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Forecasting

Strategy MTF ScannerDescription: Stop guessing which timeframe is best for your strategy. This tool performs a "Top-Down Analysis" instantly by running a unified strategy simulation across 5 different timeframes simultaneously. Why Use This? A strategy that fails on the 1-Hour chart might print massive returns on the 4-Hour chart due to reduced noise. This scanner calculates the Equity Curve, Max Drawdown, and Win Rate for 15m, 1H, 4H, Daily, and Weekly charts (customizable) and presents the winner in a dashboard. Features: Simultaneous Backtesting: Runs 5 independent simulations inside request.security. Equity & Drawdown Tracking: See not just how much you make, but how much risk is required on each timeframe. Instant Comparison: Identify "Fractal Resonance" where multiple timeframes align in profitability. Strategy Logic (Fully Customizable): The default entry logic is a generic EMA 9/21 Crossover with a Trend Filter. Note: This is an open-source framework. You can modify the calc_strategy_results function in the source code to substitute the crossover with your own custom entry conditions (RSI, Stochastic, Price Action, etc.). Workflow: Load this scanner to identify the dominant timeframe (e.g., 4H). Switch your chart to the 4H timeframe. Use the Strategy Grid Optimizer to fine-tune the specific EMA and ATR settings for that timeframe.
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ChiragPatnaik tarafından
Strategy Grid Optimizer (Trend & Risk)Description: This tool transforms your chart into a powerful backtesting engine that runs hundreds of simulations per second. It is designed to solve the "Parameter Stability" problem: finding the settings that work robustly, rather than curve-fitting to a single number. How It Works: Instead of testing one setting at a time, this script uses Pine Script Arrays to run a "Grid Search" on your chart history: Trend Filter: It iterates through a range of EMA Lengths (e.g., 20, 30, 40... to 200). Risk Management: It iterates through a range of ATR Multipliers (e.g., 1.0, 1.5, 2.0...) for the trailing stop. The Result: It ranks every combination based on Net Profit, Drawdown, and Win Rate, instantly highlighting the "Sweet Spot" for the current asset. Strategy Logic (Fully Customizable): By default, this script demonstrates a standard EMA 9/21 Crossover. Developers & Traders: This script is designed as a Template. You can easily open the Source Code and replace the entry_signal logic with any strategy you wish (e.g., RSI, MACD, Bollinger Bands, or your own proprietary logic). The optimizer engine will work with whatever signal you provide. Workflow: Use the MTF Scanner to find the best Timeframe. Load this Grid Optimizer on that timeframe. Adjust the "Start" and "End" ranges in settings. The table will reveal the optimal Trend/Risk combination for your strategy.
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ChiragPatnaik tarafından
MTF Probability Predictor PRO v2This indicator is designed to generate high-confidence market bias by combining price action, chart structure, momentum, divergence analysis, ATR, and VWAP-based volatility assessment. Instead of providing binary signals, the indicator presents a probability-based decision framework, displaying BUY / SELL / NEUTRAL confidence percentages in real time. This allows traders to assess signal quality, market strength, and trade suitability before taking a position. BUY Bias (Trade-Favorable Condition) A BUY setup is considered favorable when: BUY confidence exceeds 65% AND SELL confidence remains below 10% AND NEUTRAL confidence is 25% or lower ==>> This combination indicates strong directional momentum with minimal opposing pressure, suggesting a higher-probability bullish environment. SELL Bias (Trade-Favorable Condition) A SELL setup is considered favorable when: SELL confidence exceeds 65% AND BUY confidence remains below 10% AND NEUTRAL confidence is 25% or lower ==>> This reflects dominant bearish control, reduced market indecision, and a clearer downside directional bias. NEUTRAL Bias (No-Trade Zone) A NEUTRAL condition is identified when: NEUTRAL confidence rises above 50% or higher ==>> This indicates range-bound or transitional market behavior, where directional conviction is weak. During such conditions, it is recommended to avoid trading and allow the market to establish a clearer trend. Key Benefits - Probability-driven signal clarity Reduced false signals in sideways markets Suitable for scalping, intraday, and swing trading Designed to support disciplined, rules-based decision making
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DesiOptionTrader tarafından
METODO SP 50X DO TRINDADE [ZP] - v1💎 The Key to Wealth: Institutional Liquidity + 50x Method The market doesn't move by chance, it moves because of LIQUIDITY. Have you ever wondered why the price reverses exactly where you placed your Stop Loss? It's because you were the liquidity that large institutions needed. But that ends today. 🚀 The Power of Perfect Confluence This isn't just another support and resistance indicator. This is an institutional trail tracking tool. I developed this Super Indicator to map where the hidden orders, the "pockets" of liquidity, and the high-probability reversal zones are. When you combine the precision of this liquidity map with Trinity's legendary 50x Method, you don't just have a strategy; you have a dollar printing machine. 🛡️ Why is this method Extraordinary? Liquidity Mapping: See where the "sharks" are setting traps. Real Support and Resistance: Forget random lines. Work with supply and demand zones validated by volume. Simplicity and Assertiveness: Made for those who want to profit, not for those who want to complicate things. The Path to Freedom: The exact combination to consistently seek exponential returns. 🙏 A Divine Gift Before clicking "use," take a moment. Access to this knowledge is not a coincidence, it's providence. Thousands of people spend their whole lives searching for a financial solution and never find this level of clarity. Thank God every day for this opportunity. Jesus gave you the vision and the tools; now, it's up to you to act with discipline and wisdom. Honor this blessing and prepare to see your bank account and your life transformed. "The blessing of the Lord brings wealth, and he adds no sorrow to it."
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DiegoTrindademt tarafından
ALGO DEUS | PRECISION TIME MACHINEALGO DEUS | PRECISION TIME MACHINE The surgical position simulator that integrates your personal money management. Description ALGO DEUS | Precision Time Machine is not just another standard Risk/Reward tool. It is a visual engineering instrument designed for serious traders (specifically Futures MNQ/MES) who demand absolute precision in their post-session analysis or live trade planning. The Algo Deus Time Machine allows you to travel back through your chart history to place a trade simulation at the exact minute, capturing the precise opening price of the selected candle. Key Features Dual Mode (Live & History): By default, the tool displays on the current price for instant planning. Toggle "History Mode" to project your strategies onto any specific candle from the past. Automatic Profit Calculator: Simply enter your target in Dollars ($) and the number of contracts. The algorithm instantly calculates your Take Profit and Stop Loss levels based on the asset's tick value (e.g., $0.5 for MNQ). Minute-Level Precision: Thanks to optimized temporal integration, you can target a specific time (e.g., 4:05 PM) to analyze market reaction using your exact risk management parameters. "Algo Deus" Sleek Interface: High-visibility display with translucent color blocks for a clear price reading without cluttering your candles. How to use it? Live: Perfect for visualizing your financial goals before executing a trade. Backtest: Check "Enable History Mode," set the exact minute of your past setup, and observe whether your TP or SL would have been hit.
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Resilia56 tarafından
Metodo 50x do Trindade Introducing my latest indicator, specifically designed for 4-hour (4H) and 1-day (1D) charts. This indicator is a powerful tool that signals trend reversals, allowing you to buy and sell with confidence! 🌟 Highlights: Trend Reversal: Identify reversal points before the market moves! Profitability: Maximize your profits by taking advantage of the best entry and exit opportunities. Easy to Use: Intuitive interface for traders of all levels. Proven Results: Works in the market to increase your chances of success! Don't waste any more time! Use this indicator to print dollars intelligently and effectively. Follow me for more tips and updates! Instagram📈 @diegotrindademt
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DiegoTrindademt tarafından
3
Volume Delta Waterfall (Anchored, No Reset)What this helps you see (simple) Delta (ΔV) = UpVolume − DownVolume (estimated from lower timeframe). Positive ΔV ⇒ more “up” volume inside the bar → buying pressure dominates. Negative ΔV ⇒ more “down” volume inside the bar → selling pressure dominates. The waterfall is cumulative delta: each bar starts at the previous bar close and moves up/down by ΔV. Divergence idea: Bearish divergence: price makes Higher High, but cumulative delta at that swing makes Lower High → rally is weaker (often exhaustion / distribution). Bullish divergence: price makes Lower Low, but cumulative delta at that swing makes Higher Low → selloff is weaker (often absorption / reversal risk).
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salman4meus tarafından
PDH/PDL + PSH/PSL + Session Opens (UTC+10)PDH / PDL (Previous Day High/Low) “Day” = your trade day that starts at Asia open 09:00 Brisbane. At each new Asia open, it: Locks yesterday’s high/low as PDH/PDL Draws two horizontal lines labeled PDH and PDL PSH / PSL (Previous Session High/Low) Tracks the High/Low of each session: Asia 09:00–17:00 London 18:00–23:00 NY Futures 23:00–00:30 NYSE 00:30–01:00 When a session ends, it stores that high/low. At the next session open, it prints the previous session levels: At London open → shows PSH/PSL ASIA At NY Futures open → shows PSH/PSL LON At NYSE open → shows PSH/PSL NY At Asia open → shows PSH/PSL NYSE Session open markers (vertical lines) Draws an opaque-ish vertical line + tiny label at: 09:00 “ASIA 09:00” 18:00 “LON 18:00” 23:00 “NY 23:00” 00:30 “NYSE 00:30” Line behavior Horizontal lines extend to the right by extendBars (default 500 bars). Labels are small and minimal (left-anchored on the line).
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jbot-bit tarafından
ASIA | LONDON | NEW YORK - Session Range [Entry Lab]This free community indicator automatically plots the highs and lows of each trading session, helping you clearly identify where liquidity is likely to be swept. Fully customisable to your personal time zone, it removes the manual work and keeps your focus on what matters most: structure, context, and timing. Built by the EntryLab team for traders who want better entries, deeper market understanding, and a repeatable edge. Our mission is simple — make winning a common occurrence, not a rare one. #EntryLab
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ENTRYLAB tarafından
6
Fourier Smoothed Volume Zone Oscillator Forecast [QuantAlgo]🟢 Overview Volume tells the story that price alone cannot. When thousands of contracts change hands on an upward move versus a handful on a downward drift, the market communicates something meaningful about conviction and participation. The Fourier Smoothed Volume Zone Oscillator (FSVZO) captures this relationship by measuring directional volume flow, producing readings that reveal whether buyers or sellers control the tape with genuine commitment. Building on this foundation, this FSVZO Forecast indicator adds a forward-looking dimension through three distinct projection engines: a market structure model that interprets swing dynamics, a volume-weighted approach that examines accumulation and distribution flows, and a linear regression method that extrapolates recent directional behavior. What distinguishes this implementation is its dual forecasting architecture. Since FSVZO fundamentally depends on the interplay between price movement and volume activity, the indicator projects both elements independently before calculating future oscillator values, creating coherent framework for mean reversion trading across multiple asset classes and timeframes, from intraday scalping on liquid futures to swing trading equities and cryptocurrencies. 🟢 How It Works The indicator begins by calculating a Volume Zone Oscillator using a directional volume approach: it multiplies volume by the sign of price change (positive when price rises, negative when price falls), applies a weighted moving average to this directional volume, then divides by a simple moving average of total volume. The result scales to a percentage, typically oscillating between -100 and +100, with readings beyond these levels indicating exceptional momentum conditions. Multiple smoothing passes, including a triple-smoothed SMA sequence and optional additional smoothing, reduce noise while preserving meaningful signals. The forecasting mechanism operates through a two-stage process that distinguishes this indicator from simpler projection tools. First, the system estimates future price levels using the selected forecasting method. Second, it independently projects future volume using one of three volume models: average (baseline historical volume), momentum (volume adjusted for recent acceleration or deceleration), or mean reversion (volume gravitating toward longer-term norms). These dual projections then feed into a simulated FSVZO engine that replicates the actual oscillator's mathematics, calculating directional volume relationships and applying identical smoothing operations to produce projected values. Since momentum oscillators rarely travel in straight lines, the projection system incorporates dynamic price oscillation. This mechanism draws from stored patterns of recent price changes, applies mathematical wave functions tied to current volatility conditions, and factors in momentum characteristics to create natural-looking forecast trajectories. The Price Volatility input allows traders to adjust the degree of fluctuation in projections. Higher settings produce more waviness, while lower settings generate smoother trend-like forecasts. The complete system generates up to 20 bars of projected FSVZO and MA values, rendered as dashed lines extending beyond current price action. 🟢 Key Features 1. Market Structure Model This projection method analyzes price action through the lens of swing point dynamics and structural shifts. The algorithm identifies pivot highs and pivot lows within a configurable lookback range, then evaluates whether the market exhibits bullish characteristics (successive higher highs and higher lows) or bearish patterns (successive lower highs and lower lows). When price breaks previous swing levels, the model recognizes these as potential changes of character that inform projection direction. Price forecasts under this model incorporate proximity analysis to key structural levels and aggregate trend strength, measured by counting trend-confirming swings across recent history. Bullish structure combined with price near support zones biases projections upward, generating forecasted FSVZO readings that reflect potential buying momentum. Bearish structure near resistance creates downward-biased projections. ATR scaling keeps projections proportional to current market volatility. ▶ Practical Implications: Designed for traders who build strategies around support, resistance, and swing-based entries Structure-based projections provide context around pivot zones where FSVZO direction changes may coincide with price reactions Can help visualize potential divergence setups as structural shifts in price may precede FSVZO direction changes Shows how FSVZO projections shift based on proximity to detected swing highs and lows Works best when markets display clear directional swings rather than choppy consolidation May produce less useful output during extended consolidation phases with overlapping swing points Day traders can combine structural projections with session pivots for intraday momentum context 2. Volume-Weighted Model This method synthesizes multiple volume indicators to construct informed price projections that subsequently drive FSVZO forecasts. The algorithm tracks On-Balance Volume to measure cumulative buying and selling pressure over time, monitors the Accumulation/Distribution Line to assess where price settles within each bar's range relative to volume, and computes volume-weighted returns that emphasize high-activity price movements. Directional slopes of these metrics reveal whether volume patterns confirm or contradict prevailing price direction. Significant volume spikes receive heightened attention, with their directional bias incorporated into forecast calculations. When OBV slope, A/D line slope, and volume momentum align in the same direction, the model generates more assertive price projections, translating to stronger FSVZO movements. Conflicting volume signals produce dampened projections, suggesting FSVZO may consolidate rather than extend. The Volume Influence parameter allows traders to weight how heavily volume analysis affects the final projection versus pure price trend extrapolation. ▶ Practical Implications: Designed for traders who incorporate volume confirmation into their analysis Helps identify whether current price moves are accompanied by supportive volume patterns Volume-based projections can provide additional context when evaluating divergences between price and momentum Best suited for instruments with meaningful volume data Swing traders can assess whether breakout moves show volume commitment 3. Linear Regression Model The most mathematically direct of the three approaches, linear regression fits an optimal straight line through recent price data using least-squares methodology and extends that trajectory forward. These projected prices, combined with volume forecasts, generate corresponding FSVZO projections without conditional market interpretation or structural analysis. The forecast simply addresses one question: if price continues at its current rate of change with projected volume conditions, where would FSVZO readings be in upcoming bars? ▶ Practical Implications: Functions well during sustained, orderly trends where price progression remains relatively linear Responds more slowly to sudden directional shifts or volatility regime changes Works effectively on higher timeframes where trends develop more gradually Useful benchmark for comparing against structure or volume models to gauge projection differences 🟢 Universal Applications Across All Models Regardless of which forecasting method you select, the FSVZO Forecast indicator projects future oscillator positions that may assist with: ▶ Mean Reversion Trading at Extreme Zones: FSVZO displays defined overbought and oversold territories that create potential mean reversion opportunities. When FSVZO enters these upper or lower extremes, traders can monitor for potential exhaustion and reversal setups as the oscillator moves back toward neutral. Projections add a timing dimension to this analysis by showing where FSVZO may travel in upcoming bars, allowing traders to anticipate when the oscillator might approach or exit extreme zones. ▶ Trend Following with the Colored Band: The filled band between FSVZO main line and offset line delivers immediate trend visualization across all forecast models. Green coloring indicates rising FSVZO (current value higher than previous = long/buy opportunity), while red coloring indicates falling FSVZO (current value lower than previous = short/sell opportunity). This visual system provides quick reference for current momentum direction. For trend following applications, traders can monitor band color for directional bias and watch for color transitions as potential warning signals. Projections extend this visualization into future bars, showing whether the forecast anticipates continued momentum or potential direction changes. Combining band direction with FSVZO's position relative to zero provides layered context: green band above zero suggests bullish momentum, red band below zero suggests bearish momentum, while mixed readings suggest transitional conditions. ▶ Divergence Detection: Built-in divergence scanning identifies regular (R label) and hidden (H label) divergences between price and FSVZO. Regular divergences occur when price makes a higher high while FSVZO makes a lower high (bearish) or price makes a lower low while FSVZO makes a higher low (bullish). Hidden divergences signal potential trend continuation. Projections can provide context for whether developing divergences might continue or resolve. ▶ Signal Line Crossovers: The indicator tracks crossovers between FSVZO and its moving average. Crossovers from below occur when FSVZO rises above the MA, while crossovers from above occur when FSVZO falls below the MA. Projections may help anticipate when these crossovers could occur. ▶ Zero Line Analysis: FSVZO crossing above zero indicates a shift to positive directional volume flow. Crossing below zero indicates a shift to negative directional volume flow. Projections can show whether the oscillator may approach or cross the zero line in upcoming bars. ▶ White Noise Filtering: The optional Ehlers White Noise overlay displays an additional oscillator that measures the degree of randomness in price movement. This can help identify periods when price movements lack clear directional commitment, providing context for when momentum signals may be less meaningful. ▶ Multi-Model Comparison: Running different projection methods and noting where they agree or disagree provides additional analytical context. When multiple methods project similar trajectories, this alignment may warrant special attention. ▶ Trade Management: Reference projected FSVZO levels when planning stops, position adjustments, or profit targets based on anticipated momentum conditions. 🟢 Important Considerations ▶ This indicator requires volume data to function correctly. Instruments that do not report volume or report unreliable volume data will produce meaningless or zero readings. ▶ These forecasts derive from mathematical analysis of recent price and volume behavior. Markets operate as dynamic systems influenced by countless factors that no technical indicator can fully anticipate. Projected FSVZO values represent potential momentum scenarios based on current conditions, and actual readings may follow different paths than those visualized. Historical tendencies and mathematical extrapolations provide no guarantee of future market behavior. Consider these projections as one component within a comprehensive trading methodology that includes disciplined risk management, appropriate position sizing, and multiple analytical perspectives. The primary value of this script lies not in expecting precise forecasts but in developing forward-looking awareness of possible market conditions and structuring your trades accordingly.
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QuantAlgo tarafından
1
Crypto EMA Ribbon + Buy/Sell SignalsEMA Ribbon Strategy Logic (Professional-Grade) EMA Ribbon Fast EMAs: 8, 13, 21 Mid EMAs: 34, 55 Trend EMA: 200 Trend Rules Bull Trend: Price above 200 EMA Bear Trend: Price below 200 EMA Buy Signal Price above 200 EMA Fast EMAs stacked bullish (8 > 13 > 21 > 34 > 55) 8 EMA crosses above 21 EMA Sell Signal Price below 200 EMA Fast EMAs stacked bearish (8 < 13 < 21 < 34 < 55) 8 EMA crosses below 21 EMA This avoids chop and only trades momentum-aligned trends.
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panitsos tarafından
Sessions + Prev + PDH/PDL + Killzones SuiteDescription This indicator is designed to provide time-based and price-based market context by combining session ranges with commonly referenced prior levels into a single, unified framework. The purpose of the script is contextual analysis, not signal generation. What the script does The script tracks and plots the following elements directly on the price chart: • High and Low ranges for multiple trading sessions (Asia, London, New York morning, and New York afternoon) • High and Low levels from the previous occurrence of each session • Prior Day High (PDH) and Prior Day Low (PDL) • Optional session “killzone” boxes that visually mark active session time windows All calculations are performed using time-based session boundaries and price extrema (high/low) within those windows. Why these components are combined Sessions, previous session levels, and prior day levels are frequently analyzed together by discretionary traders because they represent: • Where liquidity formed earlier in the day or previous day • Where price previously paused, expanded, or reversed • Natural reference points for intraday structure and range analysis Instead of plotting these elements using multiple separate scripts, this indicator integrates them into one consistent framework so that all levels are calculated using the same timezone, session logic, and display rules. This avoids mismatched session times, duplicate levels, or conflicting calculations that can occur when multiple scripts are used simultaneously. How the script works (high-level) • Each session is defined using user-selectable session times and timezone • During a session, the script tracks the highest and lowest traded price • When a session ends, its final high and low are stored as the “previous session” levels • PDH and PDL are calculated using the completed trading day • Lines and labels are anchored to the bars where levels are formed, rather than extending indefinitely • Optional display filters allow users to show only the current trading day to reduce chart clutter No forward-looking logic, prediction, alerts, or trade execution logic is included. How to use it This script is intended to be used as a visual reference tool to help traders: • Identify session boundaries and intraday ranges • Observe how price reacts near prior session highs and lows • Assess where price is trading relative to PDH and PDL • Maintain consistent session timing across different timezones The script does not provide trade entries, exits, alerts, or performance claims. Important notes • This indicator does not generate buy or sell signals • It does not predict future price movement • It is not a trading strategy • All decisions remain the responsibility of the user Disclaimer This script is provided for educational and informational purposes only. It does not constitute financial advice. Trading involves risk, and users should apply appropriate risk management and personal judgment when using any technical tool.
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TradeswithB tarafından
MTF Target Radar [Rulph]MTF Target Radar - Multi-Timeframe Target Clustering with Machine Learning MTF Target Radar is an advanced target projection system that analyzes trendline breakouts across multiple timeframes (Daily to Biweekly) and clusters projected targets into high-probability zones. It dynamically calculates targets from actual breakout patterns and validates them through multi-timeframe confluence and machine learning, instead of using static support/resistance or fixed Fibonacci ratios. The system continuously tracks cluster performance (Reached / Lost / Timeout) and uses this history to improve future predictions through a transparent k-Nearest Neighbors (k-NN) logic, providing explainable adjustments to cluster quality rather than black-box scores. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHY COMBINE MULTI-TIMEFRAME TARGETS? ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Most target projection methods rely on a single timeframe or on arbitrary geometric ratios. MTF Target Radar is designed around three core ideas: 1. Cross-timeframe validation : A target zone where multiple higher timeframes converge (e.g., 1D, 2D, 3D, 4D, 5D, 6D, 1W, 2W) indicates a structural price magnet, where several independent trend cycles agree on a probable area of exhaustion, continuation, or reversal. 2. Dynamic projection from real patterns : Targets are computed from the geometry of each breakout (distance from the trendline to the extreme of the pattern) instead of being fixed percentages from arbitrary swing points. This makes projected levels adaptive to the actual volatility and structure of each pattern. 3. Adaptive learning : The system learns which cluster characteristics (density, strength, distance, momentum, market regime, etc.) historically lead to successful outcomes and then gently adjusts future cluster qualities in that direction. The result is a "target radar" where the most important zones stand out because they combine: multiple timeframes, favorable structure, and a positive historical profile with similar setups. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ COMPONENT 1: TRENDLINE BREAKOUT DETECTION ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ For each enabled higher timeframe (up to 8), the indicator performs the same deterministic process: 1. Swing pivot detection It finds swing highs and lows using a configurable pivot length (default 3 bars left and right), which defines local extremes for trendline construction. 2. Trendline construction - For bullish breakout setups (upward target clusters), it connects two descending swing highs to form a bearish trendline. - For bearish breakout setups (downward target clusters), it connects two ascending swing lows to form a bullish trendline. 3. Breakout detection A breakout is confirmed when the close crosses and holds beyond the trendline in the opposite direction of the preceding trend (close above a descending line for long setups, or below an ascending line for short setups), which indicates that the previous trend structure has failed. 4. Target projection The target is measured from the internal structure of the pattern, not guessed: For bullish (upward) targets: - The algorithm finds the lowest low between the second pivot and the breakout. - It computes the vertical distance from the trendline value at that bar to this lowest low. - This distance is then projected above the breakout level to obtain an initial target. For bearish (downward) targets, the logic is mirrored using the highest high within the pattern range. This makes each target a direct function of how "compressed" price was before breaking out, creating geometry-driven objectives that adapt to each pattern. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ COMPONENT 2: TARGET CLUSTERING ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ All individual targets from active timeframes are merged into clusters, which represent zones where multiple projected levels overlap or lie very close to each other. Clustering logic : - All targets are sorted by price. - Targets within a maximum distance (MAX_CLUSTER_DISTANCE, default 1.5% of price) are merged into a single cluster. - A cluster must contain at least MIN_CLUSTER_SIZE targets (default 2) to be considered valid and plotted. Cluster properties include: - Center : the average target price within the cluster. - Size : number of contributing targets; more targets imply stronger structural agreement. - Spread : the price width between the lowest and highest targets in the cluster. - Timeframe composition : which timeframes contributed (e.g., "1D, 2D, 3D, 1W"). A tight cluster where many timeframes converge is treated as a stronger and more precise target than scattered levels spread widely in price. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ COMPONENT 3: QUALITY SCORING SYSTEM ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Each cluster receives a base quality score from 0 to 1, computed as a weighted combination of four dimensions: 1. Density score (weight: 0.35) - Based on how narrow the cluster is relative to volatility. - Uses normalized spread: cluster_spread / ATR(14). - A smaller normalized spread leads to a higher density score. 2. Strength score (weight: 0.35) - Depends on the number of targets and their distribution across timeframes. - Uses a log-scaled function of cluster size and a density factor so that adding more confluences yields diminishing but still meaningful improvements. 3. Reachability score (weight: 0.20) - Based on the distance from current price to cluster center in percent terms. - Closer clusters are easier to reach; very distant ones are penalized unless the market and trend strongly support extended moves. 4. Momentum score (weight: 0.10) - Analyzes the last few candles (e.g., 5 bars) using candle bodies, wicks, and short-term rate of change to determine whether current price action supports moving into the cluster. Base quality formula : The base quality is a convex combination: Q_base = 0.35 × Density + 0.35 × Strength + 0.20 × Reachability + 0.10 × Momentum, with additional multiplicative penalties when reachability is too low or the overall market regime contradicts the direction of the cluster. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ COMPONENT 4: MACHINE LEARNING ADJUSTMENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ When the ML enhancement is enabled and there is enough history, the script uses an internal k-Nearest Neighbors approach to adjust cluster quality based on what worked or failed in the past. Feature extraction : For each cluster, the system extracts a feature vector including: - Base quality, distance to target, volatility, trend strength (ADX), RSI value, volume ratio, recent momentum, cluster size, density, market regime, volume trend, timeframe consistency, and price acceleration. Neighbor search : - Only clusters with the same direction (up or down) and with finalized outcomes (reached, lost, or timeout) are considered. - A Lorentzian distance metric is used: sum over all features of log(1 + |difference|) multiplied by per-feature weights, so that extreme outliers do not dominate. Graduated success scoring : Each historical cluster stores a continuous success_score, not just 0 or 1: - Full success when the target zone is actually reached with reasonable timing. - Partial credit when price comes very close but slightly misses the cluster or reaches only part of it. - Penalties when the cluster times out or price moves away strongly. ML adjustment of quality : The script computes an ML_probability for the active cluster by aggregating neighbors' success_score values weighted by similarity and recency. This ML-derived probability is then mixed with the base quality: Q_adjusted = Q_base × (1 − ML_weight) + ML_probability × ML_weight, where ML_weight increases gradually with the amount and reliability of historical data and is capped so that ML cannot completely override the base structural logic. Additionally, performance metrics such as recent accuracy, false positives, false negatives, and total predictions are tracked to adapt how much trust is placed in ML adjustments over time. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ COMPONENT 5: TIME-TO-TARGET PREDICTION ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ When time prediction is enabled, the indicator estimates how many bars it may take for price to reach the cluster. This is an experimental feature designed for context, not as a hard promise. Base estimate : - Uses distance to cluster and current volatility as primary inputs. - Time is scaled differently for various asset classes (e.g., crypto vs. equities), so that fast markets do not get unrealistic long estimates and slow markets do not get unrealistically short ones. ML refinement : If enough successful historical clusters with similar features are available, the script: - Filters neighbors that actually reached their targets. - Uses their real bars_to_reach values. - Computes a weighted average to refine the time estimate. The final time prediction is a blend of base estimate and ML-derived value, with a confidence measure derived from the number, similarity, and recency of matching examples. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ CLUSTER STATE MACHINE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Each cluster goes through a simple and explicit state machine: forming → active Once cluster quality rises above the minimum threshold, it becomes active and is displayed on the chart. active → reached The cluster is marked as reached when price touches at least the first target in its internal list (TP1), using direction-sensitive logic (high >= TP1 for long clusters, low <= TP1 for short clusters). active → lost If the underlying targets are structurally invalidated (e.g., fewer than MIN_CLUSTER_SIZE remain due to market movement), the cluster becomes lost. active → timeout If age exceeds MAX_CLUSTER_AGE (default 40 bars) without reaching the target, the cluster is marked as timeout, so stale setups do not stay active indefinitely. Final states (reached, lost, timeout) are recorded with snapshots of cluster features, bars_alive, bars_to_reach, and realized P&L percentage. These records feed back into the ML history. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ HOW TO USE MTF TARGET RADAR ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Basic workflow : 1. Enable the higher timeframes that are relevant to your trading style (e.g., 1D–6D + 1W for intraday or swing trading). 2. Set Min Quality Score (MIN_QUALITY) according to your risk tolerance: - 0.3–0.4 for aggressive, - 0.5–0.6 for balanced, - 0.7+ for conservative setups. 3. Optionally enable ML and time prediction once enough history is accumulated. 4. Use the trend context block (if enabled) to see whether clusters align with the dominant trend or go against it. Reading the chart : - Green boxes above price = upward target clusters (long objectives). - Red boxes below price = downward target clusters (short objectives). - Box width shows the price range of the cluster; box position shows where price is expected to gravitate. - Labels can include: contributing timeframes, cluster center, base quality, ML-adjusted quality, distance to target, and estimated time to target when enabled. Example entry logic : - For a long: price is below a strong green cluster, quality > 0.6, direction aligned with the current trend, and ML-adjusted quality is not significantly lower than base quality. - Entry can be timed using your own triggers (breakouts, pullbacks, candlestick patterns), while the cluster defines the target area rather than the exact entry. Example exit logic : - Take profit as price enters the cluster zone. - Scale out around cluster center or when realized move covers your planned R-multiple. - Exit early if the cluster flips to "lost" or if an opposite-direction high-quality cluster appears and is closer than the current one. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ WHAT MAKES MTF TARGET RADAR ORIGINAL ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ MTF Target Radar is not a simple overlay of trendlines and support/resistance; it implements a full pipeline: pattern-based target projection, cross-timeframe clustering, quality scoring, and machine learning feedback. Key aspects of originality include: - Multi-timeframe target clustering where zones are built from many independent breakouts instead of a single pattern. - Quantified cluster quality combining density, strength, reachability, and momentum in a transparent scoring model. - Graduated ML learning that uses continuous success scores and explainable k-NN, rather than opaque models. - State machine tracking of each cluster's lifecycle with explicit rules for success, failure, and timeout. - Optional time-to-target estimation that reuses the same ML history instead of guessing fixed time windows. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ CHART LEGEND ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - Green box above current price: bullish target cluster. - Red box below current price: bearish target cluster. - Historical clusters can be marked with symbols: - ✓ for reached, - ✗ for lost, - ⏱ for timeout, often accompanied by a diagonal line showing entry-to-target path and final P&L%. - Optional trend context (LazyTrend/SuperTrend-style block): - Green background: bullish regime. - Red background: bearish regime. - Neutral colors: sideways or mixed regime. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Additional Resources (Optional) : This description is complete and self-contained; no external materials are required to understand how the script works or how to use it. Any separate educational ideas or examples are optional and serve only as additional illustration. Disclaimer: MTF Target Radar is a decision-support tool, not a standalone trading system. All trading involves risk, and past cluster performance does not guarantee future results. Always backtest and apply proper risk management.
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rulphru tarafından
REVE_d“REVE” is an indicator developed by a team of four people: two Japanese, one American, and one Canadian. We selected around 20 financial and management-related indicators, mainly focusing on fundamentals, and fed historical data of past ten-bagger stocks into AI. The AI was then tasked with discovering combinations and calculation formulas that have a strong correlation with stock price movements. While predicting stocks ten years ahead is difficult, the core idea of the REVE project is this: by analyzing and verifying the common patterns found in past ten-bagger stocks, we may be able to identify stocks that could experience sharp price increases over a period of several years. As a result, we discovered an indicator that begins to rise ahead of major stock price surges. Of course, it does not apply to every stock, but its effectiveness becomes clear when tested on past stocks that doubled or tripled in price over the last few years. The name “REVE” was chosen by the Canadian member of the team. We are currently validating the indicator using both Japanese and U.S. stocks. We plan to release it through Kabu-Ojisan as an indicator for Japanese stocks, which has already been validated first. Since the verification process is still ongoing, the source code will not be made public; however, it will be shared free of charge with acquaintances. ---- How to Use ---- The usage of REVE is extremely simple. Display a stock price chart on a daily timeframe or higher, and show the REVE indicator at the same time. The REVE chart rises and falls at a relatively slow pace. The score typically changes on a roughly quarterly basis, and the key points are both the number of consecutive score increases and the score level itself. The conditions are as follows: 1. The score rises consecutively four times 2. The score reaches a predefined threshold When these two conditions occur simultaneously and a long position is taken, there is a strong expectation—based on statistical verification using historical data—that the stock price will rise significantly several months later and continue to increase over a long period. Regarding condition (2), when the score reaches 2.0, the probability of a subsequent price increase is quite high, making it a recommended entry point for a long position. However, cases where the score reaches 1.0 are also sufficiently meaningful and can serve as useful entry points. In the indicator, the chart color changes when the REVE score reaches 1.0 or higher, and changes again when it reaches 2.0 or higher, making these levels visually easy to identify. (When the REVE score is lower than 1.0, a rise in the stock price should not be expected.) Please note that after the REVE score reaches its threshold, once the stock price begins to rise, the REVE score will usually start to decline. If the REVE score continues to rise instead, it may indicate the potential for further opportunities. Below are several sample validation results using Japanese stocks.
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Kabu_ojisan tarafından
2
OTE Visualizer by AvenoirOTE Visualizer by Avenoir - Premium Fib-Based Structure Mapping OTE Visualizer by Avenoir is a clean, modern market-structure indicator designed to automatically detect and visualize Optimal Trade Entry (OTE) zones using true ICT-style fib logic. It identifies valid bullish and bearish impulse legs based on swing structure, then plots discount and premium retracement zones for high-probability entries. This tool is built for precision, clarity, and algorithmic consistency. 🔶 Key Features ✔ Automatic OTE Zones (Bullish & Bearish) Bullish OTE = deep discount zone from the prior swing low → swing high Bearish OTE = deep premium zone from the prior swing high → swing low Uses exact retracement levels: 62% – 79%, with optional 70.5% midline ✔ Active vs Old OTE Visualization The most recent OTE is highlighted Older OTE zones are automatically: Faded, or Completely hidden (optional toggle) This keeps charts clean while maintaining structure awareness. ✔ Swing Structure Detection Uses pivot-based swing identification Tracks swing highs/lows and builds legs only when structure is valid Optional labels for swing points ✔ Impulse Leg Lines Draws the actual impulse leg used for OTE generation Shows exactly which high/low produced the zone Helps traders understand the logic behind each OTE ✔ BOS (Break of Structure) Detection Marks BOS↑ when price closes above the previous swing high Marks BOS↓ when price closes below the previous swing low Useful confirmation for shift in market direction ✔ ATR-Based Impulse Filtering Optional filter to ensure OTEs only form on significant moves: Choose ATR length Choose minimum impulse size (ATR multiples) Removes noise and minor swings Produces cleaner, more reliable OTE zones ✔ Fully Customizable Visuals Choose any colors Adjust opacity Show/hide individual elements Clean, minimalist aesthetic that blends beautifully into charts 🎯 Ideal For ICT / Smart Money Concepts traders Algo/systematic traders Scalpers to swing traders Anyone wanting clear structure-based OTE zones Traders building automated or rule-based trading models 📌 How to Use Identify trend direction Wait for a bullish or bearish BOS Watch for price to retrace into the active OTE zone Combine with liquidity sweeps, displacement candles, FVGs, or other SMC/ICT techniques Execute trades in premium/discount areas with strong context ✨ Final Notes This indicator is built for precision and clarity. It does not repaint and provides an objective, consistently structured view of OTE zones across any market or timeframe. For traders who rely on execution models, structural mapping, and disciplined entries, this is your new foundation tool.
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AVENOIRxbt tarafından
2