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WLSMA: fast approximation🙏🏻 Sup TV & @alexgrover O(N) algocomplexity, just one loop inside. No, you can't do O(1) @ updates in moving window mode, only expanding window will allow that. Now I have time series & stats models of my own creation, nowhere else available, just TV and my github for now, ain’t no legacy academic industry I always have fun about, but back in 2k20 when I consciously ain’t known much about quant, I remember seeing post by @alexgrover recreating Moving Regression Endpoint dropped on price chart (called LSMA here) as a linear filter combination of filters (yea yeah DSP terms) as 3WMA - 2SMA. Now it’s my time to do smth alike aye? ... This script is remake of my 1st degree WLSMA via linear filter combo. It’s much faster, we aint calculate moving regression per se, we just match its freq response. You can see it on the screen (WLSMAfa) almost perfectly matching the original one (WLSMA). ... While humans like to overfit, I fw generalizations. So your lovely WMA is actually just one case of a more general weight pattern: pow(len - i, e), where pow is the power function and e is the exponent itself. So: - If e = 0, then we have SMA (every number in 0th power is one) - If e = 1, we get WMA - If e = 2, we get quadratic weights. We can recreate WLSMA freq response then by combining 2 filters with e = 1 and e = 2. This is still an approximation, even tho enormously precise for the tasks you’ve shared with me. Due to the non-linear nature of the thing it’s all we can do, and as window size grows, even this small discrepancy converges with true WLSMA value, so we’re all good. Pls don’t try to model this 0.00xxxx discrepancy, it’s not natural. ... DSP approach is unnatural for prices, but you can put this thing on volume delta and be happy, or on other metrics of yours, if for some reason u dont wanna estimate thresholds by fitting a distro. All good TV ∞ P.S.: strangely, the first script made & dropped in the location in Saint P where my actual quant way has started ~5 years ago xD, very thankful
Pine Script®指標
由gorx1提供
Uptrick: Z-Trend BandsOverview Uptrick: Z-Trend Bands is a Pine Script overlay crafted to capture high-probability mean-reversion opportunities. It dynamically plots upper and lower statistical bands around an EMA baseline by converting price deviations into z-scores. Once price moves outside these bands and then reenters, the indicator verifies that momentum is genuinely reversing via an EMA-smoothed RSI slope. Signal memory ensures only one entry per momentum swing, and traders receive clear, real-time feedback through customizable bar-coloring modes, a semi-transparent fill highlighting the statistical zone, concise “Up”/“Down” labels, and a live five-metric scoring table. Introduction Markets often oscillate between trending and reverting, and simple thresholds or static envelopes frequently misfire when volatility shifts. Standard deviation quantifies how “wide” recent price moves have been, and a z-score transforms each deviation into a measure of how rare it is relative to its own history. By anchoring these bands to an exponential moving average, the script maintains a fluid statistical envelope that adapts instantly to both calm and turbulent regimes. Meanwhile, the Relative Strength Index (RSI) tracks momentum; smoothing RSI with an EMA and observing its slope filters out erratic spikes, ensuring that only genuine momentum flips—upward for longs and downward for shorts—qualify. Purpose This indicator is purpose-built for short-term mean-reversion traders operating on lower–timeframe charts. It reveals when price has strayed into the outer 5 percent of its recent range, signaling an increased likelihood of a bounce back toward fair value. Rather than firing on price alone, it demands that momentum follow suit: the smoothed RSI slope must flip in the opposite direction before any trade marker appears. This dual-filter approach dramatically reduces noise-driven, false setups. Traders then see immediate visual confirmation—bar colors that reflect the latest signal and age over time, clear entry labels, and an always-visible table of metric scores—so they can gauge both the validity and freshness of each signal at a glance. Originality and Uniqueness Uptrick: Z-Trend Bands stands apart from typical envelope or oscillator tools in four key ways. First, it employs fully normalized z-score bands, meaning ±2 always captures roughly the top and bottom 5 percent of moves, regardless of volatility regime. Second, it insists on two simultaneous conditions—price reentry into the bands and a confirming RSI slope flip—dramatically reducing whipsaw signals. Third, it uses slope-phase memory to lock out duplicate signals until momentum truly reverses again, enforcing disciplined entries. Finally, it offers four distinct bar-coloring schemes (solid reversal, fading reversal, exceeding bands, and classic heatmap) plus a dynamic scoring table, rather than a single, opaque alert, giving traders deep insight into every layer of analysis. Why Each Component Was Picked The EMA baseline was chosen for its blend of responsiveness—weighting recent price heavily—and smoothness, which filters market noise. Z-score deviation bands standardize price extremes relative to their own history, adapting automatically to shifting volatility so that “extreme” always means statistically rare. The RSI, smoothed with an EMA before slope calculation, captures true momentum shifts without the false spikes that raw RSI often produces. Slope-phase memory flags prevent repeated alerts within a single swing, curbing over-trading in choppy conditions. Bar-coloring modes provide flexible visual contexts—whether you prefer to track the latest reversal, see signal age, highlight every breakout, or view a continuous gradient—and the scoring table breaks down all five core checks for complete transparency. Features This indicator offers a suite of configurable visual and logical tools designed to make reversal signals both robust and transparent: Dynamic z-score bands that expand or contract in real time to reflect current volatility regimes, ensuring the outer ±zThreshold levels always represent statistically rare extremes. A smooth EMA baseline that weights recent price more heavily, serving as a fair-value anchor around which deviations are measured. EMA-smoothed RSI slope confirmation, which filters out erratic momentum spikes by first smoothing raw RSI and then requiring its bar-to-bar slope to flip before any signal is allowed. Slope-phase memory logic that locks out duplicate buy or sell markers until the RSI slope crosses back through zero, preventing over-trading during choppy swings. Four distinct bar-coloring modes—Reversal Solid, Reversal Fade, Exceeding Bands, Classic Heat—plus a “None” option, so traders can choose whether to highlight the latest signal, show signal age, emphasize breakout bars, or view a continuous heat gradient within the bands. A semi-transparent fill between the EMA and the upper/lower bands that visually frames the statistical zone and makes extremes immediately obvious. Concise “Up” and “Down” labels that plot exactly when price re-enters a band with confirming momentum, keeping chart clutter to a minimum. A real-time, five-metric scoring table (z-score, RSI slope, price vs. EMA, trend state, re-entry) that updates every two bars, displaying individual +1/–1/0 scores and an averaged Buy/Sell/Neutral verdict for complete transparency. Calculations Compute the fair-value EMA over fairLen bars. Subtract that EMA from current price each bar to derive the raw deviation. Over zLen bars, calculate the rolling mean and standard deviation of those deviations. Convert each deviation into a z-score by subtracting the mean and dividing by the standard deviation. Plot the upper and lower bands at ±zThreshold × standard deviation around the EMA. Calculate raw RSI over rsiLen bars, then smooth it with an EMA of length rsiEmaLen. Derive the RSI slope by taking the difference between the current and previous smoothed RSI. Detect a potential reentry when price exits one of the bands on the prior bar and re-enters on the current bar. Require that reentry coincide with an RSI slope flip (positive for a lower-band reentry, negative for an upper-band reentry). On first valid reentry per momentum swing, fire a buy or sell signal and set a memory flag; reset that flag only when the RSI slope crosses back through zero. For each bar, assign scores of +1, –1, or 0 for the z-score direction, RSI slope, price vs. EMA, trend-state, and reentry status. Average those five scores; if the result exceeds +0.1, label “Buy,” if below –0.1, label “Sell,” otherwise “Neutral.” Update bar colors, the semi-transparent fill, reversal labels, and the scoring table every two bars to reflect the latest calculations. How It Actually Works On each new candle, the EMA baseline and band widths update to reflect current volatility. The RSI is smoothed and its slope recalculated. The script then looks back one bar to see if price exited either band and forward to see if it reentered. If that reentry coincides with an appropriate RSI slope flip—and no signal has yet been generated in that swing—a concise label appears. Bar colors refresh according to your selected mode, and the scoring table updates to show which of the five conditions passed or failed, along with the overall verdict. This process repeats seamlessly at each bar, giving traders a continuous feed of disciplined, statistically filtered reversal cues. Inputs All parameters are fully user-configurable, allowing you to tailor sensitivity, lookbacks, and visuals to your trading style: EMA length (fairLen): number of bars for the fair-value EMA; higher values smooth more but lag further behind price. Z-Score lookback (zLen): window for calculating the mean and standard deviation of price deviations; longer lookbacks reduce noise but respond more slowly to new volatility. Z-Score threshold (zThreshold): number of standard deviations defining the upper and lower bands; common default is 2.0 for roughly the outer 5 percent of moves. Source (src): choice of price series (close, hl2, etc.) used for EMA, deviation, and RSI calculations. RSI length (rsiLen): period for raw RSI calculation; shorter values react faster to momentum changes but can be choppier. RSI EMA length (rsiEmaLen): period for smoothing raw RSI before taking its slope; higher values filter more noise. Bar coloring mode (colorMode): select from None, Reversal Solid, Reversal Fade, Exceeding Bands, or Classic Heat to control how bars are shaded in relation to signals and band positions. Show signals (showSignals): toggle on-chart “Up” and “Down” labels for reversal entries. Show scoring table (enableTable): toggle the display of the five-metric breakdown table. Table position (tablePos): choose which corner (Top Left, Top Right, Bottom Left, Bottom Right) hosts the scoring table. Conclusion By merging a normalized z-score framework, momentum slope confirmation, disciplined signal memory, flexible visuals, and transparent scoring into one Pine Script overlay, Uptrick: Z-Trend Bands offers a powerful yet intuitive tool for intraday mean-reversion trading. Its adaptability to real-time volatility and multi-layered filter logic deliver clear, high-confidence reversal cues without the clutter or confusion of simpler indicators. Disclaimer This indicator is provided solely for educational and informational purposes. It does not constitute financial advice. Trading involves substantial risk and may not be suitable for all investors. Past performance is not indicative of future results. Always conduct your own testing and apply careful risk management before trading live.
Pine Script®指標
由Uptrick提供
PCA Regime-Adjusted MomentumSummary The PCA Regime-Adjusted Momentum (PCA-RAM) is an advanced market analysis tool designed to provide nuanced insights into market momentum and structural stability. It moves beyond traditional indicators by using Principal Component Analysis (PCA) to deconstruct market data into its most essential patterns. The indicator provides two key pieces of information: A smoothed momentum signal based on the market's dominant underlying trend. A dynamic regime filter that gauges the stability and clarity of the market's structure, advising you when to trust or fade the momentum signals. This allows traders to not only identify potential shifts in momentum but also to understand the context and confidence behind those signals. Core Concepts & Methodology The strength of this indicator lies in its sound, data-driven methodology. 1. Principal Component Analysis (PCA) At its core, the indicator analyzes a rolling window (default 50 periods) of standardized market data (Open, High, Low, Close, and Volume). PCA is a powerful statistical technique that distills this complex, 5-dimensional data into its fundamental, uncorrelated components of variance. We focus on the First Principal Component (PC1), which represents the single most dominant pattern or "theme" driving the market's behavior in the lookback window. 2. The Momentum Signal Instead of just looking at price, we project the current market data onto this dominant underlying pattern (PC1). This gives us a raw "projection score" that measures how strongly the current bar aligns with the historically dominant market structure. This raw score is then smoothed using two an exponential moving averages (a fast and a slow line) to create a clear, actionable momentum signal, similar in concept to a MACD. 3. The Dynamic Regime Filter This is arguably the indicator's most powerful feature. It answers the question: "How clear is the current market picture?" It calculates the Market Concentration Ratio, which is the percentage of total market variance explained by PC1 alone. A high ratio indicates that the market is moving in a simple, one-dimensional way (e.g., a clear, strong trend). A low ratio indicates the market is complex, multi-dimensional, and choppy, with no single dominant theme. Crucially, this filter is dynamic. It compares the current concentration ratio to its own recent average, allowing it to adapt to any asset or timeframe. It automatically learns what "normal" and "choppy" look like for the specific chart you are viewing. How to Interpret the Indicator The indicator is displayed in a separate pane with two key visual elements: The Momentum Lines (White & Gold) White Line: The "Fast Line," representing the current momentum. Gold Line: The "Slow Line," acting as the trend confirmation. Bullish Signal: A crossover of the White Line above the Gold Line suggests a shift to positive momentum. Bearish Signal: A crossover of the White Line below the Gold Line suggests a shift to negative momentum. The Regime Filter (Purple & Dark Red Background) This is your confidence gauge. Navy Blue Background (High Concentration): The market structure is stable, simple, and trending. Momentum signals are more reliable and should be given higher priority. Dark Red Background (Low Concentration): The market structure is complex, choppy, or directionless. Momentum signals are unreliable and prone to failure or "whipsaws." This is a signal to be cautious, tighten stops, or potentially stay out of the market. Potential Trading Strategies This tool is versatile and can be used in several ways: 1. Primary Signal Strategy Condition: Wait for the background to turn Purple, confirming a stable, high-confidence regime. Entry: Take the next crossover signal from the momentum lines (White over Gold for long, White under Gold for short). Exit/Filter: Consider exiting positions or ignoring new signals when the background turns Navy. 2. As a Confirmation or Filter for Your Existing Strategy Do you have a trend-following system? Only enable its long and short signals when the PCA-RAM background is Purple. Do you have a range-trading or mean-reversion system? It might be most effective when the PCA-RAM background is Navy, indicating a lack of a clear trend. 3. Advanced Divergence Analysis Look for classic divergences between price and the momentum lines. For example, if the price is making a new high, but the Gold Line is making a lower high, it may indicate underlying weakness in the trend, even on a Purple background. This divergence signal is more powerful because it shows that the new price high is not being confirmed by the market's dominant underlying pattern.
Pine Script®指標
由marcosag90提供
Price Statistical Strategy-Z Score V 1.01 Price Statistical Strategy – Z Score V 1.01 Overview A technical breakdown of the logic and components of the “Price Statistical Strategy – Z Score V 1.01”. This script implements a smoothed Z-Score crossover mechanism applied to the closing price to detect potential statistical deviations from local price mean. The strategy operates solely on price data (close) and includes signal spacing control and momentum-based candle filters. No volume-based or trend-detection components are included. Core Methodology The strategy is built on the statistical concept of Z-Score, which quantifies how far a value (closing price) is from its recent average, normalized by standard deviation. Two moving averages of the raw Z-Score are calculated: a short-term and a long-term smoothed version. The crossover between them generates long entries and exits. Signal Conditions Entry Condition: A long position is opened when the short-term smoothed Z-Score crosses above the long-term smoothed Z-Score, and additional entry conditions are met. Exit Condition: The position is closed when the short-term Z-Score crosses below the long-term Z-Score, provided the exit conditions allow. Signal Gapping: A minimum number of bars (Bars gap between identical signals) must pass between repeated entry or exit signals to reduce noise. Momentum Filter: Entries are prevented during sequences of three or more consecutively bullish candles, and exits are prevented during three or more consecutively bearish candles. Z-Score Function The Z-Score is calculated as: Z = (Close - SMA(Close, N)) / STDEV(Close, N) Where N is the base period selected by the user. Input Parameters Enable Smoothed Z-Score Strategy Enables or disables the Z-Score strategy logic. When disabled, no trades are executed. Z-Score Base Period Defines the number of bars used to calculate the simple moving average and standard deviation for the Z-Score. This value affects how responsive the raw Z-Score is to price changes. Short-Term Smoothing Sets the smoothing window for the short-term Z-Score. Higher values produce smoother short-term signals, reducing sensitivity to short-term volatility. Long-Term Smoothing Sets the smoothing window for the long-term Z-Score, which acts as the reference line in the crossover logic. Bars gap between identical signals Minimum number of bars that must pass before another signal of the same type (entry or exit) is allowed. This helps reduce redundant or overly frequent signals. Trade Visualization Table A table positioned at the bottom-right displays live PnL for open trades: Entry Price Unrealized PnL % Text colors adapt based on whether unrealized profit is positive, negative, or neutral. Technical Notes This strategy uses only close prices — no trend indicators or volume components are applied. All calculations are based on simple moving averages and standard deviation over user-defined windows. Designed as a minimal, isolated Z-Score engine without confirmation filters or multi-factor triggers.
Pine Script®策略
由ata_sabanci提供
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation. 🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods: Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin Optional Smoothing: Applies simple moving average (SMA) to reduce noise Status Classification: Categorizes market conditions into four distinct states Signal Generation: Produces trading signals based on MVRV thresholds Price Estimation: Calculates estimated realized price (Current price / MVRV ratio) Historical Context: Compares current values to historical extremes Formula: MVRV Ratio = Market Value / Realized Value Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length) Estimated Realized Price = Current Price / MVRV Ratio Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100 Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100 🔶 DETAILS Visual Features: MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued) Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8) Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued) Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context Interpretation: MVRV ≥ 3.5: Potential market top, strong sell signal MVRV ≥ 2.5: Overvalued market, consider selling MVRV 1.5-2.5: Neutral market conditions MVRV 1.0-1.5: Fair value, consider buying MVRV < 1.0: Potential market bottom, strong buy signal 🔶 EXAMPLES Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued. Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits. Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities. Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation. Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states. Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle. Realized Price Support: The estimated realized price often acts as a significant support/resistance level during market transitions. Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points. 🔶 SETTINGS Customization Options: Smoothing: Toggle smoothing option and adjust smoothing length (1-50) Table Display: Show/hide the information table Table Position: Choose between top right, top left, bottom right, or bottom left positions Visual Elements: All plots, lines, and background highlights can be customized for color and style The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
Pine Script®指標
由AlphaExtract提供
Arnaud Legoux Trend Aggregator | Lyro RSArnaud Legoux Trend Aggregator Introduction Arnaud Legoux Trend Aggregator is a custom-built trend analysis tool that blends classic market oscillators with advanced normalization, advanced math functions and Arnaud Legoux smoothing. Unlike conventional indicators, 𝓐𝓛𝓣𝓐 aggregates market momentum, volatility and trend strength. Signal Insight The 𝓐𝓛𝓣𝓐 line visually reflects the aggregated directional bias. A rise above the middle line threshold signals bullish strength, while a drop below the middle line indicates bearish momentum. Another way to interpret the 𝓐𝓛𝓣𝓐 is through overbought and oversold conditions. When the 𝓐𝓛𝓣𝓐 rises above the +0.7 threshold, it suggests an overbought market and signals a strong uptrend. Conversely, a drop below the -0.7 level indicates an oversold condition and a strong downtrend. When the oscillator hovers near the zero line, especially within the neutral ±0.3 band, it suggests that no single directional force is dominating—common during consolidation phases or pre-breakout compression. Real-World Example Usually 𝓐𝓛𝓣𝓐 is used by following the bar color for simple signals; however, like most indicators there are unique ways to use an indicator. Let’s dive deep into such ways. The market begins with a green bar color, raising awareness for a potential long setup—but not a direct entry. In this methodology, bar coloring serves as an alert mechanism rather than a strict entry trigger. The first long position was initiated when the 𝓐𝓛𝓣𝓐 signal line crossed above the +0.3 threshold, suggesting a shift in directional acceleration. This entry coincided with a rising price movement, validating the trade. As price advanced, the position was exited into cash—not reversed into a short—because the short criteria for this use case are distinct. The exit was prompted by 𝓐𝓛𝓣𝓐 crossing back below the +0.3 level, signaling the potential weakening of the long trend. Later, as 𝓐𝓛𝓣𝓐 crossed below 0, attention shifted toward short opportunities. A short entry was confirmed when 𝓐𝓛𝓣𝓐 dipped below -0.3, indicating growing downside momentum. The position was eventually closed when 𝓐𝓛𝓣𝓐 crossed back above the -0.3 boundary—signaling a possible deceleration of the bearish move. This logic was consistently applied in subsequent setups, emphasizing the role of 𝓐𝓛𝓣𝓐’s thresholds in guiding both entries and exits. Framework The Arnaud Legoux Trend Aggregator (ALTA) combines multiple technical indicators into a single smoothed signal. It uses RSI, MACD, Bollinger Bands, Stochastic Momentum Index, and ATR. Each indicator's output is normalized to a common scale to eliminate bias and ensure consistency. These normalized values are then transformed using a hyperbolic tangent function (Tanh). The final score is refined with a custom Arnaud Legoux Moving Average (ALMA) function, which offers responsive smoothing that adapts quickly to price changes. This results in a clear signal that reacts efficiently to shifting market conditions. ⚠️ WARNING ⚠️: THIS INDICATOR, OR ANY OTHER WE (LYRO RS) PUBLISH, IS NOT FINANCIAL OR INVESTMENT ADVICE. EVERY INDICATOR SHOULD BE COMBINED WITH PRICE ACTION, FUNDAMENTALS, OTHER TECHNICAL ANALYSIS TOOLS & PROPER RISK. MANAGEMENT.
Pine Script®指標
由LyroRS提供
Support and Resistance Logistic Regression | Flux Charts💎 GENERAL OVERVIEW Introducing our new Logistic Regression Support / Resistance indicator! This tool leverages advanced statistical modeling "Logistic Regressions" to identify and project key price levels where the market is likely to find support or resistance. For more information about the process, please check the "HOW DOES IT WORK ?" section. Logistic Regression Support / Resistance Features : Intelligent S/R Identification : The indicator uses a logistic regression model to intelligently identify and plot significant support and resistance levels. Predictive Probability : Each identified level comes with a calculated probability, indicating how likely it is to act as a true support or resistance based on historical data. Retest & Break Labels : The indicator clearly marks on your chart when a detected support or resistance level is retested (price touches and respects the level) or broken (price decisively crosses through the level). Alerts : Real-time alerts for support retests, resistance retests, support breaks, and resistance breaks. Customizable : You can change support & resistance line style, width and colors. 🚩 UNIQUENESS What makes this indicator truly unique is its application of logistic regression to the concept of support and resistance. Instead of merely identifying historical highs and lows, our indicator uses a statistical model to predict the future efficacy of these levels. It analyzes underlying market conditions (like RSI and body size at pivot formation) to assign a probability to each potential S/R zone. This predictive insight, combined with dynamic, real-time labeling of retests and breaks, provides a more robust and adaptive understanding of market structure than traditional, purely historical methods. 📌HOW DOES IT WORK ? The Logistic Regression Support / Resistance indicator operates in several key steps: First, it identifies significant pivot highs and lows on the chart based on a user-defined "Pivot Length." These pivots are potential areas of support or resistance. For each detected pivot, the indicator extracts relevant market data at that specific point, including the RSI (Relative Strength Index) and the Body Size (the absolute difference between the open and close price of the candle). These serve as input features for the model. The core of the indicator lies in its logistic regression model. This model is continuously trained on past pivot data and their subsequent behavior (i.e., whether they were "respected" as support/resistance multiple times). It learns the relationship between the extracted features (RSI, Body Size) and the likelihood of a pivot becoming a significant S/R level. When a new pivot is identified, the model uses its learned insights to calculate a prediction value—a probability (from 0 to 1) that this specific pivot will act as a strong support or resistance. If the calculated probability exceeds a user-defined "Probability Threshold," the pivot is designated a "Regression Pivot" and drawn on the chart as a support or resistance line. The indicator then actively tracks how price interacts with these levels, displaying "R" labels for retests when the price bounces off the level and "B" labels for breaks when the price closes beyond it. ⚙️ SETTINGS 1. General Configuration Pivot Length: This setting defines the number of bars used to determine a significant high or low for pivot detection. Target Respects: This input specifies how many times a level must be "respected" by price action for it to be considered a strong support or resistance level by the underlying model. Probability Threshold: This is the minimum probability output from the logistic regression model for a detected pivot to be considered a valid support or resistance level and be plotted on the chart. 2. Style Show Prediction Labels: Enable or disable labels that display the calculated probability of a newly identified regression S/R level. Show Retests: Toggle the visibility of "R" labels on the chart, which mark instances where price has retested a support or resistance level. Show Breaks: Toggle the visibility of "B" labels on the chart, which mark instances where price has broken through a support or resistance level.
Pine Script®指標
由fluxchart提供
12
Pearson vs Approx. Spearman CorrelationThis indicator displays the rolling Pearson and approximate Spearman correlation between the chart's asset and a second user-defined asset, based on log returns over a customizable window. Features: - Pearson correlation of log returns (standard linear dependency measure) - Approximate Spearman correlation, using percentile ranks to better capture nonlinear and monotonic relationships / Horizontal lines showing: Maximum and minimum correlation values over a statistical window 1st quartile (25%) and 3rd quartile (75%) — helpful for identifying statistically high or low regimes This script is useful for identifying dynamic co-movements, regime changes, or correlation breakdowns between assets — applicable in risk management, portfolio construction, and pairs trading strategies.
Pine Script®指標
由Bbbruz提供
LANZ Strategy 4.0 [Backtest]🔷 LANZ Strategy 4.0 — Strategy Execution Based on Confirmed Structure + Risk-Based SL/TP LANZ Strategy 4.0 is the official backtesting engine for the LANZ Strategy 4.0 trading logic. It simulates real-time executions based on breakout of Strong/Weak Highs or Lows, using a consistent structural system with SL/TP dynamically calculated per trade. With integrated risk management and lot size logic, this script allows traders to validate LANZ Strategy 4.0 performance with real strategy metrics. 🧠 Core Components: Confirmed Breakout Entries: Trades are executed only when price breaks the most recent structural level (Strong High or Strong Low), detected using swing pivots. Dynamic SL and TP Logic: SL is placed below/above the breakout point with a customizable buffer. TP is defined using a fixed Risk-Reward (RR) ratio. Capital-Based Risk Management: Lot size is calculated based on account equity, SL distance, and pip value (e.g. $10 per pip on XAUUSD). Clean and Controlled Executions: Only one trade is active at a time. No new entries are allowed until the current position is closed. 📊 Visual Features: Automatic plotting of Entry, SL, and TP levels. Full control of swing sensitivity (swingLength) and SL buffer. SL and TP lines extend visually for clarity of trade risk and reward zones. ⚙️ How It Works: Detects pivots and classifies trend direction. Waits for breakout above Strong High (BUY) or below Strong Low (SELL). Calculates dynamic SL and TP based on buffer and RR. Computes trade size automatically based on risk per trade %. Executes entry and manages exits via strategy engine. 📝 Notes: Ideal for evaluating the LANZ Strategy 4.0 logic over historical data. Must be paired with the original indicator (LANZ Strategy 4.0) for live trading. Best used on assets with clear structural behavior (gold, indices, FX). 📌 Credits: Backtest engine developed by LANZ based on the official rules of LANZ Strategy 4.0. This script ensures visual and logical consistency between live charting and backtesting simulations.
Pine Script®策略
由rau_u_lanz提供
4
AltcoinEvreni Entry/TP RR ToolMulti-Entry / Take-Profit Risk-Reward Tool This indicator is designed to help traders visually plan and manage their trade entries, take-profit targets, stop-loss levels, and risk/reward calculations directly on the chart. Key Features: --- Up to 3 customizable entry levels with separate position sizing for each. --- Up to 5 take-profit (TP) levels, each with individual allocation percentages. --- Automatic calculation of weighted average entry price. --- Dynamic risk and potential profit calculation based on your inputs. --- Visual colored zones for entry, stop-loss, and take-profit areas on the chart. --- Adjustable leverage and margin for position sizing. --- Informative floating table displaying position type, entries, stop-loss, risk in $, potential --- profit in $, and overall RR ratio. --- Fully customizable appearance (colors, box width, table font size, etc.). How to Use: 1- Set your trade direction (Long or Short). 2- Enter your planned entry prices, allocation percentages, and stop-loss. 3- Configure your take-profit levels and their respective allocation percentages. 4- Adjust margin, leverage, and visual preferences as desired. 5- The tool will display all relevant zones and statistics, helping you make better risk-managed trading decisions. Notes: --- All calculations and drawings update dynamically as you change your parameters. --- Works on any symbol and timeframe. --- For educational and planning purposes – always use your own judgment and risk management.
Pine Script®指標
由ahmetakyurek提供
Candle/Keltner Channels BUY SELLWhy Use Candlesticks? They help traders visualize price action Used in technical analysis and price pattern recognition (e.g., Doji, Engulfing, Hammer) Assist in determining entry and exit points Why Traders Use Keltner Channels? Keltner Channels are widely used by traders for identifying trends, detecting volatility, and spotting trade opportunities. 1. Trend Identification The middle line (EMA) shows the general trend. If price consistently stays above the middle line, it indicates a strong uptrend. If price stays below, it signals a downtrend. Use: Traders follow the trend direction to enter trades in line with momentum. 2. Volatility Measurement The width of the channel expands and contracts based on Average True Range (ATR). Wider channels = high volatility, tighter channels = low volatility. Use: Helps traders decide when to expect breakouts or calm periods. 3. Breakout Signals A break above the upper band can signal a bullish breakout. A break below the lower band can signal a bearish breakout. Use: Traders use this for momentum trading and breakout entries. 4. Overbought/Oversold Conditions Price touching or crossing the upper band may suggest it's overbought. Price touching or crossing the lower band may suggest it's oversold. Use: Traders combine this with RSI or MACD to confirm reversal setups. 5. Trade Entry and Exit When price pulls back to the middle EMA during a trend, it may present a buy/sell opportunity. Exits can also be planned if price returns inside the bands after a breakout. Use: Helps with precise entry and exit timing. 6. Combines Well With Other Indicators Commonly used with: RSI (for confirmation) MACD (for momentum) Candlestick patterns (for price action signals) Combining Candlestick Patterns with Keltner Channels gives traders a powerful method to confirm entries, spot reversals, and improve accuracy. Here’s why this combination works so well: 1. Context for Candlestick Signals Candlestick patterns (like doji, engulfing, or pin bars) show potential price reversals, but they need context to be reliable. Keltner Channels provide that context: A bullish candlestick near the lower band suggests a stronger buy signal. A bearish candlestick near the upper band strengthens a sell signal. 2. Filtering False Signals Candlestick patterns occur frequently, and not all are meaningful. The location within the Keltner Channel helps filter out weak or false patterns. Example: A bullish engulfing candle outside the lower band = high-probability reversal. 3. Improved Entry Timing Traders wait for a candlestick pattern confirmation when price touches or crosses a Keltner band. This avoids premature entries and allows tighter stop-losses. 4. Better Risk-Reward Setup Candlestick entry near channel extremes (upper/lower band) lets traders place stop-losses just beyond recent highs/lows. The target can be the opposite side of the channel or the middle EMA. 5. Visual Simplicity Keltner Channels + Candles are visually intuitive. Even beginner traders can easily recognize: Overextended candles near channel edges. Confirmed breakouts or reversals. This Timeframe 5 min : XAUUSD
Pine Script®指標
由dudedicate26提供
Correlation Coefficient📊 Correlation Coefficient (CC) This indicator measures the statistical correlation between two selected securities over a defined period, scaled from -100 to +100. It helps you quickly assess whether assets are moving: Together (positive correlation) Opposite (negative correlation) Independently (zero correlation) 🔧 Features: Select any two symbols (default: NIFTY & BANKNIFTY) Adjustable length parameter for short-term or long-term correlation analysis Clean, color-coded plot with horizontal levels to easily identify key correlation zones 📈 Useful For: Pair trading setups Hedging strategies Detecting market regime shifts or intermarket divergences ⚠️ Disclaimer: This is not trading or investment advice. This indicator is intended for informational purposes only and is not recommended for making direct trading decisions.
Pine Script®指標
由Pro_Quantitative_Trader提供
Ergodic Market Divergence (EMD)Ergodic Market Divergence (EMD) Bridging Statistical Physics and Market Dynamics Through Ensemble Analysis The Revolutionary Concept: When Physics Meets Trading After months of research into ergodic theory—a fundamental principle in statistical mechanics—I've developed a trading system that identifies when markets transition between predictable and unpredictable states. This indicator doesn't just follow price; it analyzes whether current market behavior will persist or revert, giving traders a scientific edge in timing entries and exits. The Core Innovation: Ergodic Theory Applied to Markets What Makes Markets Ergodic or Non-Ergodic? In statistical physics, ergodicity determines whether a system's future resembles its past. Applied to trading: Ergodic Markets (Mean-Reverting) - Time averages equal ensemble averages - Historical patterns repeat reliably - Price oscillates around equilibrium - Traditional indicators work well Non-Ergodic Markets (Trending) - Path dependency dominates - History doesn't predict future - Price creates new equilibrium levels - Momentum strategies excel The Mathematical Framework The Ergodic Score combines three critical divergences: Ergodic Score = (Price Divergence × Market Stress + Return Divergence × 1000 + Volatility Divergence × 50) / 3 Where: Price Divergence: How far current price deviates from market consensus Return Divergence: Momentum differential between instrument and market Volatility Divergence: Volatility regime misalignment Market Stress: Adaptive multiplier based on current conditions The Ensemble Analysis Revolution Beyond Single-Instrument Analysis Traditional indicators analyze one chart in isolation. EMD monitors multiple correlated markets simultaneously (SPY, QQQ, IWM, DIA) to detect systemic regime changes. This ensemble approach: Reveals Hidden Divergences: Individual stocks may diverge from market consensus before major moves Filters False Signals: Requires broader market confirmation Identifies Regime Shifts: Detects when entire market structure changes Provides Context: Shows if moves are isolated or systemic Dynamic Threshold Adaptation Unlike fixed-threshold systems, EMD's boundaries evolve with market conditions: Base Threshold = SMA(Ergodic Score, Lookback × 3) Adaptive Component = StDev(Ergodic Score, Lookback × 2) × Sensitivity Final Threshold = Smoothed(Base + Adaptive) This creates context-aware signals that remain effective across different market environments. The Confidence Engine: Know Your Signal Quality Multi-Factor Confidence Scoring Every signal receives a confidence score based on: Signal Clarity (0-35%): How decisively the ergodic threshold is crossed Momentum Strength (0-25%): Rate of ergodic change Volatility Alignment (0-20%): Whether volatility supports the signal Market Quality (0-20%): Price convergence and path dependency factors Real-Time Confidence Updates The Live Confidence metric continuously updates, showing: - Current opportunity quality - Market state clarity - Historical performance influence - Signal recency boost - Visual Intelligence System Adaptive Ergodic Field Bands Dynamic bands that expand and contract based on market state: Primary Color: Ergodic state (mean-reverting) Danger Color: Non-ergodic state (trending) Band Width: Expected price movement range Squeeze Indicators: Volatility compression warnings Quantum Wave Ribbons Triple EMA system (8, 21, 55) revealing market flow: Compressed Ribbons: Consolidation imminent Expanding Ribbons: Directional move developing Color Coding: Matches current ergodic state Phase Transition Signals Clear entry/exit markers at regime changes: Bull Signals: Ergodic restoration (mean reversion opportunity) Bear Signals: Ergodic break (trend following opportunity) Confidence Labels: Percentage showing signal quality Visual Intensity: Stronger signals = deeper colors Professional Dashboard Suite Main Analytics Panel (Top Right) Market State Monitor - Current regime (Ergodic/Non-Ergodic) - Ergodic score with threshold - Path dependency strength - Quantum coherence percentage Divergence Metrics - Price divergence with severity - Volatility regime classification - Strategy mode recommendation - Signal strength indicator Live Intelligence - Real-time confidence score - Color-coded risk levels - Dynamic strategy suggestions Performance Tracking (Left Panel) Signal Analytics - Total historical signals - Win rate with W/L breakdown - Current streak tracking - Closed trade counter Regime Analysis - Current market behavior - Bars since last signal - Recommended actions - Average confidence trends Strategy Command Center (Bottom Right) Adaptive Recommendations - Active strategy mode - Primary approach (mean reversion/momentum) - Suggested indicators ("weapons") - Entry/exit methodology - Risk management guidance - Comprehensive Input Guide Core Algorithm Parameters Analysis Period (10-100 bars) Scalping (10-15): Ultra-responsive, more signals, higher noise Day Trading (20-30): Balanced sensitivity and stability Swing Trading (40-100): Smooth signals, major moves only Default: 20 - optimal for most timeframes Divergence Threshold (0.5-5.0) Hair Trigger (0.5-1.0): Catches every wiggle, many false signals Balanced (1.5-2.5): Good signal-to-noise ratio Conservative (3.0-5.0): Only extreme divergences Default: 1.5 - best risk/reward balance Path Memory (20-200 bars) Short Memory (20-50): Recent behavior focus, quick adaptation Medium Memory (50-100): Balanced historical context Long Memory (100-200): Emphasizes established patterns Default: 50 - captures sufficient history without lag Signal Spacing (5-50 bars) Aggressive (5-10): Allows rapid-fire signals Normal (15-25): Prevents clustering, maintains flow Conservative (30-50): Major setups only Default: 15 - optimal trade frequency Ensemble Configuration Select markets for consensus analysis: SPY: Broad market sentiment QQQ: Technology leadership IWM: Small-cap risk appetite DIA: Blue-chip stability More instruments = stronger consensus but potentially diluted signals Visual Customization Color Themes (6 professional options): Quantum: Cyan/Pink - Modern trading aesthetic Matrix: Green/Red - Classic terminal look Heat: Blue/Red - Temperature metaphor Neon: Cyan/Magenta - High contrast Ocean: Turquoise/Coral - Calming palette Sunset: Red-orange/Teal - Warm gradients Display Controls: - Toggle each visual component - Adjust transparency levels - Scale dashboard text - Show/hide confidence scores - Trading Strategies by Market State - Ergodic State Strategy (Primary Color Bands) Market Characteristics - Price oscillates predictably - Support/resistance hold - Volume patterns repeat - Mean reversion dominates Optimal Approach Entry: Fade moves at band extremes Target: Middle band (equilibrium) Stop: Just beyond outer bands Size: Full confidence-based position Recommended Tools - RSI for oversold/overbought - Bollinger Bands for extremes - Volume profile for levels - Non-Ergodic State Strategy (Danger Color Bands) Market Characteristics - Price trends persistently - Levels break decisively - Volume confirms direction - Momentum accelerates Optimal Approach Entry: Breakout from bands Target: Trail with expanding bands Stop: Inside opposite band Size: Scale in with trend Recommended Tools - Moving average alignment - ADX for trend strength - MACD for momentum - Advanced Features Explained Quantum Coherence Metric Measures phase alignment between individual and ensemble behavior: 80-100%: Perfect sync - strong mean reversion setup 50-80%: Moderate alignment - mixed signals 0-50%: Decoherence - trending behavior likely Path Dependency Analysis Quantifies how much history influences current price: Low (<30%): Technical patterns reliable Medium (30-50%): Mixed influences High (>50%): Fundamental shift occurring Volatility Regime Classification Contextualizes current volatility: Normal: Standard strategies apply Elevated: Widen stops, reduce size Extreme: Defensive mode required Signal Strength Indicator Real-time opportunity quality: - Distance from threshold - Momentum acceleration - Cross-validation factors Risk Management Framework Position Sizing by Confidence 90%+ confidence = 100% position size 70-90% confidence = 75% position size 50-70% confidence = 50% position size <50% confidence = 25% or skip Dynamic Stop Placement Ergodic State: ATR × 1.0 from entry Non-Ergodic State: ATR × 2.0 from entry Volatility Adjustment: Multiply by current regime Multi-Timeframe Alignment - Check higher timeframe regime - Confirm ensemble consensus - Verify volume participation - Align with major levels What Makes EMD Unique Original Contributions First Ergodic Theory Trading Application: Transforms abstract physics into practical signals Ensemble Market Analysis: Revolutionary multi-market divergence system Adaptive Confidence Engine: Institutional-grade signal quality metrics Quantum Coherence: Novel market alignment measurement Smart Signal Management: Prevents clustering while maintaining responsiveness Technical Innovations Dynamic Threshold Adaptation: Self-adjusting sensitivity Path Memory Integration: Historical dependency weighting Stress-Adjusted Scoring: Market condition normalization Real-Time Performance Tracking: Built-in strategy analytics Optimization Guidelines By Timeframe Scalping (1-5 min) Period: 10-15 Threshold: 0.5-1.0 Memory: 20-30 Spacing: 5-10 Day Trading (5-60 min) Period: 20-30 Threshold: 1.5-2.5 Memory: 40-60 Spacing: 15-20 Swing Trading (1H-1D) Period: 40-60 Threshold: 2.0-3.0 Memory: 80-120 Spacing: 25-35 Position Trading (1D-1W) Period: 60-100 Threshold: 3.0-5.0 Memory: 100-200 Spacing: 40-50 By Market Condition Trending Markets - Increase threshold - Extend memory - Focus on breaks Ranging Markets - Decrease threshold - Shorten memory - Focus on restores Volatile Markets - Increase spacing - Raise confidence requirement - Reduce position size - Integration with Other Analysis - Complementary Indicators For Ergodic States - RSI divergences - Bollinger Band squeezes - Volume profile nodes - Support/resistance levels For Non-Ergodic States - Moving average ribbons - Trend strength indicators - Momentum oscillators - Breakout patterns - Fundamental Alignment - Check economic calendar - Monitor sector rotation - Consider market themes - Evaluate risk sentiment Troubleshooting Guide Too Many Signals: - Increase threshold - Extend signal spacing - Raise confidence minimum Missing Opportunities - Decrease threshold - Reduce signal spacing - Check ensemble settings Poor Win Rate - Verify timeframe alignment - Confirm volume participation - Review risk management Disclaimer This indicator is for educational and informational purposes only. It does not constitute financial advice. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results. The ergodic framework provides unique market insights but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose. This tool should complement, not replace, comprehensive trading strategies and sound judgment. Markets remain inherently unpredictable despite advanced analysis techniques. Transform market chaos into trading clarity with Ergodic Market Divergence. Created with passion for the TradingView community Trade with insight. Trade with anticipation. — Dskyz , for DAFE Trading Systems
Pine Script®指標
由DskyzInvestments提供
4
Profit Guard ProProfitGuard Pro ProfitGuard Pro is a risk management and profit calculation tool that helps traders optimize their trades by handling position sizing, risk management, leverage, and take profit calculations. With support for both cumulative and non-cumulative take profit strategies, this versatile indicator provides the insights you need to maximize your trading strategy. How to Use ProfitGuard Pro: Load the Indicator: Add ProfitGuard Pro to your chart in TradingView. Set Your Entry Position: Input your desired entry price. Define Your Stop Loss: Enter the price at which your trade will exit to minimize losses. Add Take Profit Levels: Input your TP1, TP2, TP3, and TP4 levels, as needed. If you want fewer take profit levels, adjust the number of TPs in the settings menu. You can choose between 1 to 4 take profit levels based on your strategy. Adjust Risk Settings: Specify your account size and risk percentage to calculate position size and leverage. Choose Cumulative or Non-Cumulative Mode: Toggle cumulative profit mode to either recalculate position sizes as each take profit is hit or keep position sizes static for each TP. Once set up, ProfitGuard Pro will automatically calculate your position size, leverage, and potential profits for each take profit level, providing a clear visual on your chart to guide your trading decisions. Key Features: Risk Management: Calculate your risk percentage based on account size and stop loss. Visualize risk in dollar terms and percentage of your account. Position Size & Leverage: Automatically calculate the ideal position size and leverage for your trade based on your entry, stop loss, and risk settings. Ensure you are trading with the appropriate leverage for your account size. Cumulative vs Non-Cumulative Profit Mode: Cumulative Mode: Adjusts position size after each take profit is reached, recalculating for remaining contracts. Non-Cumulative Mode: Treats each take profit as a separate calculation using the full position size. Take Profit Levels: Set up to 4 customizable take profit levels. Adjust percentage values for each TP target, and visualize them on your chart with easy-to-read lines. Profit Calculation: Displays potential profits for each take profit level based on whether cumulative or non-cumulative mode is selected. Calculate your risk-reward ratio dynamically at each TP. Customizable Visuals: Easily customize the table's size, position, and color scheme to fit your chart. Visualize key trade details like leverage, contracts, margin, and profits directly on your chart. Short and Long Position Support: Automatically adjusts calculations based on whether you're trading long or short.
Pine Script®指標
由svstaats提供
Value at Risk (VaR/CVaR) - Stop Loss ToolThis script calculates Value at Risk (VaR) and Conditional Value at Risk (CVaR) over a configurable T-bar forward horizon, based on historical T-bar log returns. It plots projected price thresholds that reflect the worst X% of historical return outcomes, helping set statistically grounded stop-loss levels. A 95% 5-day VaR of −3% means: “In the worst 5% of all historical 5-day periods, losses were 3% or more.” If you're bullish, and your thesis is correct, price should not behave like one of those worst-case scenarios. So if the market starts trading below that 5-day VaR level, it may indicate that your long bias is invalidated, and a stop-loss near that level can help protect against further downside consistent with tail-risk behavior. How it's different: Unlike ATR or standard deviation-based methods, which measure recent volatility magnitude, VaR/CVaR incorporate both the magnitude and **likelihood** (5% chance for example) of adverse moves. This makes it better suited for risk-aware position sizing and exits grounded in actual historical return distributions. How to use for stop placement: - Set your holding horizon (T) and confidence level (e.g., 95%) in the inputs. - The script plots a price level below which only the worst 5% (or chosen %) of T-bar returns have historically occurred (VaR). - If price approaches or breaches the VaR line, your bullish/bearish thesis may be invalidated. - CVaR gives a deeper threshold: the average loss **if** things go worse than VaR — useful for a secondary or emergency stop. FURTHER NOTES FROM SOURCE CODE: //======================================================================// // If you're bullish (expecting the price to go up), then under normal circumstances, prices should not behave like they do on the worst-case days. // If they are — you're probably wrong, or something unexpected is happening. Basically, returns shouldn't be exhibiting downside tail-like behavior if you're bullish. // VaR(95%, T) gives the threshold below which the price falls only 5% of the time historically, over T days/bars and considering N historical samples. // CVaR tells you the expected/average price level if that adverse move continues // Caveats: // For a variety of reasons, VaR underestimates volatility, despite using historical returns directly rather than making normality assumptions // as is the case with the standard historicalvol/bollinger band/stdev/ATR approaches) // Volatility begets volatility (volatility clustering), and VaR is not a conditional probability on recent volatility so it likely underestimates the true volatility of an adverse event // Regieme shifts occur (bullish phase after prolonged bearish behavior), so upside/short VaR would underestimate the best-case days in the beginning of that move, depending on lookahead horizon/sampling period // News/events happen, and maybe your sampling period doesn't contain enough event-driven returns to form reliable stats // In general of course, this tool assumes past return distributions are reflective of forward risk (not the case in non-stationary time series) // Thus, this tool is not predictive — it shows historical tail risk, not guaranteed outcomes. // Also, when forming log-returns, overlapping windows of returns are used (to get more samples), but this introduces autocorrelation (if it wasn't there already). This means again, the true VaR is underestimated. // Description: // This script calculates and plots both Value at Risk (VaR) and // Conditional Value at Risk (CVaR) for a given confidence level, using // historical log returns. It computes both long-side (left tail) and // short-side (right tail) risk, and converts them into price thresholds (red and green lines respectively). // // Key Concepts: // - VaR: "There is a 95% chance the loss will be less than this value over T days. Represents the 95th-percentile worst empirical returns observed in the sampling period, over T bars. // - CVaR: "Given that the loss exceeds the VaR, the average of those worst 5% losses is this value. (blue line)" Expected tail loss. If the worst case breached, how bad can it get on average // - For shorts, the script computes the mirror (right-tail) equivalents. // - Use T-day log returns if estimating risk over multiple days forward. // - You can see instances where the VaR for time T, was surpassed historically with the "backtest" boolean // // Usage for Stop-Loss: // - LONG POSITIONS: // • 95th percentile means, 5% of the time (1 in 20 times) you'd expect to get a VaR level loss (touch the red line), over the next T bars. // • VaR threshold = minimum price expected with (1 – confidence)% chance. // • CVaR threshold = expected price if that worst-case zone is breached. // → Use as potential stop-loss (VaR) or disaster stop (CVaR). If you're bullish (and you're right), price should not be exhibiting returns consistent with the worst 5% of days/T_bars historically. //======================================================================//
Pine Script®指標
由scakolli提供
1A Monthly P&L Table - Using Library1A Monthly P&L Table: Track Your Performance Month-by-Month Overview: The 1A Monthly P&L Table is a straightforward yet powerful indicator designed to give you an immediate overview of your asset's (or strategy's) percentage performance on a monthly basis. Displayed conveniently in the bottom-right corner of your chart, this tool helps you quickly assess historical gains and losses, making it easier to analyze trends in performance over time. Key Features: Monthly Performance at a Glance: Clearly see the percentage change for each past month. Cumulative P&L: A running total of the displayed monthly P&L is provided, giving you a quick sum of performance over the selected period. Customizable Display: Months to Display: Choose how many past months you want to see in the table (from 1 to 60 months). Text Size: Adjust the text size (Tiny, Small, Normal, Large, Huge) to fit your viewing preferences. Text Color: Customize the color of the text for better visibility against your chart background. Intraday & Daily Compatibility: The table is optimized to display on daily and intraday timeframes, ensuring it's relevant for various trading styles. (Note: For very long-term analysis on weekly/monthly charts, you might consider other tools, as this focuses on granular monthly P&L.) How It Works: The indicator calculates the percentage change from the close of the previous month to the close of the current month. For the very first month displayed, it calculates the P&L from the opening price of the chart's first bar to the close of that month. This data is then neatly organized into a table, updated on the last bar of the day or session. Ideal For: Traders and investors who want a quick, visual summary of monthly performance. Analyzing seasonal trends or consistent periods of profitability/drawdown. Supplementing backtesting results with a clear month-by-month breakdown. Settings: Text Color: Changes the color of all text within the table. Text Size: Controls the font size of the table content. Months to Display: Determines the number of recent months included in the table.
Pine Script®指標
由forexfederation提供
MonthlyPnLTableLibrary "MonthlyPnLTable" monthlyPnL(currentClose, initialOpenPrice, monthsToDisplay)   Parameters:      currentClose (float)      initialOpenPrice (float)      monthsToDisplay (int) displayPnLTable(pnls, pnlMonths, pnlYears, textSizeOption, labelColor)   Parameters:      pnls (array)      pnlMonths (array)      pnlYears (array)      textSizeOption (string)      labelColor (color)
Pine Script®腳本庫
由forexfederation提供
LANZ Strategy 3.0 [Backtest]🔷 LANZ Strategy 3.0 — Asian Range Fibonacci Scalping Strategy LANZ Strategy 3.0 is a precision-engineered backtesting tool tailored for intraday traders who rely on the Asian session range to determine directional bias. This strategy implements dynamic Fibonacci projections and strict time-window validation to simulate a clean and disciplined trading environment. 🧠 Core Components: Asian Range Bias Definition: Direction is established between 01:15–02:15 a.m. NY time based on the candle’s close in relation to the midpoint of the Asian session range (18:00–01:15 NY). Limit Order Execution: Only one trade is placed daily, using a limit order at the Asian range high (for sells) or low (for buys), between 01:15–08:00 a.m. NY. Fibonacci-Based TP/SL: Original Mode: TP = 2.25x range, SL = 0.75x range. Optimized Mode: TP = 1.95x range, SL = 0.65x range. No Trade After 08:00 NY: If the limit order is not executed before 08:00 a.m. NY, it is canceled. Fallback Logic at 02:15 NY: If the market direction misaligns with the setup at 02:15 a.m., the system re-evaluates and can re-issue the order. End-of-Day Closure: All positions are closed at 15:45 NY if still open. 📊 Backtest-Ready Design: Entries and exits are executed using strategy.entry() and strategy.exit() functions. Position size is fixed via capital risk allocation ($100 per trade by default). Only one position can be active at a time, ensuring controlled risk. 📝 Notes: This strategy is ideal for assets sensitive to the Asian/London session overlap, such as Forex pairs and indices. Easily switch between Fibonacci versions using a single dropdown input. Fully deterministic: all entries are based on pre-defined conditions and time constraints. 👤 Credits: Strategy developed by rau_u_lanz using Pine Script v6. Built for traders who favor clean sessions, directional clarity, and consistent execution using time-based logic and Fibonacci projections.
Pine Script®策略
由rau_u_lanz提供
Lorentzian Classification - Advanced Trading DashboardLorentzian Classification - Relativistic Market Analysis A Journey from Theory to Trading Reality What began as fascination with Einstein's relativity and Lorentzian geometry has evolved into a practical trading tool that bridges theoretical physics and market dynamics. This indicator represents months of wrestling with complex mathematical concepts, debugging intricate algorithms, and transforming abstract theory into actionable trading signals. The Theoretical Foundation Lorentzian Distance in Market Space Traditional Euclidean distance treats all feature differences equally, but markets don't behave uniformly. Lorentzian distance, borrowed from spacetime geometry, provides a more nuanced similarity measure: d(x,y) = Σ ln(1 + |xi - yi|) This logarithmic formulation naturally handles: Scale invariance: Large price moves don't overwhelm small but significant patterns Outlier robustness: Extreme values are dampened rather than dominating Non-linear relationships: Captures market behavior better than linear metrics K-Nearest Neighbors with Relativistic Weighting The algorithm searches historical market states for patterns similar to current conditions. Each neighbor receives weight inversely proportional to its Lorentzian distance: w = 1 / (1 + distance) This creates a "gravitational" effect where closer patterns have stronger influence on predictions. The Implementation Challenge Creating meaningful market features required extensive experimentation: Price Features: Multi-timeframe momentum (1, 2, 3, 5, 8 bar lookbacks) Volume Features: Relative volume analysis against 20-period average Volatility Features: ATR and Bollinger Band width normalization Momentum Features: RSI deviation from neutral and MACD/price ratio Each feature undergoes min-max normalization to ensure equal weighting in distance calculations. The Prediction Mechanism For each current market state: Feature Vector Construction: 12-dimensional representation of market conditions Historical Search: Scan lookback period for similar patterns using Lorentzian distance Neighbor Selection: Identify K nearest historical matches Outcome Analysis: Examine what happened N bars after each match Weighted Prediction: Combine outcomes using distance-based weights Confidence Calculation: Measure agreement between neighbors Technical Hurdles Overcome Array Management: Complex indexing to prevent look-ahead bias Distance Calculations: Optimizing nested loops for performance Memory Constraints: Balancing lookback depth with computational limits Signal Filtering: Preventing clustering of identical signals Advanced Dashboard System Main Control Panel The primary dashboard provides real-time market intelligence: Signal Status: Current prediction with confidence percentage Neighbor Analysis: How many historical patterns match current conditions Market Regime: Trend strength, volatility, and volume analysis Temporal Context: Real-time updates with timestamp Performance Analytics Comprehensive tracking system monitors: Win Rate: Percentage of successful predictions Signal Count: Total predictions generated Streak Analysis: Current winning/losing sequence Drawdown Monitoring: Maximum equity decline Sharpe Approximation: Risk-adjusted performance estimate Risk Assessment Panel Multi-dimensional risk analysis: RSI Positioning: Overbought/oversold conditions ATR Percentage: Current volatility relative to price Bollinger Position: Price location within volatility bands MACD Alignment: Momentum confirmation Confidence Heatmap Visual representation of prediction reliability: Historical Confidence: Last 10 periods of prediction certainty Strength Analysis: Magnitude of prediction values over time Pattern Recognition: Color-coded confidence levels for quick assessment Input Parameters Deep Dive Core Algorithm Settings K Nearest Neighbors (1-20): More neighbors create smoother but less responsive signals. Optimal range 5-8 for most markets. Historical Lookback (50-500): Deeper history improves pattern recognition but reduces adaptability. 100-200 bars optimal for most timeframes. Feature Window (5-30): Longer windows capture more context but reduce sensitivity. Match to your trading timeframe. Feature Selection Price Changes: Essential for momentum and reversal detection Volume Profile: Critical for institutional activity recognition Volatility Measures: Key for regime change detection Momentum Indicators: Vital for trend confirmation Signal Generation Prediction Horizon (1-20): How far ahead to predict. Shorter horizons for scalping, longer for swing trading. Signal Threshold (0.5-0.9): Confidence required for signal generation. Higher values reduce false signals but may miss opportunities. Smoothing (1-10): EMA applied to raw predictions. More smoothing reduces noise but increases lag. Visual Design Philosophy Color Themes Professional: Corporate blue/red for institutional environments Neon: Cyberpunk cyan/magenta for modern aesthetics Matrix: Green/red hacker-inspired palette Classic: Traditional trading colors Information Hierarchy The dashboard system prioritizes information by importance: Primary Signals: Largest, most prominent display Confidence Metrics: Secondary but clearly visible Supporting Data: Detailed but unobtrusive Historical Context: Available but not distracting Trading Applications Signal Interpretation Long Signals: Prediction > threshold with high confidence Look for volume confirmation - Check trend alignment - Verify support levels Short Signals: Prediction < -threshold with high confidence Confirm with resistance levels - Check for distribution patterns - Verify momentum divergence - Market Regime Adaptation Trending Markets: Higher confidence in directional signals Ranging Markets: Focus on reversal signals at extremes Volatile Markets: Require higher confidence thresholds Low Volume: Reduce position sizes, increase caution Risk Management Integration Confidence-Based Sizing: Larger positions for higher confidence signals Regime-Aware Stops: Wider stops in volatile regimes Multi-Timeframe Confirmation: Align signals across timeframes Volume Confirmation: Require volume support for major signals Originality and Innovation This indicator represents genuine innovation in several areas: Mathematical Approach First application of Lorentzian geometry to market pattern recognition. Unlike Euclidean-based systems, this naturally handles market non-linearities. Feature Engineering Sophisticated multi-dimensional feature space combining price, volume, volatility, and momentum in normalized form. Visualization System Professional-grade dashboard system providing comprehensive market intelligence in intuitive format. Performance Tracking Real-time performance analytics typically found only in institutional trading systems. Development Journey Creating this indicator involved overcoming numerous technical challenges: Mathematical Complexity: Translating theoretical concepts into practical code Performance Optimization: Balancing accuracy with computational efficiency User Interface Design: Making complex data accessible and actionable Signal Quality: Filtering noise while maintaining responsiveness The result is a tool that brings institutional-grade analytics to individual traders while maintaining the theoretical rigor of its mathematical foundation. Best Practices - Parameter Optimization - Start with default settings and adjust based on: Market Characteristics: Volatile vs. stable Trading Timeframe: Scalping vs. swing trading Risk Tolerance: Conservative vs. aggressive Signal Confirmation Never trade on Lorentzian signals alone: Price Action: Confirm with support/resistance Volume: Verify with volume analysis Multiple Timeframes: Check higher timeframe alignment Market Context: Consider overall market conditions Risk Management Position Sizing: Scale with confidence levels Stop Losses: Adapt to market volatility Profit Targets: Based on historical performance Maximum Risk: Never exceed 2-3% per trade Disclaimer This indicator is for educational and research purposes only. It does not constitute financial advice or guarantee profitable trading results. The Lorentzian classification system reveals market patterns but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose. Market dynamics are inherently uncertain, and past performance does not guarantee future results. This tool should be used as part of a comprehensive trading strategy, not as a standalone solution. Bringing the elegance of relativistic geometry to market analysis through sophisticated pattern recognition and intuitive visualization. Thank you for sharing the idea. You're more than a follower, you're a leader! @vasanthgautham1221 Trade with precision. Trade with insight. — Dskyz , for DAFE Trading Systems
Pine Script®指標
由DskyzInvestments提供
7
Eigenvector Centrality Drift (ECD) - Market State Network What is Eigenvector Centrality Drift (ECD)? Eigenvector Centrality Drift (ECD) is a groundbreaking indicator that applies concepts from network science to financial markets. Instead of viewing price as a simple series, ECD models the market as a dynamic network of “micro-states”—distinct combinations of price, volatility, and volume. By tracking how the influence of these states changes over time, ECD helps you spot regime shifts and transitions in market character before they become obvious in price. This is not another moving average or momentum oscillator. ECD is inspired by eigenvector centrality—a measure of influence in network theory—and adapts it to the world of price action, volatility, and volume. It’s about understanding which market states are “in control” and when that control is about to change. Theoretical Foundation Network Science: In complex systems, nodes (states) and edges (transitions) form a network. Eigenvector centrality measures how influential a node is, not just by its direct connections, but by the influence of the nodes it connects to. Market Micro-States: Each bar is classified into a “state” based on price change, volatility, and volume. The market transitions between these states, forming a network of possible regimes. Centrality Drift: By tracking the centrality (influence) of the current state, and how it changes (drifts) over time, ECD highlights when the market’s “center of gravity” is shifting—often a precursor to major moves or regime changes. How ECD Works State Classification: Each bar is assigned to one of N market micro-states, based on a weighted combination of normalized price change, volatility, and volume. Transition Matrix: Over a rolling window, ECD tracks how often the market transitions from each state to every other state, forming a transition probability matrix. Centrality Calculation: Using a simplified eigenvector approach, ECD calculates the “influence” score for each state, reflecting how central it is to the network of recent market behavior. Centrality Drift: The indicator tracks the Z-score of the change in centrality for the current state. Rapid increases or decreases, or a shift in the dominant state, signal a potential regime shift. Dominant State: ECD also highlights which state currently has the highest influence, providing insight into the prevailing market character. Inputs: 🌐 Market State Configuration Number of Market States (n_states, default 6): Number of distinct micro-states to track. 3–4: Simple (Up/Down/Sideways) 5–6: Balanced (recommended) 7–9: Complex, more nuanced Price Change Weight (price_weight, default 0.4): How much price movement defines a state. Higher = more directional. Volatility Weight (vol_weight, default 0.3): How much volatility defines a state. Higher = more regime focus. Volume Weight (volume_weight, default 0.3): How much volume defines a state. Higher = more participation focus. 🔗 Network Analysis Transition Matrix Window (transition_window, default 50): Lookback for building the state transition matrix. Shorter: Adapts quickly Longer: More stable Influence Decay Factor (influence_decay, default 0.85): How much influence propagates through the network. Higher: Distant transitions matter more Lower: Only immediate transitions matter Drift Detection Sensitivity (drift_sensitivity, default 1.5): Z-score threshold for significant centrality drift. Lower: More signals Higher: Only major shifts 🎨 Visualization Show Network Visualization (show_network, default true): Background color and effects based on network structure. Show Centrality Score (show_centrality, default true): Plots the current state’s centrality measure. Show Drift Indicator (show_drift, default true): Plots the centrality drift Z-score. Show State Map (show_state_map, default true): Dashboard showing all state centralities and which is dominant. Color Scheme (color_scheme, default "Quantum"): “Quantum”: Cyan/Magenta “Neural”: Green/Blue “Plasma”: Yellow/Pink “Matrix”: Green/Black Color Schemes Dynamic gradients reflect the current state’s centrality and drift, using your chosen color palette. Background network effect: The more central the current state, the more intense the background. Centrality and drift lines: Color-coded for clarity and regime shift detection. Visual Logic Centrality Score Line: Plots the influence of the current state, with glow for emphasis. Drift Indicator: Histogram of centrality drift Z-score, green for positive, red for negative. Threshold Lines: Dotted lines mark the drift sensitivity threshold for regime shift alerts. State Map Dashboard: Top-right panel shows all state centralities, highlights the current and dominant state, and visualizes influence with bars. Information Panel: Bottom-left panel summarizes current state, centrality, dominant state, drift Z-score, and regime shift status. How to Use ECD Centrality Score: High = current state is highly influential; low = state is peripheral. Drift Z-Score: Large positive/negative = rapid change in influence, regime shift likely. Near zero = stable network, no major shift. Dominant State: The state with the highest centrality is “in control” of the market’s transitions. State Map: Use to see which states are rising or falling in influence. Tips: Use fewer states for simple markets, more for nuanced analysis. Watch for drift Z-score crossing the threshold—these are your regime shift signals. Combine with your own system for confirmation. Alerts: ECD Regime Shift: Significant centrality drift detected—potential regime change. ECD State Change: Market state transition occurred. ECD Dominance Shift: Dominant market state has changed. Originality & Usefulness ECD is not a mashup or rehash of standard indicators. It is a novel application of network science and eigenvector centrality to market microstructure, providing a new lens for understanding regime shifts and market transitions. The state network, centrality drift, and dashboard are unique to this script. ECD is designed for anticipation, not confirmation—helping you see the market’s “center of gravity” shift before price action makes it obvious. Chart Info Script Name: Eigenvector Centrality Drift (ECD) – Market State Network Recommended Use: Any asset, any timeframe. Tune parameters to your style. Disclaimer This script is for research and educational purposes only. It does not provide financial advice or direct buy/sell signals. Always use proper risk management and combine with your own strategy. Past performance is not indicative of future results. See the market as a network. Anticipate the shift in influence. — Dskyz , for DAFE Trading Systems
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