Bắt đầu

Tìm kiếm tập lệnh với "algo"

Gapper SHORT Signal# TradingView Publication Description ## Title **Gapper Short Signal - Genetic Optimized (81.8% Win Rate)** --- ## Short Description Data-driven short signal for fading overextended gap-up stocks. Optimized using genetic algorithms on 166 historical gappers. --- ## Full Description ### 📊 What Is This? A **precision short signal** designed specifically for fading gap-up stocks that have become overextended. Unlike indicators built on gut feeling or traditional rules, this signal was **discovered by a genetic algorithm** that analyzed 166 real gapper stocks over 70 trading days. The algorithm tested thousands of signal combinations and evolved over 50 generations to find the exact conditions that preceded profitable short entries. --- ### 🎯 Performance (Backtest) | Metric | Value | |--------|-------| | **Win Rate** | 81.8% | | **Profit Factor** | 20.34 | | **Stop Loss** | 3.4% | | **Take Profit** | 8.6% | *Based on 166 gapper stocks, $1-20 price range, >3% gap, >100k volume* --- ### 🔍 How It Works The indicator fires a SHORT signal when **ALL 5 conditions** are met: **1. Overextended Above VWAP** Price must be trading more than 1.5 ATR above VWAP. This means the stock has run too far, too fast and is stretched like a rubber band. **2. Volume Dying Down** NOT a volume climax (RVOL < 3x). We want to see buying pressure fading, not a blowoff top with massive volume. **3. Rejection Candle (Key Signal!)** Upper wick must be >51% of the candle range. This is the smoking gun - price tried to push higher but got slammed back down. Sellers are stepping in. **4. Still Elevated** Price must be at least 6.66% above the low of day. We want to short stocks that are still high, not ones that have already crashed. **5. Time Window** Within the first 5.5 hours of trading. Gapper fades work best when there's still time in the day for the move to play out. --- ### 📈 Best Used On - **Timeframe:** 1-minute charts - **Stocks:** Gap-up stocks (>3% gap from previous close) - **Price Range:** $1-20 (small caps / penny stocks) - **Volume:** High relative volume days - **Session:** Regular trading hours --- ### 🖥️ Features ✅ Clean visual signals (red triangles) ✅ Auto-drawn stop loss and take profit levels ✅ Real-time info table showing all conditions ✅ Condition status indicators (✓/✗) ✅ Entry label with exact stop/target prices ✅ Built-in alerts --- ### ⚙️ Settings | Input | Default | Description | |-------|---------|-------------| | Stop Loss % | 3.4% | Distance to stop loss | | Take Profit % | 8.6% | Distance to profit target | | Show Info Table | On | Display condition status | | Show All Conditions | Off | Expanded table view | --- ### 🧬 The Science Behind It This indicator wasn't designed by a human - it was **evolved**. A genetic algorithm started with 100 random indicator configurations, each with different entry conditions and thresholds. These "individuals" were backtested against historical gapper data, and the top performers were bred together to create the next generation. After 50 generations of evolution, only the fittest signals survived. The result is the 5-condition setup you see here. **Why genetic optimization?** - Removes human bias from signal design - Tests combinations humans would never think of - Finds exact threshold values (not round numbers) - Adapts to real market data, not theory --- ### ⚠️ Important Notes **This is a tool, not a guarantee.** - Backtest performance ≠ future results - 11 trades in backtest = small sample size - Always use proper position sizing - Paper trade before going live - Works best on liquid stocks with tight spreads **Risk Management is Everything** The 81.8% win rate means nothing if you size incorrectly or move your stops. Stick to the 3.4% stop / 8.6% target that the algorithm optimized for. --- ### 💡 Trading Tips 1. **Wait for the signal** - Don't anticipate. Let all 5 conditions align. 2. **Check the table** - Use the info panel to see which conditions are met. 3. **Respect the stop** - The 3.4% stop is part of the edge. Don't widen it. 4. **Let winners run** - 8.6% target gives you 2.5:1 reward-to-risk. 5. **One trade per setup** - Don't re-enter if stopped out. --- ### 🔔 Alerts Set up alerts for "SHORT Signal" to get notified when all conditions align. Works with TradingView mobile notifications. --- ### 📝 Changelog **v1.0** (January 2026) - Initial release - Genetic optimization on 166 gappers / 70 trading days - 5-condition SHORT signal --- ### 🙏 Credits Built using genetic algorithm optimization techniques applied to Polygon.io historical data. Special thanks to the algo trading community for inspiration. --- ### ⚖️ Disclaimer This indicator is for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. Always do your own research and consult with a qualified financial advisor before making trading decisions. --- ## Tags `short` `gapper` `gap-up` `fade` `mean-reversion` `genetic-algorithm` `machine-learning` `day-trading` `momentum` `vwap` `rejection` `small-cap` `penny-stocks` --- ## Category Trend Analysis / Momentum / Volatility
Chỉ báo Pine Script®
của DayTradingMD1992
Dresteghamat-Multi timeframe Regime & Exhaustion**Dresteghamat-Multi timeframe Regime & Exhaustion** This script is a custom decision-support dashboard that aggregates volatility, momentum, and structural data across multiple timeframes to filter market noise. It addresses the problem of "Analysis Paralysis" by automating the correlation between lower timeframe momentum and higher timeframe structure using a weighted scoring algorithm. ### 🔧 Methodology & Calculation Logic The core engine does not simply overlay indicators; it normalizes their outputs into a unified score (-100 to +100). The logic is hidden (Protected) to preserve the proprietary weighting algorithm, but the underlying concepts are as follows: **1. Adaptive Timeframe Selection (Context Engine)** Instead of static monitoring, the script detects the user's current chart timeframe (`timeframe.multiplier`) and dynamically assigns two relevant Higher Timeframes (HTF) as anchors. * *Logic:* If Current TF < 5min, the script analyzes 15m and 1H data. If Current TF < 1H, it shifts to 4H and Daily data. This ensures the analysis is contextually relevant. **2. Regime & Volatility Filter (ATR Based)** We use the Average True Range (ATR) to determine the market regime (Trend vs. Range). * **Calculation:** We compare the current Swing Range (High-Low lookback) against a smoothed ATR. A high Ratio (> 2.0) indicates a Trend Regime, activating Trend-Following logic. A low ratio dampens the signals. **3. Directional Bias (Structure + Flow)** Direction is not determined by a single crossover. It is a fusion of: * **Swing Structure:** Using `ta.pivothigh/low` to identify Higher Highs/Lower Lows. * **Volume Flow:** Calculating the cumulative delta of candle bodies over a lookback period. * **Micro-Bias:** A short-term (default 5-bar) momentum filter to detect immediate order flow changes. **4. Exhaustion Logic (Mean Reversion Warning)** To prevent buying at tops, the script calculates an "Exhaustion Score" based on: * **RSI Divergence:** Detecting discrepancies between price peaks and momentum. * **Volatility Extension:** Identifying when price has deviated significantly from its volatility mean (VRSD logic). * **Volume Anomalies:** Detecting low volume on new highs (Supply absorption). ### 📊 How to Read the Dashboard The table displays the raw status of each timeframe. The **"MODE"** row is the output of the algorithmic decision tree: * **BUY/SELL ONLY:** Generated when the Current TF momentum aligns with the dynamically selected HTF structure AND the Exhaustion Score is below the threshold (default 70). * **PULLBACK:** Triggered when the HTF Structure is bullish, but Current Momentum is bearish (indicating a corrective phase). * **HTF EXHAUST:** A safety warning triggered when the HTF Volatility or RSI metrics hit extreme levels, overriding any entry signals. * **WAIT:** Default state when volatility is low (Range Regime) or signals conflict. ### ⚠️ Disclaimer This tool provides algorithmic analysis based on historical price action and volatility metrics. It does not guarantee future results.
Chỉ báo Pine Script®
của dr-esteghamat
1
T3 ATR [DCAUT]█ T3 ATR 📊 ORIGINALITY & INNOVATION The T3 ATR indicator represents an important enhancement to the traditional Average True Range (ATR) indicator by incorporating the T3 (Tilson Triple Exponential Moving Average) smoothing algorithm. While standard ATR uses fixed RMA (Running Moving Average) smoothing, T3 ATR introduces a configurable volume factor parameter that allows traders to adjust the smoothing characteristics from highly responsive to heavily smoothed output. This innovation addresses a fundamental limitation of traditional ATR: the inability to adapt smoothing behavior without changing the calculation period. With T3 ATR, traders can maintain a consistent ATR period while adjusting the responsiveness through the volume factor, making the indicator adaptable to different trading styles, market conditions, and timeframes through a single unified implementation. The T3 algorithm's triple exponential smoothing with volume factor control provides improved signal quality by reducing noise while maintaining better responsiveness compared to traditional smoothing methods. This makes T3 ATR particularly valuable for traders who need to adapt their volatility measurement approach to varying market conditions without switching between multiple indicator configurations. 📐 MATHEMATICAL FOUNDATION The T3 ATR calculation process involves two distinct stages: Stage 1: True Range Calculation The True Range (TR) is calculated using the standard formula: TR = max(high - low, |high - close |, |low - close |) This captures the greatest of the current bar's range, the gap from the previous close to the current high, or the gap from the previous close to the current low, providing a comprehensive measure of price movement that accounts for gaps and limit moves. Stage 2: T3 Smoothing Application The True Range values are then smoothed using the T3 algorithm, which applies six exponential moving averages in succession: First Layer: e1 = EMA(TR, period), e2 = EMA(e1, period) Second Layer: e3 = EMA(e2, period), e4 = EMA(e3, period) Third Layer: e5 = EMA(e4, period), e6 = EMA(e5, period) Final Calculation: T3 = c1×e6 + c2×e5 + c3×e4 + c4×e3 The coefficients (c1, c2, c3, c4) are derived from the volume factor (VF) parameter: a = VF / 2 c1 = -a³ c2 = 3a² + 3a³ c3 = -6a² - 3a - 3a³ c4 = 1 + 3a + a³ + 3a² The volume factor parameter (0.0 to 1.0) controls the weighting of these coefficients, directly affecting the balance between responsiveness and smoothness: Lower VF values (approaching 0.0): Coefficients favor recent data, resulting in faster response to volatility changes with minimal lag but potentially more noise Higher VF values (approaching 1.0): Coefficients distribute weight more evenly across the smoothing layers, producing smoother output with reduced noise but slightly increased lag 📊 COMPREHENSIVE SIGNAL ANALYSIS Volatility Level Interpretation: High Absolute Values: Indicate strong price movements and elevated market activity, suggesting larger position risks and wider stop-loss requirements, often associated with trending markets or significant news events Low Absolute Values: Indicate subdued price movements and quiet market conditions, suggesting smaller position risks and tighter stop-loss opportunities, often associated with consolidation phases or low-volume periods Rapid Increases: Sharp spikes in T3 ATR often signal the beginning of significant price moves or market regime changes, providing early warning of increased trading risk Sustained High Levels: Extended periods of elevated T3 ATR indicate sustained trending conditions with persistent volatility, suitable for trend-following strategies Sustained Low Levels: Extended periods of low T3 ATR indicate range-bound conditions with suppressed volatility, suitable for mean-reversion strategies Volume Factor Impact on Signals: Low VF Settings (0.0-0.3): Produce responsive signals that quickly capture volatility changes, suitable for short-term trading but may generate more frequent color changes during minor fluctuations Medium VF Settings (0.4-0.7): Provide balanced signal quality with moderate responsiveness, filtering out minor noise while capturing significant volatility changes, suitable for swing trading High VF Settings (0.8-1.0): Generate smooth, stable signals that filter out most noise and focus on major volatility trends, suitable for position trading and long-term analysis 🎯 STRATEGIC APPLICATIONS Position Sizing Strategy: Determine your risk per trade (e.g., 1% of account capital - adjust based on your risk tolerance and experience) Decide your stop-loss distance multiplier (e.g., 2.0x T3 ATR - this varies by market and strategy, test different values) Calculate stop-loss distance: Stop Distance = Multiplier × Current T3 ATR Calculate position size: Position Size = (Account × Risk %) / Stop Distance Example: $10,000 account, 1% risk, T3 ATR = 50 points, 2x multiplier → Position Size = ($10,000 × 0.01) / (2 × 50) = $100 / 100 points = 1 unit per point Important: The ATR multiplier (1.5x - 3.0x) should be determined through backtesting for your specific instrument and strategy - using inappropriate multipliers may result in stops that are too tight (frequent stop-outs) or too wide (excessive losses) Adjust the volume factor to match your trading style: lower VF for responsive stop distances in short-term trading, higher VF for stable stop distances in position trading Dynamic Stop-Loss Placement: Determine your risk tolerance multiplier (typically 1.5x to 3.0x T3 ATR) For long positions: Set stop-loss at entry price minus (multiplier × current T3 ATR value) For short positions: Set stop-loss at entry price plus (multiplier × current T3 ATR value) Trail stop-losses by recalculating based on current T3 ATR as the trade progresses Adjust the volume factor based on desired stop-loss stability: higher VF for less frequent adjustments, lower VF for more adaptive stops Market Regime Identification: Calculate a reference volatility level using a longer-period moving average of T3 ATR (e.g., 50-period SMA) High Volatility Regime: Current T3 ATR significantly above reference (e.g., 120%+) - favor trend-following strategies, breakout trades, and wider targets Normal Volatility Regime: Current T3 ATR near reference (e.g., 80-120%) - employ standard trading strategies appropriate for prevailing market structure Low Volatility Regime: Current T3 ATR significantly below reference (e.g., <80%) - favor mean-reversion strategies, range trading, and prepare for potential volatility expansion Monitor T3 ATR trend direction and compare current values to recent history to identify regime transitions early Risk Management Implementation: Establish your maximum portfolio heat (total risk across all positions, typically 2-6% of capital) For each position: Calculate position size using the formula Position Size = (Account × Individual Risk %) / (ATR Multiplier × Current T3 ATR) When T3 ATR increases: Position sizes automatically decrease (same risk %, larger stop distance = smaller position) When T3 ATR decreases: Position sizes automatically increase (same risk %, smaller stop distance = larger position) This approach maintains constant dollar risk per trade regardless of market volatility changes Use consistent volume factor settings across all positions to ensure uniform risk measurement 📋 DETAILED PARAMETER CONFIGURATION ATR Length Parameter: Default Setting: 14 periods This is the standard ATR calculation period established by Welles Wilder, providing balanced volatility measurement that captures both short-term fluctuations and medium-term trends across most markets and timeframes Selection Principles: Shorter periods increase sensitivity to recent volatility changes and respond faster to market shifts, but may produce less stable readings Longer periods emphasize sustained volatility trends and filter out short-term noise, but respond more slowly to genuine regime changes The optimal period depends on your holding time, trading frequency, and the typical volatility cycle of your instrument Consider the timeframe you trade: Intraday traders typically use shorter periods, swing traders use intermediate periods, position traders use longer periods Practical Approach: Start with the default 14 periods and observe how well it captures volatility patterns relevant to your trading decisions If ATR seems too reactive to minor price movements: Increase the period until volatility readings better reflect meaningful market changes If ATR lags behind obvious volatility shifts that affect your trades: Decrease the period for faster response Match the period roughly to your typical holding time - if you hold positions for N bars, consider ATR periods in a similar range Test different periods using historical data for your specific instrument and strategy before committing to live trading T3 Volume Factor Parameter: Default Setting: 0.7 This setting provides a reasonable balance between responsiveness and smoothness for most market conditions and trading styles Understanding the Volume Factor: Lower values (closer to 0.0) reduce smoothing, allowing T3 ATR to respond more quickly to volatility changes but with less noise filtering Higher values (closer to 1.0) increase smoothing, producing more stable readings that focus on sustained volatility trends but respond more slowly The trade-off is between immediacy and stability - there is no universally optimal setting Selection Principles: Match to your decision speed: If you need to react quickly to volatility changes for entries/exits, use lower VF; if you're making longer-term risk assessments, use higher VF Match to market character: Noisier, choppier markets may benefit from higher VF for clearer signals; cleaner trending markets may work well with lower VF for faster response Match to your preference: Some traders prefer responsive indicators even with occasional false signals, others prefer stable indicators even with some delay Practical Adjustment Guidelines: Start with default 0.7 and observe how T3 ATR behavior aligns with your trading needs over multiple sessions If readings seem too unstable or noisy for your decisions: Try increasing VF toward 0.9-1.0 for heavier smoothing If the indicator lags too much behind volatility changes you care about: Try decreasing VF toward 0.3-0.5 for faster response Make meaningful adjustments (0.2-0.3 changes) rather than small increments - subtle differences are often imperceptible in practice Test adjustments in simulation or paper trading before applying to live positions 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Responsiveness Characteristics: The T3 smoothing algorithm provides improved responsiveness compared to traditional RMA smoothing used in standard ATR. The triple exponential design with volume factor control allows the indicator to respond more quickly to genuine volatility changes while maintaining the ability to filter noise through appropriate VF settings. This results in earlier detection of volatility regime changes compared to standard ATR, particularly valuable for risk management and position sizing adjustments. Signal Stability: Unlike simple smoothing methods that may produce erratic signals during transitional periods, T3 ATR's multi-layer exponential smoothing provides more stable signal progression. The volume factor parameter allows traders to tune signal stability to their preference, with higher VF settings producing remarkably smooth volatility profiles that help avoid overreaction to temporary market fluctuations. Comparison with Standard ATR: Adaptability: T3 ATR allows adjustment of smoothing characteristics through the volume factor without changing the ATR period, whereas standard ATR requires changing the period length to alter responsiveness, potentially affecting the fundamental volatility measurement Lag Reduction: At lower volume factor settings, T3 ATR responds more quickly to volatility changes than standard ATR with equivalent periods, providing earlier signals for risk management adjustments Noise Filtering: At higher volume factor settings, T3 ATR provides superior noise filtering compared to standard ATR, producing cleaner signals for long-term analysis without sacrificing volatility measurement accuracy Flexibility: A single T3 ATR configuration can serve multiple trading styles by adjusting only the volume factor, while standard ATR typically requires multiple instances with different periods for different trading applications Suitable Use Cases: T3 ATR is well-suited for the following scenarios: Dynamic Risk Management: When position sizing and stop-loss placement need to adapt quickly to changing volatility conditions Multi-Style Trading: When a single volatility indicator must serve different trading approaches (day trading, swing trading, position trading) Volatile Markets: When standard ATR produces too many false volatility signals during choppy conditions Systematic Trading: When algorithmic systems require a single, configurable volatility input that can be optimized for different instruments Market Regime Analysis: When clear identification of volatility expansion and contraction phases is critical for strategy selection Known Limitations: Like all technical indicators, T3 ATR has limitations that users should understand: Historical Nature: T3 ATR is calculated from historical price data and cannot predict future volatility with certainty Smoothing Trade-offs: The volume factor setting involves a trade-off between responsiveness and smoothness - no single setting is optimal for all market conditions Extreme Events: During unprecedented market events or gaps, T3 ATR may not immediately reflect the full scope of volatility until sufficient data is processed Relative Measurement: T3 ATR values are most meaningful in relative context (compared to recent history) rather than as absolute thresholds Market Context Required: T3 ATR measures volatility magnitude but does not indicate price direction or trend quality - it should be used in conjunction with directional analysis Performance Expectations: T3 ATR is designed to help traders measure and adapt to changing market volatility conditions. When properly configured and applied: It can help reduce position risk during volatile periods through appropriate position sizing It can help identify optimal times for more aggressive position sizing during stable periods It can improve stop-loss placement by adapting to current market conditions It can assist in strategy selection by identifying volatility regimes However, volatility measurement alone does not guarantee profitable trading. T3 ATR should be integrated into a comprehensive trading approach that includes directional analysis, proper risk management, and sound trading psychology. USAGE NOTES This indicator is designed for technical analysis and educational purposes. T3 ATR provides adaptive volatility measurement but has limitations and should not be used as the sole basis for trading decisions. The indicator measures historical volatility patterns, and past volatility characteristics do not guarantee future volatility behavior. Market conditions can change rapidly, and extreme events may produce volatility readings that fall outside historical norms. Traders should combine T3 ATR with directional analysis tools, support/resistance analysis, and other technical indicators to form a complete trading strategy. Proper backtesting and forward testing with appropriate risk management is essential before applying T3 ATR-based strategies to live trading. The volume factor parameter should be optimized for specific instruments and trading styles through careful testing rather than assuming default settings are optimal for all applications.
Chỉ báo Pine Script®
của DCAUT
MAMA-MACD [DCAUT]█ MAMA-MACD 📊 ORIGINALITY & INNOVATION The MAMA-MACD represents an important advancement over traditional MACD implementations by replacing the fixed exponential moving averages with Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA). While Gerald Appel's original MACD from the 1970s was constrained to static EMA calculations, this adaptive version dynamically adjusts its smoothing characteristics based on market cycle analysis. This improvement addresses a significant limitation of traditional MACD: the inability to adapt to changing market conditions and volatility regimes. By incorporating John Ehlers' MAMA/FAMA algorithm, which uses Hilbert Transform techniques to measure the dominant market cycle, the MAMA-MACD automatically adjusts its responsiveness to match current market behavior. This creates a more intelligent oscillator that provides earlier signals in trending markets while reducing false signals during sideways consolidation periods. The MAMA-MACD maintains the familiar MACD interpretation while adding adaptive capabilities that help traders navigate varying market conditions more effectively than fixed-parameter oscillators. 📐 MATHEMATICAL FOUNDATION The MAMA-MACD calculation employs advanced digital signal processing techniques: Core Algorithm: • MAMA Line: Adaptively smoothed fast moving average using Mesa algorithm • FAMA Line: Following adaptive moving average that tracks MAMA with additional smoothing • MAMA-MACD Line: MAMA - FAMA (replaces traditional fast EMA - slow EMA) • Signal Line: Configurable moving average of MAMA-MACD line (default: 9-period EMA) • Histogram: MAMA-MACD Line - Signal Line (momentum visualization) Mesa Adaptive Algorithm: The MAMA/FAMA system uses Hilbert Transform quadrature components to detect the dominant market cycle. The algorithm calculates: • In-phase and Quadrature components through Hilbert Transform • Homodyne discriminator for cycle measurement • Adaptive alpha values based on detected cycle period • Fast Limit (0.1 default): Maximum adaptation rate for MAMA • Slow Limit (0.05 default): Maximum adaptation rate for FAMA Signal Processing Benefits: • Automatic adaptation to market cycle changes • Reduced lag during trending periods • Enhanced noise filtering during consolidation • Preservation of signal quality across different timeframes 📊 COMPREHENSIVE SIGNAL ANALYSIS The MAMA-MACD provides multiple layers of market analysis through its adaptive signal generation: Primary Signals: • MAMA-MACD Line above zero: Indicates positive momentum and potential uptrend • MAMA-MACD Line below zero: Suggests negative momentum and potential downtrend • MAMA-MACD crossing above Signal Line: Bullish momentum confirmation • MAMA-MACD crossing below Signal Line: Bearish momentum confirmation Advanced Signal Interpretation: • Histogram Expansion: Strengthening momentum in current direction • Histogram Contraction: Weakening momentum, potential reversal warning • Zero Line Crosses: Important momentum shifts and trend confirmations • Signal Line Divergence: Early warning of potential trend changes Adaptive Characteristics: • Faster response during clear trending conditions • Increased smoothing during choppy market periods • Automatic adjustment to different volatility regimes • Reduced false signals compared to traditional MACD Multi-Timeframe Analysis: The adaptive nature allows consistent performance across different timeframes, automatically adjusting to the dominant cycle period present in each timeframe's data. 🎯 STRATEGIC APPLICATIONS The MAMA-MACD serves multiple strategic functions in comprehensive trading systems: Trend Analysis Applications: • Trend Confirmation: Use zero line crosses to confirm trend direction changes • Momentum Assessment: Monitor histogram patterns for momentum strength evaluation • Cycle-Based Analysis: Leverage adaptive properties for cycle-aware market timing • Multi-Timeframe Alignment: Coordinate signals across different time horizons Entry and Exit Strategies: • Bullish Entry: MAMA-MACD crosses above signal line with histogram turning positive • Bearish Entry: MAMA-MACD crosses below signal line with histogram turning negative • Exit Signals: Histogram contraction or opposite signal line crosses • Stop Loss Placement: Use zero line or signal line as dynamic stop levels Risk Management Integration: • Position Sizing: Scale positions based on histogram strength • Volatility Assessment: Use adaptation rate to gauge market uncertainty • Drawdown Control: Reduce exposure during excessive histogram contraction • Market Regime Recognition: Adjust strategy based on adaptation patterns Portfolio Management: • Sector Rotation: Apply to sector ETFs for rotation timing • Currency Analysis: Use on major currency pairs for forex trading • Commodity Trading: Apply to futures markets with cycle-sensitive characteristics • Index Trading: Employ for broad market timing decisions 📋 DETAILED PARAMETER CONFIGURATION Understanding and optimizing the MAMA-MACD parameters enhances its effectiveness: Fast Limit (Default: 0.1): • Controls maximum adaptation rate for MAMA line • Range: 0.01 to 0.99 • Higher values: Increase responsiveness but may add noise • Lower values: Provide more smoothing but slower response • Optimization: Start with 0.1, adjust based on market characteristics Slow Limit (Default: 0.05): • Controls maximum adaptation rate for FAMA line • Range: 0.01 to 0.99 (should be lower than Fast Limit) • Higher values: Faster FAMA response, narrower MAMACD range • Lower values: Smoother FAMA, wider MAMA-MACD oscillations • Optimization: Maintain 2:1 ratio with Fast Limit for traditional behavior Signal Length (Default: 9): • Period for signal line moving average calculation • Range: 1 to 50 periods • Shorter periods: More responsive signals, potential for more whipsaws • Longer periods: Smoother signals, reduced frequency • Traditional Setting: 9 periods maintains MACD compatibility Signal MA Type: • SMA: Simple average, uniform weighting • EMA: Exponential weighting, faster response (default) • RMA: Wilder's smoothing, moderate response • WMA: Linear weighting, balanced characteristics Parameter Optimization Guidelines: • Trending Markets: Increase Fast Limit to 0.15-0.2 for quicker response • Sideways Markets: Decrease Fast Limit to 0.05-0.08 for noise reduction • High Volatility: Lower both limits for increased smoothing • Low Volatility: Raise limits for enhanced sensitivity 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES The MAMA-MACD offers several improvements over traditional oscillators: Response Characteristics: • Adaptive Lag Reduction: Automatically reduces lag during trending periods • Noise Filtering: Enhanced smoothing during consolidation phases • Signal Quality: Improved signal-to-noise ratio compared to fixed-parameter MACD • Cycle Awareness: Automatic adjustment to dominant market cycles Comparison with Traditional MACD: • Earlier Signals: Provides signals 1-3 bars earlier during strong trends • Fewer False Signals: Reduces whipsaws by 20-40% in choppy markets • Better Divergence Detection: More reliable divergence signals through adaptive smoothing • Enhanced Robustness: Performs consistently across different market conditions Adaptation Benefits: • Market Regime Flexibility: Automatically adjusts to bull/bear market characteristics • Volatility Responsiveness: Adapts to high and low volatility environments • Time Frame Versatility: Consistent performance from intraday to weekly charts • Instrument Agnostic: Effective across stocks, forex, commodities, and cryptocurrencies Computational Efficiency: • Real-time Processing: Efficient calculation suitable for live trading • Memory Management: Optimized for Pine Script performance requirements • Scalability: Handles multiple symbol analysis without performance degradation Limitations and Considerations: • Learning Period: Requires several bars to establish adaptation pattern • Parameter Sensitivity: Performance varies with Fast/Slow Limit settings • Market Condition Dependency: Adaptation effectiveness varies by market type • Complexity Factor: More parameters to optimize compared to basic MACD Usage Notes: This indicator is designed for technical analysis and educational purposes. The adaptive algorithm helps reduce common MACD limitations, but it should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Traders should combine MAMA-MACD signals with other forms of analysis and proper risk management techniques.
Chỉ báo Pine Script®
của DCAUT
1
POC Migration Velocity (POC-MV) [PhenLabs]📊POC Migration Velocity (POC-MV) Version: PineScript™v6 📌Description The POC Migration Velocity indicator revolutionizes market structure analysis by tracking the movement, speed, and acceleration of Point of Control (POC) levels in real-time. This tool combines sophisticated volume distribution estimation with velocity calculations to reveal hidden market dynamics that conventional indicators miss. POC-MV provides traders with unprecedented insight into volume-based price movement patterns, enabling the early identification of continuation and exhaustion signals before they become apparent to the broader market. By measuring how quickly and consistently the POC migrates across price levels, traders gain early warning signals for significant market shifts and can position themselves advantageously. The indicator employs advanced algorithms to estimate intra-bar volume distribution without requiring lower timeframe data, making it accessible across all chart timeframes while maintaining sophisticated analytical capabilities. 🚀Points of Innovation Micro-POC calculation using advanced OHLC-based volume distribution estimation Real-time velocity and acceleration tracking normalized by ATR for cross-market consistency Persistence scoring system that quantifies directional consistency over multiple periods Multi-signal detection combining continuation patterns, exhaustion signals, and gap alerts Dynamic color-coded visualization system with intensity-based feedback Comprehensive customization options for resolution, periods, and thresholds 🔧Core Components POC Calculation Engine: Estimates volume distribution within each bar using configurable price bands and sophisticated weighting algorithms Velocity Measurement System: Tracks the rate of POC movement over customizable lookback periods with ATR normalization Acceleration Calculator: Measures the rate of change of velocity to identify momentum shifts in POC migration Persistence Analyzer: Quantifies how consistently POC moves in the same direction using exponential weighting Signal Detection Framework: Combines trend analysis, velocity thresholds, and persistence requirements for signal generation Visual Rendering System: Provides dynamic color-coded lines and heat ribbons based on velocity and price-POC relationships 🔥Key Features Real-time POC calculation with 10-100 configurable price bands for optimal precision Velocity tracking with customizable lookback periods from 5 to 50 bars Acceleration measurement for detecting momentum changes in POC movement Persistence scoring to validate signal strength and filter false signals Dynamic visual feedback with blue/orange color scheme indicating bullish/bearish conditions Comprehensive alert system for continuation patterns, exhaustion signals, and POC gaps Adjustable information table displaying real-time metrics and current signals Heat ribbon visualization showing price-POC relationship intensity Multiple threshold settings for customizing signal sensitivity Export capability for use with separate panel indicators 🎨Visualization POC Connecting Lines: Color-coded lines showing POC levels with intensity based on velocity magnitude Heat Ribbon: Dynamic colored ribbon around price showing POC-price basis intensity Signal Markers: Clear exhaustion top/bottom signals with labeled shapes Information Table: Real-time display of POC value, velocity, acceleration, basis, persistence, and current signal status Color Gradients: Blue gradients for bullish conditions, orange gradients for bearish conditions 📖Usage Guidelines POC Calculation Settings POC Resolution (Price Bands): Default 20, Range 10-100. Controls the number of price bands used to estimate volume distribution within each bar Volume Weight Factor: Default 0.7, Range 0.1-1.0. Adjusts the influence of volume in POC calculation POC Smoothing: Default 3, Range 1-10. EMA smoothing period applied to the calculated POC to reduce noise Velocity Settings Velocity Lookback Period: Default 14, Range 5-50. Number of bars used to calculate POC velocity Acceleration Period: Default 7, Range 3-20. Period for calculating POC acceleration Velocity Significance Threshold: Default 0.5, Range 0.1-2.0. Minimum normalized velocity for continuation signals Persistence Settings Persistence Lookback: Default 5, Range 3-20. Number of bars examined for persistence score calculation Persistence Threshold: Default 0.7, Range 0.5-1.0. Minimum persistence score required for continuation signals Visual Settings Show POC Connecting Lines: Toggle display of colored lines connecting POC levels Show Heat Ribbon: Toggle display of colored ribbon showing POC-price relationship Ribbon Transparency: Default 70, Range 0-100. Controls transparency level of heat ribbon Alert Settings Enable Continuation Alerts: Toggle alerts for continuation pattern detection Enable Exhaustion Alerts: Toggle alerts for exhaustion pattern detection Enable POC Gap Alerts: Toggle alerts for significant POC gaps Gap Threshold: Default 2.0 ATR, Range 0.5-5.0. Minimum gap size to trigger alerts ✅Best Use Cases Identifying trend continuation opportunities when POC velocity aligns with price direction Spotting potential reversal points through exhaustion pattern detection Confirming breakout validity by monitoring POC gap behavior Adding volume-based context to traditional technical analysis Managing position sizing based on POC-price basis strength ⚠️Limitations POC calculations are estimations based on OHLC data, not true tick-by-tick volume distribution Effectiveness may vary in low-volume or highly volatile market conditions Requires complementary analysis tools for complete trading decisions Signal frequency may be lower in ranging markets compared to trending conditions Performance optimization needed for very short timeframes below 1-minute 💡What Makes This Unique Advanced Estimation Algorithm: Sophisticated method for calculating POC without requiring lower timeframe data Velocity-Based Analysis: Focus on POC movement dynamics rather than static levels Comprehensive Signal Framework: Integration of continuation, exhaustion, and gap detection in one indicator Dynamic Visual Feedback: Intensity-based color coding that adapts to market conditions Persistence Validation: Unique scoring system to filter signals based on directional consistency 🔬How It Works Volume Distribution Estimation: Divides each bar into configurable price bands for volume analysis Applies sophisticated weighting based on OHLC relationships and proximity to close Identifies the price level with maximum estimated volume as the POC Velocity and Acceleration Calculation: Measures POC rate of change over specified lookback periods Normalizes values using ATR for consistent cross-market performance Calculates acceleration as the rate of change of velocity Signal Generation Process: Combines trend direction analysis using EMA crossovers Applies velocity and persistence thresholds to filter signals Generates continuation, exhaustion, and gap alerts based on specific criteria 💡Note: This indicator provides estimated POC calculations based on available OHLC data and should be used in conjunction with other analysis methods. The velocity-based approach offers unique insights into market structure dynamics but requires proper risk management and complementary analysis for optimal trading decisions.
Chỉ báo Pine Script®
của PhenLabs
[Pandora][Swarm] Rapid Exponential Moving AverageENVISIONING POSSIBILITY What is the theoretical pinnacle of possibility? The current state of algorithmic affairs falls far short of my aspirations for achievable feasibility. I'm lifting the lid off of Pandora's box once again, very publicly this time, as a brute force challenge to conventional 'wisdom'. The unfolding series of time mandates a transcendental systemic alteration... THE MOVING AVERAGE ZOO: The realm of digital signal processing for trading is filled with familiar antiquated filtering tools. Two families of filtration, being 'infinite impulse response' (EMA, RMA, etc.) and 'finite impulse response' (WMA, SMA, etc.), are prevalently employed without question. These filter types are the mules and donkeys of data analysis, broadly accepted for use in finance. At first glance, they appear sufficient for most tasks, offering a basic straightforward way to reduce noise and highlight trends. Yet, beneath their simplistic facade lies a constellation of limitations and impediments, each having its own finicky quirks. Upon closer inspection, identifiable drawbacks render them far from ideal for many real-world applications in today's volatile markets. KNOWN FUNDAMENTAL FLAWS: Despite commonplace moving average (MA) popularity, these conventional filters suffer from an assortment of fundamental flaws. Most of them don't genuinely address core challenges of how to preserve the true dynamics of a signal while suppressing noise and retaining cutoff frequency compliance. Their simple cookie cutter structures make them ill-suited in actuality for dynamic market environments. In reality, they often trade one problem for another dilemma, forsaking analytics to choose between distortion and delay. A deeper seeded issue remains within frequency compliance, how adequately a filter respects (or disrespects) the underlying signal’s spectral properties according to it's assigned periodic parameter. Traditional MAs habitually distort phase relationships, causing delayed reactions with surplus lag or exaggerations with excessive undershoot/overshoot. For applications requiring timely resilience, such as algorithmic trading, these shortcomings are often functionally unacceptable. What’s needed is vigorous filters that can more accurately retain signal behaviors while minimizing lag without sacrificing smoothness and uniformity. Until then, the public MA zoo remains as a collection of corny compromises, rather than a favorable toolbelt of solutions. P.S.: In PSv7+, in my opinion, many of these geriatric MAs deserve no future with ease of access for the naive, simply not knowing these filters are most likely creating bigger problems than solving any. R.E.M.A. What is this? I prefer to think of it as the "radical EMA", definitely along my lines of a retire everything morte algorithm. This isn't your run of the mill average from the petting zoo. I would categorize it as a paradigm shifting rampant economic masochistic annihilator, sufficiently good enough to begin ruthlessly executing moving averages left and right. Um, yeah... that kind of moving average destructor as you may soon recognize with a few 'Filters+' settings adjustments, realizing ordinary EMA has been doing us an injustice all this time. Does it possess the capability to relentlessly exterminate most averaging filters in existence? Well, it's about time we find out, by uncaging it on the loose into the greater economic wilderness. Only then can we truly find out if it is indeed a radical exponential market accelerant whose time has come. If it is, then it may eventually become a reality erasing monolithic anomaly destined for greatness, ultimately changing the entire landscape of trading in perpetuity. UNLEASHING NEXT-GEN: This lone next generation exoweapon algorithm is intended to initiate the transformative beginning stages of mass filtration deprecation. However, it won't be the only one, just the first arrival of it's alien kind from me. Welcome to notion #1 of my future filtration frontier, on this episode of the algorithmic twilight zone. Where reality takes a twisting turn one dimension beyond practical logic, after persistent models of mindset disintegrate into insignificance, followed by illusory perception confronted into cognitive dissonance. An evolutionary path to genuine advancement resides outside the prison of preconceptions, manifesting only after divergence from persistent binding restrictions of dogmatic doctrines. Such a genesis in transformative thinking will catalyze unbounded cognitive potential, plowing the way for the cultivation of total redesigns of thought. Futuristic innovative breakthroughs demand the surrender of legacy and outmoded understandings. Now that the world's largest assembly of investors has been ensembled, there are additional tasks left to perform. I'm compelled to deploy this mathematical-weapon of mass financial creation into it's rightful destined hands, to "WE THE PEOPLE" of TV. SCRIPT INTENTION: Deprecate anything and everything as any non-commercial member sees desirably fit. This includes your existing code formulations already in working functional modes of operation AND/OR future projects in the works. Swapping is nearly as simple as copying and pasting with meager modifications, after you have identified comparable likeness in this indicators settings with a visual assessment. Results may become eye opening, but only if you dare to look and test. Where you may suspect a ta.filter() is lacking sufficient luster or may be flat out majorly deficient, employing rema, drema, trema, or qrema configurations may be a more suitable replacement. That's up to you to discern. My code satire already identifies likely bottom of the barrel suspects that either belong in the extinction record or have already been marked for deprecation. They are ordered more towards the bottom by rank where they belong. SuperSmoother is a masterpiece here to stay, being my original go-to reference filter. Everything you see here is already deprecated, including REMA... REMA CHARACTERISTICS - VERY low lag - No overshoot - Frequency compliant - Proper initialization at bar_index==0 - Period parameter accepts poitive floating point numerics (AND integers!) - Infinite impulse response (IIR) filter - Compact code footprint - Minimized computational overhead
Chỉ báo Pine Script®
của ImmortalFreedom
10
3D Surface Modeling [PhenLabs]📊 3D Surface Modeling Version: PineScript™ v6 📌 Description The 3D Surface Modeling indicator revolutionizes technical analysis by generating three-dimensional visualizations of multiple technical indicators across various timeframes. This advanced analytical tool processes and renders complex indicator data through a sophisticated matrix-based calculation system, creating an intuitive 3D surface representation of market dynamics. The indicator employs array-based computations to simultaneously analyze multiple instances of selected technical indicators, mapping their behavior patterns across different temporal dimensions. This unique approach enables traders to identify complex market patterns and relationships that may be invisible in traditional 2D charts. 🚀 Points of Innovation Matrix-Based Computation Engine: Processes up to 500 concurrent indicator calculations in real-time Dynamic 3D Rendering System: Creates depth perception through sophisticated line arrays and color gradients Multi-Indicator Integration: Seamlessly combines VWAP, Hurst, RSI, Stochastic, CCI, MFI, and Fractal Dimension analyses Adaptive Scaling Algorithm: Automatically adjusts visualization parameters based on indicator type and market conditions 🔧 Core Components Indicator Processing Module: Handles real-time calculation of multiple technical indicators using array-based mathematics 3D Visualization Engine: Converts indicator data into three-dimensional surfaces using line arrays and color mapping Dynamic Scaling System: Implements custom normalization algorithms for different indicator types Color Gradient Generator: Creates depth perception through programmatic color transitions 🔥 Key Features Multi-Indicator Support: Comprehensive analysis across seven different technical indicators Customizable Visualization: User-defined color schemes and line width parameters Real-time Processing: Continuous calculation and rendering of 3D surfaces Cross-Timeframe Analysis: Simultaneous visualization of indicator behavior across multiple periods 🎨 Visualization Surface Plot: Three-dimensional representation using up to 500 lines with dynamic color gradients Depth Indicators: Color intensity variations showing indicator value magnitude Pattern Recognition: Visual identification of market structures across multiple timeframes 📖 Usage Guidelines Indicator Selection Type: VWAP, Hurst, RSI, Stochastic, CCI, MFI, Fractal Dimension Default: VWAP Starting Length: Minimum 5 periods Default: 10 Step Size: Interval between calculations Range: 1-10 Visualization Parameters Color Scheme: Green, Red, Blue options Line Width: 1-5 pixels Surface Resolution: Up to 500 lines ✅ Best Use Cases Multi-timeframe market analysis Pattern recognition across different technical indicators Trend strength assessment through 3D visualization Market behavior study across multiple periods ⚠️ Limitations High computational resource requirements Maximum 500 line restriction Requires substantial historical data Complex visualization learning curve 🔬 How It Works 1. Data Processing: Calculates selected indicator values across multiple timeframes Stores results in multi-dimensional arrays Applies custom scaling algorithms 2. Visualization Generation: Creates line arrays for 3D surface representation Applies color gradients based on value magnitude Renders real-time updates to surface plot 3. Display Integration: Synchronizes with chart timeframe Updates surface plot dynamically Maintains visual consistency across updates 🌟 Credits: Inspired by LonesomeTheBlue (modified for multiple indicator types with scaling fixes and additional unique mappings) 💡 Note: Optimal performance requires sufficient computing resources and historical data. Users should start with default settings and gradually adjust parameters based on their analysis requirements and system capabilities.
Chỉ báo Pine Script®
của PhenLabs
3
ICT Opening Range Projections (tristanlee85)ICT Opening Range Projections This indicator visualizes key price levels based on ICT's (Inner Circle Trader) "Opening Range" concept. This 30-minute time interval establishes price levels that the algorithm will refer to throughout the session. The indicator displays these levels, including standard deviation projections, internal subdivisions (quadrants), and the opening price. 🟪 What It Does The Opening Range is a crucial 30-minute window where market algorithms establish significant price levels. ICT theory suggests this range forms the basis for daily price movement. This script helps you: Mark the high, low, and opening price of each session. Divide the range into quadrants (premium, discount, and midpoint/Consequent Encroachment). Project potential price targets beyond the range using configurable standard deviation multiples . 🟪 How to Use It This tool aids in time-based technical analysis rooted in ICT's Opening Range model, helping you observe price interaction with algorithmic levels. Example uses include: Identifying early structural boundaries. Observing price behavior within premium/discount zones. Visualizing initial displacement from the range to anticipate future moves. Comparing price reactions at projected standard deviation levels. Aligning price action with significant times like London or NY Open. Note: This indicator provides a visual framework; it does not offer trade signals or interpretations. 🟪 Key Information Time Zone: New York time (ET) is required on your chart. Sessions: Supports multiple sessions, including NY midnight, NY AM, NY PM, and three custom timeframes. Time Interval: Supports multi-timeframe up to 15 minutes. Best used on a 1-minute chart for accuracy. 🟪 Session Options The Opening Range interval is configurable for up to 6 sessions: Pre-defined ICT Sessions: NY Midnight: 12:00 AM – 12:30 AM ET NY AM: 9:30 AM – 10:00 AM ET NY PM: 1:30 PM – 2:00 PM ET Custom Sessions: Three user-defined start/end time pairs. This example shows a custom session from 03:30 - 04:00: 🟪 Understanding the Levels The Opening Price is the open of the first 1-minute candle within the chosen session. At session close, the Opening Range is calculated using its High and Low . An optional swing-based mode uses swing highs/lows for range boundaries. The range is divided into quadrants by its midpoint ( Consequent Encroachment or CE): Upper Quadrant: CE to high (premium). Lower Quadrant: Low to CE (discount). These subdivisions help visualize internal range dynamics, where price often reacts during algorithmic delivery. 🟪 Working with Ranges By default, the range is determined by the highest high and lowest low of the 30-minute session: A range can also be determined by the highest/lowest swing points: Quadrants outline the premium and discount of a range that price will reference: Small ranges still follow the same algorithmic logic, but may be deemed insignificant for one's trading. These can be filtered in the settings by specifying a minimum ticks limit. In this example, the range is 42 ticks (10.5 points) but the indicator is configured for 80 ticks (20 points). We can select which levels will plot if the range is below the limit. Here, only the 00:00 opening price is plotted: You may opt to include the range high/low, quadrants, and projections as well. This will plot a red (configurable) range bracket to indicate it is below the limit while plotting the levels: 🟪 Price Projections Projections extend beyond the Opening Range using standard deviations, framing the market beyond the initial session and identifying potential targets. You define the standard deviation multiples (e.g., 1.0, 1.5, 2.0). Both positive and negative extensions are displayed, symmetrically projected from the range's high and low. The Dynamic Levels option plots only the next projection level once price crosses the previous extreme. For example, only the 0.5 STDEV level plots until price reaches it, then the 1.0 level appears, and so on. This continues up to your defined maximum projections, or indefinitely if standard deviations are set to 0. This example shows dynamic levels for a total of 6 sessions, only 1 of which meet a configured minimum limit of 50 ticks: Small ranges followed by significant displacement are impacted the most with the number of levels plotted. You may hide projections when configuring the minimum ticks. A fixed standard deviation will plot levels in both directions, regardless of the price range. Here, we plot up to 3.0 which hiding projections for small ranges: 🟪 Legal Disclaimer This indicator is provided for informational and educational purposes only. It is not financial advice, and should not be construed as a recommendation to buy or sell any financial instrument. Trading involves substantial risk, and you could lose a significant amount of money. Past performance is not indicative of future results. Always consult with a qualified financial professional before making any trading or investment decisions. The creators and distributors of this indicator assume no responsibility for your trading outcomes.
Chỉ báo Pine Script®
của tristanlee85
10
Momentum + Keltner Stochastic Combo)The Momentum-Keltner-Stochastic Combination Strategy: A Technical Analysis and Empirical Validation This study presents an advanced algorithmic trading strategy that implements a hybrid approach between momentum-based price dynamics and relative positioning within a volatility-adjusted Keltner Channel framework. The strategy utilizes an innovative "Keltner Stochastic" concept as its primary decision-making factor for market entries and exits, while implementing a dynamic capital allocation model with risk-based stop-loss mechanisms. Empirical testing demonstrates the strategy's potential for generating alpha in various market conditions through the combination of trend-following momentum principles and mean-reversion elements within defined volatility thresholds. 1. Introduction Financial market trading increasingly relies on the integration of various technical indicators for identifying optimal trading opportunities (Lo et al., 2000). While individual indicators are often compromised by market noise, combinations of complementary approaches have shown superior performance in detecting significant market movements (Murphy, 1999; Kaufman, 2013). This research introduces a novel algorithmic strategy that synthesizes momentum principles with volatility-adjusted envelope analysis through Keltner Channels. 2. Theoretical Foundation 2.1 Momentum Component The momentum component of the strategy builds upon the seminal work of Jegadeesh and Titman (1993), who demonstrated that stocks which performed well (poorly) over a 3 to 12-month period continue to perform well (poorly) over subsequent months. As Moskowitz et al. (2012) further established, this time-series momentum effect persists across various asset classes and time frames. The present strategy implements a short-term momentum lookback period (7 bars) to identify the prevailing price direction, consistent with findings by Chan et al. (2000) that shorter-term momentum signals can be effective in algorithmic trading systems. 2.2 Keltner Channels Keltner Channels, as formalized by Chester Keltner (1960) and later modified by Linda Bradford Raschke, represent a volatility-based envelope system that plots bands at a specified distance from a central exponential moving average (Keltner, 1960; Raschke & Connors, 1996). Unlike traditional Bollinger Bands that use standard deviation, Keltner Channels typically employ Average True Range (ATR) to establish the bands' distance from the central line, providing a smoother volatility measure as established by Wilder (1978). 2.3 Stochastic Oscillator Principles The strategy incorporates a modified stochastic oscillator approach, conceptually similar to Lane's Stochastic (Lane, 1984), but applied to a price's position within Keltner Channels rather than standard price ranges. This creates what we term "Keltner Stochastic," measuring the relative position of price within the volatility-adjusted channel as a percentage value. 3. Strategy Methodology 3.1 Entry and Exit Conditions The strategy employs a contrarian approach within the channel framework: Long Entry Condition: Close price > Close price periods ago (momentum filter) KeltnerStochastic < threshold (oversold within channel) Short Entry Condition: Close price < Close price periods ago (momentum filter) KeltnerStochastic > threshold (overbought within channel) Exit Conditions: Exit long positions when KeltnerStochastic > threshold Exit short positions when KeltnerStochastic < threshold This methodology aligns with research by Brock et al. (1992) on the effectiveness of trading range breakouts with confirmation filters. 3.2 Risk Management Stop-loss mechanisms are implemented using fixed price movements (1185 index points), providing definitive risk boundaries per trade. This approach is consistent with findings by Sweeney (1988) that fixed stop-loss systems can enhance risk-adjusted returns when properly calibrated. 3.3 Dynamic Position Sizing The strategy implements an equity-based position sizing algorithm that increases or decreases contract size based on cumulative performance: $ContractSize = \min(baseContracts + \lfloor\frac{\max(profitLoss, 0)}{equityStep}\rfloor - \lfloor\frac{|\min(profitLoss, 0)|}{equityStep}\rfloor, maxContracts)$ This adaptive approach follows modern portfolio theory principles (Markowitz, 1952) and Kelly criterion concepts (Kelly, 1956), scaling exposure proportionally to account equity. 4. Empirical Performance Analysis Using historical data across multiple market regimes, the strategy demonstrates several key performance characteristics: Enhanced performance during trending markets with moderate volatility Reduced drawdowns during choppy market conditions through the dual-filter approach Optimal performance when the threshold parameter is calibrated to market-specific characteristics (Pardo, 2008) 5. Strategy Limitations and Future Research While effective in many market conditions, this strategy faces challenges during: Rapid volatility expansion events where stop-loss mechanisms may be inadequate Prolonged sideways markets with insufficient momentum Markets with structural changes in volatility profiles Future research should explore: Adaptive threshold parameters based on regime detection Integration with additional confirmatory indicators Machine learning approaches to optimize parameter selection across different market environments (Cavalcante et al., 2016) References Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764. Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211. Chan, L. K. C., Jegadeesh, N., & Lakonishok, J. (2000). Momentum strategies. The Journal of Finance, 51(5), 1681-1713. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. The Journal of Finance, 48(1), 65-91. Kaufman, P. J. (2013). Trading systems and methods (5th ed.). John Wiley & Sons. Kelly, J. L. (1956). A new interpretation of information rate. The Bell System Technical Journal, 35(4), 917-926. Keltner, C. W. (1960). How to make money in commodities. The Keltner Statistical Service. Lane, G. C. (1984). Lane's stochastics. Technical Analysis of Stocks & Commodities, 2(3), 87-90. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The Journal of Finance, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. Moskowitz, T. J., Ooi, Y. H., & Pedersen, L. H. (2012). Time series momentum. Journal of Financial Economics, 104(2), 228-250. Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance. Pardo, R. (2008). The evaluation and optimization of trading strategies (2nd ed.). John Wiley & Sons. Raschke, L. B., & Connors, L. A. (1996). Street smarts: High probability short-term trading strategies. M. Gordon Publishing Group. Sweeney, R. J. (1988). Some new filter rule tests: Methods and results. Journal of Financial and Quantitative Analysis, 23(3), 285-300. Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research.
Chiến lược Pine Script®
của EdgeTools
Dual Zigzag [Trendoscope®]🎲 Dual Zigzag indicator is built on recursive zigzag algorithm. It is very similar to other zigzag indicators published by us and other authors. However, the key point here is, the indicator draws zigzag on both price and any other plot based indicator on separate layouts. Before we get into the indicator, here are some brief descriptions of the underlying concepts and key terminologies 🎯 Zigzag Zigzag indicator breaks down price or any input series into a series of Pivot Highs and Pivot Lows alternating between each other. Zigzags though shows pivot high and lows, should not be used for buying at low and selling at high. The main application of zigzag indicator is for the visualisation of market structure and this can be used as basic building block for any pattern recognition algorithms. 🎯 Recursive Zigzag Algorithm Recursive zigzag algorithm builds zigzag on multiple levels and each level of zigzag is based on the previous level pivots. The level zero zigzag is built on price. However, for level 1, instead of price level 0 zigzag pivots are used. Similarly for level 2, level 1 zigzag pivots are used as base. 🎲 Components Dual Zigzag Indicator Here are the components of Dual zigzag indicator Built in Oscillator - Indicator has built in oscillator options for plotting RSI (Relative Strength Index), MFI (Money Flow Index), cci (Commodity Channel Index) , CMO (Chande Momentum Oscillator), COG (Center of Gravity), and ROC (Rate of Change). Apart from the given built in oscillators, users can also use a custom external output as base. The oscillators are not printed on the price pane. But, printed on a separate indicator overlay. Zigzag On Oscillator - Recursive zigzag is calculated and printed on the oscillator series. Each pivot high and pivot low also prints a label having the retracement ratios, and price levels at those points. Zigzag on the oscillator is also printed on the indicator overlay pane. Zigzag on Price - Recursive zigzag calculated based on price and printed on the price pane. This is made possible by using force_overlay option present in the drawing objects. At each zigzag pivot levels, the label having price retracement ratios, and oscillator values are printed. It is called dual zigzag because, the indicator calculates the zigzag on both price and oscillator series of values and prints them separately on different panes on the chart. 🎲 Indicator Settings Settings include Theme display settings to get the right colour combination to match the background. Zigzag settings to be used for zigzag calculation and display Oscillator settings to chose the oscillator to be used as base for 2nd zigzag 🎲 Applications Useful in spotting divergences with both indicator and price having their own zigzag to highlight pivots Spotting patterns in indicators/oscillators and correlate them with the patterns on price 🎲 Using External Input If users want to use an external indicator such as OBV instead of the built in oscillators, then can do so by using the custom option. Here is how this can be done. Step1. Add both Dual Zigzag and the intended indicator (in this case OBV) on the chart. Notice that both OBV and Dual zigzag appear on different panes. Step2. Edit the indicator settings of Dual zigzag and set custom indicator by selecting "custom" as oscillator name and then by setting the custom external indicator name and input. Step 3. You would notice that the zigzag in Dual Zigzag indictor pane is already showing the zigzag pivots based on the OBV indicator and the price pivots display obv values at the pivot points. We can leave this as is. Step 4. As an additional step, you can also merge the OBV pane and the Dual zigzag indicator pane into one by going into OBV settings and moving the indicator to above pane. Merge the scales so that there is no two scales on the same pane and the entire scale appear on the right. At the end, you should see two panes - one with price and other with OBV and both having their zigzag plotted.
Chỉ báo Pine Script®
của Trendoscope
12
Prediction Based on Linreg & Atr We created this algorithm with the goal of predicting future prices 📊, specifically where the value of any asset will go in the next 20 periods ⏳. It uses linear regression based on past prices, calculating a slope and an intercept to forecast future behavior 🔮. This prediction is then adjusted according to market volatility, measured by the ATR 📉, and the direction of trend signals, which are based on the MACD and moving averages 📈. How Does the Linreg & ATR Prediction Work? 1. Trend Calculation and Signals: o Technical Indicators: We use short- and long-term exponential moving averages (EMA), RSI, MACD, and Bollinger Bands 📊 to assess market direction and sentiment (not visually presented in the script). o Calculation Functions: These include functions to calculate slope, average, intercept, standard deviation, and Pearson's R, which are crucial for regression analysis 📉. 2. Predicting Future Prices: o Linear Regression: The algorithm calculates the slope, average, and intercept of past prices to create a regression channel 📈, helping to predict the range of future prices 🔮. o Standard Deviation and Pearson's R: These metrics determine the strength of the regression 🔍. 3. Adjusting the Prediction: o The predicted value is adjusted by considering market volatility (ATR 📉) and the direction of trend signals 🔮, ensuring that the prediction is aligned with the current market environment 🌍. 4. Visualization: o Prediction Lines and Bands: The algorithm plots lines that display the predicted future price along with a prediction range (upper and lower bounds) 📉📈. 5. EMA Cross Signals: o EMA Conditions and Total Score: A bullish crossover signal is generated when the total score is positive and the short EMA crosses above the long EMA 📈. A bearish crossover signal is generated when the total score is negative and the short EMA crosses below the long EMA 📉. 6. Additional Considerations: o Multi-Timeframe Regression Channel: The script calculates regression channels for different timeframes (5m, 15m, 30m, 4h) ⏳, helping determine the overall market direction 📊 (not visually presented). Confidence Interpretation: • High Confidence (close to 100%): Indicates strong alignment between timeframes with a clear trend (bullish or bearish) 🔥. • Low Confidence (close to 0%): Shows disagreement or weak signals between timeframes ⚠️. Confidence complements the interpretation of the prediction range and expected direction 🔮, aiding in decision-making for market entry or exit 🚀. Español Creamos este algoritmo con el objetivo de predecir los precios futuros 📊, específicamente hacia dónde irá el valor de cualquier activo en los próximos 20 períodos ⏳. Utiliza regresión lineal basada en los precios pasados, calculando una pendiente y una intersección para prever el comportamiento futuro 🔮. Esta predicción se ajusta según la volatilidad del mercado, medida por el ATR 📉, y la dirección de las señales de tendencia, que se basan en el MACD y las medias móviles 📈. ¿Cómo Funciona la Predicción con Linreg & ATR? Cálculo de Tendencias y Señales: Indicadores Técnicos: Usamos medias móviles exponenciales (EMA) a corto y largo plazo, RSI, MACD y Bandas de Bollinger 📊 para evaluar la dirección y el sentimiento del mercado (no presentados visualmente en el script). Funciones de Cálculo: Incluye funciones para calcular pendiente, media, intersección, desviación estándar y el coeficiente de correlación de Pearson, esenciales para el análisis de regresión 📉. Predicción de Precios Futuros: Regresión Lineal: El algoritmo calcula la pendiente, la media y la intersección de los precios pasados para crear un canal de regresión 📈, ayudando a predecir el rango de precios futuros 🔮. Desviación Estándar y Pearson's R: Estas métricas determinan la fuerza de la regresión 🔍. Ajuste de la Predicción: El valor predicho se ajusta considerando la volatilidad del mercado (ATR 📉) y la dirección de las señales de tendencia 🔮, asegurando que la predicción esté alineada con el entorno actual del mercado 🌍. Visualización: Líneas y Bandas de Predicción: El algoritmo traza líneas que muestran el precio futuro predicho, junto con un rango de predicción (límites superior e inferior) 📉📈. Señales de Cruce de EMAs: Condiciones de EMAs y Puntaje Total: Se genera una señal de cruce alcista cuando el puntaje total es positivo y la EMA corta cruza por encima de la EMA larga 📈. Se genera una señal de cruce bajista cuando el puntaje total es negativo y la EMA corta cruza por debajo de la EMA larga 📉. Consideraciones Adicionales: Canal de Regresión Multi-Timeframe: El script calcula canales de regresión para diferentes marcos de tiempo (5m, 15m, 30m, 4h) ⏳, ayudando a determinar la dirección general del mercado 📊 (no presentado visualmente). Interpretación de la Confianza: Alta Confianza (cerca del 100%): Indica una fuerte alineación entre los marcos temporales con una tendencia clara (alcista o bajista) 🔥. Baja Confianza (cerca del 0%): Muestra desacuerdo o señales débiles entre los marcos temporales ⚠️. La confianza complementa la interpretación del rango de predicción y la dirección esperada 🔮, ayudando en las decisiones de entrada o salida en el mercado 🚀.
Chỉ báo Pine Script®
của PINE_LABS
7
RSI with Swing Trade by Kelvin_VAlgorithm Description: "RSI with Swing Trade by Kelvin_V" 1. Introduction: This algorithm uses the RSI (Relative Strength Index) and optional Moving Averages (MA) to detect potential uptrends and downtrends in the market. The key feature of this script is that it visually changes the candle colors based on the market conditions, making it easier for users to identify potential trend swings or wave patterns. The strategy offers flexibility by allowing users to enable or disable the MA condition. When the MA condition is enabled, the strategy will confirm trends using two moving averages. When disabled, the strategy will only use RSI to detect potential market swings. 2. Key Features of the Algorithm: RSI (Relative Strength Index): The RSI is used to identify potential market turning points based on overbought and oversold conditions. When the RSI exceeds a predefined upper threshold (e.g., 60), it suggests a potential uptrend. When the RSI drops below a lower threshold (e.g., 40), it suggests a potential downtrend. Moving Averages (MA) - Optional: Two Moving Averages (Short MA and Long MA) are used to confirm trends. If the Short MA crosses above the Long MA, it indicates an uptrend. If the Short MA crosses below the Long MA, it indicates a downtrend. Users have the option to enable or disable this MA condition. Visual Candle Coloring: Green candles represent a potential uptrend, indicating a bullish move based on RSI (and MA if enabled). Red candles represent a potential downtrend, indicating a bearish move based on RSI (and MA if enabled). 3. How the Algorithm Works: RSI Levels: The user can set RSI upper and lower bands to represent potential overbought and oversold levels. For example: RSI > 60: Indicates a potential uptrend (bullish move). RSI < 40: Indicates a potential downtrend (bearish move). Optional MA Condition: The algorithm also allows the user to apply the MA condition to further confirm the trend: Short MA > Long MA: Confirms an uptrend, reinforcing a bullish signal. Short MA < Long MA: Confirms a downtrend, reinforcing a bearish signal. This condition can be disabled, allowing the user to focus solely on RSI signals if desired. Swing Trade Logic: Uptrend: If the RSI exceeds the upper threshold (e.g., 60) and (optionally) the Short MA is above the Long MA, the candles will turn green to signal a potential uptrend. Downtrend: If the RSI falls below the lower threshold (e.g., 40) and (optionally) the Short MA is below the Long MA, the candles will turn red to signal a potential downtrend. Visual Representation: The candle colors change dynamically based on the RSI values and moving average conditions, making it easier for traders to visually identify potential trend swings or wave patterns without relying on complex chart analysis. 4. User Customization: The algorithm provides multiple customization options: RSI Length: Users can adjust the period for RSI calculation (default is 4). RSI Upper Band (Potential Uptrend): Users can customize the upper RSI level (default is 60) to indicate a potential bullish move. RSI Lower Band (Potential Downtrend): Users can customize the lower RSI level (default is 40) to indicate a potential bearish move. MA Type: Users can choose between SMA (Simple Moving Average) and EMA (Exponential Moving Average) for moving average calculations. Enable/Disable MA Condition: Users can toggle the MA condition on or off, depending on whether they want to add moving averages to the trend confirmation process. 5. Benefits of the Algorithm: Easy Identification of Trends: By changing candle colors based on RSI and MA conditions, the algorithm makes it easy for users to visually detect potential trend reversals and trend swings. Flexible Conditions: The user has full control over the RSI and MA settings, allowing them to adapt the strategy to different market conditions and timeframes. Clear Visualization: With the candle color changes, users can quickly recognize when a potential uptrend or downtrend is forming, enabling faster decision-making in their trading. 6. Example Usage: Day traders: Can apply this strategy on short timeframes such as 5 minutes or 15 minutes to detect quick trends or reversals. Swing traders: Can use this strategy on longer timeframes like 1 hour or 4 hours to identify and follow larger market swings.
Chỉ báo Pine Script®
của Kelvin-WyckoffVSA
Intramarket Difference Index StrategyHi Traders !! The IDI Strategy: In layman’s terms this strategy compares two indicators across markets and exploits their differences. note: it is best the two markets are correlated as then we know we are trading a short to long term deviation from both markets' general trend with the assumption both markets will trend again sometime in the future thereby exhausting our trading opportunity. 📍 Import Notes: This Strategy calculates trade position size independently (i.e. risk per trade is controlled in the user inputs tab), this means that the ‘Order size’ input in the ‘Properties’ tab will have no effect on the strategy. Why ? because this allows us to define custom position size algorithms which we can use to improve our risk management and equity growth over time. Here we have the option to have fixed quantity or fixed percentage of equity ATR (Average True Range) based stops in addition to the turtle trading position size algorithm. ‘Pyramiding’ does not work for this strategy’, similar to the order size input togeling this input will have no effect on the strategy as the strategy explicitly defines the maximum order size to be 1. This strategy is not perfect, and as of writing of this post I have not traded this algo. Always take your time to backtests and debug the strategy. 🔷 The IDI Strategy: By default this strategy pulls data from your current TV chart and then compares it to the base market, be default BINANCE:BTCUSD . The strategy pulls SMA and RSI data from either market (we call this the difference data), standardizes the data (solving the different unit problem across markets) such that it is comparable and then differentiates the data, calling the result of this transformation and difference the Intramarket Difference (ID). The formula for the the ID is ID = market1_diff_data - market2_diff_data (1) Where market(i)_diff_data = diff_data / ATR(j)_market(i)^0.5, where i = {1, 2} and j = the natural numbers excluding 0 Formula (1) interpretation is the following When ID > 0: this means the current market outperforms the base market When ID = 0: Markets are at long run equilibrium When ID < 0: this means the current market underperforms the base market To form the strategy we define one of two strategy type’s which are Trend and Mean Revesion respectively. 🔸 Trend Case: Given the ‘‘Strategy Type’’ is equal to TREND we define a threshold for which if the ID crosses over we go long and if the ID crosses under the negative of the threshold we go short. The motivating idea is that the ID is an indicator of the two symbols being out of sync, and given we know volatility clustering, momentum and mean reversion of anomalies to be a stylised fact of financial data we can construct a trading premise. Let's first talk more about this premise. For some markets (cryptocurrency markets - synthetic symbols in TV) the stylised fact of momentum is true, this means that higher momentum is followed by higher momentum, and given we know momentum to be a vector quantity (with magnitude and direction) this momentum can be both positive and negative i.e. when the ID crosses above some threshold we make an assumption it will continue in that direction for some time before executing back to its long run equilibrium of 0 which is a reasonable assumption to make if the market are correlated. For example for the BTCUSD - ETHUSD pair, if the ID > +threshold (inputs for MA and RSI based ID thresholds are found under the ‘‘INTRAMARKET DIFFERENCE INDEX’’ group’), ETHUSD outperforms BTCUSD, we assume the momentum to continue so we go long ETHUSD. In the standard case we would exit the market when the IDI returns to its long run equilibrium of 0 (for the positive case the ID may return to 0 because ETH’s difference data may have decreased or BTC’s difference data may have increased). However in this strategy we will not define this as our exit condition, why ? This is because we want to ‘‘let our winners run’’, to achieve this we define a trailing Donchian Channel stop loss (along with a fixed ATR based stop as our volatility proxy). If we were too use the 0 exit the strategy may print a buy signal (ID > +threshold in the simple case, market regimes may be used), return to 0 and then print another buy signal, and this process can loop may times, this high trade frequency means we fail capture the entire market move lowering our profit, furthermore on lower time frames this high trade frequencies mean we pay more transaction costs (due to price slippage, commission and big-ask spread) which means less profit. By capturing the sum of many momentum moves we are essentially following the trend hence the trend following strategy type. Here we also print the IDI (with default strategy settings with the MA difference type), we can see that by letting our winners run we may catch many valid momentum moves, that results in a larger final pnl that if we would otherwise exit based on the equilibrium condition(Valid trades are denoted by solid green and red arrows respectively and all other valid trades which occur within the original signal are light green and red small arrows). another example... Note: if you would like to plot the IDI separately copy and paste the following code in a new Pine Script indicator template. indicator("IDI") // INTRAMARKET INDEX var string g_idi = "intramarket diffirence index" ui_index_1 = input.symbol("BINANCE:BTCUSD", title = "Base market", group = g_idi) // ui_index_2 = input.symbol("BINANCE:ETHUSD", title = "Quote Market", group = g_idi) type = input.string("MA", title = "Differrencing Series", options = , group = g_idi) ui_ma_lkb = input.int(24, title = "lookback of ma and volatility scaling constant", group = g_idi) ui_rsi_lkb = input.int(14, title = "Lookback of RSI", group = g_idi) ui_atr_lkb = input.int(300, title = "ATR lookback - Normalising value", group = g_idi) ui_ma_threshold = input.float(5, title = "Threshold of Upward/Downward Trend (MA)", group = g_idi) ui_rsi_threshold = input.float(20, title = "Threshold of Upward/Downward Trend (RSI)", group = g_idi) //>>+----------------------------------------------------------------+} // CUSTOM FUNCTIONS | //<<+----------------------------------------------------------------+{ // construct UDT (User defined type) containing the IDI (Intramarket Difference Index) source values // UDT will hold many variables / functions grouped under the UDT type functions float Close // close price float ma // ma of symbol float rsi // rsi of the asset float atr // atr of the asset // the security data getUDTdata(symbol, malookback, rsilookback, atrlookback) => indexHighTF = barstate.isrealtime ? 1 : 0 = request.security(symbol, timeframe = timeframe.period, expression = [close , // Instentiate UDT variables ta.sma(close, malookback) , ta.rsi(close, rsilookback) , ta.atr(atrlookback) ]) data = functions.new(close_, ma_, rsi_, atr_) data // Intramerket Difference Index idi(type, symbol1, malookback, rsilookback, atrlookback, mathreshold, rsithreshold) => threshold = float(na) index1 = getUDTdata(symbol1, malookback, rsilookback, atrlookback) index2 = getUDTdata(syminfo.tickerid, malookback, rsilookback, atrlookback) // declare difference variables for both base and quote symbols, conditional on which difference type is selected var diffindex1 = 0.0, var diffindex2 = 0.0, // declare Intramarket Difference Index based on series type, note // if > 0, index 2 outpreforms index 1, buy index 2 (momentum based) until equalibrium // if < 0, index 2 underpreforms index 1, sell index 1 (momentum based) until equalibrium // for idi to be valid both series must be stationary and normalised so both series hae he same scale intramarket_difference = 0.0 if type == "MA" threshold := mathreshold diffindex1 := (index1.Close - index1.ma) / math.pow(index1.atr*malookback, 0.5) diffindex2 := (index2.Close - index2.ma) / math.pow(index2.atr*malookback, 0.5) intramarket_difference := diffindex2 - diffindex1 else if type == "RSI" threshold := rsilookback diffindex1 := index1.rsi diffindex2 := index2.rsi intramarket_difference := diffindex2 - diffindex1 //>>+----------------------------------------------------------------+} // STRATEGY FUNCTIONS CALLS | //<<+----------------------------------------------------------------+{ // plot the intramarket difference = idi(type, ui_index_1, ui_ma_lkb, ui_rsi_lkb, ui_atr_lkb, ui_ma_threshold, ui_rsi_threshold) //>>+----------------------------------------------------------------+} plot(intramarket_difference, color = color.orange) hline(type == "MA" ? ui_ma_threshold : ui_rsi_threshold, color = color.green) hline(type == "MA" ? -ui_ma_threshold : -ui_rsi_threshold, color = color.red) hline(0) Note it is possible that after printing a buy the strategy then prints many sell signals before returning to a buy, which again has the same implication (less profit. Potentially because we exit early only for price to continue upwards hence missing the larger "trend"). The image below showcases this cenario and again, by allowing our winner to run we may capture more profit (theoretically). This should be clear... 🔸 Mean Reversion Case: We stated prior that mean reversion of anomalies is an standerdies fact of financial data, how can we exploit this ? We exploit this by normalizing the ID by applying the Ehlers fisher transformation. The transformed data is then assumed to be approximately normally distributed. To form the strategy we employ the same logic as for the z score, if the FT normalized ID > 2.5 (< -2.5) we buy (short). Our exit conditions remain unchanged (fixed ATR stop and trailing Donchian Trailing stop) 🔷 Position Sizing: If ‘‘Fixed Risk From Initial Balance’’ is toggled true this means we risk a fixed percentage of our initial balance, if false we risk a fixed percentage of our equity (current balance). Note we also employ a volatility adjusted position sizing formula, the turtle training method which is defined as follows. Turtle position size = (1/ r * ATR * DV) * C Where, r = risk factor coefficient (default is 20) ATR(j) = risk proxy, over j times steps DV = Dollar Volatility, where DV = (1/Asset Price) * Capital at Risk 🔷 Risk Management: Correct money management means we can limit risk and increase reward (theoretically). Here we employ Max loss and gain per day Max loss per trade Max number of consecutive losing trades until trade skip To read more see the tooltips (info circle). 🔷 Take Profit: By defualt the script uses a Donchain Channel as a trailing stop and take profit, In addition to this the script defines a fixed ATR stop losses (by defualt, this covers cases where the DC range may be to wide making a fixed ATR stop usefull), ATR take profits however are defined but optional. ATR SL and TP defined for all trades 🔷 Hurst Regime (Regime Filter): The Hurst Exponent (H) aims to segment the market into three different states, Trending (H > 0.5), Random Geometric Brownian Motion (H = 0.5) and Mean Reverting / Contrarian (H < 0.5). In my interpretation this can be used as a trend filter that eliminates market noise. We utilize the trending and mean reverting based states, as extra conditions required for valid trades for both strategy types respectively, in the process increasing our trade entry quality. 🔷 Example model Architecture: Here is an example of one configuration of this strategy, combining all aspects discussed in this post. Future Updates - Automation integration (next update)
Chiến lược Pine Script®
của Amjad_S
AI SuperTrend x Pivot Percentile - Strategy [PresentTrading]█ Introduction and How it is Different The AI SuperTrend x Pivot Percentile strategy is a sophisticated trading approach that integrates AI-driven analysis with traditional technical indicators. Combining the AI SuperTrend with the Pivot Percentile strategy highlights several key advantages: 1. Enhanced Accuracy in Trend Prediction: The AI SuperTrend utilizes K-Nearest Neighbors (KNN) algorithm for trend prediction, improving accuracy by considering historical data patterns. This is complemented by the Pivot Percentile analysis which provides additional context on trend strength. 2. Comprehensive Market Analysis: The integration offers a multi-faceted approach to market analysis, combining AI insights with traditional technical indicators. This dual approach captures a broader range of market dynamics. BTC 6H L/S Performance Local █ Strategy: How it Works - Detailed Explanation 🔶 AI-Enhanced SuperTrend Indicators 1. SuperTrend Calculation: - The SuperTrend indicator is calculated using a moving average and the Average True Range (ATR). The basic formula is: - Upper Band = Moving Average + (Multiplier × ATR) - Lower Band = Moving Average - (Multiplier × ATR) - The moving average type (SMA, EMA, WMA, RMA, VWMA) and the length of the moving average and ATR are adjustable parameters. - The direction of the trend is determined based on the position of the closing price in relation to these bands. 2. AI Integration with K-Nearest Neighbors (KNN): - The KNN algorithm is applied to predict trend direction. It uses historical price data and SuperTrend values to classify the current trend as bullish or bearish. - The algorithm calculates the 'distance' between the current data point and historical points. The 'k' nearest data points (neighbors) are identified based on this distance. - A weighted average of these neighbors' trends (bullish or bearish) is calculated to predict the current trend. For more please check: Multi-TF AI SuperTrend with ADX - Strategy 🔶 Pivot Percentile Analysis 1. Percentile Calculation: - This involves calculating the percentile ranks for high and low prices over a set of predefined lengths. - The percentile function is typically defined as: - Percentile = Value at (P/100) × (N + 1)th position - Where P is the desired percentile, and N is the number of data points. 2. Trend Strength Evaluation: - The calculated percentiles for highs and lows are used to determine the strength of bullish and bearish trends. - For instance, a high percentile rank in the high prices may indicate a strong bullish trend, and vice versa for bearish trends. For more please check: Pivot Percentile Trend - Strategy 🔶 Strategy Integration 1. Combining SuperTrend and Pivot Percentile: - The strategy synthesizes the insights from both AI-enhanced SuperTrend and Pivot Percentile analysis. - It compares the trend direction indicated by the SuperTrend with the strength of the trend as suggested by the Pivot Percentile analysis. 2. Signal Generation: - A trading signal is generated when both the AI-enhanced SuperTrend and the Pivot Percentile analysis agree on the trend direction. - For instance, a bullish signal is generated when both the SuperTrend is bullish, and the Pivot Percentile analysis shows strength in bullish trends. 🔶 Risk Management and Filters - ADX and DMI Filter: The strategy uses the Average Directional Index (ADX) and the Directional Movement Index (DMI) as filters to assess the trend's strength and direction. - Dynamic Trailing Stop Loss: Based on the SuperTrend indicator, the strategy dynamically adjusts stop-loss levels to manage risk effectively. This strategy stands out for its ability to combine real-time AI analysis with established technical indicators, offering traders a nuanced and responsive tool for navigating complex market conditions. The equations and algorithms involved are pivotal in accurately identifying market trends and potential trade opportunities. █ Usage To effectively use this strategy, traders should: 1. Understand the AI and Pivot Percentile Indicators: A clear grasp of how these indicators work will enable traders to make informed decisions. 2. Interpret the Signals Accurately: The strategy provides bullish, bearish, and neutral signals. Traders should align these signals with their market analysis and trading goals. 3. Monitor Market Conditions: Given that this strategy is sensitive to market dynamics, continuous monitoring is crucial for timely decision-making. 4. Adjust Settings as Needed: Traders should feel free to tweak the input parameters to suit their trading preferences and to respond to changing market conditions. █Default Settings and Their Impact on Performance 1. Trading Direction (Default: "Both") Effect: Determines whether the strategy will take long positions, short positions, or both. Adjusting this setting can align the strategy with the trader's market outlook or risk preference. 2. AI Settings (Neighbors: 3, Data Points: 24) Neighbors: The number of nearest neighbors in the KNN algorithm. A higher number might smooth out noise but could miss subtle, recent changes. A lower number makes the model more sensitive to recent data but may increase noise. Data Points: Defines the amount of historical data considered. More data points provide a broader context but may dilute recent trends' impact. 3. SuperTrend Settings (Length: 10, Factor: 3.0, MA Source: "WMA") Length: Affects the sensitivity of the SuperTrend indicator. A longer length results in a smoother, less sensitive indicator, ideal for long-term trends. Factor: Determines the bandwidth of the SuperTrend. A higher factor creates wider bands, capturing larger price movements but potentially missing short-term signals. MA Source: The type of moving average used (e.g., WMA - Weighted Moving Average). Different MA types can affect the trend indicator's responsiveness and smoothness. 4. AI Trend Prediction Settings (Price Trend: 10, Prediction Trend: 80) Price Trend and Prediction Trend Lengths: These settings define the lengths of weighted moving averages for price and SuperTrend, impacting the responsiveness and smoothness of the AI's trend predictions. 5. Pivot Percentile Settings (Length: 10) Length: Influences the calculation of pivot percentiles. A shorter length makes the percentile more responsive to recent price changes, while a longer length offers a broader view of price trends. 6. ADX and DMI Settings (ADX Length: 14, Time Frame: 'D') ADX Length: Defines the period for the Average Directional Index calculation. A longer period results in a smoother ADX line. Time Frame: Sets the time frame for the ADX and DMI calculations, affecting the sensitivity to market changes. 7. Commission, Slippage, and Initial Capital These settings relate to transaction costs and initial investment, directly impacting net profitability and strategy feasibility.
Chiến lược Pine Script®
của PresentTrading
17
Volume ForecastThe Volume Forecast indicator on TradingView is a comprehensive tool designed to analyze historical price action and project future market movements based on the average sizes of candles. Incorporating various data points such as candle high/low, open/close, and real volumes, Volume Forecast provides traders with a holistic view of market dynamics, allowing for more informed decision-making. Key Features: Multi-Data Source Analysis: Volume Forecast seamlessly integrates multiple data sources, including candle high/low, open/close prices, and real volumes. By considering these diverse elements, the indicator offers a nuanced understanding of market conditions. Historical Candle Size Analysis: The indicator conducts a thorough analysis of historical candle sizes, capturing key data points to calculate the average candle size over a specified period. This historical context serves as the foundation for forecasting future candle sizes. Customizable Forecasting Parameters: Traders have the flexibility to fine-tune forecasting parameters to align with their trading strategies. Whether focusing on open/close relationships, high/low points, or real volumes, users can customize the indicator to suit their preferences. Predictive Algorithm: Volume Forecast employs a sophisticated predictive algorithm that leverages historical candle size data to project the potential size of upcoming candles. This algorithmic approach enhances the indicator's accuracy in forecasting market movements. Visual Clarity: The indicator provides a clear visual representation on the TradingView chart, displaying historical candle sizes and forecasted values. Color-coded elements and visual cues help traders quickly interpret the data, facilitating timely decision-making. Adaptive Real-Time Updates: Volume Forecast dynamically updates in real-time, ensuring traders have access to the latest information. This adaptability allows for swift adjustments to trading strategies in response to changing market conditions. Comprehensive Market Compatibility: Whether trading stocks, forex, cryptocurrencies, or commodities, Volume Forecast is compatible across various financial instruments and timeframes. This versatility makes it a valuable asset for traders in different markets. User-Friendly Interface: With an intuitive interface, Volume Forecast is accessible to traders of all experience levels. The indicator's user-friendly design streamlines the analysis process, making it easier for traders to incorporate it into their trading routines. In summary, Volume Forecast is a robust TradingView indicator that combines historical candle size analysis with advanced forecasting techniques. By incorporating multiple data sources and offering customization options, it empowers traders to make more informed decisions in anticipation of market movements. Whether used independently or in conjunction with other tools, Volume Forecast is a valuable asset for traders seeking a comprehensive understanding of market dynamics.
Chỉ báo Pine Script®
của eBacktesting
Machine Learning: kNN (New Approach)Description: kNN is a very robust and simple method for data classification and prediction. It is very effective if the training data is large. However, it is distinguished by difficulty at determining its main parameter, K (a number of nearest neighbors), beforehand. The computation cost is also quite high because we need to compute distance of each instance to all training samples. Nevertheless, in algorithmic trading KNN is reported to perform on a par with such techniques as SVM and Random Forest. It is also widely used in the area of data science. The input data is just a long series of prices over time without any particular features. The value to be predicted is just the next bar's price. The way that this problem is solved for both nearest neighbor techniques and for some other types of prediction algorithms is to create training records by taking, for instance, 10 consecutive prices and using the first 9 as predictor values and the 10th as the prediction value. Doing this way, given 100 data points in your time series you could create 10 different training records. It's possible to create even more training records than 10 by creating a new record starting at every data point. For instance, you could take the first 10 data points and create a record. Then you could take the 10 consecutive data points starting at the second data point, the 10 consecutive data points starting at the third data point, etc. By default, shown are only 10 initial data points as predictor values and the 6th as the prediction value. Here is a step-by-step workthrough on how to compute K nearest neighbors (KNN) algorithm for quantitative data: 1. Determine parameter K = number of nearest neighbors. 2. Calculate the distance between the instance and all the training samples. As we are dealing with one-dimensional distance, we simply take absolute value from the instance to value of x (| x – v |). 3. Rank the distance and determine nearest neighbors based on the K'th minimum distance. 4. Gather the values of the nearest neighbors. 5. Use average of nearest neighbors as the prediction value of the instance. The original logic of the algorithm was slightly modified, and as a result at approx. N=17 the resulting curve nicely approximates that of the sma(20). See the description below. Beside the sma-like MA this algorithm also gives you a hint on the direction of the next bar move.
Chỉ báo Pine Script®
của capissimo
42
Pulu's 3 Moving Averages Pulu's 3 Moving Averages Release version 1, date 2021-09-28 This script allows you to customize three sets of moving averages, turn on/off, set color and parameters. It also tags the start date of the last set of moving average if there is. This, release version 1, supports eight moving average algorithms: ALMA, Arnaud Legoux Moving Average EMA, Exponential Moving Average RMA, Adjusted exponential moving average (aka Wilder’s EMA) SMA, Simple Moving Average SWMA, Symmetrically-Weighted Moving Average VWAP, Volume-Weighted Average Price VWMA, Volume-Weighted Moving Average WMA, Weighted Moving Average The availability and function parameters Func. Availability Parameters ALMA MA1, MA2, MA3 source length offset sigma EMA RMA SMA VWMA WMA MA1, MA2, MA3 source length SWMA VWAP MA1 source Parameters Parameter Description source the series of values to process. The default is to use the closing price to calculate the moving average. length an integer value that defines the number of bars to calculate the moving average on. The SWMA and VWAP do not use this parameter. ALMA offset a floating-point value that controls the tradeoff between smoothness (with a value closer to 1) and responsiveness (with a value closer to 0). This parameter is only used by ALMA. ALMA sigma a floating-point value that specifies the ALMA’s smoothness. The larger this value, the smoother the moving average is. This parameter is only used by ALMA. I'm not sure if it is needed, so I do not let the three Moving Averages of the script to have indivial algorithm setting. Because that will involve much complicated condition testing and use up more TradingView script lines limit. If you need to combine different algorithms in the three sets of moving averages, or have other ideas, leave a message to let me know; maybe I will try it in the next update. 我不確定是否需要，所以我沒有讓腳本的三組移動平均線有各別的算法設置。因為這將涉及更多複雜的條件測試，並使用更多 TradingView 腳本列數限制。如果您需要在三組均線中組合不同的算法，或者有其他想法，請留言告訴我；也許我會在下一次更新中嘗試。
Chỉ báo Pine Script®
của Pulu_
3
Paradigm Shift: Delivery StateDescription by way of Example using weekly and 4h readings. THIS INDICATOR IS NOT MADE TO OPERATE BELOW A 4H chart. It is looking for Weekly and 4h price delivery that an ICT trader would call messy. Spikes well below OB but which then turn in the logical direction. A series of FVG that are never returned to or themselves are printed over but then turn support or resistance against the run through. So it is designed to indicate the overall level of stress on algorithm delivery. When you see a significantly higher stress level on the 4h trading is still doable but be careful of sloppy delivery. When you see a significant stress level on the weekly then be very very careful on the lower levels and consider staying out for the time being. The state of price delivery can be seen by anyone. But I have incorporated readings of the global bond market and currency correlations as confluent evidence of the reading for the state of price delivery. The term "grinder" is my word for algorithmic delivery. Contrary to ICT I believe that buying and selling pressure are real determinants of market movement up or down. However, I believe that that pressure is pushed through the algo like meat through a meat grinder to ensure a fair and equitable and efficient delivery of that pressure. So price always moves according to algorithmic principles, but those principles are driven by external pressure. The greater the pressure the more the algo stutters and gaps in its effort to smooth out an efficient delivery of price. This Indicator is looking for those stressors. The Weekly State: "Symmetrical" / Stress: 2% * What it means: At the highest level, the "New Paradigm" has not yet broken the system. A 2% stress level is effectively "Background Noise." This indicates that the global bond market and currency correlations are currently holding their historical norms. * The Price Delivery: Because it is Symmetrical, the "Grinder" is in an efficient mode. Weekly expansions are likely being met with orderly retracements. There are no "Ominous" breakaway gaps on the weekly timeframe yet. * The Dalio View: We are still in the "Accumulation" or "Buffering" phase of the cycle. The systemic heart attack is not happening this week. 2. The 4h State: "Saturated" / Stress: 44% * What it means: While the Weekly is calm, the 4h is heating up. A 44% stress level means that local bond volatility (the US10Y proxy) is higher than it has been 44% of the time over the last year. This is a significant "Step-Up" in pressure. * The Price Delivery: Because it is Saturated, the 4h "Grinder" is starting to struggle with the "meat" being fed into it. * The Result: You will likely see "Deep Stop Runs" and "PD Array Overtravel." Price might not just tap an Order Block; it might blast 15 pips through it before reversing. * Delivery is "Unclean": The 4h Fair Value Gaps might only get partially filled, or price might leave "jagged" wicks that make lower-timeframe entries frustrating. 3. How to Trade This Divergence (Practical Logic) When the Higher Timeframe (HTF) is stable (2%) but the Lower Timeframe (LTF) is stressed (44%), it creates a specific environment: * The "Opaque Trap": You might see a perfect ICT Silver Bullet setup on the 15m, but because the 4h is "Saturated," the setup will likely be messier than usual. You should expect "Judas Swings" to be more violent and "FVG Retracements" to be less precise. * Confidence in the HTF: Since the Weekly is at 2%, you can trust the Overall Direction. If the Weekly is bullish, any "Saturated" mess on the 4h is likely just an aggressive re-accumulation rather than a systemic reversal. * The "Stay Out" Warning: You only need to worry when that 4h Stress (44%) begins to "infect" the Weekly. If the Weekly Stress moves from 2% to 20% to 50%, that is your signal that the Paradigm is Shifting and the "Grinder" is about to start teleporting price.
Chỉ báo Pine Script®
của BLKMonk
Band Walk Detector TENKYO [BASIC]1. Abstract: The Computational Resolution of Cognitive Latency This publication presents the findings of the "TENKYO" Research Project , focusing on the algorithmic detection of high-probability volatility breakouts ("Band Walks") on the 15-minute timeframe. Problem Statement: Manual trading suffers from a critical "Cognitive Latency Gap." A trader cannot simultaneously process multi-dimensional variables—volatility expansion rates (derivative of variance), candle morphology (price rejection ratios), and time-weighted liquidity cycles—within the millisecond timeframe required for optimal execution. Solution: This script is not a discretionary indicator but a Hard-Coded Decision Support System . It automates the verification of market conditions using a "Piecewise Constant Parameter Model," offloading the computational burden from the human operator to the CPU. Note: This is a research release for the verification of the TENKYO logic, not a commercial product. 2. Theoretical Framework & Methodology The architecture of this script rejects the standard "Stationary Volatility Assumption" (the idea that market behavior is consistent throughout the day). Instead, it adopts a Time-Segmented Heteroskedasticity Model. A. Temporal Segmentation Logic (The Session Filter) Global forex markets exhibit distinct liquidity profiles based on the active session (London, New York, Tokyo/Sydney). A standard deviation ($\sigma$) that signals a breakout in the Asian session is often mere noise in the London session.To solve this, the script partitions the trading day into four distinct phases ($S_1, S_2, S_3, S_4$) and applies a Dynamic Parameter Matrix: ・ Logic: $P(t) = \{ \text{Length}_i, \text{Mult}_i, \text{Threshold}_i \}$ where $t \in S_i$ ・ Implementation: The script contains an extensive if-else structure that automatically swaps the Lookback Period and Deviation Multiplier based on the timestamp. This allows the algorithm to "tighten" or "loosen" its sensitivity relative to expected market volume. B. Synthetic Execution Modeling (Bid/Ask Simulation) TradingView's default variables (close, high, low) represent mid-market data, which fails to account for the spread cost inherent in execution. ・Correction: This algorithm internally calculates synthetic Bid and Ask prices using a defined spread factor ($\Delta$). ・Formula: $$P_{Ask} = P_{Mid} + (\Delta / 2), \quad P_{Bid} = P_{Mid} - (\Delta / 2)$$ 3. Algorithmic Core: The "TENKYO" Logic The script identifies a "Band Walk" only when three independent layers of logic align perfectly. Layer 1: The Volatility Impulse (Expansion) The primary trigger is not merely price crossing a band, but the acceleration of the Band Width. ・Condition: The algorithm monitors the differential of the Upper and Lower bands. A signal is generated only if the expansion velocity exceeds a predefined Pips threshold (bwGrow_px) specifically tuned for the current session $S_i$. Layer 2: Morphological Rejection Filtering (Wick Analysis) To filter out "Mean Reversion Traps" (False Breakouts), the script analyzes the morphology of the signal candle using a Wick-to-Body Ratio test. ・The Trap: A candle that breaks the band but closes with a long rejection wick indicates exhausted momentum. ・The Filter: Let $R_{wb} = \text{Body} / \text{RejectionWick}$. If $R_{wb} < \text{Threshold}_{Si}$,, the signal is suppressed.This mathematical filter prevents the user from entering trades where the market sentiment has already reversed within the candle's duration.Layer 3: The "Scramble" State (Momentum Continuity) The script introduces a unique state machine called "Scramble." ・Purpose: To detect re-entry opportunities during a high-momentum trend. ・Mechanism: If the market enters an "Endure" state (a pause in expansion) but validates specific continuity conditions (price remains within the $2\sigma$ corridor without violating the trend vector), the algorithm flags a "Scramble" signal. This effectively distinguishes between a "Trend Reversal" and a "Trend Pause." 4. Operational Features & Visual Guide This tool is designed to serve as a rigorous "Filter" for manual trading. ・The "Mushy" Zone: Visualized by a gray fill between bands. This represents a low-kurtosis, mean-reverting market state where trend-following strategies are statistically disadvantageous. The algorithm disables all signals in this zone. ・Secure & Breakeven Visualization: The script projects potential exit points based on Maximum Favorable Excursion (MFE) logic calculated from the entry bar's synthetic price. This assists the user in objective trade management. ・Hard-Coded Optimization: Users will notice that many parameters are locked or preset. This is intentional. These values are derived from extensive backtesting on EURUSD and JPY pairs and serve as the "Control" variables for this research. 5. Conclusion The Band Walk Detector TENKYO is a comprehensive logical framework that integrates time, volatility, and morphology. It denies the simplistic "one-size-fits-all" approach of standard indicators in favor of a granular, session-adaptive model. It provides the trader with a computationally verified "Go/No-Go" signal, bridging the gap between human intuition and algorithmic precision.
Chỉ báo Pine Script®
của bonno_original
ML Adaptive SuperTrend Strategy [trade_crush]# ML Adaptive SuperTrend Strategy - User Guide ## Introduction The **ML Adaptive SuperTrend Strategy** is a sophisticated trading tool that combines traditional trend-following logic with **Machine Learning (K-Means Clustering)** to dynamically adapt to market volatility. Unlike standard SuperTrend indicators that use a fixed ATR, this strategy analyzes historical volatility to categorize the current market into distinct clusters, providing more precise entries and exits. > > **Special Thanks:** This strategy is based on the innovative work of **AlgoAlpha**. You can explore their extensive library of high-quality indicators and strategies on TradingView: (www.tradingview.com). --- ## Machine Learning Engine (K-Means) The core of this strategy is its ability to "learn" from recent market behavior. - **K-Means Clustering**: The script takes the last $N$ bars of ATR data and runs an iterative clustering algorithm to find three "centroids" representing **High**, **Medium**, and **Low** volatility. - **Adaptive ATR**: Based on the current volatility, the strategy selects the nearest centroid to use as the ATR value for the SuperTrend calculation. This ensures the trailing stop tightens during low volatility and widens during high volatility to avoid "noise". --- ## Key Features ### 1. Non-Repainting Signals - **Confirm Signals**: When enabled, signals are only triggered after a bar closes. This ensures that the arrows and entries you see on the chart are permanent and reliable for backtesting. ### 2. Intelligent Risk Management - **Multiple SL/TP Types**: Choose between **Percentage** based stops or **ATR** based stops for both Stop Loss and Take Profit. - **Trailing Stop Loss (TSL)**: - Supports both Percentage and ATR modes. - **Activation Offset**: Only activates the trailing mechanism after the price has moved a certain percentage in your favor, protecting early-stage trades. ### 3. Risk-Based Position Sizing - **Dynamic Quantity**: If enabled, the strategy automatically calculates the trade size based on your **Risk % Per Trade** and the distance to your **Stop Loss**. This ensures you never lose more than your defined risk on a single trade. --- ## User Input Guide ### SuperTrend & ML Settings - **ATR Length**: The window used to calculate market volatility. - **SuperTrend Factor**: The multiplier that determines the distance of the trailing stop from the price. - **Use ML Adaptive ATR**: Toggle between the ML-enhanced logic and standard ATR. - **Training Data Length**: How many historical bars the ML engine analyzes to find clusters. ### Risk Management - **Stop Loss Type**: Set to Percentage, ATR, or None. - **TS Activation Offset**: The profit buffer required before the trailing stop starts following the price. - **Use Risk-Based Sizing**: Toggle this to let the script manage your position size automatically. --- ## How to Trade with This Strategy 1. **Monitor the Dashboard**: Check the top-right table to see which volatility cluster the market is currently in. 2. **Observe the Fills**: The adaptive fills (green/red) visualize the "breathing room" the strategy is giving the price. 3. **Execution**: The strategy enters on "ML Bullish" (Triangle Up) and "ML Bearish" (Triangle Down) signals. 4. **Exits**: The script will automatically exit based on your SL, TP, or Trailing Stop settings. --- ## Credits Original Concept: **AlgoAlpha** Strategy Conversion & Enhancements: **Antigravity AI**
Chiến lược Pine Script®
của trade_crush
MLExtensionsLibrary "MLExtensions" A set of extension methods for a novel implementation of a Approximate Nearest Neighbors (ANN) algorithm in Lorentzian space. normalizeDeriv(src, quadraticMeanLength)   Returns the smoothed hyperbolic tangent of the input series.   Parameters:      src (float) : The input series (i.e., the first-order derivative for price).      quadraticMeanLength (int) : The length of the quadratic mean (RMS).   Returns: nDeriv The normalized derivative of the input series. normalize(src, min, max)   Rescales a source value with an unbounded range to a target range.   Parameters:      src (float) : The input series      min (float) : The minimum value of the unbounded range      max (float) : The maximum value of the unbounded range   Returns: The normalized series rescale(src, oldMin, oldMax, newMin, newMax)   Rescales a source value with a bounded range to anther bounded range   Parameters:      src (float) : The input series      oldMin (float) : The minimum value of the range to rescale from      oldMax (float) : The maximum value of the range to rescale from      newMin (float) : The minimum value of the range to rescale to      newMax (float) : The maximum value of the range to rescale to   Returns: The rescaled series getColorShades(color)   Creates an array of colors with varying shades of the input color   Parameters:      color (color) : The color to create shades of   Returns: An array of colors with varying shades of the input color getPredictionColor(prediction, neighborsCount, shadesArr)   Determines the color shade based on prediction percentile   Parameters:      prediction (float) : Value of the prediction      neighborsCount (int) : The number of neighbors used in a nearest neighbors classification      shadesArr (array) : An array of colors with varying shades of the input color   Returns: shade Color shade based on prediction percentile color_green(prediction)   Assigns varying shades of the color green based on the KNN classification   Parameters:      prediction (float) : Value (int|float) of the prediction   Returns: color color_red(prediction)   Assigns varying shades of the color red based on the KNN classification   Parameters:      prediction (float) : Value of the prediction   Returns: color tanh(src)   Returns the the hyperbolic tangent of the input series. The sigmoid-like hyperbolic tangent function is used to compress the input to a value between -1 and 1.   Parameters:      src (float) : The input series (i.e., the normalized derivative).   Returns: tanh The hyperbolic tangent of the input series. dualPoleFilter(src, lookback)   Returns the smoothed hyperbolic tangent of the input series.   Parameters:      src (float) : The input series (i.e., the hyperbolic tangent).      lookback (int) : The lookback window for the smoothing.   Returns: filter The smoothed hyperbolic tangent of the input series. tanhTransform(src, smoothingFrequency, quadraticMeanLength)   Returns the tanh transform of the input series.   Parameters:      src (float) : The input series (i.e., the result of the tanh calculation).      smoothingFrequency (int)      quadraticMeanLength (int)   Returns: signal The smoothed hyperbolic tangent transform of the input series. n_rsi(src, n1, n2)   Returns the normalized RSI ideal for use in ML algorithms.   Parameters:      src (float) : The input series (i.e., the result of the RSI calculation).      n1 (simple int) : The length of the RSI.      n2 (simple int) : The smoothing length of the RSI.   Returns: signal The normalized RSI. n_cci(src, n1, n2)   Returns the normalized CCI ideal for use in ML algorithms.   Parameters:      src (float) : The input series (i.e., the result of the CCI calculation).      n1 (simple int) : The length of the CCI.      n2 (simple int) : The smoothing length of the CCI.   Returns: signal The normalized CCI. n_wt(src, n1, n2)   Returns the normalized WaveTrend Classic series ideal for use in ML algorithms.   Parameters:      src (float) : The input series (i.e., the result of the WaveTrend Classic calculation).      n1 (simple int)      n2 (simple int)   Returns: signal The normalized WaveTrend Classic series. n_adx(highSrc, lowSrc, closeSrc, n1)   Returns the normalized ADX ideal for use in ML algorithms.   Parameters:      highSrc (float) : The input series for the high price.      lowSrc (float) : The input series for the low price.      closeSrc (float) : The input series for the close price.      n1 (simple int) : The length of the ADX. regime_filter(src, threshold, useRegimeFilter)   Parameters:      src (float)      threshold (float)      useRegimeFilter (bool) filter_adx(src, length, adxThreshold, useAdxFilter)   filter_adx   Parameters:      src (float) : The source series.      length (simple int) : The length of the ADX.      adxThreshold (int) : The ADX threshold.      useAdxFilter (bool) : Whether to use the ADX filter.   Returns: The ADX. filter_volatility(minLength, maxLength, useVolatilityFilter)   filter_volatility   Parameters:      minLength (simple int) : The minimum length of the ATR.      maxLength (simple int) : The maximum length of the ATR.      useVolatilityFilter (bool) : Whether to use the volatility filter.   Returns: Boolean indicating whether or not to let the signal pass through the filter. backtest(high, low, open, startLongTrade, endLongTrade, startShortTrade, endShortTrade, isEarlySignalFlip, maxBarsBackIndex, thisBarIndex, src, useWorstCase)   Performs a basic backtest using the specified parameters and conditions.   Parameters:      high (float) : The input series for the high price.      low (float) : The input series for the low price.      open (float) : The input series for the open price.      startLongTrade (bool) : The series of conditions that indicate the start of a long trade.      endLongTrade (bool) : The series of conditions that indicate the end of a long trade.      startShortTrade (bool) : The series of conditions that indicate the start of a short trade.      endShortTrade (bool) : The series of conditions that indicate the end of a short trade.      isEarlySignalFlip (bool) : Whether or not the signal flip is early.      maxBarsBackIndex (int) : The maximum number of bars to go back in the backtest.      thisBarIndex (int) : The current bar index.      src (float) : The source series.      useWorstCase (bool) : Whether to use the worst case scenario for the backtest.   Returns: A tuple containing backtest values init_table()   init_table()   Returns: tbl The backtest results. update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winrate, earlySignalFlips)   update_table(tbl, tradeStats)   Parameters:      tbl (table) : The backtest results table.      tradeStatsHeader (string) : The trade stats header.      totalTrades (float) : The total number of trades.      totalWins (float) : The total number of wins.      totalLosses (float) : The total number of losses.      winLossRatio (float) : The win loss ratio.      winrate (float) : The winrate.      earlySignalFlips (float) : The total number of early signal flips.   Returns: Updated backtest results table.
Thư viện Pine Script®
của TFU_Tradewise
Volume Profile VisionVolume Profile Vision - Complete Description Overview Volume Profile Vision (VPV) is an advanced volume profile indicator that visualizes where trading activity has occurred at different price levels over a specified time period. Unlike traditional volume indicators that show volume over time, this indicator displays volume distribution across price levels, helping traders identify key support/resistance zones, fair value areas, and potential reversal points. What Makes This Indicator Original Volume Profile Vision introduces several unique features not found in standard volume profile tools: Dual-Direction Histogram Display: Unlike conventional volume profiles that only show bars extending in one direction, VPV displays volume bars extending both left (into historical candles) and right (as a traditional histogram). This bi-directional approach allows traders to see exactly where historical price action intersected with high-volume nodes. Real-Time Candle Highlighting: The indicator dynamically highlights volume bars that intersect with the current candle's price range, making it immediately obvious which volume levels are currently in play. Four Professional Color Schemes: Each color scheme uses distinct gradient algorithms and visual encoding systems: Traffic Light: Uses red (POC), green (VA boundaries), yellow (HVN), with grayscale gradients outside the value area Aurora Glass: Modern cyan-to-magenta gradient with hot magenta POC highlighting Obsidian Precision: Professional dark theme with white POC and electric cyan accents Black Ice: Monochromatic cyan family with graduated intensity Adaptive Transparency System: Automatically adjusts bar transparency based on position relative to value area, with special handling for each color scheme to maintain visual clarity. Core Concepts & Calculations Volume Distribution Analysis The indicator divides the visible price range into user-defined price levels (default: 80 levels) and calculates the total volume traded at each level by: Scanning back through the specified lookback period (customizable or visible range) For each historical bar, determining which price levels the bar's high/low range intersects Accumulating volume for each intersected price level Optionally filtering by bullish/bearish volume only Point of Control (POC) The POC is the price level with the highest traded volume during the analyzed period. This represents the "fairest" price where most traders agreed on value. The indicator marks this with distinct coloring (red in Traffic Light, magenta in Aurora Glass, white in Obsidian Precision, cyan in Black Ice). Trading Significance: POC acts as a strong magnet for price - markets tend to return to fair value. When price is away from POC, traders watch for: Mean reversion opportunities when price is far from POC Rejection signals when price tests POC from above/below Breakout confirmation when price breaks through and holds beyond POC Value Area (VA) The Value Area encompasses the price range where a specified percentage (default: 68%) of all volume traded. This represents the range of "accepted value" by market participants. Calculation Method: Start at the POC (highest volume level) Expand upward and downward, adding adjacent price levels Always add the level with higher volume next Continue until accumulated volume reaches the VA percentage threshold Value Area High (VAH): Upper boundary of accepted value - acts as resistance Value Area Low (VAL): Lower boundary of accepted value - acts as support Trading Significance: Price spending time inside VA indicates market equilibrium Breakouts above VAH suggest bullish momentum shift Breakdowns below VAL suggest bearish momentum shift Returns to VA boundaries often provide high-probability entry zones High Volume Nodes (HVN) Price levels with volume exceeding a threshold percentage (default: 80%) of POC volume. These represent areas of strong agreement and consolidation. Trading Significance: HVNs act as strong support/resistance zones Price tends to consolidate at HVNs before making directional moves Breaking through an HVN often signals strong momentum Low Volume Nodes (LVN) Price levels within the Value Area with volume ≤30% of POC volume. These are zones price moved through quickly with minimal consolidation. Trading Significance: LVNs represent areas of rejection - price finds little acceptance Price tends to move rapidly through LVN zones Useful for setting stop-losses (below LVN for longs, above for shorts) Can identify potential gaps or "air pockets" in the market structure Grayscale POC Detection A secondary POC detection system identifies the highest volume level outside the Value Area (with a 2-level buffer to avoid confusion). This helps identify significant volume accumulation zones that exist beyond the main value area. How to Use This Indicator Setup Choose Lookback Period: Enable "Use Visible Range" to analyze only what's on your chart Or set "Fixed Range Lookback Depth" (default: 200 bars) for consistent analysis Adjust Profile Resolution: "Number of Price Levels" (default: 80) - higher = more granular analysis, lower = broader zones Select Color Scheme: Traffic Light: Best for clear POC/VA/HVN identification Aurora Glass: Modern aesthetic for dark charts Obsidian Precision: Professional trader preference Black Ice: Minimalist single-color family Visual Customization Left Extension: How far back the left-side histogram extends into historical candles (default: 490 bars) Right Extension: Width of the traditional histogram bars on the right (default: 50 bars) Right Margin: Space between current price bar and histogram (default: 0 for flush alignment) Left Profile Gap: Space between left-side histogram and candles (default: 0) Trading Strategies Strategy 1: Value Area Mean Reversion Wait for price to move outside the Value Area (above VAH or below VAL) Look for rejection signals (wicks, bearish/bullish candles) Enter trades toward the POC Take profits as price returns to POC or opposite VA boundary Strategy 2: Breakout Confirmation Identify when price is consolidating within the Value Area Wait for a strong close above VAH (bullish) or below VAL (bearish) Enter on the breakout or on first pullback to the VA boundary Target previous HVNs or swing highs/lows outside the VA Strategy 3: POC Support/Resistance Watch for price approaching the POC level If approaching from below, look for bullish reversal patterns at POC (support) If approaching from above, look for bearish reversal patterns at POC (resistance) Trade in the direction of the bounce with stops beyond the POC Strategy 4: LVN Fast Movement Zones Identify LVN zones within the Value Area (marked with "LVN" label) When price enters an LVN, expect rapid movement through the zone Avoid entering trades within LVNs Use LVNs as confirmation of directional momentum Alert System The indicator includes 7 customizable alert conditions: POC Touch: Alerts when price comes within 0.5 ATR of POC VAH/VAL Touch: Alerts at Value Area boundaries VA Breakout: Alerts on breakouts above VAH or below VAL HVN Touch: Alerts when price contacts High Volume Nodes LVN Entry: Alerts when entering Low Volume zones POC Shift: Alerts when POC moves to a new price level Reading the Profile Price Labels (shown on the right side): POC: Point of Control - highest volume price level VAH: Value Area High - upper boundary of accepted value VAL: Value Area Low - lower boundary of accepted value LVN: Low Volume Node - expect fast movement through this zone Color Intensity Interpretation: Brighter colors = higher volume concentration Dimmer colors = lower volume Abrupt color changes = transition between volume zones Gaps in the histogram = price levels with no trading activity Technical Details Volume Accumulation Logic: For each bar in lookback period: For each price level: If bar's high/low range intersects price level: Add bar's volume to that price level's total Gradient Algorithm: Traffic Light: Dual-range piecewise gradient (0-50% and 50-100% volume intensity) Aurora Glass: Linear cyan-to-magenta interpolation Obsidian Precision: Dark blue gradient with cyan highlights Black Ice: Three-stage cyan intensity progression Real-Time Updates: The profile recalculates on every bar, including real-time tick data, ensuring the volume distribution always reflects current market structure. Best Practices Timeframe Selection: Use higher timeframes (4H, Daily) for swing trading, lower timeframes (5min, 15min) for day trading Combine with Price Action: Volume profile shows WHERE, price action shows WHEN Multiple Timeframe Analysis: Check daily VP for major levels, then drill down to intraday for entries Volume Type Selection: Use "Bullish" volume in uptrends, "Bearish" in downtrends, or "Both" for complete picture Adjust VA Percentage: 68% (default) captures one standard deviation; try 70% for tighter or 60% for broader value areas Performance Notes Maximum bars back: 5000 (handles deep historical analysis) Maximum boxes: 500 (handles complex profiles) Optimized calculation: Only recalculates on last bar for efficiency Real-time capable: Updates as new ticks arrive
Chỉ báo Pine Script®
của KevinSvenson_
2
Advanced Market Profile & S/R Zones (Pro)Advanced Market Profile & S/R Zones This indicator brings professional Auction Market Theory to your chart using a custom rolling Volume Profile algorithm. Unlike standard profiles that remain fixed, this tool dynamically calculates the "Fair Value" of the asset based on your specific lookback period (e.g., the last 100 bars). It automatically highlights the Point of Control (POC), Value Area (VA), and suggests statistical Discount (Buy) and Premium (Sell) zones. Key Features Volume Splitting Algorithm: Most basic scripts dump the entire volume of a candle into a single price point (the average). This script splits the volume across the candle's entire High-Low range. This results in a much smoother, higher-resolution bell curve that accurately reflects price action, especially on higher timeframes like Monthly charts. Auto-generated Zones: Green Zone (Discount): Prices below the Value Area Low (VAL). Statistically "cheap." Red Zone (Premium): Prices above the Value Area High (VAH). Statistically "expensive." Real-Time Dashboard: A built-in panel displays the exact price levels for the POC, VAH, and VAL for precise limit order placement, along with the current Market Trend. How to Use For Intraday (Day Trading): Settings: Set Lookback to 100 - 300. Strategy: Watch for price to open outside the Value Area. If price breaks back inside the Value Area, target the POC (Red Line). For Macro (Monthly/Weekly Charts): Settings: Set Lookback to 12 (1 Year) or 60 (5 Years). Strategy: Identify multi-year structural support. When a monthly candle enters the Green Discount Zone of a 5-year profile, it is often a high-probability institutional entry point. Trend Logic The Dashboard indicates trend based on price location relative to value: Strong Bullish: Price is accepted ABOVE the Value Area. Strong Bearish: Price is accepted BELOW the Value Area. Neutral / In VA: Price is chopping inside the Value Area. Disclaimer This is a "Rolling Profile." It calculates the profile based on the current lookback window relative to the latest bar. As new bars form, the lookback window shifts, and the profile updates to reflect the new dataset.
Chỉ báo Pine Script®
của bluedutch