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Squeeze Momentum Early In and Out CandlesJohn Carter presented some candles called "Early In and Out Candles". Although I couldn't imitate the exact candles and warnings I create better indications and bars in my opinion. When the Candles are above Donchian MA then we have a bullish Momentum. When the Candles are bellow Donchian MA then we have bearish momentum. This indicator works best to get an WARNING to enter and close EARLY positions. Bullish: When the candles are Light Blue then we have early warning to enter. When the candles are Dark Blue then we have early warning to close the position. Bearish: When the candles are Red then we have early warning to enter. When the candles are Yellow then we have early warning to close the position. IMPORTANT NOTES: Always combine it with the Squeeze Pro indicator. Suggested Donchian MA: 5 (You can adjust it). Don't let candles only to be your closing indication once again there are EARLY WARNINGS therefore can move your stop loses to maximize your profits when you are exiting. I tested my self and I found that is the best strategy when we get Dark Blue candle in the Bullish move I move my stop loss little bit bellow the candle. Therefore here we go we have early warnings for In and Out. Thank you and Good Luck.
مؤشر Pine Script®
من ‎P1xsel‎
BTC CME Gaps Detector [SwissAlgo]BTC CME Gaps Detector Track Unfilled Gaps & Identify Price Magnets ------------------------------------------------------ Overview The BTC CME Gap Detector identifies and tracks unfilled price gaps on any timeframe (1-minute recommended for scalping) to gauge potential trading bias. Verify Gap Behavior Yourself : Use TradingView's Replay Mode on the 1-Minute chart to observe how the price interacts with gaps. Load the BTC1! ticker (Bitcoin CME Futures), enable Replay Mode, and play forward through time (for example: go back 15 days). You may observe patterns such as price frequently returning to fill gaps, nearest gaps acting as near-term targets, and gaps serving as potential support/resistance zones. Some gaps may fill quickly, while others may remain open for longer periods. This hands-on analysis lets you independently assess how gaps may influence price movement in real market conditions and whether you may use this indicator as a complement to your trading analysis. ------------------------------------------------------ Purpose Price gaps occur when there is a discontinuity between consecutive candles - when the current candle's low is above the previous candle's high (gap up), or when the current candle's high is below the previous candle's low (gap down). This indicator identifies and tracks these gaps on any timeframe to help traders: Identify gap zones that may attract price (potential "price magnets") Monitor gap fill progression Assess potential directional bias based on nearest unfilled gaps (long, short) Analyze market structure and liquidity imbalances ------------------------------------------------------ Why Use This Indicator? Universal Gap Detection : Identifies all gaps on any timeframe (1-minute, hourly, daily, etc.) Multi-Candle Mitigation Tracking : Detects gap fills that occur across multiple candles Distance Analysis : Shows percentage distance to nearest bullish and bearish gaps Visual Representation : Color-coded boxes indicate gap status (active vs. mitigated) Age Filtering : Option to display only gaps within specified time periods (3/6/12/24 months), as older gaps may lose relevance ATR-Based Sizing : Minimum gap size adjusts to instrument volatility to filter noise (i.e. small gaps) ------------------------------------------------------ Trading Concept Gaps represent price zones where no trading occurred. Historical market behavior suggests that unfilled gaps may attract price action as markets tend to revisit areas of incomplete price discovery. This phenomenon creates potential trading opportunities: Bullish gaps (above current price) may act as upside targets where the price could move to fill the gap Bearish gaps (below current price) may act as downside targets where price could move to fill the gap The nearest gap often provides directional bias, as closer gaps may have a higher probability of being filled in the near term This indicator helps quantify gap proximity and provides a visual reference for these potential target zones. EXAMPLE Step 1: Bearish Gaps Appear Below Price Step 2: Price Getting Close to Fill Gap Step 3: Gap Mitigated Gap ------------------------------------------------------ Recommended Setup Timeframe: 1-minute chart recommended for maximum gap detection frequency. Works on all timeframes (higher timeframes will show fewer, larger gaps). Symbol: Any tradable instrument. Originally designed for BTC1! (CME Bitcoin Futures) but compatible with all symbols. Settings: ATR Length: 14 (default) Min Gap Size: 0.5x ATR (adjust based on timeframe and noise level) Gap Age Limit: 3 months (configurable) Max Historical Gaps: 300 (adjustable 1-500) ------------------------------------------------------ How It Works Gap Detection : Identifies price discontinuities on every candle where: Gap up: current candle low > previous candle high Gap down: current candle high < previous candle low Minimum gap size filter (ATR-based) eliminates insignificant gaps Mitigation Tracking : Monitors when price touches both gap boundaries. A gap is marked as filled when the price has touched both the top and bottom of the gap zone, even if this occurs across multiple candles. Visual Elements : Green boxes: Unfilled gaps above current price (potential bullish targets) Red boxes: Unfilled gaps below current price (potential bearish targets) Gray boxes: Filled gaps (historical reference) Labels: Display gap type, price level, and distance percentage Analysis Table: Shows : Distance % to nearest bullish gap (above price) Distance % to nearest bearish gap (below price) Trade bias (LONG if nearest gap is above, SHORT if nearest gap is below) ------------------------------------------------------ Key Features Detects gaps on any timeframe (1m, 5m, 1h, 1D, etc.) Boxes extend 500 bars forward for active gaps, stop at the fill bar for mitigated gaps Real-time distance calculations update on every candle Configurable age filter removes outdated gaps ATR multiplier ensures gap detection adapts to market volatility and timeframe ------------------------------------------------------ Disclaimer This indicator is provided for informational and educational purposes only. It does not constitute financial advice, investment recommendations, or trading signals. The concept that gaps attract price is based on historical observation and does not guarantee future results. Gap fills are not certain - gaps may remain unfilled indefinitely, or the price may reverse before reaching a gap. This indicator should not be used as the sole basis for trading decisions. All trading involves substantial risk, including the potential loss of principal. Users should conduct their own research, apply proper risk management, test strategies thoroughly, and consult with qualified financial professionals before making trading decisions. The authors and publishers are not responsible for any losses incurred through the use of this indicator.
مؤشر Pine Script®
من ‎SwissAlgo‎
Lightning Session LevelsLightning Session Levels (LSL) draws clean, non-repainting levels for the major market sessions and a compact HUD in the top-right corner. It’s built to be lightweight, readable, and “set-and-forget” for intraday traders. What it shows Session High/Low and Open/Close levels for: ASIA (00:00–08:00 UTC) EUROPE (07:00–16:00 UTC) US (13:30–20:00 UTC) OVERNIGHT (20:00–24:00 UTC) HUD panel: Current active session Countdown to the next US session (auto-calculated from UTC) How it works (non-repainting) Levels are anchored at session close. Each line is created once on the confirmed closing bar of the session (x2 = session end). Optional Extend Right keeps the level projecting forward without changing the anchor (no “drifting”). All drawings are pinned to the right price scale for stable reading. Inputs Show HUD — toggle the top-right panel. Show Levels — master switch for drawing levels. Draw High/Low — H/L session levels. Draw Open/Close — O/C session levels. Extend Right — extend all session lines to the future. Keep N past sessions per market — FIFO limit per session group (default 12). ASIA / EUROPE / US / OVERNIGHT — enable/disable specific sessions. Style & palette Consistent “Lightning” colors: ASIA = Cyan, EUROPE = Violet, US = Amber, OVERNIGHT = Teal Labels are always size: Normal for readability. HUD uses a dark, subtle two-tone background to stay out of the way. Recommended use Timeframes: intraday (1m → 4h). On 1D and higher, TradingView’s session-window time() filters won’t match intraday windows, so levels won’t plot (by design). Markets: crypto, indices, FX, equities — any symbol where intraday session context helps. Notes & limitations Fixed UTC windows. The US window is set to 13:30–20:00 UTC. Daylight-saving shifts (DST) are not auto-adjusted; if you need region-specific DST behavior, treat this as a consistent UTC model. The HUD timer counts down to the next US open from the current UTC clock. Draw limits are capped (500 lines, 500 labels) for performance and stability. Quick start Add Lightning Session Levels to your chart. Toggle Draw High/Low and/or Draw Open/Close. Turn on Extend Right if you want the levels to project forward. Enable only the sessions you care about (e.g., just EUROPE and US). Use Keep N past sessions to control clutter (e.g., 6–12). Disclaimer This tool is for educational/informational purposes only and is not financial advice. Past session behavior does not guarantee future results. Always manage risk.
مؤشر Pine Script®
من ‎MahaTrend‎
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent. Table of contents What the script does at a glance Momentum, what it is, how to know if it is present Volatility targeting, why and how it is done here Portfolio construction modes: Long Only, Short Only, Delta Neutral Regime filter and when the strategy goes to cash Transaction cost modelling in this script Backtest metrics and definitions Performance attribution chart Monte Carlo simulation Scatter plot analysis modes Asset allocation pie chart Inputs, presets, and deployment checklist Suggested workflow 1) What the script does at a glance Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility. Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter. Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite. Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views. 2) Momentum: definition, detection, and validation Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints. Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization. How to know momentum is present . Leaders and laggards persist across adjacent windows rather than flipping every bar. Spread between average momentum of leaders and laggards is materially positive in sample. Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak. Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries. Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime. 3) Volatility targeting: purpose and implementation here Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk. Computation in this script (per asset, rolling): Return series ret = log(price/price ). Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays). Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0). This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero. Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations. Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure. 4) Portfolio construction modes Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties. Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult. Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs. Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3). Notes . The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage. If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta. 5) Regime filter A fast EMA(12) vs EMA(21) filter gates exposure. Long Only active when EMA12 > EMA21. Otherwise the book is set to cash. Short Only active when EMA12 < EMA21. Otherwise cash. Delta Neutral is always active. This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar. 6) Transaction cost modelling There are two cost touchpoints in the script. Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar. Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots. Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders. Practical advice . If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue. For crypto perp microcaps, stress higher cost assumptions and add slippage buffers. If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag. 7) Backtest metrics and definitions The script computes a standard set of portfolio statistics once the start date is reached. Net Profit percent over the full test. Max Drawdown percent, tracked from running peaks. Annualized Mean and Stdev using the chosen trading day count. Variance is the square of annualized stdev. Sharpe uses daily mean adjusted by risk-free rate and annualized. Sortino uses downside stdev only. Omega ratio of sum of gains to sum of losses. Gain-to-Pain total gains divided by total losses absolute. CAGR compounded annual growth from start date to now. Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM. Skewness of daily returns. VaR 95 linear-interpolated 5th percentile of daily returns. CVaR average of the worst 5 percent of daily returns. Benchmark Buy-and-Hold equity path for comparison. 8) Performance attribution Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels. 9) Monte Carlo simulation The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed. Normal uses Box-Muller with your seed. Good for quick, smooth envelopes. Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption. Bands show 10th, 25th, 75th, 90th percentiles and the path mean. 10) Scatter plot analysis Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels. Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite. Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations. Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta. Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk. 11) Asset allocation pie chart Builds a wheel of current allocations. Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent. Short Only, weights show the short book as positive slices that sum to 100 percent. Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional. Labels can show asset, percent, and current leverage. 12) Inputs and quick presets Core Portfolio Strategy . Long Only, Short Only, Delta Neutral. Initial Capital . For equity scaling in the panel. Trading Days/Year . 252 for stocks, 365 for crypto. Target Volatility . Annualized, drives volMult. Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above. Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier. Start Date . Ensure every symbol has data back to this date to avoid bias. Benchmark . Used for alpha, beta, and B&H line. Diagnostics Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill. Attribution panel. Toggle winners only to focus on what matters. Monte Carlo mode with Normal or Bootstrap and confidence bands. Scatter plot type and styling, labels, and portfolio star. Pie chart and labels for current allocation. Presets Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands. Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward. Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta. 13) Suggested workflow Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees. Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe. Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity. Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear. Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it. Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence. Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier. Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs. Important implementation details mapped to code Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting. Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult. Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively. Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate. Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison. Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures. Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds. Final notes Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call. Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it. Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
مؤشر Pine Script®
من ‎BackQuant‎
TrendDetectorLibLibrary "TrendDetector_Lib" method formatTF(timeframe)   Namespace types: series string, simple string, input string, const string   Parameters:      timeframe (string) : (string) The timeframe to convert (e.g., "15", "60", "240").   Returns: (string) The formatted timeframe (e.g., "15M", "1H", "4H"). f_ma(type, src, len)   Computes a Moving Average value based on type and length.   Parameters:      type (simple string) : (string) One of: "SMA", "EMA", "RMA", "WMA", "VWMA".      src (float) : (series float) Source series for MA (e.g., close).      len (simple int) : (simple int) Length of the MA.   Returns: (float) The computed MA series. render(tbl, trendDetectorSwitch, frameColor, frameWidth, borderColor, borderWidth, textColor, ma1ShowTrendData, ma1Timeframe, ma1Value, ma2ShowTrendData, ma2Timeframe, ma2Value, ma3ShowTrendData, ma3Timeframe, ma3Value)   Fills the provided table with Trend Detector contents. @desc This renderer does NOT plot and does NOT create tables; call from indicator after your table exists.   Parameters:      tbl (table) : (table) Existing table to render into.      trendDetectorSwitch (bool) : (bool) Master toggle to draw the table content.      frameColor (color) : (color) Table frame color.      frameWidth (int) : (int) Table frame width (0–5).      borderColor (color) : (color) Table border color.      borderWidth (int) : (int) Table border width (0–5).      textColor (color) : (color) Table text color.      ma1ShowTrendData (bool) : (bool) Show MA #1 in table.      ma1Timeframe (simple string) : (string) MA #1 timeframe.      ma1Value (float)      ma2ShowTrendData (bool) : (bool) Show MA #2 in table.      ma2Timeframe (simple string) : (string) MA #2 timeframe.      ma2Value (float)      ma3ShowTrendData (bool) : (bool) Show MA #3 in table.      ma3Timeframe (simple string) : (string) MA #3 timeframe.      ma3Value (float)
مكتبة Pine Script®
من ‎rithsilanew2020‎
Swing High/Low Support ResistanceThis indicator detects recent swing highs and swing lows using Pine Script pivots and marks them with visible chart labels. These points highlight potential turning areas in price action and can help identify short-term support or resistance for intraday or swing trading. How to Apply Locate the indicator in TradingView’s “Indicators” library; search by its name or author. Click the star icon to mark it as a favourite for quick future access. Apply directly to your chosen chart and timeframe with a single click—no need to enter or paste code. Adjust the input parameters from the settings panel if desired to personalize swing sensitivity. Choose Your Timeframe: Apply to any intraday or swing timeframe; shorter lengths show more frequent pivots. Set Sensitivity: Use the “Swing Detection Length” input to adjust how many bars define a pivot, making swings more or less sensitive to price action. How to Analyze Swing High Labels: Mark recent local peaks, suggesting resistance zones or possible reversal points. Swing Low Labels: Highlight recent bottoms, indicating support or bounce areas. Monitor labels for clustering or repeated appearance at similar levels, which may strengthen their importance as price reacts near those points. Track how price behaves after forming new pivots—multiple tests can affirm the relevance of a level. What Traders Should Watch Price reaction at labeled areas: frequent tests may anticipate reversals or breakouts. Transition between higher highs/higher lows (uptrend) vs. lower highs/lower lows (downtrend). Combine the swing levels with other analysis methods, such as volume, RSI, or EMA, for better signal quality. Features Included Dynamic swing high and low detection via confirmed pivots. Direct labeling on the chart for market structure clarity. No repainting—labels show only after complete formation. Fully automatic updates as price action unfolds. No promotional, external, or non-compliant elements; open source and safe for public or private use. Compliance Notes No signals, buy/sell calls, financial advice, or performance claims. No hidden code, advertising, or off-platform contacts. Pure educational and analytical utility; adheres to all TradingView house rules and script publishing policies.​ Disclaimer This indicator is for informational purposes only and does not constitute advice. Always do your own research and use proper risk management.
مؤشر Pine Script®
من ‎darshakssc‎
4
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion . Core Architecture Why These Components Are Combined: The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention. The Four Trading Agents 1. Spoofing Detector Agent 🎭 Detects iceberg orders through persistent volume at similar price levels over 5 bars Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average) Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model) Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation Best Markets: Consolidations, institutional trading windows, low-liquidity hours 2. Exhaustion Detector Agent ⚡ Calculates RSI divergence between price movement and momentum indicator over 5-bar window Detects VWAP exhaustion (price at 2σ bands with declining volume) Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure Signal Logic: Counter-trend—enters when momentum extreme shows weakness Best Markets: Trending markets reaching climax points, over-extended moves 3. Liquidity Void Detector Agent 💧 Measures Bollinger Band squeeze (width <60% of 50-period average) Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR) Signal Logic: Breakout anticipation—enters after liquidity grab but before main move Best Markets: Range-bound pre-breakout, volatility compression zones 4. Mean Reversion Agent 📊 Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ) Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model) Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability) Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments Adaptive Selection: Multi-Armed Bandit System The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance: Thompson Sampling (Default - Recommended): Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent) Balances exploration (trying underused agents) vs. exploitation (using proven winners) Each agent's win/loss history informs its selection probability Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation UCB1 (Upper Confidence Bound): Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls) Deterministic algorithm favoring agents with high uncertainty (potential upside) More conservative than Thompson Sampling Epsilon-Greedy: Exploits best-performing agent (1-ε)% of the time Explores randomly ε% of the time (default 10%, configurable 1-50%) Simple, transparent, easily tuned via epsilon parameter Gradient Bandit: Uses softmax probability distribution over agent preference weights Updates weights via gradient ascent based on rewards Best for Blend mode where all agents contribute Selection Modes: Switch Mode: Uses only the selected agent's signal (clean, decisive) Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified) Lock Agent Feature: Optional manual override to force one specific agent Useful after identifying which agent dominates your specific instrument Only applies in Switch mode Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion Memory System Dual-Layer Architecture: Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50) Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars) Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20% Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds) How Memory Works: Agent generates signal → outcome tracked after 8 bars (performance horizon) Result stored in short-term memory (win = 1.0, loss = 0.0) Short-term average influences agent's future scores (positive feedback loop) After threshold met (default 10 results), episode averaged into long-term storage Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost Market Microstructure Analysis These advanced metrics quantify institutional order flow dynamics: Order Flow Toxicity (Simplified VPIN): Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol) Detects informed trading activity (institutional players with non-public information) Values >0.4 indicate "toxic flow" (informed traders active) Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification Price Impact Analysis (Simplified Kyle's Lambda): Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10) Low values = large orders with minimal price impact ( stealth accumulation ) High values = retail-dominated moves with high slippage Lite Approximation: Uses simplified denominator instead of regression-based signed order flow Market Randomness (Entropy Analysis): Counts unique price changes over 20 bars / 20 Measures market predictability High entropy (>0.6) = human-driven, chaotic price action Low entropy (<0.25) = algorithmic trading dominance (predictable patterns) Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x)) Order Clustering (Simplified Hawkes Process): Tracks self-exciting event intensity (coordinated order activity) Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average High intensity (>0.7) indicates clustering (potential spoofing/accumulation) Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE Signal Generation Process Multi-Stage Validation: Stage 1: Agent Scoring Each agent calculates internal score based on its detection criteria Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier) Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0) Stage 2: Memory Boost Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance) Successful agents get amplified, failing agents get dampened Stage 3: Bandit Selection/Blending If Adaptive Mode ON: Switch: Bandit selects single best agent, uses only its signal Blend: All agents combined using softmax-weighted confidence scores If Adaptive Mode OFF: Traditional consensus voting with confidence-squared weighting Signal fires when consensus exceeds threshold (default 70%) Stage 4: Confirmation Filter Raw signal must repeat for consecutive bars (default 3, configurable 2-4) Minimum confidence threshold: 0.25 (25%) enforced regardless of mode Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2 Stage 5: Cooldown Enforcement Minimum bars between signals (default 10, configurable 5-15) Prevents over-trading during choppy conditions Stage 6: Performance Tracking After 8 bars (performance horizon), signal outcome evaluated Win = price moved in signal direction, Loss = price moved against Results fed back into memory and bandit statistics Trading Modes (Presets) Pre-configured parameter sets: Conservative: 85% consensus, 4 confirmations, 15-bar cooldown Expected: 60-70% win rate, 3-8 signals/week Best for: Swing trading, capital preservation, beginners Balanced: 70% consensus, 3 confirmations, 10-bar cooldown Expected: 55-65% win rate, 8-15 signals/week Best for: Day trading, most traders, general use Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown Expected: 50-58% win rate, 15-30 signals/week Best for: Scalping, high-frequency trading, active management Elite: 75% consensus, 3 confirmations, 12-bar cooldown Expected: 58-68% win rate, 5-12 signals/week Best for: Selective trading, high-conviction setups Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown Expected: Varies based on learning Best for: Experienced users leveraging bandit system How to Use 1. Initial Setup (5 Minutes): Select Trading Mode matching your style (start with Balanced) Enable Adaptive Learning (recommended for automatic agent selection) Choose Thompson Sampling algorithm (best all-around performance) Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume) 2. Agent Tuning (Optional): Adjust Agent Sensitivity multipliers (0.5-2.0): <0.8 = Highly selective (fewer signals, higher quality) 0.9-1.2 = Balanced (recommended starting point) 1.3 = Aggressive (more signals, lower individual quality) Monitor dashboard for 20-30 signals to identify dominant agent If one agent consistently outperforms, consider using Lock Agent feature 3. Bandit Configuration (Advanced): Blend Temperature (0.1-2.0): 0.3 = Sharp decisions (best agent dominates) 0.5 = Balanced (default) 1.0+ = Smooth (equal weighting, democratic) Memory Decay (0.8-0.99): 0.90 = Fast adaptation (volatile markets) 0.95 = Balanced (most instruments) 0.97+ = Long memory (stable trends) 4. Signal Interpretation: Green triangle (▲): Long signal confirmed Red triangle (▼): Short signal confirmed Dashboard shows: Active agent (highlighted row with ► marker) Win rate per agent (green >60%, yellow 40-60%, red <40%) Confidence bars (█████ = maximum confidence) Memory size (short-term buffer count) Colored zones display: Entry level (current close) Stop-loss (1.5× ATR) Take-profit 1 (2.0× ATR) Take-profit 2 (3.5× ATR) 5. Risk Management: Never risk >1-2% per signal (use ATR-based stops) Signals are entry triggers, not complete strategies Combine with your own market context analysis Consider fundamental catalysts and news events Use "Confirming" status to prepare entries (not to enter early) 6. Memory Persistence (Optional): After 50-100 trades, check Memory Export Panel Record displayed alpha/beta/weight values for each agent Record VPIN and Kyle threshold values Enable "Restore From Memory" and input saved values to continue learning Useful when switching timeframes or restarting indicator Visual Components On-Chart Elements: Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend) Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers) Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR) Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal) Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets Signal Markers: Triangle up/down at validated entry points Dashboard (Configurable Position & Size): Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear) Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus) Agent Performance Table: Real-time win%, confidence, and memory stats Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled) Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress Memory Panel (Configurable Position & Size): Live Parameter Export: Alpha, beta, and weight values per agent Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold Save Reminder: Visual indicator if parameters should be recorded What Makes This Original This script's originality lies in three key innovations: 1. Genuine Meta-Learning Framework: Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning. 2. Episodic Memory Architecture with Transfer Learning: The dual-layer memory system mimics human learning patterns: Short-term memory captures recent performance (recency bias) Long-term memory preserves historical patterns (experience) Automatic transfer mechanism consolidates knowledge Memory boost creates positive feedback loops (successful strategies become stronger) This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates. 3. Institutional Microstructure Integration: Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis. Mashup Justification: The four agents are combined specifically for risk diversification across failure modes: Spoofing Detector: Prevents false breakout losses from manipulation Exhaustion Detector: Prevents chasing extended trends into reversals Liquidity Void: Exploits volatility compression (different regime than trending) Mean Reversion: Provides mathematical anchoring when patterns fail The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals. Why "ML-lite"? Simplifications and Approximations This is the "lite" version due to necessary simplifications for Pine Script execution: 1. Simplified VPIN Calculation: Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume) Impact: May misclassify volume during ranging/choppy markets; works best in directional moves 2. Pseudo-Random Sampling: Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100 Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection 3. Hawkes Process Approximation: Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event) Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments 4. Kyle's Lambda Simplification: Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals 5. Entropy Calculation: Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude This Implementation: Simple ratio of unique price changes to total observations (variety measure) Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape 6. Memory System Constraints: Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking 7. Limited Feature Engineering: Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations 8. Single-Instrument Data: Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes This Implementation: Only OHLCV data from displayed instrument Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets 9. Fixed Performance Horizon: Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement This Implementation: Fixed 8-bar evaluation window Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach Performance Impact Summary: These simplifications make the script: ✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations ✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers ✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models) ✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint ⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations ⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows ⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized When to Upgrade to Full Implementation: Consider professional Python/C++ versions with institutional data feeds if: Trading with >$100K capital where precision differences materially impact returns Operating in microsecond-competitive environments (HFT, market making) Requiring regulatory-grade audit trails and reproducibility Backtesting with tick-level precision for strategy validation Need true real-time adaptation with neural network-based learning For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact. Technical Notes All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap ) VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above) Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling No repainting: All calculations use confirmed bar data (no forward-looking) Maximum lookback: 500 bars (set via max_bars_back parameter) Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed) Confidence threshold: Minimum 0.25 (25%) enforced on all signals Memory arrays: Dynamic sizing with FIFO queue management Limitations and Disclaimers Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty. Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context. Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms. Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts. Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets. No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss. Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics. Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes. Recommended Settings Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning) Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices) Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable) Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens) Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0) After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent Alert Configuration The script includes built-in alert conditions: Long Signal: Fires when validated long entry confirmed Short Signal: Fires when validated short entry confirmed Alerts fire once per bar (after confirmation requirements met) Set alert to "Once Per Bar Close" for reliability Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
مؤشر Pine Script®
من ‎DskyzInvestments‎
9
Range breaking indicatorDescription Bull/Bear Area Ratio (last N candles) helps identify potential end-of-range situations by analyzing the relative strength of bullish vs bearish candles over a rolling window of N bars. Instead of simply counting up or down candles, this script measures the "area" of each candle — the absolute distance between open and close, optionally weighted by volume. By summing these areas over the last N bars, it calculates the percentage of bullish and bearish energy within that period. When both sides become balanced (near 50/50), it often signals range exhaustion or possible trend transition. How it works Calculates the bullish and bearish area of each candle (abs(close - open), optionally × volume). Maintains rolling buffers of the last N bars to compute running totals. Plots both Bullish % (green) and Bearish % (red). Highlights possible range-ending zones when the bullish ratio nears 50% ± threshold. Displays a label showing the current balance. Includes an alert condition when equilibrium is detected. Inputs Number of candles (N) – Rolling window length. Use volume weighting – Multiplies each candle’s area by its volume. Balance threshold (%) – Sensitivity for detecting equilibrium (default: 10%). Best use Combine with volume or volatility indicators to confirm market compression or expansion. Use on higher timeframes (H1, H4, D1) to detect early signs of accumulation or distribution. Works across all asset types: crypto, forex, stocks, indices, etc. Alerts An alert is triggered when: “The range of the last N candles is balanced (possible end of range).”
مؤشر Pine Script®
من ‎sudo_delete_evil‎
cd_sfp_CxGeneral: This indicator is designed to assist users who trade the Swing Failure Pattern ( SFP ). In technical literature (various definitions exist), an SFP is a situation where the price violates a previous swing level but fails to close beyond that level. • (Liquidity Sweep) • (Buyer or seller dominance) • (Stop hunt) • (Turtle Soup) The general strategy is built upon seeking trade opportunities after an SFP is formed and conviction is established that the market direction has changed. Components used to gather confirmation: • Determining Bias: Periodic SAR • Obtaining Breakout/Reversal Confirmation: Change in State Delivery (CISD) • Defining the Buyer/Seller Block (Supply/Demand Zones): Mitg Blocks (Mitigation Blocks), FVG (Fair Value Gaps), and Standard Deviation Projection • Key Levels: Previous HTF (Higher Time Frame) levels • Setting Targets: Standard Deviation Projection • Trade Management: Anchored VWAP and opposing blocks • Time-Based Context: Session Killzone times • Notifications: An alarm/alert system will be utilized to stay informed. ________________________________________ Details: Swing and Swing Failure Pattern: Swing Sweep Types (Liquidity Sweep): 1. Single 2. Consecutive (The liquidity of the entity that swept the liquidity is being swept) Bias Determination We need to filter out the numerous SFPs that occur across all time frames. Our first strong filter will be the Bias. We will only look for trades aligned with our bias. We will use Periodic SAR (Stop and Reverse) to determine the bias. We compare the price with the SAR value from a Higher Time Frame than the one we are trading on. • Price > SAR => Bullish Bias • Price < SAR => Bearish Bias Depending on the pair, H1 SAR may be chosen for scalp trades, and Daily/Weekly SAR for intraday and swing trades. Key Levels Strategies looking for trades after a liquidity grab generally state that the sweep / stop hunt movement should occur at a significant price level. The most fundamental Key Level levels are (User can customize): • Previous Week High & Low • Previous Day High & Low • Previous H4 High & Low • Previous H1 High & Low • Asia Killzone High & Low • London Killzone High & Low • New York Killzone High & Low • Monday Range High & Low values We will prefer SFP formations that occur when these levels are swept. When Key Levels are violated, an information label appears on the screen. Blocks / Zones To strengthen our hand, we will use three types of blocks/zones, either with Key Levels or separately. When an SFP structure is formed in these areas (along with bias and breakout confirmation), our expectation is for the price to continue in our desired direction. These regions are: 1. Mitigation Blocks (Mtg) o (Details can be found in the cd_VWAP_mtg_Cx indicator) o In short: A second candle, following a bullish candle, crosses its high but fails to close above it. We call this a sweep / SFP. When the price, which was expected to go to the low, instead makes a new high/close, an Mtg block is formed. (Buyers are dominant) 2. FVGs (Fair Value Gaps) o We use classic FVG structures. 3. Standard Deviation Projection Boxes o When we get an SFP structure + breakout confirmation (CISD), we use the Standard Deviation Projection to determine our profit-taking and take-profit levels. o Based on the idea that the price often respects the range between -2 and -2.5 of the projection values, we box this range and use it as our area of interest. (Our expectation is for the price to reverse after reaching this target). o Let's mark it on the chart. Confirmation To summarize what has been explained so far: we look for the price to form an SFP structure in levels/zones we deem important, aligned with our bias, and for the breakout to be confirmed with a CISD. No single component is strong on its own, but the success rate increases when they occur together. We observe the following as additional confirmation along with the CISD: a new Mtg block forming in the direction of the breakout, high-volume movement (with FVG and a large body), and respect for VWAPs, the resistance/support line, and the defense block. Additional Confirmations with Breakouts: • Defence block, new mtg and VWAP • Resistance / Support Line: Indicator Signals The indicator marks all formed sweeps, selected key levels, blocks, the projection, and CISD confirmations on the screen. The candle where the CISD confirmation occurs is indicated by an arrow. • Arrows with double short lines signify a CISD that follows an SFP occurring at a Key Level. • All other CISD candle indications are shown with single-line arrows. Trade Management When selecting profit targets in trades (preferably), the projection, opposing blocks, and structures that have formed are taken into account. Do not neglect to look at the structures that have formed against you when entering a trade. Menu Settings: • For Mtg blocks, the trading timeframe or a higher timeframe can be selected. • FVGs formed in the current timeframe are displayed when the price creates an SFP (in "Fvg" option). • Deviation boxes are displayed when the price creates an SFP (in box). • The SAR HTF setting (H1) for scalp trades may vary depending on the pair. Users trying trades on higher timeframes should increase the HTF setting. o Example: If you are looking for a trade with an SFP structure on H1, the SAR HTF setting should be H4 or higher. • VWAP lines are refreshed starting from the candle that executed the sweep when the price forms an SFP. The only setting to adjust is the source selection setting (hlc3 is selected). • Time frames and Killzone / Special Zone settings for Key Levels can be changed/should be checked. Alarms / Alerts: The conditions that will trigger an alert can be selected from the menu. • To receive an alert aligned with the bias, the "Alignment with bias" checkbox must be selected. • The alert should be set on the timeframe where you plan to enter the trade. • The display options do not affect the alarm conditions. (Example: FVGs are monitored even when the menu selection is "off"). • If the necessary conditions are met, the alarm is triggered on the new candle that opens after the CISD confirmation. • The alarm will not be triggered more than once at the same Key Level. The user can preferably select alerts: • Bias-aligned or Bias-independent • Sweep (without waiting for CISD) • Sweep + CISD (without looking for other conditions) • Sweep + Key Level + CISD (the swept level is a Key Level) • Sweep + Mtg / Fvg / Dev. + CISD (SFP formed in any of the blocks) • Sweep + Mtg + CISD (SFP formed in the Mtg block) • Sweep + Fvg + CISD (SFP formed inside the FVG) • Sweep + Deviation Box + CISD (SFP formed inside the Dev. Box) • Sweep + Key Level + Mtg / Fvg / Dev. + CISD (SFP formed simultaneously at a Key Level and any of the blocks) Trade Example: • Conditions: Bias-aligned + Sweep + Mtg/Fvg/Dev (at least one) + CISD • Extra Confirmations: Respect for the Defense Block + Respect for VWAP • Target (TP): Projection between -2 and -2.5 I welcome your thoughts and suggestions regarding my indicator, which I believe will be successful in the long run by adhering to uncompromising risk management and a strict trading plan. Happy Trading!
مؤشر Pine Script®
من ‎cdikici71‎
29
JonnyFutures ScalpomaticThis indicator combines Fair Value Gap (FVG) detection with Exponential Moving Average (EMA) signals to help identify potential trade entry points based on market inefficiencies and trend direction. ⚙️ Main Components 1. EMAs Two EMAs are calculated: EMA 20 (short-term trend) EMA 50 (medium-term trend) ema20 = ta.ema(close, len20) ema50 = ta.ema(close, len50) These are plotted on the chart: Orange line = EMA 20 Purple line = EMA 50 2. Fair Value Gaps (FVGs) The script looks for gaps in price structure (imbalances): Bullish FVG → when the high from two bars ago is less than the current bar’s low → Indicates a potential bullish inefficiency. Bearish FVG → when the low from two bars ago is greater than the current bar’s high → Indicates a potential bearish inefficiency. bullishFVG = high < low bearishFVG = low > high When found, the background color changes: Light Green for bullish FVGs Light Red for bearish FVGs bgcolor(bullishFVG ? color.new(color.green,90) : na) bgcolor(bearishFVG ? color.new(color.red,90) : na) 3. EMA Crossover Signals The script plots visual buy/sell markers when the closing price crosses above or below each EMA: For EMA 20: ✅ Long signal (triangle up, lime): close crosses above EMA 20 ❌ Short signal (triangle down, red): close crosses below EMA 20 For EMA 50: ✅ Long signal (triangle up, aqua): close crosses above EMA 50 ❌ Short signal (triangle down, maroon): close crosses below EMA 50 These signals help identify potential trend-following entries or exits. 📈 How to Use It Trend Confirmation: Watch the EMAs — if EMA20 > EMA50, the trend is generally bullish; if EMA20 < EMA50, it’s bearish. FVG Zones: When a bullish or bearish FVG appears, it highlights potential retracement zones where price may rebalance before continuing the trend. Entry Timing: Combine FVGs with EMA cross signals for confluence: Long bias → bullish FVG + price crossing above EMA 20 or 50. Short bias → bearish FVG + price crossing below EMA 20 or 50. 🎯 Summary This indicator: Detects Fair Value Gaps and shades them visually. Plots EMA 20 and EMA 50 to define short- and mid-term trends. Marks entry/exit signals when price crosses these EMAs. Is designed for trend-following trades with FVG confirmation
مؤشر Pine Script®
من ‎jonathanjohnston7‎
MSB Trend Breakout Indicator**MSB Trend Breakout Indicator: The Low-Risk Trend Catcher** This indicator is built on a proprietary system designed to identify and confirm high-momentum price breakouts within the prevailing market trend. **What It Does:** * **Confirms Trend Bias:** Uses a customized Moving Average filter to determine the direction of institutional flow (Long or Short). * **Identifies Entry Momentum:** Plots signals only when price breaks a calculated short-term support/resistance level, confirming strong directional momentum. * **Filters Noise:** Excludes signals during market chop or counter-trend movement, focusing on higher probability setups. **Optimized For:** XAUUSD (Gold) on 15m/30m charts. --- **Important Note & Risk Disclosure:** The underlying logic is designed to optimize risk management. This tool is for informational and educational use only. Past performance is not indicative of future results, and this indicator does not guarantee profits. Please use appropriate risk tolerance when trading.
مؤشر Pine Script®
من ‎kmozkan‎
Bitcoin 50-Week MA “Cycle Line” — Bull/Bear RegimeOverview. This indicator plots the true 50-week simple moving average (SMA) on any timeframe by requesting weekly data, then colors the chart to show a bull/bear regime: green when price is above the 50-week MA, red when below. Many Bitcoin analysts treat this average as a cycle “line in the sand”—multiple weekly closes below it have historically coincided with the end of bull phases and the start of broader drawdowns, while holding above has aligned with continued strength. Why it matters. Recently, research notes and market commentary have flagged the 50-week MA near ~$100k–$103k (feed-dependent) as a critical support/resistance area to watch. Use this tool to track that threshold on your own exchange feed and timeframe. How it works. Pulls weekly closes via request.security() and computes a 50-week SMA (not an EMA). Plots the line on your current chart and applies a regime color (background or candles, toggle in settings). Works on any symbol; optimized for BTC spot and futures. Notes. MA values vary by data source/exchange; confirm on a weekly chart if needed. A single intrawEEK break can whipsaw; many analysts look for confirmed weekly closes. Pair with your macro/liquidity view and risk rules.
مؤشر Pine Script®
من ‎csliddell‎
Stochastic + Bollinger Bands Multi-Timeframe StrategyThis strategy fuses the Stochastic Oscillator from the 4-hour timeframe with Bollinger Bands from the 1-hour timeframe, operating on a 10-hour chart to capture a unique volatility rhythm and temporal alignment discovered through observational alpha. By blending momentum confirmation from the higher timeframe with short-term volatility extremes, the strategy leverages what some traders refer to as “rotating volatility” — a phenomenon where multi-timeframe oscillations sync to reveal hidden trade opportunities. 🧠 Strategy Logic ✅ Long Entry Condition: Stochastic on the 4H timeframe: %K crosses above %D Both %K and %D are below 20 (oversold zone) Bollinger Bands on the 1H timeframe: Price crosses above the lower Bollinger Band, indicating a potential reversal → A long trade is opened when both momentum recovery and volatility reversion align. ✅ Long Exit Condition: Stochastic on the 4H: %K crosses below %D Both %K and %D are above 80 (overbought zone) Bollinger Bands on the 1H: Price reaches or exceeds the upper Bollinger Band, suggesting exhaustion → The long trade is closed when either signal suggests a potential reversal or overextension. 🧬 Temporal Structure & Alpha This strategy is deployed on a 10-hour chart — a non-standard timeframe that may align more effectively with multi-timeframe mean reversion dynamics. This subtle adjustment exploits what some traders identify as “temporal drift” — the desynchronization of volatility across timeframes that creates hidden rhythm in price action. → For example, Stochastic on 4H (lookback 17) and Bollinger Bands on 1H (lookback 20) may periodically sync around 10H intervals, offering unique alpha windows. 📊 Indicator Components 🔹 Stochastic Oscillator (4H, Length 17) Detects momentum reversals using %K and %D crossovers Helps define overbought/oversold zones from a mid-term view 🔹 Bollinger Bands (1H, Length 20, ±2 StdDev) Measures price volatility using standard deviation around a moving average Entry occurs near lower band (support), exits near upper band (resistance) 🔹 Multi-Timeframe Logic Uses request.security() to safely reference 4H and 1H indicators from a 10H chart Avoids repainting by using closed higher-timeframe candles only 📈 Visualization A plot selector input allows toggling between: Stochastic Plot (%K & %D, with overbought/oversold levels) Bollinger Bands Plot (Upper, Basis, Lower from 1H data) This helps users visually confirm entry/exit triggers in real time. 🛠 Customization Fully configurable Stochastic and BB settings Timeframes are independently adjustable Strategy settings like position sizing, slippage, and commission are editable ⚠️ Disclaimer This strategy is intended for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. Market conditions vary, and past performance does not guarantee future results. Always test any trading strategy in a simulated environment and consult a licensed financial advisor before making real-world investment decisions.
استراتيجية Pine Script®
من ‎RWCS_LTD‎
Moyennes Mobiles PersonnaliséesThis script simply combines the moving averages I use most frequently in my market analysis: EMA 13 and 21, along with SMA 50 and 200. The goal is to gather all of these essential indicators into a single tool, avoiding the need to load multiple separate scripts and keeping the chart clean and efficient. There’s no complex logic or advanced functionality here — the script’s purpose is purely to display these moving averages in a convenient, streamlined way. I’m providing a more detailed description primarily to meet TradingView’s publication requirements, which ask for a minimum amount of explanatory text before an indicator can be published.
مؤشر Pine Script®
من ‎Gringalet‎
Gap Zones - Daily & Intraday (v9)Gap Zones (wick-fill, custom colors, hide filled) What’s new • Wick-based gap fills • A gap is now considered filled as soon as the price wick touches the far edge of the gap. • Gap up → filled when low ≤ lower edge. • Gap down → filled when high ≥ upper edge. • Custom colors & transparency • New inputs: • Gap Up Color • Gap Down Color • Gap Fill Transparency • Lets you style bullish / bearish gaps and adjust how strong the zones appear on the chart. • Hide filled gaps (optional) • New toggle: Hide Gaps Once Filled. • When ON, lines and fills are deleted as soon as the gap is filled. • When OFF, gaps stop extending and are faded out (colors set to na). • Daily + Intraday modes preserved • Gap Mode still supports both: • "Daily" → gaps between daily candles (using higher-timeframe data). • "Intraday Bars" → gaps between consecutive intraday bars. Stability / bug fixes • Fixed all line.new() and label.new() syntax issues by keeping function calls on a single line. • Added a safety guard so the script doesn’t try to read from empty arrays on the first bar (if count > 0). • Cleaned up the gap detection logic so there are no ta.change() warnings.
مؤشر Pine Script®
من ‎gtotten10‎
3