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Pro RSI + Stoch + ATR Dashboard (MASTER) by AliMulti-Timeframe Momentum + Volatility Alignment System Trend Filtered MASTER Signals Designed for Structured, Discipline-Based Trading Pro RSI + Stoch + ATR Dashboard (MASTER) by Ali Full Mathematical Logic & Professional Usage Guide This indicator is a multi-timeframe momentum and volatility dashboard designed to combine: RSI (Trend & Strength) Stochastic (Timing & Trigger) ATR & ATR% (Volatility & Risk Control) Alignment Scoring (Market Structure Confirmation) MASTER Signal Logic (Filtered Trade Execution) The goal is simple: Reduce overtrading. Eliminate counter-trend entries. Trade only when momentum alignment confirms structure. 1️⃣ Core Indicators & Mathematical Foundation 🔹 RSI (Relative Strength Index) RSI measures the ratio between average gains and average losses over a defined period. Formula: RSI = 100 − RS = Average Gain / Average Loss Default Length: 14 Key Level: RSI ≥ 50 → bullish momentum bias RSI < 50 → bearish momentum bias In this system, RSI 50 is used as: Trend filter Alignment scoring basis 🔹 Stochastic (K & D) Stochastic measures where price closes relative to its recent range. K line = smoothed momentum line D line = signal line (smoothed K) Default: Length = 14 K smoothing = 3 D smoothing = 3 Trigger logic: K crossing above D → bullish short-term trigger K crossing below D → bearish short-term trigger ⚠ Stochastic alone does NOT generate signals. It only acts as a timing mechanism inside MASTER logic. 🔹 ATR (Average True Range) ATR measures market volatility. True Range (TR) = max(High−Low, |High−PrevClose|, |Low−PrevClose|) ATR = Moving average of TR over N periods Default Length: 14 ATR% = (ATR / Close) × 100 Why ATR% matters: Higher ATR% → wider stops required Lower ATR% → tighter structure, breakout potential 2️⃣ Multi-Timeframe Structure (MTF Logic) The dashboard evaluates 4 timeframes simultaneously: 15m → Entry & Timing 1H → Short-term bias 4H → Structural trend 1D → Market regime Each timeframe displays: RSI Stoch K/D ATR Status This allows structured decision making instead of single-timeframe guessing. 3️⃣ MASTER Logic (Trend + Alignment + Trigger) A MASTER signal is generated ONLY when all three layers align: 🔹 1) Trend Filter (RSI 50 on selected TF) User selects Trend TF (1H / 4H / 1D) If RSI ≥ 50 → LONG ONLY If RSI < 50 → SHORT ONLY This prevents counter-trend trading. tr.tradingview.com 🔹 2) Alignment Score (MTF Confirmation) Alignment counts how many timeframes have RSI ≥ 50. bullCount = sum of bullish RSI conditions (0–4) Score% = (bullCount / 4) × 100 Example: 4/4 (100%) → Full bullish alignment 2/4 (50%) → Mixed / chop conditions 0/4 (0%) → Full bearish regime This prevents trading in unstable market environments. tr.tradingview.com 🔹 3) Trigger (15m Stoch Cross) Entry confirmation uses: 15m K crossing above D → bullish trigger 15m K crossing below D → bearish trigger 4️⃣ MASTER BUY / SELL Conditions MASTER BUY: Trend = LONG ONLY bullCount ≥ Min Bull TF setting 15m Stoch bullish cross MASTER SELL: Trend = SHORT ONLY bullCount ≤ Max Bull TF setting 15m Stoch bearish cross This ensures: Trend + Structure + Timing = One controlled signal. 5️⃣ Reading the Dashboard Table Columns: TIME → timeframe RSI → momentum strength K / D → stochastic timing ATR → volatility STATUS → simplified momentum state STATUS is informational only. It does NOT replace MASTER logic. 6️⃣ Center Info Label Explained Displays: TREND → Active direction filter ALIGN → MTF momentum strength ATR% → Volatility intensity across TFs Professional usage: ALIGN ≥ 75% → Strong trend continuation ALIGN ≤ 50% → Chop / mean reversion zone High ATR% → Adjust risk and position size Low ATR% → Breakout probability increases 7️⃣ Recommended Settings by Trader Type 🔹 General / Balanced: Trend TF: 4H Min Bull BUY: 3 Max Bull SELL: 1 🔹 Intraday: Trend TF: 1H or 4H Min Bull BUY: 2–3 Max Bull SELL: 1–2 🔹 Scalper: Trend TF: 1H Min Bull BUY: 2 Max Bull SELL: 2 🔹 Swing: Trend TF: 1D Min Bull BUY: 3 Max Bull SELL: 1 8️⃣ Professional Trade Workflow Check TREND first Confirm ALIGN strength Wait for 15m trigger Enter with structure-based stop Adjust position size using ATR% This indicator is a structured decision framework, not a random signal generator. Built for traders who value structure over impulse. ⚠ Risk Disclaimer This tool enhances structure and discipline. It does not predict markets. Best performance occurs in aligned trending environments.
Pine Script®指標
由dokuyucuali006提供
Key Level Breakout [Powersup8]# Key Level Breakout — Single Symbol Visual indicator that plots breakout arrows directly on your chart when price closes through key levels. ## Features - **Green triangle-up** (bullish) / **Red triangle-down** (bearish) with text labels (e.g. "PM H", "Yest L") - **Optional level lines** — Horizontal lines for all active levels (off by default to reduce clutter) - **First Cross Only** — One clean signal per level per day (on by default); turn off for re-test analysis - **`alert()` calls** — One alert covers all levels with descriptive messages - **11 `alertcondition()` entries** — 8 individual (one per level) + "Any Bullish" + "Any Bearish" + "Any Breakout" ## Setup 1. Open TradingView Pine Editor, paste `KeyLevelBreakout.pine`, click **Add to chart** on a 5-min US stock chart 2. Enable **Extended Trading Hours** in chart settings (required for premarket levels) 3. Set up alerts: - **Quick (recommended)**: Add one alert → Condition: `Key Level Breakout` → `Any alert() function call` — covers all levels, one toggle to switch on/off - **Granular**: Use individual `alertcondition()` entries from the dropdown for specific levels - Visual markers (triangles + labels) always appear on the chart regardless of alert setup — alerts and visuals fire from the same signals ## Inputs | Input | Default | Description | |-------|---------|-------------| | Premarket High/Low | On | Track and alert on premarket levels | | Yesterday High/Low | On | Track and alert on previous day levels | | Last Week High/Low | On | Track and alert on previous week levels | | ORB High/Low | On | Track and alert on opening range levels | | First Cross Only | On | One signal per level per day | | Show Level Lines | Off | Plot horizontal lines for active levels | ## Alert Messages When using `Any alert() function call`, messages look like: - `Bullish breakout above Premarket High` - `Bearish breakout below Yesterday Low` - `Bullish breakout above ORB High`
Pine Script®指標
由mabvvy61提供
[blackcat] L1 ASR Floating Chip Ratio MACD█ OVERVIEW The ASR Floating Chip Ratio MACD is a volume-based momentum indicator that combines chip distribution analysis with MACD-style signal generation. It measures the proportion of floating chips (tradable shares) within a specific price range to identify potential trend reversals and momentum shifts. █ CONCEPTS This indicator utilizes volume profile analysis to calculate chip distribution across price levels, then applies the ASR (Asymmetric Supply Ratio) methodology to quantify floating chip concentration. Key Principles • Chip Distribution — Analyzes volume distribution across price levels to understand where trading activity concentrates • Floating Chip Ratio — Measures the proportion of chips that are "floating" (actively traded) relative to the total supply • MACD-style Momentum — Applies fast/slow line crossover logic to detect momentum changes in the chip ratio • Volume-Weighted Analysis — Uses historical volume data to weight the significance of different price levels █ HOW TO USE Setup Instructions 1 — Add the indicator to your chart from the indicators menu 2 — Adjust the lookback period based on your trading timeframe (default: 500 bars) 3 — Configure price levels for volume profile granularity (default: 100 levels) 4 — Observe the fast/slow line crossovers and histogram color changes Parameter Guidance • Chip Lookback Period (100-5000, default: 500) — Defines the historical range for chip distribution calculation. Higher values provide more stable signals but with more lag. • Price Levels (10-200, default: 100) — Sets the granularity of the volume profile. More levels provide finer resolution but require more computation. Signal Interpretation • Fast line (yellow) crossing above slow line (white) suggests bullish momentum • Fast line crossing below slow line suggests bearish momentum • Histogram bars indicate the strength and direction of momentum █ SIGNALS Visual Elements • Fast Line (Yellow) — Short-term EMA-based momentum of ASR ratio • Slow Line (White) — Longer-term EMA-based momentum of ASR ratio • Red Histogram Bars — Positive momentum strengthening (bullish) • Light Red Bars — Positive momentum weakening (bullish exhaustion) • Green Histogram Bars — Negative momentum strengthening (bearish) • Light Green Bars — Negative momentum weakening (bearish exhaustion) • Golden Cross (Red Triangle Up) — Fast line crosses above slow line (potential buy signal) • Death Cross (Green Triangle Down) — Fast line crosses below slow line (potential sell signal) • Zero Axis (Dashed Gray) — Reference line for momentum direction █ FEATURES • Volume profile-based chip distribution analysis • MACD-style crossover signals with visual markers • Four-color histogram for momentum strength visualization • Configurable lookback period for different trading styles • EMA-smoothed baseline for noise reduction █ LIMITATIONS • Requires sufficient historical data (minimum 100+ bars recommended) • Performance may vary on low-volume or illiquid assets • Not suitable as a standalone trading signal — use with confirmation • The indicator repaints on new bars as volume profile updates • Higher lookback periods increase computational load █ NOTES • Best used in conjunction with price action analysis and other indicators • Consider market context and fundamental factors before making trading decisions • The chip distribution model assumes volume reflects genuine trading interest • Works well on daily and intraday timeframes with adequate liquidity ═════════════════════════════════════════════════════════════════════════ For questions or feedback, use comments section below.
Pine Script®指標
由blackcat1402提供
Unified Master Stack Unified Master Stack Overview The Unified Master Stack is an all-in-one market analysis and execution system designed for professional intraday traders. It integrates four distinct logic modules—Bias, Safety, Execution, and Context—into a single synchronized HUD. The system features an optional Consensus Engine that prevents execution signals if the broader market context does not align with the local price action. The Four Core Modules 1. Bias Scanner (Accumulation/Distribution) This module analyzes volume flow and wick pressure over a user-defined lookback period. Logic: It aggregates volume on up-candles vs. down-candles and compares the ratio of upper vs. lower wicks to determine if the "Lean" is bullish (accumulation) or bearish (distribution), filtering out noise from basic indicators. 2. Scalper Guard (The Safety Filter) Designed to prevent "FOMO" entries at the tops or bottoms of moves. Logic: It monitors the distance from a core EMA and calculates candle "health" based on wick-to-body ratios. If the price is over-extended or rejecting a level with a long wick, the Guard shifts to a "WAIT" state, overriding execution signals. 3. Apex Predator (Execution Engine) The primary signal generator based on high-probability engulfing patterns. Logic: It looks for specific "touch-and-go" price action. It requires a price interaction with a fast EMA (The Magnet), followed by a bullish or bearish engulfing candle that occurs in the direction of the primary trend. 4. Logic Engine (Institutional Context) Tuned for the Los Angeles (Pacific) Timezone, this module tracks institutional session boundaries. Macro Timers: Tracks specific algorithmic windows (6:50 AM and 7:50 AM PT) where liquidity typically surges. ADR Fuel Gauge: Calculates the Average Daily Range to identify if the market is "Overheated" (volatility exhaustion). State Machine: Classifies the market into 6 states: Stop Runs, Reversals, Bullish/Bearish Trends, Traps, or Quiet Ranges. Key Feature: System Consensus The unique value of this script lies in its Consensus Logic. When enabled, the "Apex Predator" signals are automatically filtered by the "Bias Scanner" and "Logic Engine." If the execution engine wants to buy, but the Logic Engine detects a "Bull Trap" or the Bias Scanner shows "Distribution," the signal is blocked to protect the trader. How to Use Monitor the HUD: The table provides a real-time checklist of all four modules. Identify the State: Check the Logic Engine to see if the market is in a "Trap" or "Trend." The Snipe: Execute when the HUD displays "BUY SIGNAL" or "SELL SIGNAL," which only occurs when all enabled filters are satisfied.
Pine Script®指標
由DarwinIsidro提供
Proximal Range Filter [LuxAlgo]The Proximal Range Filter indicator provides a robust range/noise-filtering solution that utilizes an L1 soft-thresholding approach to determine market trends while minimizing lag and erratic price movements. 🔶 USAGE The indicator is primarily used to identify the current trend direction and significant price shifts while ignoring minor market noise. It appears as a colored line on the price chart, transitioning between bullish and bearish states. 🔹 Trend Identification Users can determine the current market sentiment by looking at the color of the filter line and the associated gradient fill: A green line and fill indicate a bullish trend, suggesting that price is consistently overcoming the upper noise threshold. A red line and fill indicate a bearish trend, suggesting that price is consistently breaking below the lower noise threshold. 🔹 Trend Switches The indicator plots "Trend Switch Dots" at the specific point where a trend reversal is confirmed. These dots appear at the previous filter level to highlight the origin of the new trend direction. This visual cue helps traders identify the exact moment the filter "stepped" in a new direction. 🔹 Responsiveness and Volatility By adjusting the inputs, traders can tailor the filter to different trading styles: For high-volatility assets (like Crypto), increasing the ATR Multiplier can help filter out "fakeouts" that occur during consolidation. In trending markets, a higher Adaptation Rate (μ) allows the filter to track price changes more aggressively once the noise threshold is exceeded, reducing lag. A lower ATR Multiplier combined with a lower Adaptation Rate creates a more "stepped" filter, useful for identifying major support/resistance levels created by the filter's flat periods. 🔶 DETAILS The core of this indicator is the L1 Proximal Filter logic. Unlike standard moving averages that react to every price tick, this filter uses a "Soft-Thresholding" mechanism to isolate meaningful price action from random volatility. 🔹 Calculation Logic The filter operates through a specific prediction-adaptation cycle: Noise Threshold : The script calculates a dynamic threshold using a 200-period ATR multiplied by the user-defined setting. This ensures the filter's sensitivity scales automatically with the asset's current volatility. State Prediction : The algorithm predicts the next state based on the previous filtered value and the current velocity (the rate of change). Adaptive Blending : The prediction is blended with the new incoming source data using the Adaptation Rate (μ). This creates a temporary "candidate" value for the filter. Soft-Thresholding : The difference between the candidate value and the previous filter state is evaluated. If the absolute difference is less than the threshold, the velocity is set to zero (the filter remains flat). If it exceeds the threshold, the threshold value is subtracted from the absolute difference to calculate the "meaningful" signal. This mathematical approach ensures that only price movements strong enough to overcome the statistical noise (the ATR threshold) result in a change to the filter's value. 🔹 Trend Switch Dots The Trend Switch Dots are plotted with a -1 offset. This is because a trend change is only confirmed once the current bar's filter value moves relative to the previous bar. The dot marks the price level where the "breakout" from the previous noise range occurred. 🔶 SETTINGS Source : The price data used for calculation (typically the Close price). ATR Multiplier : Defines the noise threshold. Higher values require larger price movements to change the filter's value, resulting in a smoother output that ignores more "whipsaws." Adaptation Rate (μ) : Controls how fast the internal prediction adapts to price changes. A value of 1.0 reacts most aggressively to new data, while lower values provide more smoothing during the adaptation phase.
Pine Script®指標
由LuxAlgo提供
ASO Money Maker by Bruno MoscanHere’s a natural, human-style explanation you can use: --- The **ASO Pro (Average Sentiment Oscillator)** is a momentum-based indicator designed to measure the balance between buying and selling pressure in the market. It works by analyzing both intrabar price behavior and grouped price movement over a defined period, then smoothing the results to create two lines: a blue line representing bullish strength and a red line representing bearish strength. When the blue line rises, it indicates increasing buying pressure. When the red line rises, it reflects growing selling pressure. By observing how these two lines behave relative to each other and to predefined overbought (OB) and oversold (OS) zones, traders can better understand shifts in market sentiment and potential turning points. In addition to showing momentum, this version of the indicator tracks significant extremes. Whenever the bullish line reaches a strong top (overbought condition), the indicator records the closing price of that candle. The same happens when the bearish line reaches its extreme, marking a potential bottom. It then calculates the average of the most recent selected number of these top and bottom prices (for example, the last 5 or 10 occurrences, depending on user preference). These calculated averages are displayed directly on the indicator panel: * The average price of recent tops appears in the upper-right corner. * The average price of recent bottoms appears in the lower-right corner. This gives traders a dynamic reference of where the market has historically peaked or bottomed according to momentum conditions — helping identify potential support and resistance zones based on real sentiment extremes rather than arbitrary price levels. In short, the ASO Pro combines momentum analysis with practical price-level tracking, offering both directional insight and actionable reference levels in one tool.
Pine Script®指標
由BrunoMoscan提供
Multi-Timeframe (MTF) Breakout StrategyThis is a Multi-Timeframe (MTF) Breakout Strategy designed to identify high-probability trend entries by combining price action with market-wide and asset-specific filters. The strategy triggers trades when the current price breaks above (or below) the combined historical highs (or lows) of three different user-defined timeframes (e.g., Weekly, Monthly, 3-Month). It utilizes a "layered filter" approach to ensure trades are only taken during periods of high volume and trend strength. Key Features Multi-Timeframe Breakout: Dynamically calculates resistance and support levels based on the maximum/minimum values of three higher timeframes. Market Sentiment Filter: Uses an external index (e.g., PSX, Nadaq, Tasi, SPY) to ensure you aren't buying in a bearish broader market. Relative Strength (RS): Compares the asset's performance against the index over a specific lookback period to ensure the asset is an "outperformer." Triple EMA Trend Alignment: Confirms the trend using three EMAs (e.g., 13, 50, 200) to ensure price is in a clear bullish or bearish structure. Volume Validation: Requires current volume to be above its simple moving average to confirm breakout conviction.
Pine Script®策略
由bilal1947提供
QMF- Market Structure & Signal Suite [BullByte]QUANTUM MOMENTUM FUSION - Market Structure and Signal Suite OVERVIEW Quantum Momentum Fusion is a comprehensive market analysis framework built around a multi-dimensional momentum oscillator. This indicator was designed to give traders a complete analytical workspace in a single tool, combining momentum measurement, market structure identification, trendline analysis, divergence detection, and multi-timeframe context into one unified system. The core philosophy behind QMF is that successful trading decisions come from understanding multiple aspects of market behavior simultaneously, not from relying on any single indicator or signal. The oscillator serves as the analytical foundation, and every other component builds upon it to create a complete picture of current market conditions. This description will walk through each component of the indicator, explaining what it measures, why that information matters, and how to interpret what you see on the chart. Whether you are an experienced trader familiar with oscillator analysis or newer to technical indicators, each section aims to make the concepts accessible and practical. THE QUANTUM ENGINE: UNDERSTANDING THE CORE OSCILLATOR (why its original and not a mashup) At the heart of this indicator is the Quantum Momentum Fusion oscillator, displayed in its own pane below the price chart. Unlike traditional oscillators that measure a single aspect of price behavior, the QMF oscillator synthesizes four distinct market dimensions into one unified reading. WHAT IS AN OSCILLATOR For those less familiar with the term, an oscillator is a technical indicator that fluctuates between defined boundaries, typically showing whether an asset is experiencing strong buying pressure, strong selling pressure, or neutral conditions. The QMF oscillator moves between 0 and 100, with 50 representing the neutral midpoint. When the oscillator is high (above 70), it suggests the market has experienced significant upward momentum and may be approaching exhaustion. When low (below 30), it suggests the market has experienced significant downward momentum and may be due for a bounce. The space between these extremes represents normal market fluctuation. THE FOUR DIMENSIONS What makes the QMF oscillator different from standard momentum indicators is that it combines four separate measurements into its calculation. Each dimension captures a different aspect of market behavior: VELOCITY DIMENSION This measures how quickly momentum itself is changing. Think of it like acceleration in a car. Knowing the car is moving forward (direction) is useful, but knowing whether the driver is pressing the accelerator or the brake (acceleration) tells you what is likely to happen next. The velocity dimension calculates the rate of change of the rate of change, providing early warning when momentum is about to shift direction. In practical terms, this can show momentum weakening before price actually reverses. Why it matters: Price can continue in one direction for a while even after the underlying momentum starts to fade. By measuring acceleration, you can identify potential turning points earlier than with simple momentum indicators. How it appears: This dimension is calculated internally and combined with the others. You do not see it separately, but its effect shows in the oscillator responding earlier to momentum shifts. VOLUME DIMENSION This measures price movement weighted by trading volume. A price move accompanied by high volume has different significance than the same price move on low volume. High volume suggests conviction and participation from larger traders. Low volume suggests the move may lack follow-through. The volume dimension multiplies price change by a volume ratio (current volume compared to average volume), giving greater weight to moves that have volume confirmation behind them. Why it matters: Volume often precedes price. Strong volume on a move suggests institutional participation and increases the probability that the move will continue. Weak volume on a move suggests it may be easily reversed. How it appears: Moves with strong volume conviction will push the oscillator more definitively, while low-volume moves will have muted effect on the reading. VOLATILITY DIMENSION This normalizes price movement against the current volatility environment. Markets go through periods of high volatility (large price swings) and low volatility (small price swings). A 1% move during a low volatility period is more significant than a 1% move during a high volatility period. The volatility dimension divides price change by Average True Range (ATR), which measures typical price range. This tells you whether current movement is significant relative to what is normal for this market right now. Why it matters: Without volatility normalization, the oscillator would react the same way to all price moves regardless of context. By adjusting for volatility, the oscillator identifies moves that are genuinely significant versus normal noise within the current regime. How it appears: During quiet markets, smaller price moves can still register as significant if they exceed normal volatility. During volatile markets, the oscillator will not overreact to moves that are within expected range. SESSION DIMENSION This tracks where price is positioned relative to the session Volume Weighted Average Price (VWAP). VWAP represents the average price at which trading has occurred during the session, weighted by volume. Institutional traders often use VWAP as a benchmark for fair value. When price is consistently above VWAP, it suggests buyers are willing to pay above average prices, indicating accumulation. When price is consistently below VWAP, it suggests sellers are accepting below average prices, indicating distribution. Why it matters: VWAP positioning provides insight into whether institutional traders are likely accumulating or distributing. Price repeatedly returning to and bouncing from VWAP can indicate support, while price repeatedly failing at VWAP can indicate resistance. How it appears: The session dimension contributes bullish readings when price maintains above VWAP and bearish readings when price maintains below VWAP. ADAPTIVE WEIGHTING The four dimensions are combined using configurable weights, and the system can operate in Adaptive Mode. When Adaptive Mode is enabled, the indicator automatically adjusts its sensitivity based on the current volatility regime. During high volatility periods, sensitivity increases to capture larger moves. During low volatility periods, sensitivity decreases to filter out noise. This means the oscillator adapts to changing market conditions without requiring manual adjustment. READING THE OSCILLATOR: DISPLAY MODES AND ZONES The QMF oscillator can be displayed in four different visual formats. Each shows the same underlying data but presents it differently based on trader preference. ENERGY CANDLES This mode displays the oscillator as candlestick-style candles. Just as price candles show open, high, low, and close for price, energy candles show these values for the QMF oscillator. Green candles indicate the oscillator closed higher than it opened (bullish momentum). Red candles indicate the oscillator closed lower than it opened (bearish momentum). The body size shows how much the oscillator moved during the period. Larger bodies indicate stronger momentum conviction. This format is useful for traders who are comfortable reading candlestick patterns and want to apply similar visual analysis to the oscillator. QMF LINE This mode displays the oscillator as a traditional line chart with a signal line overlay. The main QMF line shows current momentum. The signal line is a smoothed average of the QMF that helps identify direction changes. When the QMF line is above the signal line, momentum is bullish. When below, momentum is bearish. Crossovers between the two lines can indicate momentum shifts. This format is familiar to traders who use indicators like MACD and prefer clean line-based visualization. IMPULSE BARS This mode displays the oscillator as a histogram centered on the 50 midline. Bars above 50 indicate bullish momentum, bars below 50 indicate bearish momentum. Bar height shows momentum strength. The color intensity changes based on momentum direction. Bars that are increasing in the bullish direction show brighter color. Bars that are decreasing show muted color. This makes it easy to see momentum acceleration and deceleration at a glance. HEIKIN FLOW This mode applies Heikin-Ashi smoothing to the energy candles. Heikin-Ashi is a Japanese technique that averages price data to create smoother trends with fewer reversals. The result is cleaner visual trends that are easier to follow, though with slightly more lag than standard energy candles. This format is useful for identifying sustained momentum moves without getting distracted by minor fluctuations. OSCILLATOR ZONES Regardless of display mode, the oscillator pane includes horizontal reference lines that define important zones: Midline at 50: The neutral point. When the oscillator is above 50, overall momentum is bullish. When below 50, overall momentum is bearish. Overbought level at 70: When the oscillator crosses above this level, the market is showing strong bullish momentum. However, this also means prices have risen significantly and bearish reversal probability increases the longer the oscillator stays elevated. Oversold level at 30: When the oscillator crosses below this level, the market is showing strong bearish momentum. However, this also means prices have fallen significantly and bullish reversal probability increases. Extreme overbought at 85: Maximum bullish exhaustion. At this level, almost all short-term buying pressure has been expended. Reversal probability is high. Extreme oversold at 15: Maximum bearish exhaustion. At this level, almost all short-term selling pressure has been expended. Reversal probability is high. Understanding these zones helps you assess the current market condition before looking at any other indicator components. MARKET STRUCTURE: DYNAMIC SUPPORT AND RESISTANCE The second major component of the indicator is market structure analysis through dynamic support and resistance levels. Unlike price-based support and resistance, these levels are calculated directly on the oscillator. WHAT ARE OSCILLATOR-BASED S/R LEVELS When the QMF oscillator reaches a high point and then reverses lower, that high point becomes a resistance level on the oscillator. When the oscillator reaches a low point and then reverses higher, that low point becomes a support level. These levels represent momentum thresholds that the market has previously found difficult to exceed. They answer the question: At what momentum reading has the oscillator historically reversed? WHY THIS MATTERS Oscillator support and resistance provides different information than price support and resistance. Price S/R tells you where buyers and sellers have previously entered the market. Oscillator S/R tells you what level of momentum the market has been able to sustain. If the oscillator approaches its resistance level, it suggests momentum is reaching the upper bounds of what has been achievable recently. Either momentum will break through (indicating unusually strong conditions) or it will reverse (indicating normal mean reversion). Similarly, if the oscillator approaches support, it suggests momentum is reaching exhaustion levels that have previously triggered bounces. HOW IT APPEARS ON THE CHART Resistance is displayed as a horizontal red line with a RES label on the oscillator pane. Support is displayed as a horizontal cyan line with a SUP label. These lines update dynamically as new pivots form. When the oscillator breaks through these levels, markers appear: R with up arrow: Resistance level broken, indicating unusually strong bullish momentum S with down arrow: Support level broken, indicating unusually strong bearish momentum R with checkmark: Resistance held, price rejected at this level S with checkmark: Support held, price bounced from this level The dashboard also shows current S/R status: whether the oscillator recently broke resistance, broke support, is currently at resistance, is currently at support, or is in clear space between levels. AUTOMATED TRENDLINES: MOMENTUM TREND STRUCTURE The third major component is automated trendline detection on the oscillator. This identifies trending behavior in momentum itself, separate from price trends. WHAT ARE OSCILLATOR TRENDLINES Just as you can draw trendlines on a price chart connecting swing lows (uptrend) or swing highs (downtrend), the indicator draws trendlines on the oscillator connecting pivot points. Support trendlines connect oscillator pivot lows and project forward with a flat or rising slope. These show upward trending momentum where each pullback finds support at a higher level. Resistance trendlines connect oscillator pivot highs and project forward with a flat or falling slope. These show downward trending momentum where each rally faces resistance at a lower level. WHY THIS MATTERS Price trends and momentum trends do not always align. Price can continue making higher highs while momentum makes lower highs, a condition called bearish divergence. Momentum trendlines help visualize this behavior. When momentum is making higher lows (rising support trendline), it suggests underlying strength even if price is consolidating. When momentum is making lower highs (falling resistance trendline), it suggests underlying weakness even if price is holding. Breaks of these trendlines often precede price moves. If a falling momentum resistance trendline breaks upward, it suggests bearish pressure is releasing and bullish momentum may follow. If a rising momentum support trendline breaks downward, it suggests bullish pressure is failing and bearish momentum may follow. HOW IT APPEARS ON THE CHART Support trendlines appear in blue/cyan, resistance trendlines appear in pink/magenta. Lines extend forward from the most recent pivot point to show projected levels. Small circle markers can optionally appear at each pivot point used to construct the trendlines, helping you verify the anchor points. When the oscillator breaks through a trendline, markers appear: TL with up arrow: Resistance trendline broken upward (bullish breakout) TL with down arrow: Support trendline broken downward (bearish breakdown) Trendline strength is calculated based on three factors: how many pivot points validate the line, how recently it formed, and the angle of the slope. Stronger trendlines have more touches, formed recently, and have moderate slopes. You can filter trendlines by strength to show only the most significant ones. Optional trendline zones can display a shaded area around each trendline rather than just a single line, showing a zone of influence rather than a precise level. DIVERGENCE: WHEN PRICE AND MOMENTUM DISAGREE The fourth major component is divergence detection, which identifies discrepancies between price action and oscillator behavior. WHAT IS DIVERGENCE Divergence occurs when price makes a new high or low, but the oscillator fails to confirm it. This disagreement between price and momentum often precedes reversals. There are four types of divergence: REGULAR BULLISH DIVERGENCE Price makes a lower low (new low point below the previous low), but the oscillator makes a higher low (its low point is above its previous low). This suggests that despite price going lower, selling momentum is actually weakening. The implication is that sellers are losing conviction and a bounce or reversal may be approaching. Visual example: Imagine price drops from 100 to 95, bounces to 97, then drops again to 93. At the same time, the oscillator drops to 25, bounces to 35, then drops only to 30. Price made a lower low (93 vs 95) but the oscillator made a higher low (30 vs 25). This is regular bullish divergence. REGULAR BEARISH DIVERGENCE Price makes a higher high (new high point above the previous high), but the oscillator makes a lower high (its high point is below its previous high). This suggests that despite price going higher, buying momentum is actually weakening. The implication is that buyers are losing conviction and a pullback or reversal may be approaching. HIDDEN BULLISH DIVERGENCE Price makes a higher low (its low point is above its previous low), but the oscillator makes a lower low (new low below its previous low). This occurs during uptrends and suggests the trend will continue. Price is holding higher but momentum briefly dipped further, indicating a temporary pullback within a larger uptrend. HIDDEN BEARISH DIVERGENCE Price makes a lower high (its high point is below its previous high), but the oscillator makes a higher high (new high above its previous high). This occurs during downtrends and suggests the trend will continue. Price is staying lower but momentum briefly spiked higher, indicating a temporary bounce within a larger downtrend. Regular divergence suggests reversal. Hidden divergence suggests continuation. HOW IT APPEARS ON THE CHART When divergence is confirmed, labels appear on the oscillator: BULL DIV: Regular bullish divergence confirmed BEAR DIV: Regular bearish divergence confirmed H-BULL: Hidden bullish divergence confirmed H-BEAR: Hidden bearish divergence confirmed Dotted lines connect the pivot points on the oscillator to show the divergence pattern. Regular divergence uses solid colored lines, hidden divergence uses dashed lines. The dashboard shows divergence status in real-time: CHECKING BULL: A potential bullish divergence pattern is forming but not yet confirmed CHECKING BEAR: A potential bearish divergence pattern is forming but not yet confirmed BULL CONFIRMED: Bullish divergence has been validated BEAR CONFIRMED: Bearish divergence has been validated NONE: No divergence currently active Divergence strength is calculated from the magnitude of the oscillator discrepancy. Only divergences meeting the minimum strength threshold are displayed to filter out minor, less significant patterns. FLOW RIBBONS: VISUALIZING MOMENTUM ALIGNMENT The fifth major component is the Flow Ribbon system, which displays multiple moving averages of the QMF oscillator to visualize momentum trend and alignment. WHAT ARE FLOW RIBBONS Flow ribbons consist of three Exponential Moving Averages (EMAs) applied to the QMF oscillator values. Think of them as smoothed versions of the oscillator at different speeds: Fast Ribbon : Responds quickly to momentum changes, showing recent momentum direction Medium Ribbon: Balances responsiveness with smoothness, showing intermediate momentum Slow Ribbon: Moves slowly and shows longer-term momentum context When these three lines are plotted together with filled area between them, they create a visual ribbon that expands and contracts based on momentum conditions. WHY RIBBON ALIGNMENT MATTERS The relationship between these three averages tells you about momentum structure: BULLISH ALIGNMENT (Fast above Medium above Slow) When the ribbons are stacked with fast on top, medium in middle, and slow on bottom, momentum is aligned bullishly across multiple timeframes. Short-term momentum leads, with medium and long-term momentum confirming. This is the strongest bullish configuration. BEARISH ALIGNMENT (Fast below Medium below Slow) When the ribbons are inverted with fast on bottom, medium in middle, and slow on top, momentum is aligned bearishly across multiple timeframes. Short-term momentum leads downward, with medium and long-term momentum confirming. This is the strongest bearish configuration. MIXED/TRANSITIONING When the ribbons are not properly stacked, momentum is in transition. This often occurs during consolidation, trend changes, or choppy conditions. Trading during mixed ribbon states carries higher uncertainty. RIBBON EXPANSION AND CONTRACTION Beyond alignment, the distance between the fast and slow ribbon provides additional information: EXPANDING RIBBON When the gap between fast and slow ribbon is increasing, momentum is accelerating. In a bullish alignment with expansion, upward momentum is strengthening. In a bearish alignment with expansion, downward momentum is strengthening. Expansion confirms trend conviction. CONTRACTING RIBBON When the gap between fast and slow ribbon is decreasing, momentum is decelerating. The current trend may be losing steam. Contraction often precedes consolidation or reversal. It serves as an early warning that the current move may be exhausting. HOW IT APPEARS ON THE CHART The fast ribbon appears as a thicker line, the slow ribbon as a thinner line. The area between them fills with color: Green fill: Bullish ribbon alignment Red fill: Bearish ribbon alignment Gray fill: Neutral or transitioning state The dashboard shows ribbon state as BULL, BEAR, or NEUT, and indicates whether ribbons are expanding (EXP) or contracting (CON). Ribbon crossovers occur when the fast ribbon crosses the slow ribbon, signaling potential momentum shifts. These crossovers are confirmed only after the bar closes to prevent false signals from intrabar movement. REVERSAL CLOUDS: PROBABILITY ZONES The sixth major component is the Reversal Cloud system, which visualizes zones where momentum reversals have elevated probability. WHAT ARE REVERSAL CLOUDS Reversal clouds are shaded areas around the QMF oscillator that indicate probability zones for mean reversion. They answer the question: How far from average has momentum extended, and what is the probability it will revert? When the oscillator moves far from its normal range, it creates stretched conditions. Like a rubber band pulled to its limit, the probability increases that it will snap back toward center. Reversal clouds visualize these stretched conditions. CLOUD CALCULATION METHODS Five different calculation methods are available, each with different characteristics: DYNAMIC BOLLINGER Uses statistical standard deviation to create bands that adapt to oscillator volatility. When the oscillator is volatile, bands widen. When the oscillator is calm, bands narrow. This method identifies moves that are statistically significant relative to recent oscillator behavior. GOLDEN RATIO Applies Fibonacci proportions (0.214 and 0.786) to the oscillator range. These ratios appear throughout nature and markets. Some traders believe these proportions have psychological significance in market behavior. ADAPTIVE HALO Scales cloud width based on price ATR rather than oscillator volatility. This connects cloud width to actual price volatility, making the clouds wider during volatile price action and narrower during calm periods. VOLATILITY SQUEEZE Uses short-term standard deviation to create bands that contract during low volatility and expand during high volatility. This method is particularly useful for identifying potential breakout conditions when volatility is compressed. ICHIMOKU RSI Applies concepts from Ichimoku Kinko Hyo equilibrium theory to create balanced zones. Uses multiple lookback periods to establish equilibrium levels where the oscillator tends to find balance. HOW TO READ THE CLOUDS The oscillator moves through the cloud area as momentum fluctuates: When QMF enters the upper cloud region, it indicates extended bullish momentum. The higher into the cloud, the greater the probability of bearish reversal through mean reversion. When QMF enters the lower cloud region, it indicates extended bearish momentum. The deeper into the cloud, the greater the probability of bullish reversal through mean reversion. Cloud opacity adjusts based on reversal probability. More opaque coloring indicates higher reversal probability. Subtle coloring indicates lower reversal probability. IMPORTANT UNDERSTANDING Clouds show probability zones, not certainty. Price can remain in extreme zones longer than expected, particularly during strong trends. Clouds are most useful when combined with other components like divergence, S/R breaks, and ribbon alignment rather than used in isolation. MULTI-TIMEFRAME ANALYSIS: SEEING THE BIGGER PICTURE The seventh major component is Multi-Timeframe (MTF) analysis, which calculates QMF values across multiple timeframes to assess momentum alignment at different time perspectives. WHY MULTIPLE TIMEFRAMES MATTER The timeframe you trade on shows only one perspective of market momentum. A bullish signal on a 15-minute chart may occur within a larger bearish trend on the 4-hour chart. Understanding momentum context from higher timeframes helps you assess whether you are trading with or against the larger flow. When multiple timeframes align in the same direction, the probability of a successful trade increases. When timeframes conflict, the situation is more uncertain and requires additional caution. HOW MTF ANALYSIS WORKS The indicator calculates the full QMF oscillator independently on four configurable timeframes. By default, these are set to 5-minute, 15-minute, 60-minute (1 hour), and 240-minute (4 hour), but you can configure them to any timeframes that suit your trading style. For each timeframe, the system determines the current momentum bias: OB - Overbought: QMF above 70, indicating extended bullish momentum that may reverse B+ - Strong Bullish: QMF above 55 and above its signal line, indicating solid bullish momentum B - Bullish: QMF above its signal line, indicating mild bullish momentum N - Neutral: QMF near 50 with no clear direction S - Bearish: QMF below its signal line, indicating mild bearish momentum S+ - Strong Bearish: QMF below 45 and below its signal line, indicating solid bearish momentum OS - Oversold: QMF below 30, indicating extended bearish momentum that may reverse ALIGNMENT SCORING The dashboard displays an alignment score showing how many of the four timeframes agree with each directional bias. This appears as a fraction like 3/4 or 2/4. 4/4 Bullish: All four timeframes show bullish readings - maximum bullish alignment 3/4 Bullish: Three timeframes bullish, one diverging - strong bullish alignment 2/4: Split between bullish and bearish - no clear alignment, use caution 3/4 Bearish: Three timeframes bearish, one diverging - strong bearish alignment 4/4 Bearish: All four timeframes show bearish readings - maximum bearish alignment Higher alignment scores indicate more reliable momentum context. Trading with 3/4 or 4/4 alignment in your favor provides better odds than trading against alignment or during mixed conditions. NON-REPAINTING MTF DATA The multi-timeframe data uses proper request.security settings with lookahead disabled and gaps handled correctly. This ensures the MTF readings you see in backtesting match what you would see in real-time trading, with no future data leakage that could create misleading results. LIVE MOMENTUM SCORING: REAL-TIME MARKET ASSESSMENT The eighth major component is the Live Momentum Scoring system, which provides continuous real-time feedback on current market conditions. WHAT IS LIVE MOMENTUM SCORING Unlike signals which only appear when specific patterns complete, live momentum scores update every bar to show the current balance between bullish and bearish factors. This answers the question: Right now, how do the bullish factors compare to the bearish factors? The system evaluates six categories for each direction and adds up points: ZONE POSITION (0-25 points) Rewards positioning in favorable oscillator zones. Deep oversold positioning adds points to the bullish score. Deep overbought positioning adds points to the bearish score. Extreme zones receive maximum points, moderate zones receive partial points, neutral zones receive zero. DIVERGENCE (0-20 points) Rewards active or forming divergence patterns. Confirmed divergence receives full points. Forming (checking) divergence receives partial credit. No divergence receives zero points. TREND ALIGNMENT (0-20 points) Rewards proper EMA stacking and trend MA positioning. Full bullish EMA stack (fast above medium above slow above trend MA) receives maximum bullish points. Partial alignment receives partial points. MOMENTUM DIRECTION (0-15 points) Rewards current momentum direction and acceleration. Accelerating momentum in the favorable direction receives maximum points. Simple directional momentum receives moderate points. Histogram turning (early reversal signs) receives partial points. RIBBON STATE (0-10 points) Rewards proper ribbon alignment and expansion. Aligned and expanding ribbons receive maximum points. Aligned but contracting ribbons receive moderate points. Mixed ribbons receive zero points. MULTI-TIMEFRAME (0-10 points) Rewards higher timeframe alignment. 4/4 alignment receives maximum points, scaling down as alignment decreases. READING THE LIVE SCORES The dashboard displays current scores for both directions: BULL: Shows bullish score as percentage (0-100) and letter grade (A through D) BEAR: Shows bearish score as percentage (0-100) and letter grade (A through D) BIAS: Shows which direction currently dominates (BULL, BEAR, or NEUTRAL if close) Grade thresholds: A Grade: 70% or higher - Strong momentum factors aligned B Grade: 50-69% - Moderate momentum factors present C Grade: 30-49% - Some momentum factors but incomplete D Grade: Below 30% - Weak or missing momentum factors The dominant bias shows which direction currently has stronger factors. When one side leads by more than 10 points, it shows that direction. Otherwise, it shows NEUTRAL indicating balanced or mixed conditions. WHY LIVE SCORING MATTERS Live scores help you understand current market conditions even when no signal has fired. You can see momentum building or fading in real-time. A rising bullish score suggests conditions are improving for potential long opportunities. A rising bearish score suggests conditions are deteriorating. This continuous feedback helps with: - Anticipating potential signals before they fire - Assessing whether to act on signals that do fire - Understanding why a signal did or did not appear - Monitoring open positions for changing conditions THE DASHBOARD: YOUR ANALYSIS CONTROL CENTER The dashboard provides a comprehensive real-time summary of all indicator components in one organized table. It displays on the price chart using force overlay so it remains visible regardless of which pane you are focused on. DASHBOARD LAYOUT The dashboard can be configured in three detail levels: COMPACT MODE Shows only essential information: QMF value, zone status, S/R status, and volume. Uses minimal screen space for traders who want the indicator to remain unobtrusive. STANDARD MODE Shows balanced detail including QMF values, zone status, last signal information, grade statistics, divergence status, S/R and volume status, live momentum scores, and MTF panel. Suitable for most traders. FULL MODE Shows maximum detail including everything in Standard mode plus EMA structure, ribbon state, volatility regime, signal statistics breakdown, and trendline counts. For traders who want complete information access. DASHBOARD ROWS EXPLAINED Row 1 - HEADER Shows indicator name for identification. Row 2 - QMF VALUES Displays three values: - QMF with directional arrow showing current oscillator value and whether it is rising, falling, or unchanged - SIG showing the signal line value - Histogram value with plus or minus sign showing the difference between QMF and signal line Row 3 - PROGRESS BAR Visual representation of oscillator position from 0 to 100 using text characters. Provides quick visual reference without needing to look at the oscillator pane. Row 4 - ZONE STATUS Text classification of current zone with color coding: - EXTREME OB (red): Oscillator at or above extreme overbought level - OVERBOUGHT (light red): Oscillator in overbought zone - BULLISH (light green): Oscillator above 55 but below overbought - NEUTRAL (gray): Oscillator between 45 and 55 - BEARISH (light red): Oscillator below 45 but above oversold - OVERSOLD (light blue): Oscillator in oversold zone - EXTREME OS (blue): Oscillator at or below extreme oversold level Row 5 - LAST SIGNAL (Standard and Full mode) Shows information about the most recent signal: - Direction and grade (LONG A, SHORT B, etc.) - Bars ago since signal fired - Entry price when signal fired - Current profit/loss from that price level This helps track performance of recent signals and manage any open positions based on them. Row 6 - GRADE STATISTICS (Standard and Full mode) Running count of signals generated: - A: Count of Grade A signals - B: Count of Grade B signals - C: Count of Grade C signals - T: Total signal count This provides perspective on signal frequency and grade distribution over the visible chart period. Row 7 - DIVERGENCE STATUS (Standard and Full mode) Current state of divergence detection: - CHECKING BULL: Bullish divergence pattern forming, not yet confirmed - CHECKING BEAR: Bearish divergence pattern forming, not yet confirmed - BULL CONFIRMED: Bullish divergence validated - BEAR CONFIRMED: Bearish divergence validated - NONE: No divergence currently active Row 8 - S/R AND VOLUME Two pieces of information: - S/R status: Shows R BROKEN (resistance broken upward), S BROKEN (support broken downward), AT RES (testing resistance), AT SUP (testing support), or CLEAR (between levels) - Volume status: Shows HIGH (volume 1.5x or more above average), MID (volume near average), or LOW (volume below average) Row 9 - LIVE MOMENTUM (Standard and Full mode) Real-time momentum scoring: - BULL: Bullish percentage and letter grade - BEAR: Bearish percentage and letter grade - Dominant bias indicator Row 10-11 - MTF PANEL (when enabled, Standard and Full mode) Multi-timeframe status: - Top row shows the four timeframe labels - Bottom row shows the status code for each timeframe (OB, B+, B, N, S, S+, OS) - Final cell shows alignment score as X/4 FULL MODE ADDITIONAL ROWS Structure row: Shows EMA stack status (BULL STACK, BEAR STACK, or relationship between fast and slow) and trend MA position (ABOVE MA or BELOW MA) Stats row: Shows count of long signals, short signals, and active trendlines Ribbon row: Shows ribbon state (BULL, BEAR, NEUT), expansion status (EXP or CON), and volatility regime (H-VOL for high volatility, L-VOL for low volatility, N-VOL for normal) DASHBOARD POSITIONING AND SIZING Position options: Top Left, Top Center, Top Right, Middle Left, Middle Right, Bottom Left, Bottom Center, Bottom Right Size options: Tiny (minimal space), Small (balanced), Normal (maximum readability) Choose a position that does not obscure important price action on your chart and a size that balances readability with space efficiency. HOW SIGNALS EMERGE FROM CONFLUENCE After understanding all the individual components, it becomes clear how signals are generated. Signals in QMF are not arbitrary triggers based on single conditions. They emerge when multiple independent factors align to create confluence. THE PATTERN-BASED APPROACH The signal system uses a hierarchical pattern-based approach. Rather than calculating a score from random factors and labeling it, the system actively hunts for specific predefined pattern combinations. The system first checks for Grade A patterns. If none are found, it checks for Grade B patterns. If none are found, it checks for Grade C patterns. Each grade represents specific combinations of factors that must be present together. GRADE A REQUIREMENTS Grade A patterns require multiple strong factors aligned. Examples of Grade A pattern combinations: Pattern A1 - Perfect Storm Reversal: - Extreme zone positioning (deeply oversold or overbought) - Confirmed regular divergence - Structural break (resistance broken or support broken or trendline broken) - Strong volume conviction (1.3x or higher) - High MTF alignment (3 or more timeframes agreeing) Pattern A2 - Breakout Conviction: - Resistance or support broken - Accelerating momentum in the breakout direction - Full EMA stack aligned - Ribbon aligned and expanding - Strong volume conviction (1.4x or higher) - Good MTF alignment (2 or more timeframes) Pattern A3 - Zone Reversal Multi-Confirmation: - Extreme or standard zone positioning - Regular or hidden divergence confirmed - Active bounce from zone - EMA crossover or MA break in reversal direction - Good MTF alignment (2 or more timeframes) - Volume conviction present (1.2x or higher) All factors in the pattern must be present simultaneously. Missing any single factor disqualifies the Grade A pattern. GRADE B REQUIREMENTS Grade B patterns require fewer but still meaningful confirmations. These patterns fire only when no Grade A pattern is detected: Pattern B1 - Zone with Confirmation: - Oversold or overbought zone positioning - Momentum in reversal direction - Hidden divergence, EMA crossover, or trendline break present - Minimum MTF alignment met Pattern B2 - Divergence with Structure: - Regular or hidden divergence confirmed - Structural break (S/R or trendline or MA) - Momentum confirming direction - Volume at least average Pattern B3 - Clean Trend Continuation: - Above or below trend MA - Ribbon aligned in direction - Oscillator crossed signal line - EMA stack complete GRADE C REQUIREMENTS Grade C patterns require basic confirmations. These patterns fire only when no Grade A or Grade B pattern is detected: Pattern C1 - Early Zone Entry: - Zone positioning or approaching zone - Momentum in expected direction - Oscillator or EMA crossover present Pattern C2 - Momentum Shift: - Histogram turning in expected direction - Oscillator crossover confirmed - Oscillator on expected side of midline SIGNAL QUALITY CONTROLS Beyond pattern detection, several quality controls must be satisfied: COOLDOWN A minimum number of bars must pass between any two signals. This prevents signal clustering during volatile conditions and ensures each signal represents a distinct opportunity. DIRECTION ALTERNATION When enabled, signals must alternate between LONG and SHORT. After a LONG signal, only SHORT signals can fire until direction changes. This prevents multiple consecutive signals in the same direction. PULLBACK REQUIREMENT After a signal fires, the oscillator must retrace a minimum percentage before another same-direction signal can fire. This ensures re-entry signals occur after meaningful pullbacks rather than immediately after the first signal. VOLUME CONFIRMATION (Optional) When enabled, volume must meet minimum threshold relative to average. This filters out signals during low-volume periods when moves may lack follow-through. BAR CONFIRMATION All signals require barstate.isconfirmed, meaning they only fire after the bar closes. This prevents signals from appearing and disappearing during live bar formation, ensuring backtest results match live behavior. A comprehensive example that combines signal generation logic, grading system, with all elements clearly annotated for easy understanding. SETTINGS REFERENCE This section provides a reference for the main configurable settings organized by category. QUANTUM ENGINE SETTINGS Sensitivity (5-50): Primary lookback period for momentum calculations. Lower values respond faster but may include more noise. Higher values smooth the oscillator but increase lag. Default 14 balances responsiveness with stability. Smoothing (1-10): Exponential smoothing applied to final QMF value. Higher values reduce noise, lower values preserve detail. Default 3 provides good noise reduction. Adaptive Mode: When enabled, automatically adjusts sensitivity based on volatility regime. Increases sensitivity during high volatility, decreases during low volatility. Dimension Toggles: Enable or disable each of the four dimensions (Velocity, Volume, Volatility, Session) individually. Useful for customizing the oscillator for specific instruments or conditions. Dimension Weights: Adjust relative contribution of each dimension. Weights are normalized so they do not need to sum to 1.0. Higher weight means that dimension has more influence on the final value. Signal Length: EMA period for the signal line. Lower values make signal line more responsive, higher values make it smoother. DISPLAY SETTINGS Display Mode: Choose between Energy Candles, QMF Line, Impulse Bars, or Heikin Flow visualization. Candle Glow: Adds luminous glow effect around energy candles based on momentum strength. Visually striking but can impact performance on slower systems. Glow Layers: Number of glow layers when candle glow is enabled. More layers create smoother gradient but use more resources. VISUAL SETTINGS Theme: Choose between Tokyo Night (dark blue with vibrant accents), Dracula (purple-grey with high contrast), or Nord (muted arctic tones). Each theme is designed for extended trading sessions. Glow Intensity: Controls transparency of glow effects. Lower values create more visible glows, higher values more subtle. Enable Glow Effects: Master toggle for all glow effects around candles and levels. REVERSAL CLOUD SETTINGS Enable Reversal Clouds: Toggle cloud display on or off. Cloud Style: Choose calculation method (Dynamic Bollinger, Golden Ratio, Adaptive Halo, Volatility Squeeze, Ichimoku RSI). Cloud Transparency: Higher values make clouds more transparent, lower values more visible. Cloud Width: Multiplier for cloud width. Higher values create wider reversal zones. FLOW RIBBON SETTINGS Enable Ribbons: Toggle ribbon display. Fast/Medium/Slow Ribbon: Period for each ribbon EMA. Faster periods respond quicker, slower periods show longer-term trend. DIVERGENCE SETTINGS Enable Divergence: Toggle divergence detection. Pivot Sensitivity: Bars required on each side to confirm pivot point. Higher values detect more significant pivots but may miss shorter-term divergences. Confirmation Bars: Bars to wait after pivot detection before confirming divergence. Min Strength Pct: Minimum divergence strength percentage to display. Higher values filter out weaker divergences. Show Lines: Draw connecting lines between divergence pivots. Min/Max Distance: Range of bars between pivots for valid divergence. SIGNAL SYSTEM SETTINGS Enable Signals: Toggle signal generation. Show Signals: Filter by grade (A Only, A and B, All Grades). Cooldown Bars: Minimum bars between signals. Pullback Required Pct: Percentage pullback needed before same-direction signal. Require Direction Alternation: Force signals to alternate LONG and SHORT. Fast/Slow EMA: Periods for EMA crossover analysis. Trend MA: Period for trend-defining moving average. Min MTF Alignment: Minimum timeframes that must align for higher grades. Require Volume Confirmation: Make volume threshold mandatory for signals. Min Volume Ratio: Minimum volume relative to average when required. TRENDLINE SETTINGS Enable Trendlines: Toggle automated trendline detection. Pivot Left/Right: Bars for pivot detection. Extension Bars: How far to extend lines into future. Min Touch Points: Minimum pivots to validate line. Enable Strength Filter: Filter by calculated strength. Minimum Strength: Threshold for strength filter. Show Trendline Zones: Display shaded zones around lines. Zone Width StdDev: Standard deviation multiplier for zone width. Line Style: Solid, Dashed, or Dotted. Line Width: Thickness in pixels. Show Touch Points: Display circle markers at pivots. Show Strength Labels: Display strength percentage at line end. SUPPORT RESISTANCE SETTINGS Enable S/R: Toggle dynamic S/R display. Pivot Lookback: Period for detecting S/R pivots. DASHBOARD SETTINGS Enable Dashboard: Toggle dashboard display. Position: Screen position (8 options). Size: Tiny, Small, or Normal. Style: Compact, Standard, or Full detail level. MTF Panel: Include or exclude multi-timeframe panel. MTF 1-4: Timeframe selections for MTF analysis. LEVEL SETTINGS Overbought/Oversold: Standard zone thresholds. Extreme OB/OS: Extreme zone thresholds. PRACTICAL EXAMPLE: READING THE COMPLETE PICTURE This example walks through analyzing a chart using all the indicator components together. SCENARIO: You are analyzing a 15-minute chart looking for trading opportunities. STEP 1: ASSESS OSCILLATOR ZONE You look at the QMF oscillator and see it reading 24, which is in the oversold zone. The dashboard confirms this showing OVERSOLD in the zone status. The progress bar shows the oscillator is in the lower portion of its range. Initial assessment: The market has experienced significant selling pressure and is in territory where bullish reversals have elevated probability. STEP 2: CHECK STRUCTURE You look at the dynamic S/R levels. The oscillator recently touched its support level at 22 and bounced. You see an S with checkmark marker indicating support held. The dashboard shows AT SUP status. Assessment update: The oscillator found support at a level that has held before. This adds to the bullish case. STEP 3: EXAMINE TRENDLINES You notice a resistance trendline connecting recent oscillator highs that has been declining. The oscillator is currently approaching this trendline from below. No break has occurred yet. Assessment update: There is overhead resistance that will need to be cleared. A break above would be significant. STEP 4: CHECK DIVERGENCE The dashboard shows BULL CONFIRMED in the divergence status. Looking at the oscillator, you see a BULL DIV label with a dotted line connecting two pivot lows. The oscillator made a higher low while price made a lower low. Assessment update: Confirmed bullish divergence suggests selling momentum is weakening despite price continuing lower. This is a meaningful signal of potential reversal. STEP 5: EVALUATE RIBBONS The ribbons are currently mixed with fast below medium but both above slow. Ribbon fill is gray indicating transitioning state. However, you notice the fast ribbon is turning upward and approaching the medium ribbon from below. Assessment update: Ribbons are not yet aligned bullish, but appear to be transitioning. A bullish crossover may be approaching. STEP 6: CHECK MTF ALIGNMENT The dashboard MTF panel shows: 5m is B+, 15m is B, 1H is N, 4H is S. The alignment shows 2/4 bullish. Assessment update: Lower timeframes support bullish bias, but higher timeframes are neutral or bearish. This is mixed alignment, suggesting caution. Any bullish move may face resistance from higher timeframe sellers. STEP 7: REVIEW LIVE MOMENTUM SCORES Dashboard shows BULL at 52% Grade B, BEAR at 28% Grade D. Dominant bias shows BULL. Assessment update: Bullish factors currently outweigh bearish factors. The score suggests moderate bullish conditions, not yet strong. STEP 8: SYNTHESIS Putting it together: - Oversold zone positioning (bullish factor) - Support held (bullish factor) - Bullish divergence confirmed (strong bullish factor) - Ribbons transitioning but not yet aligned (neutral) - MTF alignment mixed at 2/4 (caution factor) - Live score favors bullish moderately (supporting factor) - Resistance trendline overhead (risk factor) Conclusion: Conditions favor a bullish reversal but with caution warranted due to mixed MTF alignment and overhead resistance. This would not qualify for a Grade A signal due to insufficient MTF alignment. If a signal fires, it would likely be Grade B. STEP 9: SIGNAL FIRES Several bars later, the oscillator crosses above its signal line while still in oversold territory. The EMA fast crosses above EMA slow. A LONG B signal appears at 85% confluence. The signal represents: Oversold positioning plus confirmed divergence plus momentum crossover, meeting Grade B pattern requirements. STEP 10: MONITORING After entry, you monitor the dashboard for changing conditions. Live momentum scores continue rising. The resistance trendline breaks (TL up arrow marker appears). Ribbons align bullish. MTF alignment improves to 3/4 as the 1H turns bullish. The improving conditions confirm the trade thesis. You hold the position as conditions strengthen. ALERTS AVAILABLE 28 alert conditions are available covering all major events. To set up alerts, click the alert icon in TradingView, select this indicator, and choose the desired condition. SIGNAL ALERTS - A-Grade LONG Signal: Highest probability bullish entry - A-Grade SHORT Signal : Highest probability bearish entry - B-Grade LONG Signal: Solid bullish entry - B-Grade SHORT Signal: Solid bearish entry - Any LONG Signal: Any bullish signal regardless of grade - Any SHORT Signal: Any bearish signal regardless of grade DIVERGENCE ALERTS - Regular Bullish Divergence: Classic bullish reversal pattern - Regular Bearish Divergence: Classic bearish reversal pattern - Hidden Bullish Divergence: Bullish continuation pattern - Hidden Bearish Divergence: Bearish continuation pattern - Any Bullish Divergence: Either regular or hidden bullish - Any Bearish Divergence: Either regular or hidden bearish STRUCTURE ALERTS - Trendline Break Up : Resistance trendline broken - Trendline Break Down: Support trendline broken - Resistance Broken: S/R resistance level broken - Support Broken: S/R support level broken CROSSOVER ALERTS - EMA Cross Up : Fast EMA crossed above slow EMA - EMA Cross Down : Fast EMA crossed below slow EMA - Trend MA Break Up: Oscillator crossed above trend MA - Trend MA Break Down: Oscillator crossed below trend MA ZONE ALERTS - Entered Overbought Zone: Oscillator entered overbought - Entered Oversold Zone: Oscillator entered oversold - Entered Extreme Overbought: Oscillator reached extreme overbought - Entered Extreme Oversold: Oscillator reached extreme oversold RIBBON ALERTS - Ribbon Cross Up: Fast ribbon crossed above slow ribbon - Ribbon Cross Down: Fast ribbon crossed below slow ribbon BOUNCE ALERTS - Bounce From Oversold: Active reversal from oversold zone - Bounce From Overbought : Active reversal from overbought zone NON-REPAINTING Structure All visual elements and signals in this indicator are non-repainting: - Signals use barstate.isconfirmed to fire only after bar close - Divergence confirmation waits for pivot validation - Trendline breaks confirm after bar close - S/R breaks confirm after bar close - MTF data uses lookahead off setting - No future data is used in any calculation What you see in backtesting accurately represents what would have appeared in real-time trading. RISK DISCLAIMER This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment advice, or trading advice. Trading financial instruments involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. The analysis provided by this indicator should not be relied upon as the sole basis for any trading decision. Before trading: - Understand you may lose some or all of your investment - Never trade with money you cannot afford to lose - Conduct your own research and due diligence - Consider consulting with a qualified financial advisor - Practice with paper trading before risking real capital - Implement proper risk management with recommended maximum 1-2% risk per trade By using this indicator, you acknowledge that you have read and understood this disclaimer and accept full responsibility for your trading decisions.
Pine Script®指標
由BullByte提供
2
VWAP (ARCHI)VWAP Bands — Volume Weighted Average Price with Deviation Bands Overview **VWAP Bands** is an advanced VWAP indicator featuring automatic standard deviation bands (±1σ, ±2σ, ±3σ), multiple anchor period options, and fully customizable visuals. Designed for intraday and swing traders who use volume-weighted levels for decision-making. 🔑 Key Features 📐 **Anchored VWAP** — Choose your reset period: Session (default), Week, Month, Quarter, or Year. VWAP resets at the beginning of each period. 📊 **Deviation Bands** — Three levels of standard deviation bands (±1σ, ±2σ, ±3σ) highlight statistical zones of overextension relative to the volume-weighted mean price. 🎨 **Full Customization** — Adjustable colors, line widths, band fills, and edge labels. Toggle each band on/off independently. 📍 **Previous VWAP Close** — Optionally display the closing VWAP level from the previous session as a key reference point. 📈 How to Use Identifying Value Zones - Price **at VWAP** = fair value. Institutional traders benchmark execution quality against VWAP. - Price **above VWAP** = buyers in control, bullish bias. - Price **below VWAP** = sellers in control, bearish bias. Mean Reversion Trading - Price touches **±2σ** = extreme deviation, look for a reversal back to VWAP. - Price touches **±3σ** = very rare event, high probability of a pullback. - **±1σ** = normal volatility range, not a standalone signal. Trend vs Range - **Trending market**: Price stays on one side of VWAP, bands widen. - **Ranging market**: Price oscillates around VWAP, bands contract. ### Confluence Trading Combine VWAP Bands with horizontal support/resistance levels. When a VWAP band aligns with a key level — that's a high-probability zone for entries. ⚙️ Settings | Parameter | Default | Description | |-----------|---------|-------------| | Anchor Period | Session | VWAP reset period (Session/Week/Month/Quarter/Year) | | Source | hlc3 | Price source (hlc3, close, ohlc4) | | Band 1 Multiplier | 1.0 | First band (±1σ) | | Band 2 Multiplier | 2.0 | Second band (±2σ) | | Band 3 Multiplier | 3.0 | Third band (±3σ) | | Fill Bands | ✅ | Color fill between bands | | Show Labels | ✅ | Labels at band edges | | Prev VWAP Close | ❌ | Previous session VWAP close level | 💡 Tips 1. **Intraday (M5–H1):** Use Anchor = Session. VWAP resets daily. 2. **Swing (H4–D1):** Use Anchor = Week or Month for broader context. 3. **Forex/Gold:** Source `hlc3` provides the closest approximation to tick-based VWAP. 4. **Stocks:** Session VWAP matches the standard institutional benchmark. ⚠️ Note This indicator calculates VWAP from bar data (OHLCV), not tick data. On lower timeframes (M1–M5), accuracy is near-perfect. On higher timeframes (H4+), slight deviations from tick-based VWAP may occur — this is a fundamental limitation of bar-based calculation.
Pine Script®指標
由tamerlanmma94提供
Scalping Strategy with Targets1. Indicator Overview Property Value Name Scalping Strategy with Targets Version Pine Script v6 Type Technical Indicator with Trading Signals Overlay Yes (displayed on price chart) 2. Core Functionality This indicator generates buy and sell signals based on a combination of: Exponential Moving Averages (EMA) crossover RSI (Relative Strength Index) Stochastic Oscillator MACD (optional confirmation) ATR (for dynamic TP/SL levels) 3. Technical Indicators Used Indicator Parameters Purpose Fast EMA Period: 9 Short-term trend detection Slow EMA Period: 21 Long-term trend detection RSI Period: 14, Overbought: 70, Oversold: 30 Momentum measurement Stochastic K: 14, D: 3, Overbought: 80, Oversold: 20 Overbought/Oversold zones MACD Fast: 12, Slow: 26, Signal: 9 Trend confirmation (optional) ATR Period: 14 Dynamic TP/SL calculation 4. Signal Modes Mode 1: Strong (EMA + Trend) BUY = EMA Crossover + Fast EMA Rising + (RSI < 50 OR Uptrend) SELL = EMA Crossunder + Fast EMA Falling + (RSI > 50 OR Downtrend) Mode 2: Very Strong (EMA + RSI + Trend) BUY = EMA Crossover + RSI < 40 + Fast EMA Rising + Price > Fast EMA SELL = EMA Crossunder + RSI > 60 + Fast EMA Falling + Price < Fast EMA Mode 3: Confirmations Mode Requires a user-defined number of confirmations (1-3) Each condition counts as one confirmation: EMA Crossover/Crossunder Fast MA Rising/Falling RSI below/above 50 Price above/below Fast MA Uptrend/Downtrend MACD Bullish/Bearish (if enabled) 5. Filters (Optional) Filter Condition Purpose Volume Filter Volume > Volume SMA Ensure sufficient liquidity ATR Filter ATR > Minimum ATR Avoid low volatility periods 6. TP/SL Calculation All levels are calculated dynamically using ATR multiplier: For BUY Signals: ├── TP1 = Entry Price + (ATR × 1.5) ├── TP2 = Entry Price + (ATR × 2.5) └── SL = Entry Price - (ATR × 0.5) For SELL Signals: ├── TP1 = Entry Price - (ATR × 1.5) ├── TP2 = Entry Price - (ATR × 2.5) └── SL = Entry Price + (ATR × 0.5) 7. Signal Tracking Logic ┌─────────────────────────────────────────────────────────────┐ │ 1. Signal Triggered │ │ → Record entry price │ │ → Calculate TP1, TP2, SL │ │ → Set signalActive = true │ ├─────────────────────────────────────────────────────────────┤ │ 2. Active Trade Monitoring │ │ → Check if price hits TP1 (mark as hit) │ │ → Check if price hits TP2 (mark as hit) │ │ → Check if price hits SL (mark as hit) │ ├─────────────────────────────────────────────────────────────┤ │ 3. Trade Closure │ │ → When TP1 AND TP2 hit → Close trade │ │ → When SL hit → Close trade │ │ → Reset all values │ └─────────────────────────────────────────────────────────────┘ 8. Dashboard Display The indicator displays an interactive dashboard showing: Row Content 1 Signal Type (BUY/SELL) + PnL % 2 Entry Price + Current Price + Trend Direction 3 Target Price (TP2) + Percentage + Status 4 Stop Loss + Percentage + Status 5 Separator 6 ATR Value + RSI Value 7 Stochastic + MACD Status 8 Volume + Confirmations Count 9. Visual Elements On Chart: Fast EMA (Orange line) Slow EMA (Blue line) BUY Signal (Green triangle below bar) SELL Signal (Red triangle above bar) TP1 Line (Lime/Red dashed line - visible on last bar only) TP2 Line (Semi-transparent Lime/Red - visible on last bar only) SL Line (Red line - visible on last bar only) Dashboard Colors: Element Color BUY Signal Green (#00FF00) SELL Signal Red (#FF0000) Uptrend Green Downtrend Red Target Hit ✓✓ (Green) SL Hit ✓ (Red) Pending ○ (Gray) 10. Alerts The indicator provides alert conditions for: Buy Signal triggered Sell Signal triggered 11. Key Features ✅ 3 Signal Modes - Choose between Strong, Very Strong, or Confirmations ✅ Optional Filters - Volume and ATR filters ✅ Dynamic TP/SL - Based on ATR volatility ✅ Interactive Dashboard - Real-time trade monitoring ✅ Visual Targets - TP/SL lines on chart ✅ Alert System - TradingView notifications ✅ Multi-timeframe Compatible - Works on any timeframe
Pine Script®指標
由iqraa888提供
Market Regime AnalyzerStatistical regime detection with forward-looking transition probabilities. Combines drift testing, variance ratios, and volume delta to classify markets into 5 regimes and quantify transition probabilities. What Regime Are We In, and What's Likely Next? That's the question this indicator answers with statistical rigor and forward-looking probabilities. The Problem: Most traders classify regimes arbitrarily: "Bull if price > 200 MA" or "Bear if RSI < 30." These rules ignore statistical significance, volume confirmation, and mean reversion patterns. The result? Late entries, false signals, and confusion when markets transition. The Solution: Market Regime Analyzer combines drift detection, variance ratio testing, and volume delta analysis to classify markets into 5 distinct regimes. Then it calculates the probability of transitioning to each regime based on historical patterns. The Benefit: Know not just where you are, but where you're likely going - with probabilities, not guesses. The Five Market Regimes 🟢 Strong Bull (Regime 1) - Statistically significant upward drift (t-stat > 1.96) - Strong buying pressure (volume delta > 0.3) - No mean reversion detected - **Trade:** Trend-following strategies, ride the momentum 🟢 Weak Bull (Regime 2) - Upward drift present - BUT weak volume OR mean reversion detected - **Trade:** Reduce position size, tighten stops, prepare for consolidation ⚪ Consolidation (Regime 3) - No statistically significant drift - Mixed volume signals - Mean reversion likely present - **Trade:** Range-trading, avoid trend-following systems 🔴 Weak Bear (Regime 4) - Downward drift present - BUT weak volume pressure - **Trade:** Cautious shorts, reduce exposure, prepare for bounce 🔴 Strong Bear (Regime 5) - Statistically significant downward drift (t-stat < -1.96) - Strong selling pressure (volume delta < -0.3) - No mean reversion detected - **Trade:** Trend-following shorts, protective puts The Statistical Framework 1. Drift Detection with T-Statistics Instead of guessing if there's a trend, we test it statistically. How it works: - Calculates mean return over lookback period - Standardizes by volatility - Compares to significance threshold (default 1.96 = 95% confidence) What it tells you: - T-stat > 1.96: Statistically significant uptrend - T-stat < -1.96: Statistically significant downtrend - In between: No significant trend (consolidation) Why it matters: Only trades trends that are statistically validated, not just visually apparent. 2. Mean Reversion Testing (Variance Ratio) Based on Lo & MacKinlay (1988) research, this detects when markets are range-bound. How it works: - Compares variance at different time scales - Variance Ratio < 0.8 indicates mean reversion What it tells you: - Mean reversion = NO: Trends can continue - Mean reversion = YES: Expect price to return to mean, not breakout Why it matters: Prevents chasing breakouts in range-bound markets. 3. Volume Delta Analysis Total volume tells you HOW MUCH traded. Volume delta tells you WHO won. How it works: - Buying pressure - Selling pressure = Volume Delta - Normalized to show relative strength What it tells you: - Strong positive delta (>0.3): Buyers in control - Strong negative delta (<-0.3): Sellers in control - Weak delta: No clear winner Why it matters: Price can move up on weak buying or down on weak selling. Volume delta reveals the truth. 4. Transition Probability Matrix Historical regime changes predict future regime changes. How it works: - Tracks every regime transition over last 100 bars (configurable) - Builds probability distribution for next regime - Updates continuously Example: Current: Strong Bull Historical transitions from Strong Bull: - Stayed Strong Bull: 45% - Became Weak Bull: 30% - Became Consolidation: 20% - Became Weak Bear: 4% - Became Strong Bear: 1% What it tells you: Strong Bull has 75% chance of staying bullish (45% + 30%), only 5% chance of bearish turn. Why it matters: Adapts to your specific market's behavior patterns. How to Use This Indicator Strategy Adaptation In Strong Bull/Bear Regimes: - Use trend-following strategies - Wider stops, let winners run - Add to positions on pullbacks - High confidence in directional trades In Weak Bull/Bear Regimes: - Reduce position sizes by 50% - Tighter stops - Take profits earlier - Prepare for regime change In Consolidation: - Switch to range-trading strategies - Avoid trend-following systems - Sell resistance, buy support - Wait for regime change before trend trades Risk Management Position Sizing: - Strong regime + high continuation probability (>60%) = Normal size - Weak regime OR high transition probability = Half size - Consolidation = Quarter size or skip Stop Loss Placement: - Strong regime: Use wider stops (2x ATR) - Weak regime: Tighter stops (1x ATR) - Consolidation: Very tight stops (0.5x ATR) Entry Timing Best entries: - Regime just changed to Strong Bull/Bear - High probability (>50%) of staying in current regime - No divergence signals present - Drift and volume delta aligned Avoid entries: - High probability of regime change - Divergence signals appearing - Mean reversion detected in trending regime - Weak volume despite price movement Reading the Dashboard Current Regime Color-coded for instant recognition: - Dark Green = Strong Bull - Light Green = Weak Bull - Gray = Consolidation - Light Red = Weak Bear - Dark Red = Strong Bear Annualized Drift Expected annual return based on recent trend. - Positive = Upward bias - Negative = Downward bias - Near zero = No directional edge T-Statistic Measures statistical significance of drift. - > 1.96 = 95% confident in uptrend - < -1.96 = 95% confident in downtrend - Between = Not statistically significant Mean Reversion - Yes = Expect price to return to mean (range-bound) - No = Trends can continue (trending market) Volume Pressure Normalized volume delta strength. - > 0.3 = Strong buying - < -0.3 = Strong selling - Near 0 = Balanced Transition Probabilities Shows most likely next regime. - Highest probability = Most likely outcome - Evenly distributed = High uncertainty - Concentrated = High confidence in direction Practical Examples Example 1: Strong Bull with High Continuation Dashboard shows: Current Regime: Strong Bull Drift: +22% annualized T-Stat: 3.2 Mean Reversion: No Volume Pressure: +0.45 Probabilities: → Strong Bull: 50% → Weak Bull: 25% → Consolidation: 20% → Bears: 5% Interpretation: - Strong uptrend (t-stat 3.2 >> 1.96) - No mean reversion = trends can continue - Strong buying pressure (0.45 > 0.3) - 75% chance stays bullish (50% + 25%) Action: - Full position size on long setups - Use trend-following entries - Wider stops (2x ATR) - High conviction trades Example 2: Weak Bull Before Consolidation Dashboard shows: Current Regime: Weak Bull Drift: +8% annualized T-Stat: 1.2 Mean Reversion: Yes Volume Pressure: +0.15 Probabilities: → Strong Bull: 10% → Weak Bull: 30% → Consolidation: 50% → Weak Bear: 10% Interpretation: - Weak drift (t-stat 1.2 < 1.96) - Mean reversion detected = range-bound likely - Weak volume (0.15 < 0.3) - 50% chance of consolidation Action: - Reduce long positions - Tighten stops - Prepare for range-bound trading - Avoid new trend trades Example 3: Regime Transition Alert Previous: Weak Bull Current: Consolidation Volume divergence signal appeared: Price made new high, volume delta weakened Interpretation: - Trend exhausted - Buyers losing control - Regime confirmed the transition Action: - Exit trend-following longs - Switch to range-trading approach - Wait for new regime before new directional trades Settings Guide ### Regime Detection Period (50) Number of bars for statistical calculations. - **30-40:** More responsive, catches changes faster, more regime switches - **50 (default):** Balanced for daily/4H charts - **75-100:** More stable, fewer false regime changes, slower to adapt Transition History Depth (100) How much history to use for probabilities. - **50-75:** Adapts quickly to recent behavior - **100 (default):** Balanced robustness - **150-200:** More stable probabilities, slower to adapt Volume Delta Period (14) Period for volume calculations. - **7-10:** More sensitive to volume shifts - **14 (default):** Standard period - **20-30:** Smoother, less noise Significance Threshold (1.96) T-statistic required for trend classification. - **1.64:** 90% confidence, more trend regimes detected - **1.96 (default):** 95% confidence, balanced - **2.58:** 99% confidence, very conservative, mostly consolidation Best Practices Do: - Wait for regime confirmation (at least 3-5 bars in new regime) - Use probabilities to size positions appropriately - Combine with support/resistance for entries - Respect mean reversion signals - Adapt strategy to current regime Don't: - Trade every regime change immediately - Ignore high transition probabilities - Use trend strategies in consolidation - Override statistical signals with gut feel - Trade against Strong regimes without clear setup Timeframe Recommendations Daily Charts: - Default settings work well - Most reliable regime detection - Best for swing trading 4H Charts: - Use default or slightly higher lookback (60-75) - Good for active swing trading - More regime changes than daily 1H Charts: - Reduce lookback to 30-40 - More noise, use with caution - Better for intraday position trading 15M and below: - Not recommended - Too much noise for statistical validity - Regimes change too frequently Combining with Other Indicators Works Well With: Moving Averages - Use regime for directional bias - MAs for specific entry/exit points Support/Resistance - Regime shows context - S/R shows specific levels - High probability at confluence Volume Profile - Regime shows regime - Profile shows where volume is - Target high-volume nodes RSI/MACD - Regime provides context - Momentum shows entry timing - Combine for higher probability Example Combined Setup Regime: Strong Bull Price: Above 200 MA Level: Pullback to support RSI: Oversold (30) Volume Delta: Still positive Setup: Long entry Reason: Trend intact, healthy pullback, buyers still present Divergence Signals The indicator shows volume divergence warnings: Bearish Divergence (Red Triangle Down) - Price makes new high - Volume delta makes lower high - Warning: Buyers weakening, potential reversal Bullish Divergence (Green Triangle Up) - Price makes new low - Volume delta makes higher low - Warning: Sellers weakening, potential reversal How to use: - Divergence in Strong regime = early warning of regime change - Confirms when regime actually transitions - Don't trade divergence alone, wait for regime confirmation Limitations This Indicator Cannot: **Predict black swan events** - Unexpected news overrides all technical regimes **Work in all markets** - Needs liquid markets with reliable volume data **Guarantee profits** - Probabilities are not certainties **Replace fundamental analysis** - Technical regimes can diverge from fundamentals Works Best: - Liquid markets (major indices, forex, crypto, large-cap stocks) - Daily and 4H timeframes - Combined with other analysis - With proper risk management - In normal market conditions Common Questions "Why did the regime stay consolidation despite strong price move?" The indicator detected mean reversion (variance ratio < 0.8), indicating the move will likely reverse. Or the move wasn't statistically significant (t-stat < 1.96). Trust the statistics over visual appearance. "Probabilities show 30% for each regime. What does that mean?" High uncertainty. The market is at an inflection point. Reduce position sizes and wait for clearer regime formation. "Can I use this for day trading?" Not recommended on timeframes below 1H. Statistical tests need sufficient data. Better suited for swing trading. "Why does this show Strong Bull when my momentum indicators show weakness?" Momentum can weaken while the trend remains statistically significant. The indicator focuses on drift and volume, not momentum. Consider it a different perspective. Technical Notes Volume Delta Approximation Uses OHLCV data to approximate order flow: - Buy volume ≈ Volume on up-closes - Sell volume ≈ Volume on down-closes - Delta = Buy - Sell **Note:** Real order flow (from futures or Level 2) is more precise. This approximation works well on liquid markets. Statistical Tests Drift T-Test: - Null hypothesis: No drift (mean return = 0) - Reject if |t-stat| > threshold - Based on standard hypothesis testing Variance Ratio: - Compares 2-period variance to 1-period variance - Ratio = 1 for random walk - Ratio < 1 for mean reversion - Threshold of 0.8 based on empirical testing Transition Probability Implementation Due to Pine Script v5 limitations (no native 2D arrays), the 5×5 transition matrix is stored as a flat 1D array of 25 elements: - Position maps to index: `row × 5 + col` - Example: Transition from Regime 2 to Regime 4 is at index `1 × 5 + 3 = 8` - Laplace smoothing (0.1) prevents zero probabilities - Row sums normalized to calculate probabilities This approach is computationally efficient and maintains statistical accuracy. No Repainting All calculations confirmed on bar close. Regime changes appear when the bar closes, not during formation. Historical analysis is accurate. Alert Conditions Regime Change - Triggers when regime transitions to any new state - Message shows new regime number (1-5) Bearish Divergence - Triggers when price makes new high but volume delta doesn't confirm Bullish Divergence - Triggers when price makes new low but volume delta doesn't confirm Disclaimer FOR EDUCATIONAL PURPOSES ONLY This indicator uses statistical methods to analyze market regimes. It does not predict the future or guarantee trading success. Markets are probabilistic, not deterministic. A 70% probability of staying bullish means 30% chance of regime change. Always use proper risk management. Past regime transitions do not guarantee future transitions. Market structure can change. Statistical relationships can break down. Never risk more than you can afford to lose. Use stop losses on every trade. Test thoroughly before live trading. Consult a qualified financial advisor. © 2026 | Open Source Statistical rigor meets practical application
Pine Script®指標
由DavidEH提供
Bifurcation Zone - CAEBifurcation Zone — Cognitive Adversarial Engine (BZ-CAE) Bifurcation Zone — CAE (BZ-CAE) is a next-generation divergence detection system enhanced by a Cognitive Adversarial Engine that evaluates both sides of every potential trade before presenting signals. Unlike traditional divergence indicators that show every price-oscillator disagreement regardless of context, BZ-CAE applies comprehensive market-state intelligence to identify only the divergences that occur in favorable conditions with genuine probability edges. The system identifies structural bifurcation points — critical junctures where price and momentum disagree, signaling potential reversals or continuations — then validates these opportunities through five interconnected intelligence layers: Trend Conviction Scoring , Directional Momentum Alignment , Multi-Factor Exhaustion Modeling , Adversarial Validation , and Confidence Scoring . The result is a selective, context-aware signal system that filters noise and highlights high-probability setups. This is not a "buy the arrow" indicator. It's a decision support framework that teaches you how to read market state, evaluate divergence quality, and make informed trading decisions based on quantified intelligence rather than hope. What Sets BZ-CAE Apart: Technical Architecture The Problem With Traditional Divergence Indicators Most divergence indicators operate on a simple rule: if price makes a higher high and RSI makes a lower high, show a bearish signal. If price makes a lower low and RSI makes a higher low, show a bullish signal. This creates several critical problems: Context Blindness : They show counter-trend signals in powerful trends that rarely reverse, leading to repeated losses as you fade momentum. Signal Spam : Every minor price-oscillator disagreement generates an alert, overwhelming you with low-quality setups and creating analysis paralysis. No Quality Ranking : All signals are treated identically. A marginal divergence in choppy conditions receives the same visual treatment as a high-conviction setup at a major exhaustion point. Single-Sided Evaluation : They ask "Is this a good long?" without checking if the short case is overwhelmingly stronger, leading you into obvious bad trades. Static Configuration : You manually choose RSI 14 or Stochastic 14 and hope it works, with no systematic way to validate if that's optimal for your instrument. BZ-CAE's Solution: Cognitive Adversarial Intelligence BZ-CAE solves these problems through an integrated five-layer intelligence architecture: 1. Trend Conviction Score (TCS) — 0 to 1 Scale Most indicators check if ADX is above 25 to determine "trending" conditions. This binary approach misses nuance. TCS is a weighted composite metric: Formula : 0.35 × normalize(ADX, 10, 35) + 0.35 × structural_strength + 0.30 × htf_alignment Structural Strength : 10-bar SMA of consecutive directional bars. Captures persistence — are bulls or bears consistently winning? HTF Alignment : Multi-timeframe EMA stacking (20/50/100/200). When all EMAs align in the same direction, you're in institutional trend territory. Purpose : Quantifies how "locked in" the trend is. When TCS exceeds your threshold (default 0.80), the system knows to avoid counter-trend trades unless other factors override. Interpretation : TCS > 0.85: Very strong trend — counter-trading is extremely high risk TCS 0.70-0.85: Strong trend — favor continuation, require exhaustion for reversals TCS 0.50-0.70: Moderate trend — context matters, both directions viable TCS < 0.50: Weak/choppy — reversals more viable, range-bound conditions 2. Directional Momentum Alignment (DMA) — ATR-Normalized Formula : (EMA21 - EMA55) / ATR14 This isn't just "price above EMA" — it's a regime-aware momentum gauge. The same $100 price movement reads completely differently in high-volatility crypto versus low-volatility forex. By normalizing with ATR, DMA adapts its interpretation to current market conditions. Purpose : Quantifies the directional "force" behind current price action. Positive = bullish push, negative = bearish push. Magnitude = strength. Interpretation : DMA > 0.7: Strong bullish momentum — bearish divergences risky DMA 0.3 to 0.7: Moderate bullish bias DMA -0.3 to 0.3: Balanced/choppy conditions DMA -0.7 to -0.3: Moderate bearish bias DMA < -0.7: Strong bearish momentum — bullish divergences risky 3. Multi-Factor Exhaustion Modeling — 0 to 1 Probability Single-metric exhaustion detection (like "RSI > 80") misses complex market states. BZ-CAE aggregates five independent exhaustion signals: Volume Spikes : Current volume versus 50-bar average 2.5x average: 0.25 weight 2.0x average: 0.15 weight 1.5x average: 0.10 weight Divergence Present : The fact that a divergence exists contributes 0.30 weight — structural momentum disagreement is itself an exhaustion signal. RSI Extremes : Captures oscillator climax zones RSI > 80 or < 20: 0.25 weight RSI > 75 or < 25: 0.15 weight Pin Bar Detection : Identifies rejection candles (2:1 wick-to-body ratio, indicating failed breakout attempts): 0.15 weight Extended Runs : Consecutive bars above/below EMA20 without pullback 30+ bars: 0.15 weight (market hasn't paused to consolidate) Total exhaustion score is the sum of all applicable weights, capped at 1.0. Purpose : Detects when strong trends become vulnerable to reversal. High exhaustion can override trend filters, allowing counter-trend trades at genuine turning points that basic indicators would miss. Interpretation : Exhaustion > 0.75: High probability of climax — yellow background shading alerts you visually Exhaustion 0.50-0.75: Moderate overextension — watch for confirmation Exhaustion < 0.50: Fresh move — trend can continue, counter-trend trades higher risk 4. Adversarial Validation — Game Theory Applied to Trading This is BZ-CAE's signature innovation. Before approving any signal, the engine quantifies BOTH sides of the trade simultaneously: For Bullish Divergences , it calculates: Bull Case Score (0-1+) : Distance below EMA20 (pullback quality): up to 0.25 Bullish EMA alignment (close > EMA20 > EMA50): 0.25 Oversold RSI (< 40): 0.25 Volume confirmation (> 1.2x average): 0.25 Bear Case Score (0-1+) : Price below EMA50 (structural weakness): 0.30 Very oversold RSI (< 30, indicating knife-catching): 0.20 Differential = Bull Case - Bear Case If differential < -0.10 (default threshold), the bear case is dominating — signal is BLOCKED or ANNOTATED. For Bearish Divergences , the logic inverts (Bear Case vs Bull Case). Purpose : Prevents trades where you're fighting obvious strength in the opposite direction. This is institutional-grade risk management — don't just evaluate your trade, evaluate the counter-trade simultaneously. Why This Matters : You might see a bullish divergence at a local low, but if price is deeply below major support EMAs with strong bearish momentum, you're catching a falling knife. The adversarial check catches this and blocks the signal. 5. Confidence Scoring — 0 to 1 Quality Assessment Every signal that passes initial filters receives a comprehensive quality score: Formula : 0.30 × normalize(TCS) // Trend context + 0.25 × normalize(|DMA|) // Momentum magnitude + 0.20 × pullback_quality // Entry distance from EMA20 + 0.15 × state_quality // ADX + alignment + structure + 0.10 × divergence_strength // Slope separation magnitude + adversarial_bonus (0-0.30) // Your side's advantage Purpose : Ranks setup quality for filtering and position sizing decisions. You can set a minimum confidence threshold (default 0.35) to ensure only quality setups reach your chart. Interpretation : Confidence > 0.70: Premium setup — consider increased position size Confidence 0.50-0.70: Good quality — standard size Confidence 0.35-0.50: Acceptable — reduced size or skip if conservative Confidence < 0.35: Marginal — blocked in Filtering mode, annotated in Advisory mode CAE Operating Modes: Learning vs Enforcement Off : Disables all CAE logic. Raw divergence pipeline only. Use for baseline comparison. Advisory : Shows ALL signals regardless of CAE evaluation, but annotates signals that WOULD be blocked with specific warnings (e.g., "Bull: strong downtrend (TCS=0.87)" or "Adversarial bearish"). This is your learning mode — see CAE's decision logic in action without missing educational opportunities. Filtering : Actively blocks low-quality signals. Only setups that pass all enabled gates (Trend Filter, Adversarial Validation, Confidence Gating) reach your chart. This is your live trading mode — trust the system to enforce discipline. CAE Filter Gates: Three-Layer Protection When CAE is enabled, signals must pass through three independent gates (each can be toggled on/off): Gate 1: Strong Trend Filter If TCS ≥ tcs_threshold (default 0.80) And signal is counter-trend (bullish in downtrend or bearish in uptrend) And exhaustion < exhaustion_required (default 0.50) Then: BLOCK signal Logic: Don't fade strong trends unless the move is clearly overextended Gate 2: Adversarial Validation Calculate both bull case and bear case scores If opposing case dominates by more than adv_threshold (default 0.10) Then: BLOCK signal Logic: Avoid trades where you're fighting obvious strength in the opposite direction Gate 3: Confidence Gating Calculate composite confidence score (0-1) If confidence < min_confidence (default 0.35) Then: In Filtering mode, BLOCK signal; in Advisory mode, ANNOTATE with warning Logic: Only take setups with minimum quality threshold All three gates work together. A signal must pass ALL enabled gates to fire. Visual Intelligence System Bifurcation Zones (Supply/Demand Blocks) When a divergence signal fires, BZ-CAE draws a semi-transparent box extending 15 bars forward from the signal pivot: Demand Zones (Bullish) : Theme-colored box (cyan in Cyberpunk, blue in Professional, etc.) labeled "Demand" — marks where smart money likely placed buy orders as price diverged at the low. Supply Zones (Bearish) : Theme-colored box (magenta in Cyberpunk, orange in Professional) labeled "Supply" — marks where smart money likely placed sell orders as price diverged at the high. Theory : Divergences represent institutional disagreement with the crowd. The crowd pushed price to an extreme (new high or low), but momentum (oscillator) is waning, indicating smart money is taking the opposite side. These zones mark order placement areas that become future support/resistance. Use Cases : Exit targets: Take profit when price returns to opposite-side zone Re-entry levels: If price returns to your entry zone, consider adding Stop placement: Place stops just beyond your zone (below demand, above supply) Auto-Cleanup : System keeps the last 20 zones to prevent chart clutter. Adversarial Bar Coloring — Real-Time Market Debate Heatmap Each bar is colored based on the Bull Case vs Bear Case differential: Strong Bull Advantage (diff > 0.3): Full theme bull color (e.g., cyan) Moderate Bull Advantage (diff > 0.1): 50% transparency bull Neutral (diff -0.1 to 0.1): Gray/neutral theme Moderate Bear Advantage (diff < -0.1): 50% transparency bear Strong Bear Advantage (diff < -0.3): Full theme bear color (e.g., magenta) This creates a real-time visual heatmap showing which side is "winning" the market debate. When bars flip from cyan to magenta (or vice versa), you're witnessing a shift in adversarial advantage — a leading indicator of potential momentum changes. Exhaustion Shading When exhaustion score exceeds 0.75, the chart background displays a semi-transparent yellow highlight. This immediate visual warning alerts you that the current move is at high risk of reversal, even if trend indicators remain strong. Visual Themes — Six Aesthetic Options Cyberpunk : Cyan/Magenta/Yellow — High contrast, neon aesthetic, excellent for dark-themed trading environments Professional : Blue/Orange/Green — Corporate color palette, suitable for presentations and professional documentation Ocean : Teal/Red/Cyan — Aquatic palette, calming for extended monitoring sessions Fire : Orange/Red/Coral — Warm aggressive colors, high energy Matrix : Green/Red/Lime — Code aesthetic, homage to classic hacker visuals Monochrome : White/Gray — Minimal distraction, maximum focus on price action All visual elements (signal markers, zones, bar colors, dashboard) adapt to your selected theme. Divergence Engine — Core Detection System What Are Divergences? Divergences occur when price action and momentum indicators disagree, creating structural tension that often resolves in a change of direction: Regular Divergence (Reversal Signal) : Bearish Regular : Price makes higher high, oscillator makes lower high → Potential trend reversal down Bullish Regular : Price makes lower low, oscillator makes higher low → Potential trend reversal up Hidden Divergence (Continuation Signal) : Bearish Hidden : Price makes lower high, oscillator makes higher high → Downtrend continuation Bullish Hidden : Price makes higher low, oscillator makes lower low → Uptrend continuation Both types can be enabled/disabled independently in settings. Pivot Detection Methods BZ-CAE uses symmetric pivot detection with separate lookback and lookforward periods (default 5/5): Pivot High : Bar where high > all highs within lookback range AND high > all highs within lookforward range Pivot Low : Bar where low < all lows within lookback range AND low < all lows within lookforward range This ensures structural validity — the pivot must be a clear local extreme, not just a minor wiggle. Divergence Validation Requirements For a divergence to be confirmed, it must satisfy: Slope Disagreement : Price slope and oscillator slope must move in opposite directions (for regular divs) or same direction with inverted highs/lows (for hidden divs) Minimum Slope Change : |osc_slope| > min_slope_change / 100 (default 1.0) — filters weak, marginal divergences Maximum Lookback Range : Pivots must be within max_lookback bars (default 60) — prevents ancient, irrelevant divergences ATR-Normalized Strength : Divergence strength = min(|price_slope| × |osc_slope| × 10, 1.0) — quantifies the magnitude of disagreement in volatility context Regular divergences receive 1.0× weight; hidden divergences receive 0.8× weight (slightly less reliable historically). Oscillator Options — Five Professional Indicators RSI (Relative Strength Index) : Classic overbought/oversold momentum indicator. Best for: General purpose divergence detection across all instruments. Stochastic : Range-bound %K momentum comparing close to high-low range. Best for: Mean reversion strategies and range-bound markets. CCI (Commodity Channel Index) : Measures deviation from statistical mean, auto-normalized to 0-100 scale. Best for: Cyclical instruments and commodities. MFI (Money Flow Index) : Volume-weighted RSI incorporating money flow. Best for: Volume-driven markets like stocks and crypto. Williams %R : Inverse stochastic looking back over period, auto-adjusted to 0-100. Best for: Reversal detection at extremes. Each oscillator has adjustable length (2-200, default 14) and smoothing (1-20, default 1). You also set overbought (50-100, default 70) and oversold (0-50, default 30) thresholds. Signal Timing Modes — Understanding Repainting BZ-CAE offers two timing policies with complete transparency about repainting behavior: Realtime (1-bar, peak-anchored) How It Works : Detects peaks 1 bar ago using pattern: high > high AND high > high Signal prints on the NEXT bar after peak detection (bar_index) Visual marker anchors to the actual PEAK bar (bar_index - 1, offset -1) Signal locks in when bar CONFIRMS (closes) Repainting Behavior : On the FORMING bar (before close), the peak condition may change as new prices arrive Once bar CLOSES (barstate.isconfirmed), signal is locked permanently This is preview/early warning behavior by design Best For : Active monitoring and immediate alerts Learning the system (seeing signals develop in real-time) Responsive entry if you're watching the chart live Confirmed (lookforward) How It Works : Uses Pine Script's built-in ta.pivothigh() and ta.pivotlow() functions Requires full pivot validation period (lookback + lookforward bars) Signal prints pivot_lookforward bars after the actual peak (default 5-bar delay) Visual marker anchors to the actual peak bar (offset -pivot_lookforward) No Repainting Behavior Best For : Backtesting and historical analysis Conservative entries requiring full confirmation Automated trading systems Swing trading with larger timeframes Tradeoff : Delayed entry by pivot_lookforward bars (typically 5 bars) On a 5-minute chart, this is a 25-minute delay On a 4-hour chart, this is a 20-hour delay Recommendation : Use Confirmed for backtesting to verify system performance honestly. Use Realtime for live monitoring only if you're actively watching the chart and understand pre-confirmation repainting behavior. Signal Spacing System — Anti-Spam Architecture Even after CAE filtering, raw divergences can cluster. The spacing system enforces separation: Three Independent Filters 1. Min Bars Between ANY Signals (default 12): Prevents rapid-fire clustering across both directions If last signal (bull or bear) was within N bars, block new signal Ensures breathing room between all setups 2. Min Bars Between SAME-SIDE Signals (default 24, optional enforcement): Prevents bull-bull or bear-bear spam Separate tracking for bullish and bearish signal timelines Toggle enforcement on/off 3. Min ATR Distance From Last Signal (default 0, optional): Requires price to move N × ATR from last signal location Ensures meaningful price movement between setups 0 = disabled, 0.5-2.0 = typical range for enabled All three filters work independently. A signal must pass ALL enabled filters to proceed. Practical Guidance : Scalping (1-5m) : Any 6-10, Same-side 12-20, ATR 0-0.5 Day Trading (15m-1H) : Any 12, Same-side 24, ATR 0-1.0 Swing Trading (4H-D) : Any 20-30, Same-side 40-60, ATR 1.0-2.0 Dashboard — Real-Time Control Center The dashboard (toggleable, four corner positions, three sizes) provides comprehensive system intelligence: Oscillator Section Current oscillator type and value State: OVERBOUGHT / OVERSOLD / NEUTRAL (color-coded) Length parameter Cognitive Engine Section TCS (Trend Conviction Score) : Current value with emoji state indicator 🔥 = Strong trend (>0.75) 📊 = Moderate trend (0.50-0.75) 〰️ = Weak/choppy (<0.50) Color: Red if above threshold (trend filter active), yellow if moderate, green if weak DMA (Directional Momentum Alignment) : Current value with emoji direction indicator 🐂 = Bullish momentum (>0.5) ⚖️ = Balanced (-0.5 to 0.5) 🐻 = Bearish momentum (<-0.5) Color: Green if bullish, red if bearish Exhaustion : Current value with emoji warning indicator ⚠️ = High exhaustion (>0.75) 🟡 = Moderate (0.50-0.75) ✓ = Low (<0.50) Color: Red if high, yellow if moderate, green if low Pullback : Quality of current distance from EMA20 Values >0.6 are ideal entry zones (not too close, not too far) Bull Case / Bear Case (if Adversarial enabled): Current scores for both sides of the market debate Differential with emoji indicator: 📈 = Bull advantage (>0.2) ➡️ = Balanced (-0.2 to 0.2) 📉 = Bear advantage (<-0.2) Last Signal Metrics Section (New Feature) When a signal fires, this section captures and displays: Signal type (BULL or BEAR) Bars elapsed since signal Confidence % at time of signal TCS value at signal time DMA value at signal time Purpose : Provides a historical reference for learning. You can see what the market state looked like when the last signal fired, helping you correlate outcomes with conditions. Statistics Section Total Signals : Lifetime count across session Blocked Signals : Count and percentage (filter effectiveness metric) Bull Signals : Total bullish divergences Bear Signals : Total bearish divergences Purpose : System health monitoring. If blocked % is very high (>60%), filters may be too strict. If very low (<10%), filters may be too loose. Advisory Annotations When CAE Mode = Advisory, this section displays warnings for signals that would be blocked in Filtering mode: Examples: "Bull spacing: wait 8 bars" "Bear: strong uptrend (TCS=0.87)" "Adversarial bearish" "Low confidence 32%" Multiple warnings can stack, separated by " | ". This teaches you CAE's decision logic transparently. How to Use BZ-CAE — Complete Workflow Phase 1: Initial Setup (First Session) Apply BZ-CAE to your chart Select your preferred Visual Theme (Cyberpunk recommended for visibility) Set Signal Timing to "Confirmed (lookforward)" for learning Choose your Oscillator Type (RSI recommended for general use, length 14) Set Overbought/Oversold to 70/30 (standard) Enable both Regular Divergence and Hidden Divergence Set Pivot Lookback/Lookforward to 5/5 (balanced structure) Enable CAE Intelligence Set CAE Mode to "Advisory" (learning mode) Enable all three CAE filters: Strong Trend Filter , Adversarial Validation , Confidence Gating Enable Show Dashboard , position Top Right, size Normal Enable Draw Bifurcation Zones and Adversarial Bar Coloring Phase 2: Learning Period (Weeks 1-2) Goal : Understand how CAE evaluates market state and filters signals. Activities : Watch the dashboard during signals : Note TCS values when counter-trend signals fail — this teaches you the trend strength threshold for your instrument Observe exhaustion patterns at actual turning points — learn when overextension truly matters Study adversarial differential at signal times — see when opposing cases dominate Review blocked signals (orange X-crosses): In Advisory mode, you see everything — signals that would pass AND signals that would be blocked Check the advisory annotations to understand why CAE would block Track outcomes: Were the blocks correct? Did those signals fail? Use Last Signal Metrics : After each signal, check the dashboard capture of confidence, TCS, and DMA Journal these values alongside trade outcomes Identify patterns: Do confidence >0.70 signals work better? Does your instrument respect TCS >0.85? Understand your instrument's "personality" : Trending instruments (indices, major forex) may need TCS threshold 0.85-0.90 Choppy instruments (low-cap stocks, exotic pairs) may work best with TCS 0.70-0.75 High-volatility instruments (crypto) may need wider spacing Low-volatility instruments may need tighter spacing Phase 3: Calibration (Weeks 3-4) Goal : Optimize settings for your specific instrument, timeframe, and style. Calibration Checklist : Min Confidence Threshold : Review confidence distribution in your signal journal Identify the confidence level below which signals consistently fail Set min_confidence slightly above that level Day trading : 0.35-0.45 Swing trading : 0.40-0.55 Scalping : 0.30-0.40 TCS Threshold : Find the TCS level where counter-trend signals consistently get stopped out Set tcs_threshold at or slightly below that level Trending instruments : 0.85-0.90 Mixed instruments : 0.80-0.85 Choppy instruments : 0.75-0.80 Exhaustion Override Level : Identify exhaustion readings that marked genuine reversals Set exhaustion_required just below the average Typical range : 0.45-0.55 Adversarial Threshold : Default 0.10 works for most instruments If you find CAE is too conservative (blocking good trades), raise to 0.15-0.20 If signals are still getting caught in opposing momentum, lower to 0.07-0.09 Spacing Parameters : Count bars between quality signals in your journal Set min bars ANY to ~60% of that average Set min bars SAME-SIDE to ~120% of that average Scalping : Any 6-10, Same 12-20 Day trading : Any 12, Same 24 Swing : Any 20-30, Same 40-60 Oscillator Selection : Try different oscillators for 1-2 weeks each Track win rate and average winner/loser by oscillator type RSI : Best for general use, clear OB/OS Stochastic : Best for range-bound, mean reversion MFI : Best for volume-driven markets CCI : Best for cyclical instruments Williams %R : Best for reversal detection Phase 4: Live Deployment Goal : Disciplined execution with proven, calibrated system. Settings Changes : Switch CAE Mode from Advisory to Filtering System now actively blocks low-quality signals Only setups passing all gates reach your chart Keep Signal Timing on Confirmed for conservative entries OR switch to Realtime if you're actively monitoring and want faster entries (accept pre-confirmation repaint risk) Use your calibrated thresholds from Phase 3 Enable high-confidence alerts: "⭐ High Confidence Bullish/Bearish" (>0.70) Trading Discipline Rules : Respect Blocked Signals : If CAE blocks a trade you wanted to take, TRUST THE SYSTEM Don't manually override — if you consistently disagree, return to Phase 2/3 calibration The block exists because market state failed intelligence checks Confidence-Based Position Sizing : Confidence >0.70: Standard or increased size (e.g., 1.5-2.0% risk) Confidence 0.50-0.70: Standard size (e.g., 1.0% risk) Confidence 0.35-0.50: Reduced size (e.g., 0.5% risk) or skip if conservative TCS-Based Management : High TCS + counter-trend signal: Use tight stops, quick exits (you're fading momentum) Low TCS + reversal signal: Use wider stops, trail aggressively (genuine reversal potential) Exhaustion Awareness : Exhaustion >0.75 (yellow shading): Market is overextended, reversal risk is elevated — consider early exit or tighter trailing stops even on winning trades Exhaustion <0.30: Continuation bias — hold for larger move, wide trailing stops Adversarial Context : Strong differential against you (e.g., bullish signal with bear diff <-0.2): Use very tight stops, consider skipping Strong differential with you (e.g., bullish signal with bull diff >0.2): Trail aggressively, this is your tailwind Practical Settings by Timeframe & Style Scalping (1-5 Minute Charts) Objective : High frequency, tight stops, quick reversals in fast-moving markets. Oscillator : Type: RSI or Stochastic (fast response to quick moves) Length: 9-11 (more responsive than standard 14) Smoothing: 1 (no lag) OB/OS: 65/35 (looser thresholds ensure frequent crossings in fast conditions) Divergence : Pivot Lookback/Lookforward: 3/3 (tight structure, catch small swings) Max Lookback: 40-50 bars (recent structure only) Min Slope Change: 0.8-1.0 (don't be overly strict) CAE : Mode: Advisory first (learn), then Filtering Min Confidence: 0.30-0.35 (lower bar for speed, accept more signals) TCS Threshold: 0.70-0.75 (allow more counter-trend opportunities) Exhaustion Required: 0.45-0.50 (moderate override) Strong Trend Filter: ON (still respect major intraday trends) Adversarial: ON (critical for scalping protection — catches bad entries quickly) Spacing : Min Bars ANY: 6-10 (fast pace, many setups) Min Bars SAME-SIDE: 12-20 (prevent clustering) Min ATR Distance: 0 or 0.5 (loose) Timing : Realtime (speed over precision, but understand repaint risk) Visuals : Signal Size: Tiny (chart clarity in busy conditions) Show Zones: Optional (can clutter on low timeframes) Bar Coloring: ON (helps read momentum shifts quickly) Dashboard: Small size (corner reference, not main focus) Key Consideration : Scalping generates noise. Even with CAE, expect lower win rate (45-55%) but aim for favorable R:R (2:1 or better). Size conservatively. Day Trading (15-Minute to 1-Hour Charts) Objective : Balance quality and frequency. Standard divergence trading approach. Oscillator : Type: RSI or MFI (proven reliability, volume confirmation with MFI) Length: 14 (industry standard, well-studied) Smoothing: 1-2 OB/OS: 70/30 (classic levels) Divergence : Pivot Lookback/Lookforward: 5/5 (balanced structure) Max Lookback: 60 bars Min Slope Change: 1.0 (standard strictness) CAE : Mode: Filtering (enforce discipline from the start after brief Advisory learning) Min Confidence: 0.35-0.45 (quality filter without being too restrictive) TCS Threshold: 0.80-0.85 (respect strong trends) Exhaustion Required: 0.50 (balanced override threshold) Strong Trend Filter: ON Adversarial: ON Confidence Gating: ON (all three filters active) Spacing : Min Bars ANY: 12 (breathing room between all setups) Min Bars SAME-SIDE: 24 (prevent bull/bear clusters) Min ATR Distance: 0-1.0 (optional refinement, typically 0.5-1.0) Timing : Confirmed (1-bar delay for reliability, no repainting) Visuals : Signal Size: Tiny or Small Show Zones: ON (useful reference for exits/re-entries) Bar Coloring: ON (context awareness) Dashboard: Normal size (full visibility) Key Consideration : This is the "sweet spot" timeframe for BZ-CAE. Market structure is clear, CAE has sufficient data, and signal frequency is manageable. Expect 55-65% win rate with proper execution. Swing Trading (4-Hour to Daily Charts) Objective : Quality over quantity. High conviction only. Larger stops and targets. Oscillator : Type: RSI or CCI (robust on higher timeframes, smooth longer waves) Length: 14-21 (capture larger momentum swings) Smoothing: 1-3 OB/OS: 70/30 or 75/25 (strict extremes) Divergence : Pivot Lookback/Lookforward: 5/5 or 7/7 (structural purity, major swings only) Max Lookback: 80-100 bars (broader historical context) Min Slope Change: 1.2-1.5 (require strong, undeniable divergence) CAE : Mode: Filtering (strict enforcement, premium setups only) Min Confidence: 0.40-0.55 (high bar for entry) TCS Threshold: 0.85-0.95 (very strong trend protection — don't fade established HTF trends) Exhaustion Required: 0.50-0.60 (higher bar for override — only extreme exhaustion justifies counter-trend) Strong Trend Filter: ON (critical on HTF) Adversarial: ON (avoid obvious bad trades) Confidence Gating: ON (quality gate essential) Spacing : Min Bars ANY: 20-30 (substantial separation) Min Bars SAME-SIDE: 40-60 (significant breathing room) Min ATR Distance: 1.0-2.0 (require meaningful price movement) Timing : Confirmed (purity over speed, zero repaint for swing accuracy) Visuals : Signal Size: Small or Normal (clear markers on zoomed-out view) Show Zones: ON (important HTF levels) Bar Coloring: ON (long-term trend awareness) Dashboard: Normal or Large (comprehensive analysis) Key Consideration : Swing signals are rare but powerful. Expect 2-5 signals per month per instrument. Win rate should be 60-70%+ due to stringent filtering. Position size can be larger given confidence. Dashboard Interpretation Reference TCS (Trend Conviction Score) States 0.00-0.50: Weak/Choppy Emoji: 〰️ Color: Green/cyan Meaning: No established trend. Range-bound or consolidating. Both reversal and continuation signals viable. Action: Reversals (regular divs) are safer. Use wider profit targets (market has room to move). Consider mean reversion strategies. 0.50-0.75: Moderate Trend Emoji: 📊 Color: Yellow/neutral Meaning: Developing trend but not locked in. Context matters significantly. Action: Check DMA and exhaustion. If DMA confirms trend and exhaustion is low, favor continuation (hidden divs). If exhaustion is high, reversals are viable. 0.75-0.85: Strong Trend Emoji: 🔥 Color: Orange/warning Meaning: Well-established trend with persistence. Counter-trend is high risk. Action: Require exhaustion >0.50 for counter-trend entries. Favor continuation signals. Use tight stops on counter-trend attempts. 0.85-1.00: Very Strong Trend Emoji: 🔥🔥 Color: Red/danger (if counter-trading) Meaning: Locked-in institutional trend. Extremely high risk to fade. Action: Avoid counter-trend unless exhaustion >0.75 (yellow shading). Focus exclusively on continuation opportunities. Momentum is king here. DMA (Directional Momentum Alignment) Zones -2.0 to -1.0: Strong Bearish Momentum Emoji: 🐻🐻 Color: Dark red Meaning: Powerful downside force. Sellers are in control. Action: Bullish divergences are counter-momentum (high risk). Bearish divergences are with-momentum (lower risk). Size down on longs. -0.5 to 0.5: Neutral/Balanced Emoji: ⚖️ Color: Gray/neutral Meaning: No strong directional bias. Choppy or consolidating. Action: Both directions have similar probability. Focus on confidence score and adversarial differential for edge. 1.0 to 2.0: Strong Bullish Momentum Emoji: 🐂🐂 Color: Bright green/cyan Meaning: Powerful upside force. Buyers are in control. Action: Bearish divergences are counter-momentum (high risk). Bullish divergences are with-momentum (lower risk). Size down on shorts. Exhaustion States 0.00-0.50: Fresh Move Emoji: ✓ Color: Green Meaning: Trend is healthy, not overextended. Room to run. Action: Counter-trend trades are premature. Favor continuation. Hold winners for larger moves. Avoid early exits. 0.50-0.75: Mature Move Emoji: 🟡 Color: Yellow Meaning: Move is aging. Watch for signs of climax. Action: Tighten trailing stops on winning trades. Be ready for reversals. Don't add to positions aggressively. 0.75-0.85: High Exhaustion Emoji: ⚠️ Color: Orange Background: Yellow shading appears Meaning: Move is overextended. Reversal risk elevated significantly. Action: Counter-trend reversals are higher probability. Consider early exits on with-trend positions. Size up on reversal divergences (if CAE allows). 0.85-1.00: Critical Exhaustion Emoji: ⚠️⚠️ Color: Red Background: Yellow shading intensifies Meaning: Climax conditions. Reversal imminent or underway. Action: Aggressive reversal trades justified. Exit all with-trend positions. This is where major turns occur. Confidence Score Tiers 0.00-0.30: Low Quality Color: Red Status: Blocked in Filtering mode Action: Skip entirely. Setup lacks fundamental quality across multiple factors. 0.30-0.50: Moderate Quality Color: Yellow/orange Status: Marginal — passes in Filtering only if >min_confidence Action: Reduced position size (0.5-0.75% risk). Tight stops. Conservative profit targets. Skip if you're selective. 0.50-0.70: High Quality Color: Green/cyan Status: Good setup across most quality factors Action: Standard position size (1.0-1.5% risk). Normal stops and targets. This is your bread-and-butter trade. 0.70-1.00: Premium Quality Color: Bright green/gold Status: Exceptional setup — all factors aligned Visual: Double confidence ring appears Action: Consider increased position size (1.5-2.0% risk, maximum). Wider stops. Larger targets. High probability of success. These are rare — capitalize when they appear. Adversarial Differential Interpretation Bull Differential > 0.3 : Visual: Strong cyan/green bar colors Meaning: Bull case strongly dominates. Buyers have clear advantage. Action: Bullish divergences favored (with-advantage). Bearish divergences face headwind (reduce size or skip). Momentum is bullish. Bull Differential 0.1 to 0.3 : Visual: Moderate cyan/green transparency Meaning: Moderate bull advantage. Buyers have edge but not overwhelming. Action: Both directions viable. Slight bias toward longs. Differential -0.1 to 0.1 : Visual: Gray/neutral bars Meaning: Balanced debate. No clear advantage either side. Action: Rely on other factors (confidence, TCS, exhaustion) for direction. Adversarial is neutral. Bear Differential -0.3 to -0.1 : Visual: Moderate red/magenta transparency Meaning: Moderate bear advantage. Sellers have edge but not overwhelming. Action: Both directions viable. Slight bias toward shorts. Bear Differential < -0.3 : Visual: Strong red/magenta bar colors Meaning: Bear case strongly dominates. Sellers have clear advantage. Action: Bearish divergences favored (with-advantage). Bullish divergences face headwind (reduce size or skip). Momentum is bearish. Last Signal Metrics — Post-Trade Analysis After a signal fires, dashboard captures: Type : BULL or BEAR Bars Ago : How long since signal (updates every bar) Confidence : What was the quality score at signal time TCS : What was trend conviction at signal time DMA : What was momentum alignment at signal time Use Case : Post-trade journaling and learning. Example: "BULL signal 12 bars ago. Confidence: 68%, TCS: 0.42, DMA: -0.85" Analysis : This was a bullish reversal (regular div) with good confidence, weak trend (TCS), but strong bearish momentum (DMA). The bet was that momentum would reverse — a counter-momentum play requiring exhaustion confirmation. Check if exhaustion was high at that time to justify the entry. Track patterns: Do your best trades have confidence >0.65? Do low-TCS signals (<0.50) work better for you? Are you more successful with-momentum (DMA aligned with signal) or counter-momentum? Troubleshooting Guide Problem: No Signals Appearing Symptoms : Chart loads, dashboard shows metrics, but no divergence signals fire. Diagnosis Checklist : Check dashboard oscillator value : Is it crossing OB/OS levels (70/30)? If oscillator stays in 40-60 range constantly, it can't reach extremes needed for divergence detection. Are pivots forming? : Look for local swing highs/lows on your chart. If price is in tight consolidation, pivots may not meet lookback/lookforward requirements. Is spacing too tight? : Check "Last Signal" metrics — how many bars since last signal? If <12 and your min_bars_ANY is 12, spacing filter is blocking. Is CAE blocking everything? : Check dashboard Statistics section — what's the blocked signal count? High blocks indicate overly strict filters. Solutions : Loosen OB/OS Temporarily : Try 65/35 to verify divergence detection works If signals appear, the issue was threshold strictness Gradually tighten back to 67/33, then 70/30 as appropriate Lower Min Confidence : Try 0.25-0.30 (diagnostic level) If signals appear, filter was too strict Raise gradually to find sweet spot (0.35-0.45 typical) Disable Strong Trend Filter Temporarily : Turn off in CAE settings If signals appear, TCS threshold was blocking everything Re-enable and lower TCS_threshold to 0.70-0.75 Reduce Min Slope Change : Try 0.7-0.8 (from default 1.0) Allows weaker divergences through Helpful on low-volatility instruments Widen Spacing : Set min_bars_ANY to 6-8 Set min_bars_SAME_SIDE to 12-16 Reduces time between allowed signals Check Timing Mode : If using Confirmed, remember there's a pivot_lookforward delay (5+ bars) Switch to Realtime temporarily to verify system is working Realtime has no delay but repaints Verify Oscillator Settings : Length 14 is standard but might not fit all instruments Try length 9-11 for faster response Try length 18-21 for slower, smoother response Problem: Too Many Signals (Signal Spam) Symptoms : Dashboard shows 50+ signals in Statistics, confidence scores mostly <0.40, signals clustering close together. Solutions : Raise Min Confidence : Try 0.40-0.50 (quality filter) Blocks bottom-tier setups Targets top 50-60% of divergences only Tighten OB/OS : Use 70/30 or 75/25 Requires more extreme oscillator readings Reduces false divergences in mid-range Increase Min Slope Change : Try 1.2-1.5 (from default 1.0) Requires stronger, more obvious divergences Filters marginal slope disagreements Raise TCS Threshold : Try 0.85-0.90 (from default 0.80) Stricter trend filter blocks more counter-trend attempts Favors only strongest trend alignment Enable ALL CAE Gates : Turn on Trend Filter + Adversarial + Confidence Triple-layer protection Blocks aggressively — expect 20-40% reduction in signals Widen Spacing : min_bars_ANY: 15-20 (from 12) min_bars_SAME_SIDE: 30-40 (from 24) Creates substantial breathing room Switch to Confirmed Timing : Removes realtime preview noise Ensures full pivot validation 5-bar delay filters many false starts Problem: Signals in Strong Trends Get Stopped Out Symptoms : You take a bullish divergence in a downtrend (or bearish in uptrend), and it immediately fails. Dashboard showed high TCS at the time. Analysis : This is INTENDED behavior — CAE is protecting you from low-probability counter-trend trades. Understanding : Check Last Signal Metrics in dashboard — what was TCS when signal fired? If TCS was >0.85 and signal was counter-trend, CAE correctly identified it as high risk Strong trends rarely reverse cleanly without major exhaustion Your losses here are the system working as designed (blocking bad odds) If You Want to Override (Not Recommended) : Lower TCS_threshold to 0.70-0.75 (allows more counter-trend) Lower exhaustion_required to 0.40 (easier override) Disable Strong Trend Filter entirely (very risky) Better Approach : TRUST THE FILTER — it's preventing costly mistakes Wait for exhaustion >0.75 (yellow shading) before counter-trending strong TCS Focus on continuation signals (hidden divs) in high-TCS environments Use Advisory mode to see what CAE is blocking and learn from outcomes Problem: Adversarial Blocking Seems Wrong Symptoms : You see a divergence that "looks good" visually, but CAE blocks with "Adversarial bearish/bullish" warning. Diagnosis : Check dashboard Bull Case and Bear Case scores at that moment Look at Differential value Check adversarial bar colors — was there strong coloring against your intended direction? Understanding : Adversarial catches "obvious" opposing momentum that's easy to miss Example: Bullish divergence at a local low, BUT price is deeply below EMA50, bearish momentum is strong, and RSI shows knife-catching conditions Bull Case might be 0.20 while Bear Case is 0.55 Differential = -0.35, far beyond threshold Block is CORRECT — you'd be fighting overwhelming opposing flow If You Disagree Consistently Review blocked signals on chart — scroll back and check outcomes Did those blocked signals actually work, or did they fail as adversarial predicted? Raise adv_threshold to 0.15-0.20 (more permissive, allows closer battles) Disable Adversarial Validation temporarily (diagnostic) to isolate its effect Use Advisory mode to learn adversarial patterns over 50-100 signals Remember : Adversarial is conservative BY DESIGN. It prevents "obvious" bad trades where you're fighting strong strength the other way. Problem: Dashboard Not Showing or Incomplete Solutions : Toggle "Show Dashboard" to ON in settings Try different dashboard sizes (Small/Normal/Large) Try different positions (Top Left/Right, Bottom Left/Right) — might be off-screen Some sections require CAE Enable = ON (Cognitive Engine section won't appear if CAE is disabled) Statistics section requires at least 1 lifetime signal to populate Check that visual theme is set (dashboard colors adapt to theme) Problem: Performance Lag, Chart Freezing Symptoms : Chart loading is slow, indicator calculations cause delays, pinch-to-zoom lags. Diagnosis : Visual features are computationally expensive, especially adversarial bar coloring (recalculates every bar). Solutions (In Order of Impact) : Disable Adversarial Bar Coloring (MOST EXPENSIVE): Turn OFF "Adversarial Bar Coloring" in settings This is the single biggest performance drain Immediate improvement Reduce Vertical Lines : Lower "Keep last N vertical lines" to 20-30 Or set to 0 to disable entirely Moderate improvement Disable Bifurcation Zones : Turn OFF "Draw Bifurcation Zones" Reduces box drawing calculations Moderate improvement Set Dashboard Size to Small : Smaller dashboard = fewer cells = less rendering Minor improvement Use Shorter Max Lookback : Reduce max_lookback to 40-50 (from 60+) Fewer bars to scan for divergences Minor improvement Disable Exhaustion Shading : Turn OFF "Show Market State" Removes background coloring calculations Minor improvement Extreme Performance Mode : Disable ALL visual enhancements Keep only triangle markers Dashboard Small or OFF Use Minimal theme if available Problem: Realtime Signals Repainting Symptoms : You see a signal appear, but on next bar it disappears or moves. Explanation : Realtime mode detects peaks 1 bar ago: high > high AND high > high On the FORMING bar (before close), this condition can change as new prices arrive Example: At 10:05, high (10:04 bar) was 100, current high is 99 → peak detected At 10:05:30, new high of 101 arrives → peak condition breaks → signal disappears At 10:06 (bar close), final high is 101 → no peak at 10:04 anymore → signal gone permanently This is expected behavior for realtime responsiveness. You get preview/early warning, but it's not locked until bar confirms. Solutions : Use Confirmed Timing : Switch to "Confirmed (lookforward)" mode ZERO repainting — pivot must be fully validated 5-bar delay (pivot_lookforward) What you see in history is exactly what would have appeared live Accept Realtime Repaint as Tradeoff : Keep Realtime mode for speed and alerts Understand that pre-confirmation signals may vanish Only trade signals that CONFIRM at bar close (check barstate.isconfirmed) Use for live monitoring, NOT for backtesting Trade Only After Confirmation : In Realtime mode, wait 1 full bar after signal appears before entering If signal survives that bar close, it's locked This adds 1-bar delay but removes repaint risk Recommendation : Use Confirmed for backtesting and conservative trading. Use Realtime only for active monitoring with full understanding of preview behavior. Risk Management Integration BZ-CAE is a signal generation system, not a complete trading strategy. You must integrate proper risk management: Position Sizing by Confidence Confidence 0.70-1.00 (Premium) : Risk: 1.5-2.0% of account (MAXIMUM) Reasoning: High-quality setup across all factors Still cap at 2% — even premium setups can fail Confidence 0.50-0.70 (High Quality) : Risk: 1.0-1.5% of account Reasoning: Standard good setup Your bread-and-butter risk level Confidence 0.35-0.50 (Moderate Quality) : Risk: 0.5-1.0% of account Reasoning: Marginal setup, passes minimum threshold Reduce size or skip if you're selective Confidence <0.35 (Low Quality) : Risk: 0% (blocked in Filtering mode) Reasoning: Insufficient quality factors System protects you by not showing these Stop Placement Strategies For Reversal Signals (Regular Divergences) : Place stop beyond the divergence pivot plus buffer Bullish : Stop below the divergence low - 1.0-1.5 × ATR Bearish : Stop above the divergence high + 1.0-1.5 × ATR Reasoning: If price breaks the pivot, divergence structure is invalidated For Continuation Signals (Hidden Divergences) : Place stop beyond recent swing in opposite direction Bullish continuation : Stop below recent swing low (not the divergence pivot itself) Bearish continuation : Stop above recent swing high Reasoning: You're trading with trend, allow more breathing room ATR-Based Stops : 1.5-2.0 × ATR is standard Scale by timeframe: Scalping (1-5m): 1.0-1.5 × ATR (tight) Day trading (15m-1H): 1.5-2.0 × ATR (balanced) Swing (4H-D): 2.0-3.0 × ATR (wide) Never Use Fixed Dollar/Pip Stops : Markets have different volatility 50-pip stop on EUR/USD ≠ 50-pip stop on GBP/JPY Always normalize by ATR or pivot structure Profit Targets and Scaling Primary Target : 2-3 × ATR from entry (minimum 2:1 reward-risk) Example : Entry at 100, ATR = 2, stop at 97 (1.5 × ATR) → target at 106 (3 × ATR) = 2:1 R:R Scaling Out Strategy : Take 50% off at 1.5 × ATR (secure partial profit) Move stop to breakeven Trail remaining 50% with 1.0 × ATR trailing stop Let winners run if trend persists Targets by Confidence : High Confidence (>0.70) : Aggressive targets (3-4 × ATR), trail wider (1.5 × ATR) Standard Confidence (0.50-0.70) : Normal targets (2-3 × ATR), standard trail (1.0 × ATR) Low Confidence (0.35-0.50) : Conservative targets (1.5-2 × ATR), tight trail (0.75 × ATR) Use Bifurcation Zones : If opposite-side zone is visible on chart (from previous signal), use it as target Example : Bullish signal at 100, prior supply zone at 110 → use 110 as target Zones mark institutional resistance/support Exhaustion-Based Exits : If you're in a trade and exhaustion >0.75 develops (yellow shading), consider early exit Market is overextended — reversal risk is high Take profit even if target not reached Trade Management by TCS High TCS + Counter-Trend Trade (Risky) : Use very tight stops (1.0-1.5 × ATR) Conservative targets (1.5-2 × ATR) Quick exit if trade doesn't work immediately You're fading momentum — respect it Low TCS + Reversal Trade (Safer) : Use wider stops (2.0-2.5 × ATR) Aggressive targets (3-4 × ATR) Trail with patience Genuine reversal potential in weak trend High TCS + Continuation Trade (Safest) : Standard stops (1.5-2.0 × ATR) Very aggressive targets (4-5 × ATR) Trail wide (1.5-2.0 × ATR) You're with institutional momentum — let it run Educational Value — Learning Machine Intelligence BZ-CAE is designed as a learning platform, not just a tool: Advisory Mode as Teacher Most indicators are binary: signal or no signal. You don't learn WHY certain setups are better. BZ-CAE's Advisory mode shows you EVERY potential divergence, then annotates the ones that would be blocked in Filtering mode with specific reasons: "Bull: strong downtrend (TCS=0.87)" teaches you that TCS >0.85 makes counter-trend very risky "Adversarial bearish" teaches you that the opposing case was dominating "Low confidence 32%" teaches you that the setup lacked quality across multiple factors "Bull spacing: wait 8 bars" teaches you that signals need breathing room After 50-100 signals in Advisory mode, you internalize the CAE's decision logic. You start seeing these factors yourself BEFORE the indicator does. Dashboard Transparency Most "intelligent" indicators are black boxes — you don't know how they make decisions. BZ-CAE shows you ALL metrics in real-time: TCS tells you trend strength DMA tells you momentum alignment Exhaustion tells you overextension Adversarial shows both sides of the debate Confidence shows composite quality You learn to interpret market state holistically, a skill applicable to ANY trading system beyond this indicator. Divergence Quality Education Not all divergences are equal. BZ-CAE teaches you which conditions produce high-probability setups: Quality divergence : Regular bullish div at a low, TCS <0.50 (weak trend), exhaustion >0.75 (overextended), positive adversarial differential, confidence >0.70 Low-quality divergence : Regular bearish div at a high, TCS >0.85 (strong uptrend), exhaustion <0.30 (not overextended), negative adversarial differential, confidence <0.40 After using the system, you can evaluate divergences manually with similar intelligence. Risk Management Discipline Confidence-based position sizing teaches you to adjust risk based on setup quality, not emotions: Beginners often size all trades identically Or worse, size UP on marginal setups to "make up" for losses BZ-CAE forces systematic sizing: premium setups get larger size, marginal setups get smaller size This creates a probabilistic approach where your edge compounds over time. What This Indicator Is NOT Complete transparency about limitations and positioning: Not a Prediction System BZ-CAE does not predict future prices. It identifies structural divergences (price-momentum disagreements) and assesses current market state (trend, exhaustion, adversarial conditions). It tells you WHEN conditions favor a potential reversal or continuation, not WHAT WILL HAPPEN. Markets are probabilistic. Even premium-confidence setups fail ~30-40% of the time. The system improves your probability distribution over many trades — it doesn't eliminate risk. Not Fully Automated This is a decision support tool, not a trading robot. You must: Execute trades manually based on signals Manage positions (stops, targets, trailing) Apply discretionary judgment (news events, liquidity, context) Integrate with your broader strategy and risk rules The confidence scores guide position sizing, but YOU determine final risk allocation based on your account size, risk tolerance, and portfolio context. Not Beginner-Friendly BZ-CAE requires understanding of: Divergence trading concepts (regular vs hidden, reversal vs continuation) Market state interpretation (trend vs range, momentum, exhaustion) Basic technical analysis (pivots, support/resistance, EMAs) Risk management fundamentals (position sizing, stops, R:R) This is designed for intermediate to advanced traders willing to invest time learning the system. If you want "buy the arrow" simplicity, this isn't the tool. Not a Holy Grail There is no perfect indicator. BZ-CAE filters noise and improves signal quality significantly, but: Losing trades are inevitable (even at 70% win rate, 30% still fail) Market conditions change rapidly (yesterday's strong trend becomes today's chop) Black swan events occur (fundamentals override technicals) Execution matters (slippage, fees, emotional discipline) The system provides an EDGE, not a guarantee. Your job is to execute that edge consistently with proper risk management over hundreds of trades. Not Financial Advice BZ-CAE is an educational and analytical tool. All trading decisions are your responsibility. Past performance (backtested or live) does not guarantee future results. Only risk capital you can afford to lose. Consult a licensed financial advisor for investment advice specific to your situation. Ideal Market Conditions Best Performance Characteristics Liquid Instruments : Major forex pairs (EUR/USD, GBP/USD, USD/JPY) Large-cap stocks and index ETFs (SPY, QQQ, AAPL, MSFT) High-volume crypto (BTC, ETH) Major commodities (Gold, Oil, Natural Gas) Reasoning: Clean price structure, clear pivots, meaningful oscillator behavior Trending with Consolidations : Markets that trend for 20-40 bars, then consolidate 10-20 bars, repeat Creates divergences at consolidation boundaries (reversals) and within trends (continuations) Both regular and hidden divs find opportunities 5-Minute to Daily Timeframes : Below 5m: too much noise, false pivots, CAE metrics unstable Above daily: too few signals, edge diminishes (fundamentals dominate) Sweet spot: 15m to 4H for most traders Consistent Volume and Participation : Regular trading sessions (not holidays or thin markets) Predictable volatility patterns Avoid instruments with sudden gaps or circuit breakers Challenging Conditions Extremely Low Liquidity : Penny stocks, exotic forex pairs, low-volume crypto Erratic pivots, unreliable oscillator readings CAE metrics can't assess market state properly Very Low Timeframes (1-Minute or Below) : Dominated by market microstructure noise Divergences are everywhere but meaningless CAE filtering helps but still unreliable Extended Sideways Consolidation : 100+ bars of tight range with no clear pivots Oscillator hugs midpoint (45-55 range) No divergences to detect Fundamentally-Driven Gap Markets : Earnings releases, economic data, geopolitical events Price gaps over stops and targets Technical structure breaks down Recommendation: Disable trading around known events Calculation Methodology — Technical Depth For users who want to understand the math: Oscillator Computation Each oscillator type calculates differently, but all normalize to 0-100: RSI : ta.rsi(close, length) — Standard Relative Strength Index Stochastic : ta.stoch(high, low, close, length) — %K calculation CCI : (ta.cci(hlc3, length) + 100) / 2 — Normalized from -100/+100 to 0-100 MFI : ta.mfi(hlc3, length) — Volume-weighted RSI equivalent Williams %R : ta.wpr(length) + 100 — Inverted stochastic adjusted to 0-100 Smoothing: If smoothing > 1, apply ta.sma(oscillator, smoothing) Divergence Detection Algorithm Identify Pivots : Price high pivot: ta.pivothigh(high, lookback, lookforward) Price low pivot: ta.pivotlow(low, lookback, lookforward) Oscillator high pivot: ta.pivothigh(osc, lookback, lookforward) Oscillator low pivot: ta.pivotlow(osc, lookback, lookforward) Store Recent Pivots : Maintain arrays of last 10 pivots with bar indices When new pivot confirmed, unshift to array, pop oldest if >10 Scan for Slope Disagreements : Loop through last 5 pivots For each pair (current pivot, historical pivot): Check if within max_lookback bars Calculate slopes: (current - historical) / bars_between Regular bearish: price_slope > 0, osc_slope < 0, |osc_slope| > min_threshold Regular bullish: price_slope < 0, osc_slope > 0, |osc_slope| > min_threshold Hidden bearish: price_slope < 0, osc_slope > 0, osc_slope > min_threshold Hidden bullish: price_slope > 0, osc_slope < 0, |osc_slope| > min_threshold Important Disclaimers and Terms Performance Disclosure Past performance, whether backtested or live-traded, does not guarantee future results. Markets change. What works today may not work tomorrow. Hypothetical or simulated performance results have inherent limitations and do not represent actual trading. Risk of Loss Trading involves substantial risk of loss. Only trade with risk capital you can afford to lose entirely. The high degree of leverage often available in trading can work against you as well as for you. Leveraged trading may result in losses exceeding your initial deposit. Not Financial Advice BZ-CAE is an educational and analytical tool for technical analysis. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument. All trading decisions are your sole responsibility. Consult a licensed financial advisor for advice specific to your circumstances. Technical Indicator Limitations BZ-CAE is a technical analysis tool based on price and volume data. It does not account for: Fundamental analysis (earnings, economic data, financial health) Market sentiment and positioning Geopolitical events and news Liquidity conditions and market microstructure changes Regulatory changes or exchange rules Integrate with broader analysis and strategy. Do not rely solely on technical indicators for trading decisions. Repainting Acknowledgment As disclosed throughout this documentation: Realtime mode may repaint on forming bars before confirmation (by design for preview functionality) Confirmed mode has zero repainting (fully validated pivots only) Choose timing mode appropriate for your use case. Understand the tradeoffs. Testing Recommendation ALWAYS test on demo/paper accounts before committing real capital. Validate the indicator's behavior on your specific instruments and timeframes. Learn the system thoroughly in Advisory mode before using Filtering mode. Learning Resources : In-indicator tooltips (hover over setting names for detailed explanations) This comprehensive publishing statement (save for reference) User guide in script comments (top of code) Final Word — Philosophy of BZ-CAE BZ-CAE is not designed to replace your judgment — it's designed to enhance it. The indicator identifies structural inflection points (bifurcations) where price and momentum disagree. The Cognitive Engine evaluates market state to determine if this disagreement is meaningful or noise. The Adversarial model debates both sides of the trade to catch obvious bad setups. The Confidence system ranks quality so you can choose your risk appetite. But YOU still execute. YOU still manage risk. YOU still learn from outcomes. This is intelligence amplification, not intelligence replacement. Use Advisory mode to learn how expert traders evaluate market state. Use Filtering mode to enforce discipline when emotions run high. Use the dashboard to develop a systematic approach to reading markets. Use confidence scores to size positions probabilistically. The system provides an edge. Your job is to execute that edge with discipline, patience, and proper risk management over hundreds of trades. Markets are probabilistic. No system wins every trade. But a systematic edge + disciplined execution + proper risk management compounds over time. That's the path to consistent profitability. BZ-CAE gives you the edge. The discipline and risk management are on you. Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
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RSI Overbought/Oversold + Divergence Indicator (new)//@version=5 indicator('CryptoSignalScanner - RSI Overbought/Oversold + Divergence Indicator (new)', //--------------------------------------------------------------------------------------------------------------------------------- //--- Define Colors --------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------- vWhite = #FFFFFF vViolet = #C77DF3 vIndigo = #8A2BE2 vBlue = #009CDF vGreen = #5EBD3E vYellow = #FFB900 vRed = #E23838 longColor = color.green shortColor = color.red textColor = color.white bullishColor = color.rgb(38,166,154,0) //Used in the display table bearishColor = color.rgb(239,83,79,0) //Used in the display table nomatchColor = color.silver //Used in the display table //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Functions-------------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- TF2txt(TF) => switch TF "S" => "RSI 1s:" "5S" => "RSI 5s:" "10S" => "RSI 10s:" "15S" => "RSI 15s:" "30S" => "RSI 30s" "1" => "RSI 1m:" "3" => "RSI 3m:" "5" => "RSI 5m:" "15" => "RSI 15m:" "30" => "RSI 30m" "45" => "RSI 45m" "60" => "RSI 1h:" "120" => "RSI 2h:" "180" => "RSI 3h:" "240" => "RSI 4h:" "480" => "RSI 8h:" "D" => "RSI 1D:" "1D" => "RSI 1D:" "2D" => "RSI 2D:" "3D" => "RSI 2D:" "3D" => "RSI 3W:" "W" => "RSI 1W:" "1W" => "RSI 1W:" "M" => "RSI 1M:" "1M" => "RSI 1M:" "3M" => "RSI 3M:" "6M" => "RSI 6M:" "12M" => "RSI 12M:" //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Show/Hide Settings ---------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsiShowInput = input(true, title='Show RSI', group='Show/Hide Settings') maShowInput = input(false, title='Show MA', group='Show/Hide Settings') showRSIMAInput = input(true, title='Show RSIMA Cloud', group='Show/Hide Settings') rsiBandShowInput = input(true, title='Show Oversold/Overbought Lines', group='Show/Hide Settings') rsiBandExtShowInput = input(true, title='Show Oversold/Overbought Extended Lines', group='Show/Hide Settings') rsiHighlightShowInput = input(true, title='Show Oversold/Overbought Highlight Lines', group='Show/Hide Settings') DivergenceShowInput = input(true, title='Show RSI Divergence Labels', group='Show/Hide Settings') //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Table Settings -------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsiShowTable = input(true, title='Show RSI Table Information box', group="RSI Table Settings") rsiTablePosition = input.string(title='Location', defval='middle_right', options= , group="RSI Table Settings", inline='1') rsiTextSize = input.string(title=' Size', defval='small', options= , group="RSI Table Settings", inline='1') rsiShowTF1 = input(true, title='Show TimeFrame1', group="RSI Table Settings", inline='tf1') rsiTF1 = input.timeframe("15", title=" Time", group="RSI Table Settings", inline='tf1') rsiShowTF2 = input(true, title='Show TimeFrame2', group="RSI Table Settings", inline='tf2') rsiTF2 = input.timeframe("60", title=" Time", group="RSI Table Settings", inline='tf2') rsiShowTF3 = input(true, title='Show TimeFrame3', group="RSI Table Settings", inline='tf3') rsiTF3 = input.timeframe("240", title=" Time", group="RSI Table Settings", inline='tf3') rsiShowTF4 = input(true, title='Show TimeFrame4', group="RSI Table Settings", inline='tf4') rsiTF4 = input.timeframe("D", title=" Time", group="RSI Table Settings", inline='tf4') rsiShowHist = input(true, title='Show RSI Historical Columns', group="RSI Table Settings", tooltip='Show the information of the 2 previous closed candles') //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- RSI Input Settings ---------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsiSourceInput = input.source(close, 'Source', group='RSI Settings') rsiLengthInput = input.int(14, minval=1, title='RSI Length', group='RSI Settings', tooltip='Here we set the RSI lenght') rsiColorInput = input.color(#26a69a, title="RSI Color", group='RSI Settings') rsimaColorInput = input.color(#ef534f, title="RSIMA Color", group='RSI Settings') rsiBandColorInput = input.color(#787B86, title="RSI Band Color", group='RSI Settings') rsiUpperBandExtInput = input.int(title='RSI Overbought Extended Line', defval=80, minval=50, maxval=100, group='RSI Settings') rsiUpperBandInput = input.int(title='RSI Overbought Line', defval=70, minval=50, maxval=100, group='RSI Settings') rsiLowerBandInput = input.int(title='RSI Oversold Line', defval=30, minval=0, maxval=50, group='RSI Settings') rsiLowerBandExtInput = input.int(title='RSI Oversold Extended Line', defval=20, minval=0, maxval=50, group='RSI Settings') //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- MA Input Settings ----------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- maTypeInput = input.string("EMA", title="MA Type", options= , group="MA Settings") maLengthInput = input.int(14, title="MA Length", group="MA Settings") maColorInput = input.color(color.yellow, title="MA Color", group='MA Settings') //#7E57C2 //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Divergence Input Settings --------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- lbrInput = input(title="Pivot Lookback Right", defval=2, group='RSI Divergence Settings') lblInput = input(title="Pivot Lookback Left", defval=2, group='RSI Divergence Settings') lbRangeMaxInput = input(title="Max of Lookback Range", defval=10, group='RSI Divergence Settings') lbRangeMinInput = input(title="Min of Lookback Range", defval=2, group='RSI Divergence Settings') plotBullInput = input(title="Plot Bullish", defval=true, group='RSI Divergence Settings') plotHiddenBullInput = input(title="Plot Hidden Bullish", defval=true, group='RSI Divergence Settings') plotBearInput = input(title="Plot Bearish", defval=true, group='RSI Divergence Settings') plotHiddenBearInput = input(title="Plot Hidden Bearish", defval=true, group='RSI Divergence Settings') //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- RSI Calculation ------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsi = ta.rsi(rsiSourceInput, rsiLengthInput) rsiprevious = rsi = request.security(syminfo.tickerid, rsiTF1, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on) = request.security(syminfo.tickerid, rsiTF2, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on) = request.security(syminfo.tickerid, rsiTF3, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on) = request.security(syminfo.tickerid, rsiTF4, [rsi, rsi , rsi ], lookahead=barmerge.lookahead_on) //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- MA Calculation ------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- ma(source, length, type) => switch type "SMA" => ta.sma(source, length) "Bollinger Bands" => ta.sma(source, length) "EMA" => ta.ema(source, length) "SMMA (RMA)" => ta.rma(source, length) "WMA" => ta.wma(source, length) "VWMA" => ta.vwma(source, length) rsiMA = ma(rsi, maLengthInput, maTypeInput) rsiMAPrevious = rsiMA //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Stoch RSI Settings + Calculation -------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- showStochRSI = input(false, title="Show Stochastic RSI", group='Stochastic RSI Settings') smoothK = input.int(title="Stochastic K", defval=3, minval=1, maxval=10, group='Stochastic RSI Settings') smoothD = input.int(title="Stochastic D", defval=4, minval=1, maxval=10, group='Stochastic RSI Settings') lengthRSI = input.int(title="Stochastic RSI Lenght", defval=14, minval=1, group='Stochastic RSI Settings') lengthStoch = input.int(title="Stochastic Lenght", defval=14, minval=1, group='Stochastic RSI Settings') colorK = input.color(color.rgb(41,98,255,0), title="K Color", group='Stochastic RSI Settings', inline="1") colorD = input.color(color.rgb(205,109,0,0), title="D Color", group='Stochastic RSI Settings', inline="1") StochRSI = ta.rsi(rsiSourceInput, lengthRSI) k = ta.sma(ta.stoch(StochRSI, StochRSI, StochRSI, lengthStoch), smoothK) //Blue Line d = ta.sma(k, smoothD) //Red Line //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Divergence Settings ------------------------------------------------------------------------------------------------------------------------------------------ //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- bearColor = color.red bullColor = color.green hiddenBullColor = color.new(color.green, 50) hiddenBearColor = color.new(color.red, 50) //textColor = color.white noneColor = color.new(color.white, 100) osc = rsi plFound = na(ta.pivotlow(osc, lblInput, lbrInput)) ? false : true phFound = na(ta.pivothigh(osc, lblInput, lbrInput)) ? false : true _inRange(cond) => bars = ta.barssince(cond == true) lbRangeMinInput <= bars and bars <= lbRangeMaxInput //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Define Plot & Line Colors --------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- rsiColor = rsi >= rsiMA ? rsiColorInput : rsimaColorInput //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Plot Lines ------------------------------------------------------------------------------------------------------------------------------------------------------ //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- // Create a horizontal line at a specific price level myLine = line.new(bar_index , 75, bar_index, 75, color = color.rgb(187, 14, 14), width = 2) bottom = line.new(bar_index , 50, bar_index, 50, color = color.rgb(223, 226, 28), width = 2) mymainLine = line.new(bar_index , 60, bar_index, 60, color = color.rgb(13, 154, 10), width = 3) hline(50, title='RSI Baseline', color=color.new(rsiBandColorInput, 50), linestyle=hline.style_solid, editable=false) hline(rsiBandExtShowInput ? rsiUpperBandExtInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false) hline(rsiBandShowInput ? rsiUpperBandInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false) hline(rsiBandShowInput ? rsiLowerBandInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false) hline(rsiBandExtShowInput ? rsiLowerBandExtInput : na, title='RSI Upper Band', color=color.new(rsiBandColorInput, 10), linestyle=hline.style_dashed, editable=false) bgcolor(rsiHighlightShowInput ? rsi >= rsiUpperBandExtInput ? color.new(rsiColorInput, 70) : na : na, title="Show Extended Oversold Highlight", editable=false) bgcolor(rsiHighlightShowInput ? rsi >= rsiUpperBandInput ? rsi < rsiUpperBandExtInput ? color.new(#64ffda, 90) : na : na: na, title="Show Overbought Highlight", editable=false) bgcolor(rsiHighlightShowInput ? rsi <= rsiLowerBandInput ? rsi > rsiLowerBandExtInput ? color.new(#F43E32, 90) : na : na : na, title="Show Extended Oversold Highlight", editable=false) bgcolor(rsiHighlightShowInput ? rsi <= rsiLowerBandInput ? color.new(rsimaColorInput, 70) : na : na, title="Show Oversold Highlight", editable=false) maPlot = plot(maShowInput ? rsiMA : na, title='MA', color=color.new(maColorInput,0), linewidth=1) rsiMAPlot = plot(showRSIMAInput ? rsiMA : na, title="RSI EMA", color=color.new(rsimaColorInput,0), editable=false, display=display.none) rsiPlot = plot(rsiShowInput ? rsi : na, title='RSI', color=color.new(rsiColor,0), linewidth=1) fill(rsiPlot, rsiMAPlot, color=color.new(rsiColor, 60), title="RSIMA Cloud") plot(showStochRSI ? k : na, title='Stochastic K', color=colorK, linewidth=1) plot(showStochRSI ? d : na, title='Stochastic D', color=colorD, linewidth=1) //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Plot Divergence ------------------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- // Regular Bullish // Osc: Higher Low oscHL = osc > ta.valuewhen(plFound, osc , 1) and _inRange(plFound ) // Price: Lower Low priceLL = low < ta.valuewhen(plFound, low , 1) bullCond = plotBullInput and priceLL and oscHL and plFound plot( plFound ? osc : na, offset=-lbrInput, title="Regular Bullish", linewidth=2, color=(bullCond ? bullColor : noneColor) ) plotshape( DivergenceShowInput ? bullCond ? osc : na : na, offset=-lbrInput, title="Regular Bullish Label", text=" Bull ", style=shape.labelup, location=location.absolute, color=bullColor, textcolor=textColor ) //------------------------------------------------------------------------------ // Hidden Bullish // Osc: Lower Low oscLL = osc < ta.valuewhen(plFound, osc , 1) and _inRange(plFound ) // Price: Higher Low priceHL = low > ta.valuewhen(plFound, low , 1) hiddenBullCond = plotHiddenBullInput and priceHL and oscLL and plFound plot( plFound ? osc : na, offset=-lbrInput, title="Hidden Bullish", linewidth=2, color=(hiddenBullCond ? hiddenBullColor : noneColor) ) plotshape( DivergenceShowInput ? hiddenBullCond ? osc : na : na, offset=-lbrInput, title="Hidden Bullish Label", text=" H Bull ", style=shape.labelup, location=location.absolute, color=bullColor, textcolor=textColor ) //------------------------------------------------------------------------------ // Regular Bearish // Osc: Lower High oscLH = osc < ta.valuewhen(phFound, osc , 1) and _inRange(phFound ) // Price: Higher High priceHH = high > ta.valuewhen(phFound, high , 1) bearCond = plotBearInput and priceHH and oscLH and phFound plot( phFound ? osc : na, offset=-lbrInput, title="Regular Bearish", linewidth=2, color=(bearCond ? bearColor : noneColor) ) plotshape( DivergenceShowInput ? bearCond ? osc : na : na, offset=-lbrInput, title="Regular Bearish Label", text=" Bear ", style=shape.labeldown, location=location.absolute, color=bearColor, textcolor=textColor ) //------------------------------------------------------------------------------ // Hidden Bearish // Osc: Higher High oscHH = osc > ta.valuewhen(phFound, osc , 1) and _inRange(phFound ) // Price: Lower High priceLH = high < ta.valuewhen(phFound, high , 1) hiddenBearCond = plotHiddenBearInput and priceLH and oscHH and phFound plot( phFound ? osc : na, offset=-lbrInput, title="Hidden Bearish", linewidth=2, color=(hiddenBearCond ? hiddenBearColor : noneColor) ) plotshape( DivergenceShowInput ? hiddenBearCond ? osc : na : na, offset=-lbrInput, title="Hidden Bearish Label", text=" H Bear ", style=shape.labeldown, location=location.absolute, color=bearColor, textcolor=textColor ) //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Check RSI Lineup ------------------------------------------------------------------------------------------------------------------------------------------------ //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- bullTF = rsi > rsi and rsi > rsi bearTF = rsi < rsi and rsi < rsi bullTF1 = rsi1 > rsi1_1 and rsi1_1 > rsi1_2 bearTF1 = rsi1 < rsi1_1 and rsi1_1 < rsi1_2 bullTF2 = rsi2 > rsi2_1 and rsi2_1 > rsi2_2 bearTF2 = rsi2 < rsi2_1 and rsi2_1 < rsi2_2 bullTF3 = rsi3 > rsi3_1 and rsi3_1 > rsi3_2 bearTF3 = rsi3 < rsi3_1 and rsi3_1 < rsi3_2 bullTF4 = rsi4 > rsi4_1 and rsi4_1 > rsi4_2 bearTF4 = rsi4 < rsi4_1 and rsi4_1 < rsi4_2 bbTxt(bull,bear) => bull ? "BULLISH" : bear ? "BEARISCH" : 'NO LINEUP' bbColor(bull,bear) => bull ? bullishColor : bear ? bearishColor : nomatchColor newTC(tBox, col, row, txt, width, txtColor, bgColor, txtHA, txtSize) => table.cell(table_id=tBox,column=col, row=row, text=txt, width=width,text_color=txtColor,bgcolor=bgColor, text_halign=txtHA, text_size=txtSize) //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- //--- Define RSI Table Setting ---------------------------------------------------------------------------------------------------------------------------------------- //--------------------------------------------------------------------------------------------------------------------------------------------------------------------- width_c0 = 0 width_c1 = 0 if rsiShowTable var tBox = table.new(position=rsiTablePosition, columns=5, rows=6, bgcolor=color.rgb(18,22,33,50), frame_color=color.black, frame_width=1, border_color=color.black, border_width=1) newTC(tBox, 0,1,"RSI Current",width_c0,color.orange,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,1,str.format(" {0,number,#.##} ", rsi),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,1,bbTxt(bullTF, bearTF),width_c0,vWhite,bbColor(bullTF, bearTF),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,1,str.format(" {0,number,#.##} ", rsi ),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,1,str.format(" {0,number,#.##} ", rsi ),width_c0,vWhite,rsi < 50 ? bearishColor:bullishColor,'left',rsiTextSize) if rsiShowTF1 newTC(tBox, 0,2,TF2txt(rsiTF1),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,2,str.format(" {0,number,#.##} ", rsi1),width_c0,vWhite,rsi1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,2,bbTxt(bullTF1, bearTF1),width_c0,vWhite,bbColor(bullTF1,bearTF1),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,2,str.format(" {0,number,#.##} ", rsi1_1),width_c0,vWhite,rsi1_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,2,str.format(" {0,number,#.##} ", rsi1_2),width_c0,vWhite,rsi1_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) if rsiShowTF2 newTC(tBox, 0,3,TF2txt(rsiTF2),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,3,str.format(" {0,number,#.##} ", rsi2),width_c0,vWhite,rsi2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,3,bbTxt(bullTF2, bearTF2),width_c0,vWhite,bbColor(bullTF2,bearTF2),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,3,str.format(" {0,number,#.##} ", rsi2_1),width_c0,vWhite,rsi2_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,3,str.format(" {0,number,#.##} ", rsi2_2),width_c0,vWhite,rsi2_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) if rsiShowTF3 newTC(tBox, 0,4,TF2txt(rsiTF3),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,4,str.format(" {0,number,#.##} ", rsi3),width_c0,vWhite,rsi3 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,4,bbTxt(bullTF3, bearTF3),width_c0,vWhite,bbColor(bullTF3,bearTF3),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,4,str.format(" {0,number,#.##} ", rsi3_1),width_c0,vWhite,rsi3_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,4,str.format(" {0,number,#.##} ", rsi3_2),width_c0,vWhite,rsi3_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) if rsiShowTF4 newTC(tBox, 0,5,TF2txt(rsiTF4),width_c0,vWhite,color.rgb(0,0,0,100),'right',rsiTextSize) newTC(tBox, 1,5,str.format(" {0,number,#.##} ", rsi4),width_c0,vWhite,rsi4 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 4,5,bbTxt(bullTF4, bearTF4),width_c0,vWhite,bbColor(bullTF4,bearTF4),'center',rsiTextSize) if rsiShowHist newTC(tBox, 2,5,str.format(" {0,number,#.##} ", rsi4_1),width_c0,vWhite,rsi4_1 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) newTC(tBox, 3,5,str.format(" {0,number,#.##} ", rsi4_2),width_c0,vWhite,rsi4_2 < 50 ? bearishColor:bullishColor,'left',rsiTextSize) //------------------------------------------------------ //--- Alerts ------------------------------------------- //------------------------------------------------------
Pine Script®指標
由RandyDahlin提供
Relative Strength Index Remastered [CHE]Relative Strength Index Remastered — Enhanced RSI with robust divergence detection using price-based pivots and line-of-sight validation to reduce false signals compared to the standard RSI indicator. Summary RSI Remastered builds on the classic Relative Strength Index by adding a more reliable divergence detection system that relies on price pivots rather than RSI pivots alone, incorporating a line-of-sight check to ensure the RSI path between points remains clear. This approach filters out many false divergences that occur in the original RSI indicator due to its volatile pivot detection on the RSI line itself. Users benefit from clearer reversal and continuation signals, especially in noisy markets, with optional hidden divergence support for trend confirmation. The core RSI calculation and smoothing options remain familiar, but the divergence logic provides materially fewer alerts while maintaining sensitivity. Motivation: Why this design? The standard RSI indicator often generates misleading divergence signals because it detects pivots directly on the RSI values, which can fluctuate erratically in volatile conditions, leading to frequent false positives that confuse traders during ranging or choppy price action. RSI Remastered addresses this by shifting pivot detection to the underlying price highs and lows, which are more stable, and adding a validation step that confirms the RSI line does not cross the direct path between pivot points. This design targets the real problem of over-signaling in the original, promoting more actionable insights without altering the RSI's core momentum measurement. What’s different vs. standard approaches? - Reference baseline: The classical TradingView RSI indicator, which uses simple RSI-based pivot detection for divergences. - Architecture differences: - Pivot identification on price extremes (highs and lows) instead of RSI values, extracting RSI levels at those points for comparison. - Addition of a line-of-sight validation that checks the RSI path bar by bar between pivots to prevent signals where the line is interrupted. - Inclusion of hidden divergence types alongside regular ones, using the same robust framework. - Configurable drawing of connecting lines between validated pivot RSI points for visual clarity. - Practical effect: Charts show fewer but higher-quality divergence markers and lines, reducing clutter from the original's frequent RSI pivot triggers; this matters for avoiding whipsaws in intraday trading, where the standard version might flag dozens of invalid setups per session. Key Comparison Aspects Aspect: Title/Shorttitle Original RSI: "Relative Strength Index" / "RSI" Robust Variant: "Relative Strength Index Remastered " / "RSI RM" Aspect: Max. Lines/Labels Original RSI: No specification (Standard: 50/50) Robust Variant: max_lines_count=200, max_labels_count=200 (for more lines/markers in divergences) Aspect: RSI Calculation & Plots Original RSI: Identical: RSI with RMA, Plots (line, bands, gradient fills) Robust Variant: Identical: RSI with RMA, Plots (line, bands, gradient fills) Aspect: Smoothing (MA) Original RSI: Identical: Inputs for MA types (SMA, EMA etc.), Bollinger Bands optional Robust Variant: Identical: Inputs for MA types (SMA, EMA etc.), Bollinger Bands optional Aspect: Divergence Activation Original RSI: input.bool(false, "Calculate Divergence") (disabled by default) Robust Variant: input.bool(true, "Calculate Divergence") (enabled by default, with tooltip) Aspect: Pivot Calculation Original RSI: Pivots on RSI (ta.pivotlow/high on RSI values) Robust Variant: Pivots on price (ta.pivotlow/high on low/high), RSI values then extracted Aspect: Lookback Values Original RSI: Fixed: lookbackLeft=5, lookbackRight=5 Robust Variant: Input: L=5 (Pivot Left), R=5 (Pivot Right), adjustable (min=1, max=50) Aspect: Range Between Pivots Original RSI: Fixed: rangeUpper=60, rangeLower=5 (via _inRange function) Robust Variant: Input: rangeUpper=60 (Max Bars), rangeLower=5 (Min Bars), adjustable (min=1–6, max=100–300) Aspect: Divergence Types Original RSI: Only Regular Bullish/Bearish: - Bull: Price LL + RSI HL - Bear: Price HH + RSI LH Robust Variant: Regular + Hidden (optional via showHidden=true): - Regular Bull: Price LL + RSI HL - Regular Bear: Price HH + RSI LH - Hidden Bull: Price HL + RSI LL - Hidden Bear: Price LH + RSI HH Aspect: Validation Original RSI: No additional check (only pivot + range check) Robust Variant: Line-of-Sight Check: RSI line must not cross the connecting line between pivots (line_clear function with slope calculation and loop for each bar in between) Aspect: Signals (Plots/Shapes) Original RSI: - Plot of pivot points (if divergence) - Shapes: "Bull"/"Bear" at RSI value, offset=-5 Robust Variant: - No pivot plots, instead shapes at RSI , offset=-R (adjustable) - Shapes: "Bull"/"Bear" (Regular), "HBull"/"HBear" (Hidden) - Colors: Lime/Red (Regular), Teal/Orange (Hidden) Aspect: Line Drawing Original RSI: No lines Robust Variant: Optional (showLines=true): Lines between RSI pivots (thick for regular, dashed/thin for hidden), extend=none Aspect: Alerts Original RSI: Only Regular Bullish/Bearish (with pivot lookback reference) Robust Variant: Regular Bullish/Bearish + Hidden Bullish/Bearish (specific "at latest pivot low/high") Aspect: Robustness Original RSI: Simple, prone to false signals (RSI pivots can be volatile) Robust Variant: Higher: Price pivots are more stable, line-of-sight filters "broken" divergences, hidden support for trend continuations Aspect: Code Length/Structure Original RSI: ~100 lines, simple if-blocks for bull/bear Robust Variant: ~150 lines, extended helper functions (e.g., inRange, line_clear), var group for inputs How it works (technical) The indicator first computes the core RSI value based on recent price changes, separating upward and downward movements over the specified length and smoothing them to derive a momentum reading scaled between zero and one hundred. This value is then plotted in a separate pane with fixed upper and lower reference lines at seventy and thirty, along with optional gradient fills to highlight overbought and oversold zones. For smoothing, a moving average type is applied to the RSI if enabled, with an option to add bands around it based on the variability of recent RSI values scaled by a multiplier. Divergence detection activates on confirmed price pivots: lows for bullish checks and highs for bearish. At each new pivot, the system retrieves the bar index and values (price and RSI) for the current and prior pivot, ensuring they fall within a configurable bar range to avoid unrelated points. Comparisons then assess whether the price has made a lower low (or higher high) while the RSI at those points moves in the opposite direction—higher for bullish regular, lower for bearish regular. For hidden types, the directions reverse to capture trend strength. The line-of-sight check calculates the straight path between the two RSI points and verifies that the actual RSI values in between stay entirely above (for bullish) or below (for bearish) that path, breaking the signal if any bar violates it. Valid signals trigger shapes at the RSI level of the new pivot and optional lines connecting the points. Initialization uses built-in functions to track prior occurrences, with states persisting across bars for accurate historical comparisons. No higher timeframe data is used, so confirmation occurs after the right pivot bars close, minimizing live-bar repaints. Parameter Guide Length — Controls the period for measuring price momentum changes — Default: 14 — Trade-offs/Tips: Shorter values increase responsiveness but add noise and more false signals; longer smooths trends but delays entries in fast markets. Source — Selects the price input for RSI calculation — Default: Close — Trade-offs/Tips: Use high or low for volatility focus, but close works best for most assets; mismatches can skew overbought/oversold reads. Calculate Divergence — Enables the enhanced divergence logic — Default: True — Trade-offs/Tips: Disable for pure RSI view to save computation; essential for signal reliability over the standard method. Type (Smoothing) — Chooses the moving average applied to RSI — Default: SMA — Trade-offs/Tips: None for raw RSI; EMA for quicker adaptation, but SMA reduces whipsaws; Bollinger Bands option adds volatility context at cost of added lines. Length (Smoothing) — Period for the smoothing average — Default: 14 — Trade-offs/Tips: Match RSI length for consistency; shorter boosts signal speed but amplifies noise in the smoothed line. BB StdDev — Multiplier for band width around smoothed RSI — Default: 2.0 — Trade-offs/Tips: Lower narrows bands for tighter signals, risking more touches; higher widens for fewer but stronger breakouts. Pivot Left — Bars to the left for confirming price pivots — Default: 5 — Trade-offs/Tips: Increase for stricter pivots in noisy data, reducing signals; too high delays confirmation excessively. Pivot Right — Bars to the right for confirming price pivots — Default: 5 — Trade-offs/Tips: Balances with left for symmetry; longer right ensures maturity but shifts signals backward. Max Bars Between Pivots — Upper limit on distance for valid pivot pairs — Default: 60 — Trade-offs/Tips: Tighten for short-term trades to focus recent action; widen for swing setups but risks unrelated comparisons. Min Bars Between Pivots — Lower limit to avoid clustered pivots — Default: 5 — Trade-offs/Tips: Raise to filter micro-moves; too low invites overlapping signals like the original RSI. Detect Hidden — Includes trend-continuation hidden types — Default: True — Trade-offs/Tips: Enable for full trend analysis; disable simplifies to reversals only, akin to basic RSI. Draw Lines — Shows connecting lines between valid pivots — Default: True — Trade-offs/Tips: Turn off for cleaner charts; helps visually confirm line-of-sight in backtests. Reading & Interpretation The main RSI line oscillates between zero and one hundred, crossing above fifty suggesting building momentum and below indicating weakness; touches near seventy or thirty flag potential extremes. The optional smoothed line and bands provide a filtered view—price above the upper band on the RSI pane hints at overextension. Divergence shapes appear as upward labels for bullish (lime for regular, teal for hidden) and downward for bearish (red regular, orange hidden) at the pivot's RSI level, signaling a mismatch only after validation. Connecting lines, if drawn, slope between points without RSI interference, their color matching the shape type; a dashed style denotes hidden. Fewer shapes overall compared to the standard RSI mean higher conviction, but always confirm with price structure. Practical Workflows & Combinations - Trend following: Enter longs on regular bullish shapes near support with higher highs in price; filter hidden bullish for pullback buys in uptrends, pairing with a rising smoothed RSI above fifty. - Exits/Stops: Use bearish regular as reversal warnings to tighten stops; hidden bearish in downtrends confirms continuation—exit if lines show RSI crossing the path. - Multi-asset/Multi-TF: Defaults suit forex and stocks on one-hour charts; for crypto volatility, widen pivot ranges to ten; scale min/max bars proportionally on daily for swings, avoiding the original's intraday spam. Behavior, Constraints & Performance Signals confirm only after the right pivot bars close, so live bars may show tentative pivots that vanish on close, unlike the standard RSI's immediate RSI-pivot triggers—plan for this delay in automation. No higher timeframe calls, so no security-related repaints. Resources include up to two hundred lines and labels for dense charts, with a loop in validation scanning up to three hundred bars between pivots, which is efficient but could slow on very long histories. Known limits: Slight lag at pivot confirmation in trending markets; volatile RSI might rarely miss fine path violations; not ideal for gap-heavy assets where pivots skip. Sensible Defaults & Quick Tuning Start with defaults for balanced momentum and divergence on most timeframes. For too many signals (like the original), raise pivot left/right to eight and min bars to ten to filter noise. If sluggish in trends, shorten RSI length to nine and enable EMA smoothing for faster adaptation. In high-volatility assets, widen max bars to one hundred but disable hidden to focus essentials. For clean reversal hunts, set smoothing to none and lines on. What this indicator is—and isn’t RSI Remastered serves as a refined momentum and divergence visualization tool, enhancing the standard RSI for better signal quality in technical analysis setups. It is not a standalone trading system, nor does it predict price moves—pair it with volume, structure breaks, and risk rules for decisions. Use alongside position sizing and broader context, not in isolation. Disclaimer The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts. Best regards and happy trading Chervolino
Pine Script®指標
由chervolino提供
RSMPatternLibLibrary "RSMPatternLib" RSM Pattern Library - All chart patterns from PATTERNS.md Implements: Candlestick patterns, Support/Resistance, Gaps, Triangles, Volume Divergence, and more ALL PATTERNS ARE OWN IMPLEMENTATION - No external dependencies EDGE CASES HANDLED: - Zero/tiny candle bodies - Missing volume data - Low bar count scenarios - Integer division issues - Price normalization for different instruments bullishEngulfing(minBodyRatio, minPrevBodyRatio)   Detects Bullish Engulfing pattern   Parameters:      minBodyRatio (float) : Minimum body size as ratio of total range (default 0.3)      minPrevBodyRatio (float) : Minimum previous candle body ratio to filter dojis (default 0.1)   Returns: bool True when bullish engulfing detected EDGE CASES: Handles doji previous candle, zero range, tiny bodies bearishEngulfing(minBodyRatio, minPrevBodyRatio)   Detects Bearish Engulfing pattern   Parameters:      minBodyRatio (float) : Minimum body size as ratio of total range (default 0.3)      minPrevBodyRatio (float) : Minimum previous candle body ratio to filter dojis (default 0.1)   Returns: bool True when bearish engulfing detected EDGE CASES: Handles doji previous candle, zero range, tiny bodies doji(maxBodyRatio, minRangeAtr)   Detects Doji candle (indecision)   Parameters:      maxBodyRatio (float) : Maximum body size as ratio of total range (default 0.1)      minRangeAtr (float) : Minimum range as multiple of ATR to filter flat candles (default 0.3)   Returns: bool True when doji detected EDGE CASES: Filters out no-movement bars, handles zero range shootingStar(wickMultiplier, maxLowerWickRatio, minBodyAtrRatio)   Detects Shooting Star (bearish reversal)   Parameters:      wickMultiplier (float) : Upper wick must be at least this times the body (default 2.0)      maxLowerWickRatio (float) : Lower wick max as ratio of body (default 0.5)      minBodyAtrRatio (float) : Minimum body size as ratio of ATR (default 0.1)   Returns: bool True when shooting star detected EDGE CASES: Handles zero body (uses range-based check), tiny bodies hammer(wickMultiplier, maxUpperWickRatio, minBodyAtrRatio)   Detects Hammer (bullish reversal)   Parameters:      wickMultiplier (float) : Lower wick must be at least this times the body (default 2.0)      maxUpperWickRatio (float) : Upper wick max as ratio of body (default 0.5)      minBodyAtrRatio (float) : Minimum body size as ratio of ATR (default 0.1)   Returns: bool True when hammer detected EDGE CASES: Handles zero body (uses range-based check), tiny bodies invertedHammer(wickMultiplier, maxLowerWickRatio)   Detects Inverted Hammer (bullish reversal after downtrend)   Parameters:      wickMultiplier (float) : Upper wick must be at least this times the body (default 2.0)      maxLowerWickRatio (float) : Lower wick max as ratio of body (default 0.5)   Returns: bool True when inverted hammer detected EDGE CASES: Same as shootingStar but requires bullish close hangingMan(wickMultiplier, maxUpperWickRatio)   Detects Hanging Man (bearish reversal after uptrend)   Parameters:      wickMultiplier (float) : Lower wick must be at least this times the body (default 2.0)      maxUpperWickRatio (float) : Upper wick max as ratio of body (default 0.5)   Returns: bool True when hanging man detected NOTE: Identical to hammer - context (uptrend) determines meaning morningStar(requireGap, minAvgBars)   Detects Morning Star (3-candle bullish reversal)   Parameters:      requireGap (bool) : Whether to require gap between candles (default false for crypto/forex)      minAvgBars (int) : Minimum bars for average body calculation (default 14)   Returns: bool True when morning star pattern detected EDGE CASES: Gap is optional, handles low bar count, uses shifted average eveningStar(requireGap, minAvgBars)   Detects Evening Star (3-candle bearish reversal)   Parameters:      requireGap (bool) : Whether to require gap between candles (default false for crypto/forex)      minAvgBars (int) : Minimum bars for average body calculation (default 14)   Returns: bool True when evening star pattern detected EDGE CASES: Gap is optional, handles low bar count gapUp()   Detects Gap Up   Returns: bool True when current bar opens above previous bar's high gapDown()   Detects Gap Down   Returns: bool True when current bar opens below previous bar's low gapSize()   Returns gap size in price   Returns: float Gap size (positive for gap up, negative for gap down, 0 for no gap) gapPercent()   Returns gap size as percentage   Returns: float Gap size as percentage of previous close gapType(volAvgLen, breakawayMinPct, highVolMult)   Classifies gap type based on volume   Parameters:      volAvgLen (int) : Length for volume average (default 20)      breakawayMinPct (float) : Minimum gap % for breakaway (default 1.0)      highVolMult (float) : Volume multiplier for high volume (default 1.5)   Returns: string Gap type: "Breakaway", "Common", "Continuation", or "None" EDGE CASES: Handles missing volume data, low bar count swingHigh(leftBars, rightBars)   Detects swing high using pivot   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)   Returns: float Swing high price or na swingLow(leftBars, rightBars)   Detects swing low using pivot   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)   Returns: float Swing low price or na higherHigh(leftBars, rightBars, lookback)   Checks if current swing high is higher than previous swing high   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when higher high pattern detected EDGE CASES: Searches backwards for pivots instead of using var (library-safe) higherLow(leftBars, rightBars, lookback)   Checks if current swing low is higher than previous swing low   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when higher low pattern detected lowerHigh(leftBars, rightBars, lookback)   Checks if current swing high is lower than previous swing high   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when lower high pattern detected lowerLow(leftBars, rightBars, lookback)   Checks if current swing low is lower than previous swing low   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : How many bars back to search for previous pivot (default 50)   Returns: bool True when lower low pattern detected bullishTrend(leftBars, rightBars, lookback)   Detects Bullish Trend (HH + HL within lookback)   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : Lookback period (default 50)   Returns: bool True when making higher highs AND higher lows bearishTrend(leftBars, rightBars, lookback)   Detects Bearish Trend (LH + LL within lookback)   Parameters:      leftBars (int) : Bars to left for pivot (default 5)      rightBars (int) : Bars to right for pivot (default 5)      lookback (int) : Lookback period (default 50)   Returns: bool True when making lower highs AND lower lows nearestResistance(lookback, leftBars, rightBars)   Finds nearest resistance level above current price   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: float Nearest resistance level or na EDGE CASES: Pre-computes pivots, handles bounds properly nearestSupport(lookback, leftBars, rightBars)   Finds nearest support level below current price   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: float Nearest support level or na resistanceBreakout(lookback, leftBars, rightBars)   Detects resistance breakout   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: bool True when price breaks above resistance EDGE CASES: Uses previous bar's resistance to avoid lookahead supportBreakdown(lookback, leftBars, rightBars)   Detects support breakdown   Parameters:      lookback (int) : Number of bars to look back (default 50)      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)   Returns: bool True when price breaks below support bullishVolumeDivergence(leftBars, rightBars, lookback)   Detects Bullish Volume Divergence (price makes lower low, volume decreases)   Parameters:      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)      lookback (int) : Bars to search for previous pivot (default 50)   Returns: bool True when bullish volume divergence detected EDGE CASES: Library-safe (no var), searches for previous pivot bearishVolumeDivergence(leftBars, rightBars, lookback)   Detects Bearish Volume Divergence (price makes higher high, volume decreases)   Parameters:      leftBars (int) : Pivot left bars (default 5)      rightBars (int) : Pivot right bars (default 5)      lookback (int) : Bars to search for previous pivot (default 50)   Returns: bool True when bearish volume divergence detected rangeContracting(lookback)   Detects if price is in a contracting range (triangle formation)   Parameters:      lookback (int) : Bars to analyze (default 20)   Returns: bool True when range is contracting EDGE CASES: Uses safe integer division, checks minimum lookback ascendingTriangle(lookback, flatTolerance)   Detects Ascending Triangle (flat top, rising bottom)   Parameters:      lookback (int) : Bars to analyze (default 20)      flatTolerance (float) : Max normalized slope for "flat" line (default 0.002)   Returns: bool True when ascending triangle detected EDGE CASES: Safe division, normalized slope, minimum lookback descendingTriangle(lookback, flatTolerance)   Detects Descending Triangle (falling top, flat bottom)   Parameters:      lookback (int) : Bars to analyze (default 20)      flatTolerance (float) : Max normalized slope for "flat" line (default 0.002)   Returns: bool True when descending triangle detected symmetricalTriangle(lookback, minSlope)   Detects Symmetrical Triangle (converging trend lines)   Parameters:      lookback (int) : Bars to analyze (default 20)      minSlope (float) : Minimum normalized slope magnitude (default 0.0005)   Returns: bool True when symmetrical triangle detected doubleBottom(tolerance, minSpanBars, lookback)   Detects Double Bottom (W pattern) - OWN IMPLEMENTATION Two swing lows at similar price levels with a swing high between them   Parameters:      tolerance (float) : Max price difference between lows as % (default 3)      minSpanBars (int) : Minimum bars between the two lows (default 5)      lookback (int) : Max bars to search for pattern (default 100)   Returns: bool True when double bottom detected doubleTop(tolerance, minSpanBars, lookback)   Detects Double Top (M pattern) - OWN IMPLEMENTATION Two swing highs at similar price levels with a swing low between them   Parameters:      tolerance (float) : Max price difference between highs as % (default 3)      minSpanBars (int) : Minimum bars between the two highs (default 5)      lookback (int) : Max bars to search for pattern (default 100)   Returns: bool True when double top detected tripleBottom(tolerance, minSpanBars, lookback)   Detects Triple Bottom - OWN IMPLEMENTATION Three swing lows at similar price levels   Parameters:      tolerance (float) : Max price difference between lows as % (default 3)      minSpanBars (int) : Minimum total bars for pattern (default 10)      lookback (int) : Max bars to search for pattern (default 150)   Returns: bool True when triple bottom detected tripleTop(tolerance, minSpanBars, lookback)   Detects Triple Top - OWN IMPLEMENTATION Three swing highs at similar price levels   Parameters:      tolerance (float) : Max price difference between highs as % (default 3)      minSpanBars (int) : Minimum total bars for pattern (default 10)      lookback (int) : Max bars to search for pattern (default 150)   Returns: bool True when triple top detected bearHeadShoulders()   Detects Bearish Head and Shoulders (OWN IMPLEMENTATION) Head is higher than both shoulders, shoulders roughly equal, with valid neckline STRICT VERSION - requires proper structure, neckline, and minimum span   Returns: bool True when bearish H&S detected bullHeadShoulders()   Detects Bullish (Inverse) Head and Shoulders (OWN IMPLEMENTATION) Head is lower than both shoulders, shoulders roughly equal, with valid neckline STRICT VERSION - requires proper structure, neckline, and minimum span   Returns: bool True when bullish H&S detected bearAscHeadShoulders()   Detects Bearish Ascending Head and Shoulders (variant)   Returns: bool True when pattern detected bullAscHeadShoulders()   Detects Bullish Ascending Head and Shoulders (variant)   Returns: bool True when pattern detected bearDescHeadShoulders()   Detects Bearish Descending Head and Shoulders (variant)   Returns: bool True when pattern detected bullDescHeadShoulders()   Detects Bullish Descending Head and Shoulders (variant)   Returns: bool True when pattern detected isSwingLow()   Re-export: Detects swing low   Returns: bool True when swing low detected isSwingHigh()   Re-export: Detects swing high   Returns: bool True when swing high detected swingHighPrice(idx)   Re-export: Gets swing high price at index   Parameters:      idx (int) : Index (0 = most recent)   Returns: float Swing high price swingLowPrice(idx)   Re-export: Gets swing low price at index   Parameters:      idx (int) : Index (0 = most recent)   Returns: float Swing low price swingHighBarIndex(idx)   Re-export: Gets swing high bar index   Parameters:      idx (int) : Index (0 = most recent)   Returns: int Bar index of swing high swingLowBarIndex(idx)   Re-export: Gets swing low bar index   Parameters:      idx (int) : Index (0 = most recent)   Returns: int Bar index of swing low cupBottom(smoothLen, minDepthAtr, maxDepthAtr)   Detects Cup and Handle pattern formation Uses price acceleration and depth analysis   Parameters:      smoothLen (int) : Smoothing length for price (default 10)      minDepthAtr (float) : Minimum cup depth as ATR multiple (default 1.0)      maxDepthAtr (float) : Maximum cup depth as ATR multiple (default 5.0)   Returns: bool True when potential cup bottom detected EDGE CASES: Added depth filter, ATR validation cupHandle(lookback, maxHandleRetraceRatio)   Detects potential handle formation after cup   Parameters:      lookback (int) : Bars to look back for cup (default 30)      maxHandleRetraceRatio (float) : Maximum handle retracement of cup depth (default 0.5)   Returns: bool True when handle pattern detected bullishPatternCount()   Returns count of bullish patterns detected   Returns: int Number of bullish patterns currently active bearishPatternCount()   Returns count of bearish patterns detected   Returns: int Number of bearish patterns currently active detectedPatterns()   Returns string description of detected patterns   Returns: string Comma-separated list of detected patterns
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Adaptive Market Wave Theory - ProAdaptive Market Wave Theory 🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation. The Wave Analysis Problem: Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses: 1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition. 2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw. 3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management. 4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024. 5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve. The AMWT Solution: AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning: • Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed. • Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated. • No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3." • Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time. • No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance. The Core Insight: "Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'" 🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS Why Hidden Markov Models? Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables. This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations. HMM Components: 1. Hidden States (S) : The unobservable market conditions 2. Observations (O) : What we can measure (price, volume, indicators) 3. Transition Matrix (A) : Probability of moving between states 4. Emission Matrix (B) : Probability of observations given each state 5. Initial Distribution (π) : Starting state probabilities AMWT's Six Market States: State 0: IMPULSE_UP • Definition: Strong bullish momentum with high participation • Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising • Typical Duration: 5-20 bars depending on timeframe • What It Means: Institutional buying pressure, trend acceleration phase State 1: IMPULSE_DN • Definition: Strong bearish momentum with high participation • Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling • Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster) • What It Means: Institutional selling pressure, panic or distribution acceleration State 2: CORRECTION • Definition: Counter-trend consolidation with declining momentum • Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing • Typical Duration: 8-30 bars • What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg State 3: ACCUMULATION • Definition: Base-building near lows where informed participants absorb supply • Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range • Typical Duration: 15-50 bars • What It Means: Smart money buying from weak hands, preparing for markup phase State 4: DISTRIBUTION • Definition: Top-forming near highs where informed participants distribute holdings • Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range • Typical Duration: 15-50 bars • What It Means: Smart money selling to late buyers, preparing for markdown phase State 5: TRANSITION • Definition: Regime change period with mixed signals and elevated uncertainty • Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction • Typical Duration: 5-15 bars • What It Means: Market deciding next direction, dangerous for directional trades The Transition Matrix: The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online: From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02 IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02 CORR 0.15 0.15 0.50 0.10 0.10 0.00 ACCUM 0.30 0.05 0.15 0.40 0.05 0.05 DIST 0.05 0.30 0.15 0.05 0.40 0.05 TRANS 0.20 0.20 0.20 0.15 0.15 0.10 Key Insights from Transition Probabilities: • Impulse states are sticky (70% self-transition): Once trending, markets tend to continue • Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain • Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies • Distribution strongly favors IMP_DN transition (30%): Topping leads to declines The Viterbi Algorithm: Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space. Mathematical Formulation: δ_t(j) = max_i × B_j(O_t) Where: δ_t(j) = probability of most likely path ending in state j at time t A_ij = transition probability from state i to state j B_j(O_t) = emission probability of observation O_t given state j AMWT Implementation: AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting: • Current state estimate • State confidence (probability of current state vs alternatives) • State sequence for pattern detection Online Learning (Baum-Welch Adaptation): Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior: f_onlineUpdateHMM(prev_state, curr_state, observation, decay) => // Update transition matrix A *= decay A += (1.0 - decay) // Renormalize row // Update emission matrix B *= decay B += (1.0 - decay) // Renormalize row The decay parameter (default 0.85) controls adaptation speed: • Higher decay (0.95): Slower adaptation, more stable, better for consistent markets • Lower decay (0.80): Faster adaptation, more reactive, better for regime changes Why This Matters for Trading: Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment : "There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION." This enables: • Position sizing by confidence : 90% confidence = full size; 60% confidence = half size • Risk management by transition probability : High correction probability = tighten stops • Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in 🎰 THE 3-BANDIT CONSENSUS SYSTEM The Multi-Agent Philosophy: No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets. AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions. Agent 1: TREND AGENT Philosophy : Markets trend. Follow the trend until it ends. Analytical Components: • EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish) • MACD Histogram: Direction and rate of change • Price Momentum: Close relative to ATR-normalized movement • VWAP Position: Price above/below volume-weighted average price Signal Generation: Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP → Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4 Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1 → Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3 Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP → Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4 Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1 → Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3 When Trend Agent Excels: • Trend days (IB extension >1.5x) • Post-breakout continuation • Institutional accumulation/distribution phases When Trend Agent Fails: • Range-bound markets (ADX <20) • Chop zones after volatility spikes • Reversal days at major levels Agent 2: REVERSION AGENT Philosophy: Markets revert to mean. Extreme readings reverse. Analytical Components: • Bollinger Band Position: Distance from bands, percent B • RSI Extremes: Overbought (>70) and oversold (<30) • Stochastic: %K/%D crossovers at extremes • Band Squeeze: Bollinger Band width contraction Signal Generation: Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25 → Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01 Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75 → Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01 Squeeze Fire Bull: Band squeeze ending AND close > upper band → Signal: +1 (Long), Confidence: 0.65 Squeeze Fire Bear: Band squeeze ending AND close < lower band → Signal: -1 (Short), Confidence: 0.65 When Reversion Agent Excels: • Rotation days (price stays within IB) • Range-bound consolidation • After extended moves without pullback When Reversion Agent Fails: • Strong trend days (RSI can stay overbought for days) • Breakout moves • News-driven directional moves Agent 3: STRUCTURE AGENT Philosophy: Market structure reveals institutional intent. Follow the smart money. Analytical Components: • Break of Structure (BOS): Price breaks prior swing high/low • Change of Character (CHOCH): First break against prevailing trend • Higher Highs/Higher Lows: Bullish structure • Lower Highs/Lower Lows: Bearish structure • Liquidity Sweeps: Stop runs that reverse Signal Generation: BOS Bull: Price breaks above prior swing high with momentum → Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bull: First higher low after downtrend, breaking structure → Signal: +1 (Long), Confidence: 0.75 BOS Bear: Price breaks below prior swing low with momentum → Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bear: First lower high after uptrend, breaking structure → Signal: -1 (Short), Confidence: 0.75 Liquidity Sweep Long: Price sweeps below swing low then reverses strongly → Signal: +1 (Long), Confidence: 0.80 Liquidity Sweep Short: Price sweeps above swing high then reverses strongly → Signal: -1 (Short), Confidence: 0.80 When Structure Agent Excels: • After liquidity grabs (stop runs) • At major swing points • During institutional accumulation/distribution When Structure Agent Fails: • Choppy, structureless markets • During news events (structure becomes noise) • Very low timeframes (noise overwhelms structure) Thompson Sampling: The Bandit Algorithm With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives). Thompson Sampling Solution: Each agent maintains a Beta distribution representing its success/failure history: Agent success rate modeled as Beta(α, β) Where: α = number of successful signals + 1 β = number of failed signals + 1 On Each Bar: 1. Sample from each agent's Beta distribution 2. Weight agent signals by sampled probabilities 3. Combine weighted signals into consensus 4. Update α/β based on trade outcomes Mathematical Implementation: // Beta sampling via Gamma ratio method f_beta_sample(alpha, beta) => g1 = f_gamma_sample(alpha) g2 = f_gamma_sample(beta) g1 / (g1 + g2) // Thompson Sampling selection for each agent: sampled_prob = f_beta_sample(agent.alpha, agent.beta) weight = sampled_prob / sum(all_sampled_probs) consensus += agent.signal × agent.confidence × weight Why Thompson Sampling? • Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution) • Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff • Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation • Self-Correcting : Poor performers naturally get lower weights over time Example Evolution: Day 1 (Initial): Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty) After 50 Signals: Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence) Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming) Structure Agent: Beta(32,19) → samples ~0.63 (outperforming) Result: Structure Agent now receives highest weight in consensus Consensus Requirements by Mode: Aggressive Mode: • Minimum 1/3 agents agreeing • Consensus threshold: 45% • Use case: More signals, higher risk tolerance Balanced Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 55% • Use case: Standard trading Conservative Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 65% • Use case: Higher quality, fewer signals Institutional Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 75% • Additional: Session quality >0.65, mode adjustment +0.10 • Use case: Highest quality signals only 🌀 INTELLIGENT CHOP DETECTION ENGINE The Chop Problem: Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike. AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades. Five-Factor Chop Analysis: Factor 1: ADX Component (25% weight) ADX (Average Directional Index) measures trend strength regardless of direction. ADX < 15: Very weak trend (high chop score) ADX 15-20: Weak trend (moderate chop score) ADX 20-25: Developing trend (low chop score) ADX > 25: Strong trend (minimal chop score) adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100 Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop. Factor 2: Choppiness Index (25% weight) The Choppiness Index measures price efficiency using the ratio of ATR sum to price range: CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n) CI > 61.8: Choppy (range-bound, inefficient movement) CI < 38.2: Trending (directional, efficient movement) CI 38.2-61.8: Transitional chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100 Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range). Factor 3: Range Compression (20% weight) Compares recent range to longer-term range, detecting volatility squeezes: recent_range = Highest(20) - Lowest(20) longer_range = Highest(50) - Lowest(50) compression = 1 - (recent_range / longer_range) compression > 0.5: Strong squeeze (potential breakout imminent) compression < 0.2: No compression (normal volatility) range_compression_score = compression × 100 Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet. Factor 4: Channel Position (15% weight) Tracks price position within the macro channel: channel_position = (close - channel_low) / (channel_high - channel_low) position 0.4-0.6: Center of channel (indecision zone) position <0.2 or >0.8: Near extremes (potential reversal or breakout) channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability. Factor 5: Volume Quality (15% weight) Assesses volume relative to average: vol_ratio = volume / SMA(volume, 20) vol_ratio < 0.7: Low volume (lack of conviction) vol_ratio 0.7-1.3: Normal volume vol_ratio > 1.3: High volume (conviction present) volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0 Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall. Combined Chop Intensity: chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) + (range_compression_score × 0.20) + (channel_chop × 0.15) + (volume_chop × i_volumeChopWeight × 0.15) Regime Classifications: Based on chop intensity and component analysis: • Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively • Trending (20-35%): ADX >20, moderate directional bias, trade normally • Transitioning (35-50%): Mixed signals, regime change possible, reduce size • Mid-Range (50-60%): Price trapped in channel center, avoid new positions • Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only • Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout • Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely Signal Suppression: When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification. Chop Box Visualization: When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions. 💧 LIQUIDITY ANCHORING SYSTEM The Liquidity Concept: Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing. AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence. Liquidity Event Types: Type 1: Volume Spikes Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x) Interpretation: Sudden volume surge indicates institutional activity • Near swing low + reversal: Likely accumulation • Near swing high + reversal: Likely distribution • With continuation: Institutional conviction in direction Type 2: Stop Runs (Liquidity Sweeps) Definition: Price briefly exceeds swing high/low then reverses within N bars Detection: • Price breaks above recent swing high (triggering buy stops) • Then closes back below that high within 3 bars • Signal: Bullish stop run complete, reversal likely Or inverse for bearish: • Price breaks below recent swing low (triggering sell stops) • Then closes back above that low within 3 bars • Signal: Bearish stop run complete, reversal likely Type 3: Absorption Events Definition: High volume with small candle body Detection: • Volume > 2x average • Candle body < 30% of candle range • Interpretation: Large orders being filled without moving price • Implication: Accumulation (at lows) or distribution (at highs) Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity) BSL (Buy-Side Liquidity): • Cluster of swing highs within ATR proximity • Stop losses from shorts sit above these highs • Breaking BSL triggers short covering (fuel for rally) SSL (Sell-Side Liquidity): • Cluster of swing lows within ATR proximity • Stop losses from longs sit below these lows • Breaking SSL triggers long liquidation (fuel for decline) Liquidity Pool Mapping: AMWT continuously scans for and maps liquidity pools: // Detect swing highs/lows using pivot function swing_high = ta.pivothigh(high, 5, 5) swing_low = ta.pivotlow(low, 5, 5) // Track recent swing points if not na(swing_high) bsl_levels.push(swing_high) if not na(swing_low) ssl_levels.push(swing_low) // Display on chart with labels Confluence Scoring Integration: When signals fire near identified liquidity events, confluence scoring increases: • Signal near volume spike: +10% confidence • Signal after liquidity sweep: +15% confidence • Signal at BSL/SSL pool: +10% confidence • Signal aligned with absorption zone: +10% confidence Why Liquidity Anchoring Matters: Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst. 📊 SIGNAL GRADING SYSTEM The Quality Problem: Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence. Confluence Components (100 points total): 1. Bandit Consensus Strength (25 points) consensus_str = weighted average of agent confidences score = consensus_str × 25 Example: Trend Agent: +1 signal, 0.80 confidence, 0.35 weight Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight Structure Agent: +1 signal, 0.75 confidence, 0.40 weight Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77 Score = 0.77 × 25 = 19.25 points 2. HMM State Confidence (15 points) score = hmm_confidence × 15 Example: HMM reports 82% probability of IMPULSE_UP Score = 0.82 × 15 = 12.3 points 3. Session Quality (15 points) Session quality varies by time: • London/NY Overlap: 1.0 (15 points) • New York Session: 0.95 (14.25 points) • London Session: 0.70 (10.5 points) • Asian Session: 0.40 (6 points) • Off-Hours: 0.30 (4.5 points) • Weekend: 0.10 (1.5 points) 4. Energy/Participation (10 points) energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25 score = min(energy, 1.0) × 10 5. Volume Confirmation (10 points) if volume > SMA(volume, 20) × 1.5: score = 10 else if volume > SMA(volume, 20): score = 5 else: score = 0 6. Structure Alignment (10 points) For long signals: • Bullish structure (HH + HL): 10 points • Higher low only: 6 points • Neutral structure: 3 points • Bearish structure: 0 points Inverse for short signals 7. Trend Alignment (10 points) For long signals: • Price > EMA21 > EMA50: 10 points • Price > EMA21: 6 points • Neutral: 3 points • Against trend: 0 points 8. Entry Trigger Quality (5 points) • Strong trigger (multiple confirmations): 5 points • Moderate trigger (single confirmation): 3 points • Weak trigger (marginal): 1 point Grade Scale: Total Score → Grade 85-100 → A+ (Exceptional—all factors aligned) 70-84 → A (Strong—high probability) 55-69 → B (Acceptable—proceed with caution) Below 55 → C (Marginal—filtered by default) Grade-Based Signal Brightness: Signal arrows on the chart have transparency based on grade: • A+: Full brightness (alpha = 0) • A: Slight fade (alpha = 15) • B: Moderate fade (alpha = 35) • C: Significant fade (alpha = 55) This visual hierarchy helps traders instantly identify signal quality. Minimum Grade Filter: Configurable filter (default: C) sets the minimum grade for signal display: • Set to "A" for only highest-quality signals • Set to "B" for moderate selectivity • Set to "C" for all signals (maximum quantity) 🕐 SESSION INTELLIGENCE Why Sessions Matter: Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality. Session Definitions: Asian Session (18:00-03:00 ET) • Characteristics: Lower volatility, range-bound tendency, fewer institutional participants • Quality Score: 0.40 (40% of peak quality) • Strategy Implications: Fade extremes, expect ranges, smaller position sizes • Best For: Mean-reversion setups, accumulation/distribution identification London Session (03:00-12:00 ET) • Characteristics: European institutional activity, volatility pickup, trend initiation • Quality Score: 0.70 (70% of peak quality) • Strategy Implications: Watch for trend development, breakouts more reliable • Best For: Initial trend identification, structure breaks New York Session (08:00-17:00 ET) • Characteristics: Highest liquidity, US institutional activity, major moves • Quality Score: 0.95 (95% of peak quality) • Strategy Implications: Best environment for directional trades • Best For: Trend continuation, momentum plays London/NY Overlap (08:00-12:00 ET) • Characteristics: Peak liquidity, both European and US participants active • Quality Score: 1.0 (100%—maximum quality) • Strategy Implications: Highest probability for successful breakouts and trends • Best For: All signal types—this is prime time Off-Hours • Characteristics: Thin liquidity, erratic price action, gaps possible • Quality Score: 0.30 (30% of peak quality) • Strategy Implications: Avoid new positions, wider stops if holding • Best For: Waiting Smart Weekend Detection: AMWT properly handles the Sunday evening futures open: // Traditional (broken): isWeekend = dayofweek == saturday OR dayofweek == sunday // AMWT (correct): anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime) isWeekend = calendarWeekend AND NOT anySessionActive This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend." Session Transition Boosts: Certain session transitions create trading opportunities: • Asian → London transition: +15% confidence boost (volatility expansion likely) • London → Overlap transition: +20% confidence boost (peak liquidity approaching) • Overlap → NY-only transition: -10% confidence adjustment (liquidity declining) • Any → Off-Hours transition: Signal suppression recommended 📈 TRADE MANAGEMENT SYSTEM The Signal Spam Problem: Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance. Trade Lock Mechanism: Once a signal fires, the system enters a "trade lock" state: Trade Lock Duration: Configurable (default 30 bars) Early Exit Conditions: • TP3 hit (full target reached) • Stop Loss hit (trade failed) • Lock expiration (time-based exit) During lock: • No new signals of same type displayed • Opposite signals can override (reversal) • Trade status tracked in dashboard Target Levels: Each signal generates three profit targets based on ATR: TP1 (Conservative Target) • Default: 1.0 × ATR • Purpose: Quick partial profit, reduce risk • Action: Take 30-40% off position, move stop to breakeven TP2 (Standard Target) • Default: 2.5 × ATR • Purpose: Main profit target • Action: Take 40-50% off position, trail stop TP3 (Extended Target) • Default: 5.0 × ATR • Purpose: Runner target for trend days • Action: Close remaining position or continue trailing Stop Loss: • Default: 1.9 × ATR from entry • Purpose: Define maximum risk • Placement: Below recent swing low (longs) or above recent swing high (shorts) Invalidation Level: Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies: invalidation = entry - (ATR × INVALIDATION_MULT × 1.5) If price reaches invalidation, the current market interpretation is wrong—not just the trade. Visual Trade Management: During active trades, AMWT displays: • Entry arrow with grade label (▲A+, ▼B, etc.) • TP1, TP2, TP3 horizontal lines in green • Stop Loss line in red • Invalidation line in orange (dashed) • Progress indicator in dashboard Persistent Execution Markers: When targets or stops are hit, permanent markers appear: • TP hit: Green dot with "TP1"/"TP2"/"TP3" label • SL hit: Red dot with "SL" label These persist on the chart for review and statistics. 💰 PERFORMANCE TRACKING & STATISTICS Tracked Metrics: • Total Trades: Count of all signals that entered trade lock • Winning Trades: Signals where at least TP1 was reached before SL • Losing Trades: Signals where SL was hit before any TP • Win Rate: Winning / Total × 100% • Total R Profit: Sum of R-multiples from winning trades • Total R Loss: Sum of R-multiples from losing trades • Net R: Total R Profit - Total R Loss Currency Conversion System: AMWT can display P&L in multiple formats: R-Multiple (Default) • Shows risk-normalized returns • "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades • Best for comparing across different position sizes Currency Conversion (USD/EUR/GBP/JPY/INR) • Converts R-multiples to currency based on: - Dollar Risk Per Trade (user input) - Tick Value (user input) - Selected currency Example Configuration: Dollar Risk Per Trade: $100 Display Currency: USD If Net R = +4.2R Display: Net P&L: +$420.00 | 78 trades Ticks • For futures traders who think in ticks • Converts based on tick value input Statistics Reset: Two reset methods: 1. Toggle Reset • Turn "Reset Statistics" toggle ON then OFF • Clears all statistics immediately 2. Date-Based Reset • Set "Reset After Date" (YYYY-MM-DD format) • Only trades after this date are counted • Useful for isolating recent performance 🎨 VISUAL FEATURES Macro Channel: Dynamic regression-based channel showing market boundaries: • Upper/lower bounds calculated from swing pivot linear regression • Adapts to current market structure • Shows overall trend direction and potential reversal zones Chop Boxes: Semi-transparent overlay during high-chop periods: • Purple/orange coloring indicates dangerous conditions • Visual reminder to avoid new positions Confluence Heat Zones: Background shading indicating setup quality: • Darker shading = higher confluence • Lighter shading = lower confluence • Helps identify optimal entry timing EMA Ribbon: Trend visualization via moving average fill: • EMA 8/21/50 with gradient fill between • Green fill when bullish aligned • Red fill when bearish aligned • Gray when neutral Absorption Zone Boxes: Marks potential accumulation/distribution areas: • High volume + small body = absorption • Boxes drawn at these levels • Often act as support/resistance Liquidity Pool Lines: BSL/SSL levels with labels: • Dashed lines at liquidity clusters • "BSL" label above swing high clusters • "SSL" label below swing low clusters Six Professional Themes: • Quantum: Deep purples and cyans (default) • Cyberpunk: Neon pinks and blues • Professional: Muted grays and greens • Ocean: Blues and teals • Matrix: Greens and blacks • Ember: Oranges and reds 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: Learning the System (Week 1) Goal: Understand AMWT concepts and dashboard interpretation Setup: • Signal Mode: Balanced • Display: All features enabled • Grade Filter: C (see all signals) Actions: • Paper trade ONLY—no real money • Observe HMM state transitions throughout the day • Note when agents agree vs disagree • Watch chop detection engage and disengage • Track which grades produce winners vs losers Key Learning Questions: • How often do A+ signals win vs B signals? (Should see clear difference) • Which agent tends to be right in current market? (Check dashboard) • When does chop detection save you from bad trades? • How do signals near liquidity events perform vs signals in vacuum? Phase 2: Parameter Optimization (Week 2) Goal: Tune system to your instrument and timeframe Signal Mode Testing: • Run 5 days on Aggressive mode (more signals) • Run 5 days on Conservative mode (fewer signals) • Compare: Which produces better risk-adjusted returns? Grade Filter Testing: • Track A+ only for 20 signals • Track A and above for 20 signals • Track B and above for 20 signals • Compare win rates and expectancy Chop Threshold Testing: • Default (80%): Standard filtering • Try 70%: More aggressive filtering • Try 90%: Less filtering • Which produces best results for your instrument? Phase 3: Strategy Development (Weeks 3-4) Goal: Develop personal trading rules based on system signals Position Sizing by Grade: • A+ grade: 100% position size • A grade: 75% position size • B grade: 50% position size • C grade: 25% position size (or skip) Session-Based Rules: • London/NY Overlap: Take all A/A+ signals • NY Session: Take all A+ signals, selective on A • Asian Session: Only A+ signals with extra confirmation • Off-Hours: No new positions Chop Zone Rules: • Chop >70%: Reduce position size 50% • Chop >80%: No new positions • Chop <50%: Full position size allowed Phase 4: Live Micro-Sizing (Month 2) Goal: Validate paper trading results with minimal risk Setup: • 10-20% of intended full position size • Take ONLY A+ signals initially • Follow trade management religiously Tracking: • Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus • Calculate: Win rate by grade, by session, by chop level • Compare to paper trading (should be within 15%) Red Flags: • Win rate diverges significantly from paper trading: Execution issues • Consistent losses during certain sessions: Adjust session rules • Losses cluster when specific agent dominates: Review that agent's logic Phase 5: Scaling Up (Months 3-6) Goal: Gradually increase to full position size Progression: • Month 3: 25-40% size (if micro-sizing profitable) • Month 4: 40-60% size • Month 5: 60-80% size • Month 6: 80-100% size Scale-Up Requirements: • Minimum 30 trades at current size • Win rate ≥50% • Net R positive • No revenge trading incidents • Emotional control maintained 💡 DEVELOPMENT INSIGHTS Why HMM Over Simple Indicators: Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend. The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP." The Multi-Agent Evolution: Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way. The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection. Chop Detection Revelation: Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones. Liquidity Anchoring Discovery: Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals. The Grade System Impact: Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What AMWT Is NOT: • NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty. • NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment. • NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis. • NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate. Core Assumptions: 1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure. 2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals. 3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns. 4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation. Performs Best On: • Liquid Futures : ES, NQ, MNQ, MES, CL, GC • Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY • Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap) • Liquid Crypto : BTC, ETH on major exchanges Performs Poorly On: • Illiquid Instruments : Low volume stocks, exotic pairs • Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal) • Binary Event Days : Earnings, FDA approvals, court rulings • Manipulated Markets : Penny stocks, low-cap altcoins Known Weaknesses: • Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable. • Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance. • Complexity : More parameters than simple indicators. Requires understanding to use effectively. ⚠️ RISK DISCLOSURE Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses. AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results. Hidden Markov Model assumptions may not hold during: • Major news events disrupting normal market behavior • Flash crashes or circuit breaker events • Low liquidity periods with erratic price action • Algorithmic manipulation or spoofing Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear. Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing. Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception. By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes. "Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable." Initial Public Release Core Engine: • True Hidden Markov Model with online Baum-Welch learning • Viterbi algorithm for optimal state sequence decoding • 6-state market regime classification Agent System: • 3-Bandit consensus (Trend, Reversion, Structure) • Thompson Sampling with true Beta distribution sampling • Adaptive weight learning based on performance Signal Generation: • Quality-based confluence grading (A+/A/B/C) • Four signal modes (Aggressive/Balanced/Conservative/Institutional) • Grade-based visual brightness Chop Detection: • 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume) • 7 regime classifications • Configurable signal suppression threshold Liquidity: • Volume spike detection • Stop run (liquidity sweep) identification • BSL/SSL pool mapping • Absorption zone detection Trade Management: • Trade lock with configurable duration • TP1/TP2/TP3 targets • ATR-based stop loss • Persistent execution markers Session Intelligence: • Asian/London/NY/Overlap detection • Smart weekend handling (Sunday futures open) • Session quality scoring Performance: • Statistics tracking with reset functionality • 7 currency display modes • Win rate and Net R calculation Visuals: • Macro channel with linear regression • Chop boxes • EMA ribbon • Liquidity pool lines • 6 professional themes Dashboards: • Main Dashboard: Market State, Consensus, Trade Status, Statistics • AMWT Advisor : Market Pulse, Agent Matrix, Structure, Watch For Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
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Adaptive Market Wave TheoryAdaptive Market Wave Theory 🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation. The Wave Analysis Problem: Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses: 1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition. 2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw. 3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management. 4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024. 5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve. The AMWT Solution: AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning: • Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed. • Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated. • No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3." • Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time. • No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance. The Core Insight: "Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'" 🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS Why Hidden Markov Models? Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables. This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations. HMM Components: 1. Hidden States (S) : The unobservable market conditions 2. Observations (O) : What we can measure (price, volume, indicators) 3. Transition Matrix (A) : Probability of moving between states 4. Emission Matrix (B) : Probability of observations given each state 5. Initial Distribution (π) : Starting state probabilities AMWT's Six Market States: State 0: IMPULSE_UP • Definition: Strong bullish momentum with high participation • Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising • Typical Duration: 5-20 bars depending on timeframe • What It Means: Institutional buying pressure, trend acceleration phase State 1: IMPULSE_DN • Definition: Strong bearish momentum with high participation • Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling • Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster) • What It Means: Institutional selling pressure, panic or distribution acceleration State 2: CORRECTION • Definition: Counter-trend consolidation with declining momentum • Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing • Typical Duration: 8-30 bars • What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg State 3: ACCUMULATION • Definition: Base-building near lows where informed participants absorb supply • Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range • Typical Duration: 15-50 bars • What It Means: Smart money buying from weak hands, preparing for markup phase State 4: DISTRIBUTION • Definition: Top-forming near highs where informed participants distribute holdings • Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range • Typical Duration: 15-50 bars • What It Means: Smart money selling to late buyers, preparing for markdown phase State 5: TRANSITION • Definition: Regime change period with mixed signals and elevated uncertainty • Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction • Typical Duration: 5-15 bars • What It Means: Market deciding next direction, dangerous for directional trades The Transition Matrix: The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online: From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02 IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02 CORR 0.15 0.15 0.50 0.10 0.10 0.00 ACCUM 0.30 0.05 0.15 0.40 0.05 0.05 DIST 0.05 0.30 0.15 0.05 0.40 0.05 TRANS 0.20 0.20 0.20 0.15 0.15 0.10 Key Insights from Transition Probabilities: • Impulse states are sticky (70% self-transition): Once trending, markets tend to continue • Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain • Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies • Distribution strongly favors IMP_DN transition (30%): Topping leads to declines The Viterbi Algorithm: Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space. Mathematical Formulation: δ_t(j) = max_i × B_j(O_t) Where: δ_t(j) = probability of most likely path ending in state j at time t A_ij = transition probability from state i to state j B_j(O_t) = emission probability of observation O_t given state j AMWT Implementation: AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting: • Current state estimate • State confidence (probability of current state vs alternatives) • State sequence for pattern detection Online Learning (Baum-Welch Adaptation): Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior: f_onlineUpdateHMM(prev_state, curr_state, observation, decay) => // Update transition matrix A *= decay A += (1.0 - decay) // Renormalize row // Update emission matrix B *= decay B += (1.0 - decay) // Renormalize row The decay parameter (default 0.85) controls adaptation speed: • Higher decay (0.95): Slower adaptation, more stable, better for consistent markets • Lower decay (0.80): Faster adaptation, more reactive, better for regime changes Why This Matters for Trading: Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment : "There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION." This enables: • Position sizing by confidence : 90% confidence = full size; 60% confidence = half size • Risk management by transition probability : High correction probability = tighten stops • Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in 🎰 THE 3-BANDIT CONSENSUS SYSTEM The Multi-Agent Philosophy: No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets. AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions. Agent 1: TREND AGENT Philosophy : Markets trend. Follow the trend until it ends. Analytical Components: • EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish) • MACD Histogram: Direction and rate of change • Price Momentum: Close relative to ATR-normalized movement • VWAP Position: Price above/below volume-weighted average price Signal Generation: Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP → Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4 Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1 → Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3 Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP → Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4 Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1 → Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3 When Trend Agent Excels: • Trend days (IB extension >1.5x) • Post-breakout continuation • Institutional accumulation/distribution phases When Trend Agent Fails: • Range-bound markets (ADX <20) • Chop zones after volatility spikes • Reversal days at major levels Agent 2: REVERSION AGENT Philosophy: Markets revert to mean. Extreme readings reverse. Analytical Components: • Bollinger Band Position: Distance from bands, percent B • RSI Extremes: Overbought (>70) and oversold (<30) • Stochastic: %K/%D crossovers at extremes • Band Squeeze: Bollinger Band width contraction Signal Generation: Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25 → Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01 Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75 → Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01 Squeeze Fire Bull: Band squeeze ending AND close > upper band → Signal: +1 (Long), Confidence: 0.65 Squeeze Fire Bear: Band squeeze ending AND close < lower band → Signal: -1 (Short), Confidence: 0.65 When Reversion Agent Excels: • Rotation days (price stays within IB) • Range-bound consolidation • After extended moves without pullback When Reversion Agent Fails: • Strong trend days (RSI can stay overbought for days) • Breakout moves • News-driven directional moves Agent 3: STRUCTURE AGENT Philosophy: Market structure reveals institutional intent. Follow the smart money. Analytical Components: • Break of Structure (BOS): Price breaks prior swing high/low • Change of Character (CHOCH): First break against prevailing trend • Higher Highs/Higher Lows: Bullish structure • Lower Highs/Lower Lows: Bearish structure • Liquidity Sweeps: Stop runs that reverse Signal Generation: BOS Bull: Price breaks above prior swing high with momentum → Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bull: First higher low after downtrend, breaking structure → Signal: +1 (Long), Confidence: 0.75 BOS Bear: Price breaks below prior swing low with momentum → Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2 CHOCH Bear: First lower high after uptrend, breaking structure → Signal: -1 (Short), Confidence: 0.75 Liquidity Sweep Long: Price sweeps below swing low then reverses strongly → Signal: +1 (Long), Confidence: 0.80 Liquidity Sweep Short: Price sweeps above swing high then reverses strongly → Signal: -1 (Short), Confidence: 0.80 When Structure Agent Excels: • After liquidity grabs (stop runs) • At major swing points • During institutional accumulation/distribution When Structure Agent Fails: • Choppy, structureless markets • During news events (structure becomes noise) • Very low timeframes (noise overwhelms structure) Thompson Sampling: The Bandit Algorithm With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives). Thompson Sampling Solution: Each agent maintains a Beta distribution representing its success/failure history: Agent success rate modeled as Beta(α, β) Where: α = number of successful signals + 1 β = number of failed signals + 1 On Each Bar: 1. Sample from each agent's Beta distribution 2. Weight agent signals by sampled probabilities 3. Combine weighted signals into consensus 4. Update α/β based on trade outcomes Mathematical Implementation: // Beta sampling via Gamma ratio method f_beta_sample(alpha, beta) => g1 = f_gamma_sample(alpha) g2 = f_gamma_sample(beta) g1 / (g1 + g2) // Thompson Sampling selection for each agent: sampled_prob = f_beta_sample(agent.alpha, agent.beta) weight = sampled_prob / sum(all_sampled_probs) consensus += agent.signal × agent.confidence × weight Why Thompson Sampling? • Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution) • Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff • Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation • Self-Correcting : Poor performers naturally get lower weights over time Example Evolution: Day 1 (Initial): Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty) Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty) After 50 Signals: Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence) Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming) Structure Agent: Beta(32,19) → samples ~0.63 (outperforming) Result: Structure Agent now receives highest weight in consensus Consensus Requirements by Mode: Aggressive Mode: • Minimum 1/3 agents agreeing • Consensus threshold: 45% • Use case: More signals, higher risk tolerance Balanced Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 55% • Use case: Standard trading Conservative Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 65% • Use case: Higher quality, fewer signals Institutional Mode: • Minimum 2/3 agents agreeing • Consensus threshold: 75% • Additional: Session quality >0.65, mode adjustment +0.10 • Use case: Highest quality signals only 🌀 INTELLIGENT CHOP DETECTION ENGINE The Chop Problem: Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike. AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades. Five-Factor Chop Analysis: Factor 1: ADX Component (25% weight) ADX (Average Directional Index) measures trend strength regardless of direction. ADX < 15: Very weak trend (high chop score) ADX 15-20: Weak trend (moderate chop score) ADX 20-25: Developing trend (low chop score) ADX > 25: Strong trend (minimal chop score) adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100 Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop. Factor 2: Choppiness Index (25% weight) The Choppiness Index measures price efficiency using the ratio of ATR sum to price range: CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n) CI > 61.8: Choppy (range-bound, inefficient movement) CI < 38.2: Trending (directional, efficient movement) CI 38.2-61.8: Transitional chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100 Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range). Factor 3: Range Compression (20% weight) Compares recent range to longer-term range, detecting volatility squeezes: recent_range = Highest(20) - Lowest(20) longer_range = Highest(50) - Lowest(50) compression = 1 - (recent_range / longer_range) compression > 0.5: Strong squeeze (potential breakout imminent) compression < 0.2: No compression (normal volatility) range_compression_score = compression × 100 Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet. Factor 4: Channel Position (15% weight) Tracks price position within the macro channel: channel_position = (close - channel_low) / (channel_high - channel_low) position 0.4-0.6: Center of channel (indecision zone) position <0.2 or >0.8: Near extremes (potential reversal or breakout) channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability. Factor 5: Volume Quality (15% weight) Assesses volume relative to average: vol_ratio = volume / SMA(volume, 20) vol_ratio < 0.7: Low volume (lack of conviction) vol_ratio 0.7-1.3: Normal volume vol_ratio > 1.3: High volume (conviction present) volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0 Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall. Combined Chop Intensity: chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) + (range_compression_score × 0.20) + (channel_chop × 0.15) + (volume_chop × i_volumeChopWeight × 0.15) Regime Classifications: Based on chop intensity and component analysis: • Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively • Trending (20-35%): ADX >20, moderate directional bias, trade normally • Transitioning (35-50%): Mixed signals, regime change possible, reduce size • Mid-Range (50-60%): Price trapped in channel center, avoid new positions • Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only • Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout • Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely Signal Suppression: When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification. Chop Box Visualization: When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions. 💧 LIQUIDITY ANCHORING SYSTEM The Liquidity Concept: Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing. AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence. Liquidity Event Types: Type 1: Volume Spikes Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x) Interpretation: Sudden volume surge indicates institutional activity • Near swing low + reversal: Likely accumulation • Near swing high + reversal: Likely distribution • With continuation: Institutional conviction in direction Type 2: Stop Runs (Liquidity Sweeps) Definition: Price briefly exceeds swing high/low then reverses within N bars Detection: • Price breaks above recent swing high (triggering buy stops) • Then closes back below that high within 3 bars • Signal: Bullish stop run complete, reversal likely Or inverse for bearish: • Price breaks below recent swing low (triggering sell stops) • Then closes back above that low within 3 bars • Signal: Bearish stop run complete, reversal likely Type 3: Absorption Events Definition: High volume with small candle body Detection: • Volume > 2x average • Candle body < 30% of candle range • Interpretation: Large orders being filled without moving price • Implication: Accumulation (at lows) or distribution (at highs) Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity) BSL (Buy-Side Liquidity): • Cluster of swing highs within ATR proximity • Stop losses from shorts sit above these highs • Breaking BSL triggers short covering (fuel for rally) SSL (Sell-Side Liquidity): • Cluster of swing lows within ATR proximity • Stop losses from longs sit below these lows • Breaking SSL triggers long liquidation (fuel for decline) Liquidity Pool Mapping: AMWT continuously scans for and maps liquidity pools: // Detect swing highs/lows using pivot function swing_high = ta.pivothigh(high, 5, 5) swing_low = ta.pivotlow(low, 5, 5) // Track recent swing points if not na(swing_high) bsl_levels.push(swing_high) if not na(swing_low) ssl_levels.push(swing_low) // Display on chart with labels Confluence Scoring Integration: When signals fire near identified liquidity events, confluence scoring increases: • Signal near volume spike: +10% confidence • Signal after liquidity sweep: +15% confidence • Signal at BSL/SSL pool: +10% confidence • Signal aligned with absorption zone: +10% confidence Why Liquidity Anchoring Matters: Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst. 📊 SIGNAL GRADING SYSTEM The Quality Problem: Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence. Confluence Components (100 points total): 1. Bandit Consensus Strength (25 points) consensus_str = weighted average of agent confidences score = consensus_str × 25 Example: Trend Agent: +1 signal, 0.80 confidence, 0.35 weight Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight Structure Agent: +1 signal, 0.75 confidence, 0.40 weight Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77 Score = 0.77 × 25 = 19.25 points 2. HMM State Confidence (15 points) score = hmm_confidence × 15 Example: HMM reports 82% probability of IMPULSE_UP Score = 0.82 × 15 = 12.3 points 3. Session Quality (15 points) Session quality varies by time: • London/NY Overlap: 1.0 (15 points) • New York Session: 0.95 (14.25 points) • London Session: 0.70 (10.5 points) • Asian Session: 0.40 (6 points) • Off-Hours: 0.30 (4.5 points) • Weekend: 0.10 (1.5 points) 4. Energy/Participation (10 points) energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25 score = min(energy, 1.0) × 10 5. Volume Confirmation (10 points) if volume > SMA(volume, 20) × 1.5: score = 10 else if volume > SMA(volume, 20): score = 5 else: score = 0 6. Structure Alignment (10 points) For long signals: • Bullish structure (HH + HL): 10 points • Higher low only: 6 points • Neutral structure: 3 points • Bearish structure: 0 points Inverse for short signals 7. Trend Alignment (10 points) For long signals: • Price > EMA21 > EMA50: 10 points • Price > EMA21: 6 points • Neutral: 3 points • Against trend: 0 points 8. Entry Trigger Quality (5 points) • Strong trigger (multiple confirmations): 5 points • Moderate trigger (single confirmation): 3 points • Weak trigger (marginal): 1 point Grade Scale: Total Score → Grade 85-100 → A+ (Exceptional—all factors aligned) 70-84 → A (Strong—high probability) 55-69 → B (Acceptable—proceed with caution) Below 55 → C (Marginal—filtered by default) Grade-Based Signal Brightness: Signal arrows on the chart have transparency based on grade: • A+: Full brightness (alpha = 0) • A: Slight fade (alpha = 15) • B: Moderate fade (alpha = 35) • C: Significant fade (alpha = 55) This visual hierarchy helps traders instantly identify signal quality. Minimum Grade Filter: Configurable filter (default: C) sets the minimum grade for signal display: • Set to "A" for only highest-quality signals • Set to "B" for moderate selectivity • Set to "C" for all signals (maximum quantity) 🕐 SESSION INTELLIGENCE Why Sessions Matter: Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality. Session Definitions: Asian Session (18:00-03:00 ET) • Characteristics: Lower volatility, range-bound tendency, fewer institutional participants • Quality Score: 0.40 (40% of peak quality) • Strategy Implications: Fade extremes, expect ranges, smaller position sizes • Best For: Mean-reversion setups, accumulation/distribution identification London Session (03:00-12:00 ET) • Characteristics: European institutional activity, volatility pickup, trend initiation • Quality Score: 0.70 (70% of peak quality) • Strategy Implications: Watch for trend development, breakouts more reliable • Best For: Initial trend identification, structure breaks New York Session (08:00-17:00 ET) • Characteristics: Highest liquidity, US institutional activity, major moves • Quality Score: 0.95 (95% of peak quality) • Strategy Implications: Best environment for directional trades • Best For: Trend continuation, momentum plays London/NY Overlap (08:00-12:00 ET) • Characteristics: Peak liquidity, both European and US participants active • Quality Score: 1.0 (100%—maximum quality) • Strategy Implications: Highest probability for successful breakouts and trends • Best For: All signal types—this is prime time Off-Hours • Characteristics: Thin liquidity, erratic price action, gaps possible • Quality Score: 0.30 (30% of peak quality) • Strategy Implications: Avoid new positions, wider stops if holding • Best For: Waiting Smart Weekend Detection: AMWT properly handles the Sunday evening futures open: // Traditional (broken): isWeekend = dayofweek == saturday OR dayofweek == sunday // AMWT (correct): anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime) isWeekend = calendarWeekend AND NOT anySessionActive This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend." Session Transition Boosts: Certain session transitions create trading opportunities: • Asian → London transition: +15% confidence boost (volatility expansion likely) • London → Overlap transition: +20% confidence boost (peak liquidity approaching) • Overlap → NY-only transition: -10% confidence adjustment (liquidity declining) • Any → Off-Hours transition: Signal suppression recommended 📈 TRADE MANAGEMENT SYSTEM The Signal Spam Problem: Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance. Trade Lock Mechanism: Once a signal fires, the system enters a "trade lock" state: Trade Lock Duration: Configurable (default 30 bars) Early Exit Conditions: • TP3 hit (full target reached) • Stop Loss hit (trade failed) • Lock expiration (time-based exit) During lock: • No new signals of same type displayed • Opposite signals can override (reversal) • Trade status tracked in dashboard Target Levels: Each signal generates three profit targets based on ATR: TP1 (Conservative Target) • Default: 1.0 × ATR • Purpose: Quick partial profit, reduce risk • Action: Take 30-40% off position, move stop to breakeven TP2 (Standard Target) • Default: 2.5 × ATR • Purpose: Main profit target • Action: Take 40-50% off position, trail stop TP3 (Extended Target) • Default: 5.0 × ATR • Purpose: Runner target for trend days • Action: Close remaining position or continue trailing Stop Loss: • Default: 1.9 × ATR from entry • Purpose: Define maximum risk • Placement: Below recent swing low (longs) or above recent swing high (shorts) Invalidation Level: Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies: invalidation = entry - (ATR × INVALIDATION_MULT × 1.5) If price reaches invalidation, the current market interpretation is wrong—not just the trade. Visual Trade Management: During active trades, AMWT displays: • Entry arrow with grade label (▲A+, ▼B, etc.) • TP1, TP2, TP3 horizontal lines in green • Stop Loss line in red • Invalidation line in orange (dashed) • Progress indicator in dashboard Persistent Execution Markers: When targets or stops are hit, permanent markers appear: • TP hit: Green dot with "TP1"/"TP2"/"TP3" label • SL hit: Red dot with "SL" label These persist on the chart for review and statistics. 💰 PERFORMANCE TRACKING & STATISTICS Tracked Metrics: • Total Trades: Count of all signals that entered trade lock • Winning Trades: Signals where at least TP1 was reached before SL • Losing Trades: Signals where SL was hit before any TP • Win Rate: Winning / Total × 100% • Total R Profit: Sum of R-multiples from winning trades • Total R Loss: Sum of R-multiples from losing trades • Net R: Total R Profit - Total R Loss Currency Conversion System: AMWT can display P&L in multiple formats: R-Multiple (Default) • Shows risk-normalized returns • "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades • Best for comparing across different position sizes Currency Conversion (USD/EUR/GBP/JPY/INR) • Converts R-multiples to currency based on: - Dollar Risk Per Trade (user input) - Tick Value (user input) - Selected currency Example Configuration: Dollar Risk Per Trade: $100 Display Currency: USD If Net R = +4.2R Display: Net P&L: +$420.00 | 78 trades Ticks • For futures traders who think in ticks • Converts based on tick value input Statistics Reset: Two reset methods: 1. Toggle Reset • Turn "Reset Statistics" toggle ON then OFF • Clears all statistics immediately 2. Date-Based Reset • Set "Reset After Date" (YYYY-MM-DD format) • Only trades after this date are counted • Useful for isolating recent performance 🎨 VISUAL FEATURES Macro Channel: Dynamic regression-based channel showing market boundaries: • Upper/lower bounds calculated from swing pivot linear regression • Adapts to current market structure • Shows overall trend direction and potential reversal zones Chop Boxes: Semi-transparent overlay during high-chop periods: • Purple/orange coloring indicates dangerous conditions • Visual reminder to avoid new positions Confluence Heat Zones: Background shading indicating setup quality: • Darker shading = higher confluence • Lighter shading = lower confluence • Helps identify optimal entry timing EMA Ribbon: Trend visualization via moving average fill: • EMA 8/21/50 with gradient fill between • Green fill when bullish aligned • Red fill when bearish aligned • Gray when neutral Absorption Zone Boxes: Marks potential accumulation/distribution areas: • High volume + small body = absorption • Boxes drawn at these levels • Often act as support/resistance Liquidity Pool Lines: BSL/SSL levels with labels: • Dashed lines at liquidity clusters • "BSL" label above swing high clusters • "SSL" label below swing low clusters Six Professional Themes: • Quantum: Deep purples and cyans (default) • Cyberpunk: Neon pinks and blues • Professional: Muted grays and greens • Ocean: Blues and teals • Matrix: Greens and blacks • Ember: Oranges and reds 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: Learning the System (Week 1) Goal: Understand AMWT concepts and dashboard interpretation Setup: • Signal Mode: Balanced • Display: All features enabled • Grade Filter: C (see all signals) Actions: • Paper trade ONLY—no real money • Observe HMM state transitions throughout the day • Note when agents agree vs disagree • Watch chop detection engage and disengage • Track which grades produce winners vs losers Key Learning Questions: • How often do A+ signals win vs B signals? (Should see clear difference) • Which agent tends to be right in current market? (Check dashboard) • When does chop detection save you from bad trades? • How do signals near liquidity events perform vs signals in vacuum? Phase 2: Parameter Optimization (Week 2) Goal: Tune system to your instrument and timeframe Signal Mode Testing: • Run 5 days on Aggressive mode (more signals) • Run 5 days on Conservative mode (fewer signals) • Compare: Which produces better risk-adjusted returns? Grade Filter Testing: • Track A+ only for 20 signals • Track A and above for 20 signals • Track B and above for 20 signals • Compare win rates and expectancy Chop Threshold Testing: • Default (80%): Standard filtering • Try 70%: More aggressive filtering • Try 90%: Less filtering • Which produces best results for your instrument? Phase 3: Strategy Development (Weeks 3-4) Goal: Develop personal trading rules based on system signals Position Sizing by Grade: • A+ grade: 100% position size • A grade: 75% position size • B grade: 50% position size • C grade: 25% position size (or skip) Session-Based Rules: • London/NY Overlap: Take all A/A+ signals • NY Session: Take all A+ signals, selective on A • Asian Session: Only A+ signals with extra confirmation • Off-Hours: No new positions Chop Zone Rules: • Chop >70%: Reduce position size 50% • Chop >80%: No new positions • Chop <50%: Full position size allowed Phase 4: Live Micro-Sizing (Month 2) Goal: Validate paper trading results with minimal risk Setup: • 10-20% of intended full position size • Take ONLY A+ signals initially • Follow trade management religiously Tracking: • Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus • Calculate: Win rate by grade, by session, by chop level • Compare to paper trading (should be within 15%) Red Flags: • Win rate diverges significantly from paper trading: Execution issues • Consistent losses during certain sessions: Adjust session rules • Losses cluster when specific agent dominates: Review that agent's logic Phase 5: Scaling Up (Months 3-6) Goal: Gradually increase to full position size Progression: • Month 3: 25-40% size (if micro-sizing profitable) • Month 4: 40-60% size • Month 5: 60-80% size • Month 6: 80-100% size Scale-Up Requirements: • Minimum 30 trades at current size • Win rate ≥50% • Net R positive • No revenge trading incidents • Emotional control maintained 💡 DEVELOPMENT INSIGHTS Why HMM Over Simple Indicators: Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend. The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP." The Multi-Agent Evolution: Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way. The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection. Chop Detection Revelation: Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones. Liquidity Anchoring Discovery: Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals. The Grade System Impact: Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What AMWT Is NOT: • NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty. • NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment. • NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis. • NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate. Core Assumptions: 1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure. 2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals. 3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns. 4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation. Performs Best On: • Liquid Futures : ES, NQ, MNQ, MES, CL, GC • Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY • Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap) • Liquid Crypto : BTC, ETH on major exchanges Performs Poorly On: • Illiquid Instruments : Low volume stocks, exotic pairs • Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal) • Binary Event Days : Earnings, FDA approvals, court rulings • Manipulated Markets : Penny stocks, low-cap altcoins Known Weaknesses: • Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable. • Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance. • Complexity : More parameters than simple indicators. Requires understanding to use effectively. ⚠️ RISK DISCLOSURE Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses. AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results. Hidden Markov Model assumptions may not hold during: • Major news events disrupting normal market behavior • Flash crashes or circuit breaker events • Low liquidity periods with erratic price action • Algorithmic manipulation or spoofing Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear. Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing. Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception. By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes. "Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable." Initial Public Release Core Engine: • True Hidden Markov Model with online Baum-Welch learning • Viterbi algorithm for optimal state sequence decoding • 6-state market regime classification Agent System: • 3-Bandit consensus (Trend, Reversion, Structure) • Thompson Sampling with true Beta distribution sampling • Adaptive weight learning based on performance Signal Generation: • Quality-based confluence grading (A+/A/B/C) • Four signal modes (Aggressive/Balanced/Conservative/Institutional) • Grade-based visual brightness Chop Detection: • 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume) • 7 regime classifications • Configurable signal suppression threshold Liquidity: • Volume spike detection • Stop run (liquidity sweep) identification • BSL/SSL pool mapping • Absorption zone detection Trade Management: • Trade lock with configurable duration • TP1/TP2/TP3 targets • ATR-based stop loss • Persistent execution markers Session Intelligence: • Asian/London/NY/Overlap detection • Smart weekend handling (Sunday futures open) • Session quality scoring Performance: • Statistics tracking with reset functionality • 7 currency display modes • Win rate and Net R calculation Visuals: • Macro channel with linear regression • Chop boxes • EMA ribbon • Liquidity pool lines • 6 professional themes Dashboards: • Main Dashboard: Market State, Consensus, Trade Status, Statistics 📋 AMWT vs AMWT-PRO: This version includes all core AMWT functionality: ✓ Full Hidden Markov Model state estimation ✓ 3-Bandit Thompson Sampling consensus system ✓ Complete 5-factor chop detection engine ✓ All four signal modes ✓ Full trade management with TP/SL tracking ✓ Main dashboard with complete statistics ✓ All visual features (channels, zones, pools) ✓ Identical signal generation to PRO ✓ Six professional themes ✓ Full alert system The PRO version adds the AMWT Advisor panel—a secondary dashboard providing: • Real-time Market Pulse situation assessment • Agent Matrix visualization (individual agent votes) • Structure analysis breakdown • "Watch For" upcoming setups • Action Command coaching Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals. Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
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Flux Momentum Oscillator[BullByte]Flux Momentum Oscillator is a professional-grade momentum analysis system built on an original methodology called Momentum Flux Bars (MFB). Unlike conventional oscillators that measure momentum over fixed time periods, this indicator constructs synthetic momentum bars based on actual price movement, creating a pure representation of directional pressure independent of time-based noise. This is NOT a mashup or combination of existing indicators. The entire system is built from the ground up around a single cohesive concept: measuring momentum through price-triggered synthetic bars rather than time-triggered calculations. CORE INNOVATION: MOMENTUM FLUX BARS (MFB) Traditional momentum indicators calculate values at fixed time intervals, which means a slow, grinding move receives the same measurement weight as a fast, explosive move occurring over the same number of bars. This creates distortion in momentum readings. Momentum Flux Bars solve this problem by forming only when price travels a volatility-adjusted distance. Each MFB represents genuine directional commitment from market participants. Key Properties of Momentum Flux Bars: - Form based on price movement, not time passage - Automatically adjust their formation threshold based on current volatility - Capture the velocity of price movement (how quickly each bar forms) - Record volume participation during formation - Create a noise-filtered view of true market momentum The oscillator then analyzes the pattern, velocity, and characteristics of recent MFB formations to produce its readings. WHY THIS APPROACH MATTERS FOR TRADERS Time-Based Problem: A 14-period RSI on a choppy day produces the same calculation structure as on a trending day, even though market behavior differs completely. The indicator cannot distinguish between meaningful moves and noise. Flux-Based Solution: When price chops sideways, fewer MFBs form because price fails to travel the required distance. When price trends strongly, MFBs form rapidly in sequence. The oscillator inherently adapts to actual market behavior. Practical Benefits: - Cleaner signals during trending conditions - Automatic noise reduction during consolidation - Earlier detection of momentum shifts through velocity analysis - Reduced false signals in choppy markets - No manual adjustment needed across different market conditions COMPLETE FEATURE BREAKDOWN FEATURE 1: AUTO-OPTIMIZATION ENGINE The indicator includes an optional auto-optimization system that continuously evaluates different sensitivity parameters and selects the configuration producing the cleanest momentum measurement for current conditions. How It Works: - Tests multiple ATR multiplier values against recent price history - Scores each configuration based on trend capture efficiency - Automatically applies the optimal setting - Re-evaluates periodically to adapt to changing conditions Trader Benefit: Eliminates the guesswork of parameter tuning. The indicator finds its own optimal settings. FEATURE 2: MARKET REGIME CLASSIFICATION The system classifies current market conditions into four distinct regimes based on MFB formation patterns: EXPLOSIVE: Rapid MFB formation with strong directional bias and high volume participation. Indicates powerful trending conditions with high momentum. STEADY: Consistent MFB formation in a primary direction with normal velocity. Represents healthy, sustainable trends suitable for trend-following approaches. CONSOLIDATING: Mixed direction MFB formation with decreasing velocity. Suggests range-bound conditions where breakout strategies may be appropriate. DEAD: Minimal MFB formation activity. Indicates extremely low volatility or market indecision. Often precedes significant moves. Trader Benefit: Instantly understand current market character and adjust strategy accordingly. FEATURE 3: VELOCITY DIVERGENCE DETECTION This advanced feature monitors the formation speed of Momentum Flux Bars and compares it against price direction. Velocity Divergence Bearish: Price making higher highs but MFBs forming progressively slower. Suggests buying pressure is weakening despite higher prices. Velocity Divergence Bullish: Price making lower lows but MFBs forming progressively slower. Suggests selling pressure is weakening despite lower prices. Trader Benefit: Early warning system for potential reversals before they appear on price charts. FEATURE 4: MOMENTUM EXHAUSTION DETECTION The system identifies when a trending move may be running out of energy by analyzing the duration pattern of consecutive same-direction MFBs. Exhaustion Pattern: When each successive MFB in a trend takes progressively longer to form, it indicates diminishing momentum even though direction remains unchanged. States Displayed: - BUILDING: Momentum is increasing or stable - PEAK: Maximum momentum velocity reached - EXHAUSTING: Progressive slowdown detected Trader Benefit: Know when a trend is losing steam before price reverses. FEATURE 5: HIGHER TIMEFRAME ALIGNMENT The indicator checks whether higher timeframe MFB direction supports or conflicts with current timeframe momentum. ALIGNED BULL: Both timeframes showing bullish MFB direction ALIGNED BEAR: Both timeframes showing bearish MFB direction DIVERGENT: Timeframes showing opposing directions NEUTRAL: Higher timeframe direction unclear Trader Benefit: Trade with higher timeframe support for higher probability setups. FEATURE 6: CHOPPY MARKET DETECTION A dedicated algorithm analyzes recent MFB patterns to determine if the market is in a choppy, directionless state. Detection Factors: - Frequency of direction changes in recent MFBs - Lack of consecutive same-direction formations - Weak directional bias in the MFB sequence Trader Benefit: Avoid trend-following strategies when market conditions do not support them. FEATURE 7: TREND STRENGTH MEASUREMENT A percentage-based strength reading derived from MFB pattern analysis. Flux Momentum Oscillator Chart Example Chart Overview: Bitcoin 15-Minute Chart (Dec 21, 2025) BTCUSD Market Snapshot Price: $88,854.53 | Oscillator: 77.38 | Direction: BULLISH | Regime: EXPLOSIVE 1. EXPLOSIVE REGIME DETECTION (Current State - Right Side) 2. MOMENTUM EXHAUSTION ZONE (Mid-Chart) 3. CHOP/CONSOLIDATION PERIOD (Before Breakout) 4. VELOCITY DIVERGENCE (Around 21:00 the previous day) 5. BULLISH MOMENTUM SHIFT (Around 09:00) 6. FORMATION PROGRESS BAR (Bottom of Oscillator) 7. TREND STRENGTH INDICATOR (Bottom Bar) 8. EXTREME ZONES (Top and Bottom Boundaries) Reading Interpretation: - Above 70%: Strong trending conditions - 40% to 70%: Moderate trend or developing move - Below 40%: Weak trend or choppy conditions Visual representation provided via the strength bar at the bottom of the indicator panel. HOW TO READ THE OSCILLATOR PLOT OSCILLATOR LINE (Main Line): - Ranges from -100 to +100 - Above zero indicates bullish momentum - Below zero indicates bearish momentum - Color intensity reflects momentum direction and strength - Glow effect (optional) enhances visibility of the main reading SIGNAL LINE (Secondary Line): - Smoothed version of the oscillator - Crossovers indicate momentum shifts - Purple/accent colored for visual distinction HISTOGRAM BARS: - Represent the difference between oscillator and signal line - Increasing histogram in direction of oscillator confirms momentum - Decreasing histogram warns of potential momentum shift - Bright colors indicate increasing momentum - Faded colors indicate decreasing momentum ZONE INTERPRETATION: +75 to +100 (Extreme Bullish Zone): Very strong bullish momentum. Price has moved significantly and rapidly. Watch for exhaustion patterns. Not ideal for new long entries. Consider profit-taking on existing longs. +50 to +75 (Strong Bullish Zone): Healthy bullish momentum. Good conditions for trend-following long strategies. Pullbacks to signal line often provide continuation opportunities. 0 to +50 (Mild Bullish Zone): Positive but moderate momentum. Trend may be developing or maturing. Watch for strength building or fading. 0 to -50 (Mild Bearish Zone): Negative but moderate momentum. Downtrend may be developing or maturing. Watch for weakness building or recovering. -50 to -75 (Strong Bearish Zone): Healthy bearish momentum. Good conditions for trend-following short strategies. Rallies to signal line often provide continuation opportunities. -75 to -100 (Extreme Bearish Zone): Very strong bearish momentum. Price has moved significantly and rapidly to downside. Watch for exhaustion patterns. Not ideal for new short entries. Consider profit-taking on existing shorts. HOW TO READ THE DASHBOARD The dashboard provides comprehensive market analysis at a glance. Each row displays specific information: OSCILLATOR ROW: Shows current oscillator value with directional icon. indicates reading above +50 (High) indicates reading below -50 (Low) DIRECTION ROW: Current MFB direction. BULLISH: Recent MFB formed upward BEARISH: Recent MFB formed downward NEUTRAL: No recent MFB or unclear REGIME ROW: Current market regime classification. EXPLOSIVE / STEADY / CONSOLIDATING / DEAD Color coded for quick recognition. MARKET ROW: Trend state assessment. TRENDING UP: Confirmed uptrend in progress TRENDING DN: Confirmed downtrend in progress CHOPPY: No clear trend, high direction changes MIXED: Partial trend characteristics STRENGTH ROW: Visual bar showing trend strength percentage. More filled bars indicate stronger trend. Color shifts from red (weak) to yellow (moderate) to green (strong). VELOCITY ROW: MFB formation speed status. ACCELERATING: MFBs forming faster over time STEADY: Consistent formation speed DECELERATING: MFBs forming slower over time MOMENTUM ROW: Momentum development status. BUILDING: Momentum increasing PEAK: Maximum momentum reached EXHAUSTING: Momentum declining despite same direction HTF ALIGN ROW: Higher timeframe alignment status. BULL: HTF supports bullish bias BEAR: HTF supports bearish bias DIVERGENT: HTF opposes current direction NEUTRAL: HTF unclear FORMING ROW: Progress toward next MFB formation. Visual bar fills as price approaches formation threshold. Helps anticipate when next MFB will complete. Additional rows (when not in Compact Mode): - Flux Size: Current MFB formation threshold value - ATR Mult: Current optimized ATR multiplier (when auto-optimization enabled) - Regime %: Numerical regime score FORMATION PROGRESS INDICATOR The horizontal line near the bottom of the indicator panel shows progress toward the next MFB formation. Reading the Progress Line: - Starts at baseline after each MFB completion - Rises as price moves toward formation threshold - Higher position indicates imminent MFB formation - Color changes from neutral to accent to warning as formation approaches Practical Use: - Anticipate when new momentum data will become available - Gauge intra-bar momentum development - Understand why signals occur when they do TREND STRENGTH BAR The horizontal bar at the very bottom of the indicator displays trend strength visually. Components: - Gray background bar represents full scale (0-100%) - Colored fill represents current strength reading - Label displays exact percentage value Color Interpretation: - Green fill: Strong trend (above 70%) - Yellow fill: Moderate trend (40-70%) - Red fill: Weak trend (below 40%) RECOMMENDED USAGE GUIDELINES TIMEFRAME RECOMMENDATIONS: Scalping (1m to 5m): - Use lower Flux Period (8-10) for faster response - Focus on oscillator crossovers and histogram momentum - Regime should be STEADY or EXPLOSIVE for best results Day Trading (5m to 30m): - Default settings work well - Use HTF alignment with 1H or 4H for confirmation - Avoid trading when regime shows DEAD Swing Trading (1H to 4H): - Consider higher Flux Period (18-21) for smoother signals - Regime classification becomes very valuable - Velocity divergence provides excellent early warnings Position Trading (Daily and above): - Higher Flux Period (21-30) recommended - Focus on regime changes and exhaustion patterns - HTF alignment less relevant, oscillator zones more important ASSET CLASS NOTES: Forex: Works well on major pairs. Consider slightly higher sensitivity on less volatile pairs. Crypto: Higher volatility may require lower sensitivity multiplier. Regime detection particularly useful. Stocks: Excellent for liquid stocks. Less effective on illiquid names due to gappy price action. Indices: Very effective. Clean price action produces clean MFB patterns. Commodities: Works well, especially on gold and oil. Adjust sensitivity for different volatility profiles. SETTINGS OVERVIEW MODE AND THEME: - Trading Mode: Simple (clean), Pro (full data), Hybrid (balanced) - Visual Theme: Dark, Light, Neon, Stealth - Compact Dashboard: Reduces dashboard rows FLUX ENGINE: - Flux Calculation Method: Choose optimization approach - Enable Auto-Optimization: Let indicator find optimal parameters - Flux Period: Base volatility calculation period - Sensitivity Multiplier: Adjust MFB formation threshold - Optimization Lookback: Bars analyzed for optimization - Optimization Frequency: How often to re-optimize OSCILLATOR: - Oscillator Smoothing: Main line smoothness - Signal Line Length: Signal line responsiveness - Momentum Depth: MFBs analyzed for oscillator - Histogram Scale: Visual scaling of histogram MARKET STATE: - Chop Detection Window: MFBs analyzed for chop detection - Chop Threshold: Sensitivity of chop classification - Min Trend Confirmation: Consecutive bars for trend confirmation ADVANCED ANALYSIS: - Enable Regime Classification: Market regime detection - Enable Velocity Divergence: Formation speed analysis - Enable Exhaustion Detection: Trend exhaustion warnings - Enable HTF Alignment: Higher timeframe checking - Higher Timeframe: Which timeframe to check VISUALS: - Glow Effect: Visual enhancement on oscillator - Show Zone Fills: Background zone coloring - Show Formation Progress: Progress indicator display - Show Trend Strength Bar: Bottom strength bar - Show Dashboard: Information panel display - Dashboard Position: Corner placement SIGNAL INTERPRETATION GUIDELINES BULLISH MOMENTUM SHIFT: Oscillator crosses above signal line while not in extreme bearish territory. Suggests emerging bullish momentum. Stronger when occurring near zero line or in mild bearish zone. BEARISH MOMENTUM SHIFT: Oscillator crosses below signal line while not in extreme bullish territory. Suggests emerging bearish momentum. Stronger when occurring near zero line or in mild bullish zone. STRONG TREND CONDITIONS: Oscillator beyond +/-55, in direction of signal line, trend strength above 55%, not choppy. Indicates conditions favorable for trend-following approaches. EXTREME ZONES: Oscillator beyond +/-75. Diamond markers appear. Exercise caution with new positions in trend direction. Watch for exhaustion and divergence signals. ALERT SYSTEM The indicator includes comprehensive alerts for automated monitoring: Momentum Alerts: - Bullish Momentum Shift - Bearish Momentum Shift - Strong Uptrend Initiated - Strong Downtrend Initiated Zone Alerts: - Extreme Bullish Zone Reached - Extreme Bearish Zone Reached Market State Alerts: - Choppy Conditions Detected - Choppy Conditions Cleared - Explosive Regime Entered - Dead Regime Entered Advanced Alerts: - Velocity Divergence Detected - Exhaustion Warning Triggered - HTF Aligned Bullish - HTF Aligned Bearish - HTF Divergence Detected MFB Alerts: - Bullish MFB Formed - Bearish MFB Formed WHAT THIS INDICATOR IS NOT This indicator is NOT: - A buy/sell signal generator (it provides momentum context, not trade signals) - A standalone trading system (combine with price action and other analysis) - A guarantee of profitability (no indicator can guarantee results) - A replacement for risk management (always use proper position sizing and stops) - A mashup of existing indicators (this is original methodology) ORIGINALITY STATEMENT The Momentum Flux Bars concept was designed specifically to address limitations of time-based momentum calculations. Every component of this system serves the central MFB methodology: - The oscillator measures MFB directional weight - The regime classifier interprets MFB patterns - The velocity analysis tracks MFB formation speed - The exhaustion detector monitors MFB duration progression - The HTF alignment checks MFB direction across timeframes This is a unified analytical framework, not a collection of separate indicators. TECHNICAL NOTES Non-Repainting Confirmation: All signal generation uses confirmed bar data only. MFB formations occur on bar close. Historical signals will not change after they appear. Performance Considerations: Auto-optimization runs periodically, not every bar, to maintain performance. MFB history is trimmed to prevent memory issues on extended sessions. Reduce Max MFB History if experiencing performance issues. Symbol and Timeframe Handling: The indicator resets its MFB history when symbol or timeframe changes. This ensures clean analysis without carryover from previous contexts. DISCLAIMER This indicator is provided for educational and informational purposes only. It is not financial advice and should not be considered as such. Trading involves substantial risk of loss. Past performance of any trading methodology or indicator does not guarantee future results. The author makes no representations regarding the profitability or suitability of this indicator for any particular purpose. Users are solely responsible for their own trading decisions. Always use proper risk management, including appropriate position sizing and stop-loss orders. Never risk more than you can afford to lose. Before using this or any indicator in live trading, thoroughly test it on historical data and in a demo environment. Understand its behavior across different market conditions. The author is not liable for any losses incurred through the use of this indicator. Developed by BullByte Version 1.0.0
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7
A Physicist's Bitcoin Trading Strategy 1. Summary This strategy and indicator were designed for, and intended to be used to guide trading activity in, crypto markets, particularly Bitcoin. This strategy uses a custom indicator to determine the state of the market (bullish vs bearish) and allocates funds accordingly. This particular variation also uses the custom indicator to determine when the market is significantly oversold and takes advantage of the opportunity (it buys the dip). The specific mathematical formula that is used to calculate the underlying custom indicator allows the trader to get in before, or near the start of, the bull trends, and get out before the bear trends. The strategy's properties dialogue box includes many display settings and parameters for optimization and customization to meet the user's needs and risk tolerance; this is both a tool to gauge the market, as well as a trading strategy to beat the market. Guidelines for parameter settings are provided. A sample dataset of backtest results using randomized parameters, both within the guidelines and outside the guidelines, is available upon request; notably, all trials outperformed the intended market (Bitcoin) during the 9-year backtest period. 2. The Indicator and Strategy 2.1. The Indicator A mathematical formula is used to determine the state of the market according to three different "frequencies", which, for lack of better terminology, are called fast, moderate, and slow indicators. There are two parameters for each of the three indicators, one called response time and the other is a simple look-back period. Finally, four exponential moving averages are used to smooth each indicator. In total, there are 18 different levels of bullishness/bearishness. The purpose of using three indicators, rather than one, is to capture the full character of the market, from a macro/global scope down to a micro/local scope. I.e. the full indicator looks at the forest, the trees, and the branches, simultaneously. 2.2. The Strategy The trend-trading strategy is very simple; there are only four types of orders: 1) The entire position (e.g. all bitcoins held) is sold (if it hasn't already been totally sold) when the indicator becomes maximally bearish, 2) When the movement of the indicator is in the bullish direction, the strategy dollar-cost-average (DCA) buys at an exponentially decreasing rate, i.e. it buys more in the early stages of the transition from bear->bull. 3) When the indicator is maximally bullish, it goes "all-in" † (if it hasn't already gone all-in), i.e. it converts all available cash into the underlying security/token. And, 4) when the movement of the indicator is in the bearish direction, the strategy DCA sells (again, exponentially decreasing) to get out quickly. No leverage is used in this strategy. The strategy never takes a short position. A second "buy-the-dip" strategy is also used, and it is the synergy of these two strategies, together, that is responsible for most of the outperformance in the backtests (this strategy handily beats the non-dip-buying variation in backtests). To do this, the custom indicator is used to determine when the market is significantly oversold on a short-term basis, and the strategy responds by DCA buying. However, unlike the DCA buying during bull/bear transitions, the buy-the-dip DCA buying increases with time. Specifically, within each candle that is short-term oversold, the strategy converts 10% x # of candles since becoming oversold (up to a max of 6 candles) of available cash into the underlying security/token. I.e. the first buy is 10% of available cash and occurs in the first oversold candle, the second buy is 20% of available cash and occurs in the second oversold candle, etc. up to six consecutive oversold candles. Lastly, to ensure no conflicting orders and no leverage (buying more than what is affordable with the available cash in the fund), buy-the-dip orders take precedence over the trend-trading orders enumerated in the previous paragraph. † Technically the strategy goes 99.5% in when it goes "all-in". This is to ensure no leverage is used given that there may be a commission of 0.5%. 3. Backtest Results Backtest results demonstrate significant outperformance over buy-and-hold. The default parameters of the strategy/indicator have been set by the author to achieve maximum (or, close to maximum) outperformance on backtests executed on the BTCUSD (Bitcoin) chart. However, significant outperformance over buy-and-hold is still easily achievable using non-default parameters. Basically, as long as the parameters are set to adequately capture the full character of the market, significant outperformance on backtests is achievable and is quite easy. In fact, after some experimentation, it seems as if underperformance hardly achievable and requires deliberately setting the parameters illogically (e.g. setting one parameter of the slow indicator faster than the fast indicator). In the interest of providing a quality product to the user, suggestions and guidelines for parameter settings are provided in section (6). Finally, some metrics of the strategy's outperformance on the BTCUSD chart are listed below, both for the default (optimal) parameters as well as for a random sample of parameter settings that adhere to the guidelines set forth in section (6). Using the default parameters, relative to buy-and-hold strategy, backtested from August 2011 to August 2020, Total cumulative outperformance (total return of strategy minus total return of buy-n-hold): 13,000,000%. Rolling 1-year outperformance: mean 318%, median 84%, 1st quartile 55%, 3rd quartile, 430%. Rolling 1-month outperformance: mean 2.8% (annualized, 39%), median -2.1%, 1st quartile -7.7%, 3rd quartile 13.2%, 10th percentile -13.9%, 90th percentile 24.5%. Using the default parameters, relative to buy-and-hold strategy, during specific periods, Cumulative outperformance during the past year (August 2019-August 2020): 37%. 12/17/2016 - 12/17/2017 (2017 bull market) absolute performance of 2563% vs buy-n-hold absolute performance of 2385% 11/29/2012 - 11/29/2013 (2013 bull market) absolute performance of 14033% vs buy-n-hold absolute performance of 9247% Using a random sample (n=20) of combinations of parameter settings that adhere to the guidelines outlined in section (6), relative to buy-and-hold strategy, backtested from August 2011 to August 2020, Average total cumulative outperformance, from August 2011 to August 2020: 2,000,000%. Median total cumulative outperformance, from August 2011 to August 2020: 1,000,000%. 4. Limitations This strategy is basically a DCA-swing trading strategy, and as such it is intended to be used on the 6-hr chart. Similar performance is expected on daily chart, 12-hr chart, and 4-hr chart, but performance is likely to be limited when used on charts of shorter time-frames. However, due to the flexibility afforded by the large quantity of parameters, as well as the tools included, it may be possible to tweak the indicator settings to get some outperformance on smaller time-frames. Admittedly, the author did not spend much time investigating this. As is apparent in the backtests, this strategy has very limited absolute performance during large bear markets, such as Bitcoin's 2018 bear market. As described, it does outperform the underlying security by a large amount in backtests, but a large absolute return is unlikely during large and prolonged declines (unless, of course, your unit of account is the underlying token, in which case an outperformance of the underlying is, by definition, an absolute positive return). This strategy is likely to underperform if used to trade ETFs of broad equity markets. This strategy may produce a small amount of outperformance when used to trade precious metals ETFs, given that the parameters are set optimally by the user. 5. Use The default parameters have already been set for highly optimal backtest results on the chart of BTCUSD (Bitcoin / US Dollar BITSTAMP), (although, a different combination of parameter settings may yet produce better results). Still, there is a great number of combinations that can be explored, so the user is free to tweak the settings to meet his/her/their needs. Some display options are provided to give the user a visual aid while tweaking the parameters. These include a blue/red background display of the custom indicator, a calibration system, and options to display information about the backtest results. The background pattern represents the various levels of bullishness/bearishness as semi-transparent layers of blue and red, with blue corresponding with bullish and red corresponding with bearish. The parameters that affect the indicator are the response times, the periods, and some EMA lengths. The parameters that affect the quantity of contracts (tokens/shares/bitcoins/etc) to be bought/sold are the transitionary buy/sell rates. There are also two sets of date parameters. The response time and period parameters are direct inputs into the underlying math formula and are used to create the base-level indicators (fast, moderate, and slow). The response times control the speed of each of the three indicators (shorter is fast, longer is slower) and the period controls how much historical data is used in computation. Information about how these should be set are included in section (6). Another set of parameters control EMA look-back periods that serve to smooth the base-level indicators. Increasing these EMA lengths makes the overall indicator less sensitive to short-term price action, while reducing them does the opposite. The effect of these parameters are obvious when the background blue/red visualization is displayed. Another EMA length is an EMA for the entire indicator. Increasing this parameter reduces the responsiveness of the trading strategy (buy/sell orders) to quick/small changes of the overall level of the indicator, so as to avoid unnecessary buying and selling in times of relatively small and balanced price perturbations. Note, changing this parameter does not have an effect on the overall indicator itself, and thus will not affect the blue/red background representation. The transitionary buy/sell rates control the portion of the available asset to be converted to the other. E.g. if the buy rate is set to 90%, then 90% of the available cash will be used to buy contracts/tokens/shares/bitcoins during transitions bullish transitions, e.g. if the available cash at the start of the bullish transition is $10,000 and the parameter is set to 90%, then $9,000 will be used to buy in the first candle during which the transition is bullish, then $900 will be used to buy in the second candle, then $90 in the third candle, etc. There are two dates that can be set. The first is the date at which the strategy goes all in. This is included because the buy-and-hold strategy is the benchmark against which this strategy is compared, so setting this date to some time before the strategy starts to make trades will show, very clearly, the outperformance of the strategy, especially when the initial capital parameter in the Properties tab is equal to the price of one unit of the underlying security on the date that is set, e.g. all-in on Bitcoin on 8/20/2011 and set initial capital to the BTCUSD price on that date, which was $11.70. The second date is a date to control when the strategy can begin to place trades. Finally (actually, firstly in the Inputs dialogue box), a set of checkbox inputs controls whether or not the backtest is on or off, and what is displayed. The display options are the blue/red (bull/bear) background layers †, a set of calibrators, a plot of the total strategy equity, a plot of the cash position of the strategy, a plot of the size of the position of the strategy in contracts/shares/units (labeled as BTC position), and a plot of the rolling 1-year performances of buy-and-hold and the strategy. About the calibrators: The calibration system allows the user to quickly assess and calibrate how well the indicator... indicates. Quite simply, the system has two parts: one plot that is the cumulative sum of the product of the indicator level and the change in the underlying price, i.e. sum of ‡, over all candles. The second part is a similar plot that is reduced according to the quickness with which the indicator changes, i.e. sum of . Maximizing the first plot at the expense of the second will cause the indicator to match the price action very well but therefore it will change very rapidly, from bullish to bearish, which is visualized by a background pattern that changes frequently from blue to red to blue. Ignoring the first plot and maximizing the second will also cause the indicator to more closely match the price action, but the transitions will be slower and less frequent, and will therefore focus on identifying the major trends of the market. † The blue/red background has many layers and will make the chart lag as the user interacts with it. ‡ Bearish states are coded as negative quantities, so a bearish state x negative price action = positive number, and bullish state x positive price action = positive number. 6. Suggestions and Guidelines As described in section (2.1), the indicator used in this strategy was designed to determine the state of the market--whether it is bullish or bearish--as well as the change in the state of the market--whether it is increasingly bullish or increasingly bearish. As such, the following suggestions are provided based on the principles of the indicator's design, 1. Response Time 1 should be less than (<) Response Time 2 which should be < Response Time 3 2. Fast Period < Moderate Period < Slow Period 3. In terms of the period of a full market cycle (e.g. ~ 4 years for BTC, ~ 5.5 years for equities, etc.), response times 1, 2, and 3 should be about 0.3% to 1%, 3% to 20%, and 20% to 50% of a full market cycle period, respectively. However, this is a loose guideline. 4. In terms of the period of a full market cycle, periods 1, 2, and 3 should all be about 25% to 75% of a full cycle period. Again, this is a loose guideline. 4. EMA 1 Length < EMA 2 Length < EMA 3 Length < EMA 4 Length 5. EMA Lengths 1, 2, 3, and 4 should be limited to about 1/4th the length of a full market cycle. Note, EMA lengths are measured in bars (candles), not in days. 1/4th of 1000 days is 250 days which is 250 x 4 = 1000 6-hr candles. The following guidelines are provided based on results of over 100 backtests on the BTCUSD chart using randomized parameters †, 1. 9 days < Response Time 1 < 14 days 2. 5 days < EMA 1 Length < 100 days 3. 600 days < EMA 4 length < 1000 days 4. The ratio of the EMA range (EMA 4 len - EMA 1 len) to the sum of EMA lengths (EMA 1 len + EMA 2 len + ...) be greater than 0.4 5. The ratio of the sum of EMA 1 and EMA 2 lengths to the sum of EMA 3 and EMA 4 lengths be less than 0.3. A suggestion from the author: Given that backtests show a high degree of outperformance using the guidelines enumerated above, a good trading strategy may be to not rely on any one particular combination of parameters. Rather, a random set of combinations of parameter settings that adhere to the guidelines above could be used to create multiple instances of the strategy in a TradingView chart, each of which varies by a small amount due to their unique parameter settings. The proportion of the entire set of strategy instances that agree about the current state of the market could indicate to the trader the level of confidence of the indicator, in aggregate. † A sample dataset of backtest results using randomized parameters is available upon request; notably, all trials outperformed the intended market (Bitcoin). 7. General Remarks About the Indicator Other than some exponential moving averages, no traditional technical indicators or technical analysis tools are employed in this strategy. No MACD, no RSI, no CMF, no Bollinger bands, parabolic SARs, Ichimoku clouds, hoosawatsits, XYZs, ABCs, whatarethese. No tea leaves can be found in this strategy, only mathematics. It is in the nature of the underlying math formula, from which the indicator is produced, to quickly identify trend changes. 8. Remarks About Expectations of Future Results and About Backtesting 8.1. In General As it's been stated in many prospectuses and marketing literature, "past performance is no guarantee of future results." Backtest results are retrospective, and hindsight is 20/20. Therefore, no guarantee can, nor should, be expressed by me or anybody else who is selling a financial product (unless you have a money printer, like the Federal Reserve does). 8.2. Regarding This Strategy No guarantee of future results using this strategy is expressed by the author, not now nor at any time in the future. With that written, the author is free to express his own expectations and opinions based on his intimate knowledge of how the indicator works, and the author will take that liberty by writing the following: As described in section (7), this trading strategy does not include any traditional technical indicators or TA tools (other than smoothing EMAs). Instead, this strategy is based on a principle that does not change, it employs a complex indicator that is based on a math formula that does not change, and it places trades based on five simple rules that do not change. And, as described in section (2.1), the indicator is designed to capture the full character of the market, from a macro/global scope down to a micro/local scope. Additionally, as described in section (3), outperformance of the market for which this strategy was intended during backtesting does not depend on luckily setting the parameters "just right." In fact, all random combinations of parameter settings that followed the guidelines outperformed the intended market in backtests. Additionally, no parameters are included within the underlying math formula from which the indicator is produced; it is not as if the formula contains a "5" and future outperformance would depend on that "5" being a "6" instead. And, again as described, it is in the nature of the formula to quickly identify trend changes. Therefore, it is the opinion of the author that the outperformance of this strategy in backtesting is directly attributable to the fundamental nature of the math formula from which the indicator is produced. As such, it is also the opinion of the author that continued outperformance by using this strategy, applied to the crypto (Bitcoin) market, is likely, given that the parameter settings are set reasonably and in accordance with the guidelines. The author does not, however, expect future outperformance of this strategy to match or exceed the outperformance observed in backtests using the default parameters, i.e. it probably won't outperform by anything close to 13,000,000% during the next 9 years. Additionally, based on the rolling 1-month outperformance data listed in section (3), expectations of short-term outperformance should be kept low; the median 1-month outperformance was -2%, so it's basically a 50/50 chance that any significant outperformance is seen in any given month. The true strength of this strategy is to be out of the market during large, sharp declines and capitalizing on the opportunities presented at the bottom of those declines by buying the dip. Given that such price action does not happen every month, outperformance in the initial months of use is approximately as likely as underperformance. 9. Access Those who are interested in using this strategy may send a personal message to inquire about how to gain access. Those who are interested in acquiring the sample dataset of backtest results may send a personal message to request a copy of the data.
Pine Script®策略
由anon2414691提供
7
MTF Confluence Reporter - Trend & Momentum AlignmentThis indicator is a multi-timeframe confluence dashboard designed to answer one question clearly: “Across my key timeframes, is the market leaning Bullish, Bearish, or Mixed—and how strong is that lean?” It combines two separate “votes” per timeframe: 4MA Direction (trend alignment / slope bias) StochRSI State (momentum bias) Those votes are then blended into a single Confluence result, shown as a clean readout with a 0–100 Strength score, plus hysteresis to reduce flicker near the decision boundary. What you see in the table 1) 4MA This is the trend component. It summarizes whether the selected timeframes are generally Bull or Bear based on the moving-average direction logic (your 4MA engine). 2) Stoch This is the momentum component. It summarizes whether StochRSI across the selected timeframes is leaning Bull or Bear. 3) Qualified (YES/NO) A safety gate. “Qualified = YES” means the internal conditions required for a valid confluence read are met (i.e., enough alignment/consistency to treat the output as actionable). If it’s NO, treat the market as mixed / transitional and tighten risk. 4) Strength (0–100) Your blended score (trend + momentum). Higher = stronger agreement across timeframes. A simple way to interpret it: 80–100: Strong alignment (clean regime) 60–79: Moderate alignment (tradable, but expect chop) 50–59: Weak / transitioning (be cautious) < 50: Bearish side of the regime logic (or mixed turning down) 5) Strength Bar A visual “battery meter” for the Strength score. This is meant to be read at a glance during fast decision-making. 6) Confluence (BULL/BEAR) The actual regime output. This is the “final answer” based on the Strength score and hysteresis rules. 7) Hysteresis (Enter / Exit thresholds) This is the anti-flicker system. Example shown on the chart: Enter > 60 Exit < 50 Meaning: The script only “flips ON” a Bull regime when strength becomes convincingly Bullish (above 60). It won’t “flip OFF” until strength meaningfully weakens (below 50). This reduces rapid flipping during 50/50 conditions. How to use it (practical workflow) Step 1 — Use Confluence as your “market mode” BULL: Favor longs, trend-following entries, buying pullbacks. BEAR: Favor defense, shorts/hedges (if you trade them), or wait for reset. Qualified = NO: Reduce size, tighten stops, or wait—conditions are not clean. Step 2 — Use Strength to time aggressiveness Strength rising: Momentum is joining trend → entries tend to have better follow-through. Strength falling: Alignment is fading → take profit quicker or tighten risk. Step 3 — Use hysteresis as your “noise filter” If you’re a swing trader, hysteresis is your friend: Don’t overreact to a single bar change. Let the regime confirm and stay confirmed. Best use-cases Swing trading / position bias (daily/weekly context) Hedge decisions (when alignment flips and stays flipped) Filtering entries from other tools (only take signals that match the regime) Settings notes: This script is designed to be flexible: You can choose which timeframes matter most to you (commonly 1H / 4H / 1D / 1W / 1M). If your version includes weighting, you can tune weights to match your trading style (short-term vs swing). Thresholds (Enter/Exit) can be tightened for faster flips or widened for smoother regimes. Important notes / disclaimer (TradingView-safe) This tool is an informational confluence dashboard, not financial advice. No indicator can predict the future. Always confirm with market structure, risk management, and your own plan. Past behavior on a chart does not guarantee future results. How I Use This Indicator (Example Workflow) I use this tool primarily as a market-bias and risk-filter, not as a standalone entry signal. Establish the regime first I start by checking the Confluence row: BULL: I focus on long-side ideas and bullish continuation setups. BEAR: I become defensive, avoid counter-trend trades, or look for short/hedge opportunities where applicable. Qualified = NO: I treat the market as transitional and reduce risk. Use Strength to adjust aggressiveness When Strength is elevated and rising, I am more comfortable holding positions and allowing trades more room to develop. When Strength is declining, I tighten stops, reduce position size, or manage trades more actively. Let hysteresis do the work I do not react to every minor fluctuation near the midpoint. The built-in hysteresis thresholds help me stay aligned with the prevailing regime instead of over-trading during indecision. Entries come from other tools Actual entries are taken using price structure, support/resistance, or other indicators. This dashboard simply tells me whether the broader environment supports that idea or not. In short, I treat this indicator as a context and confirmation layer—it helps answer when to be aggressive, cautious, or patient.
Pine Script®指標
由Dinjin提供
Institutional Confluence Mapper [JOAT]Institutional Confluence Mapper (ICM) Introduction The Institutional Confluence Mapper is an open-source multi-factor analysis tool that combines five analytical modules into a unified confluence scoring system. It synthesizes institutional trading concepts including Relative Rotation analysis, Smart Money flow detection, Liquidity zone mapping, Session-based timing, and Volatility regime classification. Rather than relying on a single indicator, ICM evaluates market conditions through multiple lenses simultaneously, presenting a clear confluence score (0-100%) that reflects the alignment of various market factors. This script is fully open-source under the Mozilla Public License 2.0. Originality and Purpose This indicator is NOT a random mashup of existing indicators. It is an original implementation that creates a unified institutional analysis framework: Why Multiple Modules? Most retail traders struggle because they rely on single indicators that provide conflicting signals. Institutional traders evaluate markets through multiple frameworks simultaneously. ICM bridges this gap by providing a unified view of complementary analysis methods. The Confluence Scoring System: Each module contributes to a weighted confluence score (0-100%). Scores above 65% indicate bullish confluence; below 35% indicates bearish confluence. How Components Work Together: RRG (Relative Rotation) determines macro bias - is this asset outperforming or underperforming its benchmark? Institutional Flow confirms smart money activity - are institutions accumulating or distributing? Volatility Regime determines strategy selection - trend-follow or mean-revert? Liquidity Detection identifies key levels - where are the stop hunts happening? Session Analysis optimizes timing - when should you trade? The Five Core Modules 1. Relative Rotation Momentum Matrix (RRG) Compares the current symbol against a benchmark (default: SPY) using the JdK RS-Ratio methodology with double-smoothed EMA. Assets rotate through four quadrants: LEADING: Outperforming with positive momentum (strongest bullish) WEAKENING: Outperforming but losing momentum LAGGING: Underperforming with negative momentum (strongest bearish) IMPROVING: Underperforming but gaining momentum 2. Institutional Flow Analysis Analyzes volume patterns to detect smart money activity: Volume Z-Score measures how unusual current volume is Buy/Sell pressure estimation based on candle structure Unusual volume detection highlights institutional activity 3. Volatility Regime System Uses ATR percentile ranking to classify market conditions: COMPRESSION: Low volatility (ATR < 20th percentile) - potential breakout EXPANSION: High volatility (ATR > 80th percentile) - trending TRENDING_BULL/BEAR: Directional trends based on EMA alignment RANGING: Sideways consolidation 4. Liquidity Detection Identifies institutional liquidity targets using swing point analysis: Swing highs/lows are tracked and displayed as dashed lines Purple dashed lines mark resistance/sell-side liquidity Teal dashed lines mark support/buy-side liquidity Gold diamonds appear when liquidity sweeps are detected (potential reversals) 5. Session Momentum Profiler Tracks trading sessions based on your selected timezone: Asian Session: 7PM - 4AM EST London Session: 3AM - 12PM EST New York Session: 9:30AM - 4PM EST London/NY Overlap: 8AM - 12PM EST (peak liquidity) Visual Elements Main Dashboard (Top-Right): BIAS: Overall direction with confluence percentage RRG: Current quadrant and momentum FLOW: Smart money bias and volume status REGIME: Market condition and volatility percentile SESSION: Active trading session and current time LIQUIDITY: Active zones and grab signals SIGNAL: Actionable recommendation Chart Elements: Gold Diamond: Liquidity grab (potential reversal point) Teal Dashed Line: Support / Buy-side liquidity zone Purple Dashed Line: Resistance / Sell-side liquidity zone EMA 21/55/200: Trend structure with cloud fill Volatility Bands: ATR-based channels How to Use Step 1: Check the BIAS row for overall market direction Step 2: Check REGIME to understand market conditions Step 3: Identify key levels using liquidity zones and EMAs Step 4: Wait for confluence above 65% (bullish) or below 35% (bearish) Step 5: Look for gold diamond signals at key levels Best Setups Bullish: Confluence >65%, RRG in LEADING/IMPROVING, bullish flow, price near teal support zone. Bearish: Confluence <35%, RRG in LAGGING/WEAKENING, bearish flow, price near purple resistance zone. Reversal: Gold diamond appears after price sweeps a liquidity zone. Key Input Parameters Benchmark Symbol: Compare against (default: SPY) RS-Ratio/Momentum Lookback: RRG calculation periods Volume Analysis Period: Flow detection lookback Swing Length: Liquidity zone detection ATR Period/Rank Period: Regime classification Timezone: Session detection timezone Alerts Liquidity Grab Bull: Bullish sweep detected Liquidity Grab Bear: Bearish sweep detected High Confluence Bull: Confluence above 70% High Confluence Bear: Confluence below 30% Best Practices Use on 1H, 4H, or Daily timeframes for reliable signals Combine with price action for confirmation Respect the regime - don't fight strong trends Trade during London/NY overlap for best liquidity Wait for high confluence scores before entering Always use proper risk management Limitations Works best on liquid markets with sufficient volume Session features optimized for forex/crypto markets RRG requires a valid benchmark symbol No indicator predicts the future - use proper risk management Disclaimer This indicator is for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. -Made with passion by officialjackofalltrades
Pine Script®指標
由officialjackofalltrades提供
6
[NBK] Cover Buy Sell for BTC Cover Buy Sell for BTC— Engulfing Reversals with EMA/ATR Trend & Quality Filters {Update for BTC} What it does This indicator flags high-quality bullish/bearish reversal candles only when they align with a short-term trend and pass several objective quality filters. It is not a simple mashup: each component serves a distinct role and they work together to keep early/low-quality signals out. How it works (components & interaction) Pattern engine (entry candidates) Bullish side (Cover Buy): Body Engulf: current green body fully covers the prior red body, or Piercing (relaxed): prior red → current green closes above the prior body’s midpoint (not beyond prior open). Bearish side (Cover Sell): Full-candle Engulf: current red candle (body + wicks) covers the entire prior candle, or Body Engulf: current red body fully covers the prior body, or Dark-Cloud (relaxed): prior green → current red closes below the prior body’s midpoint. Short-term trend gate (non-repainting) Trend is defined by the EMA slope between bar-1 and bar-2, scaled by ATR to require minimum strength. Slope < 0 → only bullish candidates pass. Slope > 0 → only bearish candidates pass. Body-size filter (noise control) Rejects tiny candles: each body is compared with the lookback average body size. For bearish candidates an additional ratio check requires current body ≥ a fraction of the prior body (to avoid weak top-ticks). Peak filters for bearish signals (late, cleaner tops) Distance above EMA: the high must be at least X × ATR above EMA (avoids mid-range noise). Near local high: the high of the current bar (or bar-1) must be close to the highest high in a recent window. Break confirmation: close must break low by at least Y × ATR (filters shallow dark-clouds). Only when a candidate satisfies the pattern ➝ trend ➝ size ➝ peak sequence is a signal printed/alerted. Inputs (key parameters) EMA length, Min EMA slope vs ATR, ATR length: trend strength. Lookback for average body, Min body vs average, Bear body ratio: body-quality filters. High distance above EMA (×ATR), Local high lookback, Tolerance to local high (×ATR), Min break of low (×ATR): bearish peak confirmation. Alerts Built-in alerts fire on bar close for both Cover Buy and Cover Sell. How to use Increase High distance above EMA / Local high lookback / Min break of low to reduce early Cover Sell in ranges. If you miss good tops, ease those thresholds slightly. Works across symbols/timeframes; evaluated on bar close; no repaint from the trend gate. Notes This tool is a signal screener, not financial advice. For best results, combine with your structure/SR zones, risk management, and execution rules. 13 thg 10 Phát hành các Ghi chú Cover Buy Sell — Engulfing Reversals with EMA/ATR Trend & Quality/Peak Filters What this script does Flags high-quality bullish/bearish engulfing reversals only when short-term trend and price-action quality conditions are met. Signals evaluate on bar close. No promotions, no links, no external calls. Why this is an original combination (mashup justification) Raw engulfing patterns are noisy in ranges. This script is a pipeline where each module solves a specific failure mode, not a simple merge of indicators: Pattern engine (candidates): Bullish (Cover Buy): body-engulf of prior red body, or relaxed Piercing (close above prior body midpoint, not beyond prior open). Bearish (Cover Sell): full-candle engulf (body+wicks), body-engulf, or relaxed Dark-Cloud (close below prior body midpoint). Short-term trend gate (non-repainting): EMA slope is measured between bar-1 and bar-2 and must exceed an ATR-scaled threshold (slopeAtrPct). Slope < 0 → only bullish candidates pass. Slope > 0 → only bearish candidates pass. Body-size filter (noise control): Bodies must not be tiny vs the average over lenBodyRef. For bearish candidates, an alternate check allows current body ≥ bearRatioMin × prior body to keep decisive tops. Bearish peak filters (late, cleaner tops): High must be far above EMA (≥ emaDistAtrMin × ATR), near the local high (current or prior bar within nearHighLen & nearHighTol × ATR), and the close must break prior low by ≥ breakAtrMin × ATR. These remove premature Cover Sell flags in chop and keep the later, higher-quality reversal. Only candidates that pass Pattern → Trend → Size → Peak become signals. This staged design is the core originality. How to use (practical guidance) To reduce early Cover Sell in ranges: raise emaDistAtrMin, raise nearHighLen, and/or raise breakAtrMin. If you miss clean tops: slightly lower those thresholds. Typical starting points: lenTrend=4–6, slopeAtrPct=0.03–0.06, atrLen=14; adjust to instrument/TF volatility. Combine with structure/SR zones and your risk rules. This tool is for analysis only and is not investment advice. Alerts Two alert conditions are included: Cover Buy and Cover Sell, both evaluated on bar close. Non-repainting note The trend gate uses confirmed data (EMA slope from bar-1 vs bar-2). Pattern and filters also use confirmed values; no forward-looking references. English translations of UI strings (if your on-chart UI uses another language) “Strict engulf (không cho bằng nhau)” → Strict engulf (no equality) “EMA length (trend ngắn hạn)” → EMA length (short-term trend) “Lookback tính thân trung bình” → Lookback for average body size “Ngưỡng thân tối thiểu vs trung bình” → Min body vs average (ratio) “Bear: thân hiện tại ≥ rRatio * thân trước” → Bear: current body ≥ rRatio × prior body “Bear: HIGH cách EMA tối thiểu (x ATR)” → Bear: HIGH distance above EMA (× ATR) min “Bear: cửa sổ đỉnh cục bộ (bars)” → Bear: local high lookback (bars) “Bear: dung sai tới đỉnh (x ATR)” → Bear: tolerance to local high (× ATR) “Bear: mức phá LOW tối thiểu (x ATR)” → Bear: min break of LOW (× ATR) Compliance notes English-only title, English appears first in this description; no ads, logos, links, or solicitations. This is an original, closed-source script with a meaningful explanation of what, how, why, and how to use. (Vietnamese short summary) Chỉ báo bắt tín hiệu engulfing chất lượng cao khi có xu hướng EMA ngắn hạn (đo bằng độ dốc/ATR) và vượt qua lọc kích thước thân + điều kiện đỉnh cho tín hiệu bán. Chuỗi xử lý Mẫu → Xu hướng → Kích thước → Đỉnh giúp loại tín hiệu sớm trong vùng đi ngang. Có alert Cover Buy/Sell, đánh giá theo bar close, không quảng cáo/đường link, và không phải khuyến nghị đầu tư.
Pine Script®指標
由Impressive6868提供