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.
Prediction
Next Candle PredictorAdvanced TradingView Indicator for Precise Buy and Sell Signals
Overview:
The Predicta Futures - Next Candle Predictor is a cutting-edge TradingView indicator designed to forecast the next candle's direction in futures and cryptocurrency markets. Leveraging a multi-indicator confluence strategy, this tool provides traders with actionable long and short prediction percentages, enhanced by dynamic ADX-based thresholds and visual projection candles. Ideal for scalping, day trading, or swing trading on platforms like MEXC or Binance futures, it combines Supertrend, MACD, RSI, Stochastic, ADX, and volume analysis to deliver high-probability buy and sell signals while minimizing false positives.
Key Features:
• Multi-Indicator Confluence Scoring:
Integrates Supertrend for trend direction, EMAs (8, 21, 50) for alignment, MACD for momentum crossovers, RSI for overbought/oversold conditions, Stochastic for divergence detection, ADX for trend strength, and volume ratios for confirmation. A customizable confluence score (0-6) ensures signals meet user-defined criteria, reducing whipsaws in volatile markets.
• Dynamic Prediction Thresholds:
ADX-driven adjustments lower the required prediction percentage (e.g., 60% in strong trends) for "PERFECT TIME" entries, adapting to market conditions like ranging or trending phases.
• Visual Analysis Table:
A sleek, color-coded dashboard displays progress bars for each indicator, prediction percentages, and status (e.g., "PERFECT TIME" or "WAIT"). Supports long and short analyses with intuitive ASCII bars for quick scans.
• Projection Candles:
Simulates potential next-candle outcomes with volatility-scaled (via Bollinger Bands width) green long and red short candles, aiding in visualizing price targets.
• Buy/Sell Signals and Alerts:
Generates labeled "BUY" and "SELL" arrows on EMA crossovers within confirmed trends, with separate alerts for basic signals and high-confluence "PERFECT TIME" opportunities.
• Customizable Inputs:
Adjust ATR periods, Supertrend factors, minimum confluence scores, and volume ratios to tailor the indicator for stocks, forex, or crypto perpetual futures.
How It Works:
This TradingView script calculates long and short scores using weighted contributions from key indicators, normalizing them into prediction percentages. A confluence check—factoring trend, EMA alignment, MACD, Stochastic, volume, and ADX—triggers "PERFECT TIME" only when conditions align robustly. For example:
• In a downtrend (Supertrend red), with bearish MACD and Stochastic, and sufficient volume, the indicator highlights short opportunities.
• Dynamic thresholds ensure aggressive entries in strong trends (ADX >25) and conservative ones in weak trends.
• Backtested for reliability, it excels in identifying reversals and continuations, making it a must-have for traders seeking an edge in futures trading strategies.
Usage Instructions:
1. Add the indicator to your TradingView chart. (Search: Next Candle Predictor)
2. Customize settings via the inputs panel (e.g., set minConfluence to 5 for stricter signals).
3. Monitor the analysis table for predictions and confluence scores.
4. Act on "BUY/SELL" labels or "PERFECT TIME" alerts, combining with your risk management.
5. Enable projection candles for visual forecasting of the next bar.
Compatible with all timeframes, from 1-minute scalping to daily swings. Note: This is not financial advice; always verify signals with additional analysis.
Join thousands of traders enhancing their strategies—add it to your charts today and elevate your trading performance!
Please rate and review if it boosts your trades!
Thank you!
Next Candle PredictorAdvanced TradingView Indicator for Precise Buy and Sell Signals
Overview:
The Predicta Futures - Next Candle Predictor is a cutting-edge TradingView indicator designed to forecast the next candle's direction in futures and cryptocurrency markets. Leveraging a multi-indicator confluence strategy, this tool provides traders with actionable long and short prediction percentages, enhanced by dynamic ADX-based thresholds and visual projection candles. Ideal for scalping, day trading, or swing trading on platforms like MEXC or Binance futures, it combines Supertrend, MACD, RSI, Stochastic, ADX, and volume analysis to deliver high-probability buy and sell signals while minimizing false positives.
Key Features:
* Multi-Indicator Confluence Scoring: Integrates Supertrend for trend direction, EMAs (8, 21, 50) for alignment, MACD for momentum crossovers, RSI for overbought/oversold conditions, Stochastic for divergence detection, ADX for trend strength, and volume ratios for confirmation. A customizable confluence score (0-6) ensures signals meet user-defined criteria, reducing whipsaws in volatile markets.
* Dynamic Prediction Thresholds: ADX-driven adjustments lower the required prediction percentage (e.g., 60% in strong trends) for "PERFECT TIME" entries, adapting to market conditions like ranging or trending phases.
* Visual Analysis Table: A sleek, color-coded dashboard displays progress bars for each indicator, prediction percentages, and status (e.g., "PERFECT TIME" or "WAIT"). Supports long and short analyses with intuitive ASCII bars for quick scans.
* Projection Candles: Simulates potential next-candle outcomes with volatility-scaled (via Bollinger Bands width) green long and red short candles, aiding in visualizing price targets.
Buy/Sell Signals and Alerts: Generates labeled "BUY" and "SELL" arrows on EMA crossovers within confirmed trends, with separate alerts for basic signals and high-confluence "PERFECT TIME" opportunities.
* Customizable Inputs: Adjust ATR periods, Supertrend factors, minimum confluence scores, and volume ratios to tailor the indicator for stocks, forex, or crypto perpetual futures.
How It Works:
This TradingView script calculates long and short scores using weighted contributions from key indicators, normalizing them into prediction percentages. A confluence check—factoring trend, EMA alignment, MACD, Stochastic, volume, and ADX—triggers "PERFECT TIME" only when conditions align robustly. For example:
In a downtrend (Supertrend red), with bearish MACD and Stochastic, and sufficient volume, the indicator highlights short opportunities.
Dynamic thresholds ensure aggressive entries in strong trends (ADX >25) and conservative ones in weak trends.
Backtested for reliability, it excels in identifying reversals and continuations, making it a must-have for traders seeking an edge in futures trading strategies.
Usage Instructions:
1. Add the indicator to your TradingView chart.
2. Customize settings via the inputs panel (e.g., set minConfluence to 5 for stricter signals).
3. Monitor the analysis table for predictions and confluence scores.
4. Act on "BUY/SELL" labels or "PERFECT TIME" alerts, combining with your risk management.
5. Enable projection candles for visual forecasting of the next bar.
Compatible with all timeframes, from 1-minute scalping to daily swings. Note: This is not financial advice; always verify signals with additional analysis.
Rate and review if it boosts your trades!
Thank you!
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.
Bassi Enhanced Next Candle Prediction with Neural Network & SMCOverview
This advanced all-in-one indicator combines machine learning-based next candle direction prediction with comprehensive Smart Money Concepts (SMC/ICT) tools, classic technical indicators, and visual aids for price action traders. It predicts whether the next candle will close bullish (green), bearish (red), or neutral — with a confidence percentage — using either a logistic regression neural network approximation (pre-trained on historical data) or a rule-based decision tree ensemble.
Perfect for scalpers, day traders, and swing traders seeking confluence from multiple sources.
Key Features
Next Candle Prediction
Real-time probability and direction (BUY/SELL/HOLD) with confidence level (0-100%).
Visual simulated future candle (one bar ahead) based on ATR-scaled body size.
Background coloring for predicted up/down moves.
Large label on the chart showing prediction, strength, confidence, and recent patterns.
Machine Learning Models (toggle via inputs)
NN Mode: Logistic regression (single-layer neural net) using normalized features from RSI, MACD, Stochastic, EMA, Bollinger Bands, ATR, OBV, Ichimoku, VWAP, CCI, Williams %R, MFI, and volume.
Tree Mode: Ensemble of 6 decision trees incorporating trend, volume, oscillators, candlestick patterns, divergences, and SMC elements.
Smart Money Concepts (SMC/ICT)
Order Blocks (Bullish/Bearish) with auto-extension and labels.
Fair Value Gaps (FVG) with volume-confirmed 3-candle detection and minimum size filter.
Breaker Blocks (when OB is broken).
Liquidity Sweeps (fakeouts at recent highs/lows).
Market Structure: Break of Structure (BOS) and Change of Character (CHoCH) labels.
Mitigation Blocks, Equal Highs/Lows, Imbalances.
Divergence Detection (Regular & Hidden)
RSI, MACD, and Stochastic divergences with lines and labels.
Classic Indicators & Tools
EMA, Ichimoku Cloud, Bollinger Bands, Parabolic SAR, SuperTrend, VWAP with bands.
ADX trend strength, Volume confirmation, Candlestick patterns (Engulfing, Hammer, Shooting Star).
Fibonacci Retracement from recent fractals (auto-updating on last bar).
Volume Profile (POC, VAH, VAL) over lookback period.
Visual & Info Enhancements
Customizable info table (Full/Summary/Mobile modes) showing key metrics, predictions, and statuses.
Trend background coloring.
Auto-cleanup of old drawings to prevent chart clutter.
Alerts
Buy/Sell/Hold predictions.
Patterns, divergences, SMC events (OB, FVG, BOS, CHoCH, Liquidity Sweeps, etc.).
How to Use
Add to any chart/timeframe (best on 1-15min for predictions).
Watch the next-candle label and simulated candle for directional bias.
Use SMC zones for entries/exits, confirmed by prediction confidence >66% (STRONG).
Combine with table for quick confluence overview.
Enable alerts for real-time notifications.
Disclaimer
No indicator guarantees profits. This is a tool for confluence — always use proper risk management. Backtest thoroughly on your assets/timeframes.
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.
Pivot Points Standard w/ Future PivotsPivot Points Standard with Future Projections
This indicator displays traditional pivot point levels with an added feature to project future pivot levels based on the current period's price action.
Key Features:
Multiple Pivot Types: Choose from Traditional, Fibonacci, Woodie, Classic, DM, and Camarilla pivot calculations
Flexible Timeframes: Auto-detect or manually select Daily, Weekly, Monthly, Quarterly, Yearly, and multi-year periods
Future Pivot Projections: Visualize potential pivot levels for the next period based on current price movement
Custom Price Scenarios: Test "what-if" scenarios by entering a custom close price to see resulting pivot levels
Customizable Display: Adjust line styles, colors, opacity, and label positioning for both historical and future pivots
Historical Pivots: View up to 200 previous pivot periods for context
Future Pivot Options:
The unique future pivot feature calculates what the next period's support and resistance levels would be using the current period's High, Low, Open, and either the current price or a custom price you specify for the closing value. Future pivots are displayed with customizable line styles (solid, dashed, dotted) and opacity to distinguish them from historical levels.
Use Cases:
Plan entries and exits based on projected support/resistance
Scenario analysis with custom price targets
Identify key levels before the period closes
Multi-timeframe pivot analysis
Works on all timeframes and instruments.
AI Probabilistic OrderFlow Scalper⭐ Main Name
AI Probabilistic OrderFlow Scalper
⭐Description:
📌 AI Probabilistic OrderFlow Scalper — Predictive Auction Theory Model for Futures
This script combines Order Flow, Auction Market Theory, Volume Imbalance, Market Structure (HH/LL), RSI bias filtering, and a probability-based direction model inspired by AI and Revenue Management.
It produces high-precision scalping entries designed for fast markets such as Nasdaq Futures (NQ), while remaining compatible with all markets (indices, crypto, forex, metals).
This is not a typical indicator — it is a probabilistic predictive model engineered to provide sniper entries, a tick-based Take Profit, a volatility-adaptive ATR Stop Loss, and optional Value Area levels (VAH/VAL/POC).
⭐ Main Features
🔥 Directional probability model (AI-style weighted scoring)
📊 Order Flow imbalance (delta-like logic)
📈 HH/LL market structure detection
🎯 Smart RSI bias filter
🚀 One signal per trend shift (anti-spam)
🎯 Tick-based Take Profit (perfect for NQ / futures)
🛡️ ATR-based dynamic Stop Loss
📉 Value Area display: VAH, VAL, POC
🔊 Volume confirmation filter
📡 Directional probability plot
✔️ Works for Futures, Crypto, Forex, Indices
🧠 Probabilistic AI Approach
The model uses a 3-factor scoring system:
Order Flow imbalance
Market structure (HH/LL)
RSI trend bias
Each validated condition = 1 point.
The total score is converted into Buy/Sell probabilities, and the higher-probability direction is selected.
When probability exceeds the threshold (e.g. 80%), the system triggers a high-confidence sniper signal.
This mirrors Hight probability decision:
→ Only take a decision when probability of success is maximized.
🎯 Buy/Sell Signals (Sniper Entries)
🔵 Green triangle under the candle = high-probability Buy
🔴 Red triangle above the candle = high-probability Sell
✔️ Only one signal per directional shift
✔️ Signals appear only when all strict filters are satisfied
📌 Automatic TP / SL
TP: fixed tick-based (e.g. 100 ticks for NQ scalping)
SL: ATR-based, adapts to volatility
TP/SL display can be enabled or disabled
Perfectly calibrated for high-speed scalping.
📘 How to Use
Use any timeframe
Adjust probability threshold (75–90 recommended)
Enable strict mode for maximum precision
Let the model filter entries automatically
Choose a TP suitable for your market
Optionally display VAH/VAL/POC for Auction Theory context
Always test using backtesting before going live
🏆 Advantages
Extremely fast for scalping
High win-rate potential via probabilistic filtering
Clean signals (no noise or spam)
Combines the strongest trading frameworks:
Order Flow
Market Structure
Statistical modeling
Volume profiling
Automated risk management
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.
Predicta Futures – Scalping Predictor with Confidence FilterPredicta Futures is an advanced short-term forecasting indicator that combines historical pattern similarity analysis with weighted technical signals to predict price movements 1–10 minutes ahead.
**Core Functionality**
The script scans up to 5,000 historical bars to identify structurally similar price patterns. It aggregates forward outcomes from matched patterns and integrates real-time signals from RSI, MACD, Bollinger Bands, volume momentum, and volatility. A composite confidence score filters signals, displaying only those meeting the user-defined threshold (default ≥68%).
**Key Outputs**
- Buy/sell triangles with text labels
- Dashed projection line to predicted price
- Dotted target and ATR-based stop lines
- Info panel showing forecast direction, confidence %, expected move %, pattern count, order book status, and data access details
**Customization & Performance**
- Execution modes: Fast, Balanced, Accurate
- Adaptive sampling with recency bias option
- Filters for volatility and market hours
- Adjustable weights, lookback period, and prediction horizon
**Use Cases**
Scalping, intraday trading, futures, cryptocurrencies, equities.
*Order book metrics are simulated (platform limitation). Technical analysis tool; not financial advice.*
Machine Learning Moving Average [BackQuant]Machine Learning Moving Average
A powerful tool combining clustering, pseudo-machine learning, and adaptive prediction, enabling traders to understand and react to price behavior across multiple market regimes (Bullish, Neutral, Bearish). This script uses a dynamic clustering approach based on percentile thresholds and calculates an adaptive moving average, ideal for forecasting price movements with enhanced confidence levels.
What is Percentile Clustering?
Percentile clustering is a method that sorts and categorizes data into distinct groups based on its statistical distribution. In this script, the clustering process relies on the percentile values of a composite feature (based on technical indicators like RSI, CCI, ATR, etc.). By identifying key thresholds (lower and upper percentiles), the script assigns each data point (price movement) to a cluster (Bullish, Neutral, or Bearish), based on its proximity to these thresholds.
This approach mimics aspects of machine learning, where we “train” the model on past price behavior to predict future movements. The key difference is that this is not true machine learning; rather, it uses data-driven statistical techniques to "cluster" the market into patterns.
Why Percentile Clustering is Useful
Clustering price data into meaningful patterns (Bullish, Neutral, Bearish) helps traders visualize how price behavior can be grouped over time.
By leveraging past price behavior and technical indicators, percentile clustering adapts dynamically to evolving market conditions.
It helps you understand whether price behavior today aligns with past bullish or bearish trends, improving market context.
Clusters can be used to predict upcoming market conditions by identifying regimes with high confidence, improving entry/exit timing.
What This Script Does
Clustering Based on Percentiles : The script uses historical price data and various technical features to compute a "composite feature" for each bar. This feature is then sorted and clustered based on predefined percentile thresholds (e.g., 10th percentile for lower, 90th percentile for upper).
Cluster-Based Prediction : Once clustered, the script uses a weighted average, cluster momentum, or regime transition model to predict future price behavior over a specified number of bars.
Dynamic Moving Average : The script calculates a machine-learning-inspired moving average (MLMA) based on the current cluster, adjusting its behavior according to the cluster regime (Bullish, Neutral, Bearish).
Adaptive Confidence Levels : Confidence in the predicted return is calculated based on the distance between the current value and the other clusters. The further it is from the next closest cluster, the higher the confidence.
Visual Cluster Mapping : The script visually highlights different clusters on the chart with distinct colors for Bullish, Neutral, and Bearish regimes, and plots the MLMA line.
Prediction Output : It projects the predicted price based on the selected method and shows both predicted price and confidence percentage for each prediction horizon.
Trend Identification : Using the clustering output, the script colors the bars based on the current cluster to reflect whether the market is trending Bullish (green), Bearish (red), or is Neutral (gray).
How Traders Use It
Predicting Price Movements : The script provides traders with an idea of where prices might go based on past market behavior. Traders can use this forecast for short-term and long-term predictions, guiding their trades.
Clustering for Regime Analysis : Traders can identify whether the market is in a Bullish, Neutral, or Bearish regime, using that information to adjust trading strategies.
Adaptive Moving Average for Trend Following : The adaptive moving average can be used as a trend-following indicator, helping traders stay in the market when it’s aligned with the current trend (Bullish or Bearish).
Entry/Exit Strategy : By understanding the current cluster and its associated trend, traders can time entries and exits with higher precision, taking advantage of favorable conditions when the confidence in the predicted price is high.
Confidence for Risk Management : The confidence level associated with the predicted returns allows traders to manage risk better. Higher confidence levels indicate stronger market conditions, which can lead to higher position sizes.
Pseudo Machine Learning Aspect
While the script does not use conventional machine learning models (e.g., neural networks or decision trees), it mimics certain aspects of machine learning in its approach. By using clustering and the dynamic adjustment of a moving average, the model learns from historical data to adjust predictions for future price behavior. The "learning" comes from how the script uses past price data (and technical indicators) to create patterns (clusters) and predict future market movements based on those patterns.
Why This Is Important for Traders
Understanding market regimes helps to adjust trading strategies in a way that adapts to current market conditions.
Forecasting price behavior provides an additional edge, enabling traders to time entries and exits based on predicted price movements.
By leveraging the clustering technique, traders can separate noise from signal, improving the reliability of trading signals.
The combination of clustering and predictive modeling in one tool reduces the complexity for traders, allowing them to focus on actionable insights rather than manual analysis.
How to Interpret the Output
Bullish (Green) Zone : When the price behavior clusters into the Bullish zone, expect upward price movement. The MLMA line will help confirm if the trend remains upward.
Bearish (Red) Zone : When the price behavior clusters into the Bearish zone, expect downward price movement. The MLMA line will assist in tracking any downward trends.
Neutral (Gray) Zone : A neutral market condition signals indecision or range-bound behavior. The MLMA line can help track any potential breakouts or trend reversals.
Predicted Price : The projected price is shown on the chart, based on the cluster's predicted behavior. This provides a useful reference for where the price might move in the near future.
Prediction Confidence : The confidence percentage helps you gauge the reliability of the predicted price. A higher percentage indicates stronger market confidence in the forecasted move.
Tips for Use
Combining with Other Indicators : Use the output of this indicator in combination with your existing strategy (e.g., RSI, MACD, or moving averages) to enhance signal accuracy.
Position Sizing with Confidence : Increase position size when the prediction confidence is high, and decrease size when it’s low, based on the confidence interval.
Regime-Based Strategy : Consider developing a multi-strategy approach where you use this tool for Bullish or Bearish regimes and a separate strategy for Neutral markets.
Optimization : Adjust the lookback period and percentile settings to optimize the clustering algorithm based on your asset’s characteristics.
Conclusion
The Machine Learning Moving Average offers a novel approach to price prediction by leveraging percentile clustering and a dynamically adapting moving average. While not a traditional machine learning model, this tool mimics the adaptive behavior of machine learning by adjusting to evolving market conditions, helping traders predict price movements and identify trends with improved confidence and accuracy.
AI MEDEA FORECASTAI MEDEA searches for similar historical patterns and uses them to generate predictions. The longer it runs, the more data it gathers and the better the predictions become.
Important:
The indicator must remain enabled to:
- Collect predictions and check their accuracy
- Have as much data as possible for comparison
- Provide more accurate results
Recommendation:
Let the indicator run for several days on different timeframes (15m, 30m, 1H, 4H). The accuracy table will show the actual accuracy only after gathering enough predictions.
Volume Surprise [LuxAlgo]The Volume Surprise tool displays the trading volume alongside the expected volume at that time, allowing users to spot unexpected trading activity on the chart easily.
The tool includes an extrapolation of the estimated volume for future periods, allowing forecasting future trading activity.
🔶 USAGE
We define Volume Surprise as a situation where the actual trading volume deviates significantly from its expected value at a given time.
Being able to determine if trading activity is higher or lower than expected allows us to precisely gauge the interest of market participants in specific trends.
A histogram constructed from the difference between the volume and expected volume is provided to easily highlight the difference between the two and may be used as a standalone.
The tool can also help quantify the impact of specific market events, such as news about an instrument. For example, an important announcement leading to volume below expectations might be a sign of market participants underestimating the impact of the announcement.
Like in the example above, it is possible to observe cases where the volume significantly differs from the expected one, which might be interpreted as an anomaly leading to a correction.
🔹 Detecting Rare Trading Activity
Expected volume is defined as the mean (or median if we want to limit the impact of outliers) of the volume grouped at a specific point in time. This value depends on grouping volume based on periods, which can be user-defined.
However, it is possible to adjust the indicator to overestimate/underestimate expected volume, allowing for highlighting excessively high or low volume at specific times.
In order to do this, select "Percentiles" as the summary method, and change the percentiles value to a value that is close to 100 (overestimate expected volume) or to 0 (underestimate expected volume).
In the example above, we are only interested in detecting volume that is excessively high, we use the 95th percentile to do so, effectively highlighting when volume is higher than 95% of the volumes recorded at that time.
🔶 DETAILS
🔹 Choosing the Right Periods
Our expected volume value depends on grouping volume based on periods, which can be user-defined.
For example, if only the hourly period is selected, volumes are grouped by their respective hours. As such, to get the expected volume for the hour 7 PM, we collect and group the historical volumes that occurred at 7 PM and average them to get our expected value at that time.
Users are not limited to selecting a single period, and can group volume using a combination of all the available periods.
Do note that when on lower timeframes, only having higher periods will lead to less precise expected values. Enabling periods that are too low might prevent grouping. Finally, enabling a lot of periods will, on the other hand, lead to a lot of groups, preventing the ability to get effective expected values.
In order to avoid changing periods by navigating across multiple timeframes, an "Auto Selection" setting is provided.
🔹 Group Length
The length setting allows controlling the maximum size of a volume group. Using higher lengths will provide an expected value on more historical data, further highlighting recurring patterns.
🔹 Recommended Assets
Obtaining the expected volume for a specific period (time of the day, day of the week, quarter, etc) is most effective when on assets showing higher signs of periodicity in their trading activity.
This is visible on stocks, futures, and forex pairs, which tend to have a defined, recognizable interval with usually higher trading activity.
Assets such as cryptocurrencies will usually not have a clearly defined periodic trading activity, which lowers the validity of forecasts produced by the tool, as well as any conclusions originating from the volume to expected volume comparisons.
🔶 SETTINGS
Length: Maximum number of records in a volume group for a specific period. Older values are discarded.
Smooth: Period of a SMA used to smooth volume. The smoothing affects the expected value.
🔹 Periods
Auto Selection: Automatically choose a practical combination of periods based on the chart timeframe.
Custom periods can be used if disabling "Auto Selection". Available periods include:
- Minutes
- Hours
- Days (can be: Day of Week, Day of Month, Day of Year)
- Months
- Quarters
🔹 Summary
Method: Method used to obtain the expected value. Options include Mean (default) or Percentile.
Percentile: Percentile number used if "Method" is set to "Percentile". A value of 50 will effectively use a median for the expected value.
🔹 Forecast
Forecast Window: Number of bars ahead for which the expected volume is predicted.
Style: Style settings of the forecast.
AI Money FlowAI Money Flow is a revolutionary trading indicator that combines cutting-edge artificial intelligence technologies with traditional Smart Money concepts. This indicator provides comprehensive market analysis with emphasis on signal accuracy and reliability.
Key Features:
Volume Profile with Smart Money Analysis - Displays real money flow instead of just volume, identifying key support and resistance levels based on actual trader activity.
Volatility-Based Support & Resistance - Intelligent support and resistance levels that dynamically adapt to market volatility in real-time for maximum accuracy.
Order Flow Analysis - Advanced detection of buying and selling pressure that reveals the true intentions of large market players.
Machine Learning Optimization - Futuristic AI technology that automatically learns and optimizes settings for each specific asset and timeframe.
Risk Management - Advanced volatility and price spike detection for better risk management and capital protection.
Real-time Dashboard - Modern dashboard with color-coded signals provides instant overview of market conditions and trends.
Accuracy: 88-93%
Machine Learning Price Predictor: Ridge AR [Bitwardex]🔹Machine Learning Price Predictor: Ridge AR is a research-oriented indicator demonstrating the use of Regularized AutoRegression (Ridge AR) for short-term price forecasting.
The model combines autoregressive structure with Ridge regularization , providing stability under noisy or volatile market conditions.
The latest version introduces Bull and Bear signals , visually representing the current momentum phase and model direction directly on the chart.
Unlike traditional linear regression, Ridge AR minimizes overfitting, stabilizes coefficient dynamics, and enhances predictive consistency in correlated datasets.
The script plots:
Fit Line — in-sample fitted data;
Forecast Line — out-of-sample projection;
Trend Segments — color-coded bullish/bearish sections;
Bull/Bear Labels 🐂🐻 — dynamic visual signals showing directional bias.
Designed for researchers, students, and developers, this tool helps explore regularized time-series forecasting in Pine Script™.
🧩 Ridge AR Settings
Training Window — number of bars used for model training;
Forecast Horizon — forecast length (bars ahead);
AR Order — number of lags used as features;
Ridge Strength (λ) — regularization coefficient;
Damping Factor — exponential trend decay rate;
Trend Length — period for trend/volatility estimation;
Momentum Weight — strength of the recent move;
Mean Reversion — pullback intensity toward the mean.
🧮 Data Processing
Prefilter:
None — raw close price;
EMA — exponential smoothing;
SuperSmoother — Ehlers filter for noise reduction.
EMA Length, SuperSmoother Length — smoothing parameters.
🖥️ Display Settings
Update Mode:
Lock — static model;
Update Once Reached — rebuild after forecast horizon;
Continuous — update every bar.
Forecast Color — projection line color;
Bullish/Bearish Colors — colors for trend segments.
🐂🐻 Bull/Bear Signal System
The Bull/Bear Signal System adds directional visual cues to highlight local momentum shifts and model-based trend confirmation.
Bull (🐂) — appears when upward momentum is confirmed (momentum > 0) .
Displayed below the bar, colored with Bullish Color.
Bear (🐻) — appears when downward momentum is dominant (momentum < 0) .
Displayed above the bar, colored with Bearish Color.
Signals are generated during model recalculations or when the directional bias changes in Continuous mode.
These visual markers are analytical aids , not trading triggers.
🧠 Core Algorithmic Components
Regularized AutoRegression (Ridge AR):
Solves: (X′X+λI)−1X′y
to derive stable regression coefficients.
Matrix and Pseudoinverse Operations — implemented natively in Pine Script™.
Prefiltering (EMA / Ehlers SuperSmoother) — stabilizes noisy data.
Forecast Dynamics — integrates damping, momentum, and mean reversion.
Trend Visualization — color-coded bullish/bearish line segments.
Bull/Bear Signal Engine — visualizes real-time impulse direction.
📊 Applications
Academic and educational purposes;
Demonstration of Ridge Regression and AR models;
Analysis of bull/bear market phase transitions;
Visualization of time-series dependencies.
⚠️ Disclaimer
This script is provided for educational and research purposes only.
It does not provide trading or investment advice.
The author assumes no liability for financial losses resulting from its use.
Use responsibly and at your own risk.
Estimated Manipulation Movement Signal [AlgoPoint]Follow the Footprints of Whale Movements That Drive the Market
Overview
The market is not always driven by natural supply and demand. Large players—often called "whales" or institutions—can create artificial price movements to trigger stop-losses, induce panic or FOMO, and build their large positions at favorable prices. These events are known as "stop hunts" or "liquidity grabs."
The EMMS indicator is a specialized tool designed to detect these specific moments of potential market manipulation. It does not follow trends in a traditional sense; instead, it identifies high-probability reversal points created by the calculated actions of Smart Money trapping other market participants.
How It Works: The 3-Module Logic
The indicator uses a multi-stage confirmation process to identify a potential stop hunt:
1. Anomaly Detection: The engine first scans the chart for "Anomaly Candles." These are candles with unusually high volume and a very long wick relative to their body. This combination signals a sudden, forceful, and potentially unnatural price push.
2. Liquidity Zone Detection: The indicator automatically identifies and tracks recent significant swing highs and lows. These levels are considered "Liquidity Zones" because they are areas where a large number of stop-loss orders are likely clustered. These are the "hunting grounds" for whales.
3. The Stop Hunt Signal: A final signal is generated only when these two events align in a specific sequence:
An Anomaly Candle (high volume, long wick) spikes through a previously identified Liquidity Zone.
The same candle then reverses, closing back inside the previous price range.
This sequence confirms that the move was likely a "trap" designed to engineer liquidity, and a reversal in the opposite direction is now highly probable.
How to Interpret & Use This Indicator
BUY Signal: A BUY signal appears after a sharp price drop that pierces a recent swing low (taking out the stops of long positions) and then aggressively reverses to close higher. This suggests that Smart Money has absorbed the panic selling they just induced. The signal indicates a potential move UP.
SELL Signal: A SELL signal appears after a sharp price spike that pierces a recent swing high (taking out the stops of short positions) and then aggressively reverses to close lower. This suggests that Smart Money has sold into the FOMO buying they just created. The signal indicates a potential move DOWN.
This indicator is best used as a high-probability confirmation tool, ideally in conjunction with your understanding of the overall market trend and structure.
Cyclic Reversal Engine [AlgoPoint]Overview
Most indicators focus on price and momentum, but they often ignore a critical third dimension: time. Markets move in rhythmic cycles of expansion and contraction, but these cycles are not fixed; they speed up in trending markets and slow down in choppy conditions.
The Cyclic Reversal Engine is an advanced analytical tool designed to decode this rhythm. Instead of relying on static, lagging formulas, this indicator learns from past market behavior to anticipate when the current trend is statistically likely to reach its exhaustion point, providing high-probability reversal signals.
It achieves this by combining a sophisticated time analysis with a robust price-action confirmation.
How It Works: The Core Logic
The indicator operates on a multi-stage process to identify potential turning points in the market.
1. Market Regime Analysis (The Brain): Before analyzing any cycles, the indicator first diagnoses the current "personality" of the market. Using a combination of the ADX, Choppiness Index, and RSI, it classifies the market into one of three primary regimes:
- Trending: Strong, directional movement.
- Ranging: Sideways, non-directional chop.
- Reversal: An over-extended state (overbought/oversold) where a turn is imminent.
2. Adaptive Cycle Learning (The "Machine Learning" Aspect): This is the indicator's smartest feature. It constantly analyzes past cycles by measuring the bar-count between significant swing highs and swing lows. Crucially, it learns the average cycle duration for each specific market regime. For example, it learns that "in a strong trending market, a new swing low tends to occur every 35 bars," while "in a ranging market, this extends to 60 bars."
3. The Countdown & Timing Signal: The indicator identifies the last major swing high or low and starts a bar-by-bar countdown. Based on the current market regime, it selects the appropriate learned cycle length from its memory. When the bar count approaches this adaptive target, the indicator determines that a reversal is "due" from a timing perspective.
4. Price Confirmation (The Trigger): A signal is never generated based on timing alone. Once the timing condition is met (the cycle is "due"), the indicator waits for a final price-action confirmation. The default confirmation is the RSI entering an extreme overbought or oversold zone, signaling momentum exhaustion. The signal is only triggered when Time + Price Confirmation align.
How to Use This Indicator
- The Dashboard: The panel in the bottom-right corner is your command center.
- Market Regime: Shows the current market personality analyzed by the engine.
- Adaptive Cycle / Bar Count: This is the core of the indicator. It shows the target cycle length for the current regime (e.g., 50) and the current bar count since the last swing point (e.g., 45). The background turns orange when the bar count enters the "due zone," indicating that you should be on high alert for a reversal.
- BUY/SELL Signals: A label appears on the chart only when the two primary conditions are met:
The timing is right (Bar Count has reached the Adaptive Cycle target).
The price confirms exhaustion (RSI is in an extreme zone).
A BUY signal suggests a downtrend cycle is likely complete, and a SELL signal suggests an uptrend cycle is likely complete.
Key Settings
- Pivot Lookback: Controls the sensitivity of the swing point detection. Higher values will identify more significant, longer-term cycles.
- Market Regime Engine: The ADX, Choppiness, and RSI settings can be fine-tuned to adjust how the indicator classifies the market's personality.
- Require Price Confirmation: You can toggle the RSI confirmation on or off. It is highly recommended to keep it enabled for higher-quality signals.
HorizonSigma Pro [CHE]HorizonSigma Pro
Disclaimer
Not every timeframe will yield good results . Very short charts are dominated by microstructure noise, spreads, and slippage; signals can flip and the tradable edge shrinks after costs. Very high timeframes adapt more slowly, provide fewer samples, and can lag regime shifts. When you change timeframe, you also change the ratios between horizon, lookbacks, and correlation windows—what works on M5 won’t automatically hold on H1 or D1. Liquidity, session effects (overnight gaps, news bursts), and volatility do not scale linearly with time. Always validate per symbol and timeframe, then retune horizon, z-length, correlation window, and either the neutral band or the z-threshold. On fast charts, “components” mode adapts quicker; on slower charts, “super” reduces noise. Keep prior-shift and calibration enabled, monitor Hit Rate with its confidence interval and the Brier score, and execute only on confirmed (closed-bar) values.
For example, what do “UP 61%” and “DOWN 21%” mean?
“UP 61%” is the model’s estimated probability that the close will be higher after your selected horizon—directional probability, not a price target or profit guarantee. “DOWN 21%” still reports the probability of up; here it’s 21%, which implies 79% for down (a short bias). The label switches to “DOWN” because the probability falls below your short threshold. With a neutral-band policy, for example ±7%, signals are: Long above 57%, Short below 43%, Neutral in between. In z-score mode, fixed z-cutoffs drive the call instead of percentages. The arrow length on the chart is an ATR-scaled projection to visualize reach; treat it as guidance, not a promise.
Part 1 — Scientific description
Objective.
The indicator estimates the probability that price will be higher after a user-defined horizon (a chosen number of bars) and emits long, short, or neutral decisions under explicit thresholds. It combines multi‑feature, z‑normalized inputs, adaptive correlation‑based weighting, a prior‑shifted sigmoid mapping, optional rolling probability calibration, and repaint‑safe confirmation. It also visualizes an ATR‑scaled forward projection and prints a compact statistics panel.
Data and labeling.
For each bar, the target label is whether price increased over the past chosen horizon. Learning is deliberately backward‑looking to avoid look‑ahead: features are associated with outcomes that are only known after that horizon has elapsed.
Feature engineering.
The feature set includes momentum, RSI, stochastic %K, MACD histogram slope, a normalized EMA(20/50) trend spread, ATR as a share of price, Bollinger Band width, and volume normalized by its moving average. All features are standardized over rolling windows. A compressed “super‑feature” is available that aggregates core trend and momentum components while penalizing excessive width (volatility). Users can switch between a “components” mode (weighted sum of individual features) and a “super” mode (single compressed driver).
Weighting and learning.
Weights are the rolling correlations between features (evaluated one horizon ago) and realized directional outcomes, smoothed by an EMA and optionally clamped to a bounded range to stabilize outliers. This produces an adaptive, regime‑aware weighting without explicit machine‑learning libraries.
Scoring and probability mapping.
The raw score is either the weighted component sum or the weighted super‑feature. The score is standardized again and passed through a sigmoid whose steepness is user‑controlled. A “prior shift” moves the sigmoid’s midpoint to the current base rate of up moves, estimated over the evaluation window, so that probabilities remain well‑calibrated when markets drift bullish or bearish. Probabilities and standardized scores are EMA‑smoothed for stability.
Decision policy.
Two modes are supported:
- Neutral band: go long if the probability is above one half plus a user‑set band; go short if it is below one half minus that band; otherwise stay neutral.
- Z‑score thresholds: use symmetric positive/negative cutoffs on the standardized score to trigger long/short.
Repaint protection.
All values used for decisions can be locked to confirmed (closed) bars. Intrabar updates are available as a preview, but confirmed values drive evaluation and stats.
Calibration.
An optional rolling linear calibration maps past confirmed probabilities to realized outcomes over the evaluation window. The mapping is clipped to the unit interval and can be injected back into the decision logic if desired. This improves reliability (probabilities that “mean what they say”) without necessarily improving raw separability.
Evaluation metrics.
The table reports: hit rate on signaled bars; a Wilson confidence interval for that hit rate at a chosen confidence level; Brier score as a measure of probability accuracy; counts of long/short trades; average realized return by side; profit factor; net return; and exposure (signal density). All are computed on rolling windows consistent with the learning scheme.
Visualization.
On the chart, an arrowed projection shows the predicted direction from the current bar to the chosen horizon, with magnitude scaled by ATR (optionally scaled by the square‑root of the horizon). Labels display either the decision probability or the standardized score. Neutral states can display a configurable icon for immediate recognition.
Computational properties.
The design relies on rolling means, standard deviations, correlations, and EMAs. Per‑bar cost is constant with respect to history length, and memory is constant per tracked series. Graphical objects are updated in place to obey platform limits.
Assumptions and limitations.
The method is correlation‑based and will adapt after regime changes, not before them. Calibration improves probability reliability but not necessarily ranking power. Intrabar previews are non‑binding and should not be evaluated as historical performance.
Part 2 — Trader‑facing description
What it does.
This tool tells you how likely price is to be higher after your chosen number of bars and converts that into Long / Short / Neutral calls. It learns, in real time, which components—momentum, trend, volatility, breadth, and volume—matter now, adjusts their weights, and shows you a probability line plus a forward arrow scaled by volatility.
How to set it up.
1) Choose your horizon. Intraday scalps: 5–10 bars. Swings: 10–30 bars. The default of 14 bars is a balanced starting point.
2) Pick a feature mode.
- components: granular and fast to adapt when leadership rotates between signals.
- super: cleaner single driver; less noise, slightly slower to react.
3) Decide how signals are triggered.
- Neutral band (probability based): intuitive and easy to tune. Widen the band for fewer, higher‑quality trades; tighten to catch more moves.
- Z‑score thresholds: consistent numeric cutoffs that ignore base‑rate drift.
4) Keep reliability helpers on. Leave prior shift and calibration enabled to stabilize probabilities across bullish/bearish regimes.
5) Smoothing. A short EMA on the probability or score reduces whipsaws while preserving turns.
6) Overlay. The arrow shows the call and a volatility‑scaled reach for the next horizon. Treat it as guidance, not a promise.
Reading the stats table.
- Hit Rate with a confidence interval: your recent accuracy with an uncertainty range; trust the range, not only the point.
- Brier Score: lower is better; it checks whether a stated “70%” really behaves like 70% over time.
- Profit Factor, Net Return, Exposure: quick triage of tradability and signal density.
- Average Return by Side: sanity‑check that the long and short calls each pull their weight.
Typical adjustments.
- Too many trades? Increase the neutral band or raise the z‑threshold.
- Missing the move? Tighten the band, or switch to components mode to react faster.
- Choppy timeframe? Lengthen the z‑score and correlation windows; keep calibration on.
- Volatility regime change? Revisit the ATR multiplier and enable square‑root scaling of horizon.
Execution and risk.
- Size positions by volatility (ATR‑based sizing works well).
- Enter on confirmed values; use intrabar previews only as early signals.
- Combine with your market structure (levels, liquidity zones). This model is statistical, not clairvoyant.
What it is not.
Not a black‑box machine‑learning model. It is transparent, correlation‑weighted technical analysis with strong attention to probability reliability and repaint safety.
Suggested defaults (robust starting point).
- Horizon 14; components mode; weight EMA 10; correlation window 500; z‑length 200.
- Neutral band around seven percentage points, or z‑threshold around one‑third of a standard deviation.
- Prior shift ON, Calibration ON, Use calibrated for decisions OFF to start.
- ATR multiplier 1.0; square‑root horizon scaling ON; EMA smoothing 3.
- Confidence setting equivalent to about 95%.
Disclaimer
No indicator guarantees profits. HorizonSigma Pro is a decision aid; always combine with solid risk management and your own judgment. Backtest, forward test, and size responsibly.
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Enhance your trading precision and confidence 🚀
Best regards
Chervolino
Essa - Market Structure Crystal Ball SystemEssa - Market Structure Crystal Ball V2.0
Ever wished you had a glimpse into the market's next move? Stop guessing and start anticipating with the Market Structure Crystal Ball!
This isn't just another indicator that tells you what has happened. This is a comprehensive analysis tool that learns from historical price action to forecast the most probable future structure. It combines advanced pattern recognition with essential trading concepts to give you a unique analytical edge.
Key Features
The Predictive Engine (The Crystal Ball)
This is the core of the indicator. It doesn't just identify market structure; it predicts it.
Know the Odds: Get a real-time probability score (%) for the next structural point: Higher High (HH), Higher Low (HL), Lower Low (LL), or Lower High (LH).
Advanced Analysis: The engine considers the pattern sequence, the speed (velocity) of the move, and its size to find the most accurate historical matches.
Dynamic Learning: The indicator constantly updates its analysis as new price data comes in.
The All-in-One Dashboard
Your command center for at-a-glance information. No need to clutter your screen!
Market Phase: Instantly know if the market is in a "🚀 Strong Uptrend," "📉 Steady Downtrend," or "↔️ Consolidation."
Live Probabilities: See the updated forecasts for HH, HL, LL, and LH in a clean, easy-to-read format.
Confidence Level: The dashboard tells you how confident the algorithm is in its current prediction (Low, Medium, or High).
🎯 Dynamic Prediction Zones
Turn probabilities into actionable price areas.
Visual Targets: Based on the highest probability outcome, the indicator draws a target zone on your chart where the next structure point is likely to form.
Context-Aware: These zones are calculated using recent volatility and average swing sizes, making them adaptive to the current market conditions.
🔍 Fair Value Gap (FVG) Detector
Automatically identify and track key price imbalances.
Price Magnets: FVGs are automatically detected and drawn, acting as potential targets for price.
Smart Tracking: The indicator tracks the status of each FVG (Fresh, Partially Filled, or Filled) and uses this data to refine its predictions.
🌍 Trading Session Analysis
Never lose track of key session levels again.
Visualize Sessions: See the Asia, London, and New York sessions highlighted with colored backgrounds.
Key Levels: Automatically plots the high and low of each session, which are often critical support and resistance levels.
Breakout Alerts: Get notified when price breaks a session high or low.
📈 Multi-Timeframe (MTF) Context
Understand the bigger picture by integrating higher timeframe analysis directly onto your chart.
BOS & MSS: Automatically identifies Breaks of Structure (trend continuation) and Market Structure Shifts (potential reversals) from up to two higher timeframes.
Trade with the Trend: Align your intraday trades with the dominant trend for higher probability setups.
⚙️ How It Works in Simple Terms
1️⃣ It Learns: The indicator first identifies all the past swing points (HH, HL, LL, LH) and analyzes their characteristics (speed, size, etc.).
2️⃣ It Finds a Match: It looks at the most recent price action and searches through hundreds of historical bars to find moments that were almost identical.
3️⃣ It Analyzes the Outcome: It checks what happened next in those similar historical scenarios.
4️⃣ It Predicts: Based on that historical data, it calculates the probability of each potential outcome and presents it to you.
🚀 How to Use This Indicator in Your Trading
Confirmation Tool: Use a high probability score (e.g., >60% for a HH) to confirm your own bullish analysis before entering a trade.
Finding High-Probability Zones: Use the Prediction Zones as potential areas to take profit, or as reversal zones to watch for entries in the opposite direction.
Gauging Market Sentiment: Check the "Market Phase" on the dashboard. Avoid forcing trades when the indicator shows "😴 Low Volatility."
Confluence is Key: This indicator is incredibly powerful when combined with your existing strategy. Use it alongside supply/demand zones, moving averages, or RSI for ultimate confirmation.
We hope this tool gives you a powerful new perspective on the market. Dive into the settings to customize it to your liking!
If you find this indicator helpful, please give it a Boost 👍 and leave a comment with your feedback below! Happy trading!
Disclaimer: All predictions are probabilistic and based on historical data. Past performance is not indicative of future results. Always use proper risk management.
ADR Plots + OverlayADR Plots + Overlay
This tool calculates and displays Average Daily Range (ADR) levels on your chart, giving traders a quick visual reference for expected daily price movement. It plots guide levels above and below the daily open and shows how much of the day's typical range has already been covered—all in one interactive table and on-chart overlay.
What It Does
ADR Calculation:
Uses daily high-low differences over a user-defined period (default 14 days), smoothed via RMA, SMA, EMA, or WMA to calculate the average daily range.
Projected Levels:
Plots four reference levels relative to the current day's open price:
+100% ADR: Open + ADR
+50% ADR: Open + 50% of ADR
−50% ADR: Open − 50% of ADR
−100% ADR: Open − ADR
Coverage %:
Tracks intraday high and low prices to calculate what percentage of the ADR has already been covered for the current session:
Coverage % = (High − Low) ÷ ADR × 100
Interactive Table:
Shows the ADR value and today's ADR coverage percentage in a customizable table overlay. The table position, colors, border, transparency, and an optional empty top row can all be adjusted via settings.
Customization Options
Table Settings:
Position the table (top/bottom × left/right).
Change background color, text color, border color and thickness.
Toggle an empty top row for spacing.
Line Settings:
Choose color, line style (solid/dotted/dashed), and width.
Lines automatically reposition each day based on that day's open price and ADR calculation.
General Inputs:
ADR length (number of days).
Smoothing method (RMA, SMA, EMA, WMA).
How to Use It for Trading
Measure Daily Movement: Instantly know the expected daily price range based on historical volatility.
Identify Overextension: Use the coverage % to see if the market has already moved close to or beyond its typical daily range.
Plan Entries & Exits: Align trade targets and stops with ADR levels for more objective intraday planning.
Visual Reference: Horizontal guide lines and table update automatically as new data comes in, helping traders stay informed without manual calculations.
Ideal For
Intraday traders tracking daily volatility limits.
Swing traders wanting a quick reference for expected price movement per day.
Anyone seeking a volatility-based framework for planning targets, stops, or identifying extended market conditions.
Probability Grid [LuxAlgo]The Probability Grid tool allows traders to see the probability of where and when the next reversal would occur, it displays a 10x10 grid and/or dashboard with the probability of the next reversal occurring beyond each cell or within each cell.
🔶 USAGE
By default, the tool displays deciles (percentiles from 0 to 90), users can enable, disable and modify each percentile, but two of them must always be enabled or the tool will display an error message alerting of it.
The use of the tool is quite simple, as shown in the chart above, the further the price moves on the grid, the higher the probability of a reversal.
In this case, the reversal took place on the cell with a probability of 9%, which means that there is a probability of 91% within the square defined by the last reversal and this cell.
🔹 Grid vs Dashboard
The tool can display a grid starting from the last reversal and/or a dashboard at three predefined locations, as shown in the chart above.
🔶 DETAILS
🔹 Raw Data vs Normalized Data
By default the tool displays the normalized data, this means that instead of using the raw data (price delta between reversals) it uses the returns between each reversal, this is useful to make an apples to apples comparison of all the data in the dataset.
This can be seen in the left side of the chart above (BTCUSD Daily chart) where normalize data is disabled, the percentiles from 0 to 40 overlap and are indistinguishable from each other because the tool uses the raw price delta over the entire bitcoin history, with normalize data enabled as we can see in the right side of the chart we can have a fair comparison of the data over the entire history.
🔹 Probability Beyond or Within Each Cell
Two different probability modes are available, the default mode is Probability Beyond Each Cell, the number displayed in each cell is the probability of the next reversal to be located in the area beyond the cell, for example, if the cell displays 20%, it means that in the area formed by the square starting from the last reversal and ending at the cell, there is an 80% probability and outside that square there is a 20% probability for the location of the next reversal.
The second probability mode is the probability within each cell, this outlines the chance that the next reversal will be within the cell, as we can see on the right chart above, when using deciles as percentiles (default settings), each cell has the same 1% probability for the 10x10 grid.
🔶 SETTINGS
Swing Length: The maximum length in bars used to identify a swing
Maximum Reversals: Maximum number of reversals included in calculations
Normalize Data: Use returns between swings instead of raw price
Probability: Choose between two different probability modes: beyond and inside each cell
Percentiles: Enable/disable each of the ten percentiles and select the percentile number and line style
🔹 Dashboard
Show Dashboard: Enable or disable the dashboard
Position: Choose dashboard location
Size: Choose dashboard size
🔹 Style
Show Grid: Enable or disable the grid
Size: Choose grid text size
Colors: Choose grid background colors
Show Marks: Enable/disable reversal markers
Bitcoin Polynomial Regression ModelThis is the main version of the script. Click here for the Oscillator part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines. The Oscillator version can be found here.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
Bitcoin Polynomial Regression OscillatorThis is the oscillator version of the script. Click here for the other part of the script.
💡Why this model was created:
One of the key issues with most existing models, including our own Bitcoin Log Growth Curve Model , is that they often fail to realistically account for diminishing returns. As a result, they may present overly optimistic bull cycle targets (hence, we introduced alternative settings in our previous Bitcoin Log Growth Curve Model).
This new model however, has been built from the ground up with a primary focus on incorporating the principle of diminishing returns. It directly responds to this concept, which has been briefly explored here .
📉The theory of diminishing returns:
This theory suggests that as each four-year market cycle unfolds, volatility gradually decreases, leading to more tempered price movements. It also implies that the price increase from one cycle peak to the next will decrease over time as the asset matures. The same pattern applies to cycle lows and the relationship between tops and bottoms. In essence, these price movements are interconnected and should generally follow a consistent pattern. We believe this model provides a more realistic outlook on bull and bear market cycles.
To better understand this theory, the relationships between cycle tops and bottoms are outlined below:https://www.tradingview.com/x/7Hldzsf2/
🔧Creation of the model:
For those interested in how this model was created, the process is explained here. Otherwise, feel free to skip this section.
This model is based on two separate cubic polynomial regression lines. One for the top price trend and another for the bottom. Both follow the general cubic polynomial function:
ax^3 +bx^2 + cx + d.
In this equation, x represents the weekly bar index minus an offset, while a, b, c, and d are determined through polynomial regression analysis. The input (x, y) values used for the polynomial regression analysis are as follows:
Top regression line (x, y) values:
113, 18.6
240, 1004
451, 19128
655, 65502
Bottom regression line (x, y) values:
103, 2.5
267, 211
471, 3193
676, 16255
The values above correspond to historical Bitcoin cycle tops and bottoms, where x is the weekly bar index and y is the weekly closing price of Bitcoin. The best fit is determined using metrics such as R-squared values, residual error analysis, and visual inspection. While the exact details of this evaluation are beyond the scope of this post, the following optimal parameters were found:
Top regression line parameter values:
a: 0.000202798
b: 0.0872922
c: -30.88805
d: 1827.14113
Bottom regression line parameter values:
a: 0.000138314
b: -0.0768236
c: 13.90555
d: -765.8892
📊Polynomial Regression Oscillator:
This publication also includes the oscillator version of the this model which is displayed at the bottom of the screen. The oscillator applies a logarithmic transformation to the price and the regression lines using the formula log10(x) .
The log-transformed price is then normalized using min-max normalization relative to the log-transformed top and bottom regression line with the formula:
normalized price = log(close) - log(bottom regression line) / log(top regression line) - log(bottom regression line)
This transformation results in a price value between 0 and 1 between both the regression lines.
🔍Interpretation of the Model:
In general, the red area represents a caution zone, as historically, the price has often been near its cycle market top within this range. On the other hand, the green area is considered an area of opportunity, as historically, it has corresponded to the market bottom.
The top regression line serves as a signal for the absolute market cycle peak, while the bottom regression line indicates the absolute market cycle bottom.
Additionally, this model provides a predicted range for Bitcoin's future price movements, which can be used to make extrapolated predictions. We will explore this further below.
🔮Future Predictions:
Finally, let's discuss what this model actually predicts for the potential upcoming market cycle top and the corresponding market cycle bottom. In our previous post here , a cycle interval analysis was performed to predict a likely time window for the next cycle top and bottom:
In the image, it is predicted that the next top-to-top cycle interval will be 208 weeks, which translates to November 3rd, 2025. It is also predicted that the bottom-to-top cycle interval will be 152 weeks, which corresponds to October 13th, 2025. On the macro level, these two dates align quite well. For our prediction, we take the average of these two dates: October 24th 2025. This will be our target date for the bull cycle top.
Now, let's do the same for the upcoming cycle bottom. The bottom-to-bottom cycle interval is predicted to be 205 weeks, which translates to October 19th, 2026, and the top-to-bottom cycle interval is predicted to be 259 weeks, which corresponds to October 26th, 2026. We then take the average of these two dates, predicting a bear cycle bottom date target of October 19th, 2026.
Now that we have our predicted top and bottom cycle date targets, we can simply reference these two dates to our model, giving us the Bitcoin top price prediction in the range of 152,000 in Q4 2025 and a subsequent bottom price prediction in the range of 46,500 in Q4 2026.
For those interested in understanding what this specifically means for the predicted diminishing return top and bottom cycle values, the image below displays these predicted values. The new values are highlighted in yellow:
And of course, keep in mind that these targets are just rough estimates. While we've done our best to estimate these targets through a data-driven approach, markets will always remain unpredictable in nature. What are your targets? Feel free to share them in the comment section below.
