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Arnaud Legoux Gaussian Flow | AlphaNattArnaud Legoux Gaussian Flow | AlphaNatt A sophisticated trend-following and mean-reversion indicator that combines the power of the Arnaud Legoux Moving Average (ALMA) with advanced Gaussian distribution analysis to identify high-probability trading opportunities. 🎯 What Makes This Indicator Unique? This indicator goes beyond traditional moving averages by incorporating Gaussian mathematics at multiple levels: ALMA uses Gaussian distribution for superior price smoothing with minimal lag Dynamic envelopes based on Gaussian probability zones Multi-layer gradient visualization showing probability density Adaptive envelope modes that respond to market conditions 📊 Core Components 1. Arnaud Legoux Moving Average (ALMA) The ALMA is a highly responsive moving average that uses Gaussian distribution to weight price data. Unlike simple moving averages, ALMA can be fine-tuned to balance responsiveness and smoothness through three key parameters: ALMA Period: Controls the lookback window (default: 21) Gaussian Offset: Shifts the Gaussian curve to adjust lag vs. responsiveness (default: 0.85) Gaussian Sigma: Controls the width of the Gaussian distribution (default: 6.0) 2. Gaussian Envelope System The indicator features three envelope calculation modes: Fixed Mode: Uses ATR-based fixed width for consistent envelope sizing Adaptive Mode: Dynamically adjusts based on price acceleration and volatility Hybrid Mode: Combines ATR and standard deviation for balanced adaptation The envelopes represent statistical probability zones. Price moving beyond these zones suggests potential mean reversion opportunities. 3. Momentum-Adjusted Envelopes The envelope width automatically expands during strong trends and contracts during consolidation, providing context-aware support and resistance levels. ⚡ Key Features Multi-Layer Gradient Visualization The indicator displays 10 gradient layers between the ALMA and envelope boundaries, creating a visual "heat map" of probability density. This helps traders quickly assess: Distance from the mean Potential support/resistance strength Overbought/oversold conditions in context Dynamic Color Coding Cyan gradient: Price below ALMA (bullish zone) Magenta gradient: Price above ALMA (bearish zone) The ALMA line itself changes color based on price position Trend Regime Detection The indicator automatically identifies market regimes: Strong Uptrend: Trend strength > 0.5% with price above ALMA Strong Downtrend: Trend strength < -0.5% with price below ALMA Weak trends and ranging conditions 📈 Trading Strategies Mean Reversion Strategy Look for price entering the extreme Gaussian zones (beyond 95% of envelope width) when trend strength is moderate. These represent statistical extremes where mean reversion is probable. Signals: Long: Price in lower Gaussian zone with trend strength > -0.5% Short: Price in upper Gaussian zone with trend strength < 0.5% Trend Continuation Strategy Enter when price crosses the ALMA during confirmed strong trend conditions, riding momentum while using the envelope as a trailing stop reference. Signals: Long: Price crosses above ALMA during strong uptrend Short: Price crosses below ALMA during strong downtrend 🎨 Visualization Guide The gradient layers create a "probability cloud" around the ALMA: Darker shades (near ALMA): High probability zone - price tends to stay here Lighter shades (near envelope edges): Lower probability - potential reversal zones Price at envelope extremes: Statistical outliers - strongest mean reversion setups ⚙️ Customization Options ALMA Parameters Adjust period for different timeframes (lower for day trading, higher for swing trading) Modify offset to tune responsiveness vs. smoothness Change sigma to control distribution width Envelope Configuration Choose envelope mode based on market characteristics Adjust multiplier to match instrument volatility Modify gradient depth for visual preference (5-15 layers) Signal Enhancement Momentum Length: Lookback for trend strength calculation Signal Smoothing: Additional EMA smoothing to reduce noise 🔔 Built-in Alerts The indicator includes six pre-configured alert conditions: ALMA Trend Long - Price crosses above ALMA in strong uptrend ALMA Trend Short - Price crosses below ALMA in strong downtrend Mean Reversion Long - Price enters lower Gaussian zone Mean Reversion Short - Price enters upper Gaussian zone Strong Uptrend Detected - Momentum confirms strong bullish regime Strong Downtrend Detected - Momentum confirms strong bearish regime 💡 Best Practices Use on clean, liquid markets with consistent volatility Combine with volume analysis for confirmation Adjust envelope multiplier based on backtesting for your specific instrument Higher timeframes (4H+) generally provide more reliable signals Use adaptive mode for trending markets, hybrid for mixed conditions ⚠️ Important Notes This indicator works best in markets with normal price distribution Extreme news events can invalidate Gaussian assumptions temporarily Always use proper risk management - no indicator is perfect Backtest parameters on your specific instrument and timeframe 🔬 Technical Background The Arnaud Legoux Moving Average was developed to solve the classic dilemma of moving averages: the trade-off between lag and noise. By applying Gaussian distribution weighting, ALMA achieves superior smoothing while maintaining responsiveness to price changes. The envelope system extends this concept by creating probability zones based on volatility and momentum, effectively mapping where price is "likely" vs "unlikely" to be found based on statistical principles. Created by AlphaNatt - For educational purposes. Always practice proper risk management. Not financial advice. Always DYOR.
Wskaźnik Pine Script®
od AlphaNatt
1
Smooth Theil-SenI wanted to build a Theil-Sen estimator that could run on more than one bar and produce smoother output than the standard implementation. Theil-Sen regression is a non-parametric method that calculates the median slope between all pairs of points in your dataset, which makes it extremely robust to outliers. The problem is that median operations produce discrete jumps, especially when you're working with limited sample sizes. Every time the median shifts from one value to another, you get a step change in your regression line, which creates visual choppiness that can be distracting even though the underlying calculations are sound. The solution I ended up going with was convolving a Gaussian kernel around the center of the sorted lists to get a more continuous median estimate. Instead of just picking the middle value or averaging the two middle values when you have an even sample size, the Gaussian kernel weights the values near the center more heavily and smoothly tapers off as you move away from the median position. This creates a weighted average that behaves like a median in terms of robustness but produces much smoother transitions as new data points arrive and the sorted list shifts. There are variance tradeoffs with this approach since you're no longer using the pure median, but they're minimal in practice. The kernel weighting stays concentrated enough around the center that you retain most of the outlier resistance that makes Theil-Sen useful in the first place. What you gain is a regression line that updates smoothly instead of jumping discretely, which makes it easier to spot genuine trend changes versus just the statistical noise of median recalculation. The smoothness is particularly noticeable when you're running the estimator over longer lookback periods where the sorted list is large enough that small kernel adjustments have less impact on the overall center of mass. The Gaussian kernel itself is a bell curve centered on the median position, with a standard deviation you can tune to control how much smoothing you want. Tighter kernels stay closer to the pure median behavior and give you more discrete steps. Wider kernels spread the weighting further from the center and produce smoother output at the cost of slightly reduced outlier resistance. The default settings strike a balance that keeps the estimator robust while removing most of the visual jitter. Running Theil-Sen on multiple bars means calculating slopes between all pairs of points across your lookback window, sorting those slopes, and then applying the Gaussian kernel to find the weighted center of that sorted distribution. This is computationally more expensive than simple moving averages or even standard linear regression, but Pine Script handles it well enough for reasonable lookback lengths. The benefit is that you get a trend estimate that doesn't get thrown off by individual spikes or anomalies in your price data, which is valuable when working with noisy instruments or during volatile periods where traditional regression lines can swing wildly. The implementation maintains sorted arrays for both the slope calculations and the final kernel weighting, which keeps everything organized and makes the Gaussian convolution straightforward. The kernel weights are precalculated based on the distance from the center position, then applied as multipliers to the sorted slope values before summing to get the final smoothed median slope. That slope gets combined with an intercept calculation to produce the regression line values you see plotted on the chart. What this really demonstrates is that you can take classical statistical methods like Theil-Sen and adapt them with signal processing techniques like kernel convolution to get behavior that's more suited to real-time visualization. The pure mathematical definition of a median is discrete by nature, but financial charts benefit from smooth, continuous lines that make it easier to track changes over time. By introducing the Gaussian kernel weighting, you preserve the core robustness of the median-based approach while gaining the visual smoothness of methods that use weighted averages. Whether that smoothness is worth the minor variance tradeoff depends on your use case, but for most charting applications, the improved readability makes it a good compromise.
Wskaźnik Pine Script®
od The_Peaceful_Lizard
4
Luxy Adaptive MA Cloud - Trend Strength & Signal Tracker V2Luxy Adaptive MA Cloud - Professional Trend Strength & Signal Tracker Next-generation moving average cloud indicator combining ultra-smooth gradient visualization with intelligent momentum detection. Built for traders who demand clarity, precision, and actionable insights. ═══════════════════════════════════════════════ WHAT MAKES THIS INDICATOR SPECIAL? ═══════════════════════════════════════════════ Unlike traditional MA indicators that show static lines, Luxy Adaptive MA Cloud creates a living, breathing visualization of market momentum. Here's what sets it apart: Exponential Gradient Technology This isn't just a simple fill between two lines. It's a professionally engineered gradient system with 26 precision layers using exponential density distribution. The result? An organic, cloud-like appearance where the center is dramatically darker (15% transparency - where crossovers and price action occur), while edges fade gracefully (75% transparency). Think of it as a visual "heat map" of trend strength. Dynamic Momentum Intelligence Most MA clouds only show structure (which MA is on top). This indicator shows momentum strength in real-time through four intelligent states: - 🟢 Bright Green = Explosive bullish momentum (both MAs rising strongly) - 🔵 Blue = Weakening bullish (structure intact, but momentum fading) - 🟠 Orange = Caution zone (bearish structure forming, weak momentum) - 🔴 Deep Red = Strong bearish momentum (both MAs falling) The cloud literally tells you when trends are accelerating or losing steam. Conditional Performance Architecture Every calculation is optimized for speed. Disable a feature? It stops calculating entirely—not just hidden, but not computed . The 26-layer gradient only renders when enabled. Toggle signals off? Those crossover checks don't run. This makes it one of the most efficient cloud indicators available, even with its advanced visual system. Zero Repaint Guarantee All signals and momentum states are based on confirmed bar data only . What you see in historical data is exactly what you would have seen trading live. No lookahead bias. No repainting tricks. No signals that "magically" appear perfect in hindsight. If a signal shows in history, it would have triggered in real-time at that exact moment. Educational by Design Every single input includes comprehensive tooltips with: - Clear explanations of what each parameter does - Practical examples of when to use different settings - Recommended configurations for scalping, day trading, and swing trading - Real-world trading impact ("This affects entry timing" vs "This is visual only") You're not just getting an indicator—you're learning how to use it effectively . ═══════════════════════════════════════════════ THE GRADIENT CLOUD - TECHNICAL DETAILS ═══════════════════════════════════════════════ Architecture: 26 precision layers for silk-smooth transitions Exponential density curve - layers packed tightly near center (where crossovers happen), spread wider at edges 75%-15% transparency range - center is highly opaque (15%), edges fade gracefully (75%) V-Gradient design - emphasizes the action zone between Fast and Medium MAs The Four Momentum States: 🟢 GREEN - Strong Bullish Fast MA above Medium MA Both MAs rising with momentum > 0.02% Action: Enter/hold LONG positions, strong uptrend confirmed 🔵 BLUE - Weak Bullish Fast MA above Medium MA Weak or flat momentum Action: Caution - bullish structure but losing strength, consider trailing stops 🟠 ORANGE - Weak Bearish Medium MA above Fast MA Weak or flat momentum Action: Warning - bearish structure developing, consider exits 🔴 RED - Strong Bearish Medium MA above Fast MA Both MAs falling with momentum < -0.02% Action: Enter/hold SHORT positions, strong downtrend confirmed Smooth Transitions: The momentum score is smoothed using an 8-bar EMA to eliminate noise and prevent whipsaws. You see the true trend , not every minor fluctuation. ═══════════════════════════════════════════════ FLEXIBLE MOVING AVERAGE SYSTEM ═══════════════════════════════════════════════ Three Customizable MAs: Fast MA (default: EMA 10) - Reacts quickly to price changes, defines short-term momentum Medium MA (default: EMA 20) - Balances responsiveness with stability, core trend reference Slow MA (default: SMA 200, optional) - Long-term trend filter, major support/resistance Six MA Types Available: EMA - Exponential; faster response, ideal for momentum and day trading SMA - Simple; smooth and stable, best for swing trading and trend following WMA - Weighted; middle ground between EMA and SMA VWMA - Volume-weighted; reflects market participation, useful for liquid markets RMA - Wilder's smoothing; used in RSI/ADX, excellent for trend filters HMA - Hull; extremely responsive with minimal lag, aggressive option Recommended Settings by Trading Style: Scalping (1m-5m): Fast: EMA(5-8) Medium: EMA(10-15) Slow: Not needed or EMA(50) Day Trading (5m-1h): Fast: EMA(10-12) Medium: EMA(20-21) Slow: SMA(200) for bias Swing Trading (4h-1D): Fast: EMA(10-20) Medium: EMA(34-50) Slow: SMA(200) Pro Tip: Start with Fast < Medium < Slow lengths. The gradient works best when there's clear separation between Fast and Medium MAs. ═══════════════════════════════════════════════ CROSSOVER SIGNALS - CLEAN & RELIABLE ═══════════════════════════════════════════════ Golden Cross ⬆ LONG Signal Fast MA crosses above Medium MA Classic bullish reversal or trend continuation signal Most reliable when accompanied by GREEN cloud (strong momentum) Death Cross ⬇ SHORT Signal Fast MA crosses below Medium MA Classic bearish reversal or trend continuation signal Most reliable when accompanied by RED cloud (strong momentum) Signal Intelligence: Anti-spam filter - Minimum 5 bars between signals prevents noise Clean labels - Placed precisely at crossover points Alert-ready - Built-in ALERTS for automated trading systems No repainting - Signals based on confirmed bars only Signal Quality Assessment: High-Quality Entry: Golden Cross + GREEN cloud + Price above both MAs = Strong bullish setup ✓ Low-Quality Entry (skip or wait): Golden Cross + ORANGE cloud + Choppy price action = Weak bullish setup, likely whipsaw ✗ ═══════════════════════════════════════════════ REAL-TIME INFO PANEL ═══════════════════════════════════════════════ An at-a-glance dashboard showing: Trend Strength Indicator: Visual display of current momentum state Color-coded header matching cloud color Instant recognition of market bias MA Distance Table: Shows percentage distance of price from each enabled MA: Green rows : Price ABOVE MA (bullish) Red rows : Price BELOW MA (bearish) Gray rows : Price AT MA (rare, decision point) Distance Interpretation: +2% to +5%: Healthy uptrend +5% to +10%: Getting extended, caution +10%+: Overextended, expect pullback -2% to -5%: Testing support -5% to -10%: Oversold zone -10%+: Deep correction or downtrend Customization: 4 corner positions 5 font sizes (Tiny to Huge) Toggle visibility on/off ═══════════════════════════════════════════════ HOW TO USE - PRACTICAL TRADING GUIDE ═══════════════════════════════════════════════ STRATEGY 1: Trend Following Identify trend : Wait for GREEN (bullish) or RED (bearish) cloud Enter on signal : Golden Cross in GREEN cloud = LONG, Death Cross in RED cloud = SHORT Hold position : While cloud maintains color Exit signals : • Cloud turns ORANGE/BLUE = momentum weakening, tighten stops • Opposite crossover = close position • Cloud turns opposite color = full reversal STRATEGY 2: Pullback Entries Confirm trend : GREEN cloud established (bullish bias) Wait for pullback : Price touches or crosses below Fast MA Enter when : Price rebounds back above Fast MA with cloud still GREEN Stop loss : Below Medium MA or recent swing low Target : Previous high or when cloud weakens STRATEGY 3: Momentum Confirmation Your setup triggers : (e.g., chart pattern, support/resistance) Check cloud color : • GREEN = proceed with LONG • RED = proceed with SHORT • BLUE/ORANGE = skip or reduce size Use gradient as confluence : Not as primary signal, but as momentum filter Risk Management Tips: Never enter against the cloud color (don't LONG in RED cloud) Reduce position size during BLUE/ORANGE (transition periods) Place stops beyond Medium MA for swing trades Use Slow MA (200) as final trend filter - don't SHORT above it in uptrends ═══════════════════════════════════════════════ PERFORMANCE & OPTIMIZATION ═══════════════════════════════════════════════ Tested On: Crypto: BTC, ETH, major altcoins Stocks: SPY, AAPL, TSLA, QQQ Forex: EUR/USD, GBP/USD, USD/JPY Indices: S&P 500, NASDAQ, DJI ═══════════════════════════════════════════════ TRANSPARENCY & RELIABILITY ═══════════════════════════════════════════════ Educational Focus: Detailed tooltips on every input Clear documentation of methodology Practical examples in descriptions Teaches you why , not just what Open Logic: Momentum calculation: (Fast slope + Medium slope) / 2 Smoothing: 8-bar EMA to reduce noise Thresholds: ±0.02% for strong momentum classification Everything is transparent and explainable ═══════════════════════════════════════════════ COMPLETE FEATURE LIST ═══════════════════════════════════════════════ Visual Components: 26-layer exponential gradient cloud 3 customizable moving average lines Golden Cross / Death Cross labels Real-time info panel with trend strength MA distance table Calculation Features: 6 MA types (EMA, SMA, WMA, VWMA, RMA, HMA) Momentum-based cloud coloring Smoothed trend strength scoring Conditional performance optimization Customization Options: All MA lengths adjustable All colors customizable (when gradient disabled) Panel position (4 corners) Font sizes (5 options) Toggle any feature on/off Signal Features: Anti-spam filter (configurable gap) Clean, non-overlapping labels Built-in alert conditions No repainting guarantee ═══════════════════════════════════════════════ IMPORTANT DISCLAIMERS ═══════════════════════════════════════════════ This indicator is for educational and informational purposes only Not financial advice - always do your own research Past performance does not guarantee future results Use proper risk management - never risk more than you can afford to lose Test on paper/demo accounts before using with real money Combine with other analysis methods - no single indicator is perfect Works best in trending markets; less effective in choppy/sideways conditions Signals may perform differently in different timeframes and market conditions The indicator uses historical data for MA calculations - allow sufficient lookback period ═══════════════════════════════════════════════ CREDITS & TECHNICAL INFO ═══════════════════════════════════════════════ Version: 2.0 Release: October 2025 Special Thanks: TradingView community for feedback and testing Pine Script documentation for technical reference ═══════════════════════════════════════════════ SUPPORT & UPDATES ═══════════════════════════════════════════════ Found a bug? Comment below with: Ticker symbol Timeframe Screenshot if possible Steps to reproduce Feature requests? I'm always looking to improve! Share your ideas in the comments. Questions? Check the tooltips first (hover over any input) - most answers are there. If still stuck, ask in comments. ═══════════════════════════════════════════════ Happy Trading! Remember: The best indicator is the one you understand and use consistently. Take time to learn how the cloud behaves in different market conditions. Practice on paper before going live. Trade smart, manage risk, and may the trends be with you! 🚀
Wskaźnik Pine Script®
od orenluxy
11
SuperBandsI've been seeing a lot of volatility band indicators pop up recently, and after watching this trend for a while, I figured it was time to throw my two chips in. The original spark for this idea came years ago from RicardoSantos's Vector Flow Channel script, which used decay channels with timed events in an interesting way. That concept stuck with me, and I kept thinking about how to build something that captured the same kind of dynamic envelope behavior but with a different mathematical foundation. What I ended up with is a hybrid that takes the core logic of supertrend trailing stops, smooths them heavily with exponential moving averages, and wraps them in Donchian-style filled bands with momentum-based color gradients. The basic mechanism here is pretty straightforward. Standard supertrend calculates a trailing stop based on ATR offset from price, then flips direction when price crosses the trail. This implementation does the same thing but adds EMA smoothing to the trail calculation itself, which removes a lot of the choppiness you get from raw supertrend during sideways periods. The smoothing period is adjustable, so you can tune how reactive versus stable you want the bands to be. Lower smoothing values make the bands track price more aggressively, higher values create wider, slower-moving envelopes that only respond to sustained directional moves. Where this diverges from typical supertrend implementations is in the visual presentation and the separate treatment of bullish and bearish conditions. Instead of a single flipping line, you get persistent upper and lower bands that each track their own trailing stops independently. The bullish band trails below price and stays active as long as price doesn't break below it. The bearish band trails above price and remains active until price breaks above. Both bands can be visible simultaneously, which gives you a dynamic channel that adapts to volatility on both sides of price action. When price is trending strongly, one band will dominate and the other will disappear. During consolidation, both bands tend to compress toward price. The color gradients are calculated by measuring the rate of change in each band's position and converting that delta into an angle using arctangent scaling. Steeper angles, which correspond to the band moving quickly to catch up with accelerating price, get brighter colors. Flatter angles, where the band is moving slowly or staying relatively stable, fade toward more muted tones. This gives you a visual sense of momentum within the bands themselves, not just from price movement. A rapidly brightening band often precedes expansion or breakout conditions, while fading colors suggest the trend is losing steam or entering consolidation. The filled regions between price and each band serve a similar function to Donchian channels or Keltner bands, creating clearly defined zones that represent normal price behavior relative to recent volatility. When price hugs one band and the fill area compresses, you're in a strong directional regime. When price bounces between both bands and the fills expand, you're in a ranging environment. The transparency gradients in the fills make it easier to see when price is near the edge of the envelope versus safely inside it. Configuration is split between bullish and bearish settings, which lets you asymmetrically tune the indicator if you find that your market or timeframe has different characteristics in uptrends versus downtrends. You can adjust ATR period, ATR multiplier, and smoothing independently for each direction. This flexibility is useful for instruments that exhibit different volatility profiles during bull and bear phases, or for strategies that want tighter trailing on longs than shorts, or vice versa. The ATR period controls the lookback window for volatility measurement. Shorter periods make the bands react quickly to recent volatility spikes, which can be beneficial in fast-moving markets but also leads to more frequent whipsaws. Longer periods smooth out volatility estimates and create more stable bands at the cost of slower adaptation. The multiplier scales the ATR offset, directly controlling how far the bands sit from price. Smaller multipliers keep the bands tight, triggering more frequent direction changes. Larger multipliers create wider envelopes that give price more room to move without breaking the trail. One thing to note is that this indicator doesn't generate explicit buy or sell signals in the traditional sense. It's a regime filter and envelope tool. You can use band breaks as directional cues if you want, but the primary value comes from understanding the current volatility environment and whether price is respecting or violating its recent behavioral boundaries. Pairing this with momentum oscillators or volume analysis tends to work better than treating band breaks as standalone entries. From an implementation perspective, the supertrend state machine tracks whether each direction's trail is active, handles resets when price breaks through, and manages the EMA smoothing on the trail points themselves rather than just post-processing the supertrend output. This means the smoothing is baked into the trailing logic, which creates a different response curve than if you just applied an EMA to a standard supertrend line. The angle calculations use RMS estimation for the delta normalization range, which adapts to changing volatility and keeps the color gradients responsive across different market conditions. What this really demonstrates is that there are endless ways to combine basic technical concepts into something that feels fresh without reinventing mathematics. ATR offsets, trailing stops, EMA smoothing, and Donchian fills are all standard building blocks, but arranging them in a particular way produces behavior that's distinct from each component alone. Whether this particular arrangement works better than other volatility band systems depends entirely on your market, timeframe, and what you're trying to accomplish. For me, it scratched the itch I had from seeing Vector Flow years ago and wanting to build something in that same conceptual space using tools I'm more comfortable with.
Wskaźnik Pine Script®
od The_Peaceful_Lizard
7
MACD-V Adaptive FluxProMACD-V Adaptive FluxPro Type: Multi-Factor Volatility-Normalized Momentum & Regime Framework Overlay: ✅ Yes (on price chart) Purpose: Detect high-probability trend continuation or reversal zones through volatility-adjusted momentum, VWAP structure, and adaptive filters. 🧩 Concept Overview MACD-V Adaptive FluxPro is a next-generation, multi-factor analytical framework that merges the principles of Linda Raschke’s 3-10-16 MACD with modern volatility normalization and adaptive filtering. Instead of generating raw buy/sell signals, it builds a probability-driven environment model — showing when price action, volatility, and structure align for high-confidence trades. The “V” in MACD-V stands for Volatility Normalization: every MACD component is divided by ATR to stabilize amplitude across fast or slow markets. This enables the indicator to remain consistent across timeframes, instruments, and volatility regimes. ⚙️ Core Components 1️⃣ Volatility-Normalized MACD (MACD-V) A traditional MACD built on Linda Raschke’s 3-10-16 structure, but adjusted by ATR to create a volatility-invariant momentum profile. You can toggle to alternative presets (Scalp / Swing / Trend) for faster or slower environments. 2️⃣ Dynamic Regime Detection A slope-based classifier that identifies whether the market is: Trend Up 🟢 Trend Down 🔴 Compression / Squeeze 🟧 Transition / Neutral ⚫ The background color updates dynamically as momentum, volatility, and slope shift between these states. 3️⃣ VWAP Structure Bands Adaptive VWAP with inner and outer ATR-scaled envelopes. These act as short-term mean-reversion and breakout zones. The indicator can optionally gate entries to occur only within defined VWAP proximity. 4️⃣ EMAs for Micro-Trend Confirmation Includes 9-EMA and 21-EMA, color-configurable for visual crossovers and short-term momentum bias. 5️⃣ Multi-Timeframe Confirmation Tiles Top-center dashboard tiles display directional bias from higher timeframes (e.g., 15m / 1h / 4h). When all align, it confirms multi-frame trend coherence. 6️⃣ Adaptive Probability Engine All subsystems — MACD-V, slope, compression, volume z-score, and VWAP distance — feed into a logistic scoring model that outputs a real-time AOI Probability (0-100%). When conditions align, probabilities rise above 60% (long bias) or drop below 40% (short bias). These are your high-probability “Areas of Interest.” 7️⃣ Dashboard HUD The top-right status console provides a one-glance view of system state: Field Meaning AOI Prob Long Real-time probability of bullish bias Regime Market state (Trend, Transition, Compression) Risk Gate ATR-based volatility filter News Mute Manual toggle for event-risk suppression ATR (≈ risk) Real-time volatility readout Status ✅ Trading OK / 🧱 Risk Gate / 🔇 News Mute / 🟧 Compression 🎯 Interpretation Guide Visual Meaning 🟢 Green background Confirmed uptrend regime 🔴 Red background Confirmed downtrend regime 🟧 Orange background Volatility compression (squeeze forming) ⚫ Gray background Transitional / indecisive structure Teal % (AOI Prob Long) Bullish probability > 60% Arrows Optional: appear only when all gates align (rare, filtered signals) 🧮 Mathematical Notes MACD-V = (EMA_fast(src) − EMA_slow(src)) / ATR(n) Normalized score is smoothed, scaled 0–100 via logistic curve Slope = Δ(EMA(src, n)) / ATR(n) Probabilities gated by: Minimum slope magnitude (minAbsSlope) VWAP proximity (maxVWAPDistATR) Multi-TF agreement Cooldown interval (cooldownBars) ATR-based risk gate No repainting — all calculations use barstate.isconfirmed. ⚡ Use Cases ✅ Identify trend regime changes before major expansions ✅ Filter breakout vs. compression setups ✅ Quantify volatility conditions before entries ✅ Confirm multi-timeframe alignment ✅ Serve as a visual regime map for automated systems or discretionary traders 🧠 Recommended Presets Market Type Setting Preset Behavior Index Futures (ES/NQ) LBR 3-10-16 SMA (default) Classic swing/momentum balance Scalping (1m–5m) Fast Adaptive Higher frequency, shorter cooldown Swing Trading (1h–4h) Smooth ATR Broader, trend-only signals Trend-Following Futures Wide ATR Bands Filters noise, favors strong continuation ⚠️ Notes Non-repainting, bar-confirmed calculations Signal arrows are optional and rare — intended for precision setups ATR and slope thresholds should be tuned per instrument Compatible with all TradingView markets and resolutions 🏁 Summary “MACD-V Adaptive FluxPro” is not a simple MACD — it’s a volatility-normalized market state engine that adapts to changing conditions. It fuses Linda Raschke’s timeless MACD logic with modern volatility, slope, and multi-timeframe analytics — giving you a live market dashboard that tells you when not to trade just as clearly as when you should.
Wskaźnik Pine Script®
od inkadinka_ka
5
PPI Inflation Monitor (Change YoY & MoM)📊 PPI Inflation Monitor - Leading Inflation Indicator The Producer Price Index (PPI) measures wholesale/producer-level prices and serves as a critical leading indicator for consumer inflation trends. This tool helps you anticipate CPI movements and identify corporate margin pressures before they show up in earnings. 🎯 KEY FEATURES: - Dual Perspective Analysis: - Year-over-Year (YoY): Histogram bars showing annual producer price inflation - Month-over-Month (MoM): Line overlay showing monthly wholesale price changes - Visual Reference System: - Dashed line at 2% (typical target for producer price inflation) - Dotted line at 0.17% (equivalent monthly target) - Color-coded bars: Red above target, Green below target - Real-Time Data Table: - Current PPI Index value - YoY inflation rate with color coding - MoM inflation rate with color coding - Deviation from target level - Automated Alerts: - YoY crosses above/below target - MoM crosses above/below target - Early warning system for inflation trends 📈 WHY PPI IS YOUR EARLY WARNING SYSTEM: PPI typically leads CPI by 1-3 months because: - Producers face cost increases first - These costs are eventually passed to consumers - Shows whether companies can maintain pricing power Rising PPI with stable CPI = Margin compression → Bearish for stocks Rising PPI followed by rising CPI = Broad inflation → Fed hawkishness incoming Falling PPI = Disinflationary trend starting → Positive for risk assets 🔍 TRADING APPLICATIONS: 1. Lead Time Advantage: Position before CPI confirms PPI trends 2. Sector Rotation: High PPI = favor companies with pricing power 3. Margin Analysis: PPI-CPI divergence = margin pressure/expansion signals 4. Fed Anticipation: PPI acceleration = Fed likely to turn hawkish soon 💡 STRATEGIC USE CASES: - Value vs. Growth: Rising PPI favors value stocks with pricing power - Commodities: PPI often correlates with commodity price trends - Small Caps: More vulnerable to input cost increases (high PPI = cautious) - Corporate Earnings: Anticipate margin pressure before quarterly reports 🔄 COMBINE WITH: - CPI: Confirm if producer costs reach consumers - PCE: Validate Fed's preferred inflation metric response - Fed Funds Rate: Assess if Fed is behind/ahead of curve 📊 DATA SOURCE: Official PPI data from FRED (Federal Reserve Economic Data), updated monthly when new data releases occur. 🎨 CUSTOMIZATION: Fully customizable: - Toggle YoY/MoM displays - Adjust reference target levels - Customize colors - Show/hide absolute PPI values Perfect for: Macro traders, fundamental analysts, earnings traders, and investors seeking early inflation signals before they appear in consumer prices. ⚡ Remember: PPI leads CPI. Use this advantage to position ahead of the crowd.
Wskaźnik Pine Script®
od Phillipklh
Logit RSI [AdaptiveRSI]The traditional 0–100 RSI scale makes statistical overlays, such as Bollinger Bands or even moving averages, technically invalid. This script solves this issue by placing RSI on an unbounded, continuous scale, enabling these tools to work as intended. The Logit function takes bounded data, such as RSI values ranging from 0 to 100, and maps them onto an unbounded scale ranging from negative infinity (−∞) to positive infinity (+∞). An RSI reading of 50 becomes 0 on the Logit scale, indicating a balanced market. Readings above 50 map to positive Logit values (price above Wilder’s EMA / RSI above 50), while readings below 50 map to negative values (price below Wilder’s EMA / RSI below 50). For the detailed formula, which calculates RSI as a scaled distance from Wilder’s EMA, check the RSI : alternative derivation script. The main issue with the 0–100 RSI scale is that different lookback periods produce very different distributions of RSI values. The histograms below illustrate how often RSIs of various lengths spend time within each 5-point range. On RSI(2), the tallest bars appear at the edges (0–5 and 95–100), meaning short-term RSI spends most of its time at the extremes. For longer lookbacks, the bars cluster around the center and rarely reach 70 or 30. This behavior makes it difficult to generalize the two most common RSI techniques: Fixed 70/30 thresholds: These overbought and oversold levels only make sense for short- or mid-range lookbacks (around the low teens). For very short periods, RSI spends most of its time above or below these levels, while for long-term lookbacks, RSI rarely reaches them. Bollinger Bands (±2 standard deviations): When applied directly to RSI, the bands often extend beyond the 0–100 limits (especially for short-term lookbacks) making them mathematically invalid. While the issue is less visible on longer settings, it remains conceptually incorrect. To address this, we apply the Logit Transform : Logit RSI = LN(RSI / (100 − RSI)) The transformed data fits a smooth bell-shaped curve, allowing statistical tools like Bollinger Bands to function properly for the first time. Why Logit RSI Matters: Makes RSI statistically consistent across all lookback periods. Greatly improves the visual clarity of short-term RSIs Allows proper use of volatility tools (like Bollinger Bands) on RSI. Replaces arbitrary 70/30 levels with data-driven thresholds. Simplifies RSI interpretation for both short- and long-term analysis. INPUTS: RSI Length — set the RSI lookback period used in calculations. RSI Type — choose between Regular RSI or Logit RSI . Plot Bollinger Bands — ON/OFF toggle to overlay statistical envelopes around RSI or Logit RSI. SMA and Standard Deviation Length — defines the lookback period for both the SMA (Bollinger Bands midline) and Standard Deviation calculations. Standard Deviation Multiplier — controls the width of the Bollinger Bands (e.g., 2.0 for ±2σ). While simple, the Logit transformation represents an unexplored yet powerful mathematically grounded improvement to the classic RSI. It offers traders a structured, intuitive, and statistically consistent way to use RSI across all timeframes. I welcome your feedback, suggestions, and code improvements—especially regarding performance and efficiency. Your insights are greatly appreciated.
Wskaźnik Pine Script®
od AdaptiveRSI
[Fune]-Trend Technology🌊 - Trend Technology “Flow with the trend — read every wave.” 🎯 Concept Micro EMA (White) – Short-term pulse Mid EMA (Aqua) – Medium-term direction Macro EMA (Orange) – Long-term flow Mid- to long-term references: 100 EMA = Yellow (trend balance) 300 EMA = Blue (structural anchor) In addition, the PLR (Periodic Linear Regression) reveals the cyclical rhythm of the market trend — a recurring regression curve that reflects the underlying heartbeat of price movement. 📊 Trend Logic Summary Condition Color Meaning Action Mid > Macro 🟢 Green background Bullish trend Look for long opportunities Mid < Macro 🔴 Red background Bearish trend Look for short opportunities PLR slope > 0 📈 Upward bias Confirms bullish momentum PLR slope < 0 📉 Downward bias Confirms bearish momentum Micro EMA (White) dominant ⚪ White background Neutral / Resting phase Stand aside and wait 🧭 Trading Guidance 🟢 Long Setup: Green background + PLR slope upward + price above 100/300 EMA 🔴 Short Setup: Red background + PLR slope downward + price below 100/300 EMA ⚪ No Trade: White background, EMAs converging, or PLR slope flattening ⚓ Philosophy of “ (The Boat) is a vessel sailing across the ocean of the market. The EMAs are its sails, the PLR its compass. The trader holds the helm, while the divine wind guides the waves. Only those who move with the current — not against it — will one day reach the state of ‘mindless clarity.’”
Wskaźnik Pine Script®
od FUNE11120188040922
Bitcoin Power Law Corridor + Z-score This script visualizes the long-term Bitcoin Power Law Corridor, a conceptual model originally discussed by Harold Christopher Burger, and enhances it with a logarithmic Z-Score framework. The indicator plots Bitcoin’s long-term regression curve together with estimated resistance and support bands based on power-law relationships between price and time since inception. The added Z-Score expresses the statistical distance between price and the central regression line, using logarithmic scaling: Z ≈ 0 → price near its long-term fair-value trajectory. Z ≈ +2 → price near the lower corridor boundary (historically undervalued region). Z ≈ −2 → price near the upper corridor boundary (historically overheated region). This indicator is designed for visual and educational purposes only. It should not be considered financial advice, a predictive model, or a signal provider. Users should always combine this tool with other forms of technical, fundamental, and sentiment analysis to confirm confluence before making any decision.
Wskaźnik Pine Script®
od S0905B
RSI Donchian Channel [DCAUT]█ RSI Donchian Channel 📊 ORIGINALITY & INNOVATION The RSI Donchian Channel represents an important synthesis of two complementary analytical frameworks: momentum oscillators and breakout detection systems. This indicator addresses a common limitation in traditional RSI analysis by replacing fixed overbought/oversold thresholds with adaptive zones derived from historical RSI extremes. Key Enhancement: Traditional RSI analysis relies on static threshold levels (typically 30/70), which may not adequately reflect changing market volatility regimes. This indicator adapts the reference zones dynamically based on the actual RSI behavior over the lookback period, helping traders identify meaningful momentum extremes relative to recent price action rather than arbitrary fixed levels. The implementation combines the proven momentum measurement capabilities of RSI with Donchian Channel's breakout detection methodology, creating a framework that identifies both momentum exhaustion points and potential continuation signals through the same analytical lens. 📐 MATHEMATICAL FOUNDATION Core Calculation Process: Step 1: RSI Calculation The Relative Strength Index measures momentum by comparing the magnitude of recent gains to recent losses: Calculate price changes between consecutive periods Separate positive changes (gains) from negative changes (losses) Apply selected smoothing method (RMA standard, also supports SMA, EMA, WMA) to both gain and loss series Compute Relative Strength (RS) as the ratio of smoothed gains to smoothed losses Transform RS into bounded 0-100 scale using the formula: RSI = 100 - (100 / (1 + RS)) Step 2: Donchian Channel Application The Donchian Channel identifies the highest and lowest RSI values within the specified lookback period: Upper Channel: Highest RSI value over the lookback period, represents the recent momentum peak Lower Channel: Lowest RSI value over the lookback period, represents the recent momentum trough Middle Channel (Basis): Average of upper and lower channels, serves as equilibrium reference Channel Width Dynamics: The distance between upper and lower channels reflects RSI volatility. Wide channels indicate high momentum variability, while narrow channels suggest momentum consolidation and potential breakout preparation. The indicator monitors channel width over a 100-period window to identify squeeze conditions that often precede significant momentum shifts. 📊 COMPREHENSIVE SIGNAL ANALYSIS Primary Signal Categories: Breakout Signals: Upper Breakout: RSI crosses above the upper channel, indicates momentum reaching new relative highs and potential trend continuation, particularly significant when accompanied by price confirmation Lower Breakout: RSI crosses below the lower channel, suggests momentum reaching new relative lows and potential trend exhaustion or reversal setup Breakout strength is enhanced when the channel is narrow prior to the breakout, indicating a transition from consolidation to directional movement Mean Reversion Signals: Upper Touch Without Breakout: RSI reaches the upper channel but fails to break through, may indicate momentum exhaustion and potential reversal opportunity Lower Touch Without Breakout: RSI reaches the lower channel without breakdown, suggests potential bounce as momentum reaches oversold extremes Return to Basis: RSI moving back toward the middle channel after touching extremes signals momentum normalization Trend Strength Assessment: Sustained Upper Channel Riding: RSI consistently remains near or above the upper channel during strong uptrends, indicates persistent bullish momentum Sustained Lower Channel Riding: RSI stays near or below the lower channel during strong downtrends, reflects persistent bearish pressure Basis Line Position: RSI position relative to the middle channel helps identify the prevailing momentum bias Channel Compression Patterns: Squeeze Detection: Channel width narrowing to 100-period lows indicates momentum consolidation, often precedes significant directional moves Expansion Phase: Channel widening after a squeeze confirms the initiation of a new momentum regime Persistent Narrow Channels: Extended periods of tight channels suggest market indecision and accumulation/distribution phases 🎯 STRATEGIC APPLICATIONS Trend Continuation Strategy: This approach focuses on identifying and trading momentum breakouts that confirm established trends: Identify the prevailing price trend using higher timeframe analysis or trend-following indicators Wait for RSI to break above the upper channel in uptrends (or below the lower channel in downtrends) Enter positions in the direction of the breakout when price action confirms the momentum shift Place protective stops below the recent swing low (long positions) or above swing high (short positions) Target profit levels based on prior swing extremes or use trailing stops to capture extended moves Exit when RSI crosses back through the basis line in the opposite direction Mean Reversion Strategy: This method capitalizes on momentum extremes and subsequent corrections toward equilibrium: Monitor for RSI reaching the upper or lower channel boundaries Look for rejection signals (price reversal patterns, volume divergence) when RSI touches the channels Enter counter-trend positions when RSI begins moving back toward the basis line Use the basis line as the initial profit target for mean reversion trades Implement tight stops beyond the channel extremes to limit risk on failed reversals Scale out of positions as RSI approaches the basis line and closes the position when RSI crosses the basis Breakout Preparation Strategy: This approach positions traders ahead of potential volatility expansion from consolidation phases: Identify squeeze conditions when channel width reaches 100-period lows Monitor price action for consolidation patterns (triangles, rectangles, flags) during the squeeze Prepare conditional orders for breakouts in both directions from the consolidation Enter positions when RSI breaks out of the narrow channel with expanding width Use the channel width expansion as a confirmation signal for the breakout's validity Manage risk with stops just inside the opposite channel boundary Multi-Timeframe Confluence Strategy: Combining RSI Donchian Channel analysis across multiple timeframes can improve signal reliability: Identify the primary trend direction using a higher timeframe RSI Donchian Channel (e.g., daily or weekly) Use a lower timeframe (e.g., 4-hour or hourly) to time precise entry points Enter long positions when both timeframes show RSI above their respective basis lines Enter short positions when both timeframes show RSI below their respective basis lines Avoid trades when timeframes provide conflicting signals (e.g., higher timeframe below basis, lower timeframe above) Exit when the higher timeframe RSI crosses its basis line in the opposite direction Risk Management Guidelines: Effective risk management is essential for all RSI Donchian Channel strategies: Position Sizing: Calculate position sizes based on the distance between entry point and stop loss, limiting risk to 1-2% of capital per trade Stop Loss Placement: For breakout trades, place stops just inside the opposite channel boundary; for mean reversion trades, use stops beyond the channel extremes Profit Targets: Use the basis line as a minimum target for mean reversion trades; for trend trades, target prior swing extremes or use trailing stops Channel Width Context: Increase position sizes during narrow channels (lower volatility) and reduce sizes during wide channels (higher volatility) Correlation Awareness: Monitor correlations between traded instruments to avoid over-concentration in similar setups 📋 DETAILED PARAMETER CONFIGURATION RSI Source: Defines the price data series used for RSI calculation: Close (Default): Standard choice providing end-of-period momentum assessment, suitable for most trading styles and timeframes High-Low Average (HL2): Reduces the impact of closing auction dynamics, useful for markets with significant end-of-day volatility High-Low-Close Average (HLC3): Provides a more balanced view incorporating the entire period's range Open-High-Low-Close Average (OHLC4): Offers the most comprehensive price representation, helpful for identifying overall period sentiment Strategy Consideration: Use Close for end-of-period signals, HL2 or HLC3 for intraday volatility reduction, OHLC4 for capturing full period dynamics RSI Length: Controls the number of periods used for RSI calculation: Short Periods (5-9): Highly responsive to recent price changes, produces more frequent signals with increased false signal risk, suitable for short-term trading and volatile markets Standard Period (14): Widely accepted default balancing responsiveness with stability, appropriate for swing trading and intermediate-term analysis Long Periods (21-28): Produces smoother RSI with fewer signals but more reliable trend identification, better for position trading and reducing noise in choppy markets Optimization Approach: Test different lengths against historical data for your specific market and timeframe, consider using longer periods in ranging markets and shorter periods in trending markets RSI MA Type: Determines the smoothing method applied to price changes in RSI calculation: RMA (Relative Moving Average - Default): Wilder's original smoothing method providing stable momentum measurement with gradual response to changes, maintains consistency with classical RSI interpretation SMA (Simple Moving Average): Treats all periods equally, responds more quickly to changes than RMA but may produce more whipsaws in volatile conditions EMA (Exponential Moving Average): Weights recent periods more heavily, increases responsiveness at the cost of potential noise, suitable for traders prioritizing early signal generation WMA (Weighted Moving Average): Applies linear weighting favoring recent data, offers a middle ground between SMA and EMA responsiveness Selection Guidance: Maintain RMA for consistency with traditional RSI analysis, use EMA or WMA for more responsive signals in fast-moving markets, apply SMA for maximum simplicity and transparency DC Length: Specifies the lookback period for Donchian Channel calculation on RSI values: Short Periods (10-14): Creates tight channels that adapt quickly to changing momentum conditions, generates more frequent trading signals but increases sensitivity to short-term RSI fluctuations Standard Period (20): Balances channel responsiveness with stability, aligns with traditional Bollinger Bands and moving average periods, suitable for most trading styles Long Periods (30-50): Produces wider, more stable channels that better represent sustained momentum extremes, reduces signal frequency while improving reliability, appropriate for position traders and higher timeframes Calibration Strategy: Match DC length to your trading timeframe (shorter for day trading, longer for swing trading), test channel width behavior during different market regimes, consider using adaptive periods that adjust to volatility conditions Market Adaptation: Use shorter DC lengths in trending markets to capture momentum shifts earlier, apply longer periods in ranging markets to filter noise and focus on significant extremes Parameter Combination Recommendations: Scalping/Day Trading: RSI Length 5-9, DC Length 10-14, EMA or WMA smoothing for maximum responsiveness Swing Trading: RSI Length 14, DC Length 20, RMA smoothing for balanced analysis (default configuration) Position Trading: RSI Length 21-28, DC Length 30-50, RMA or SMA smoothing for stable signals High Volatility Markets: Longer RSI periods (21+) with standard DC length (20) to reduce noise Low Volatility Markets: Standard RSI length (14) with shorter DC length (10-14) to capture subtle momentum shifts 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Adaptive Threshold Mechanism: Unlike traditional RSI analysis with fixed 30/70 thresholds, this indicator's Donchian Channel approach provides several improvements: Context-Aware Extremes: Overbought/oversold levels adjust automatically based on recent momentum behavior rather than arbitrary fixed values Volatility Adaptation: In low volatility periods, channels narrow to reflect tighter momentum ranges; in high volatility, channels widen appropriately Market Regime Recognition: The indicator implicitly adapts to different market conditions without manual threshold adjustments False Signal Reduction: Adaptive channels help reduce premature reversal signals that often occur with fixed thresholds during strong trends Signal Quality Characteristics: The indicator's dual-purpose design provides distinct advantages for different trading objectives: Breakout Trading: Channel boundaries offer clear, objective breakout levels that update dynamically, eliminating the ambiguity of when momentum becomes "too high" or "too low" Mean Reversion: The basis line provides a natural profit target for reversion trades, representing the midpoint of recent momentum extremes Trend Strength: Persistent channel boundary riding offers an objective measure of trend strength without additional indicators Consolidation Detection: Channel width analysis provides early warning of potential volatility expansion from compression phases Comparative Analysis: When compared to traditional RSI implementations and other momentum frameworks: vs. Fixed Threshold RSI: Provides market-adaptive reference levels rather than static values, helping to reduce false signals during trending markets where RSI can remain "overbought" or "oversold" for extended periods vs. RSI Bollinger Bands: Offers clearer breakout signals and more intuitive extreme identification through actual high/low boundaries rather than statistical standard deviations vs. Stochastic Oscillator: Maintains RSI's momentum measurement advantages (unbounded calculation avoiding scale compression) while adding the breakout detection capabilities of Donchian Channels vs. Standard Donchian Channels: Applies breakout methodology to momentum space rather than price, providing earlier signals of potential trend changes before price breakouts occur Performance Characteristics: The indicator exhibits specific behavioral patterns across different market conditions: Trending Markets: Excels at identifying momentum continuation through channel breakouts, RSI tends to ride one channel boundary during strong trends, providing trend confirmation Ranging Markets: Channel width narrows during consolidation, offering early preparation signals for potential breakout trading opportunities High Volatility: Channels widen to reflect increased momentum variability, automatically adjusting signal sensitivity to match market conditions Low Volatility: Channels contract, making the indicator more sensitive to subtle momentum shifts that may be significant in calm market environments Transition Periods: Channel squeezes often precede major trend changes, offering advance warning of potential regime shifts Limitations and Considerations: Users should be aware of certain operational characteristics: Lookback Dependency: Channel boundaries depend entirely on the lookback period, meaning the indicator has no predictive element beyond identifying current momentum relative to recent history Lag Characteristics: As with all moving average-based indicators, RSI calculation introduces lag, and channel boundaries update only as new extremes occur within the lookback window Range-Bound Sensitivity: In extremely tight ranges, channels may become very narrow, potentially generating excessive signals from minor momentum fluctuations Trending Persistence: During very strong trends, RSI may remain at channel extremes for extended periods, requiring patience for mean reversion setups or commitment to trend-following approaches No Absolute Levels: Unlike traditional RSI, this indicator provides no fixed reference points (like 50), making it less suitable for strategies that depend on absolute momentum readings USAGE NOTES This indicator is designed for technical analysis and educational purposes to help traders understand momentum dynamics and identify potential trading opportunities. The RSI Donchian Channel has limitations and should not be used as the sole basis for trading decisions. Important considerations: Performance varies significantly across different market conditions, timeframes, and instruments Historical signal patterns do not guarantee future results, as market behavior continuously evolves Effective use requires understanding of both RSI momentum principles and Donchian Channel breakout concepts Risk management practices (stop losses, position sizing, diversification) are essential for any trading application Consider combining with additional analytical tools such as volume analysis, price action patterns, or trend indicators for confirmation Backtest thoroughly on your specific instruments and timeframes before live trading implementation Be aware that optimization on historical data may lead to curve-fitting and poor forward performance The indicator performs best when used as part of a comprehensive trading methodology that incorporates multiple forms of market analysis, sound risk management, and realistic expectations about win rates and drawdowns.
Wskaźnik Pine Script®
od DCAUT
Stochastic Enhanced [DCAUT]█ Stochastic Enhanced 📊 ORIGINALITY & INNOVATION The Stochastic Enhanced indicator builds upon George Lane's classic momentum oscillator (developed in the late 1950s) by providing comprehensive smoothing algorithm flexibility. While traditional implementations limit users to Simple Moving Average (SMA) smoothing, this enhanced version offers 21 advanced smoothing algorithms, allowing traders to optimize the indicator's characteristics for different market conditions and trading styles. Key Improvements: Extended from single SMA smoothing to 21 professional-grade algorithms including adaptive filters (KAMA, FRAMA), zero-lag methods (ZLEMA, T3), and advanced digital filters (Kalman, Laguerre) Maintains backward compatibility with traditional Stochastic calculations through SMA default setting Unified smoothing algorithm applies to both %K and %D lines for consistent signal processing characteristics Enhanced visual feedback with clear color distinction and background fill highlighting for intuitive signal recognition Comprehensive alert system covering crossovers and zone entries for systematic trade management Differentiation from Traditional Stochastic: Traditional Stochastic indicators use fixed SMA smoothing, which introduces consistent lag regardless of market volatility. This enhanced version addresses the limitation by offering adaptive algorithms that adjust to market conditions (KAMA, FRAMA), reduce lag without sacrificing smoothness (ZLEMA, T3, HMA), or provide superior noise filtering (Kalman Filter, Laguerre filters). The flexibility helps traders balance responsiveness and stability according to their specific needs. 📐 MATHEMATICAL FOUNDATION Core Stochastic Calculation: The Stochastic Oscillator measures the position of the current close relative to the high-low range over a specified period: Step 1: Raw %K Calculation %K_raw = 100 × (Close - Lowest Low) / (Highest High - Lowest Low) Where: Close = Current closing price Lowest Low = Lowest low over the %K Length period Highest High = Highest high over the %K Length period Result ranges from 0 (close at period low) to 100 (close at period high) Step 2: Smoothed %K Calculation %K = MA(%K_raw, K Smoothing Period, MA Type) Where: MA = Selected moving average algorithm (SMA, EMA, etc.) K Smoothing = 1 for Fast Stochastic, 3+ for Slow Stochastic Traditional Fast Stochastic uses %K_raw directly without smoothing Step 3: Signal Line %D Calculation %D = MA(%K, D Smoothing Period, MA Type) Where: %D acts as a signal line and moving average of %K D Smoothing typically set to 3 periods in traditional implementations Both %K and %D use the same MA algorithm for consistent behavior Available Smoothing Algorithms (21 Options): Standard Moving Averages: SMA (Simple): Equal-weighted average, traditional default, consistent lag characteristics EMA (Exponential): Recent price emphasis, faster response to changes, exponential decay weighting RMA (Rolling/Wilder's): Smoothed average used in RSI, less reactive than EMA WMA (Weighted): Linear weighting favoring recent data, moderate responsiveness VWMA (Volume-Weighted): Incorporates volume data, reflects market participation intensity Advanced Moving Averages: HMA (Hull): Reduced lag with smoothness, uses weighted moving averages and square root period ALMA (Arnaud Legoux): Gaussian distribution weighting, minimal lag with good noise reduction LSMA (Least Squares): Linear regression based, fits trend line to data points DEMA (Double Exponential): Reduced lag compared to EMA, uses double smoothing technique TEMA (Triple Exponential): Further lag reduction, triple smoothing with lag compensation ZLEMA (Zero-Lag Exponential): Lag elimination attempt using error correction, very responsive TMA (Triangular): Double-smoothed SMA, very smooth but slower response Adaptive & Intelligent Filters: T3 (Tilson T3): Six-pass exponential smoothing with volume factor adjustment, excellent smoothness FRAMA (Fractal Adaptive): Adapts to market fractal dimension, faster in trends, slower in ranges KAMA (Kaufman Adaptive): Efficiency ratio based adaptation, responds to volatility changes McGinley Dynamic: Self-adjusting mechanism following price more accurately, reduced whipsaws Kalman Filter: Optimal estimation algorithm from aerospace engineering, dynamic noise filtering Advanced Digital Filters: Ultimate Smoother: Advanced digital filter design, superior noise rejection with minimal lag Laguerre Filter: Time-domain filter with N-order implementation, adjustable lag characteristics Laguerre Binomial Filter: 6-pole Laguerre filter, extremely smooth output for long-term analysis Super Smoother: Butterworth filter implementation, removes high-frequency noise effectively 📊 COMPREHENSIVE SIGNAL ANALYSIS Absolute Level Interpretation (%K Line): %K Above 80: Overbought condition, price near period high, potential reversal or pullback zone, caution for new long entries %K in 70-80 Range: Strong upward momentum, bullish trend confirmation, uptrend likely continuing %K in 50-70 Range: Moderate bullish momentum, neutral to positive outlook, consolidation or mild uptrend %K in 30-50 Range: Moderate bearish momentum, neutral to negative outlook, consolidation or mild downtrend %K in 20-30 Range: Strong downward momentum, bearish trend confirmation, downtrend likely continuing %K Below 20: Oversold condition, price near period low, potential bounce or reversal zone, caution for new short entries Crossover Signal Analysis: %K Crosses Above %D (Bullish Cross): Momentum shifting bullish, faster line overtakes slower signal, consider long entry especially in oversold zone, strongest when occurring below 20 level %K Crosses Below %D (Bearish Cross): Momentum shifting bearish, faster line falls below slower signal, consider short entry especially in overbought zone, strongest when occurring above 80 level Crossover in Midrange (40-60): Less reliable signals, often in choppy sideways markets, require additional confirmation from trend or volume analysis Multiple Failed Crosses: Indicates ranging market or choppy conditions, reduce position sizes or avoid trading until clear directional move Advanced Divergence Patterns (%K Line vs Price): Bullish Divergence: Price makes lower low while %K makes higher low, indicates weakening bearish momentum, potential trend reversal upward, more reliable when %K in oversold zone Bearish Divergence: Price makes higher high while %K makes lower high, indicates weakening bullish momentum, potential trend reversal downward, more reliable when %K in overbought zone Hidden Bullish Divergence: Price makes higher low while %K makes lower low, indicates trend continuation in uptrend, bullish trend strength confirmation Hidden Bearish Divergence: Price makes lower high while %K makes higher high, indicates trend continuation in downtrend, bearish trend strength confirmation Momentum Strength Analysis (%K Line Slope): Steep %K Slope: Rapid momentum change, strong directional conviction, potential for extended moves but also increased reversal risk Gradual %K Slope: Steady momentum development, sustainable trends more likely, lower probability of sharp reversals Flat or Horizontal %K: Momentum stalling, potential reversal or consolidation ahead, wait for directional break before committing %K Oscillation Within Range: Indicates ranging market, sideways price action, better suited for range-trading strategies than trend following 🎯 STRATEGIC APPLICATIONS Mean Reversion Strategy (Range-Bound Markets): Identify ranging market conditions using price action or Bollinger Bands Wait for Stochastic to reach extreme zones (above 80 for overbought, below 20 for oversold) Enter counter-trend position when %K crosses %D in extreme zone (sell on bearish cross above 80, buy on bullish cross below 20) Set profit targets near opposite extreme or midline (50 level) Use tight stop-loss above recent swing high/low to protect against breakout scenarios Exit when Stochastic reaches opposite extreme or %K crosses %D in opposite direction Trend Following with Momentum Confirmation: Identify primary trend direction using higher timeframe analysis or moving averages Wait for Stochastic pullback to oversold zone (<20) in uptrend or overbought zone (>80) in downtrend Enter in trend direction when %K crosses %D confirming momentum shift (bullish cross in uptrend, bearish cross in downtrend) Use wider stops to accommodate normal trend volatility Add to position on subsequent pullbacks showing similar Stochastic pattern Exit when Stochastic shows opposite extreme with failed cross or bearish/bullish divergence Divergence-Based Reversal Strategy: Scan for divergence between price and Stochastic at swing highs/lows Confirm divergence with at least two price pivots showing divergent Stochastic readings Wait for %K to cross %D in direction of anticipated reversal as entry trigger Enter position in divergence direction with stop beyond recent swing extreme Target profit at key support/resistance levels or Fibonacci retracements Scale out as Stochastic reaches opposite extreme zone Multi-Timeframe Momentum Alignment: Analyze Stochastic on higher timeframe (4H or Daily) for primary trend bias Switch to lower timeframe (1H or 15M) for precise entry timing Only take trades where lower timeframe Stochastic signal aligns with higher timeframe momentum direction Higher timeframe Stochastic in bullish zone (>50) = only take long entries on lower timeframe Higher timeframe Stochastic in bearish zone (<50) = only take short entries on lower timeframe Exit when lower timeframe shows counter-signal or higher timeframe momentum reverses Zone Transition Strategy: Monitor Stochastic for transitions between zones (oversold to neutral, neutral to overbought, etc.) Enter long when Stochastic crosses above 20 (exiting oversold), signaling momentum shift from bearish to neutral/bullish Enter short when Stochastic crosses below 80 (exiting overbought), signaling momentum shift from bullish to neutral/bearish Use zone midpoint (50) as dynamic support/resistance for position management Trail stops as Stochastic advances through favorable zones Exit when Stochastic fails to maintain momentum and reverses back into prior zone 📋 DETAILED PARAMETER CONFIGURATION %K Length (Default: 14): Lower Values (5-9): Highly sensitive to price changes, generates more frequent signals, increased false signals in choppy markets, suitable for very short-term trading and scalping Standard Values (10-14): Balanced sensitivity and reliability, traditional default (14) widely used,适合 swing trading and intraday strategies Higher Values (15-21): Reduced sensitivity, smoother oscillations, fewer but potentially more reliable signals, better for position trading and lower timeframe noise reduction Very High Values (21+): Slow response, long-term momentum measurement, fewer trading signals, suitable for weekly or monthly analysis %K Smoothing (Default: 3): Value 1: Fast Stochastic, uses raw %K calculation without additional smoothing, most responsive to price changes, generates earliest signals with higher noise Value 3: Slow Stochastic (default), traditional smoothing level, reduces false signals while maintaining good responsiveness, widely accepted standard Values 5-7: Very slow response, extremely smooth oscillations, significantly reduced whipsaws but delayed entry/exit timing Recommendation: Default value 3 suits most trading scenarios, active short-term traders may use 1, conservative long-term positions use 5+ %D Smoothing (Default: 3): Lower Values (1-2): Signal line closely follows %K, frequent crossover signals, useful for active trading but requires strict filtering Standard Value (3): Traditional setting providing balanced signal line behavior, optimal for most trading applications Higher Values (4-7): Smoother signal line, fewer crossover signals, reduced whipsaws but slower confirmation, better for trend trading Very High Values (8+): Signal line becomes slow-moving reference, crossovers rare and highly significant, suitable for long-term position changes only Smoothing Type Algorithm Selection: For Trending Markets: ZLEMA, DEMA, TEMA: Reduced lag for faster trend entry, quick response to momentum shifts, suitable for strong directional moves HMA, ALMA: Good balance of smoothness and responsiveness, effective for clean trend following without excessive noise EMA: Classic choice for trending markets, faster than SMA while maintaining reasonable stability For Ranging/Choppy Markets: Kalman Filter, Super Smoother: Superior noise filtering, reduces false signals in sideways action, helps identify genuine reversal points Laguerre Filters: Smooth oscillations with adjustable lag, excellent for mean reversion strategies in ranges T3, TMA: Very smooth output, filters out market noise effectively, clearer extreme zone identification For Adaptive Market Conditions: KAMA: Automatically adjusts to market efficiency, fast in trends and slow in congestion, reduces whipsaws during transitions FRAMA: Adapts to fractal market structure, responsive during directional moves, conservative during uncertainty McGinley Dynamic: Self-adjusting smoothing, follows price naturally, minimizes lag in trending markets while filtering noise in ranges For Conservative Long-Term Analysis: SMA: Traditional choice, predictable behavior, widely understood characteristics RMA (Wilder's): Smooth oscillations, reduced sensitivity to outliers, consistent behavior across market conditions Laguerre Binomial Filter: Extremely smooth output, ideal for weekly/monthly timeframe analysis, eliminates short-term noise completely Source Selection: Close (Default): Standard choice using closing prices, most common and widely tested HLC3 or OHLC4: Incorporates more price information, reduces impact of sudden spikes or gaps, smoother oscillator behavior HL2: Midpoint of high-low range, emphasizes intrabar volatility, useful for markets with wide intraday ranges Custom Source: Can use other indicators as input (e.g., Heikin Ashi close, smoothed price), creates derivative momentum indicators 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Responsiveness Characteristics: Traditional SMA-Based Stochastic: Fixed lag regardless of market conditions, consistent delay of approximately (K Smoothing + D Smoothing) / 2 periods Equal treatment of trending and ranging markets, no adaptation to volatility changes Predictable behavior but suboptimal in varying market regimes Enhanced Version with Adaptive Algorithms: KAMA and FRAMA reduce lag by up to 40-60% in strong trends compared to SMA while maintaining similar smoothness in ranges ZLEMA and T3 provide near-zero lag characteristics for early entry signals with acceptable noise levels Kalman Filter and Super Smoother offer superior noise rejection, reducing false signals in choppy conditions by estimations of 30-50% compared to SMA Performance improvements vary by algorithm selection and market conditions Signal Quality Improvements: Adaptive algorithms help reduce whipsaw trades in ranging markets by adjusting sensitivity dynamically Advanced filters (Kalman, Laguerre, Super Smoother) provide clearer extreme zone readings for mean reversion strategies Zero-lag methods (ZLEMA, DEMA, TEMA) generate earlier crossover signals in trending markets for improved entry timing Smoother algorithms (T3, Laguerre Binomial) reduce false extreme zone touches for more reliable overbought/oversold signals Comparison with Standard Implementations: Versus Basic Stochastic: Enhanced version offers 21 smoothing options versus single SMA, allowing optimization for specific market characteristics and trading styles Versus RSI: Stochastic provides range-bound measurement (0-100) with clear extreme zones, RSI measures momentum speed, Stochastic offers clearer visual overbought/oversold identification Versus MACD: Stochastic bounded oscillator suitable for mean reversion, MACD unbounded indicator better for trend strength, Stochastic excels in range-bound and oscillating markets Versus CCI: Stochastic has fixed bounds (0-100) for consistent interpretation, CCI unbounded with variable extremes, Stochastic provides more standardized extreme readings across different instruments Flexibility Advantages: Single indicator adaptable to multiple strategies through algorithm selection rather than requiring different indicator variants Ability to optimize smoothing characteristics for specific instruments (e.g., smoother for crypto volatility, faster for forex trends) Multi-timeframe analysis with consistent algorithm across timeframes for coherent momentum picture Backtesting capability with algorithm as optimization parameter for strategy development Limitations and Considerations: Increased complexity from multiple algorithm choices may lead to over-optimization if parameters are curve-fitted to historical data Adaptive algorithms (KAMA, FRAMA) have adjustment periods during market regime changes where signals may be less reliable Zero-lag algorithms sacrifice some smoothness for responsiveness, potentially increasing noise sensitivity in very choppy conditions Performance characteristics vary significantly across algorithms, requiring understanding and testing before live implementation Like all oscillators, Stochastic can remain in extreme zones for extended periods during strong trends, generating premature reversal signals USAGE NOTES This indicator is designed for technical analysis and educational purposes to provide traders with enhanced flexibility in momentum analysis. The Stochastic Oscillator has limitations and should not be used as the sole basis for trading decisions. Important Considerations: Algorithm performance varies with market conditions - no single smoothing method is optimal for all scenarios Extreme zone signals (overbought/oversold) indicate potential reversal areas but not guaranteed turning points, especially in strong trends Crossover signals may generate false entries during sideways choppy markets regardless of smoothing algorithm Divergence patterns require confirmation from price action or additional indicators before trading Past indicator characteristics and backtested results do not guarantee future performance Always combine Stochastic analysis with proper risk management, position sizing, and multi-indicator confirmation Test selected algorithm on historical data of specific instrument and timeframe before live trading Market regime changes may require algorithm adjustment for optimal performance The enhanced smoothing options are intended to provide tools for optimizing the indicator's behavior to match individual trading styles and market characteristics, not to create a perfect predictive tool. Responsible usage includes understanding the mathematical properties of selected algorithms and their appropriate application contexts.
Wskaźnik Pine Script®
od DCAUT
ADAM Projection - Efficiency Ratio Adaptive)Overview The ADAM Projection is a visualization of how a price path might extend from its recent motion, expressed as a continuation (trend reflection) or anti-trend (mean reversion) pattern. This indicator expands upon Jim Sloman’s original ADAM projection—introduced in “The Adam Theory of Markets or What Matters Is Profit” (1983)—by adding a modern quantitative framework for Efficiency Ratio (ER) weighting, time-scaled path normalization, and smooth blending between continuation and anti-trend projections. What Is the ADAM Theory? Jim Sloman’s original ADAM projection was designed to model pure trend continuation. He proposed that every market motion could be mirrored around a central anchor price (the “Adam line”), effectively reflecting past price movements forward in time to visualize what a continuation of the same geometric path would look like. This reflection concept captured the idea that market structure exhibits self-similarity and that price trends often extend symmetrically beyond recent pivots. How This Script Extends It This version generalizes Sloman’s concept by introducing an adjustable blend between continuation (reflection) and anti-trend (forward paste) behavior, weighted by an adaptive ER domain. Anchor Axis The reflection axis (anchorPrice) can be Close, HL2, HLC3, or OHLC4. The projection is drawn forward from this anchor for a user-defined horizon (len bars). Dual Paths Continuation (Reflection): Mirrors historical closes across the anchor. Anti-trend (Forward Paste): Extends historical closes directly forward without inversion. Efficiency Ratio (ER) The Efficiency Ratio measures how directional recent price movement has been: ER = |Net Change| / Σ|Δi| Values near +1 indicate strong directionality (favoring continuation); values near 0 indicate noise or consolidation (favoring anti-trend behavior). Signed ER Normalization ER values are mapped into a user-defined domain between erMin and erMax, with: erSharp (γ) controlling the steepness of the blend curve erFloor providing stability when ER ≈ 0 beta (β) weighting volatility across time (β = 0.5 approximates √time scaling) Blended Projection Each projected point is a weighted combination of the two paths: y_proj = (1 − w) * y_fade + w * y_cont The blend factor w is derived from the normalized ER domain and gamma shaping, producing a smooth morph between the anti-trend and continuation geometries. Visualization The teal projection line shows the dynamically blended continuation/anti-trend forecast for the next len bars. The gray anchor line marks the reflection axis. Each segment adapts in real time based on ER magnitude and recent path structure. Key Parameters Core: len, anchorPrice, lineThin — projection horizon and appearance Lines: showProj, colProj — show or recolor projection ER Domain: erMin, erMax, erSharp, erFloor, beta — control domain scaling, shaping, and time weighting Practical Use High ER values emphasize continuation (trend-following behavior). Low or negative ER values emphasize fading or mean reversion. The projection helps visualize whether recent structure supports trend persistence or weakening. Interpretation The ADAM Projection is not a predictive indicator but a geometric tool for studying market symmetry and efficiency. It provides a structured way to visualize how recent movements would look if extended forward under both continuation and anti-trend assumptions. This blends Sloman’s original reflection concept with modern ER-based adaptivity. Summary Origin: Jim Sloman (1983) — trend continuation via reflection symmetry. Extension: Adds ER-driven blending to model both continuation and anti-trend regimes. Concept: Price reflection vs. direct forward extension. Purpose: Study of geometric price symmetry and efficiency, not a trade signal.
Wskaźnik Pine Script®
od Tradenometry
Crypto Exchange PremiumDescription: Crypto Exchange Premium The Crypto Exchange Premium indicator is designed to quantify and visualize price disparities between different types of crypto markets — specifically between spot and perpetual futures markets, or between any two customizable sources of price data. By consolidating live data from multiple major exchanges, it creates a unified, cross-market measure of premium (or discount), helping traders identify institutional activity (i. e. by comparing exchanges with high institutional activity against others), arbitrage opportunities, and shifts in market sentiment before they become visible in price action alone. Concept and Purpose In cryptocurrency markets, price divergence between spot and perpetual pairs reflects the real-time interaction of demand and liquidity across market segments. When perpetual prices trade above spot, it implies aggressive long positioning or bullish leverage (positive funding expectations). Conversely, when spot trades above perps, it may reflect net selling pressure in futures or strong spot accumulation. Unlike most tools that rely on funding rates or open interest alone, this indicator measures the actual traded price spread dynamically across exchanges. This allows traders to visualize the “premium curve” of the crypto market in a clear, data-driven format. How It Works The indicator aggregates real-time prices from a wide selection of exchanges, normalizes them into groups, and computes the difference (“premium”) between two chosen reference markets. 1. Exchange Aggregation: Users can toggle individual exchanges for both spot and perpetual aggregation groups. The script automatically calculates group averages by dividing the sum of all enabled exchange prices by the number of valid feeds. Non-USD exchanges (e.g., KRW pairs on Upbit or Bithumb) are automatically converted into USD using live FX data (USDKRW) for accurate normalization. 2. Flexible Comparison Logic: Each leg of the comparison (First vs. Second Source) can be chosen as one of: Local chart symbol Custom symbol Aggregated Spot group Aggregated Perpetual group This allows users to compare, for example: Binance Spot vs. Global Perp Average Coinbase Spot vs. Binance Perp BTCUSD vs. BTCUSDT.P (or any cross-exchange combination) 3. Premium Calculation: The final value is computed as: Premium = First Source Price − Second Source Price and is plotted as a histogram (positive = green, negative = red). This visual instantly shows whether the first source trades at a premium or discount relative to the second. How to Use Select Data Sources: Configure the “First Symbol” and “Second Symbol” in the settings. For most use cases: First Symbol → Perps (Aggregated) Second Symbol → Spot (Aggregated) Adjust Exchange Selection: Enable or disable individual exchanges to fine-tune your data set. For instance, disabling Korean exchanges filters out regional FX distortions. Originality and Value While many exchange difference or “premium indicators” track one or two exchanges, this script introduces multi-exchange aggregation, cross-market normalization, and user-configurable pairing, resulting in a more holistic and accurate reflection of market structure. It bridges a gap between macro market breadth and microstructural price dynamics, empowering traders to: Detect arbitrage inefficiencies between spot and perps. Track regional price dislocations (USD vs. KRW). Gauge the intensity of speculative leverage over time. Anticipate funding rate shifts and liquidation clusters before they happen.
Wskaźnik Pine Script®
od mxdvt07
Trend Pivots Profile [BigBeluga]🔵 OVERVIEW The Trend Pivots Profile is a dynamic volume profile tool that builds profiles around pivot points to reveal where liquidity accumulates during trend shifts. When the market is in an uptrend , the indicator generates profiles at low pivots . In a downtrend , it builds them at high pivots . Each profile is constructed using lower timeframe volume data for higher resolution, making it highly precise even in limited space. A colored trendline helps traders instantly recognize the prevailing trend and anticipate which type of profile (bullish or bearish) will form. 🔵 CONCEPTS Pivot-Driven Profiles : Profiles are only created when a new pivot forms, aligning liquidity analysis with market structure shifts. Trend-Contextual : Profiles form at low pivots in uptrends and at high pivots in downtrends. Lower Timeframe Data : Volume and close values are pulled from smaller timeframes to provide detailed, high-resolution profiles inside larger pivot windows. Adaptive Bin Sizing : Bin size is automatically calculated relative to ATR, ensuring consistent precision across different markets and volatility conditions. Point of Control (PoC) : The highest-volume level within each profile is marked with a PoC line that extends until the next pivot forms. Trendline Visualization : A wide, semi-transparent line follows the rolling average of highs and lows, colored blue in uptrends and orange in downtrends. 🔵 FEATURES Pivot Length Control : Adjust how far back the script looks to detect pivots (e.g., length 5 → profiles cover 10 bars after pivot). Pivot Profile toggle : On → draw the filled pivot profile + PoC + pivot label. Off → hide profiles; show only PoC level (clean S/R mode). Trend Length Filter : Smooths trendline detection to ensure reliable up/down bias. Precise Volume Distribution : Volume is aggregated into bins, creating a smooth volume curve around the pivot range. PoC Extension : Automatically extends the most active price level until a new pivot is confirmed. Profile Visualization : Profiles appear as filled shapes anchored at the pivot candle, colored based on trend. Trendline Overlay : Thick, semi-transparent trendline provides visual guidance on directional bias. Automatic Cleanup : Old profiles are deleted once they exceed the chart’s capacity (default 25 stored profiles). 🔵 HOW TO USE Spotting Trend Liquidity : In an uptrend, monitor profiles at low pivots to see where buyers concentrated. In downtrends, use high-pivot profiles to spot sell-side pressure. Watch the PoC : The PoC line highlights the strongest traded level of the pivot structure—expect reactions when price retests it. Anticipate Trend Continuation/Reversal : Use the trendline (blue = bullish, orange = bearish) together with pivot profiles to forecast directional momentum. Combine with HTF Context : Overlay with higher timeframe structure (order blocks, liquidity zones, or FVGs) for confluence. Fine-Tune with Inputs : Adjust Pivot Length for sensitivity and Trend Length for smoother or faster trend shifts. 🔵 CONCLUSION The Trend Pivots Profile blends pivot-based structure with precise volume profiling. By dynamically plotting profiles on pivots aligned with the prevailing trend, highlighting PoCs, and overlaying a directional trendline, it equips traders with a clear view of liquidity clusters and directional momentum—ideal for anticipating reactions, pullbacks, or breakouts.
Wskaźnik Pine Script®
od BigBeluga
5
Macro Momentum – 4-Theme, Vol Target, RebalanceMacro Momentum — 4-Theme, Vol Target, Rebalance Purpose. A macro-aware strategy that blends four economic “themes”—Business Cycle, Trade/USD, Monetary Policy, and Risk Sentiment—into a single, smoothed Composite signal. It then: gates entries/exits with hysteresis bands, enforces optional regime filters (200-day bias), and sizes the position via volatility targeting with caps for long/short exposure. It’s designed to run on any chart (index, ETF, futures, single stocks) while reading external macro proxies on a chosen Signal Timeframe. How it works (high level) Build four theme signals from robust macro proxies: Business Cycle: XLI/XLU and Copper/Gold momentum, confirmed by the chart’s price vs a long SMA (default 200D). Trade / USD: DXY momentum (sign-flipped so a rising USD is bearish for risk assets). Monetary Policy: 10Y–2Y curve slope momentum and 10Y yield trend (steepening & falling 10Y = risk-on; rising 10Y = risk-off). Risk Sentiment: VIX momentum (bearish if higher) and HYG/IEF momentum (bullish if credit outperforms duration). Normalize & de-noise. Optional Winsorization (MAD or stdev) clamps outliers over a lookback window. Optional Z-score → tanh mapping compresses to ~ for stable weighting. Theme lines are SMA-smoothed; the final Composite is LSMA-smoothed (linreg). Decide direction with hysteresis. Enter/hold long when Composite ≥ Entry Band; enter/hold short when Composite ≤ −Entry Band. Exit bands are tighter than entry bands to avoid whipsaws. Apply regime & direction constraints. Optional Long-only above 200MA (chart symbol) and/or Short-only below 200MA. Global Direction control (Long / Short / Both) and Invert switch. Size via volatility targeting. Realized close-to-close vol is annualized (choose 9-5 or 24/7 market profile). Target exposure = TargetVol / RealizedVol, capped by Max Long/Max Short multipliers. Quantity is computed from equity; futures are rounded to whole contracts. Rebalance cadence & execution. Trades are placed on Weekly / Monthly / Quarterly rebalance bars or when the sign of exposure flips. Optional ATR stop/TP for single-stock style risk management. Inputs you’ll actually tweak General Signal Timeframe: Where macro is sampled (e.g., D/W). Rebalance Frequency: Weekly / Monthly / Quarterly. ROC & SMA lengths: Defaults for theme momentum and the 200D regime filter. Normalization: Z-score (tanh) on/off. Winsorization Toggle, lookback, multiplier, MAD vs Stdev. Risk / Sizing Target Annualized Vol & Realized Vol Lookback. Direction (Long/Short/Both) and Invert. Max long/short exposure caps. Advanced Thresholds Theme/Composite smoothing lengths. Entry/Exit bands (hysteresis). Regime / Execution Long-only above 200MA, Short-only below 200MA. Stops/TP (optional) ATR length and SL/TP multiples. Theme Weights Per-theme scalars so you can push/pull emphasis (e.g., overweight Policy during rate cycles). Macro Proxies Symbols for each theme (XLI, XLU, HG1!, GC1!, DXY, US10Y, US02Y, VIX, HYG, IEF). Swap to alternatives as needed (e.g., UUP for DXY). Signals & logic (under the hood) Business Cycle = ½ ROC(XLI/XLU) + ½ ROC(Copper/Gold), then confirmed by (price > 200SMA ? +1 : −1). Trade / USD = −ROC(DXY). Monetary Policy = 0.6·ROC(10Y–2Y) − 0.4·ROC(10Y). Risk Sentiment = −0.6·ROC(VIX) + 0.4·ROC(HYG/IEF). Each theme → (optional Winsor) → (robust z or scaled ROC) → tanh → SMA smoothing. Composite = weighted average → LSMA smoothing → compare to bands → dir ∈ {−1,0,+1}. Rebalance & flips. Orders fire on your chosen cadence or when the sign of exposure changes. Position size. exposure = clamp(TargetVol / realizedVol, maxLong/Short) × dir. Note: The script also exposes Gross Exposure (% equity) and Signed Exposure (× equity) as diagnostics. These can help you audit how vol-targeting and caps translate into sizing over time. Visuals & alerts Composite line + columns (color/intensity reflect direction & strength). Entry/Exit bands with green/red fills for quick polarity reads. Hidden plots for each Theme if you want to show them. Optional rebalance labels (direction, gross & signed exposure, σ). Background heatmap keyed to Composite. Alerts Enter/Inc LONG when Composite crosses up (and on rebalance bars). Enter/Inc SHORT when Composite crosses down (and on rebalance bars). Exit to FLAT when Composite returns toward neutral (and on rebalance bars). Practical tips Start higher timeframes. Daily signals with Monthly rebalance are a good baseline; weekly signals with quarterly rebalances are even cleaner. Tune Entry/Exit bands before anything else. Wider bands = fewer trades and less noise. Weights reflect regime. If policy dominates markets, raise Monetary Policy weight; if credit stress drives moves, raise Risk Sentiment. Proxies are swappable. Use UUP for USD, or futures-continuous symbols that match your data plan. Futures vs ETFs. Quantity auto-rounds for futures; ETFs accept fractional shares. Check contract multipliers when interpreting exposure. Caveats Macro proxies can repaint at the selected signal timeframe as higher-TF bars form; that’s intentional for macro sampling, but test live. Vol targeting assumes reasonably stationary realized vol over the lookback; if markets regime-shift, revisit volLook and targetVol. If you disable normalization/winsorization, themes can become spikier; expect more hysteresis band crossings. What to change first (quick start) Set Signal Timeframe = D, Rebalance = Monthly, Z-score on, Winsor on (MAD). Entry/Exit bands: 0.25 / 0.12 (defaults), then nudge until trade count and turnover feel right. TargetVol: try 10% for diversified indices; lower for single stocks, higher for vol-sell strategies. Leave weights = 1.0 until you’ve inspected the four theme lines; then tilt deliberately.
Strategia Pine Script®
od GabrielAmadeusLau
BayesStack RSI [CHE]BayesStack RSI — Stacked RSI with Bayesian outcome stats and gradient visualization Summary BayesStack RSI builds a four-length RSI stack and evaluates it with a simple Bayesian success model over a rolling window. It highlights bull and bear stack regimes, colors price with magnitude-based gradients, and reports per-regime counts, wins, and estimated win rate in a compact table. Signals seek to be more robust through explicit ordering tolerance, optional midline gating, and outcome evaluation that waits for events to mature by a fixed horizon. The design focuses on readable structure, conservative confirmation, and actionable context rather than raw oscillator flips. Motivation: Why this design? Classical RSI signals flip frequently in volatile phases and drift in calm regimes. Pure threshold rules often misclassify shallow pullbacks and stacked momentum phases. The core idea here is ordered, spaced RSI layers combined with outcome tracking. By requiring a consistent order with a tolerance and optionally gating by the midline, regime identification becomes clearer. A horizon-based maturation check and smoothed win-rate estimate provide pragmatic feedback about how often a given stack has recently worked. What’s different vs. standard approaches? Reference baseline: Traditional single-length RSI with overbought and oversold rules or simple crossovers. Architecture differences: Four fixed RSI lengths with strict ordering and a spacing tolerance. Optional requirement that all RSI values stay above or below the midline for bull or bear regimes. Outcome evaluation after a fixed horizon, then rolling counts and a prior-smoothed win rate. Dispersion measurement across the four RSIs with a percent-rank diagnostic. Gradient coloring of candles and wicks driven by stack magnitude. A last-bar statistics table with counts, wins, win rate, dispersion, and priors. Practical effect: Charts emphasize sustained momentum alignment instead of single-length crosses. Users see when regimes start, how strong alignment is, and how that regime has recently performed for the chosen horizon. How it works (technical) The script computes RSI on four lengths and forms a “stack” when they are strictly ordered with at least the chosen tolerance between adjacent lengths. A bull stack requires a descending set from long to short with positive spacing. A bear stack requires the opposite. Optional gating further requires all RSI values to sit above or below the midline. For evaluation, each detected stack is checked again after the horizon has fully elapsed. A bull event is a success if price is higher than it was at event time after the horizon has passed. A bear event succeeds if price is lower under the same rule. Rolling sums over the training window track counts and successes; a pair of priors stabilizes the win-rate estimate when sample sizes are small. Dispersion across the four RSIs is measured and converted to a percent rank over a configurable window. Gradients for bars and wicks are normalized over a lookback, then shaped by gamma controls to emphasize strong regimes. A statistics table is created once and updated on the last bar to minimize overhead. Overlay markers and wick coloring are rendered to the price chart even though the indicator runs in a separate pane. Parameter Guide Source — Input series for RSI. Default: close. Tips: Use typical price or hlc3 for smoother behavior. Overbought / Oversold — Guide levels for context. Defaults: seventy and thirty. Bounds: fifty to one hundred, zero to fifty. Tips: Narrow the band for faster feedback. Stacking tolerance (epsilon) — Minimum spacing between adjacent RSIs to qualify as a stack. Default: zero point twenty-five RSI points. Trade-off: Higher values reduce false stacks but delay entries. Horizon H — Bars ahead for outcome evaluation. Default: three. Trade-off: Longer horizons reduce noise but delay success attribution. Rolling window — Lookback for counts and wins. Default: five hundred. Trade-off: Longer windows stabilize the win rate but adapt more slowly. Alpha prior / Beta prior — Priors used to stabilize the win-rate estimate. Defaults: one and one. Trade-off: Larger priors reduce variance with sparse samples. Show RSI 8/13/21/34 — Toggle raw RSI lines. Default: on. Show consensus RSI — Weighted combination of the four RSIs. Default: on. Show OB/OS zones — Draw overbought, oversold, and midline. Default: on. Background regime — Pane background tint during bull or bear stacks. Default: on. Overlay regime markers — Entry markers on price when a stack forms. Default: on. Show statistics table — Last-bar table with counts, wins, win rate, dispersion, priors, and window. Default: on. Bull requires all above fifty / Bear requires all below fifty — Midline gate. Defaults: both on. Trade-off: Stricter regimes, fewer but cleaner signals. Enable gradient barcolor / wick coloring — Gradient visuals mapped to stack magnitude. Defaults: on. Trade-off: Clearer regime strength vs. extra rendering cost. Collection period — Normalization window for gradients. Default: one hundred. Trade-off: Shorter values react faster but fluctuate more. Gamma bars and shapes / Gamma plots — Curve shaping for gradients. Defaults: zero point seven and zero point eight. Trade-off: Higher values compress weak signals and emphasize strong ones. Gradient and wick transparency — Visual opacity controls. Defaults: zero. Up/Down colors (dark and neon) — Gradient endpoints. Defaults: green and red pairs. Fallback neutral candles — Directional coloring when gradients are off. Default: off. Show last candles — Limit for gradient squares rendering. Default: three hundred thirty-three. Dispersion percent-rank length / High and Low thresholds — Window and cutoffs for dispersion diagnostics. Defaults: two hundred fifty, eighty, and twenty. Table X/Y, Dark theme, Text size — Table anchor, theme, and typography. Defaults: right, top, dark, small. Reading & Interpretation RSI stack lines: Alignment and spacing convey regime quality. Wider spacing suggests stronger alignment. Consensus RSI: A single line that summarizes the four lengths; use as a smoother reference. Zones: Overbought, oversold, and midline provide context rather than standalone triggers. Background tint: Indicates active bull or bear stack. Markers: “Bull Stack Enter” or “Bear Stack Enter” appears when the stack first forms. Gradients: Brighter tones suggest stronger stack magnitude; dull tones suggest weak alignment. Table: Count and Wins show sample size and successes over the window. P(win) is a prior-stabilized estimate. Dispersion percent rank near the high threshold flags stretched alignment; near the low threshold flags tight clustering. Practical Workflows & Combinations Trend following: Enter only on new stack markers aligned with structure such as higher highs and higher lows for bull, or lower lows and lower highs for bear. Use the consensus RSI to avoid chasing into overbought or oversold extremes. Exits and stops: Consider reducing exposure when dispersion percent rank reaches the high threshold or when the stack loses ordering. Use the table’s P(win) as a context check rather than a direct signal. Multi-asset and multi-timeframe: Defaults travel well on liquid assets from intraday to daily. Combine with higher-timeframe structure or moving averages for regime confirmation. The script itself does not fetch higher-timeframe data. Behavior, Constraints & Performance Repaint and confirmation: Stack markers evaluate on the live bar and can flip until close. Alert behavior follows TradingView settings. Outcome evaluation uses matured events and does not look into the future. HTF and security: Not used. Repaint paths from higher-timeframe aggregation are avoided by design. Resources: max bars back is two thousand. The script uses rolling sums, percent rank, gradient rendering, and a last-bar table update. Shapes and colored wicks add draw overhead. Known limits: Lag can appear after sharp turns. Very small windows can overfit recent noise. P(win) is sensitive to sample size and priors. Dispersion normalization depends on the collection period. Sensible Defaults & Quick Tuning Start with the shipped defaults. Too many flips: Increase stacking tolerance, enable midline gates, or lengthen the collection period. Too sluggish: Reduce stacking tolerance, shorten the collection period, or relax midline gates. Sparse samples: Extend the rolling window or increase priors to stabilize P(win). Visual overload: Disable gradient squares or wick coloring, or raise transparency. What this indicator is—and isn’t This is a visualization and context layer for RSI stack regimes with simple outcome statistics. It is not a complete trading system, not predictive, and not a signal generator on its own. Use it with market structure, risk controls, and position management that fit your process. Metadata - Pine version: v6 - Overlay: false (price overlays are drawn via forced overlay where applicable) - Primary outputs: Four RSI lines, consensus line, OB/OS guides, background tint, entry markers, gradient bars and wicks, statistics table - Inputs with defaults: See Parameter Guide - Metrics and functions used: RSI, rolling sums, percent rank, dispersion across RSI set, gradient color mapping, table rendering, alerts - Special techniques: Ordered RSI stacking with tolerance, optional midline gating, horizon-based outcome maturation, prior-stabilized win rate, gradient normalization with gamma shaping - Performance and constraints: max bars back two thousand, rendering of shapes and table on last bar, no higher-timeframe data, no security calls - Recommended use-cases: Regime confirmation, momentum alignment, post-entry management with dispersion and recent outcome context - Compatibility: Works across assets and timeframes that support RSI - Limitations and risks: Sensitive to parameter choices and market regime changes; not a standalone strategy - Diagnostics: Statistics table, dispersion percent rank, gradient intensity Disclaimer The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Best regards and happy trading Chervolino. Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Wskaźnik Pine Script®
od chervolino
Long-short energy ratio /多空能量比值This indicator calculates the relative strength of bulls and bears by measuring the average candle body movement within a user-defined window (default: 50 bars). Bull Energy = average percentage change of all bullish candles in the lookback period Bear Energy = average percentage change of all bearish candles in the lookback period Energy Ratio = Bull Energy ÷ Bear Energy The ratio is plotted as a curve around the baseline of 1: Ratio > 1 → Bull side shows stronger momentum Ratio < 1 → Bear side shows stronger momentum Ratio ≈ 1 → Balanced market conditions This tool helps visualize short-term shifts in buying and selling pressure, offering a simple mean-reversion perspective or a confirmation of trend strength depending on the context.
Wskaźnik Pine Script®
od slyrtaqj79
Implied Volatility RangeThe Implied Volatility Range is a forward-looking tool that transforms option market data into probability ranges for future prices. Based on the lognormal distribution of asset prices assumed in modern option pricing models, it converts the implied volatility curve into a volatility cone with dynamic labels that show the market’s expectations for the price distribution at a specific point in time. At the selected future date, it displays projected price levels and their percentage change from today’s close across 1, 2, and 3 standard deviation (σ) ranges: 1σ range = ~68.2% probability the price will remain within this range. 2σ range = ~95.4% probability the price will remain within this range. 3σ range = ~99.7% probability the price will remain within this range. What makes this indicator especially useful is its ability to incorporate implied volatility skew. When only ATM IV (%) is entered, the indicator displays the standard Black–Scholes lognormal distribution. By adding High IV (%) and Low IV (%) values tied to strikes above and below the current price, the indicator interpolates between these inputs to approximate the implied volatility skew. This adjustment produces a market-implied probability distribution that indicates whether the option market is leaning bullish or bearish, based on the data entered in the menu: ATM IV (%) = Implied volatility at the current spot price (at-the-money). High IV (%) = Implied volatility at a strike above the current spot price. High Strike = Strike price corresponding to the High IV input (OTM call). Low IV (%) = Implied volatility at a strike below the current spot price. Low Strike = Strike price corresponding to the Low IV input (OTM put). Expiration (Day, Month, Year) = Option expiration date for the projection. Once these inputs are entered, the indicator calculates implied probability ranges and, if both High IV and Low IV values are provided, adjusts for skew to approximate the option market’s distribution. If no implied volatility data is supplied, the indicator defaults to a lognormal distribution based on historical volatility, using past realized volatility over the same forward horizon. This keeps the tool functional even without implied volatility inputs, though in that case the output represents only an approximation of ATM IV, not the actual market view. In summary, the Implied Volatility Range is a powerful tool that translates implied volatility inputs into a clear and practical estimate of the market’s expectations for future prices. It allows traders to visualize the probability of price ranges while also highlighting directional bias, a dimension often difficult to interpret from traditional implied volatility charts. It should be emphasized, however, that this tool reflects only the market’s expectations at a specific point in time, which may change as new information and trading activity reshape implied volatility.
Wskaźnik Pine Script®
od QuantitativeAlpha
1
Extremum Range MA Crossover Strategy1. Principle of Work & Strategy Logic ⚙️📈 Main idea: The strategy tries to catch the moment of a breakout from a price consolidation range (flat) and the start of a new trend. It combines two key elements: Moving Average (MA) 📉: Acts as a dynamic support/resistance level and trend filter. Range Extremes (Range High/Low) 🔺🔻: Define the borders of the recent price channel or consolidation. The strategy does not attempt to catch absolute tops and bottoms. Instead, it enters an already formed move after the breakout, expecting continuation. Type: Trend-following, momentum-based. Timeframes: Works on different TFs (H1, H4, D), but best suited for H4 and higher, where breakouts are more meaningful. 2. Justification of Indicators & Settings ⚙️ A. Moving Average (MA) 📊 Why used: Core of the strategy. It smooths price fluctuations and helps define the trend. The price (via extremes) must cross the MA → signals a potential trend shift or strengthening. Parameters: maLength = 20: Default length (≈ one trading month, 20-21 days). Good balance between sensitivity & smoothing. Lower TF → reduce (10–14). Higher TF → increase (50). maSource: Defines price source (default = Close). Alternatives (HL2, HLC3) → smoother, less noisy MA. maType: Default = EMA (Exponential MA). Why EMA? Faster reaction to recent price changes vs SMA → useful for breakout strategies. Other options: SMA 🟦 – classic, slowest. WMA 🟨 – weights recent data stronger. HMA 🟩 – near-zero lag, but “nervous,” more false signals. DEMA/TEMA 🟧 – even faster & more sensitive than EMA. VWMA 🔊 – volume-weighted. ZLEMA ⏱ – reduced lag. 👉 Choice = tradeoff between speed of reaction & false signals. B. Range Extremes (Previous High/Low) 📏 Why used: Define borders of recent trading range. prevHigh = local resistance. prevLow = local support. Break of these levels on close = trigger. Parameters: lookbackPeriod = 5: Searches for highest high / lowest low of last 5 candles. Very recent range. Higher value (10–20) → wider, stronger ranges but rarer signals. 3. Entry & Exit Rules 🎯 Long signals (BUY) 🟢📈 Condition (longCondition): Previous Low crosses MA from below upwards. → Price bounced from the bottom & strong enough to push range border above MA. Execution: Auto-close short (if any) → open long. Short signals (SELL) 🔴📉 Condition (shortCondition): Previous High crosses MA from above downwards. → Price rejected from the top, upper border failed above MA. Execution: Auto-close long (if any) → open short. Exit conditions 🚪 Exit Long (exitLongCondition): Close below prevLow. → Uptrend likely ended, range shifts down. Exit Short (exitShortCondition): Close above prevHigh. → Downtrend likely ended, range shifts up. ⚠️ Important: Exit = only on candle close beyond extremes (not just wick). 4. Trading Settings ⚒️ overlay = true → indicators shown on chart. initial_capital = 10000 💵. default_qty_type = strategy.cash, default_qty_value = 100 → trades fixed $100 per order (not lots). Can switch to % of equity. commission_type = strategy.commission.percent, commission_value = 0.1 → default broker fee = 0.1%. Adjust for your broker! slippage = 3 → slippage = 3 ticks. Adjust to asset liquidity. currency = USD. margin_long = 100, margin_short = 100 → no leverage (100% margin). 5. Visualization on Chart 📊 The strategy draws 3 lines: 🔵 MA line (thickness 2). 🔴 Previous High (last N candles). 🟢 Previous Low (last N candles). Also: entry/exit arrows & equity curve shown in backtest. Disclaimer ⚠️📌 Risk Warning: This description & code are for educational purposes only. Not financial advice. Trading (Forex, Stocks, Crypto) carries high risk and may lead to full capital loss. You trade at your own risk. Testing: Always backtest & demo test first. Past results ≠ future profits. Responsibility: Author of this strategy & description is not responsible for your trading decisions or losses.
Strategia Pine Script®
od Script_Algo
Small-Cap — Sell Every Spike (Rendon1) Small-Cap — Sell Every Spike v6 — Strict, No Look-Ahead Educational use only. This is not financial advice or a signal service. This strategy targets low/ mid-float runners (≤ ~20M) that make parabolic spikes. It shorts qualified spikes and scales out into flushes. Logic is deliberately simple and transparent to avoid curve-fit. What the strategy does Detects a parabolic up move using: Fast ROC over N bars Big range vs ATR Volume spike vs SMA Fresh higher high (no stale spikes) Enters short at bar close when conditions are met (no same-bar fills). Manages exits with ATR targets and optional % covers. Tracks float rotation intraday (manual float input) and blocks trades above a hard limit. Draws daily spike-high resistance from confirmed daily bars (no repaint / no look-ahead). Timeframes & market Designed for 1–5 minute charts. Intended for US small-caps; turn Premarket on. Works intraday; avoid illiquid tickers or names with constant halts. Entry, Exit, Risk (short side) Entry: parabolic spike (ROC + Range≥ATR×K + Vol≥SMA×K, new HH). Optional confirmations (OFF by default to “sell every spike”): upper-wick and VWAP cross-down. Stop: ATR stop above entry (default 1.2× ATR). Targets: TP1 = 1.0× ATR, TP2 = 2.0× ATR + optional 10/20/30% covers. Safety: skip trades if RVOL is low or Float Rotation exceeds your limit (default warn 5×, hard 7×). Inputs (Balanced defaults) Price band: $2–$10 Float Shares: set per ticker (from Finviz). RVOL(50) ≥ 1.5× ROC(5) ≥ 1.0%, Range ≥ 1.6× ATR, Vol ≥ 1.8× SMA Cooldown: 10 bars; Max trades/day: 6 Optional: Require wick (≥35%) and/or Require VWAP cross-down. Presets suggestion: • Balanced (defaults above) • Safer: wick+VWAP ON, Range≥1.8×, trades/day 3–4 • Micro-float (<5M): ROC 1.4–1.8%, Range≥1.9–2.2×, Vol≥2.2×, RVOL≥2.0, wick 40–50% No look-ahead / repaint notes Daily spike-highs use request.security(..., lookahead_off) and shifted → only closed daily bars. Orders arm next bar after entry; entries execute at bar close. VWAP/ATR/ROC/Vol/RVOL are computed on the chart timeframe (no HTF peeking). How to use Build a watchlist: Float <20M, RelVol >2, Today +20% (Finviz). Open 1–5m chart, enter Float Shares for the ticker. Start with Balanced, flip to Safer on halty/SSR names or repeated VWAP reclaims. Scale out into flushes; respect the stop and rotation guard. Limitations & risk Backtests on small-caps can be optimistic due to slippage, spreads, halts, SSR, and limited premarket data. Always use conservative sizing. Low-float stocks can squeeze violently. Alerts Parabolic UP (candidate short) SHORT Armed (conditions met; entry at bar close)
Strategia Pine Script®
od Rendon1
Kalman Adjusted Average True Range [BackQuant]Kalman Adjusted Average True Range A volatility-aware trend baseline that fuses a Kalman price estimate with ATR “rails” to create a smooth, adaptive guide for entries, exits, and trailing risk. Built on my original Kalman This indicator is based on my original Kalman Price Filter: That core smoother is used here to estimate the “true” price path, then blended with ATR to control step size and react proportionally to market noise. What it plots Kalman ATR Line the main baseline that turns up/down with the filtered trend. Optional Moving Average of the Kalman ATR a secondary line for confluence (SMA/Hull/EMA/WMA/DEMA/RMA/LINREG/ALMA). Candle Coloring (optional) paint bars by the baseline’s current direction. Why combine Kalman + ATR? Kalman reduces measurement noise and produces a stable path without the lag of heavy MAs. ATR rails scale the baseline’s step to current volatility, so it’s calm in chop and more responsive in expansion. The result is a single, intelligible line you can trade around: slope-up = constructive; slope-down = caution. How it works (plain English) Each bar, the Kalman filter updates an internal state (tunable via Process Noise , Measurement Noise , and Filter Order ) to estimate the underlying price. An ATR band (Period × Factor) defines the allowed per-bar adjustment. The baseline cannot “jump” beyond those rails in one step. A direction flip is detected when the baseline’s slope changes sign (upturn/downturn), and alerts are provided for both. Typical uses Trend confirmation Trade in the baseline’s direction; avoid fading a firmly rising/falling line. Pullback timing Look for entries when price mean-reverts toward a rising baseline (or exits on tags of a falling one). Trailing risk Use the baseline as a dynamic guide; many traders set stops a small buffer beyond it (e.g., a fraction of ATR). Confluence Enable the MA overlay of the Kalman ATR; alignment (baseline above its MA and rising) supports continuation. Inputs & what they do Calculation Kalman Price Source which price the filter tracks (Close by default). Process Noise how quickly the filter can adapt. Higher = more responsive (but choppier). Measurement Noise how much you distrust raw price. Higher = smoother (but slower to turn). Filter Order (N) depth of the internal state array. Higher = slightly steadier behavior. Kalman ATR Period ATR lookback. Shorter = snappier; longer = steadier. Factor scales the allowed step per bar. Larger factors permit faster drift; smaller factors clamp movement. Confluence (optional) MA Type & Period compute an MA on the Kalman ATR line , not on price. Sigma (ALMA) if ALMA is selected, this input controls the curve’s shape. (Ignored for other MA types.) Visuals Plot Kalman ATR toggle the main line. Paint Candles color bars by up/down slope. Colors choose long/short hues. Signals & alerts Trend Up baseline turns upward (slope crosses above 0). Alert: “Kalman ATR Trend Up” Trend Down baseline turns downward (slope crosses below 0). Alert: “Kalman ATR Trend Down” These are state flips , not “price crossovers,” so you avoid many one-bar head-fakes. How to start (fast presets) Swing (daily/4H) ATR Period 7–14, Factor 0.5–0.8, Process Noise 0.02–0.05, Measurement Noise 2–4, N = 3–5. Intraday (5–15m) ATR Period 5–7, Factor 0.6–1.0, Process Noise 0.05–0.10, Measurement Noise 2–3, N = 3–5. Slow assets / FX raise Measurement Noise or ATR Period for calmer lines; drop Factor if the baseline feels too jumpy. Reading the line Rising & curving upward momentum building; consider long bias until a clear downturn. Flat & choppy regime uncertainty; many traders stand aside or tighten risk. Falling & accelerating distribution lower; short bias until a clean upturn. Practical playbook Continuation entries After a Trend Up alert, wait for a minor pullback toward the baseline; enter on evidence the line keeps rising. Exit/reduce If long and the baseline flattens then turns down, trim or exit; reverse logic for shorts. Filters Add a higher-timeframe check (e.g., only take longs when the daily Kalman ATR is rising). Stops Place stops just beyond the baseline (e.g., baseline − x% ATR for longs) to avoid “tag & reverse” noise. Notes This is a guide to state and momentum, not a guarantee. Combine with your process (structure, volume, time-of-day) for decisions. Settings are asset/timeframe dependent; start with the presets and nudge Process/Measurement Noise until the baseline “feels right” for your market. Summary Kalman ATR takes the noise-reduction of a Kalman price estimate and couples it with volatility-scaled movement to produce a clean, adaptive baseline. If you liked the original Kalman Price Filter (), this is its trend-trading cousin purpose-built for cleaner state flips, intuitive trailing, and confluence with your existing
Wskaźnik Pine Script®
od BackQuant
12
Continuous Accumulation Strategy [DCA] v9🇬🇧 English: Continuous Accumulation Strategy v9.4 This script is a full-featured strategy designed to backtest the "Buy the Dip" or "Dollar Cost Averaging" (DCA) philosophy. Its core feature is the Dynamic Peak Detection logic, which solves the "lock-in" problem of previous versions. Instead of getting stuck on an old high, the strategy constantly adapts to the market by referencing the most recent peak. Key Features * Dynamic Peak Detection: You define the "Peak Lookback Period." For example, on a Daily chart, setting it to `5` references the peak of the last business week. * Stable Order Management: The strategy consistently uses a fixed cash amount (e.g., $100) for each entry, which prevents any runtime errors related to negative equity. * Publishing-Ready: To meet TradingView's requirement for a backtest report, this strategy executes a symbolic, one-time "dummy trade" (one buy and one sell) at the very beginning of the test period. This first trade should be ignored when analyzing performance , as its only purpose is to enable publication. How It Works The main logic follows an adaptive cycle: Find Dynamic Peak -> Wait for a Drop -> Buy on Crossover -> Repeat. 1. Finds the Dynamic Peak: On every bar, it identifies the highest price within your defined lookback period. 2. Calculates the Drop: It constantly calculates the percentage drop from this moving peak. 3. Executes an Entry: The moment the price crosses below a target drop percentage, it executes a buy order. 4. Continuously Adapts: As the price moves, the dynamic peak is constantly updated, meaning the strategy never gets locked and is always ready for the next opportunity. How to Use This Strategy * Focus on the Strategy Tester: After adding it to the chart, analyze the Equity Curve, Net Profit, and Max Drawdown to see how this accumulation philosophy would have performed on your favorite asset. * Optimize Parameters: Adjust the "Peak Lookback Period" and "Drop Percentages" to fit the volatility of the asset you are testing. This is a tool for testing and analyzing a "buy and accumulate" philosophy. Its main logic does not generate sell signals.
Strategia Pine Script®
od bbbirkan
1
عكفة الماكد المتقدمة - أبو فارس ©// 🔒 Advanced MACD Curve © 2025 // 💡 Idea & Creativity: Engineer Abu Elias // 🛠️ Development & Implementation: Abu Fares // 📜 All intellectual rights reserved - Copying, modifying, or redistributing is not permitted // 🚫 Any attempt to tamper with this code or violate intellectual property rights is legally prohibited // 📧 For inquiries and licensing: Please contact the developer, Abu Fares
Wskaźnik Pine Script®
od ABU_FARIS