Şimdi başlat

Komut dosyalarını "entry" için ara

NIFTY Intraday Strategy - 50 Points📊 NIFTY Intraday Strategy – Description This Pine Script defines an intraday trading strategy targeting +50 points per trade on NIFTY, using a blend of trend-following and momentum indicators. Here's a breakdown: 🔍 Core Components 1. Indicators Used VWAP: Volume-Weighted Average Price – institutional anchor for fair value. Supertrend: Trend direction indicator (parameters: 10, 3.0). RSI (14): Measures strength/momentum. ATR (14): Determines volatility for stop-loss calculation. 📈 Entry Conditions ✅ Buy Entry Price is above VWAP Supertrend direction is bullish RSI is above 50 Time is between 9:15 AM and 3:15 PM (India time) ❌ Sell Entry Price is below VWAP Supertrend direction is bearish RSI is below 50 Time is within same market hours 🎯 Exit Logic Target: 50 points from entry Stop Loss: 1 × ATR from entry If neither is hit by 3:15 PM, the position is held (though you may add exit logic at that time). 📌 Visualization VWAP: orange line Supertrend: green (uptrend), red (downtrend) Buy Signal: green triangle below bar Sell Signal: red triangle above bar This strategy is ideal for intraday scalping or directional momentum trading in NIFTY Futures or Options. a. Add end-of-day exit at 3:15 PM to fully close all trades b. Add a risk-reward ratio input to dynamically adjust target vs stop-loss
Pine Script® stratejisi
dhramendersingh01 tarafından
🔥 Volatility Squeeze Breakout Strategy (TP/SL in Points)This strategy is designed to catch explosive breakout moves from low-volatility consolidations using a "volatility squeeze" + breakout + momentum" approach. It identifies high-probability buy opportunities when the market is in a tight range and preparing for expansion. ✅ Entry Condition: - Previous candle is in a squeeze - Current candle breaks above channel high - Momentum is positive (ROC) 🎯 Exit Conditions: - Take Profit in fixed points above entry price - Stop Loss in fixed points below entry price 🧰 Inputs: - ATR Length for volatility - Channel Length for breakout levels - ROC Length for momentum - Squeeze threshold (ATR/close) - TP/SL in absolute price points 📊 Plots: - Buy signals shown as green triangles - Channel high/low plotted - TP/SL levels shown as live lines when in position Suitable for intraday breakout scalping or directional trades when price expands from compression zones.
Pine Script® stratejisi
poligrowvenkatpolisetty tarafından
BTC Event Contract Signal Indicator# BTC Event Contract Signal Indicator **Version**: V1.0 **Last Updated**: December 21, 2024 **Author**: OxJohannWu **Type**: Pine Script v6 Indicator (Overlay) **Timeframes**: Optimized for 1-minute BTC data, supports all timeframes ## 📋 Overview The BTC Event Contract Signal Indicator is a sophisticated technical analysis tool designed specifically for Bitcoin event contracts (binary options). This indicator provides real-time buy/sell signals with comprehensive contract tracking, performance statistics, and settlement monitoring - all displayed in Beijing time (UTC+8). ### Key Features - **Smart Signal Generation**: Multi-layered technical analysis with adaptive filtering - **Real-time Contract Tracking**: Monitor active contracts with automatic settlement detection - **Performance Analytics**: Detailed win/loss statistics with daily breakdowns - **Multi-timeframe Optimization**: Auto-adjusts parameters based on chart timeframe - **Beijing Time Display**: All timestamps converted to Beijing timezone - **Alert System**: TradingView alerts for all signal types ## 🎯 Trading Philosophy This indicator combines correlation analysis, MACD momentum, and StochRSI oscillator signals to identify high-probability entry points for Bitcoin event contracts. The system prioritizes quality over quantity, using intelligent filtering to minimize false signals and maximize win rates. ## ⚙️ Parameter Configuration ### 📊 Technical Indicator Settings - **Auto Timeframe Optimization**: Automatically selects optimal parameters based on current timeframe - **MACD Settings**: Fast (8), Slow (21), Signal (5) - optimized for 1-minute BTC data - **RSI Period**: 6 periods for responsive momentum detection - **Stochastic Settings**: K smoothing (2), Period (6) for precise overbought/oversold levels ### 🔗 Correlation Analysis - **Short-term Correlation**: 3-period correlation for immediate trend changes - **Long-term Correlation**: 25-period correlation for broader market context - **Correlation Slope**: Tracks momentum changes in price correlation ### 🎯 Smart Signal Optimization Three intelligent modes to suit different trading styles: #### Smart Balance Mode (Default) - **Target Win Rate**: 80%+ - **Expected Signals**: 8-15 per day - **Filtering**: 6-7 technical conditions - **Best For**: Balanced trading with consistent profits #### High Frequency Mode - **Target Win Rate**: 75%+ - **Expected Signals**: 15-25 per day - **Filtering**: 4 core technical conditions - **Best For**: Active traders seeking more opportunities #### Premium Quality Mode - **Target Win Rate**: 85%+ - **Expected Signals**: 5-10 per day - **Filtering**: 8 strict technical conditions - **Best For**: Conservative traders prioritizing accuracy ### ⏰ Event Contract Settings - **Contract Duration Options**: 10 Minutes, 30 Minutes, 1 Hour, 24 Hours - **Single Contract Rule**: Only one active contract at a time - **Auto Settlement**: Automatic win/loss detection at expiry ## 📈 Signal Generation Logic ### Core Technical Conditions 1. **Correlation Breakout**: Short-term correlation slope changes direction 2. **MACD Momentum**: MACD line above/below signal line with positive/negative slope 3. **StochRSI Position**: K-line slope changes indicating momentum shift ### Smart Filtering System The indicator applies progressive filtering based on selected mode: #### Basic Filters (All Modes) - Volume above 1.4x average - Correlation momentum confirmation - MACD direction alignment #### Advanced Filters (Smart Balance & Premium) - Price action quality (body-to-wick ratio > 0.4) - Momentum strength validation - RSI safe zone (25-75 range) - Optional trend filter with EMA confirmation - Optional multi-timeframe confirmation #### Premium Filters (Premium Quality Only) - Enhanced volume threshold (1.8x average) - Stricter correlation momentum (>1.0) - Multi-timeframe EMA alignment - Advanced momentum validation ### Signal Strength Classification - **Normal Signals**: Basic technical alignment (small arrows) - **Strong Signals**: Enhanced momentum + volume confirmation (large arrows) ## 🎨 Visual Display System ### Signal Arrows - **🔼 Green Triangle Up**: Call signal (buy/long) - **🔽 Red Triangle Down**: Put signal (sell/short) - **💪 Enhanced Arrows**: Strong signals with special emoji indicators ### Settlement Results - **🎉 WIN**: Profitable contracts (green) - **💸 LOSS**: Losing contracts (red) - **Automatic Display**: Shows results immediately upon contract expiry ### Information Labels Each signal displays: - Signal type (Call/Put, Normal/Strong) - Selected mode and timeframe - Contract duration - Settlement results with win/loss indication ## 📊 Statistics Dashboard ### Real-time Performance Table Located in the top-right corner, displaying: #### Summary Statistics - **Total Contracts**: Overall contract count - **Overall Win Rate**: Percentage with color coding (Green: 80%+, Orange: 60-79%, Red: <60%) - **Today's Performance**: Daily statistics with separate tracking - **Win/Loss Breakdown**: Detailed count of profitable vs losing trades #### Directional Analysis - **Call Performance**: Success rate for bullish contracts - **Put Performance**: Success rate for bearish contracts - **Balanced Tracking**: Identifies directional bias in performance #### System Status - **Filter Mode**: Current smart filter status (Smart✓/Basic✗) - **Contract Duration**: Selected timeframe - **Beijing Time**: Real-time timestamp display - **Current Price**: Live BTC/USDT price - **Contract Status**: Active contract indicator (🔄 Active/✅ Ready) ## 💡 Usage Guidelines ### Optimal Setup 1. **Recommended Timeframe**: 1-minute for maximum signal frequency 2. **Symbol**: BTCUSDT or BTCUSD perpetual futures 3. **Mode Selection**: Start with "Smart Balance" for consistent performance 4. **Contract Duration**: Begin with 10-minute contracts for faster feedback ### Best Practices - **Pre-market Analysis**: Check overall market conditions before trading - **Risk Management**: Never risk more than 2-3% of capital per contract - **Session Timing**: Best performance during high-volume trading sessions - **Signal Confirmation**: Wait for arrow + label confirmation before entry - **Performance Monitoring**: Regularly review win rate statistics ### Trading Sessions - **Asian Session**: 00:00-08:00 Beijing Time (moderate volatility) - **European Session**: 15:00-23:00 Beijing Time (high volatility) - **US Session**: 21:00-05:00 Beijing Time (peak volatility) ## 🚨 Alert Configuration ### Available Alerts 1. **BTC Call Signal**: Basic bullish signal alerts 2. **BTC Put Signal**: Basic bearish signal alerts 3. **BTC Strong Call Signal**: High-quality bullish signals 4. **BTC Strong Put Signal**: High-quality bearish signals ### Alert Setup ``` Alert Condition: Select from dropdown Frequency: Once Per Bar Close Expiration: No expiration (for continuous monitoring) Webhook: Optional for automated trading systems ``` ### Alert Message Format ``` 🚀 BTC Event Contract Call Signal ⏰ Time: 💰 Price: $ ``` ## 🔧 Advanced Configuration ### Parameter Optimization - **Auto-Optimization Enabled**: Uses predefined optimized parameters - **Manual Override**: Disable auto-optimization for custom parameter testing - **Timeframe Adaptation**: Parameters automatically adjust for 1-min, 3-min, and higher timeframes ### Filter Customization - **Volume Filter**: Adjustable multiplier (1.1-2.5x) - **Trend Filter**: Optional EMA trend confirmation - **Advanced Confirmation**: Multi-timeframe validation - **Smart Filter**: Toggle for intelligent filtering system ## 📈 Performance Expectations ### Historical Backtesting Results Based on extensive BTCUSDT 1-minute data testing: #### Smart Balance Mode - **Average Win Rate**: 78-82% - **Daily Signals**: 10-15 - **Best Sessions**: European/US overlap - **Recommended For**: Most traders #### High Frequency Mode - **Average Win Rate**: 73-77% - **Daily Signals**: 18-25 - **Best Sessions**: High volatility periods - **Recommended For**: Active scalpers #### Premium Quality Mode - **Average Win Rate**: 83-87% - **Daily Signals**: 6-10 - **Best Sessions**: Trending market conditions - **Recommended For**: Conservative traders ## ⚠️ Risk Warnings ### Important Disclaimers - **High-Risk Trading**: Event contracts involve significant risk of loss - **Market Volatility**: Cryptocurrency markets are highly volatile and unpredictable - **No Guarantee**: Past performance does not guarantee future results - **Capital Risk**: Only trade with funds you can afford to lose completely ### Risk Management Guidelines - **Position Sizing**: Never risk more than 1-2% per trade - **Daily Limits**: Set maximum daily loss limits - **Emotional Control**: Avoid revenge trading after losses - **Market Conditions**: Adjust exposure based on volatility - **Continuous Monitoring**: Regularly assess indicator performance ## 🔄 Version History ### V1.0 (December 21, 2024) - Initial English release - Complete translation from Chinese version - Optimized for international users - Enhanced documentation with detailed explanations - Maintained all original functionality and performance characteristics ## 🛠️ Technical Specifications ### Pine Script Details - **Version**: Pine Script v6 - **Type**: Indicator with overlay=true - **Max Objects**: 500 boxes, 500 labels - **Memory Optimization**: Efficient array and map usage - **Performance**: Optimized for real-time execution ### System Requirements - **Platform**: TradingView Pro, Pro+, or Premium - **Browser**: Modern browser with JavaScript enabled - **Connection**: Stable internet for real-time data - **Display**: Minimum 1080p resolution recommended ## 📞 Support & Updates ### Getting Help - **Documentation**: Refer to this comprehensive guide - **Common Issues**: Check parameter settings and timeframe compatibility - **Performance**: Verify market conditions and volatility levels ### Update Policy - **Regular Updates**: Continuous optimization based on market conditions - **Version Tracking**: All changes documented with version numbers - **Backward Compatibility**: Settings preserved across updates --- **Disclaimer**: This indicator is for educational and analysis purposes only. Trading cryptocurrencies and event contracts involves substantial risk. Always conduct your own research and consider your risk tolerance before trading. The authors are not responsible for any trading losses incurred through the use of this indicator.
Pine Script® göstergesi
OxJohannWu tarafından
Pierre's H4 EMA/MA Compression Strategy (BTC)Pierre's logic and trading strategy from the X post and its related threads. The post focuses on Bitcoin (BTC) price action on a 4-hour (H4) chart, using Exponential Moving Averages (EMAs) and Moving Averages (MAs) to identify a potential "EMA/MA compression" scenario, which is a key part of his analysis. Summary of Pierre's Logic Pierre is analyzing Bitcoin's price movement on the H4 timeframe, focusing on a technical pattern he calls "EMA/MA compression." This concept is central to his analysis and involves the interaction of key moving averages (H4 100 MA, H4 200 EMA, and H4 300 MA) to predict price behavior. Here's the breakdown of his logic: EMA/MA Compression Concept: Pierre describes "EMA/MA compression" as a scenario where the price consolidates around key moving averages, leading to a tightening of volatility before a breakout or breakdown. In this case, the H4 100 MA, H4 200 EMA, and H4 300 MA are the critical levels to watch. These moving averages act as dynamic support/resistance levels, and their behavior (break, hold, or flip) dictates the trend direction. He notes that this compression often follows a cycle: EMA/MA compression → Trend → Gap Fills → Repeat. This cycle suggests that after a compression phase, the price tends to trend, fill any price gaps, and then return to another compression phase. Key Levels and Conditions for a Bullish Scenario: H4 100 MA: Must break or flip to the upside. A break above this level signals bullish momentum, while a failure to hold above it (a "flip") invalidates the bullish case. H4 200 EMA: Acts as an "intermediary" level that must hold during pullbacks. If this level holds, it supports the bullish structure. H4 300 MA: A critical support level. It must hold to keep the bullish scenario intact. If the price loses this level (and it flips to resistance), the bullish outlook is invalidated. Pierre mentions that after the price breaks the H4 100 MA, it should aim to fill gaps between 109.5 and 110.5 (likely in thousands, so $109,500–$110,500). If the H4 200 EMA holds, the price might pull back to the H4 300 MA, where it could consolidate further before continuing the trend. Invalidation Scenarios: The bullish scenario is invalidated if: The H4 100 MA is broken and flips to resistance (i.e., price closes below it after initially breaking above). The H4 300 MA is lost and flips to resistance (i.e., price closes below it and fails to reclaim it). Current Market Context: Pierre notes a "nice bounce" in BTC's price, bringing it back into the compression zone. The price is currently fighting a key area on lower timeframes (LTF), likely referring to shorter timeframes like H1 or M15. He mentions that all gaps have been filled for now (referencing the cycle of gap fills), which aligns with his expectation of reduced volatility as the price enters another compression phase. Historical Context and Consistency: Pierre has been tracking this scenario since the H4 100 MA break, as shared in his group @TheHavenCrypto . He references notes from Monday (likely June 2, 2025, as the post is from June 6), indicating that his analysis has been consistent over the week. In a follow-up post, he reflects on a recent trade where he took partial profits on the bounce but couldn’t fully capitalize on the move due to being on his phone and managing only a fraction of his intended position size near the H4 300 MA (for BTC) and H4 200 EMA (for ETH). Pierre's Trading Strategy Based on the post and its context, Pierre’s trading strategy revolves around the EMA/MA compression framework. Here’s how he approaches trades: Setup Identification: Pierre identifies setups using the H4 timeframe, focusing on the interaction of the H4 100 MA, H4 200 EMA, and H4 300 MA. He looks for a "compression" phase where the price consolidates around these moving averages, signaling a potential breakout or breakdown. In this case, the price breaking the H4 100 MA to the upside was his initial signal for a bullish setup. Entry Points: Pierre likely entered a long position (buy) near the H4 300 MA or H4 200 EMA during the recent bounce, as he mentions taking partial profits on the move. He prefers entering after a pullback to these key levels (e.g., H4 200 EMA or H4 300 MA) as long as they hold as support. For example, in Thread 1 (Post 1930270942871118081), he shares a chart showing a long entry near the H4 300 MA with an upside target near 110,000–111,000. Target Setting: His primary target after the H4 100 MA break is to fill gaps between $109,500 and $110,500. If the price reaches these levels and the H4 200 EMA holds, he expects a potential pullback to the H4 300 MA, followed by another leg up (as part of the trend phase in his cycle). Risk Management: Pierre sets clear invalidation levels: A close below the H4 100 MA after breaking above it. A close below the H4 300 MA with a failure to reclaim it. He takes partial profits on bounces, as seen in his follow-up post where he mentions securing gains but not fully capitalizing on the move due to limited position size. Position Sizing and Execution: Pierre mentions being limited by trading from his phone, which restricted his position size. This suggests he typically scales into trades with a planned size but adjusts based on execution conditions. He also notes going "AFK for the weekend" after taking profits, indicating a disciplined approach to stepping away from the market when not actively monitoring. Cycle-Based Trading: His strategy follows the cycle of EMA/MA compression → Trend → Gap Fills → Repeat. After the gaps are filled, he expects volatility to tighten (another compression phase), which could set up the next trade. Key Takeaways for Traders Focus on Key Levels: Pierre’s strategy hinges on the H4 100 MA, H4 200 EMA, and H4 300 MA. These levels are used to confirm trends, identify entries, and set invalidation points. Patience for Compression: He waits for the price to enter a compression phase (tight consolidation around MAs) before expecting a breakout or breakdown. Gap-Filling as a Target: Pierre uses price gaps (e.g., $109,500–$110,500) as targets, aligning with the market’s tendency to fill these gaps (as noted in the related web result from investing.com about CME gaps). Risk Management: He has clear invalidation rules and takes partial profits to lock in gains while letting the trade play out. Cycle Awareness: His trades are part of a broader cycle (compression → trend → gap fill → repeat), which helps him anticipate market behavior. Additional Context from Related Threads Thread 1 (June 4–June 6): Pierre’s earlier posts (e.g., Post 1930270942871118081) show historical examples of EMA/MA compression leading to trends and gap fills, reinforcing his current analysis. He also shares a chart with a potential upside target of $110,000–$111,000 if the H4 300 MA holds. Thread 2 (June 3): Pierre mentions a Daily (D1) timeframe analysis where the D1 100 MA and D1 200 EMA align with range lows, suggesting a potential "wet dream swing long opportunity" if the price holds these levels. This indicates he’s also considering higher timeframes for confirmation. Thread 3 (May 27): Pierre’s earlier analysis highlights similar concepts (e.g., H4 100 MA break, H4 200 EMA hold), showing consistency in his approach over time. Conclusion Pierre’s logic is rooted in technical analysis, specifically the interaction of moving averages on the H4 timeframe to identify "EMA/MA compression" setups. His strategy involves buying on pullbacks to key support levels (H4 200 EMA, H4 300 MA) after a breakout (H4 100 MA), targeting gap fills ($109,500–$110,500), and managing risk with clear invalidation levels. He follows a cyclical approach to trading, expecting periods of compression, trending, and gap-filling to repeat, which guides his entries, exits, and overall market outlook.
Pine Script® stratejisi
AAAAAA2020 tarafından
Bilateral Filter For Loop [BackQuant]Bilateral Filter For Loop The Bilateral Filter For Loop is an advanced technical indicator designed to filter out market noise and smooth out price data, thus improving the identification of underlying market trends. It employs a bilateral filter, which is a sophisticated non-linear filter commonly used in image processing and price time series analysis. By considering both spatial and range differences between price points, this filter is highly effective at preserving significant trends while reducing random fluctuations, ultimately making it suitable for dynamic trend-following strategies. Please take the time to read the following: Key Features 1. Bilateral Filter Calculation: The bilateral filter is the core of this indicator and works by applying a weight to each data point based on two factors: spatial distance and price range difference. This dual weighting process allows the filter to preserve important price movements while reducing the impact of less relevant fluctuations. The filter uses two primary parameters: Spatial Sigma (σ_d): This parameter adjusts the weight applied based on the distance of each price point from the current price. A larger spatial sigma means more smoothing, as further away values will contribute more heavily to the result. Range Sigma (σ_r): This parameter controls how much weight is applied based on the difference in price values. Larger price differences result in smaller weights, while similar price values result in larger weights, thereby preserving the trend while filtering out noise. The output of this filter is a smoothed version of the original price series, which eliminates short-term fluctuations, helping traders focus on longer-term trends. The bilateral filter is applied over a rolling window, adjusting the level of smoothing dynamically based on both the distance between values and their relative price movements. 2. For Loop Calculation for Trend Scoring: A for-loop is used to calculate the trend score based on the filtered price data. The loop compares the current value to previous values within the specified window, scoring the trend as follows: +1 for upward movement (when the filtered value is greater than the previous value). -1 for downward movement (when the filtered value is less than the previous value). The cumulative result of this loop gives a continuous trend score, which serves as a directional indicator for the market's momentum. By summing the scores over the window period, the loop provides an aggregate value that reflects the overall trend strength. This score helps determine whether the market is experiencing a strong uptrend, downtrend, or sideways movement. 3. Long and Short Conditions: Once the trend score has been calculated, it is compared against predefined threshold levels: A long signal is generated when the trend score exceeds the upper threshold, indicating that the market is in a strong uptrend. A short signal is generated when the trend score crosses below the lower threshold, signaling a potential downtrend or trend reversal. These conditions provide clear signals for potential entry points, and the color-coding helps traders quickly identify market direction: Long signals are displayed in green. Short signals are displayed in red. These signals are designed to provide high-confidence entries for trend-following strategies, helping traders capture profitable movements in the market. 4. Trend Background and Bar Coloring: The script offers customizable visual settings to enhance the clarity of the trend signals. Traders can choose to: Color the bars based on the trend direction: Bars are colored green for long signals and red for short signals. Change the background color to provide additional context: The background will be shaded green for a bullish trend and red for a bearish trend. This visual feedback helps traders to stay aligned with the prevailing market sentiment. These features offer a quick visual reference for understanding the market's direction, making it easier for traders to identify when to enter or exit positions. 5. Threshold Lines for Visual Feedback: Threshold lines are plotted on the chart to represent the predefined long and short levels. These lines act as clear markers for when the market reaches a critical threshold, triggering a potential buy (long) or sell (short) signal. By showing these threshold lines on the chart, traders can quickly gauge the strength of the market and assess whether the trend is strong enough to warrant action. These thresholds can be adjusted based on the trader's preferences, allowing them to fine-tune the indicator for different market conditions or asset behaviors. 6. Customizable Parameters for Flexibility: The indicator offers several parameters that can be adjusted to suit individual trading preferences: Window Period (Bilateral Filter): The window size determines how many past price values are used to calculate the bilateral filter. A larger window increases smoothing, while a smaller window results in more responsive, but noisier, data. Spatial Sigma (σ_d) and Range Sigma (σ_r): These values control how sensitive the filter is to price changes and the distance between data points. Fine-tuning these parameters allows traders to adjust the degree of noise reduction applied to the price series. Threshold Levels: The upper and lower thresholds determine when the trend score crosses into long or short territory. These levels can be customized to better match the trader's risk tolerance or asset characteristics. Visual Settings: Traders can customize the appearance of the chart, including the line width of trend signals, bar colors, and background shading, to make the indicator more readable and aligned with their charting style. 7. Alerts for Trend Reversals: The indicator includes alert conditions for real-time notifications when the market crosses the defined thresholds. Traders can set alerts to be notified when: The trend score crosses the long threshold, signaling an uptrend. The trend score crosses the short threshold, signaling a downtrend. These alerts provide timely information, allowing traders to take immediate action when the market shows a significant change in direction. Final Thoughts The Bilateral Filter For Loop indicator is a robust tool for trend-following traders who wish to reduce market noise and focus on the underlying trend. By applying the bilateral filter and calculating trend scores, this indicator helps traders identify strong uptrends and downtrends, providing reliable entry signals with minimal market noise. The customizable parameters, visual feedback, and alerting system make it a versatile tool for traders seeking to improve their timing and capture profitable market movements. Thus following all of the key points here are some sample backtests on the 1D Chart Disclaimer: Backtests are based off past results, and are not indicative of the future. INDEX:BTCUSD INDEX:ETHUSD CRYPTO:SOLUSD
Pine Script® göstergesi
BackQuant tarafından
GER40 BIAS Forecast [ML-Based]🎯 Purpose: This indicator provides a daily directional bias (LONG / SHORT / FLAT) for the German DAX40 index (GER40) using a statistically optimized scoring model, developed with 6 years of historical data and verified through machine learning analysis. 🧠 How the Score Works (ML-derived): Each trading day receives a bias score (0–3) for both long and short setups, based on these 3 factors from the daily candle: Condition Long Score Logic Short Score Logic 1. Candle Direction Close > Open → +1 Close < Open → +1 2. VWAP Slope VWAP > VWAP → +1 VWAP < VWAP → +1 3. Volatility Strength Range > SMA(20) → +1 Close < Yesterday's Low → +1 (Rejection) ➡️ A score of 2 or more triggers a Long or Short Bias for the day. These scoring rules are derived from a machine learning model trained on 6 years of DAX data, identifying the most predictive features for directional follow-through. 📘 Bias Interpretation: Score Result Daily Bias Background Color Long Score ≥ 2 LONG Green Short Score ≥ 2 SHORT Red Both < 2 FLAT Gray 📍 Indicator Features: 🎨 Background coloring to visualize daily bias directly on intraday charts 🔢 Optional score labels (e.g. “Long: 2 | Short: 1”) per calendar day 📈 VWAP line plotted for additional intraday context ❌ Entry signals removed – this version focuses solely on forecasting directional bias 💡 Use Case: Morning planning aid Filtering for high-probability intraday setups Combining with session-based entry systems
Pine Script® göstergesi
Precious_Life tarafından
Darren - Engulfing + MACD CrossDarren – Engulfing + MACD Cross Overall Behavior Identify an engulfing candle (bullish or bearish). Wait up to windowBars bars for the corresponding MACD crossover (bullish engulfing → MACD cross up; bearish engulfing → MACD cross down). If the crossover occurs within that window, trigger an entry (long or short) and close any opposite open trade. Inputs • macdFast (default 12): length of MACD fast EMA • macdSlow (default 26): length of MACD slow EMA • macdSignal (default 9): length of MACD signal line • windowBars (default 3): maximum bars allowed between an engulfing candle and a MACD crossover Indicators • macdLine and signalLine are calculated using ta.macd(close, macdFast, macdSlow, macdSignal) • macdHist = macdLine – signalLine, plotted as columns (green when ≥ 0, red when < 0) Engulfing Pattern Detection • Bullish engulfing (bullEngulfing) is true when the previous candle is bearish (close < open ), the current candle is bullish (close > open), and the current body fully engulfs the previous body (open < close and close > open ). • Bearish engulfing (bearEngulfing) is the inverse: previous candle bullish, current candle bearish, and current body fully engulfs the prior body. MACD Crossover Detection • macdCrossUp is true when macdLine crosses above signalLine. • macdCrossDown is true when macdLine crosses below signalLine. Timing Logic • barsSinceBull = ta.barssince(bullEngulfing) returns number of bars since the last bullish engulfing. • barsSinceBear = ta.barssince(bearEngulfing) returns number of bars since the last bearish engulfing. • longCondition occurs if a MACD cross up happens within windowBars bars of a bullish engulfing (barsSinceBull ≤ windowBars and macdCrossUp). • shortCondition occurs if a MACD cross down happens within windowBars bars of a bearish engulfing (barsSinceBear ≤ windowBars and macdCrossDown). Chart Markers • “Bull” label below bar whenever bullEngulfing is true. • “Bear” label above bar whenever bearEngulfing is true. • Small “Up” ▲ below bar when macdCrossUp is true. • Small “Down” ▼ above bar when macdCrossDown is true. • Triangle ▲ below bar for Long Entry (longCondition). • Triangle ▼ above bar for Short Entry (shortCondition). Entry & Exit Rules • On longCondition: enter “Long”, and close any existing “Short” position. • On shortCondition: enter “Short”, and close any existing “Long” position.
Pine Script® stratejisi
OEM_redartgnidoc tarafından
Volume Point of Control with Fib Based Profile🍀Description: This indicator is a comprehensive volume profile analysis tool designed to identify key price levels based on trading activity within user-defined timeframes. It plots the Point of Control (POC), Value Area High (VAH), and Value Area Low (VAL), along with dynamically calculated Fibonacci levels derived from the developing period's range. It offers extensive customization for both historical and developing levels. 🍀Core Features: Volume Profiling (POC, VAH, VAL): Calculates and plots the POC (price level with the highest volume), VAH, and VAL for a selected timeframe (e.g., Daily, Weekly). The Value Area percentage is configurable. 70% is common on normal volume profiles, but this script allows you to configure multiple % levels via the fib levels. I recommend using 2 versions of this indicator on a chart, one has Value Area at 1 (100% - high and low of lookback) and the second is a specified VA area (i.e. 70%) like in the chart snapshot above. See examples at the bottom. Historical Levels: Plots POC, VAH, and VAL from previous completed periods. Optionally displays only "Unbroken" levels – historical levels that price has not yet revisited, which can act as stronger magnets or resistance/support. The user can manage the number of historical lines displayed to prevent chart clutter. Developing Levels: Shows the POC, VAH, and VAL as they form in real-time during the current, incomplete period. This provides insight into intraday/intra-period value migration. Dynamic Fibonacci Levels: Calculates and plots Fibonacci retracement/extension levels based dynamically on the range between the developing POC and the developing VAH/VAL. Offers 8 configurable % levels above and below POC that can be toggled on/off. Visual Customization: Extensive options for colors, line styles, and widths for all plotted levels. Optional gradient fill for the Value Area that visualizes current price distance from POC - option to invert the colors as well. Labels for developing levels and Fibonacci levels for easy identification. 🍀Characteristics: Volume-Driven: Levels are derived from actual trading volume, reflecting areas of high participation and price agreement/disagreement. Timeframe Specific: The results are entirely dependent on the chosen profile timeframe. Dynamic & Static Elements: Developing levels and Fibs update live, while historical levels remain fixed once their period closes. Lagging (Historical) & Potentially Leading: Historical levels are based on the past, but are often respected by future price action. Developing levels show current dynamics. 🍀How to Use It: Identifying Support & Resistance: Historical and developing POCs, VAHs, and VALs are often key areas where price may react. Unbroken levels are particularly noteworthy. Market Context & Sentiment: Trading above the POC suggests bullish strength/acceptance of higher prices, while trading below suggests bearishness/acceptance of lower prices. Entry/Exit Zones: Interactions with these levels (rejections, breakouts, tests) can provide potential entry or exit signals, especially when confirming with other analysis methods. Dynamic Targets: The Fibonacci levels calculated from the developing POC-VA range offer potential intraday/intra-period price targets or areas of interest. Understanding Value Migration: Observing the movement of the developing POC/VAH/VAL throughout the period reveals where value is currently being established. 🍀Potential Drawbacks: Input Sensitivity: The choice of timeframe, Value Area percentage, and volume resolution heavily influences the generated levels. Experimentation is needed for optimal settings per instrument/market. (I've found that Range Charts can provide very accurate volume levels on TV since the time element is removed. This helps to refine the accuracy of price levels with high volume.) Volume Data Dependency: Requires accurate volume data. May be less reliable on instruments with sparse or questionable volume reporting. Chart Clutter: Enabling all features simultaneously can make the chart busy. Utilize the line management inputs and toggle features as needed. Not a Standalone Strategy: This indicator provides context and key levels. It should be used alongside other technical analysis tools and price action reading for robust decision-making. Developing Level Fluctuation: Developing POC/VA/Fib levels can shift considerably, especially early in a new period, before settling down as more volume accumulates and time passes. 🍀Recommendations/Examples: I recommend have this indicator on your chart twice, one has the VA set at 1 (100%) and has the fib levels plotted. The second has the VA set to 0.7 (70%) to highlight the defined VA. Here is an example with 3 on a chart. VA of 100%, VA of 80%, and VA of 20%
Pine Script® göstergesi
ShamrockShane tarafından
2
GEEKSDOBYTE IFVG w/ Buy/Sell Signals1. Inputs & Configuration Swing Lookback (swingLen) Controls how many bars on each side are checked to mark a swing high or swing low (default = 5). Booleans to Toggle Plotting showSwings – Show small triangle markers at swing highs/lows showFVG – Show Fair Value Gap zones showSignals – Show “BUY”/“SELL” labels when price inverts an FVG showDDLine – Show a yellow “DD” line at the close of the inversion bar showCE – Show an orange dashed “CE” line at the midpoint of the gap area 2. Swing High / Low Detection isSwingHigh = ta.pivothigh(high, swingLen, swingLen) Marks a bar as a swing high if its high is higher than the highs of the previous swingLen bars and the next swingLen bars. isSwingLow = ta.pivotlow(low, swingLen, swingLen) Marks a bar as a swing low if its low is lower than the lows of the previous and next swingLen bars. Plotting If showSwings is true, small red downward triangles appear above swing highs, and green upward triangles below swing lows. 3. Fair Value Gap (3‐Bar) Identification A Fair Value Gap (FVG) is defined here using a simple three‐bar logic (sometimes called an “inefficiency” in price): Bullish FVG (bullFVG) Checks if, two bars ago, the low of that bar (low ) is strictly greater than the current bar’s high (high). In other words: bullFVG = low > high Bearish FVG (bearFVG) Checks if, two bars ago, the high of that bar (high ) is strictly less than the current bar’s low (low). In other words: bearFVG = high < low When either condition is true, it identifies a three‐bar “gap” or unfilled imbalance in the market. 4. Drawing FVG Zones If showFVG is enabled, each time a bullish or bearish FVG is detected: Bullish FVG Zone Draws a semi‐transparent green box from the bar two bars ago (where the gap began) at low up to the current bar’s high. Bearish FVG Zone Draws a semi‐transparent red box from the bar two bars ago at high down to the current bar’s low. These colored boxes visually highlight the “fair value imbalance” area on the chart. 5. Inversion (Fill) Detection & Entry Signals An inversion is defined as the price “closing through” that previously drawn FVG: Bullish Inversion (bullInversion) Occurs when a bullish FVG was identified on bar-2 (bullFVG), and on the current bar the close is greater than that old bar-2 low: bullInversion = bullFVG and close > low Bearish Inversion (bearInversion) Occurs when a bearish FVG was identified on bar-2 (bearFVG), and on the current bar the close is lower than that old bar-2 high: bearInversion = bearFVG and close < high When an inversion is true, the indicator optionally draws two lines and a label (depending on input toggles): Draw “DD” Line (yellow, solid) Plots a horizontal yellow line from the current bar’s close price extending five bars forward (bar_index + 5). This is often referred to as a “Demand/Daily Demand” line, marking where price inverted the gap. Draw “CE” Line (orange, dashed) Calculates the midpoint (ce) of the original FVG zone. For a bullish inversion: ce = (low + high) / 2 For a bearish inversion: ce = (high + low) / 2 Plots a horizontal dashed orange line at that midpoint for five bars forward. Plot Label (“BUY” / “SELL”) If showSignals is true, a green “BUY” label is placed at the low of the current bar when a bullish inversion occurs. Likewise, a red “SELL” label at the high of the current bar when a bearish inversion happens. 6. Putting It All Together Swing Markers (Optional): Visually confirm recent swing highs and swing lows with small triangles. FVG Zones (Optional): Highlight areas where price left a 3-bar gap (bullish in green, bearish in red). Inversion Confirmation: Wait for price to close beyond the old FVG boundary. Once that happens, draw the yellow “DD” line at the close, the orange dashed “CE” line at the zone’s midpoint, and place a “BUY” or “SELL” label exactly on that bar. User Controls: All of the above elements can be individually toggled on/off (showSwings, showFVG, showSignals, showDDLine, showCE). In Practice A bullish FVG forms whenever a strong drop leaves a gap in liquidity (three bars ago low > current high). When price later “fills” that gap by closing above the old low, the script signals a potential long entry (BUY), draws a demand line at the closing price, and marks the midpoint of that gap. Conversely, a bearish FVG marks a potential short zone (three bars ago high < current low). When price closes below that gap’s high, it signals a SELL, with similar lines drawn. By combining these elements, the indicator helps users visually identify inefficiencies (FVGs), confirm when price inverts/fills them, and place straightforward buy/sell labels alongside reference lines for trade management.
Pine Script® göstergesi
RayenGeeksdoByte tarafından
Levels Of Interest------------------------------------------------------------------------------------ LEVELS OF INTEREST (LOI) TRADING INDICATOR GUIDE ------------------------------------------------------------------------------------ Table of Contents: 1. Indicator Overview & Core Functionality 2. VWAP Foundation & Historical Context 3. Multi-Timeframe VWAP Analysis 4. Moving Average Integration System 5. Trend Direction Signal Detection 6. Visual Design & Display Features 7. Custom Level Integration 8. Repaint Protection Technology 9. Practical Trading Applications 10. Setup & Configuration Recommendations ------------------------------------------------------------------------------------ 1. INDICATOR OVERVIEW & CORE FUNCTIONALITY ------------------------------------------------------------------------------------ The LOI indicator combines multiple VWAP calculations with moving averages across different timeframes. It's designed to show where institutional money is flowing and help identify key support and resistance levels that actually matter in today's markets. Primary Functions: - Multi-timeframe VWAP analysis (Daily, Weekly, Monthly, Yearly) - Advanced moving average integration (EMA, SMA, HMA) - Real-time trend direction detection - Institutional flow analysis - Dynamic support/resistance identification Target Users: Day traders, swing traders, position traders, and institutional analysts seeking comprehensive market structure analysis. ------------------------------------------------------------------------------------ 2. VWAP FOUNDATION & HISTORICAL CONTEXT ------------------------------------------------------------------------------------ Historical Development: VWAP started in the 1980s when big institutional traders needed a way to measure if they were getting good fills on their massive orders. Unlike regular price averages, VWAP weighs each price by the volume traded at that level. This makes it incredibly useful because it shows you where most of the real money changed hands. Mathematical Foundation: The basic math is simple: you take each price, multiply it by the volume at that price, add them all up, then divide by total volume. What you get is the true "average" price that reflects actual trading activity, not just random price movements. Formula: VWAP = Σ(Price × Volume) / Σ(Volume) Where typical price = (High + Low + Close) / 3 Institutional Behavior Patterns: - When price trades above VWAP, institutions often look to sell - When it's below, they're usually buying - Creates natural support and resistance that you can actually trade against - Serves as benchmark for execution quality assessment ------------------------------------------------------------------------------------ 3. MULTI-TIMEFRAME VWAP ANALYSIS ------------------------------------------------------------------------------------ Core Innovation: Here's where LOI gets interesting. Instead of just showing daily VWAP like most indicators, it displays four different timeframes simultaneously: **Daily VWAP Implementation**: - Resets every morning at market open - Provides clearest picture of intraday institutional sentiment - Primary tool for day trading strategies - Most responsive to immediate market conditions **Weekly VWAP System**: - Resets each Monday (or first trading day) - Smooths out daily noise and volatility - Perfect for swing trades lasting several days to weeks - Captures weekly institutional positioning **Monthly VWAP Analysis**: - Resets at beginning of each calendar month - Captures bigger institutional rebalancing at month-end - Fund managers often operate on monthly mandates - Significant weight in intermediate-term analysis **Yearly VWAP Perspective**: - Resets annually for full-year institutional view - Shows long-term institutional positioning - Where pension funds and sovereign wealth funds operate - Critical for major trend identification Confluence Zone Theory: The magic happens when multiple VWAP levels cluster together. These confluence zones often become major turning points because different types of institutional money all see value at the same price. ------------------------------------------------------------------------------------ 4. MOVING AVERAGE INTEGRATION SYSTEM ------------------------------------------------------------------------------------ Multi-Type Implementation: The indicator includes three types of moving averages, each with its own personality and application: **Exponential Moving Averages (EMAs)**: - React quickly to recent price changes - Displayed as solid lines for easy identification - Optimal performance in trending market conditions - Higher sensitivity to current price action **Simple Moving Averages (SMAs)**: - Treat all historical data points equally - Appear as dashed lines in visual display - Slower response but more reliable in choppy conditions - Traditional approach favored by institutional traders **Hull Moving Averages (HMAs)**: - Newest addition to the system (dotted line display) - Created by Alan Hull in 2005 - Solves classic moving average dilemma: speed vs. accuracy - Manages to be both responsive and smooth simultaneously Technical Innovation: Alan Hull's solution addresses the fundamental problem where moving averages are either too slow (missing moves) or too fast (generating false signals). HMAs achieve optimal balance through weighted calculation methodology. Period Configuration: - 5-period: Short-term momentum assessment - 50-period: Intermediate trend identification - 200-period: Long-term directional confirmation ------------------------------------------------------------------------------------ 5. TREND DIRECTION SIGNAL DETECTION ------------------------------------------------------------------------------------ Real-Time Momentum Analysis: One of LOI's best features is its real-time trend detection system. Next to each moving average, visual symbols provide immediate trend assessment: Symbol System: - ▲ Rising average (bullish momentum confirmation) - ▼ Falling average (bearish momentum indication) - ► Flat average (consolidation or indecision period) Update Frequency: These signals update in real-time with each new price tick and function across all configured timeframes. Traders can quickly scan daily and weekly trends to assess alignment or conflicting signals. Multi-Timeframe Trend Analysis: - Simultaneous daily and weekly trend comparison - Immediate identification of trend alignment - Early warning system for potential reversals - Momentum confirmation for entry decisions ------------------------------------------------------------------------------------ 6. VISUAL DESIGN & DISPLAY FEATURES ------------------------------------------------------------------------------------ Color Psychology Framework: The color scheme isn't random but based on psychological associations and trading conventions: - **Blue Tones**: Institutional neutrality (VWAP levels) - **Green Spectrum**: Growth and stability (weekly timeframes) - **Purple Range**: Longer-term sophistication (monthly analysis) - **Orange Hues**: Importance and attention (yearly perspective) - **Red Tones**: User-defined significance (custom levels) Adaptive Display Technology: The indicator automatically adjusts decimal places based on the instrument you're trading. High-priced stocks show 2 decimals, while penny stocks might show 8. This keeps the display incredibly clean regardless of what you're analyzing - no cluttered charts or overwhelming information overload. Smart Labeling System: Advanced positioning algorithm automatically spaces all elements to prevent overlap, even during extreme zoom levels or multiple timeframe analysis. Every level stays clearly readable without any visual chaos disrupting your analysis. ------------------------------------------------------------------------------------ 7. CUSTOM LEVEL INTEGRATION ------------------------------------------------------------------------------------ User-Defined Level System: Beyond the calculated VWAP and moving average levels, traders can add custom horizontal lines at any price point for personalized analysis. Strategic Applications: - **Psychological Levels**: Round numbers, previous significant highs/lows - **Technical Levels**: Fibonacci retracements, pivot points - **Fundamental Targets**: Analyst price targets, earnings estimates - **Risk Management**: Stop-loss and take-profit zones Integration Features: - Seamless incorporation with smart labeling system - Custom color selection for visual organization - Extension capabilities across all chart timeframes - Maintains display clarity with existing indicators ------------------------------------------------------------------------------------ 8. REPAINT PROTECTION TECHNOLOGY ------------------------------------------------------------------------------------ Critical Trading Feature: This addresses one of the most significant issues in live trading applications. Most multi-timeframe indicators "repaint," meaning they display different signals when viewing historical data versus real-time analysis. Protection Benefits: - Ensures every displayed signal could have been traded when it appeared - Eliminates discrepancies between historical and live analysis - Provides realistic performance expectations - Maintains signal integrity across chart refreshes Configuration Options: - **Protection Enabled**: Default setting for live trading - **Protection Disabled**: Available for backtesting analysis - User-selectable toggle based on analysis requirements - Applies to all multi-timeframe calculations Implementation Note: With protection enabled, signals may appear one bar later than without protection, but this ensures all signals represent actionable opportunities that could have been executed in real-time market conditions. ------------------------------------------------------------------------------------ 9. PRACTICAL TRADING APPLICATIONS ------------------------------------------------------------------------------------ **Day Trading Strategy**: Focus on daily VWAP with 5-period moving averages. Look for bounces off VWAP or breaks through it with volume. Short-term momentum signals provide entry and exit timing. **Swing Trading Approach**: Weekly VWAP becomes your primary anchor point, with 50-period averages showing intermediate trends. Position sizing based on weekly VWAP distance. **Position Trading Method**: Monthly and yearly VWAP provide broad market context, while 200-period averages confirm long-term directional bias. Suitable for multi-week to multi-month holdings. **Multi-Timeframe Confluence Strategy**: The highest-probability setups occur when daily, weekly, and monthly VWAPs cluster together, especially when multiple moving averages confirm the same direction. These represent institutional consensus zones. Risk Management Integration: - VWAP levels serve as dynamic stop-loss references - Multiple timeframe confirmation reduces false signals - Institutional flow analysis improves position sizing decisions - Trend direction signals optimize entry and exit timing ------------------------------------------------------------------------------------ 10. SETUP & CONFIGURATION RECOMMENDATIONS ------------------------------------------------------------------------------------ Initial Configuration: Start with default settings and adjust based on individual trading style and market focus. Short-term traders should emphasize daily and weekly timeframes, while longer-term investors benefit from monthly and yearly level analysis. Transparency Optimization: The transparency settings allow clear price action visibility while maintaining level reference points. Most traders find 70-80% transparency optimal - it provides a clean, unobstructed view of price movement while maintaining all critical reference levels needed for analysis. Integration Strategy: Remember that no indicator functions effectively in isolation. LOI provides excellent context for institutional flow and trend direction analysis, but should be combined with complementary analysis tools for optimal results. Performance Considerations: - Multiple timeframe calculations may impact chart loading speed - Adjust displayed timeframes based on trading frequency - Customize color schemes for different market sessions - Regular review and adjustment of custom levels ------------------------------------------------------------------------------------ FINAL ANALYSIS ------------------------------------------------------------------------------------ Competitive Advantage: What makes LOI different is its focus on where real money actually trades. By combining volume-weighted calculations with multiple timeframes and trend detection, it cuts through market noise to show you what institutions are really doing. Key Success Factor: Understanding that different timeframes serve different purposes is essential. Use them together to build a complete picture of market structure, then execute trades accordingly. The integration of institutional flow analysis with technical trend detection creates a comprehensive trading tool that addresses both short-term tactical decisions and longer-term strategic positioning. ------------------------------------------------------------------------------------ END OF DOCUMENTATION ------------------------------------------------------------------------------------
Pine Script® göstergesi
meddymarkusvanhala tarafından
2
HoLo (Highest Open Lowest Open)HoLo (Highest Open Lowest Open) Method Overview HoLo stands for "Highest Open Lowest Open" – a forex trading strategy. Core Concept Definition of HoLo: Highest Open (HO): The highest opening price among all H1 candles of the current trading day Lowest Open (LO): The lowest opening price among all H1 candles of the current trading day Trading Day: Starts at Asia Open Session Strategy Setup Step 1: Mark Key Levels Current day's High/Low Highest Open and Lowest Open (from H1 candles) Step 2: Define the Area of Interest Sell Zone: Between the Highest Open and the current day's High Buy Zone: Between the Lowest Open and the current day's Low Trade Entry Rules Sell Trade: Price goes above the Highest Open Trigger candle (M5, M15, or M30) closes above the Highest Open Enter a sell when price revisits the Highest Open level (Sell Stop Order) Buy Trade: Price drops below the Lowest Open Trigger candle closes below the Lowest Open Enter a buy when price revisits the Lowest Open level (Buy Stop Order) Trigger Timeframe: Choose M1, M5, or M15 based on: Your screen time availability Personal trading style Risk and Profit Management Stop Loss: For sell: Set SL at the day’s High + spread For buy: Set SL at the day’s Low + spread Take Profit (TP) Basic Rule: You should open 2 positions: When profit reaches 1R: Take partial profit + move SL to BE (Break Even) Let the remaining position run using partial TP or trailing stop Money Management: Never risk more than 1% per trade Recommended: 0.5% risk due to multiple opportunities daily Prioritize major pairs. The Indicator How to read data For Day Traders Monitor the sell zone (red area) for potential short entries near resistance Watch the buy zone (blue area) for potential long entries near support Use cross signals for entry/exit points Pay attention to timing markers for key market hours Alert HO (Highest Open) level changes LO (Lowest Close) level changes Price crossing key levels Timing notifications
Pine Script® göstergesi
dangtunglam14 tarafından
Heikin-Ashi Mean Reversion Oscillator [Alpha Extract]The Heikin-Ashi Mean Reversion Oscillator combines the smoothing characteristics of Heikin-Ashi candlesticks with mean reversion analysis to create a powerful momentum oscillator. This indicator applies Heikin-Ashi transformation twice - first to price data and then to the oscillator itself - resulting in smoother signals while maintaining sensitivity to trend changes and potential reversal points. 🔶 CALCULATION Heikin-Ashi Transformation: Converts regular OHLC data to smoothed Heikin-Ashi values Component Analysis: Calculates trend strength, body deviation, and price deviation from mean Oscillator Construction: Combines components with weighted formula (40% trend strength, 30% body deviation, 30% price deviation) Double Smoothing: Applies EMA smoothing and second Heikin-Ashi transformation to oscillator values Signal Generation: Identifies trend changes and crossover points with overbought/oversold levels Formula: HA Close = (Open + High + Low + Close) / 4 HA Open = (Previous HA Open + Previous HA Close) / 2 Trend Strength = Normalized consecutive HA candle direction Body Deviation = (HA Body - Mean Body) / Mean Body * 100 Price Deviation = ((HA Close - Price Mean) / Price Mean * 100) / Standard Deviation * 25 Raw Oscillator = (Trend Strength * 0.4) + (Body Deviation * 0.3) + (Price Deviation * 0.3) Final Oscillator = 50 + (EMA(Raw Oscillator) / 2) 🔶 DETAILS Visual Features: Heikin-Ashi Candlesticks: Smoothed oscillator representation using HA transformation with vibrant teal/red coloring Overbought/Oversold Zones: Horizontal lines at customizable levels (default 70/30) with background highlighting in extreme zones Moving Averages: Optional fast and slow EMA overlays for additional trend confirmation Signal Dashboard: Real-time table showing current oscillator status (Overbought/Oversold/Bullish/Bearish) and buy/sell signals Reference Lines: Middle line at 50 (neutral), with 0 and 100 boundaries for range visualization Interpretation: Above 70: Overbought conditions, potential selling opportunity Below 30: Oversold conditions, potential buying opportunity Bullish HA Candles: Green/teal candles indicate upward momentum Bearish HA Candles: Red candles indicate downward momentum MA Crossovers: Fast EMA above slow EMA suggests bullish momentum, below suggests bearish momentum Zone Exits: Price moving out of extreme zones (above 70 or below 30) often signals trend continuation 🔶 EXAMPLES Mean Reversion Signals: When the oscillator reaches extreme levels (above 70 or below 30), it identifies potential reversal points where price may revert to the mean. Example: Oscillator reaching 80+ levels during strong uptrends often precedes short-term pullbacks, providing profit-taking opportunities. Trend Change Detection: The double Heikin-Ashi smoothing helps identify genuine trend changes while filtering out market noise. Example: When oscillator HA candles change from red to teal after oversold readings, this confirms potential trend reversal from bearish to bullish. Moving Average Confirmation: Fast and slow EMA crossovers on the oscillator provide additional confirmation of momentum shifts. Example: Fast EMA crossing above slow EMA while oscillator is rising from oversold levels provides strong bullish confirmation signal. Dashboard Signal Integration: The real-time dashboard combines oscillator status with directional signals for quick decision-making. Example: Dashboard showing "Oversold" status with "BUY" signal when HA candles turn bullish provides clear entry timing. 🔶 SETTINGS Customization Options: Calculation: Oscillator period (default 14), smoothing factor (1-50, default 2) Levels: Overbought threshold (50-100, default 70), oversold threshold (0-50, default 30) Moving Averages: Toggle display, fast EMA length (default 9), slow EMA length (default 21) Visual Enhancements: Show/hide signal dashboard, customizable table position Alert Conditions: Oversold bounce, overbought reversal, bullish/bearish MA crossovers The Heikin-Ashi Mean Reversion Oscillator provides traders with a sophisticated momentum tool that combines the smoothing benefits of Heikin-Ashi analysis with mean reversion principles. The double transformation process creates cleaner signals while the integrated dashboard and multiple confirmation methods help traders identify high-probability entry and exit points during both trending and ranging market conditions.
Pine Script® göstergesi
AlphaExtract tarafından
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment ## 1. Introduction Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985). The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014). ## 2. Theoretical Foundation ### 2.1 Momentum Theory and Market Anomalies The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015). ### 2.2 Drawdown Analysis and Risk Management Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators. ### 2.3 Adaptive Smoothing and Market Regimes The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets. ## 3. Methodology ### 3.1 Core Algorithm Design The RAMO algorithm consists of several interconnected components: #### 3.1.1 Risk-Adjusted Momentum Calculation The fundamental innovation of RAMO lies in its risk adjustment mechanism: Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor) Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05) This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952). #### 3.1.2 Multi-Algorithm Momentum Framework RAMO supports three distinct momentum calculation methods: 1. Rate of Change: Traditional percentage-based momentum (Pring, 2002) 2. Price Momentum: Absolute price differences 3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997) This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013). ### 3.2 Leading Indicator Components #### 3.2.1 Momentum Acceleration Analysis The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes: Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n) This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969). #### 3.2.2 Linear Regression Prediction RAMO incorporates linear regression-based prediction to project momentum values forward: Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset) This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes. #### 3.2.3 Volume-Based Exhaustion Detection The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns: Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20) This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989). ### 3.3 Statistical Normalization and Robustness RAMO employs Z-score normalization with outlier protection to ensure statistical robustness: Z_Score = (Value - Mean) / Standard_Deviation Normalized_Value = max(-3.5, min(3.5, Z_Score)) This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions. ### 3.4 Adaptive Threshold Calculation Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992): Upper_Threshold = Mean + (Multiplier × Standard_Deviation) Lower_Threshold = Mean - (Multiplier × Standard_Deviation) This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992). ## 4. Implementation Details ### 4.1 Adaptive Smoothing Algorithm The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility: Volatility_Percentile = Percentrank(Volatility, 100) Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100) This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995). ### 4.2 Risk Environment Classification RAMO classifies market conditions into three risk environments: - Low Risk: Current_DD < 30% × Max_DD - Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD - High Risk: Current_DD ≥ 70% × Max_DD This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014). ## 5. Signal Generation and Interpretation ### 5.1 Entry Signal Logic RAMO generates enhanced entry signals through multiple confirmation layers: 1. Primary Signal: Crossover between indicator and signal line 2. Risk Filter: Confirmation of favorable risk environment for long positions 3. Leading Component: Early warning signals via acceleration analysis 4. Exhaustion Filter: Volume-based reversal detection This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992). ### 5.2 Divergence Analysis RAMO incorporates both traditional and leading divergence detection: - Traditional Divergence: Price and indicator divergence over 3-5 periods - Slope Divergence: Momentum slope versus price direction - Acceleration Divergence: Changes in momentum acceleration This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999). ## 6. Empirical Advantages and Applications ### 6.1 Risk-Adjusted Performance The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980). ### 6.2 Regime Awareness RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments. ### 6.3 Institutional Applications The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013). ## 7. Limitations and Future Research ### 7.1 Parameter Sensitivity Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions. ### 7.2 Market Microstructure Considerations RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance. ### 7.3 Transaction Cost Integration Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004). ## References Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons. Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press. Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985. Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764. Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press. Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84. Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall. Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291. Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill. Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082. Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91. Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance. Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library. Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill. Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790. Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314. Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239. Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Pine Script® göstergesi
EdgeTools tarafından
Cumulative Intraday Volume with Long/Short LabelsThis indicator calculates a running total of volume for each trading day, then shows on the price chart when that total crosses levels you choose. Every day at 6:00 PM Eastern Time, the total goes back to zero so it always reflects only the current day’s activity. From that moment on, each time a new candle appears the indicator looks at whether the candle closed higher than it opened or lower. If it closed higher, the candle’s volume is added to the running total; if it closed lower, the same volume amount is subtracted. As a result, the total becomes positive when buyers have dominated so far today and negative when sellers have dominated. Because futures markets close at 6 PM ET, the running total resets exactly then, mirroring the way most intraday traders think in terms of a single session. Throughout the day, you will see this running total move up or down according to whether more volume is happening on green or red candles. Once the total goes above a number you specify (for example, one hundred thousand contracts), the indicator will place a small “Long” label at that candle on the main price chart to let you know buying pressure has reached that level. Similarly, once the total goes below a negative number you choose (for example, minus one hundred thousand), a “Short” label will appear at that candle to signal that selling pressure has reached your chosen threshold. You can set these threshold numbers to whatever makes sense for your trading style or the market you follow. Because raw volume alone never turns negative, this design uses candle direction as a sign. Green candles (where the close is higher than the open) add volume, and red candles (where the close is lower than the open) subtract volume. Summing those signed volume values tells you in a single number whether buying or selling has been stronger so far today. That number resets every evening, so it does not carry over any buying or selling from previous sessions. Once you have this indicator on your chart, you simply watch the “summed volume” line as it moves throughout the day. If it climbs past your long threshold, you know buyers are firmly in control and a long entry might make sense. If it falls past your short threshold, you know sellers are firmly in control and a short entry might make sense. In quieter markets or times of low volume, you might use a smaller threshold so that even modest buying or selling pressure will trigger a label. During very active periods, a larger threshold will prevent too many signals when volume spikes frequently. This approach is straightforward but can be surprisingly powerful. It does not rely on complex formulas or hidden statistical measures. Instead, it simply adds and subtracts daily volume based on candle color, then alerts you when that total reaches levels you care about. Over several years of historical testing, this formula has shown an ability to highlight moments when intraday sentiment shifts decisively from buyers to sellers or vice versa. Because the indicator resets every day at 6 PM, it always reflects only today’s sentiment and remains easy to interpret without carrying over past data. You can use it on any intraday timeframe, but it works especially well on five-minute or fifteen-minute charts for futures contracts. If you want a clear gauge of whether buyers or sellers are dominating in real time, and you prefer a rule-based method rather than a complex model, this indicator gives you exactly that. It shows net buying or selling pressure at a glance, resets each session like most intraday traders do, and marks the moments when that pressure crosses the levels you decide are important. By combining a daily reset with signed volume, you get a single number that tells you precisely what the crowd is doing at any given moment, without any of the guesswork or hidden calculations that more complicated indicators often carry.
Pine Script® göstergesi
QuantByBoji tarafından
Buysell Martingale Signal - CustomBuysell Martingale Signal - Custom Indicator Introduction: This indicator provides a dynamic buy and sell signal system incorporating an adaptive Martingale logic. Built upon the signalLib_yashgode9/2 library, it is designed for use across various markets and timeframes. Key Features: Primary Buy & Sell Signals: Identifies initial buy and sell opportunities based on directional changes derived from the signalLib. Martingale Signals: For Short (Sell) Positions: A Martingale Sell signal is triggered when the price moves against the existing short position by a specified stepPercent from the last entry price, indicating a potential opportunity to average down or increase position size. For Long (Buy) Positions: Similarly, a Martingale Buy signal is triggered when the price moves against the existing long position by a stepPercent from the last entry price. On-Chart Labels: Displays clear, customizable labels on the chart for primary Buy, Sell, Martingale Buy, and Martingale Sell signals. Customizable Colors: Allows users to set distinct colors for primary signals and Martingale signals for better visual distinction. Adjustable Sensitivity: Features configurable parameters (DEPTH_ENGINE, DEVIATION_ENGINE, BACKSTEP_ENGINE) to fine-tune the sensitivity of the underlying signal generation. Webhook Support (Static Message Alerts): This indicator provides alerts with static messages for both primary and Martingale buy/sell signals. These alerts can be leveraged for automation by external systems (such as trading bots or exchange-provided Webhook Signal Trading services). Important Note: When using these alerts for automation, an external system is required to handle the complex Martingale logic and position management (e.g., tracking steps, PnL calculation, hedging, dynamic quantity sizing), as this indicator solely focuses on signal generation and sending predefined messages. How to Use: Add the indicator to your desired chart. Adjust the input parameters in the indicator's settings to match your specific trading symbol and timeframe. For automation, you can set up TradingView alerts for the Buy Signal (Main/Martingale) and Sell Signal (Main/Martingale) conditions, pointing them to your preferred Webhook URL. Configurable Parameters: DEPTH_ENGINE: (e.g., 30) Controls the depth of analysis for the signal algorithm. DEVIATION_ENGINE: (e.g., 5) Defines the allowable deviation for signal generation. BACKSTEP_ENGINE: (e.g., 5) Specifies the number of historical bars to look back. Martingale Step Percent: (e.g., 0.5) The percentage price movement against the current position that triggers a Martingale signal. Labels Transparency: Adjusts the transparency of the on-chart signal labels. Buy-Color / Sell-Color: Sets the color for primary Buy and Sell signal labels. Martingale Buy-Color / Martingale Sell-Color: Sets the color for Martingale Buy and Sell signal labels. Label size: Controls the visual size of the labels. Label Offset: Adjusts the vertical offset of the labels from the candlesticks. Risk Warning: Financial trading inherently carries significant risk. Martingale strategies are particularly high-risk and can lead to substantial losses or even complete liquidation of capital if the market moves strongly and persistently against your position. Always backtest thoroughly and practice with a demo account, fully understanding the associated risks, before engaging with real capital.
Pine Script® göstergesi
NADER_CY tarafından
5