Binary Options Signals Provider M1-H4 [TradingFinder]🔵 Introduction
Binary Options trading is highly sensitive to timing, precision, and short-term price reactions. Unlike other trading styles, entries in binary markets must be executed at exact moments when price behavior, momentum, and liquidity conditions align within a very limited time window.
This Screener is designed to generate Binary Options trading signals based on pure price action analysis, market structure, and liquidity behavior rather than lagging indicators. The signals are not random alerts; they are produced only when price reacts at critical decision points defined by supply and demand zones.
The core logic focuses on how price behaves when it reaches areas of concentrated orders, where liquidity absorption or injection typically leads to fast directional moves. These reactions are evaluated through candlestick structure, momentum shifts, and false breakout behavior, which are essential for short-duration binary setups.
By combining order blocks, Fair Value Gaps, imbalances, and breaker structures with strict candlestick confirmation, this indicator identifies high-probability Long and Short Binary Options signals suitable for short-term expirations across multiple timeframes.
Rather than predicting the market, the indicator reacts to real-time order flow and liquidity interaction, making it a structured and disciplined tool for traders who rely on precise execution in Binary Options environments.
Long Signal :
Short Signal :
🔵 How to Use
The first step is to identify valid structural zones such as order blocks, Fair Value Gaps, imbalances, or breaker structures. These zones represent areas where order flow has previously shown a strong directional response and where future reactions are likely to occur.
Once a zone is identified, the indicator continuously monitors price behavior as it approaches and interacts with that area. A signal is generated only when price reaches a valid zone, liquidity behavior becomes evident, and a confirming candlestick structure forms in alignment with the expected direction.
This approach ensures that Binary Options signals are issued only during moments of active market participation, where short-term directional moves have the highest probability of success.
🟣 Long Signal
A Long Binary Options signal is generated when price reaches a validated demand zone, such as a bullish order block, an unfilled bullish Fair Value Gap, a lower-structure imbalance, or a bullish breaker.
As price enters the demand area, the indicator evaluates whether sell-side liquidity is being absorbed. This is reflected through changes in candlestick structure and momentum behavior.
Confirmation occurs when bullish price action patterns form, including structures such as :
Pin Bars with long lower wicks
Bullish Engulfing patterns
Rejection candles
False breakouts of local lows
Short-term momentum continuation after liquidity sweep
When these conditions align within or near the demand zone, the indicator issues a Long signal, indicating a high-probability bullish reaction suitable for Binary Options execution with short expirations.
🟣 Short Signal
A Short Binary Options signal is generated when price reaches a validated supply zone, such as a bearish order block, a bearish Fair Value Gap, an upper-structure imbalance, or a bearish breaker.
In these areas, price often collects buy-side liquidity above nearby highs before reversing. The indicator monitors this behavior and waits for clear bearish confirmation through candlestick structure and momentum shift.
Bearish confirmation patterns include :
Pin Bars with long upper wicks
Bearish Engulfing patterns
Rejection candles
Indecision followed by strong bearish displacement
False breakouts of local highs
Once price confirms rejection or liquidity exhaustion within or near the supply zone, the indicator generates a Short signal, highlighting a short-term bearish opportunity optimized for Binary Options trading.
🔵 Settings
Last Candle in Signal Direction: When On, a signal is issued only if the last candle moves in the direction required by the signal.
Signal in Nearly Zone : When enabled, the signal becomes valid even if the candle is near the zone rather than strictly inside it. When disabled, only signals formed inside the zone are allowed.
Table on Chart : This setting enables or disables the on chart screener table. When enabled, the table displays signal status, correlation information, and symbol data directly on the chart. When disabled, the chart remains clean with no table overlay.
Number of Symbols : This option controls how many symbol pairs are displayed in the screener table. Users can choose between four or six pairs depending on screen size and personal preference.
Table Size : This setting adjusts the visual scale of the screener table. Smaller sizes are suitable for minimal layouts, while larger sizes improve readability when monitoring multiple pairs simultaneously.
Table Mode : This setting offers two layout styles for the signal table.
Basic mode displays symbols in a single vertical column, using more vertical space and providing straightforward readability.
Extended mode arranges symbols in pairs side by side, optimizing screen space with a more compact and efficient layout.
Table Position : This option defines where the screener table is placed on the chart. The table can be positioned in any corner or central area to avoid overlapping with price action or other indicators.
🔵 Conclusion
Binary Options trading requires precise timing, disciplined execution, and a clear understanding of short-term market behavior. This indicator is built on the principle that high-quality binary signals emerge not from prediction, but from real-time price reactions at key liquidity zones. By combining supply and demand analysis with structural elements such as order blocks, Fair Value Gaps, imbalances, and breaker structures, the indicator filters out random price movements and focuses only on moments when the market is actively responding to order flow.
Signals are generated exclusively when price reaches a validated zone, liquidity behavior becomes evident, and a confirming candlestick forms at the correct location. This structured process helps reduce emotional or impulsive entries and maintains consistency in execution. Rather than acting as a standalone decision-maker, the indicator functions as a confirmation and timing tool, assisting traders in identifying high-probability Long and Short Binary Options setups across multiple timeframes while remaining aligned with the underlying mechanics of price and liquidity.
Forecast
Bitcoin Halving Cycles [DotGain]Halving Cycles
A lightweight, time-anchored Bitcoin halving cycle visualizer built for clean charting, repeatable process planning, and simple profit/DCA timing references.
This Code was heavily inspired by KevinSvenson_ who created Bitcoin Halving Cycle Profit .
What this indicator does
This script plots the key “cycle landmarks” relative to each halving date:
Halving (⛏) – the cycle anchor
Profit START – marks the beginning of the post-halving profit window (default: 40 weeks )
Profit END / Last Call – marks the final phase of the profit window (default: 77 weeks )
DCA START – marks the point where long-term accumulation becomes the focus again (default: 135 weeks )
How to read it
Vertical lines = the exact cycle milestones
Bottom labels = description of each milestone aligned to its line (keeps the chart clean)
Green background (optional) = active Profit Zone on existing bars
Red background (optional) = optional warning zone after Profit END
HUD Panel (top-right)
The HUD gives you a fast “where are we in the cycle?” view with two modes:
Current Cycle
Shows: Halving date, Weeks since, and time remaining to Profit START / Last Call / DCA START within the current cycle.
Next Halving (Projection)
Shows: Countdown to the next enabled future halving, plus the projected weeks from today to Profit START / Last Call / DCA START after that future halving.
Future Halvings (manual)
You can manually add up to 3 future halving dates (Halving #1–#3).
This is useful for forward planning and cycle projection even before the event happens.
Enable Halving #1 / #2 / #3
Set Year / Month / Day for each
Optional: show/hide future markers & projections
Note: background zones only shade existing bars . Future projections are shown via lines/labels.
Settings overview
Show all cycles – plots every enabled cycle (historical + optional future). If disabled, only the current cycle is drawn.
Show Profit Zone background – green shading during the active profit window (current cycle only).
Show vertical markers + labels – toggles all milestone lines + labels.
Show HUD – toggles the HUD panel.
HUD Mode – switch between Current Cycle and Next Halving (Projection).
Cycle Logic – edit offsets in weeks (Profit START / Profit END / DCA START).
Optional Warning Zone – show a post-profit warning shading for a chosen number of weeks.
Have fun :)
Disclaimer
This Halving Cycles indicator is provided for informational and educational purposes only. It does not, and should not be construed as, financial, investment, or trading advice.
This indicator is an independent implementation of a time-based Bitcoin halving cycle visualization tool and is not affiliated with, or endorsed by, any third-party trading systems, strategies, protocols, or trademarked methodologies. The cycle zones, milestone markers, and countdown values displayed by this indicator are generated by a predefined set of algorithmic rules based on historical halving dates and user-defined time offsets. They do not constitute a direct recommendation to buy, sell, or hold any financial instrument or digital asset.
All trading and investing in financial markets involves a substantial risk of loss. You may lose part or all of your invested capital. Past performance does not guarantee future results. This indicator highlights historical and projected time-based market cycles and may produce false, lagging, incomplete, or misleading signals. Market behavior is influenced by many external factors and can deviate significantly from historical patterns or expectations.
The creator DotGain assumes no responsibility or liability for any financial losses, damages, or decisions made based on the use of this indicator or the information it provides. You are solely responsible for your own trading and investment decisions. Always conduct your own research (DYOR), use proper risk management, validate insights with additional tools or analysis, and consider your personal financial situation and risk tolerance before making any financial decision.
Monte Carlo Simulation BandsMonte Carlo Simulation v2.4.2
Plots a one-bar-ahead price distribution band built from many simulated paths. The green band shows empirical percentiles of simulated final prices—these are distribution bounds, not a confidence interval of the mean.
What It Does
Simulates many one-bar price paths using a directional random walk with volatility scaling (uniform shocks, not Gaussian GBM).
Plots Mean Forecast, Median Forecast, and configurable percentile bounds (default 5th/95th).
Optional rolling HTF-days mean line (yellow) for trend context.
Optional labels and forward projection lines.
Alerts when the confirmed close breaks above or below the percentile band.
Non-Repainting & HTF Behavior (Fail-Closed)
All calculations are gated to confirmed bars only via explicit no_repaint_ok gate (barstate.isconfirmed).
If you select an HTF Resolution, the script uses a strict request.security(..., lookahead_off, gaps_off) pipeline.
If HTF data is unavailable, outputs are na—no silent fallback to chart timeframe.
A separate "HTF Alignment (lagged)" plot shows the prior HTF close (htf_price ) as visual proof of no look-ahead.
Volatility Source & Scaling
If "Use Historical Volatility" is enabled, volatility is estimated from log returns on the selected resolution (HTF if set, otherwise chart).
Annualization adapts to session type:
Equities: 6.5 hours/day, 252 trading days/year
Crypto: 24 hours/day, 365 days/year
Substeps increase path smoothness within the same one-bar horizon—they do not extend the forecast to multiple bars.
Key Inputs
• Prob Up / Prob Down — Must satisfy Prob Up + Prob Down ≤ 1.0. If violated, simulation is skipped and table shows "✗ PROB>1".
• # Simulations / # Substeps — Higher = smoother/more stable, but slower. Default 100×100 is a good balance.
• Lower/Upper Percentile — Define the band width (e.g., 5 and 95 for a 90% distribution band).
• Run On Last Bar Only — Performance mode (recommended). Skips historical computation; updates on each new confirmed bar.
• Resolution (HTF) — Leave blank for chart timeframe, or set to Weekly/Monthly for HTF-aligned simulation.
• Crypto 24/7 Session? — Enable for crypto markets to use correct annualization (365d, 24h).
How to Use (Quickstart)
Start with defaults and keep Run On Last Bar Only = true for speed.
Set Prob Up and Prob Down so their sum ≤ 1.0 (e.g., 0.5 + 0.5 = 1.0 for neutral).
Enable "Use Historical Volatility" and set a Volatility Lookback (e.g., 20 bars) for data-driven vol.
Set Resolution (HTF) if you want the model to run on higher timeframe data (e.g., 1W). Expect updates only when a new HTF interval starts.
Choose percentiles (e.g., 5 and 95) to define your distribution band width.
Enable alerts for "Price Above Upper Percentile" or "Price Below Lower Percentile" to get notified of breakouts.
Limitations & Disclosures
Forecast horizon is one bar only. Substeps do not create a multi-bar forecast.
Model uses uniform shocks with direction chosen from Prob Up/Down. This is not Geometric Brownian Motion (GBM) and is not calibrated to any option-implied distribution.
Bounds are percentiles of final simulated prices, not a statistical confidence interval of the mean.
HTF mode updates at the start of a new HTF interval (first chart bar where the HTF timestamp changes), so the band appears "step-like" in realtime.
Historical volatility requires enough bars for the selected lookback; until then, values may be na.
Performance depends on Sims × Substeps; extreme settings (e.g., 500×500) can be slow.
This indicator does not predict direction—it shows a probabilistic range based on your inputs.
Blockcircle MRS - Macroeconomic Risk ScorecardOVERVIEW
This BLOCKCIRCLE MACROECONOMIC RISK SCORECARD (MRS) is a real-time economic analysis dashboard that tracks over 30 key metrics and proprietary indicators across GDP, employment, income, consumption, industrial production, yield curves, and credit markets. It consolidates data that would otherwise require monitoring dozens of separate sources into a single, actionable interface.
The core purpose is straightforward: know when conditions support risk-taking and when caution is warranted. Whether you lean aggressive or conservative, this tool gives you the data foundation to adjust your positioning across different timeframes. It delivers both daily short-term insights and a long-term perspective in one view.
WHAT MAKES IT ORIGINAL AND DIFFERENT
This indicator represents years of research into recession forecasting and macroeconomic analysis, distilled into a unified system that goes far beyond what standard economic dashboards provide.
Seven distinct recession risk methodologies run simultaneously: M1 Proprietary Composite, M2 GDP 2-Quarter Rule, M3 Yield Curve Inversion, M4 Sahm Rule, M5 Credit Stress Index, M6 Leading Indicators, and M7 Combined Method
The M1 model is a proprietary scoring system developed through extensive backtesting against historical recession data, weighting GDP, GDI, employment, income, consumption, industrial production, and delinquency data through a calibrated formula
Historical percentage changes span eight distinct lookback periods (1P through 50P), allowing you to see momentum shifts that single-period comparisons miss entirely
Quantitative ratios, including Employment/Population, GDP/GDI divergence, Income/Consumption, Monetary Velocity, Industrial Momentum, and Real Interest Rate, provide context that raw numbers alone cannot deliver
Credit stress monitoring tracks delinquency acceleration across seven loan categories, catching deterioration before it shows up in headline figures
The combined risk score synthesizes all methodologies into a single weighted output with color-coded severity levels
CORE FEATURES
Unified dashboard structure with consistent columns across all sections: VALUE, 1P%, 2P%, 3P%, 5P%, 10P%, 20P%, 30P%, 50P%, TF, STATUS, and SIG
Standardized STATUS classifications provide immediate interpretation without requiring deep economic knowledge
TF column displays data frequency for each metric (3M for quarterly, M for monthly, W for weekly, D for daily)
Compact view toggles let you hide the TF column or extended period columns when you need a cleaner display
NBER recession shading overlays historical recession periods directly on the chart with optional start/end labels
Five fully customizable moving averages with selectable sources from any risk model or economic metric
Configurable alert system with multi-condition triggers for risk threshold breaches across any methodology
Everything on the scorecard is configurable to your exact needs and wants
METRICS COVERAGE
Core Recession Metrics: Real GDP, Gross Domestic Income, Total Nonfarm Payrolls, Civilian Employment, Real Personal Income, Real Personal Consumption, Industrial Production
Key Economic Indicators: Unemployment Rate, Yield Curve (10Y-2Y), M2 Money Supply, Fed Balance Sheet, Consumer Sentiment, Leading Economic Index, ISM Manufacturing PMI, Building Permits, NY Fed Recession Probability
Financial Stress Metrics: Financial Conditions Index (NFCI), High-Yield Spread, TED Spread, Corporate Spread (BAA-AAA), VIX, Initial Jobless Claims
Delinquency Tracking: All Loans, Consumer Loans, Credit Card, Business Loans, Residential RE, Single Family Residential, Commercial RE
Quantitative Ratios: Employment/Population Ratio, GDP/GDI Ratio, Income/Consumption Ratio, Monetary Velocity, Industrial Momentum, Real Interest Rate
USE CASES
Assess economic and monetary policy impacts before making asset allocation decisions
Monitor recessionary risk through multiple independent methodologies and the unified composite score
Track credit stress as an early warning system before problems appear in broader markets
Validate or challenge economic narratives circulating in financial media against objective, sourced data
Time entries and exits in risk assets based on macro regime identification
Compare current conditions against historical precedents using the multi-period change analysis
TECHNICAL SPECIFICATIONS
Optimized data architecture reduced script complexity from 40+ request.security() calls to 38 highly efficient calls
Function-based table rendering dramatically improves execution speed and reduces loading times
Chart labels display full metric names with MA configuration details for immediate identification
Modular dashboard sections can be individually enabled or disabled based on your focus areas
Risk threshold levels are fully adjustable to match your personal risk tolerance
You can setup precise alerts to be notified when specific recessionary risk models are forecasting a potential recession in the horizon, this can be tailored to your customized needs
Monte Carlo Option Forecast [Lite]Turn your chart into a Quantitative Trading Terminal.
Forget linear predictions. The market is driven by probability. Montecarlo Option Forecast leverages 2,000+ Monte Carlo simulations to model future price paths, assess volatility, and calculate the "fair" mathematical value of options directly on your chart.
This tool doesn't just tell you where the price might go—it visualizes the probability distribution (The Fan) and the most likely deterministic path (The Neon Line) to help you find a mathematical edge.
🔥 Key Features
1. 🧠 Smart Simulation Engine
3 Calculation Modes:
Historical (Raw): For trending assets (uses past returns).
Stationary (Flat): For ranging markets (random walk).
Ensemble: A balanced 50/50 mix.
Neon Line: A dynamic forecast line that visualizes the projected path based on your settings.
2. 🧲 Magnet Mechanics (Mean Reversion)
Markets tend to return to the mean. Adjust the Magnet Strength to simulate trends decaying or prices pulling back to fair value over time.
3. 📊 Option Desk (ATM Edition)
An embedded terminal that calculates theoretical option values (Call/Put) based on your simulations.
MC vs. Black-Scholes: Compares your custom Monte Carlo valuation against standard models to find edge.
Kelly Criterion: Suggests position sizing based on probability.
Smart Markers: ⌖ (Spot Price) and ★ (Forecast Target).
Note: This Lite edition is optimized for At-The-Money (ATM) analysis. Deep OTM strikes and wide steps are available in the PRO version.
4. 🏆 The Judge (Backtester)
The script constantly "judges" itself by running backtests on past data. It displays honest accuracy stats (Win Rate, Error %, Drift) to help you calibrate the model.
LSE Chrono-Behavior Forecast🎯 ANTICIPATE THE MOVE. TRADE THE EDGE.
The Chrono-Behavior Forecast is a revolutionary forward-looking indicator that projects future market behavior and reversal points directly onto your chart. Unlike traditional indicators that are based on lagging data, this indicator shows you what's coming next.
📊 WHAT MAKES THIS DIFFERENT
While most indicators look backward at historical price action, the Chrono-Behavior Forecast does the opposite: it plots a non-repainting forecasted line that projects market timing, behavior, and reversals for up to 24 hours into the future.
All forecasts are generated BEFORE market open - no curve fitting, no hindsight bias, no repainting. What you see is pure forward-looking analysis.
⚡ KEY FEATURES
• Non-Repainting Forecasts - The forecasted line never changes after it's plotted. What you see is what you get.
• Any Asset Class - Works on stocks, futures, forex, crypto, commodities - any tradable instrument. Place this indicator on any chart and see our forecasted line plotted right on it.
• Any Intraday Timeframe - Optimized for day trading timeframes from 1 second to 6 hours. Use shorter timeframes (1-5 min) for quick scalps, longer timeframes (15 min - 6 hr) for more deliberate entries.
• Battle-Tested - We trade these same indicators ourselves. Your success is our success.
🔬 THE METHODOLOGY
The Chrono-Behavior Forecast is the culmination of over two decades of intensive research into the hidden mechanics of market movement. We've moved beyond standard technical analysis to uncover the specific, repeatable forces that drive market behavior.
Market Energy Analysis - Our proprietary algorithm analyzes decades of historical data to decode how global exchanges influence specific asset classes over time.
Energy Forecasting - We forecast the future energy that markets are expected to exert, mapped to precise time windows throughout your trading session.
Behavioral Footprints - By mapping these "behavioral footprints" against time, we predict market impacts and reversals well before they manifest.
📈 HOW TO USE
• Identify Future Reversal Points - Use the forecasted peaks and valleys to anticipate market turning points.
• Time Your Entries & Exits - The forecast gives you the foresight to time your trades with confidence.
• Combine Multiple Markets - Layer multiple Chrono-Behavior Forecasts on a single chart to see how competing market forces converge to drive price action.
⚠️ IMPORTANT NOTES
• Best used for intraday trading on timeframes between 1 second and 6 hours.
• As with day trading in general, exercise caution during high market volatility events (e.g., NFP, FOMC announcements) and the first few minutes after US market open.
• We have forecasting indicators for 28 global exchanges including NYSE, NASDAQ, CME, LSE, TSE, SSE, and more - that can be applied to ANY chart.
🌐 CURRENTLY AVAILABLE EXCHANGES
USA: NYSE, NASDAQ, CME, ICE, CBOE
UK: LSE
Europe: Euronext, Deutsche Börse, Swiss Exchange, Nasdaq Nordic, Spanish Exchanges
Asia: TSE, SSE, SZSE, HKEX, NSE India, TWSE, KRX, SGX, SET, Bursa Malaysia, IDX
Other: TSX, TASI, ASX, JSE, ADX, B3
Custom exchange forecast development available upon request.
MTF Probability Predictor v1 (Directional + Market State)This indicator is designed to generate high-confidence market bias by combining price action, chart structure, momentum, divergence analysis, ATR, and VWAP-based volatility assessment.
Instead of providing binary signals, the indicator presents a probability-based decision framework, displaying BUY / SELL confidence percentages in real time. This allows traders to assess signal quality, market strength, and trade suitability before taking a position.
Orbedud_Rebourne V2Orbedud Rebourne Trading Indicator
A fully adaptive, multi-timeframe trend detection system.
The Orbedud Rebourne indicator analyzes market dynamics across multiple perspectives simultaneously, providing clear directional signals without requiring manual parameter optimization. The system automatically adapts to changing market conditions and different timeframes, making it suitable for futures, stocks, and forex trading.
Key Features:
Self-adapting to any market or timeframe
Consensus-based signals for high-confidence entries
Normalized strength meter (-100 to +100) for objective trend measurement
Visual trend lines with color-coded market states
Built-in signal filtering to reduce false entries
Outputs:
Master trend line with support/resistance levels
Entry signals with confirmation markers
Market strength visualization
Session level tracking
Benner Cycle (TT314)This indicator replicates the famous "Benner Cycle," first published by Samuel Benner in 1875. Originally based on commodities prices.Includes Benner’s original descriptions for zones A, B, and C directly on the chart.
i.redd.it
Yearly Projection ExplorerThis indicator helps you understand how the current market period has behaved historically by overlaying the same date window from previous years and projecting it forward from today’s price.
The script works the following way:
Aligns past years to today’s calendar date
Normalizes all paths to the last close at the start
Projects historical performance X bars forward
Displays each year as a separate performance path
Calculates and plots the mean (average) projection for quick reference
🔧 How It Works
Number of Years: choose how many past years to include (e.g. last 10, 20, or 25 years)
Projection Length: choose how many bars (days) ahead to project
Each line shows how the market moved during the same period in a specific year
Labels show the year and total return at the projection end
The mean line highlights the average historical outcome
🧠 Why This Is Useful
Identify seasonal tendencies
Compare current price action to historical analogs
Visualize best / worst historical outcomes
Set realistic expectations for short-term moves
Add context to discretionary or systematic decisions
This tool does not predict the future, but it provides a powerful historical framework to assess what has been typical, rare, or extreme for the current market window.
⚠️ Notes
Script works on timenow variable for now, and you might see unexpected periods if today is a day off.
Results depend on the selected timeframe and instrument
Past performance is not a guarantee of future results
Designed for analysis and context, not standalone signals
RSI Forecast [QuantAlgo]🟢 Overview
While standard RSI excels at measuring current momentum and identifying overbought or oversold conditions, it only reflects what has already happened in the market. The RSI Forecast indicator builds upon this foundation by projecting potential RSI trajectories into future bars, giving traders a framework to consider where momentum might head next. Three analytical models power these projections: a market structure approach that reads swing highs and lows, a volume analysis method that weighs accumulation and distribution patterns, and a linear regression model that extrapolates recent trend behavior. Each model processes market data differently, allowing traders to choose the approach that best fits their analytical style and the asset they're trading.
🟢 How It Works
At its foundation, the indicator calculates RSI using the standard methodology: comparing average upward price movements against average downward movements over a specified period, producing an oscillator that ranges from 0 to 100. Traders can apply an optional signal line using various moving average types (e.g., SMA, EMA, SMMA/RMA, WMA, or VWMA), and when SMA smoothing is selected, Bollinger Bands can be added to visualize RSI volatility ranges.
The forecasting mechanism operates by first estimating future price levels using the chosen projection method. These estimated prices then pass through a simulated RSI engine that mirrors the actual indicator's mathematics. The simulation updates the internal gain and loss averages bar by bar, applying the same RMA smoothing that powers real RSI calculations, to produce authentic projected values.
Since RSI characteristically moves in waves rather than straight lines, the projection system incorporates dynamic oscillation. This draws from stored patterns of recent RSI movements, factors in the tendency for RSI to pull back from extreme readings, and applies mathematical wave functions tied to current momentum conditions. The Oscillation Intensity control lets traders adjust how much waviness appears in projections. Signal line (RSI-based MA) projections follow the same logic, advancing the chosen moving average type forward using its proper mathematical formula. The complete system generates 15 bars of projected RSI and signal line values, displayed as dashed lines extending beyond current price action.
🟢 Key Features
1. Market Structure Model
This projection method reads price action through swing point analysis. It scans for pivot highs and pivot lows within a defined lookback range, then evaluates whether the market is building bullish patterns (successive higher highs and higher lows) or bearish patterns (successive lower highs and lower lows). The algorithm recognizes structural shifts when price violates previous swing levels in either direction.
Price projections under this model factor in proximity to key swing levels and overall trend strength, measured by tallying trend-confirming swings over recent history. When bullish structure prevails and price hovers near support, upward price bias enters the projection, pushing forecasted RSI higher. Bearish structure near resistance creates the opposite effect. The model scales its projections using ATR to keep them proportional to current volatility conditions.
▶ Practical Implications for Traders:
Aligns well with traders who focus on support, resistance, and swing-based entries
Provides context for where RSI might travel as price interacts with structural levels
Tends to perform better when markets display clear directional swings
May produce less useful output during consolidation phases with overlapping swings
Offers early visualization of potential divergence setups
Swing traders can use structure-based projections to time entries around key pivot zones
Position traders could benefit from the trend strength component when holding through larger moves
On lower timeframes, it helps scalpers identify micro-structure shifts for quick momentum plays
Useful for mapping out potential RSI behavior around breakout and breakdown levels
Day traders can combine structural projections with session highs and lows for intraday context
2. Volume-Weighted Model
This method blends multiple volume indicators to inform its price projections. It tracks On-Balance Volume to gauge cumulative buying and selling pressure, monitors the Accumulation/Distribution Line to assess where price closes relative to its range on each bar, and calculates volume-weighted returns to give heavier influence to high-volume price movements. The model examines the directional slope of these metrics to assess whether volume confirms or contradicts price direction.
Unusually high volume bars receive special attention, with their directional bias factored into projections. When all volume metrics point the same direction, the model produces more aggressive price forecasts and consequently stronger RSI movements. Conflicting volume signals lead to more muted projections, suggesting RSI may move sideways rather than trending.
▶ Practical Implications for Traders:
Suited for traders who incorporate volume confirmation into their analysis
Works best with instruments that report accurate, meaningful volume data
Useful for identifying situations where momentum lacks volume support
Less applicable to instruments with sparse or unreliable volume information
Scalpers on liquid markets can spot volume-backed momentum for quick entries and exits
Helps intraday traders distinguish between genuine moves and low-volume fakeouts
Position traders can assess whether institutional participation supports longer-term trends
Effective during news events or market opens when volume spikes often drive directional moves
Swing traders can use volume divergence in projections to anticipate potential reversals
3. Linear Regression Model
The simplest of the three methods, linear regression fits a straight line through recent price data using least-squares mathematics and extends that line forward. These projected prices then generate corresponding RSI forecasts. This creates a clean momentum projection without conditional logic or interpretation of market characteristics. The forecast simply asks: if the recent price trend continues at its current rate of change, where would RSI be in the coming bars?
▶ Practical Implications for Traders:
Delivers a clean, mathematically neutral projection baseline
Functions well during sustained, orderly trends
Involves fewer parameters and produces consistent, reproducible output
Responds more slowly when trend direction shifts
Works best in trending environments rather than ranging markets
Ideal for position traders who want to ride established trends
Useful for swing traders to gauge trend exhaustion when actual RSI deviates from linear projections
Scalpers can use the smooth output as a reference point to measure short-term momentum deviations
Effective baseline for comparing against structure or volume models to measure market complexity
Works particularly well on higher timeframes where trends develop more gradually
🟢 Universal Applications Across All Models
Regardless of which forecasting method you select, the indicator projects future RSI positions that may help with:
▶ Overbought/Oversold Planning: See whether RSI trajectories point toward extreme zones, giving you time to prepare responses before conditions develop
▶ Entry and Exit Timing: Factor projected RSI levels into your timing decisions for opening or closing positions
▶ Crossover Anticipation: Watch for projected crossings between RSI and its signal line (RSI-based MA) that might indicate upcoming momentum shifts
▶ Mean Reversion Context: When RSI sits at extremes, projections can illustrate potential paths back toward the midline
▶ Momentum Evaluation: Assess whether current directional strength appears likely to continue or fade based on projection direction
▶ Divergence Awareness: Use forecast trajectories alongside price action to spot potential divergence formations earlier
▶ Comparative Analysis: Run different projection methods and note where they agree or disagree, using alignment as an additional filter, for instance
▶ Multi-Timeframe Context: Compare RSI projections across different timeframes to identify alignment or conflict in momentum outlook
▶ Trade Management: Reference projected RSI levels when adjusting stops, scaling positions, or setting profit targets
▶ Rule-Based Systems: Incorporate projected RSI conditions into systematic trading approaches for more forward-looking signal generation
Note: It is essential to recognize that these forecasts derive from mathematical analysis of recent price behavior. Markets are dynamic environments shaped by innumerable factors that no technical tool can fully capture or foresee. The projected RSI values represent potential scenarios for how momentum might develop, and actual readings can take different paths than those visualized. Historical tendencies and past patterns offer no guarantee of future behavior. Consider these projections as one element within a comprehensive trading approach that encompasses disciplined risk management, appropriate position sizing, and diverse analytical methods. The true benefit lies not in expecting precise forecasts but in developing a forward-thinking perspective on possible market conditions and planning your responses accordingly.
Bollinger Bands Forecast with Signals (Zeiierman)█ Overview
Bollinger Bands Forecast with Signals (Zeiierman) extends classic Bollinger Bands into a forward-looking framework. Instead of only showing where volatility has been, it projects where the basis (midline) and band width are likely to drift next, based on recent trend and volatility behavior.
The projection is built from the measured slopes of the Bollinger basis, the standard deviation (or ATR, depending on the mode), and a volatility “breathing” component. On top of that, the script includes an optional projected price path that can be blended with a deterministic random walk, plus rejection signals to highlight failed band breaks.
█ How It Works
⚪ Bollinger Core
The script first computes standard Bollinger Bands using the selected Source, Length, and Multiplier:
Basis = SMA(Source, Length)
Band width = Multiplier × StDev(Source, Length)
Upper/Lower = Basis ± Width
This remains the “live” (non-forecast) structure on the chart.
⚪ Trend & Volatility Slope Estimation
To project forward, the indicator measures directional drift and volatility drift using linear regression differences:
Basis slope from the Bollinger basis
StDev slope from the Bollinger deviation
ATR slope for ATR-based projection mode
These slopes drive the forecast bands forward, reflecting the market’s recent directional and volatility regime.
⚪ Projection Engine (Forecast Bands)
At the last bar, the indicator draws projected basis, upper, and lower lines out to Forecast Bars. The projected basis can be:
Trend (straight linear projection)
Curved (ease-in/out transition toward projected endpoints)
Smoothed (extra smoothing on projected basis/width)
⚪ Price Path Projection + Optional Random Walk
In addition to projecting the bands, the script can draw a price forecast path made of a small number of zigzag swings.
Each swing targets a point offset from the projected basis by a multiple of the projected half-width (“width units”).
Decay gradually reduces swing size as the forecast deepens.
The Optional Random Walk Blend adds a deterministic drift component to the zigzag path. It’s not true randomness; it’s a stable pseudo-random sequence, so the drawing doesn’t jump around on refresh, while still adding “natural” variation.
⚪ Rejection Signals
Signals are based on failed attempts to break a band:
Bear Signal (Down): price tries to push above the upper band, then falls back inside, while still closing above the basis.
Bull Signal (Up): price tries to push below the lower band, then returns back inside, while still closing below the basis.
█ How to Use
⚪ Forward Support/Resistance Corridors
Treat the projected upper/lower bands as a future volatility envelope, not a guarantee:
The upper projection ≈ is likely a resistance level if the regime persists
The lower projection ≈ is likely a support level if the regime persists
Best used for trade planning, targets, and “where price could travel” under similar conditions.
⚪ Regime Read: Trend + Volatility
The projection shape is informative:
Rising basis + expanding width → trend with increasing volatility (needs wider stops / more caution)
Flat basis + compressing width → contraction regime (often precedes expansion)
⚪ Signals for Mean-Reversion / Failed Breakouts
The rejection markers are useful for fade-style setups:
A Down signal near/after upper-band failure can imply rotation back toward the basis.
An Up signal near/after lower-band failure can imply snap-back toward the basis.
With MA filtering enabled, signals are constrained to align with the broader bias, helping reduce chop-driven noise.
█ Related Publications
Donchian Predictive Channel (Zeiierman)
█ Settings
⚪ Bollinger Band
Controls the live Bollinger Bands on the chart.
Source – Price used for calculations.
Length – Lookback period; higher = smoother, lower = more reactive.
Multiplier – Bandwidth; higher = wider bands, lower = tighter bands.
⚪ Forecast
Controls the forward projection of the Bollinger Bands.
Forecast Bars – How far into the future the bands are projected.
Trend Length – Lookback used to estimate trend and volatility slopes.
Forecast Band Mode – Defines projection behavior (linear, curved, breathing, ATR-based, or smoothed).
⚪ Price Forecast
Controls the projected price path inside the bands.
ZigZag Swings – Number of projected oscillations.
Amplitude – Distance from basis, measured in bandwidth units.
Decay – Shrinks swings further into the forecast.
⚪ Random-Walk
Adds controlled randomness to the price path.
Enable – Toggle random-walk influence.
Blend – Strength of randomness vs. zigzag.
Step Size – Size of random steps (band-width units).
Decay – Reduces randomness as the forecast deepens.
Seed – Changes the (stable) random sequence.
⚪ Signals
Controls rejection/mean-reversion signals.
Show Signals – Enable/disable signal markers.
MA Filter (Type/Length) – Filters signals by trend direction.
-----------------
Disclaimer
The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Future Ichimoku Cloud - HorizonIchimoku Horizon is an advanced Ichimoku indicator that projects future cloud formations and component lines, giving traders unprecedented visibility into potential support/resistance zones before they form.
1. Future Ichimoku Projections
Project Ichimoku components forward in time using simulated price evolution based on rolling Tenkan/Kijun windows
Manual forecast periods up to 125 bars (all 4 components) or 500 bars (cloud only)
Smart limit management automatically adjusts to TradingView's drawing object limits while maximizing visible projections
2. Preset & Custom Ichimoku Configurations
Choose from multiple common Ichimoku presets or fully customize your own
3. Multi-Timeframe Display & Projections
Display Ichimoku from higher/lower timeframes directly on your current timeframe chart
Automatic scaling adjusts Ichimoku periods correctly across timeframes
Intelligent handling of 24/7 markets (crypto/forex) vs traditional session-based markets
Built-in detection of problematic timeframe combinations with optional MTF cloud fetching for accuracy
Automatic notifications when future projections are unavailable due to MTF constraints
4. Tenkan & Kijun Range Windows
Visual range windows that display the exact high/low range used for Tenkan and Kijun calculations
Optional High/Low markers placed at the exact bars they occur
Optional countdown labels show how many bars remain until the current High/Low expires from the rolling window
Range windows scale up and down dynamically to match display timeframe
5. Comprehensive Alert Suite
Built-in alerts for all major Ichimoku events: TK crosses, E2E entires, Kumo breakouts, etc.
All alerts are cloud-aware and displacement-correct.
How It Works
The indicator uses the traditional Donchian channel method to calculate Ichimoku components, then extends this logic forward by simulating future price action within the calculation windows (no new highs or lows). This creates a forward-looking projection of where support and resistance zones will form.
The range display feature helps traders understand why the lines are where they are by showing the exact high/low points and countdown timers for when these points will expire from the calculation.
Who This Indicator Is For:
Ichimoku traders who want future-aware context
Multi-timeframe analysts seeking correctly aligned clouds
Traders who want to understand Tenkan/Kijun mechanics
Users who need precision without manual recalculation
Notes:
Maximum 500 drawing objects limit managed automatically
Due to Pinescript/TradingView limitations, future Tenkan/Kijun line width is only modifiable in the source code.
Smart match finder🔍 Pattern Match Finder
What It Does:
This indicator finds historical price patterns that look similar to your current price action and projects what might happen next based on what happened after those past patterns.
How It Works:
📊 Captures Current Pattern - Takes the last 30 bars (configurable) of price movement as your "current pattern"
🔎 Searches History - Scans up to 2,500 bars back looking for price patterns that moved similarly
📈 Matches by Trend - When "Same Condition" is ON, it only finds patterns that moved in the same direction (bullish matches bullish, bearish matches bearish)
🎯 Quality Filter - Uses correlation (75%+ by default) to ensure matches are high quality, not random
🔮 Projects Future - Takes what happened AFTER those historical matches and draws a prediction (yellow dashed line) showing where price might go next
📊 Shows Best Match - Highlights the best matching pattern with cyan vertical lines and overlays it on your current chart
Key Features:
✅ Trend-aware matching - Finds patterns with same market direction
✅ Quality scoring - Shows correlation % and match quality (Excellent/Good/Fair)
✅ Visual projection - Yellow prediction line showing expected price movement
✅ Smart filtering - Adjustable correlation and distance thresholds
✅ No match alerts - Warns you when no similar patterns exist
Technical Strength:
This indicator employs advanced statistical correlation analysis combined with normalized pattern recognition algorithms, making it highly effective for identifying statistically significant price pattern repetitions with quantifiable confidence metrics.
⚠️ Important Disclaimer:
This tool is for educational and analytical purposes only. Pattern projections are based on historical data and should NOT be used as the sole basis for buy/sell decisions. Always combine with proper risk management, fundamental analysis, and other technical tools before making any trading decisions.
Bollinger Bands Forecast [QuantAlgo]🟢 Overview
Bollinger Bands are widely recognized for mapping volatility boundaries around price action, but they inherently lag behind market movement since they calculate based on completed bars. The Bollinger Bands Forecast addresses this limitation by adding a predictive layer that attempts to project where the upper band, lower band, and basis line might position in the future. The indicator provides three unique analytical models for generating these projections: one examines swing structure and breakout patterns, another integrates volume flow and accumulation metrics, while the third applies statistical trend fitting. Traders can select whichever methodology aligns with their market view or trading style to gain visibility into potential future volatility zones that could inform position planning, risk management, and timing decisions across various asset classes and timeframes.
🟢 How It Works
The core calculation begins with traditional Bollinger Bands: a moving average basis line (configurable as SMA, EMA, SMMA/RMA, WMA, or VWMA) with upper and lower bands positioned at a specified number of standard deviations away. The forecasting extension works by first generating predicted price values for upcoming bars using the selected method. These projected prices then feed into a rolling calculation that simulates how the basis line would update bar by bar, respecting the mathematical properties of the chosen moving average type. As each new forecasted price enters the calculation window, the oldest historical price drops out, mimicking the natural progression of the moving average. The system recalculates standard deviation across this evolving price window and applies the multiplier to determine where upper and lower bands would theoretically sit. This process repeats for each of the forecasted bars, creating a connected chain of potential future band positions that render as dashed lines on the chart.
🟢 Key Features
1. Market Structure Model
This forecasting approach interprets price through the lens of swing analysis and structural patterns. The algorithm identifies pivot highs and lows across a definable lookback window, then tracks whether price is forming higher highs and higher lows (bullish structure) or lower highs and lower lows (bearish structure). The system looks for break of structure (BOS) when price pushes beyond a previous swing point in the trending direction, or change of character (CHoCH) when price starts creating opposing swing patterns.
When projecting future prices, the model considers current distance from recent swing levels and the strength of the established trend (measured by counting higher highs versus lower lows). If bullish structure dominates and price sits near a swing low, the forecast biases upward. Conversely, bearish structure near a swing high produces downward bias. ATR scaling ensures the projection magnitude relates to actual market volatility.
Practical Implications for Traders:
Useful when you trade based on swing points and structural breaks
The Structure Influence slider (0 to 1) lets you dial in how much weight structure analysis carries versus pure trend
Helps visualize where bands could form around key structural levels you're watching
Works better in trending conditions where structure patterns are clearer
Might be less effective in choppy, sideways markets without defined swings
2. Volume-Weighted Model
This method attempts to incorporate volume flow into the price forecast. It combines three volume-based metrics: On-Balance Volume (OBV) to track cumulative buying/selling pressure, the Accumulation/Distribution Line to measure money flow, and volume-weighted price changes to emphasize moves that occur on high volume. The algorithm calculates the slope of these indicators to determine if volume is confirming price direction or diverging from it.
Volume spikes above a configurable threshold are flagged as potentially significant, with the direction of the spike (whether it occurred on an up bar or down bar) influencing the forecast. When OBV, A/D Line, and volume momentum all align in the same direction, the model projects stronger moves. When they conflict or show weak volume support, the forecast becomes more conservative.
Practical Implications for Traders:
Relevant if you use volume analysis to confirm price moves
More meaningful in markets with reliable volume data
The Volume Influence parameter (0 to 1) controls how much volume factors into the projection
Volume Spike Threshold adjusts sensitivity to what constitutes unusual volume
Helps spot scenarios where volume doesn't support a move, suggesting possible consolidation
Might be less effective in low-liquidity instruments or markets where volume reporting is unreliable
3. Linear Regression Model
The simplest of the three methods, linear regression fits a straight line through recent price data using least-squares mathematics and extends that line forward. This creates a clean trend projection without conditional logic or interpretation of market characteristics. The forecast simply asks: if the recent trend continues at its current rate of change, where would price be in 10 or 20 bars?
Practical Implications for traders:
Provides a neutral, mathematical baseline for comparison
Works well when trends are steady and consistent
Can be useful for backtesting since results are deterministic
Requires minimal configuration beyond lookback period
Might not adapt to changing market conditions as dynamically as the other methods
Best suited for trending markets rather than ranging or volatile conditions
🟢 Universal Applications Across All Models
Regardless of which forecasting method you select, the indicator projects future Bollinger Band positions that may help with:
▶ Pre-planning entries and exits: See where potential support (lower band) or resistance (upper band) might develop before price gets there
▶ Volatility context: Observe whether forecasted bands are widening (suggesting potential volatility expansion) or narrowing (possible compression or squeeze setup)
▶ Target setting: Reference projected band levels when determining profit targets or stop placement
▶ Mean reversion scenarios: Visualize potential paths back toward the basis line when price extends to a band extreme
▶ Breakout anticipation: Consider where upper or lower bands might sit if price begins a strong directional move
▶ Strategy development: Build trading rules around forecasted band interactions, such as entering when price is projected to return to the basis or exit when forecasts show band expansion
▶ Method comparison: Switch between the three forecasting models to see if they agree or diverge, potentially using consensus as a confidence filter
It's critical to understand that these forecasts are projections based on recent market behavior. Markets are complex systems influenced by countless factors that cannot be captured in a technical calculation or predicted perfectly. The forecasted bands represent one possible scenario of how volatility might unfold, so actual price action may still diverge from these projections. Past performance and historical patterns provide no assurance of future results. Use these forecasts as one input within a broader trading framework that includes proper risk management, position sizing, and multiple forms of analysis. The value lies not in prediction accuracy but in helping you think probabilistically about potential market states and plan accordingly.
Stochastic RSI Forecast [QuantAlgo]🟢 Overview
The Stochastic RSI Forecast extends the classic momentum oscillator by projecting potential future K and D line values up to 10 bars ahead. Unlike traditional indicators that only reflect historical price action, this indicator uses three proprietary forecasting models, each operating on different market data inputs (price structure, volume metrics, or linear trend), to explore potential price paths. This unique approach allows traders to form probabilistic expectations about future momentum states and incorporate these projections into both discretionary and algorithmic trading and/or analysis.
🟢 How It Works
The indicator operates through a multi-stage calculation process that extends the RSI-to-Stochastic chain forward in time. First, it generates potential future price values using one of three selectable forecasting methods, each analyzing different market dimensions (structure, volume, or trend). These projected prices are then processed through an iterative RSI calculation that maintains continuity with historical gain/loss averages, producing forecasted RSI values. Finally, the system applies the full stochastic transformation (calculating the position of each forecasted RSI within its range, smoothing with K and D periods) to project potential future oscillator values.
The forecasting models adapt to market conditions by analyzing configurable lookback periods and recalculating projections on every bar update. The implementation preserves the mathematical properties of the underlying RSI calculation while extrapolating momentum trajectories, creating visual continuity between historical and forecasted values displayed as semi-transparent dashed lines extending beyond the current bar.
🟢 Key Features
1. Market Structure Model
This algorithm applies price action analysis by tracking break of structure (BOS) and change of character (CHoCH) patterns to identify potential order flow direction. The system detects swing highs and lows using configurable pivot lengths, then analyzes sequences of higher highs or lower lows to determine bullish or bearish structure bias. When price approaches recent swing points, the forecast projects moves in alignment with the established structure, scaled by ATR (Average True Range) for volatility adjustment.
Potential Benefits for Traders:
Explores potential momentum continuation scenarios during established trends
Identifies areas where structure changes might influence momentum
Could be useful for swing traders and position traders who incorporate structure-based analysis
The Structure Influence parameter (0-1 scale) allows blending between pure trend following and structure-weighted forecasts
Helps visualize potential trend exhaustion through weakening structure patterns
2. Volume-Weighted Model
This model analyzes volume patterns by combining On-Balance Volume (OBV), Accumulation/Distribution Line, and volume-weighted price returns to assess potential capital flow. The algorithm calculates directional volume momentum and identifies volume spikes above customizable thresholds to determine accumulation or distribution phases. When volume indicators align directionally, the forecast projects stronger potential moves; when volume diverges from price trends, it suggests possible reversals or consolidation.
Potential Benefits for Traders:
Incorporates volume analysis into momentum forecasting
Attempts to filter price action by volume support or lack thereof
Could be more relevant in markets where volume data is reliable (equities, crypto, major forex pairs)
Volume Influence parameter (0-1 scale) enables adaptation to different market liquidity profiles
Highlights volume climax patterns that sometimes precede trend changes
Could be valuable for traders who incorporate volume confirmation in their analysis
3. Linear Regression Model
This mathematical approach applies least-squares regression fitting to project price trends based on recent price data. Unlike the conditional logic of the other methods, linear regression provides straightforward trend extrapolation based on the best-fit line through the lookback period.
Potential Benefits for Traders:
Delivers consistent, reproducible forecasts based on statistical principles
Works better in trending markets with clear directional bias
Useful for systematic traders building quantitative strategies requiring stable inputs
Minimal parameter sensitivity (primarily controlled by lookback period)
Computationally efficient with fast recalculation on every bar
Serves as a baseline to compare against the more complex structure and volume methods
🟢 Universal Applications Across All Models
Each forecasting method projects potential future stochastic RSI values (K and D lines), which traders can use to:
▶ Anticipate potential crossovers: Visualize possible K/D crosses several bars ahead
▶ Explore overbought/oversold scenarios: Forecast when momentum might return from extreme zones
▶ Assess divergences: Evaluate how oscillator divergences might develop
▶ Inform entry timing: Consider potential points along the forecasted momentum curve for trade entry
▶ Develop systematic strategies: Build rules based on forecasted crossovers, slope changes, or threshold levels
▶ Adapt to market conditions: Switch between methods based on current market character (trending vs range-bound, high vs low volume)
In short, the indicator's flexibility allows traders to combine forecasting projections with traditional stochastic signals, using historical K/D for immediate reference while considering forecasted values for planning and analysis. As with all technical analysis tools, the forecasts represent one possible scenario among many and should be used as part of a broader trading methodology rather than as standalone signals.
NeuroSwarm ETH — Crowd vs Experts Forecast TrackerEnglish:
NeuroSwarm — Crowd vs Experts Forecast Tracker (ETH)
This indicator visualizes monthly forecast data collected from two independent groups:
Crowd – a large sample of retail participants
Experts – a curated group of analysts and experienced market participants
For each month, the indicator plots the following values as horizontal levels on the price chart:
Median forecast (Crowd)
Average forecast (Crowd)
Median forecast (Experts)
Average forecast (Experts)
Shaded zones highlighting the difference between median and mean
All values are fixed for each month and stay unchanged historically.
This allows traders to analyze sentiment dynamics and compare how expectations from both groups align or diverge from actual price action.
Purpose:
This tool is intended for sentiment visualization and analytical insight — it does not generate trading signals.
Its main goal is to compare collective expectations of retail traders vs experts across time.
Data source:
All forecasts come from monthly surveys conducted within the NeuroSwarm project between the 1st and 5th day of each month.
Interface notice:
The script's UI may contain non-English labels for convenience, but a full English documentation is provided here in compliance with TradingView rules.
Русская версия:
NeuroSwarm — Мудрость Толпы vs Эксперты (ETH)
Индикатор отображает ежемесячные прогнозы двух групп:
Толпа: медиана и средняя прогнозов
Эксперты: медиана и средняя прогнозов
Значения фиксируются для каждого месяца и показываются горизонтальными уровнями.
Заливка отображает диапазон между медианой и средней, что упрощает визуальное сравнение настроений.
Это аналитический инструмент для визуализации настроений — не торговая стратегия.
Все данные берутся из ежемесячных опросов проекта NeuroSwarm.
NeuroSwarm BTC — Crowd vs Experts Forecast TrackerEnglish:
NeuroSwarm — Crowd vs Experts Forecast Tracker (BTC)
This indicator visualizes monthly forecasts collected from two independent groups:
Crowd – a large sample of retail traders
Experts – a smaller, curated group of analysts and experienced market participants
For each month, the following values are displayed as horizontal levels on the chart:
Median forecast of the Crowd
Average forecast of the Crowd
Median forecast of Experts
Average forecast of Experts
Shaded zones showing the range between median and mean
The values remain fixed throughout each month. This allows traders to compare sentiment dynamics between groups and see how expectations evolve relative to actual market movement.
Purpose:
This indicator is designed for sentiment analysis — NOT for generating trading signals.
It helps identify divergences between retail expectations and expert forecasts, which can be informative during trend transitions.
Data source:
All values come from monthly surveys conducted within the NeuroSwarm project (1–5 of every month).
Crowd and Expert groups are collected separately to avoid bias and to preserve independent aggregation.
Interface language note:
The indicator’s interface may contain non-English labels for ease of use, but full English documentation is provided here in compliance with TradingView House Rules.
Русская версия (optional, allowed only AFTER English):
NeuroSwarm — Мудрость Толпы vs Эксперты (BTC)
Индикатор показывает ежемесячные прогнозы двух групп:
Толпа: медиана и средняя прогнозов
Эксперты: медиана и средняя прогнозов
Значения фиксируются на весь месяц и отображаются на графике горизонтальными уровнями.
Заливка показывает диапазон между медианой и средней.
Цель индикатора — визуализировать настроение толпы и экспертов и сравнить его с реальным движением цены.
Это аналитический инструмент, а не торговая стратегия.
Данные берутся из ежемесячных опросов (1–5 числа), проводимых в рамках проекта NeuroSwarm.
Auto Seasonality Scanner by Novatrix CapitalThe Auto Seasonality Scanner analyzes historical daily price data to identify recurring seasonal patterns in the market. It highlights periods over the last 10 years where certain price movements have historically occurred. This indicator is designed for the DAILY (1D) timeframe only.
Key Features:
Visualizes historical entry and exit points for Long and Short patterns using vertical lines.
Option to exclude specific years (e.g., 2020) from the analysis.
Optional filter by US election cycles.
Calculates average returns, win rates, trade lengths, and number of trades for each pattern.
Displays results in a customizable table with color-coded Long and Short patterns.
This tool is for educational and informational purposes only. It provides a visual guide to potential recurring seasonal trends and does not constitute financial advice or trading recommendations.
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful
Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions.
⯁ Real-Time RSI Forecasting
For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions.
⯁ Highly Customizable
The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy.
⯁ Innovative and Science-Based
This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages.
⯁ Scientific Foundation: Linear Regression
Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is:
y = β₀ + β₁x + ε
where:
y = predicted variable (e.g., future RSI value)
x = explanatory variable (e.g., bar index or time)
β₀ = intercept (value of y when x = 0)
β₁ = slope (rate of change of y relative to x)
ε = random error term
The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends.
⯁ Least Squares Estimation
To minimize the error between predicted and observed values, we use the formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Linear Regression in Machine Learning
Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension.
⯁ Visual Interpretation
Imagine a time series of RSI values like this:
Time →
RSI →
The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior.
⯁ Summary of Scientific Concepts Used
Linear Regression: Models relationships between variables using a straight line.
Least Squares: Minimizes squared prediction errors for optimal fit.
Time-Series Forecasting: Predicts future values from historical patterns.
Supervised Learning: Predictive modeling based on known output values.
Statistical Smoothing: Reduces noise to highlight underlying trends.
⯁ Why This Indicator Is Revolutionary
Scientifically grounded: Built on statistical and mathematical theory.
First of its kind: The first public RSI with least-squares predictive modeling.
Intelligent: Incorporates machine-learning logic into RSI interpretation.
Forward-looking: Generates predictive, not just reactive, signals.
Customizable: Exceptionally flexible for any strategic framework.
⯁ Conclusion
By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What Is RSI?
The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength.
⯁ How RSI Works
RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones:
Overbought: RSI above 70 may signal an overbought market.
Oversold: RSI below 30 may signal an oversold market.
Neutral Zone: RSI between 30 and 70, indicating no extreme condition.
These zones help identify potential price reversals and confirm trend strength.
⯁ Entry Conditions
All conditions below are fully customizable and allow detailed control over entry signal creation.
📈 BUY
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
📉 SELL
🧲 Signal Validity: Signal remains valid for X bars.
🧲 Signal Logic: Configurable using AND or OR.
🧲 RSI > Upper
🧲 RSI < Upper
🧲 RSI > Lower
🧲 RSI < Lower
🧲 RSI > Middle
🧲 RSI < Middle
🧲 RSI > MA
🧲 RSI < MA
🧲 MA > Upper
🧲 MA < Upper
🧲 MA > Lower
🧲 MA < Lower
🧲 RSI (Crossover) Upper
🧲 RSI (Crossunder) Upper
🧲 RSI (Crossover) Lower
🧲 RSI (Crossunder) Lower
🧲 RSI (Crossover) Middle
🧲 RSI (Crossunder) Middle
🧲 RSI (Crossover) MA
🧲 RSI (Crossunder) MA
🧲 MA (Crossover)Upper
🧲 MA (Crossunder)Upper
🧲 MA (Crossover) Lower
🧲 MA (Crossunder) Lower
🧲 RSI Bullish Divergence
🧲 RSI Bearish Divergence
🔮 RSI (Crossover) Forecast MA
🔮 RSI (Crossunder) Forecast MA
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
Linear Regression Forecast
Signal Validity: Keep signals active for X bars
Signal Logic: AND/OR configuration
Condition Table: BUY/SELL
Condition Labels: BUY/SELL
Chart Labels: BUY/SELL markers above price
Automation & Alerts: BUY/SELL
Background Colors: bgcolor
Fill Colors: fill
MarketSurge EPS Line [tradeviZion]MarketSurge EPS Line
EPS trend line overlay for TradingView charts, inspired by the IBD MarketSurge (formerly MarketSmith) EPS line style.
Comparison: Left side shows IBD MarketSurge EPS line as reference. Right side shows this TradingView script producing similar output with interactive tooltips. The left side image is for reference only to demonstrate similarity - it is not part of the TradingView script.
Features:
Displays EPS trend line on price charts
Uses 4-quarter earnings moving average
Shows earnings momentum over time
Works with actual, estimated, or standardized earnings data
Customizable line color and width
Interactive tooltips with detailed earnings information
Custom symbol analysis support
How to Use:
Add script to chart
EPS line appears automatically
Adjust color and width in settings if needed
Hover over line for earnings details
Settings Explained:
Display Settings:
Show EPS Line: Toggle to show or hide the EPS trend line
EPS Line Color: Choose the color for the EPS trend line and labels
EPS Line Width: Adjust the thickness of the EPS trend line (1-5 pixels)
Symbol Settings:
By default, the indicator analyzes the EPS data for the symbol currently displayed on your chart. The Custom Symbol feature allows you to:
Analyze EPS data for a different symbol without changing your chart
Compare earnings trends of related stocks or competitors
View EPS data for one symbol while analyzing price action of another
To use Custom Symbol:
Enable "Use Custom Symbol" checkbox
Click on "Custom Symbol" field to open TradingView's symbol picker
Search and select the symbol you want to analyze
The indicator will fetch and display EPS data for the selected symbol
Note: The chart will still show price action for your current symbol, but the EPS line will reflect the custom symbol's earnings data.
Data Settings:
EPS Field: Choose which earnings data source to use:
Actual Earnings: Reported earnings from company financial statements (default). Use this to analyze historical performance based on what companies actually reported.
Estimated Earnings: Analyst consensus forecasts for future quarters. Use this to see what analysts expect and compare expectations with actual results.
Standardized Earnings: Earnings adjusted for comparability across companies. Use this when comparing multiple stocks as it normalizes accounting differences.
Display Scale:
For the indicator to display correctly on the existing chart, it uses its own axis (right scale) by default. However, you can change this, but the view will not look the same. The right scale is recommended for optimal visibility as it allows the EPS line to be clearly visible alongside price action without compression.
Example: EPS line on separate right scale (recommended) - hover over labels to view detailed earnings tooltips
Example: EPS line pinned to Scale A (not recommended - appears as straight line due to small EPS range compared to price)
Example: EPS line displayed in separate pane below price chart
Methodology Credits:
This indicator implements the EPS line visualization methodology developed by Investor's Business Daily (IBD) for their MarketSurge platform (formerly known as MarketSmith). The EPS line concept helps visualize earnings momentum alongside price action, providing a fundamental overlay for technical analysis.
Technical Details:
Designed for daily, weekly, and monthly timeframes
Minimum 4 quarters of earnings data required
Uses TradingView's built-in earnings data
Automatically handles missing or invalid data
This indicator helps you visualize earnings trends alongside price action, providing a fundamental overlay for your technical analysis.
Bollinger Bands Regression Forecast [BigBeluga]🔵 OVERVIEW
The Bollinger Bands Regression Forecast combines volatility envelopes from Bollinger Bands with a linear regression-based projection model .
It visualizes both current and future price zones by extrapolating the Bollinger channel forward in time, giving traders a statistical forecast of probable support and resistance behavior.
🔵 CONCEPTS
Classic Bollinger Bands use a moving average (basis) and standard deviation (deviation) to form dynamic envelopes around price.
This indicator enhances them with linear regression slope detection , allowing it to forecast how the band may expand or contract in the future.
Regression is applied to both the band’s basis and deviation components to predict their trajectory for a user-defined number of Forecast Bars .
The resulting forecast creates a smoothed, funnel-shaped projection that dynamically adapts to volatility.
▲ and ▼ markers highlight potential mean reversion points when price crosses the outer bounds of the bands.
🔵 FEATURES
Forecast Engine : Uses linear regression to project Bollinger Band movement into the future.
Dynamic Channel Width : Adapts standard deviation and slope for realistic volatility modeling.
Auto-Labeled Levels : Displays live upper and lower forecast values for quick reference.
Cross Signals : Marks potential overbought and oversold zones with ▲/▼ signals when price exits the band.
Trend-Adaptive Basis Color : Basis line automatically switches color to represent short-term trend direction.
Customizable Colors and Widths for complete visual control.
🔵 HOW TO USE
Apply the indicator to visualize both current Bollinger structure and its forward projection.
Use ▲/▼ breakout markers to identify short-term reversals or volatility shifts.
When price consistently rides the upper band forecast, the trend is strong and likely continuing.
When regression shows narrowing bands ahead, expect a volatility contraction or consolidation period.
For range traders, outer projected bands can be used as potential mean reversion entry points .
Combine with volume or momentum filters to confirm whether breakouts are genuine or fading.
🔵 CONCLUSION
Bollinger Bands Regression Forecast transforms classic Bollinger analysis into a predictive forecasting model .
By merging volatility dynamics with regression-based extrapolation, it provides traders with a forward-looking visualization of likely price boundaries — revealing not only where volatility is but also where it’s heading next.
Power Balance ForecasterHey trader buddy! Remember the old IBM 5150 on Wall Street back in the 80s? :) Well, I wanted to pay tribute to it with this retro-style code when MS DOS and CRT screens were the cutting edge of technology...
Analysis of the balance of power between buyers and sellers with price predictions
What This Indicator Does
The Power Balance Forecaster indicator analyzes the relationship between buyer and seller strength to predict future price movements. Here's what it does in detail:
Main Features:
Power Balance Analysis: Calculates real-time percentage of buyer power vs seller power
Price Predictions: Estimates next closing level based on current momentum
Market State Detection: Identifies 5 different market conditions
Visual Signals: Shows directional arrows and price targets
How the Trading Logic Works
Power Balance Calculation:
Analyzes Consecutive Bars - Counts consecutive bullish and bearish bars
Calculates Momentum - Uses ATR-normalized momentum to measure trend strength
Determines Market State - Assigns one of 5 market states based on conditions
Market States:
Bull Control: Strong uptrend (75% buyer power)
Bear Control: Strong downtrend (75% seller power)
Buying Pressure: Bullish pressure (65% buyer power)
Selling Pressure: Bearish pressure (65% seller power)
Balance Area: Market in equilibrium (50/50)
Prediction System:
Bullish Condition: Buyer power > 55% + Positive momentum = Bullish prediction
Bearish Condition: Seller power > 55% + Negative momentum = Bearish prediction
Price Target: Based on ATR multiplied by timeframe factor
Configurable Parameters:
Analysis Sensitivity (5-50): Controls how responsive the indicator is
Low values (5-15): More sensitive, ideal for scalping
High values (30-50): More stable, ideal for swing trading
Table Position: Choose from 9 positions to display the data table
Trading Signals:
Green Triangle ▲: Bullish signal, price expected to increase
Green Triangle ▼: Bearish signal, price expected to decrease
Dashed Line: Shows the price target projection
Label: Displays the exact target value
Recommended Timeframes:
Lower Timeframes (1-15 minutes):
Sensitivity: 10-20
Automatic Low TF mode
Higher Timeframes (1 hour - 1 day):
Sensitivity: 25-40
Automatic High TF mode
Important Notes:
Always use this indicator in combination with:
Market context analysis
Proper risk management
Confirmation from other indicators
Mandatory stop losses
The indicator works best in trending markets and may be less effective during extreme consolidation periods.
