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SuperBulls - Heiken Ashi StrategyA streamlined, trade-ready strategy from the SuperBulls universe that turns noisy charts into clear decisions. It combines a smoothed price view, adaptive momentum gating, and a dynamic support/resistance overlay so you can spot high-probability turns without overthinking every candle. Entries and exits are signalled visually and designed to work with simple position sizing — perfect for discretionary traders and systematic setups alike. Why traders like it Clean visual signals reduce analysis paralysis and speed up decision-making. Built-in momentum filter helps avoid chop and chase only the stronger moves. Dynamic S/R zones provide objective areas for targets and stop placement. Works with simple risk rules — position sizing and pyramiding kept conservative by default. Who it’s for Traders who want a reliable, low-friction strategy to trade intraday or swing setups without rebuilding indicators from scratch. Minimal tuning required; plug in your size and let the SuperBulls logic do the heavy lifting. Use it, don’t overfit it, and try not to blame the indicator when you ignore risk management.
Стратегия Pine Script®
от sarveshhcrypto
MTF 20 SMA Table - DXY**MTF 20 SMA Table - Multi-Timeframe Trend Analysis Dashboard** **Overview:** This indicator provides a comprehensive multi-timeframe analysis dashboard that displays the relationship between price and the 20-period Simple Moving Average (SMA) across four key timeframes: 15-minute, 1-hour, 4-hour, and Daily. It's designed to help traders quickly identify trend alignment and potential trading opportunities across multiple timeframes at a glance. It's definitely not perfect but has helped me speed up my backtesting efforts as it's worked well for me eliminating flipping back and forth between timeframes excpet when I have confluence on the table, then I check the HTF. **How It Works:** The indicator creates a table overlay on your chart showing three critical metrics for each timeframe: 1. **Price vs SMA (Row 1):** Shows whether price is currently above (bullish) or below (bearish) the 20 SMA - Green = Price Above SMA - Red = Price Below SMA 2. **SMA Direction (Row 2):** Indicates the trend direction of the SMA itself over a lookback period - Green (↗ Rising) = Uptrend - Red (↘ Falling) = Downtrend - Gray (→ Flat) = Ranging/Consolidation 3. **Strength (Row 3):** Displays the distance between current price and the SMA in pips - Purple background = Strong move (>50 pips away) - Orange background = Moderate move (20-50 pips) - Gray background = Weak/consolidating (<20 pips) - Text color: Green for positive distance, Red for negative **Key Features:** - **Customizable Table Position:** Place the table anywhere on your chart (9 position options) - **Adjustable SMA Lengths:** Modify the SMA period for each timeframe independently (default: 20) - **Direction Lookback Settings:** Fine-tune how far back the indicator looks to determine SMA direction for each timeframe - **Flat Threshold:** Set the pip threshold for determining when an SMA is "flat" vs trending (default: 5 pips) - **DXY Optimized:** Calculations are calibrated for the US Dollar Index (1 pip = 0.01) **Best Use Cases:** 1. **Trend Alignment:** Identify when multiple timeframes align in the same direction for higher probability trades 2. **Divergence Spotting:** Detect when lower timeframes diverge from higher timeframes (potential reversals) 3. **Entry Timing:** Use lower timeframe signals while higher timeframes confirm overall trend 4. **Strength Assessment:** Gauge how extended price is from the mean (SMA) to avoid overextended entries **Settings Guide:** - **SMA Settings Group:** Adjust the SMA period for each timeframe (15M, 1H, 4H, Daily) - **SMA Direction Group:** Control lookback periods to determine trend direction - 15M: Default 5 candles - 1H: Default 10 candles - 4H: Default 15 candles - Daily: Default 20 candles - **Flat Threshold:** Set sensitivity for "flat" detection (lower = more sensitive to ranging markets) **Trading Strategy Examples:** 1. **Trend Following:** Look for all timeframes showing the same direction (all green or all red) 2. **Pullback Trading:** When Daily/4H are green but 15M/1H show red, wait for lower timeframes to flip green for entry 3. **Ranging Markets:** When multiple SMAs show "flat", consider range-bound strategies **Important Notes:** - This is a reference tool only, not a standalone trading system - Always use proper risk management and combine with other analysis methods - Best suited for trending instruments like indices and major forex pairs - Calculations are optimized for DXY but can be used on other instruments (pip calculations may need adjustment) **Credits:** Feel free to modify and improve this code! Suggestions for enhancements are welcome in the comments. --- **Installation Instructions:** 1. Add the indicator to your TradingView chart 2. Adjust the table position via settings to avoid overlap with price action 3. Customize SMA lengths and lookback periods to match your trading style 4. Monitor the table for timeframe alignment and trend confirmation --- This indicator is published as open source for the community to learn from and improve upon. Happy trading! 📈
Индикатор Pine Script®
от stuartm69
True Average PriceTrue Average Price Overview The indicator plots a single line representing the cumulative average closing price of any symbol you choose. It lets you project a long-term mean onto your active chart, which is useful when your favourite symbol offers limited history but you still want context from an index or data-rich feed. How It Works The script retrieves all available historical bars from the selected symbol, sums their closes, counts the bars, and divides the totals to compute the lifetime average. That value is projected onto the chart you are viewing so you can compare current price action to the broader historical mean. Inputs Use Symbol : Toggle on to select an alternate symbol; leave off to default to the current chart. Symbol : Pick the data source used for the average when the toggle is enabled. Line Color : Choose the display color of the average line. Line Width : Adjust the thickness of the plotted line. Usage Tips Apply the indicator to exchanges with shallow history while sourcing the average from a complete index (e.g., INDEX:BTCUSD for crypto pairs). Experiment with different symbols to understand how alternative data feeds influence the baseline level. Disclaimer This indicator is designed as a technical analysis tool and should be used in conjunction with other forms of analysis and proper risk management. Past performance does not guarantee future results, and traders should thoroughly test any strategy before implementing it with real capital.
Индикатор Pine Script®
от dchunt-stack
Price Action Brooks ProPrice Action Brooks Pro (PABP) - Professional Trading Indicator ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 OVERVIEW Price Action Brooks Pro (PABP) is a professional-grade TradingView indicator developed based on Al Brooks' Price Action trading methodology. It integrates decades of Al Brooks' trading experience and price action analysis techniques into a comprehensive technical analysis tool, helping traders accurately interpret market structure and identify trading opportunities. • Applicable Markets: Stocks, Futures, Forex, Cryptocurrencies • Timeframes: 1-minute to Daily (5-minute chart recommended) • Theoretical Foundation: Al Brooks Price Action Trading Method ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 CORE FEATURES ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1️⃣ INTELLIGENT GAP DETECTION SYSTEM Automatically identifies and marks three critical types of gaps in the market. TRADITIONAL GAP • Detects complete price gaps between bars • Upward gap: Current bar's low > Previous bar's high • Downward gap: Current bar's high < Previous bar's low • Hollow border design - doesn't obscure price action • Color coding: Upward gaps (light green), Downward gaps (light pink) • Adjustable border: 1-5 pixel width options TAIL GAP • Detects price gaps between bar wicks/shadows • Analyzes across 3 bars for precision • Identifies hidden market structure BODY GAP • Focuses only on gaps between bar bodies (open/close) • Filters out wick noise • Disabled by default, enable as needed Trading Significance: • Gaps signal strong momentum • Gap fills provide trading opportunities • Consecutive gaps indicate trend continuation ✓ Independent alert system for all gap types ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2️⃣ RTH BAR COUNT (Trading Session Counter) Intelligent counting system designed for US stock intraday trading. FEATURES • RTH Only Display: Regular Trading Hours (09:30-15:00 EST) • 5-Minute Chart Optimized: Displays every 3 bars (15-minute intervals) • Daily Auto-Reset: Counting starts from 1 each trading day SMART COLOR CODING • 🔴 Red (Bars 18 & 48): Critical turning moments (1.5h & 4h) • 🔵 Sky Blue (Multiples of 12): Hourly markers (12, 24, 36...) • 🟢 Light Green (Bar 6): Half-hour marker (30 minutes) • ⚫ Gray (Others): Regular 15-minute interval markers Al Brooks Time Theory: • Bar 18 (90 min): First 90 minutes determine daily trend • Bar 48 (4 hours): Important afternoon turning point • Hourly markers: Track institutional trading rhythm ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 3️⃣ FOUR-LINE EMA SYSTEM Professional-grade configurable moving average system. DEFAULT CONFIGURATION • EMA 20: Short-term trend (Al Brooks' most important MA) • EMA 50: Medium-short term reference • EMA 100: Medium-long term confirmation • EMA 200: Long-term trend and bull/bear dividing line FLEXIBLE CUSTOMIZATION Each EMA can be independently configured: • On/Off toggle • Data source selection (close/high/low/open, etc.) • Custom period length • Offset adjustment • Color and transparency COLOR SCHEME • EMA 20: Dark brown, opaque (most important) • EMA 50/100/200: Blue-purple gradient, 70% transparent TRADING APPLICATIONS • Bullish Alignment: Price > 20 > 50 > 100 > 200 • Bearish Alignment: 200 > 100 > 50 > 20 > Price • EMA Confluence: All within <1% = major move precursor Al Brooks Quote: "The EMA 20 is the most important moving average. Almost all trading decisions should reference it." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 4️⃣ PREVIOUS VALUES (Key Prior Price Levels) Automatically marks important price levels that often act as support/resistance. THREE INDEPENDENT CONFIGURATIONS Each group configurable for: • Timeframe (1D/60min/15min, etc.) • Price source (close/high/low/open/CurrentOpen, etc.) • Line style and color • Display duration (Today/TimeFrame/All) SMART OPEN PRICE LABELS ⭐ • Auto-displays "Open" label when CurrentOpen selected • Label color matches line color • Customizable label size TYPICAL SETUP • 1st Line: Previous close (Support/Resistance) • 2nd Line: Previous high (Breakout target) • 3rd Line: Previous low (Support level) Al Brooks Magnet Price Theory: • Previous open: Price frequently tests opening price • Previous high/low: Strongest support/resistance • Breakout confirmation: Breaking prior levels = trend continuation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5️⃣ INSIDE & OUTSIDE BAR PATTERN RECOGNITION Automatically detects core candlestick patterns from Al Brooks' theory. ii PATTERN (Consecutive Inside Bars) • Current bar contained within previous bar • Two or more consecutive • Labels: ii, iii, iiii (auto-accumulates) • High-probability breakout setup • Stop loss: Outside both bars Trading Significance: "Inside bars are one of the most reliable breakout setups, especially three or more consecutive inside bars." - Al Brooks OO PATTERN (Consecutive Outside Bars) • Current bar engulfs previous bar • Two or more consecutive • Labels: oo, ooo (auto-accumulates) • Indicates indecision or volatility increase ioi PATTERN (Inside-Outside-Inside) • Three-bar combination: Inside → Outside → Inside • Auto-detected and labeled • Tug-of-war pattern • Breakout direction often very strong SMART LABEL SYSTEM • Auto-accumulation counting • Dynamic label updates • Customizable size and color • Positioned above bars ✓ Independent alerts for all patterns ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 USE CASES INTRADAY TRADING ✓ Bar Count (timing rhythm) ✓ Traditional Gap (strong signals) ✓ EMA 20 + 50 (quick trend) ✓ ii/ioi Patterns (breakout points) SWING TRADING ✓ Previous Values (key levels) ✓ EMA 20 + 50 + 100 (trend analysis) ✓ Gaps (trend confirmation) ✓ iii Patterns (entry timing) TREND FOLLOWING ✓ All four EMAs (alignment analysis) ✓ Gaps (continuation signals) ✓ Previous Values (targets) BREAKOUT TRADING ✓ iii Pattern (high-reliability setup) ✓ Previous Values (targets) ✓ EMA 20 (trend direction) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎨 DESIGN FEATURES PROFESSIONAL COLOR SCHEME • Gaps: Hollow borders + light colors • Bar Count: Smart multi-color coding • EMAs: Gradient colors + transparency hierarchy • Previous Values: Customizable + smart labels CLEAR VISUAL HIERARCHY • Important elements: Opaque (EMA 20, bar count) • Reference elements: Semi-transparent (other EMAs, gaps) • Hollow design: Doesn't obscure price action USER-FRIENDLY INTERFACE • Clear functional grouping • Inline layout saves space • All colors and sizes customizable ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📚 AL BROOKS THEORY CORE READING PRICE ACTION "Don't try to predict the market, read what the market is telling you." PABP converts core concepts into visual tools: • Trend Assessment: EMA system • Time Rhythm: Bar Count • Market Structure: Gap analysis • Trade Setups: Inside/Outside Bars • Support/Resistance: Previous Values PROBABILITY THINKING • ii pattern: Medium probability • iii pattern: High probability • iii + EMA 20 support: Very high probability ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ TECHNICAL SPECIFICATIONS • Pine Script Version: v6 • Maximum Objects: 500 lines, 500 labels, 500 boxes • Alert Functions: 8 independent alerts • Supported Timeframes: All (5-min recommended for Bar Count) • Compatibility: All TradingView plans, Mobile & Desktop ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 RECOMMENDED INITIAL SETTINGS GAPS • Traditional Gap: ✓ • Tail Gap: ✓ • Border Width: 2 BAR COUNT • Use Bar Count: ✓ • Label Size: Normal EMA • EMA 20: ✓ • EMA 50: ✓ • EMA 100: ✓ • EMA 200: ✓ PREVIOUS VALUES • 1st: close (Previous close) • 2nd: high (Previous high) • 3rd: low (Previous low) INSIDE & OUTSIDE BAR • All patterns: ✓ • Label Size: Large ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🌟 WHY CHOOSE PABP? ✅ Solid Theoretical Foundation Based on Al Brooks' decades of trading experience ✅ Complete Professional Features Systematizes complex price action analysis ✅ Highly Customizable Every feature adjustable to personal style ✅ Excellent Performance Optimized code ensures smooth experience ✅ Continuous Updates Constantly improving based on feedback ✅ Suitable for All Levels Benefits beginners to professionals ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📖 RECOMMENDED LEARNING Al Brooks Books: • "Trading Price Action Trends" • "Trading Price Action Trading Ranges" • "Trading Price Action Reversals" Learning Path: 1. Understand basic candlestick patterns 2. Learn EMA applications 3. Master market structure analysis 4. Develop trading system 5. Continuous practice and optimization ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ RISK DISCLOSURE IMPORTANT NOTICE: • For educational and informational purposes only • Does not constitute investment advice • Past performance doesn't guarantee future results • Trading involves risk and may result in capital loss • Trade according to your risk tolerance • Test thoroughly in demo account first RESPONSIBLE TRADING: • Always use stop losses • Control position sizes reasonably • Don't overtrade • Continuous learning and improvement • Keep trading journal ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📜 COPYRIGHT Price Action Brooks Pro (PABP) Author: © JimmC98 License: Mozilla Public License 2.0 Pine Script Version: v6 Acknowledgments: Thanks to Dr. Al Brooks for his contributions to price action trading. This indicator is developed based on his theories. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Experience professional-grade price action analysis now! "The best traders read price action, not indicators. But when indicators help you read price action better, use them." - Al Brooks
Индикатор Pine Script®
от JimmC98
1
Triangular Moving Average (TRIMA)The Triangular Moving Average (TRIMA) is a technical indicator that applies a triangular weighting scheme to price data, providing enhanced smoothing compared to simpler moving averages. Originating in the early 1970s as technical analysts sought more effective noise filtering methods, the TRIMA was first popularized through the work of market technician Arthur Merrill. Its formal mathematical properties were established in the 1980s, and the indicator gained widespread adoption in the 1990s as computerized charting became standard. TRIMA effectively filters out market noise while maintaining important trends through its unique center-weighted calculation method. ## Core Concepts * **Double-smoothing process:** TRIMA can be viewed as applying a simple moving average twice, creating more effective noise filtering * **Triangular weighting:** Uses a symmetrical weight distribution that emphasizes central data points and reduces emphasis toward both ends * **Constant-time implementation:** Two $O(1)$ SMA passes with circular buffers preserve exact triangular weights while keeping update cost constant per bar * **Market application:** Particularly effective for identifying the underlying trend in noisy market conditions where standard moving averages generate too many false signals * **Timeframe flexibility:** Works across multiple timeframes, with longer periods providing cleaner trend signals in higher timeframes The core innovation of TRIMA is its unique triangular weighting scheme, which can be viewed either as a specialized weight distribution or as a twice-applied simple moving average with adjusted period. This creates more effective noise filtering without the excessive lag penalty typically associated with longer-period averages. The symmetrical nature of the weight distribution ensures zero phase distortion, preserving the timing of important market turning points. ## Common Settings and Parameters | Parameter | Default | Function | When to Adjust | |-----------|---------|----------|---------------| | Length | 14 | Controls the lookback period | Increase for smoother signals in volatile markets, decrease for responsiveness | | Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation | **Pro Tip:** For a good balance between smoothing and responsiveness, try using a TRIMA with period N instead of an SMA with period 2N - you'll get similar smoothing characteristics but with less lag. ## Calculation and Mathematical Foundation **Simplified explanation:** TRIMA calculates a weighted average of prices where the weights form a triangle shape. The middle prices get the most weight, and weights gradually decrease toward both the recent and older ends. This creates a smooth filter that effectively removes random price fluctuations while preserving the underlying trend. **Technical formula:** TRIMA = Σ(Price × Weight ) / Σ(Weight ) Where the triangular weights form a symmetric pattern: - Weight = min(i, n-1-i) + 1 - Example for n=5: weights = - Example for n=4: weights = Alternatively, TRIMA can be calculated as: TRIMA(source, p) = SMA(SMA(source, (p+1)/2), (p+1)/2) > 🔍 **Technical Note:** The double application of SMA explains why TRIMA provides better smoothing than a single SMA or WMA. This approach effectively applies smoothing twice with optimal period adjustment, creating a -18dB/octave roll-off in the frequency domain compared to -6dB/octave for a simple moving average, and the current implementation achieves $O(1)$ complexity through circular buffers and NA-safe warmup compensation. ## Interpretation Details TRIMA can be used in various trading strategies: * **Trend identification:** The direction of TRIMA indicates the prevailing trend * **Signal generation:** Crossovers between price and TRIMA generate trade signals with fewer false alarms than SMA * **Support/resistance levels:** TRIMA can act as dynamic support during uptrends and resistance during downtrends * **Trend strength assessment:** Distance between price and TRIMA can indicate trend strength * **Multiple timeframe analysis:** Using TRIMAs with different periods can confirm trends across different timeframes ## Limitations and Considerations * **Market conditions:** Like all moving averages, less effective in choppy, sideways markets * **Lag factor:** More lag than WMA or EMA due to center-weighted emphasis * **Limited adaptability:** Fixed weighting scheme cannot adapt to changing market volatility * **Response time:** Takes longer to reflect sudden price changes than directionally-weighted averages * **Complementary tools:** Best used with momentum oscillators or volume indicators for confirmation ## References * Ehlers, John F. "Cycle Analytics for Traders." Wiley, 2013 * Kaufman, Perry J. "Trading Systems and Methods." Wiley, 2013 * Colby, Robert W. "The Encyclopedia of Technical Market Indicators." McGraw-Hill, 2002
Индикатор Pine Script®
от mihakralj
Savitzky-Golay Filter (SGF)The Savitzky-Golay Filter (SGF) is a digital filter that performs local polynomial regression on a series of values to determine the smoothed value for each point. Developed by Abraham Savitzky and Marcel Golay in 1964, it is particularly effective at preserving higher moments of the data while reducing noise. This implementation provides a practical adaptation for financial time series, offering superior preservation of peaks, valleys, and other important market structures that might be distorted by simpler moving averages. ## Core Concepts * **Local polynomial fitting:** Fits a polynomial of specified order to a sliding window of data points * **Moment preservation:** Maintains higher statistical moments (peaks, valleys, inflection points) * **Optimized coefficients:** Uses pre-computed coefficients for common polynomial orders * **Adaptive weighting:** Weight distribution varies based on polynomial order and window size * **Market application:** Particularly effective for preserving significant price movements while filtering noise The core innovation of the Savitzky-Golay filter is its ability to smooth data while preserving important features that are often flattened by other filtering methods. This makes it especially valuable for technical analysis where maintaining the shape of price patterns is crucial. ## Common Settings and Parameters | Parameter | Default | Function | When to Adjust | |-----------|---------|----------|---------------| | Window Size | 11 | Number of points used in local fitting (must be odd) | Increase for smoother output, decrease for better feature preservation | | Polynomial Order | 2 | Order of fitting polynomial (2 or 4) | Use 2 for general smoothing, 4 for better peak preservation | | Source | close | Price data used for calculation | Consider using hlc3 for more stable fitting | **Pro Tip:** A window size of 11 with polynomial order 2 provides a good balance between smoothing and feature preservation. For sharper peaks and valleys, use order 4 with a smaller window size. ## Calculation and Mathematical Foundation **Simplified explanation:** The filter fits a polynomial of specified order to a moving window of price data. The smoothed value at each point is computed from this local fit, effectively removing noise while preserving the underlying shape of the data. **Technical formula:** For a window of size N and polynomial order M, the filtered value is: y = Σ(c_i × x ) Where: - c_i are the pre-computed filter coefficients - x are the input values in the window - Coefficients depend on window size N and polynomial order M > 🔍 **Technical Note:** The implementation uses optimized coefficient calculations for orders 2 and 4, which cover most practical applications while maintaining computational efficiency. ## Interpretation Details The Savitzky-Golay filter can be used in various trading strategies: * **Pattern recognition:** Preserves chart patterns while removing noise * **Peak detection:** Maintains amplitude and width of significant peaks * **Trend analysis:** Smooths price movement without distorting important transitions * **Divergence trading:** Better preservation of local maxima and minima * **Volatility analysis:** Accurate representation of price movement dynamics ## Limitations and Considerations * **Computational complexity:** More intensive than simple moving averages * **Edge effects:** First and last few points may show end effects * **Parameter sensitivity:** Performance depends on appropriate window size and order selection * **Data requirements:** Needs sufficient points for polynomial fitting * **Complementary tools:** Best used with volume analysis and momentum indicators ## References * Savitzky, A., Golay, M.J.E. "Smoothing and Differentiation of Data by Simplified Least Squares Procedures," Analytical Chemistry, 1964 * Press, W.H. et al. "Numerical Recipes: The Art of Scientific Computing," Chapter 14 * Schafer, R.W. "What Is a Savitzky-Golay Filter?" IEEE Signal Processing Magazine, 2011
Индикатор Pine Script®
от mihakralj
Bilateral Filter (BILATERAL)The Bilateral Filter is an edge-preserving smoothing technique that combines spatial filtering with intensity filtering to achieve noise reduction while maintaining significant price structure. Originally developed in computer vision for image processing, this adaptive filter has been adapted for financial time series analysis to provide superior smoothing that preserves important market transitions. The filter intelligently reduces noise in stable price regions while preserving sharp transitions like breakouts, reversals, and other significant market structures that would be blurred by conventional filters. ## Core Concepts * **Dual-domain filtering:** Combines traditional time-based (spatial) filtering with value-based (range) filtering for adaptive smoothing * **Edge preservation:** Maintains important price transitions while aggressively smoothing areas of minor fluctuation * **Adaptive processing:** Automatically adjusts filtering strength based on local price characteristics The core innovation of the Bilateral Filter is its ability to distinguish between random noise and significant price movements. Unlike conventional filters that smooth everything equally, Bilateral filtering preserves major price transitions by reducing the influence of price points that differ significantly from the current price, effectively preserving market structure while still eliminating noise. ## Common Settings and Parameters | Parameter | Default | Function | When to Adjust | |-----------|---------|----------|---------------| | Length | 14 | Controls the lookback window size | Increase for more context in filtering decisions, decrease for quicker response | | Sigma_S_Ratio | 0.3 | Controls spatial (time) weighting | Lower values emphasize recent bars, higher values distribute influence more evenly | | Sigma_R_Mult | 2.0 | Controls range (price) sensitivity | Lower values increase edge preservation, higher values increase smoothing | | Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation | **Pro Tip:** For breakout trading strategies, try reducing Sigma_R_Mult to 1.0-1.5 to make the filter more sensitive to significant price moves, allowing it to preserve breakout signals while still filtering noise. ## Calculation and Mathematical Foundation **Simplified explanation:** The Bilateral Filter calculates a weighted average of nearby prices, where the weights depend on two factors: how far away in time the price point is (spatial weight) and how different the price value is (range weight). Points that are close in time AND similar in value get the highest weight. This means stable price regions get smoothed while significant changes are preserved. **Technical formula:** BF = (1 / Wp) × Σ_{q ∈ S} G_s(||p - q||) × G_r(|I - I |) × I Where: - G_s is the spatial Gaussian kernel: exp(-||p - q||² / (2 × σ_s²)) - G_r is the range Gaussian kernel: exp(-|I - I |² / (2 × σ_r²)) - Wp is the normalization factor (sum of all weights) > 🔍 **Technical Note:** The sigma_r parameter is typically calculated dynamically based on local price volatility (standard deviation) to provide adaptive filtering - this automatically adjusts filtering strength based on market conditions. ## Interpretation Details The Bilateral Filter can be applied in various trading contexts: * **Trend identification:** Reveals cleaner underlying price direction by removing noise while preserving trend changes * **Support/resistance identification:** Provides clearer price levels by preserving significant turning points * **Pattern recognition:** Maintains critical chart patterns while eliminating distracting minor fluctuations * **Breakout trading:** Preserves sharp price transitions for more reliable breakout signals * **Pre-processing:** Can be used as an initial filter before applying other technical indicators to reduce false signals ## Limitations and Considerations * **Computational complexity:** More intensive calculations than traditional linear filters * **Parameter sensitivity:** Performance highly dependent on proper parameter selection * **Non-linearity:** Non-linear behavior may produce unexpected results in certain market conditions * **Interpretation adjustment:** Requires different interpretation than conventional moving averages * **Complementary tools:** Best used alongside volume analysis and traditional indicators for confirmation ## References * Tomasi, C. and Manduchi, R. "Bilateral Filtering for Gray and Color Images," Proceedings of IEEE ICCV, 1998 * Paris, S. et al. "A Gentle Introduction to Bilateral Filtering and its Applications," ACM SIGGRAPH, 2008
Индикатор Pine Script®
от mihakralj
Easy Read Short‑Term EMA CrossoverThis TradingView panel shows a multi-indicator setup for analyzing the S&P 500 Index (SPX): Main chart (top): displays price candles with two Exponential Moving Averages (EMA 8 and 21). When the short-term EMA (blue) crosses above the long-term EMA (red), it gives a Buy signal; when it crosses below, a Sell signal appears — helping identify short-term trend shifts. Middle panel: shows the Easy-Read RSI, highlighting overbought (OB) and oversold (OS) zones. When RSI moves above 70 it signals potential weakness (sell), and below 30 suggests a rebound zone (buy). Bottom panel: plots the Easy-Read MACD, which tracks momentum by comparing short- and long-term moving averages. When the MACD line crosses above the Signal line, it indicates bullish momentum; crossing below suggests bearish momentum. Together, these three indicators provide a layered view of trend, momentum, and potential entry/exit points.
Индикатор Pine Script®
от samliuhouse
EMA6 or SMA6 Touch AlertThis script monitors the market and notifies you whenever the price touches either the 6-period EMA or the 6-period SMA. It helps identify potential pullbacks, reaction points, or entry zones, as price interaction with these moving averages often signals short-term market shifts. What the script does: Calculates the EMA 6 and SMA 6 Detects if price touches either moving average within the candle Plots both lines on the chart for visibility Allows you to set alerts to receive automatic notifications Best suited for: Scalping Day Trading Pullback Entries Short-term trend reactions
Индикатор Pine Script®
от gihontt
Double Weighted Moving Average (DWMA)# DWMA: Double Weighted Moving Average ## Overview and Purpose The Double Weighted Moving Average (DWMA) is a technical indicator that applies weighted averaging twice in sequence to create a smoother signal with enhanced noise reduction. Developed in the late 1990s as an evolution of traditional weighted moving averages, the DWMA was created by quantitative analysts seeking enhanced smoothing without the excessive lag typically associated with longer period averages. By applying a weighted moving average calculation to the results of an initial weighted moving average, DWMA achieves more effective filtering while preserving important trend characteristics. ## Core Concepts * **Cascaded filtering:** DWMA applies weighted averaging twice in sequence for enhanced smoothing and superior noise reduction * **Linear weighting:** Uses progressively increasing weights for more recent data in both calculation passes * **Market application:** Particularly effective for trend following strategies where noise reduction is prioritized over rapid signal response * **Timeframe flexibility:** Works across multiple timeframes but particularly valuable on daily and weekly charts for identifying significant trends The core innovation of DWMA is its two-stage approach that creates more effective noise filtering while minimizing the additional lag typically associated with longer-period or higher-order filters. This sequential processing creates a more refined output that balances noise reduction and signal preservation better than simply increasing the length of a standard weighted moving average. ## Common Settings and Parameters | Parameter | Default | Function | When to Adjust | |-----------|---------|----------|---------------| | Length | 14 | Controls the lookback period for both WMA calculations | Increase for smoother signals in volatile markets, decrease for more responsiveness | | Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation | **Pro Tip:** For trend following, use a length of 10-14 with DWMA instead of a single WMA with double the period - this provides better smoothing with less lag than simply increasing the period of a standard WMA. ## Calculation and Mathematical Foundation **Simplified explanation:** DWMA first calculates a weighted moving average where recent prices have more importance than older prices. Then, it applies the same weighted calculation again to the results of the first calculation, creating a smoother line that reduces market noise more effectively. **Technical formula:** ``` DWMA is calculated by applying WMA twice: 1. First WMA calculation: WMA₁ = (P₁ × w₁ + P₂ × w₂ + ... + Pₙ × wₙ) / (w₁ + w₂ + ... + wₙ) 2. Second WMA calculation applied to WMA₁: DWMA = (WMA₁₁ × w₁ + WMA₁₂ × w₂ + ... + WMA₁ₙ × wₙ) / (w₁ + w₂ + ... + wₙ) ``` Where: - Linear weights: most recent value has weight = n, second most recent has weight = n-1, etc. - n is the period length - Sum of weights = n(n+1)/2 **O(1) Optimization - Inline Dual WMA Architecture:** This implementation uses an advanced O(1) algorithm with two complete inline WMA calculations. Each WMA uses the dual running sums technique: 1. **First WMA (source → wma1)**: - Maintains buffer1, sum1, weighted_sum1 - Recurrence: `W₁_new = W₁_old - S₁_old + (n × P_new)` - Cached denominator norm1 after warmup 2. **Second WMA (wma1 → dwma)**: - Maintains buffer2, sum2, weighted_sum2 - Recurrence: `W₂_new = W₂_old - S₂_old + (n × WMA₁_new)` - Cached denominator norm2 after warmup **Implementation details:** - Both WMAs fully integrated inline (no helper functions) - Each maintains independent state: buffers, sums, counters, norms - Both warm up independently from bar 1 - Performance: ~16 operations per bar regardless of period (vs ~10,000 for naive O(n²) implementation) **Why inline architecture:** Unlike helper functions, the inline approach makes all state variables and calculations visible in a single scope, eliminating function call overhead and making the dual-pass nature explicit. This is ideal for educational purposes and when debugging complex cascaded filters. > 🔍 **Technical Note:** The dual-pass O(1) approach creates a filter that effectively increases smoothing without the quadratic increase in computational cost. Original O(n²) implementations required ~10,000 operations for period=100; this optimized version requires only ~16 operations, achieving a 625x speedup while maintaining exact mathematical equivalence. ## Interpretation Details DWMA can be used in various trading strategies: * **Trend identification:** The direction of DWMA indicates the prevailing trend * **Signal generation:** Crossovers between price and DWMA generate trade signals, though they occur later than with single WMA * **Support/resistance levels:** DWMA can act as dynamic support during uptrends and resistance during downtrends * **Trend strength assessment:** Distance between price and DWMA can indicate trend strength * **Noise filtering:** Using DWMA to filter noisy price data before applying other indicators ## Limitations and Considerations * **Market conditions:** Less effective in choppy, sideways markets where its lag becomes a disadvantage * **Lag factor:** More lag than single WMA due to double calculation process * **Initialization requirement:** Requires more data points for full calculation, showing more NA values at chart start * **Short-term trading:** May miss short-term trading opportunities due to increased smoothing * **Complementary tools:** Best used with momentum oscillators or volume indicators for confirmation ## References * Jurik, M. "Double Weighted Moving Averages: Theory and Applications in Algorithmic Trading Systems", Jurik Research Papers, 2004 * Ehlers, J.F. "Cycle Analytics for Traders," Wiley, 2013
Индикатор Pine Script®
от mihakralj
Weighted Moving Average (WMA)This implementation uses O(1) algorithm that eliminates the need to loop through all period values on each bar. It also generates valid WMA values from the first bar and is not returning NA when number of bars is less than period. ## Overview and Purpose The Weighted Moving Average (WMA) is a technical indicator that applies progressively increasing weights to more recent price data. Emerging in the early 1950s during the formative years of technical analysis, WMA gained significant adoption among professional traders through the 1970s as computational methods became more accessible. The approach was formalized in Robert Colby's 1988 "Encyclopedia of Technical Market Indicators," establishing it as a staple in technical analysis software. Unlike the Simple Moving Average (SMA) which gives equal weight to all prices, WMA assigns greater importance to recent prices, creating a more responsive indicator that reacts faster to price changes while still providing effective noise filtering. ## Core Concepts * **Linear weighting:** WMA applies progressively increasing weights to more recent price data, creating a recency bias that improves responsiveness * **Market application:** Particularly effective for identifying trend changes earlier than SMA while maintaining better noise filtering than faster-responding averages like EMA * **Timeframe flexibility:** Works effectively across all timeframes, with appropriate period adjustments for different trading horizons The core innovation of WMA is its linear weighting scheme, which strikes a balance between the equal-weight approach of SMA and the exponential decay of EMA. This creates an intuitive and effective compromise that prioritizes recent data while maintaining a finite lookback period, making it particularly valuable for traders seeking to reduce lag without excessive sensitivity to price fluctuations. ## Common Settings and Parameters | Parameter | Default | Function | When to Adjust | |-----------|---------|----------|---------------| | Length | 14 | Controls the lookback period | Increase for smoother signals in volatile markets, decrease for responsiveness | | Source | close | Price data used for calculation | Consider using hlc3 for a more balanced price representation | **Pro Tip:** For most trading applications, using a WMA with period N provides better responsiveness than an SMA with the same period, while generating fewer whipsaws than an EMA with comparable responsiveness. ## Calculation and Mathematical Foundation **Simplified explanation:** WMA calculates a weighted average of prices where the most recent price receives the highest weight, and each progressively older price receives one unit less weight. For example, in a 5-period WMA, the most recent price gets a weight of 5, the next most recent a weight of 4, and so on, with the oldest price getting a weight of 1. **Technical formula:** ``` WMA = (P₁ × w₁ + P₂ × w₂ + ... + Pₙ × wₙ) / (w₁ + w₂ + ... + wₙ) ``` Where: - Linear weights: most recent value has weight = n, second most recent has weight = n-1, etc. - The sum of weights for a period n is calculated as: n(n+1)/2 - For example, for a 5-period WMA, the sum of weights is 5(5+1)/2 = 15 **O(1) Optimization - Dual Running Sums:** The key insight is maintaining two running sums: 1. **Unweighted sum (S)**: Simple sum of all values in the window 2. **Weighted sum (W)**: Sum of all weighted values The recurrence relation for a full window is: ``` W_new = W_old - S_old + (n × P_new) ``` This works because when all weights decrement by 1 (as the window slides), it's mathematically equivalent to subtracting the entire unweighted sum. The implementation: - **During warmup**: Accumulates both sums as the window fills, computing denominator each bar - **After warmup**: Uses cached denominator (constant at n(n+1)/2), updates both sums in constant time - **Performance**: ~8 operations per bar regardless of period, vs ~100+ for naive O(n) implementation > 🔍 **Technical Note:** Unlike EMA which theoretically considers all historical data (with diminishing influence), WMA has a finite memory, completely dropping prices that fall outside its lookback window. This creates a cleaner break from outdated market conditions. The O(1) optimization achieves 12-25x speedup over naive implementations while maintaining exact mathematical equivalence. ## Interpretation Details WMA can be used in various trading strategies: * **Trend identification:** The direction of WMA indicates the prevailing trend with greater responsiveness than SMA * **Signal generation:** Crossovers between price and WMA generate trade signals earlier than with SMA * **Support/resistance levels:** WMA can act as dynamic support during uptrends and resistance during downtrends * **Moving average crossovers:** When a shorter-period WMA crosses above a longer-period WMA, it signals a potential uptrend (and vice versa) * **Trend strength assessment:** Distance between price and WMA can indicate trend strength ## Limitations and Considerations * **Market conditions:** Still suboptimal in highly volatile or sideways markets where enhanced responsiveness may generate false signals * **Lag factor:** While less than SMA, still introduces some lag in signal generation * **Abrupt window exit:** The oldest price suddenly drops out of calculation when leaving the window, potentially causing small jumps * **Step changes:** Linear weighting creates discrete steps in influence rather than a smooth decay * **Complementary tools:** Best used with volume indicators and momentum oscillators for confirmation ## References * Colby, Robert W. "The Encyclopedia of Technical Market Indicators." McGraw-Hill, 2002 * Murphy, John J. "Technical Analysis of the Financial Markets." New York Institute of Finance, 1999 * Kaufman, Perry J. "Trading Systems and Methods." Wiley, 2013
Индикатор Pine Script®
от mihakralj
Iriza4 -DAX EMA+HULL+ADX TP40 SL205 MIN SKALP. Additional filters improve accuracy: the strategy blocks trades after too many consecutive bullish or bearish candles (streak filter) and ignores signals when price is too far from the EMA (measured by ATR distance). Each position uses a fixed risk-to-reward ratio of 1 : 2 with clear stop-loss and take-profit targets, without partial exits or breakevens. The goal is to identify clean pullbacks inside strong trends and filter out late or exhausted entries
Стратегия Pine Script®
от Radoje1984
Simple Moving Average (SMA)## Overview and Purpose The Simple Moving Average (SMA) is one of the most fundamental and widely used technical indicators in financial analysis. It calculates the arithmetic mean of a selected range of prices over a specified number of periods. Developed in the early days of technical analysis, the SMA provides traders with a straightforward method to identify trends by smoothing price data and filtering out short-term fluctuations. Due to its simplicity and effectiveness, it remains a cornerstone indicator that forms the basis for numerous other technical analysis tools. ## What’s Different in this Implementation - **Constant streaming update:** On each bar we: 1) subtract the value leaving the window, 2) add the new value, 3) divide by the number of valid samples (early) or by `period` (once full). - **Deterministic lag, same as textbook SMA:** Once full, lag is `(period - 1)/2` bars—identical to the classic SMA. You just **don’t lose the first `period-1` bars** to `na`. - **Large windows without penalty:** Complexity is constant per tick; memory is bounded by `period`. Very long SMAs stay cheap. ## Behavior on Early Bars - **Bars < period:** returns the arithmetic mean of **available** samples. Example (period = 10): bar #3 is the average of the first 3 inputs—not `na`. - **Bars ≥ period:** behaves exactly like standard SMA over a fixed-length window. > Implication: Crosses and signals can appear earlier than with `ta.sma()` because you’re not suppressing the first `period-1` bars. ## When to Prefer This - Backtests needing early bars: You want signals and state from the very first bars. - High-frequency or very long SMAs: O(1) updates avoid per-bar CPU spikes. - Memory-tight scripts: Single circular buffer; no large temp arrays per tick. ## Caveats & Tips Backtest comparability: If you previously relied on na gating from ta.sma(), add your own warm-up guard (e.g., only trade after bar_index >= period-1) for apples-to-apples. Missing data: The function treats the current bar via nz(source); adjust if you need strict NA propagation. Window semantics: After warm-up, results match the textbook SMA window; early bars are a partial-window mean by design. ## Math Notes Running-sum update: sum_t = sum_{t-1} - oldest + newest SMA_t = sum_t / k where k = min(#valid_samples, period) Lag (full window): (period - 1) / 2 bars. ## References - Edwards & Magee, Technical Analysis of Stock Trends - Murphy, Technical Analysis of the Financial Markets
Индикатор Pine Script®
от mihakralj
ATR Money Line Bands V2The "ATR Money Line Bands V2" is a clever TradingView overlay designed for trend identification with volatility-aware bands, evolving from basic ATR envelopes. Reasoning Behind Construction: The core idea is to blend a smoothed trend line with dynamic volatility bands for reliable signals in varying markets. The "Money Line" uses linear regression (ta.linreg) on closes over a length (default 16) instead of a moving average, as it fits data via least-squares for a cleaner, forward-projected trend without lag artifacts. ATR (default 12-period) powers the bands because it measures true range volatility better than std dev in gappy assets like crypto/stocks—bands offset from the Money Line by ATR * multiplier (default 1.5). A dynamic multiplier (boosts by ~33% on spikes > prior ATR * 1.3) prevents tight bands from false breakouts during surges. Trend detection checks slope against an ATR-scaled tolerance (default 0.15) to ignore noise, labeling bull/bear/neutral—avoiding whipsaws in flats. Properties: It's an overlay with a colored Money Line (green bull, red bear, yellow neutral) and invisible bands (toggle to show gray lines) filled semi-transparently matching trend for visual pop. Dynamic adaptation makes bands widen/contract intelligently. An info table (positionable, e.g., top_right) displays real-time values: Money Line, bands, ATR, trend—great for quick scans. Limits history (2000 bars) and labels (500) for efficiency. Tips for Usage: Apply to any timeframe/asset; defaults suit medium-term (e.g., daily stocks). Watch color flips: green for longs (enter on pullbacks to lower band), red for shorts (vice versa), yellow to sit out. Use bands as S/R—breakouts signal momentum, squeezes impending vol. Tweak length for sensitivity (shorter for intraday), multiplier for width (higher for trends), tolerance for fewer neutrals. Pair with volume/RSI for confirmation; backtest to optimize. In choppy markets, disable dynamic mult to avoid over-expansion. Overall, it's adaptive and visual—helps trend-follow without overcomplicating.
Индикатор Pine Script®
от ralis24
Adaptive Trend SelectorThe Adaptive Trend Selector is a comprehensive trend-following tool designed to automatically identify the optimal moving average crossover strategy. It features adjustable parameters and an integrated backtester that delivers institutional-grade insights into the recommended strategy. The model continuously adapts to new data in real time by evaluating multiple moving average combinations, determining the best performing lengths, and presenting the backtest results in a clear, color-coded table that benchmarks performance against the buy-and-hold strategy. At its core, the model systematically backtests a wide range of moving average combinations to identify the configuration that maximizes the selected optimization metric. Users can choose to optimize for absolute returns or risk-adjusted returns using the Sharpe, Sortino, or Calmar ratios. Alternatively, users can enable manual optimization to test custom fast and slow moving average lengths and view the corresponding backtest results. The label displays the Compounded Annual Growth Rate (CAGR) of the strategy, with the buy-and-hold CAGR in parentheses for comparison. The table presents the backtest results based on the fast and slow lengths displayed at the top: Sharpe = CAGR per unit of standard deviation. Sortino = CAGR per unit of downside deviation. Calmar = CAGR relative to maximum drawdown. Max DD = Largest peak-to-trough decline in value. Beta (β) = Return sensitivity relative to buy-and-hold. Alpha (α) = Excess annualized risk-adjusted returns. Win Rate = Ratio of profitable trades to total trades. Profit Factor = Total gross profit per unit of losses. Expectancy = Average expected return per trade. Trades/Year = Average number of trades per year. This indicator is designed with flexibility in mind, enabling users to specify the start date of the backtesting period and the preferred moving average strategy. Supported strategies include the Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), and Volume-Weighted Moving Average (VWMA). To minimize overfitting, users can define constraints such as a minimum and maximum number of trades per year, as well as an optional optimization margin that prioritizes longer, more robust combinations by requiring shorter-length strategies to exceed this threshold. The table follows an intuitive color logic that enables quick performance comparison against buy-and-hold (B&H): Sharpe = Green indicates better than B&H, while red indicates worse. Sortino = Green indicates better than B&H, while red indicates worse. Calmar = Green indicates better than B&H, while red indicates worse. Max DD = Green indicates better than B&H, while red indicates worse. Beta (β) = Green indicates better than B&H, while red indicates worse. Alpha (α) = Green indicates above 0%, while red indicates below 0%. Win Rate = Green indicates above 50%, while red indicates below 50%. Profit Factor = Green indicates above 2, while red indicates below 1. Expectancy = Green indicates above 0%, while red indicates below 0%. In summary, the Adaptive Trend Selector is a powerful tool designed to help investors make data-driven decisions when selecting moving average crossover strategies. By optimizing for risk-adjusted returns, investors can confidently identify the best lengths using institutional-grade metrics. While results are based on the selected historical period, users should be mindful of potential overfitting, as past results may not persist under future market conditions. Since the model recalibrates to incorporate new data, the recommended lengths may evolve over time.
Индикатор Pine Script®
от QuantitativeAlpha
Better DEMAThe Better DEMA is a new tool designed to recreate the classical moving average DEMA, into a smoother, more reliable tool. Combining many methodologies, this script offers users a unique insight into market behavior. How does it work? First, to get a smoother signal, we need to calculate the Gaussian filter. A Gaussian filter is a smoothing filter that reduces noise and detail by averaging data with weights following a Gaussian (bell-shaped) curve. Now that we have the source, we will calculate the following: n2 = n/2 (half of the user defined length) a = 2/(1+n) ns Now that we have that out of the way, it is time to get into the core. Now we calculate 2 EMAs: slow EMA => EMA over n fast EMA => EMA over n2 period Rather then now doing this: DEMA = fast EMA * 2 - slow EMA I found this to be better: DEMA = slow EMA * (1-a) + fast EMA * a As a last touch I took a little something from the HMA, and used a EMA with period of √n to smooth the entire the thing. The Trend condition at base is the following (but feel free to FAFO with it): Long = dema > dema yesterday and dema < src Short = dema < dema yesterday and dema > src Methodology While the DEMA is an amazing tool used in many great indicators, it can be far too noisy. This made me test out many filters, out of which the Gaussian performed best. Then I tried out the non subtractive approach and that worked too, as it made it smoother. Compacting on all I learned and smoothing it bit by bit, I think I can say this is worth looking into :). Use cases: Following Trends => classic, effective :) Smoothing sources for other indicators => if done well enough, could be useful :) Easy trend visualization => Added extra options for that. Strategy development => Yes Another good thing is it does not a high lookback period, so it should be better and less overfit. That is all for today Gs, Have fun and enjoy!
Индикатор Pine Script®
от MisinkoMaster
THAIT Moving Averages Tight within # ATR EMA SMA convergence THAIT(tight) indicator is a powerful tool for identifying moving average convergence in price action. This indicator plots four user-defined moving averages (EMA or SMA). It highlights moments when the MAs converge within a user specified number of ATRs, adjusted by the 14-period ATR, signaling potential trend shifts or consolidation. A convergence is flagged when MA1 is the maximum, the spread between MAs is tight, and the price is above MA1, excluding cases where the longest MA (MA4) is the highest. The indicator alerts and visually marks convergence zones with a shaded green background, making it ideal for traders seeking precise entry or exit points.
Индикатор Pine Script®
от Imready4
Momentum Breakout Filter + ATR ZonesMomentum Breakout Filter + ATR Zones - User Guide What This Indicator Does This indicator helps you with your MACD + volume momentum strategy by: Filtering out fake breakouts - Shows ⚠️ warnings when breakouts lack confirmation Showing clear entry signals - 🚀 LONG and 🔻 SHORT labels when all conditions align Automatic stop loss & profit targets - Based on ATR (Average True Range) Visual trend confirmation - Background color + EMA alignment Signal Types 🚀 LONG Entry Signal (Green Label) Appears when ALL conditions met: ✅ MACD crosses above signal line ✅ Volume > 1.5× average ✅ Price > EMA 9 > EMA 21 > EMA 200 (bullish trend) ✅ Price closes above recent 20-bar high 🔻 SHORT Entry Signal (Red Label) Appears when ALL conditions met: ✅ MACD crosses below signal line ✅ Volume > 1.5× average ✅ Price < EMA 9 < EMA 21 < EMA 200 (bearish trend) ✅ Price closes below recent 20-bar low ⚠️ FAKE Breakout Warning (Orange Label) Appears when price breaks high/low BUT lacks confirmation: ❌ Low volume (below 1.5× average), OR ❌ Wick break only (didn't close through level), OR ❌ MACD not aligned with direction Hover over the warning label to see what's missing! ATR Stop Loss & Targets When you get a signal, colored lines automatically appear: Long Position Red solid line = Stop Loss (Entry - 1.5×ATR) Green dashed lines = Profit Targets: Target 1: Entry + 2×ATR Target 2: Entry + 3×ATR Target 3: Entry + 4×ATR Short Position Red solid line = Stop Loss (Entry + 1.5×ATR) Green dashed lines = Profit Targets: Target 1: Entry - 2×ATR Target 2: Entry - 3×ATR Target 3: Entry - 4×ATR The lines move with each bar until you exit the position. Chart Elements Moving Averages Blue line = EMA 9 (fast) Orange line = EMA 21 (medium) White line = EMA 200 (trend filter) Volume Yellow bars = High volume (above threshold) Gray bars = Normal volume Background Color Light green = Bullish trend (all EMAs aligned up) Light red = Bearish trend (all EMAs aligned down) No color = Neutral/mixed MACD (Bottom Pane) Green/Red columns = MACD Histogram Blue line = MACD Line Orange line = Signal Line Info Dashboard (Bottom Right) ItemWhat It ShowsVolumeCurrent volume vs average (✓ HIGH or ✗ Low)MACDDirection (BULLISH or BEARISH)TrendEMA alignment (BULL, BEAR, or NEUTRAL)ATRCurrent ATR value in dollarsPositionCurrent position (LONG, SHORT, or NONE)R:RRisk-to-Reward ratio (shows when in position) How To Use It Basic Workflow Wait for setup Watch for MACD to approach signal line Volume should be building Price should be near EMA structure Get confirmation Wait for 🚀 LONG or 🔻 SHORT label Check dashboard shows "✓ HIGH" volume Verify trend is aligned (green or red background) Enter the trade Enter when signal appears Note your stop loss (red line) Note your targets (green dashed lines) Manage the trade Exit at first target for partial profit Move stop to breakeven Trail remaining position What To Avoid ❌ Don't trade when you see: ⚠️ FAKE labels (wait for confirmation) Neutral background (no clear trend) "✗ Low" volume in dashboard MACD and Trend not aligned Settings You Can Adjust Volume Sensitivity High Volume Threshold: Default 1.5× Increase to 2.0× for cleaner signals (fewer trades) Decrease to 1.2× for more signals (more trades) Fake Breakout Filters You can toggle these ON/OFF: Volume Confirmation: Requires high volume Close Through: Requires candle close, not just wick MACD Alignment: Requires MACD direction match Tip: Turn all three ON for highest quality signals ATR Stop/Target Multipliers Default settings (conservative): Stop Loss: 1.5×ATR Target 1: 2×ATR (1.33:1 R:R) Target 2: 3×ATR (2:1 R:R) Target 3: 4×ATR (2.67:1 R:R) Aggressive traders might use: Stop Loss: 1.0×ATR Target 1: 2×ATR (2:1 R:R) Target 2: 4×ATR (4:1 R:R) Conservative traders might use: Stop Loss: 2.0×ATR Target 1: 3×ATR (1.5:1 R:R) Target 2: 5×ATR (2.5:1 R:R) Example Trade Scenarios Scenario 1: Perfect Long Setup ✅ Stock consolidating near EMA 21 MACD curling up toward signal line Volume bar turns yellow (high volume) 🚀 LONG label appears Red stop line and green target lines appear Result: High probability trade Scenario 2: Fake Breakout Avoided ✅ Price breaks above resistance Volume is normal (gray bar) ⚠️ FAKE label appears (hover shows "Low volume") No entry signal Price falls back below breakout level Result: Avoided losing trade Scenario 3: Premature Entry ❌ MACD crosses up Volume is high BUT trend is NEUTRAL (no background color) No signal appears (trend filter blocks it) Result: Avoided choppy/sideways market Quick Reference Entry Checklist 🚀 or 🔻 label on chart Dashboard shows "✓ HIGH" volume Dashboard shows aligned MACD + Trend Colored background (green or red) ATR lines visible No ⚠️ FAKE warning Exit Strategy Target 1 (2×ATR): Take 50% profit, move stop to breakeven Target 2 (3×ATR): Take 25% profit, trail stop Target 3 (4×ATR): Take remaining profit or trail aggressively Stop Loss: Exit entire position if hit Alerts Set up these alerts: Long Entry: Fires when 🚀 LONG signal appears Short Entry: Fires when 🔻 SHORT signal appears Fake Breakout Warning: Fires when ⚠️ appears (optional) Tips for Success Use on 5-minute charts for day trading momentum plays Only trade high volume stocks ($5-20 range works best) Wait for full confirmation - don't jump early Respect the stop loss - it's calculated based on volatility Scale out at targets - don't hold for home runs Avoid trading first 15 minutes - let market settle Best during 10am-11am and 2pm-3pm - peak momentum times Common Questions Q: Why didn't I get a signal even though MACD crossed? A: All conditions must be met - check dashboard for what's missing (likely volume or trend alignment) Q: Can I use this on any timeframe? A: Yes, but it's designed for 5-15 minute charts. On daily charts, adjust ATR multipliers higher. Q: The stop loss seems too tight, can I widen it? A: Yes, increase "Stop Loss (×ATR)" from 1.5 to 2.0 or 2.5 in settings. Q: I keep seeing FAKE warnings but price keeps going - what gives? A: The filter is conservative. You can disable some filters in settings, but expect more false signals. Q: Can I use this for swing trading? A: Yes, but use larger timeframes (1H or 4H) and adjust ATR multipliers up (3× for stops, 6-9× for targets).
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