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RTH Levels: VWAP + PDH/PDL + ONH/ONL + IBAlgo Index — Levels Pro (ONH/ONL • PDH/PDL • VWAP±Bands • IB • Gaps) Purpose. A session-aware, non-repainting levels tool for intraday decision-making. Designed for futures and indices, with clean visuals, alerts, and a one-click Minimal Mode for screenshot-ready charts. What it plots • PDH/PDL (RTH-only) – Prior Regular Trading Hours high/low, computed intraday and frozen at the RTH close (no 24h mix-ups, no repainting). • ONH/ONL – Prior Overnight high/low, held throughout RTH. • RTH VWAP with ±σ bands – Volume-weighted variance, reset each RTH. • Initial Balance (IB) – First N minutes of RTH, plus 1.5× / 2.0× extensions after IB completes. • Today’s RTH Open & Prior RTH Close – With gap detection and “gap filled” alert. • Killzone shading – NY Open (09:30–10:30 ET) and Lunch (11:15–13:30 ET). • Values panel (top-right) – Each level with live distance in points & ticks. • Right-edge level tags – With anti-overlap (stagger + vertical jitter). • Price-scale tags – Native trackprice markers that always “stick” to the axis. ⸻ New in v6.4 • Minimal Mode: one click for a clean look (thinner lines, VWAP bands/IB extensions hidden, on-chart right-edge labels off; price-scale tags remain). • Theme presets: Dark Hi-Contrast / Light Minimal / Futures Classic / Muted Dark. • Anti-overlap controls: horizontal staggering, vertical jitter, and baseline offset to keep tags readable even when levels cluster. ⸻ Quick start (2 minutes) 1. Add to chart → keep defaults. 2. Sessions (ET): • RTH Session default: 09:30–16:00 (US equities cash hours). • Overnight Session default: 18:00–09:29. Adjust for your market if you use different “day” hours (e.g., many use 08:20–13:30 ET for COMEX Gold). 3. Theme & Minimal Mode: pick a Theme Preset; enable Minimal Mode for screenshots. 4. Visibility: toggle PD/ON/VWAP/IB/References/Panel to taste. 5. Right-edge labels: turn Show Right-Edge Labels on. If they crowd, tune: • Anti-overlap: min separation (ticks) • Horizontal offset per tag (bars) • Vertical jitter per step (ticks) • Right-edge baseline offset (bars) 6. Alerts: open Add alert → Condition: and pick the events you want. ⸻ How levels are computed (no repainting) • PDH/PDL: Intraday H/L are accumulated only while in RTH and saved at RTH close for “yesterday’s” values. • ONH/ONL: Accumulated across the defined Overnight window and then held during RTH. • RTH VWAP & ±σ: Volume-weighted mean and standard deviation, reset at the RTH open. • IB: First N minutes of RTH (default 60). Extensions (1.5×/2.0×) appear after IB completes. • Gaps: Today’s RTH open vs prior RTH close; “Gap Filled” triggers when price trades back to prior close. ⸻ Practical playbooks (how to trade around the levels) 1) PDH/PDL interactions • Rejection: Price taps PDH/PDL then closes back inside → mean-reversion toward VWAP/IB. • Acceptance: Close/hold beyond PDH/PDL with momentum → continuation to next HTF/IB target. • Alert: PD Touch/Break. 2) ONH/ONL “taken” • Often one ON extreme is taken during RTH. ONH Taken / ONL Taken → check if it’s a clean break or sweep & reclaim. • Sweep + reclaim near VWAP can fuel rotations through the ON range. 3) VWAP ±σ framework • Balanced: First tag of ±1σ often reverts toward VWAP. • Trend: Persistent trade beyond ±1σ + IB break → target ±2σ/±3σ. • Alerts: VWAP Cross and VWAP Reject (cross then immediate fail back). 4) IB breaks • After IB completes, a clean IB break commonly targets 1.5× and sometimes 2.0×. • Quick return inside IB = possible fade back to the opposite IB edge/VWAP. • Alerts: IB Break Up / Down. 5) Gaps • Gap-and-go: Opening drive away from prior close + VWAP support → trend until IB completion. • Gap-fill: Weak open and VWAP overhead/underfoot → trade toward prior close; manage on Gap Filled alert. Pro tip: Stack confluences (e.g., ONL sweep + VWAP reclaim + IB hold) and respect your execution rules (e.g., require a 5-minute close in direction, or your order-flow confirmation). ⸻ Inputs you’ll actually touch • Sessions (ET): Session Timezone, RTH Session, Overnight Session. • Visibility: toggles for PD/ON/VWAP/IB/Ref/Panel. • VWAP bands: set σ multipliers (±1/±2/±3). • IB: duration (minutes) and extension multipliers (1.5× / 2.0×). • Style & Theme: Theme Preset, Main Line Width, Trackprice, Minimal Mode, and anti-overlap controls. ⸻ Alerts included • PD Touch/Break — High ≥ PDH or Low ≤ PDL • ONH Taken / ONL Taken — First in-RTH take of ONH/ONL • VWAP Cross — Close crosses VWAP • VWAP Reject — Cross then immediate fail back • IB Break Up / Down — Break of IB High/Low after IB completes • Gap Filled — Price trades back to prior RTH close Setup: Add alert → Condition: Algo Index — Levels Pro → choose event → message → Notify on app/email. ⸻ Panel guide The top-right panel shows each level plus live distance from last price: LevelValue (Δpoints | Δticks) Coloring: green if level is below current price, red if above. ⸻ Styling & screenshot tips • Use Theme Preset that matches your chart. • For dark charts, “Dark Hi-Contrast” with Main Line Width = 3 works well. • Enable Trackprice for crisp axis tags that always stick to the right edge. • Turn on Minimal Mode for cleaner screenshots (no VWAP bands or IB extensions, on-chart tags off; price-scale tags remain). • If tags crowd, increase min separation (ticks) to 30–60 and horizontal offset to 3–5; add vertical jitter (4–12 ticks) and/or push tags farther right with baseline offset (bars). ⸻ Behavior & limitations • Levels are computed incrementally; tables refresh on the last bar for efficiency. • Right-edge labels are placed at bar_index + offset and do not track extra right-margin scrolling (TradingView limitation). The price-scale tags (from trackprice) do track the axis. • “RTH” is what you define in inputs. If your market uses different day hours, change the session strings so PDH/PDL reflect your definition of “yesterday’s session.” ⸻ FAQ Q: My PDH/PDL don’t match the daily chart. A: By design this uses RTH-only highs/lows, not 24h daily bars. Adjust sessions if you want a different definition. Q: Right-edge tags overlap or don’t sit at the far right. A: Increase min separation / horizontal offset / vertical jitter and/or push tags farther with baseline offset. If you want markers that always hug the axis, rely on Trackprice. Q: Can I change killzones? A: Yes—edit the session strings in settings or request a version with user inputs for custom windows. ⸻ Disclaimer Educational use only. This is not financial advice. Always apply your own risk management and confirmation rules. ⸻ Enjoy it? Please ⭐ the script and share screenshots using Minimal Mode + a Theme Preset that fits your style.
Indicateur Pine Script®
par MyAlgoIndex
30
Swing Elite Supply & Demand MTFSwing Elite Supply & Demand MTF This indicator automates Supply & Demand zone detection using the classic base-candle pattern methodology, enhanced with multi-timeframe confluence and zone strength classification. How Zones Are Detected The algorithm identifies zones by scanning for the Leg-Base-Leg structure that defines institutional order flow areas. A valid zone requires three components: an initial directional candle (the "leg in"), one or more consolidation candles forming the base, and an explosive breakout candle (the "leg out") that confirms institutional participation. Base candles are identified using a body-to-range ratio threshold — candles where the body occupies less than 55% of the total range qualify as indecision/accumulation. The explosive candle must have a body ratio exceeding 50% and must close beyond the base boundaries, confirming genuine breakout rather than false signals. Zone boundaries are drawn from the base candles, with two width options: "Preferred" uses the candle bodies for tighter zones, while "Wider" extends to the full wick range for more conservative entries. Pattern Classification Zones are labeled by their formation pattern. RBR (Rally-Base-Rally) and DBR (Drop-Base-Rally) indicate demand zones, while DBD (Drop-Base-Drop) and RBD (Rally-Base-Drop) mark supply zones. This classification helps traders understand the preceding price action context. Level on Level (LOL) Strength When a new zone forms that overlaps an existing zone of the same type by a configurable percentage, the zones merge into a "Level on Level" formation. These stacked zones represent areas where institutional orders have accumulated multiple times, suggesting stronger support or resistance. LOL zones use stricter mitigation rules — they're only removed when price closes through them entirely, rather than the standard percentage penetration. FLIP Level Detection The indicator tracks swing highs and lows using pivot detection. When price breaks a significant swing level and subsequently forms a zone that encompasses that broken level, it's marked as a "FLIP" zone. These represent potential support-turned-resistance or resistance-turned-support areas — a key concept in price action trading. Big Brother Multi-Timeframe Confluence The "Big Brother" feature detects zones on a higher timeframe and checks whether current-timeframe zones fall within them. Zones with higher-timeframe coverage are labeled with "BB" and the confirming timeframe. This helps traders prioritize zones that align with larger structural levels. The higher timeframe auto-selects based on the zone timeframe (e.g., 15m zones check against 4H, daily zones check against weekly). Zone Management Zones are automatically removed when mitigated. Standard zones use percentage-based mitigation (default 25% penetration), while LOL zones require a full close beyond the zone. Additionally, zones that have been touched but see price move beyond a configurable R-multiple distance are removed, preventing stale zones from cluttering the chart. Gap Finder The indicator also identifies unfilled price gaps — areas where consecutive candles don't overlap. These gaps often act as magnets for price and can provide additional confluence when aligned with supply or demand zones. Practical Application This tool is designed for traders who use Supply & Demand methodology but want objective, consistent zone identification. The strength classification (standard, LOL, FLIP, BB confluence) helps prioritize which zones deserve attention. The built-in stop loss calculation provides a starting point for risk management based on zone dimensions.
Indicateur Pine Script®
par Swing_elite
Swing elite Trend DirectionSwing Elite Trend Direction This indicator provides a structured approach to market analysis by combining swing point detection with trend confirmation logic derived from Smart Money Concepts (SMC). How It Works The core algorithm identifies swing highs and lows using a configurable lookback depth, then classifies each pivot based on its relationship to prior swings. A swing high that exceeds the previous swing high is labeled HH (Higher High), while one that fails to do so becomes LH (Lower High). The same logic applies to lows, producing HL (Higher Low) and LL (Lower Low) classifications. Trend confirmation follows ICT/SMC principles: a bullish trend is confirmed only when a Higher Low is followed by a Higher High, establishing the classic bullish market structure sequence. Conversely, bearish confirmation requires a Lower High followed by a Lower Low. When price creates a counter-structure pivot (such as a Lower High appearing during a bullish sequence), the trend status shifts to "Unconfirmed," alerting traders to potential reversals before they fully develop. What Makes This Indicator Useful Rather than displaying isolated swing points, this indicator synthesizes the relationship between consecutive pivots to provide actionable trend status. The multi-timeframe dashboard extends this analysis across three user-defined timeframes, allowing traders to assess trend alignment — a key filter for higher-probability setups. Structure trendlines automatically connect relevant pivot sequences (HH→LH for resistance, LL→HL for support), visualizing the trajectory of market structure rather than arbitrary price connections. Break levels mark the specific prices where structure would shift, giving clear invalidation points for trade management. The Fibonacci retracement draws automatically between the two most recent pivots, providing potential entry zones within the established structure context. Intended Use This tool is designed for traders who use market structure analysis as their primary framework. It automates the manual process of labeling swing points and tracking structure sequences, reducing subjectivity while maintaining the discretionary trader's analytical approach. Works across all markets and timeframes. Swing elite Trend direction A comprehensive market structure indicator that identifies swing highs/lows, labels them with HH/HL/LH/LL structure, draws dynamic trendlines, and provides multi-timeframe trend analysis. 🔹 FEATURES Market Structure Analysis Automatically detects swing highs and swing lows Labels each pivot with its structure type: HH (Higher High), HL (Higher Low), LH (Lower High), LL (Lower Low) Color-coded zigzag lines based on confirmed trend direction Structure Trendlines Downtrend Line: Connects HH to LH (resistance in bearish structure) Uptrend Line: Connects LL to HL (support in bullish structure) Extended projection for potential future price interaction Swing Trendlines Connects the last 2 swing highs (resistance trendline) Connects the last 2 swing lows (support trendline) Optional extension to project future levels Break Levels Horizontal lines at key structure points (HH, HL, LL, LH) Visual reference for potential breakout/breakdown levels Customizable colors for bullish and bearish breaks Fibonacci Retracement Auto-drawn between the last two pivots Customizable levels: 0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0 Individual toggle and color settings for each level Multi-Timeframe Dashboard Displays trend status across 3 customizable timeframes Shows trend direction: Bullish / Bearish / Neutral Shows confirmation status: Confirmed / Unconfirmed Color-coded for quick visual analysis Trend Confirmation Logic (ICT/SMC Concepts) Bullish Confirmed: HL followed by HH (Higher Low → Higher High pattern) Bearish Confirmed: LH followed by LL (Lower High → Lower Low pattern) Unconfirmed: Counter-structure appears (potential reversal signal) 🔹 SETTINGS Swing Settings Depth: Lookback period for pivot detection Display Toggle zigzag lines, labels, price on labels Adjust label size and number of visible swings Zigzag Settings Line style: Solid, Dashed, Dotted Thickness and colors for bullish/bearish trends Swing Trendlines Toggle high/low trendlines independently Customizable colors, style, thickness Option to extend trendlines Structure Trendlines Toggle HH→LH and LL→HL lines independently Customizable colors, style, thickness Option to extend trendlines Break Levels Toggle HH, HL, LL, LH break levels independently Customizable colors for each level Fibonacci Toggle individual fib levels Customizable colors and line style Dashboard Position: Top Left, Top Right, Bottom Left, Bottom Right Size: Tiny, Small, Normal, Large 3 customizable timeframes 🔹 ALERTS HH Broken: Price breaks above recent Higher High HL Broken: Price breaks below recent Higher Low LL Broken: Price breaks below recent Lower Low LH Broken: Price breaks above recent Lower High 🔹 USE CASES ✅ Identify market structure and trend direction ✅ Spot potential trend reversals (unconfirmed status) ✅ Find key support/resistance levels ✅ Multi-timeframe trend alignment for trade confirmation ✅ Breakout/breakdown trading with break levels ✅ Fibonacci retracement entries 3 hours ago Release Notes Swing elite Trend direction A comprehensive market structure indicator that identifies swing highs/lows, labels them with HH/HL/LH/LL structure, draws dynamic trendlines, and provides multi-timeframe trend analysis. 🔹 FEATURES Market Structure Analysis Automatically detects swing highs and swing lows Labels each pivot with its structure type: HH (Higher High), HL (Higher Low), LH (Lower High), LL (Lower Low) Color-coded zigzag lines based on confirmed trend direction Structure Trendlines Downtrend Line: Connects HH to LH (resistance in bearish structure) Uptrend Line: Connects LL to HL (support in bullish structure) Extended projection for potential future price interaction Swing Trendlines Connects the last 2 swing highs (resistance trendline) Connects the last 2 swing lows (support trendline) Optional extension to project future levels Break Levels Horizontal lines at key structure points (HH, HL, LL, LH) Visual reference for potential breakout/breakdown levels Customizable colors for bullish and bearish breaks Fibonacci Retracement Auto-drawn between the last two pivots Customizable levels: 0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.0 Individual toggle and color settings for each level Multi-Timeframe Dashboard Displays trend status across 3 customizable timeframes Shows trend direction: Bullish / Bearish / Neutral Shows confirmation status: Confirmed / Unconfirmed Color-coded for quick visual analysis Trend Confirmation Logic (ICT/SMC Concepts) Bullish Confirmed: HL followed by HH (Higher Low → Higher High pattern) Bearish Confirmed: LH followed by LL (Lower High → Lower Low pattern) Unconfirmed: Counter-structure appears (potential reversal signal) 🔹 SETTINGS Swing Settings Depth: Lookback period for pivot detection Display Toggle zigzag lines, labels, price on labels Adjust label size and number of visible swings Zigzag Settings Line style: Solid, Dashed, Dotted Thickness and colors for bullish/bearish trends Swing Trendlines Toggle high/low trendlines independently Customizable colors, style, thickness Option to extend trendlines Structure Trendlines Toggle HH→LH and LL→HL lines independently Customizable colors, style, thickness Option to extend trendlines Break Levels Toggle HH, HL, LL, LH break levels independently Customizable colors for each level Fibonacci Toggle individual fib levels Customizable colors and line style Dashboard Position: Top Left, Top Right, Bottom Left, Bottom Right Size: Tiny, Small, Normal, Large 3 customizable timeframes 🔹 ALERTS HH Broken: Price breaks above recent Higher High HL Broken: Price breaks below recent Higher Low LL Broken: Price breaks below recent Lower Low LH Broken: Price breaks above recent Lower High 🔹 USE CASES ✅ Identify market structure and trend direction ✅ Spot potential trend reversals (unconfirmed status) ✅ Find key support/resistance levels ✅ Multi-timeframe trend alignment for trade confirmation ✅ Breakout/breakdown trading with break levels ✅ Fibonacci retracement entries
Indicateur Pine Script®
par Swing_elite
X-Trend Reversal (Lite version)🚀 X-TREND REVERSAL (LITE): Market Extremes Detector X-Trend Reversal is a professional algorithm designed to identify high-probability trend inflection points. We have stripped away the "noise" to provide you with deterministic, clean execution signals. Unlike standard lagging indicators, X-Trend analyzes market structure to find the exact moment a trend is exhausted, giving you the "Sniper Entry" advantage. 💎 KEY FEATURES: 🛡 Immutable Signals: Guaranteed Non-Repainting technology. What you see on historical data is exactly what happened in real-time. No disappearing signals. 🎯 High-Probability Setups: Advanced filtering logic eliminates false moves to pinpoint the most reliable counter-trend entry points. 🌍 Multi-Timeframe Scalability: Universal adaptation to the volatility of any asset class (Crypto, Forex, Indices) and any timeframe (from 15s scalping to 4H swing trading). 🏛 Institutional Confidence: Trade reversals with the clarity usually reserved for institutional desks. 📊 X-TREND ECOSYSTEM This indicator is a powerful standalone tool, but it is designed to be part of a larger professional suite. For a comprehensive market view and deeper confluence analysis, we highly recommend pairing this tool with our X-Trend Dashboard and other advanced modules from the X-Trend series. Combining these tools provides a 360-degree view of market sentiment and liquidity. 🔒 HOW TO GET ACCESS (INVITE-ONLY) This script is a closed-source proprietary tool. Access is granted exclusively through our official channels. ⛔ NOTE: Please do not ask for access in the comment section below. Use the links above for instant support and license activation.
Indicateur Pine Script®
par Dev0880
MACD Enhanced [DCAUT]█ MACD Enhanced 📊 ORIGINALITY & INNOVATION The MACD Enhanced represents a significant improvement over traditional MACD implementations. While Gerald Appel's original MACD from the 1970s was limited to exponential moving averages (EMA), this enhanced version expands algorithmic options by supporting 21 different moving average calculations for both the main MACD line and signal line independently. This improvement addresses an important limitation of traditional MACD: the inability to adapt the indicator's mathematical foundation to different market conditions. By allowing traders to select from algorithms ranging from simple moving averages (SMA) for stability to advanced adaptive filters like Kalman Filter for noise reduction, this implementation changes MACD from a fixed-algorithm tool into a flexible instrument that can be adjusted for specific market environments and trading strategies. The enhanced histogram visualization system uses a four-color gradient that helps communicate momentum strength and direction more clearly than traditional single-color histograms. 📐 MATHEMATICAL FOUNDATION The core calculation maintains the proven MACD formula: Fast MA(source, fastLength) - Slow MA(source, slowLength), but extends it with algorithmic flexibility. The signal line applies the selected smoothing algorithm to the MACD line over the specified signal period, while the histogram represents the difference between MACD and signal lines. Available Algorithms: The implementation supports a comprehensive spectrum of technical analysis algorithms: Basic Averages: SMA (arithmetic mean), EMA (exponential weighting), RMA (Wilder's smoothing), WMA (linear weighting) Advanced Averages: HMA (Hull's low-lag), VWMA (volume-weighted), ALMA (Arnaud Legoux adaptive) Mathematical Filters: LSMA (least squares regression), DEMA (double exponential), TEMA (triple exponential), ZLEMA (zero-lag exponential) Adaptive Systems: T3 (Tillson T3), FRAMA (fractal adaptive), KAMA (Kaufman adaptive), MCGINLEY_DYNAMIC (reactive to volatility) Signal Processing: ULTIMATE_SMOOTHER (low-pass filter), LAGUERRE_FILTER (four-pole IIR), SUPER_SMOOTHER (two-pole Butterworth), KALMAN_FILTER (state-space estimation) Specialized: TMA (triangular moving average), LAGUERRE_BINOMIAL_FILTER (binomial smoothing) Each algorithm responds differently to price action, allowing traders to match the indicator's behavior to market characteristics: trending markets benefit from responsive algorithms like EMA or HMA, while ranging markets require stable algorithms like SMA or RMA. 📊 COMPREHENSIVE SIGNAL ANALYSIS Histogram Interpretation: Positive Values: Indicate bullish momentum when MACD line exceeds signal line, suggesting upward price pressure and potential buying opportunities Negative Values: Reflect bearish momentum when MACD line falls below signal line, indicating downward pressure and potential selling opportunities Zero Line Crosses: MACD crossing above zero suggests transition to bullish bias, while crossing below indicates bearish bias shift Momentum Changes: Rising histogram (regardless of positive/negative) signals accelerating momentum in the current direction, while declining histogram warns of momentum deceleration Advanced Signal Recognition: Divergences: Price making new highs/lows while MACD fails to confirm often precedes trend reversals Convergence Patterns: MACD line approaching signal line suggests impending crossover and potential trade setup Histogram Peaks: Extreme histogram values often mark momentum exhaustion points and potential reversal zones 🎯 STRATEGIC APPLICATIONS Comprehensive Trend Confirmation Strategies: Primary Trend Validation Protocol: Identify primary trend direction using higher timeframe (4H or Daily) MACD position relative to zero line Confirm trend strength by analyzing histogram progression: consistent expansion indicates strong momentum, contraction suggests weakening Use secondary confirmation from MACD line angle: steep angles (>45°) indicate strong trends, shallow angles suggest consolidation Validate with price structure: trending markets show consistent higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend) Entry Timing Techniques: Pullback Entries in Uptrends: Wait for MACD histogram to decline toward zero line without crossing, then enter on histogram expansion with MACD line still above zero Breakout Confirmations: Use MACD line crossing above zero as confirmation of upward breakouts from consolidation patterns Continuation Signals: Look for MACD line re-acceleration (steepening angle) after brief consolidation periods as trend continuation signals Advanced Divergence Trading Systems: Regular Divergence Recognition: Bullish Regular Divergence: Price creates lower lows while MACD line forms higher lows. This pattern is traditionally considered a potential upward reversal signal, but should be combined with other confirmation signals Bearish Regular Divergence: Price makes higher highs while MACD shows lower highs. This pattern is traditionally considered a potential downward reversal signal, but trading decisions should incorporate proper risk management Hidden Divergence Strategies: Bullish Hidden Divergence: Price shows higher lows while MACD displays lower lows, indicating trend continuation potential. Use for adding to existing long positions during pullbacks Bearish Hidden Divergence: Price creates lower highs while MACD forms higher highs, suggesting downtrend continuation. Optimal for adding to short positions during bear market rallies Multi-Timeframe Coordination Framework: Three-Timeframe Analysis Structure: Primary Timeframe (Daily): Determine overall market bias and major trend direction. Only trade in alignment with daily MACD direction Secondary Timeframe (4H): Identify intermediate trend changes and major entry opportunities. Use for position sizing decisions Execution Timeframe (1H): Precise entry and exit timing. Look for MACD line crossovers that align with higher timeframe bias Timeframe Synchronization Rules: Daily MACD above zero + 4H MACD rising = Strong uptrend context for long positions Daily MACD below zero + 4H MACD declining = Strong downtrend context for short positions Conflicting signals between timeframes = Wait for alignment or use smaller position sizes 1H MACD signals only valid when aligned with both higher timeframes Algorithm Considerations by Market Type: Trending Markets: Responsive algorithms like EMA, HMA may be considered, but effectiveness should be tested for specific market conditions Volatile Markets: Noise-reducing algorithms like KALMAN_FILTER, SUPER_SMOOTHER may help reduce false signals, though results vary by market Range-Bound Markets: Stability-focused algorithms like SMA, RMA may provide smoother signals, but individual testing is required Short Timeframes: Low-lag algorithms like ZLEMA, T3 theoretically respond faster but may also increase noise Important Note: All algorithm choices and parameter settings should be thoroughly backtested and validated based on specific trading strategies, market conditions, and individual risk tolerance. Different market environments and trading styles may require different configuration approaches. 📋 DETAILED PARAMETER CONFIGURATION Comprehensive Source Selection Strategy: Price Source Analysis and Optimization: Close Price (Default): Most commonly used, reflects final market sentiment of each period. Best for end-of-day analysis, swing trading, daily/weekly timeframes. Advantages: widely accepted standard, good for backtesting comparisons. Disadvantages: ignores intraday price action, may miss important highs/lows HL2 (High+Low)/2: Midpoint of the trading range, reduces impact of opening gaps and closing spikes. Best for volatile markets, gap-prone assets, forex markets. Calculation impact: smoother MACD signals, reduced noise from price spikes. Optimal when asset shows frequent gaps, high volatility during specific sessions HLC3 (High+Low+Close)/3: Weighted average emphasizing the close while including range information. Best for balanced analysis, most asset classes, medium-term trading. Mathematical effect: 33% weight to high/low, 33% to close, provides compromise between close and HL2. Use when standard close is too noisy but HL2 is too smooth OHLC4 (Open+High+Low+Close)/4: True average of all price points, most comprehensive view. Best for complete price representation, algorithmic trading, statistical analysis. Considerations: includes opening sentiment, smoothest of all options but potentially less responsive. Optimal for markets with significant opening moves, comprehensive trend analysis Parameter Configuration Principles: Important Note: Different moving average algorithms have distinct mathematical characteristics and response patterns. The same parameter settings may produce vastly different results when using different algorithms. When switching algorithms, parameter settings should be re-evaluated and tested for appropriateness. Length Parameter Considerations: Fast Length (Default 12): Shorter periods provide faster response but may increase noise and false signals, longer periods offer more stable signals but slower response, different algorithms respond differently to the same parameters and may require adjustment Slow Length (Default 26): Should maintain a reasonable proportional relationship with fast length, different timeframes may require different parameter configurations, algorithm characteristics influence optimal length settings Signal Length (Default 9): Shorter lengths produce more frequent crossovers but may increase false signals, longer lengths provide better signal confirmation but slower response, should be adjusted based on trading style and chosen algorithm characteristics Comprehensive Algorithm Selection Framework: MACD Line Algorithm Decision Matrix: EMA (Standard Choice): Mathematical properties: exponential weighting, recent price emphasis. Best for general use, traditional MACD behavior, backtesting compatibility. Performance characteristics: good balance of speed and smoothness, widely understood behavior SMA (Stability Focus): Equal weighting of all periods, maximum smoothness. Best for ranging markets, noise reduction, conservative trading. Trade-offs: slower signal generation, reduced sensitivity to recent price changes HMA (Speed Optimized): Hull Moving Average, designed for reduced lag. Best for trending markets, quick reversals, active trading. Technical advantage: square root period weighting, faster trend detection. Caution: can be more sensitive to noise KAMA (Adaptive): Kaufman Adaptive MA, adjusts smoothing based on market efficiency. Best for varying market conditions, algorithmic trading. Mechanism: fast smoothing in trends, slow smoothing in sideways markets. Complexity: requires understanding of efficiency ratio Signal Line Algorithm Optimization Strategies: Matching Strategy: Use same algorithm for both MACD and signal lines. Benefits: consistent mathematical properties, predictable behavior. Best when backtesting historical strategies, maintaining traditional MACD characteristics Contrast Strategy: Use different algorithms for optimization. Common combinations: MACD=EMA, Signal=SMA for smoother crossovers, MACD=HMA, Signal=RMA for balanced speed/stability, Advanced: MACD=KAMA, Signal=T3 for adaptive behavior with smooth signals Market Regime Adaptation: Trending markets: both fast algorithms (EMA/HMA), Volatile markets: MACD=KALMAN_FILTER, Signal=SUPER_SMOOTHER, Range-bound: both slow algorithms (SMA/RMA) Parameter Sensitivity Considerations: Impact of Parameter Changes: Length Parameter Sensitivity: Small parameter adjustments can significantly affect signal timing, while larger adjustments may fundamentally change indicator behavior characteristics Algorithm Sensitivity: Different algorithms produce different signal characteristics. Thoroughly test the impact on your trading strategy before switching algorithms Combined Effects: Changing multiple parameters simultaneously can create unexpected effects. Recommendation: adjust parameters one at a time and thoroughly test each change 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Response Characteristics by Algorithm: Fastest Response: ZLEMA, HMA, T3 - minimal lag but higher noise Balanced Performance: EMA, DEMA, TEMA - good trade-off between speed and stability Highest Stability: SMA, RMA, TMA - reduced noise but increased lag Adaptive Behavior: KAMA, FRAMA, MCGINLEY_DYNAMIC - automatically adjust to market conditions Noise Filtering Capabilities: Advanced algorithms like KALMAN_FILTER and SUPER_SMOOTHER help reduce false signals compared to traditional EMA-based MACD. Noise-reducing algorithms can provide more stable signals in volatile market conditions, though results will vary based on market conditions and parameter settings. Market Condition Adaptability: Unlike fixed-algorithm MACD, this enhanced version allows real-time optimization. Trending markets benefit from responsive algorithms (EMA, HMA), while ranging markets perform better with stable algorithms (SMA, RMA). The ability to switch algorithms without changing indicators provides greater flexibility. Comparative Performance vs Traditional MACD: Algorithm Flexibility: 21 algorithms vs 1 fixed EMA Signal Quality: Reduced false signals through noise filtering algorithms Market Adaptability: Optimizable for any market condition vs fixed behavior Customization Options: Independent algorithm selection for MACD and signal lines vs forced matching Professional Features: Advanced color coding, multiple alert conditions, comprehensive parameter control USAGE NOTES This indicator is designed for technical analysis and educational purposes. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always combine with proper risk management and thorough strategy testing.
Indicateur Pine Script®
par DCAUT
Flux-Tensor Singularity [ML/RL PRO]Flux-Tensor Singularity This version of the Flux-Tensor Singularity (FTS) represents a paradigm shift in technical analysis by treating price movement as a physical system governed by volume-weighted forces and volatility dynamics. Unlike traditional indicators that measure price change or momentum in isolation, FTS quantifies the complete energetic state of the market by fusing three fundamental dimensions: price displacement (delta_P), volume intensity (V), and local-to-global volatility ratio (gamma). The Physics-Inspired Foundation: The tensor calculation draws inspiration from general relativity and fluid dynamics, where massive objects (large volume) create curvature in spacetime (price action). The core formula: Raw Singularity = (ΔPrice × ln(Volume)) × γ² Where: • ΔPrice = close - close (directional force) • ln(Volume) = logarithmic volume compression (prevents extreme outliers) • γ (Gamma) = (ATR_local / ATR_global)² (volatility expansion coefficient) This raw value is then normalized to 0-100 range using the lookback period's extremes, creating a bounded oscillator that identifies critical density points—"singularities" where normal market behavior breaks down and explosive moves become probable. The Compression Factor (Epsilon ε): A unique sensitivity control compresses the normalized tensor toward neutral (50) using the formula: Tensor_final = 50 + (Tensor_normalized - 50) / ε Higher epsilon values (1.5-3.0) make threshold breaches rare and significant, while lower values (0.3-0.7) increase signal frequency. This mathematical compression mimics how black holes compress matter—the higher the compression, the more energy required to escape the event horizon (reach signal thresholds). Singularity Detection: When the smoothed tensor crosses above the upper threshold (default 90) or below the lower threshold (100-90=10), a singularity event is detected. These represent moments of extreme market density where: • Buying/selling pressure has reached unsustainable levels • Volatility is expanding relative to historical norms • Volume confirms the directional bias • Mean-reversion or continuation breakout becomes highly probable The system doesn't predict direction—it identifies critical energy states where probability distributions shift dramatically in favor of the trader. 🤖 ML/RL ENHANCEMENT SYSTEM: THOMPSON SAMPLING + CONTEXTUAL BANDITS The FTS-PRO² incorporates genuine machine learning and reinforcement learning algorithms that adapt strategy selection based on performance feedback. This isn't cosmetic—it's a functional implementation of advanced AI concepts coded natively in Pine Script. Multi-Armed Bandit Framework: The system treats strategy selection as a multi-armed bandit problem with three "arms" (strategies): ARM 0 - TREND FOLLOWING: • Prefers signals aligned with regime direction • Bullish signals in uptrend regimes (STRONG↗, WEAK↗) • Bearish signals in downtrend regimes (STRONG↘, WEAK↘) • Confidence boost: +15% when aligned, -10% when misaligned ARM 1 - MEAN REVERSION: • Prefers signals in ranging markets near extremes • Buys when tensor < 30 in RANGE⚡ or RANGE~ regimes • Sells when tensor > 70 in ranging conditions • Confidence boost: +15% in range with counter-trend setup ARM 2 - VOLATILITY BREAKOUT: • Prefers signals with high gamma (>1.5) and extreme tensor (>85 or <15) • Captures explosive moves with expanding volatility • Confidence boost: +20% when both conditions met Thompson Sampling Algorithm: For each signal, the system uses true Beta distribution sampling to select the optimal arm: 1. Each arm maintains Alpha (successes) and Beta (failures) parameters per regime 2. Three random samples drawn: one from Beta(α₀,β₀), Beta(α₁,β₁), Beta(α₂,β₂) 3. Highest sample wins and that arm's strategy applies 4. After trade outcome: - Win → Alpha += 1.0, reward += 1.0 - Loss → Beta += 1.0, reward -= 0.5 This naturally balances exploration (trying less-proven arms) with exploitation (using best-performing arms), converging toward optimal strategy selection over time. Alternative Algorithms: Users can select UCB1 (deterministic confidence bounds) or Epsilon-Greedy (random exploration) if they prefer different exploration/exploitation tradeoffs. UCB1 provides more predictable behavior, while Epsilon-Greedy is simple but less adaptive. Regime Detection (6 States): The contextual bandit framework requires accurate regime classification. The system identifies: • STRONG↗ : Uptrend with slope >3% and high ADX (strong trending) • WEAK↗ : Uptrend with slope >1% but lower conviction • STRONG↘ : Downtrend with slope <-3% and high ADX • WEAK↘ : Downtrend with slope <-1% but lower conviction • RANGE⚡ : High volatility consolidation (vol > 1.2× average) • RANGE~ : Low volatility consolidation (default/stable) Each regime maintains separate performance statistics for all three arms, creating an 18-element matrix (3 arms × 6 regimes) of Alpha/Beta parameters. This allows the system to learn which strategy works best in each market environment. 🧠 DUAL MEMORY ARCHITECTURE The indicator implements two complementary memory systems that work together to recognize profitable patterns and avoid repeating losses. Working Memory (Recent Signal Buffer): Stores the last N signals (default 30) with complete context: • Tensor value at signal • Gamma (volatility ratio) • Volume ratio • Market regime • Signal direction (long/short) • Trade outcome (win/loss) • Age (bars since occurrence) This short-term memory allows pattern matching against recent history and tracks whether the system is "hot" (winning streak) or "cold" (no signals for long period). Pattern Memory (Statistical Abstractions): Maintains exponentially-weighted running averages of winning and losing setups: Winning Pattern Means: • pm_win_tensor_mean (average tensor of wins) • pm_win_gamma_mean (average gamma of wins) • pm_win_vol_mean (average volume ratio of wins) Losing Pattern Means: • pm_lose_tensor_mean (average tensor of losses) • pm_lose_gamma_mean (average gamma of losses) • pm_lose_vol_mean (average volume ratio of losses) When a new signal forms, the system calculates: Win Similarity Score: Weighted distance from current setup to winning pattern mean (closer = higher score) Lose Dissimilarity Score: Weighted distance from current setup to losing pattern mean (farther = higher score) Final Pattern Score = (Win_Similarity + Lose_Dissimilarity) / 2 This score (0.0 to 1.0) feeds into ML confidence calculation with 15% weight. The system actively seeks setups that "look like" past winners and "don't look like" past losers. Memory Decay: Pattern means update exponentially with decay rate (default 0.95): New_Mean = Old_Mean × 0.95 + New_Value × 0.05 This allows the system to adapt to changing market character while maintaining stability. Faster decay (0.80-0.90) adapts quickly but may overfit to recent noise. Slower decay (0.95-0.99) provides stability but adapts slowly to regime changes. 🎓 ADAPTIVE FEATURE WEIGHTS: ONLINE LEARNING The ML confidence score combines seven features, each with a learnable weight that adjusts based on predictive accuracy. The Seven Features: 1. Overall Win Rate (15% initial) : System-wide historical performance 2. Regime Win Rate (20% initial) : Performance in current market regime 3. Score Strength (15% initial) : Bull vs bear score differential 4. Volume Strength (15% initial) : Volume ratio normalized to 0-1 5. Pattern Memory (15% initial) : Similarity to winning patterns 6. MTF Confluence (10% initial) : Higher timeframe alignment 7. Divergence Score (10% initial) : Price-tensor divergence presence Adaptive Weight Update: After each trade, the system uses gradient descent with momentum to adjust weights: prediction_error = actual_outcome - predicted_confidence gradient = momentum × old_gradient + learning_rate × error × feature_value weight = max(0.05, weight + gradient × 0.01) Then weights are normalized to sum to 1.0. Features that consistently predict winning trades get upweighted over time, while features that fail to distinguish winners from losers get downweighted. The momentum term (default 0.9) smooths the gradient to prevent oscillation and overfitting. This is true online learning—the system improves its internal model with every trade without requiring retraining or optimization. Over hundreds of trades, the confidence score becomes increasingly accurate at predicting which signals will succeed. ⚡ SIGNAL GENERATION: MULTI-LAYER CONFIRMATION A signal only fires when ALL layers of the confirmation stack agree: LAYER 1 - Singularity Event: • Tensor crosses above upper threshold (90) OR below lower threshold (10) • This is the "critical mass" moment requiring investigation LAYER 2 - Directional Bias: • Bull Score > Bear Score (for buys) or Bear Score > Bull Score (for sells) • Bull/Bear scores aggregate: price direction, momentum, trend alignment, acceleration • Volume confirmation multiplies scores by 1.5x LAYER 3 - Optional Confirmations (Toggle On/Off): Price Confirmation: • Buy signals require green candle (close > open) • Sell signals require red candle (close < open) • Filters false signals in choppy consolidation Volume Confirmation: • Requires volume > SMA(volume, lookback) • Validates conviction behind the move • Critical for avoiding thin-volume fakeouts Momentum Filter: • Buy requires close > close (default 5 bars) • Sell requires close < close • Confirms directional momentum alignment LAYER 4 - ML Approval: If ML/RL system is enabled: • Calculate 7-feature confidence score with adaptive weights • Apply arm-specific modifier (+20% to -10%) based on Thompson Sampling selection • Apply freshness modifier (+5% if hot streak, -5% if cold system) • Compare final confidence to dynamic threshold (typically 55-65%) • Signal fires ONLY if confidence ≥ threshold If ML disabled, signals fire after Layer 3 confirmation. Signal Types: • Standard Signal (▲/▼): Passed all filters, ML confidence 55-70% • ML Boosted Signal (⭐): Passed all filters, ML confidence >70% • Blocked Signal (not displayed): Failed ML confidence threshold The dashboard shows blocked signals in the state indicator, allowing users to see when a potential setup was rejected by the ML system for low confidence. 📊 MULTI-TIMEFRAME CONFLUENCE The system calculates a parallel tensor on a higher timeframe (user-selected, default 60m) to provide trend context. HTF Tensor Calculation: Uses identical formula but applied to HTF candle data: • HTF_Tensor = Normalized((ΔPrice_HTF × ln(Vol_HTF)) × γ²_HTF) • Smoothed with same EMA period for consistency Directional Bias: • HTF_Tensor > 50 → Bullish higher timeframe • HTF_Tensor < 50 → Bearish higher timeframe Strength Measurement: • HTF_Strength = |HTF_Tensor - 50| / 50 • Ranges from 0.0 (neutral) to 1.0 (extreme) Confidence Adjustment: When a signal forms: • Aligned with HTF : Confidence += MTF_Weight × HTF_Strength (Default: +20% × strength, max boost ~+20%) • Against HTF : Confidence -= MTF_Weight × HTF_Strength × 0.6 (Default: -20% × strength × 0.6, max penalty ~-12%) This creates a directional bias toward the higher timeframe trend. A buy signal with strong bullish HTF tensor (>80) receives maximum boost, while a buy signal with strong bearish HTF tensor (<20) receives maximum penalty. Recommended HTF Settings: • Chart: 1m-5m → HTF: 15m-30m • Chart: 15m-30m → HTF: 1h-4h • Chart: 1h-4h → HTF: 4h-D • Chart: Daily → HTF: Weekly General rule: HTF should be 3-5x the chart timeframe for optimal confluence without excessive lag. 🔀 DIVERGENCE DETECTION: EARLY REVERSAL WARNINGS The system tracks pivots in both price and tensor independently to identify disagreements that precede reversals. Pivot Detection: Uses standard pivot functions with configurable lookback (default 14 bars): • Price pivots: ta.pivothigh(high) and ta.pivotlow(low) • Tensor pivots: ta.pivothigh(tensor) and ta.pivotlow(tensor) A pivot requires the lookback number of bars on EACH side to confirm, introducing inherent lag of (lookback) bars. Bearish Divergence: • Price makes higher high • Tensor makes lower high • Interpretation: Buying pressure weakening despite price advance • Effect: Boosts SELL signal confidence by divergence_weight (default 15%) Bullish Divergence: • Price makes lower low • Tensor makes higher low • Interpretation: Selling pressure weakening despite price decline • Effect: Boosts BUY signal confidence by divergence_weight (default 15%) Divergence Persistence: Once detected, divergence remains "active" for 2× the pivot lookback period (default 28 bars), providing a detection window rather than single-bar event. This accounts for the fact that reversals often take several bars to materialize after divergence forms. Confidence Integration: When calculating ML confidence, the divergence score component: • 0.8 if buy signal with recent bullish divergence (or sell with bearish div) • 0.2 if buy signal with recent bearish divergence (opposing signal) • 0.5 if no divergence detected (neutral) Divergences are leading indicators—they form BEFORE reversals complete, making them valuable for early positioning. ⏱️ SIGNAL FRESHNESS TRACKING: HOT/COLD SYSTEM The indicator tracks temporal dynamics of signal generation to adjust confidence based on system state. Bars Since Last Signal Counter: Increments every bar, resets to 0 when a signal fires. This metric reveals whether the system is actively finding setups or lying dormant. Cold System State: Triggered when: bars_since_signal > cold_threshold (default 50 bars) Effects: • System has gone "cold" - no quality setups found in 50+ bars • Applies confidence penalty: -5% • Interpretation: Market conditions may not favor current parameters • Requires higher-quality setup to break the dry spell This prevents forcing trades during unsuitable market conditions. Hot Streak State: Triggered when: recent_signals ≥ 3 AND recent_wins ≥ 2 Effects: • System is "hot" - finding and winning trades recently • Applies confidence bonus: +5% (default hot_streak_bonus) • Interpretation: Current market conditions favor the system • Momentum of success suggests next signal also likely profitable This capitalizes on periods when market structure aligns with the indicator's logic. Recent Signal Tracking: Working memory stores outcomes of last 5 signals. When 3+ winners occur in this window, hot streak activates. After 5 signals, the counter resets and tracking restarts. This creates rolling evaluation of recent performance. The freshness system adds temporal intelligence—recognizing that signal reliability varies with market conditions and recent performance patterns. 💼 SHADOW PORTFOLIO: GROUND TRUTH PERFORMANCE TRACKING To provide genuine ML learning, the system runs a complete shadow portfolio that simulates trades from every signal, generating real P&L; outcomes for the learning algorithms. Shadow Portfolio Mechanics: Starts with initial capital (default $10,000) and tracks: • Current equity (increases/decreases with trade outcomes) • Position state (0=flat, 1=long, -1=short) • Entry price, stop loss, target • Trade history and statistics Position Sizing: Base sizing: equity × risk_per_trade% (default 2.0%) With dynamic sizing enabled: • Size multiplier = 0.5 + ML_confidence • High confidence (0.80) → 1.3× base size • Low confidence (0.55) → 1.05× base size Example: $10,000 equity, 2% risk, 80% confidence: • Impact: $10,000 × 2% × 1.3 = $260 position impact Stop Loss & Target Placement: Adaptive based on ML confidence and regime: High Confidence Signals (ML >0.7): • Tighter stops: 1.5× ATR • Larger targets: 4.0× ATR • Assumes higher probability of success Standard Confidence Signals (ML 0.55-0.7): • Standard stops: 2.0× ATR • Standard targets: 3.0× ATR Ranging Regimes (RANGE⚡/RANGE~): • Tighter setup: 1.5× ATR stop, 2.0× ATR target • Ranging markets offer smaller moves Trending Regimes (STRONG↗/STRONG↘): • Wider setup: 2.5× ATR stop, 5.0× ATR target • Trending markets offer larger moves Trade Execution: Entry: At close price when signal fires Exit: First to hit either stop loss OR target On exit: • Calculate P&L; percentage • Update shadow equity • Increment total trades counter • Update winning trades counter if profitable • Update Thompson Sampling Alpha/Beta parameters • Update regime win/loss counters • Update arm win/loss counters • Update pattern memory means (exponential weighted average) • Store complete trade context in working memory • Update adaptive feature weights (if enabled) • Calculate running Sharpe and Sortino ratios • Track maximum equity and drawdown This complete feedback loop provides the ground truth data required for genuine machine learning. 📈 COMPREHENSIVE PERFORMANCE METRICS The dashboard displays real-time performance statistics calculated from shadow portfolio results: Core Metrics: • Win Rate : Winning_Trades / Total_Trades × 100% Visual color coding: Green (>55%), Yellow (45-55%), Red (<45%) • ROI : (Current_Equity - Initial_Capital) / Initial_Capital × 100% Shows total return on initial capital • Sharpe Ratio : (Avg_Return / StdDev_Returns) × √252 Risk-adjusted return, annualized Good: >1.5, Acceptable: >0.5, Poor: <0.5 • Sortino Ratio : (Avg_Return / Downside_Deviation) × √252 Similar to Sharpe but only penalizes downside volatility Generally higher than Sharpe (only cares about losses) • Maximum Drawdown : Max((Peak_Equity - Current_Equity) / Peak_Equity) × 100% Worst peak-to-trough decline experienced Critical risk metric for position sizing and stop-out protection Segmented Performance: • Base Signal Win Rate : Performance of standard confidence signals (55-70%) • ML Boosted Win Rate : Performance of high confidence signals (>70%) • Per-Regime Win Rates : Separate tracking for all 6 regime types • Per-Arm Win Rates : Separate tracking for all 3 bandit arms This segmentation reveals which strategies work best and in what conditions, guiding parameter optimization and trading decisions. 🎨 VISUAL SYSTEM: THE ACCRETION DISK & FIELD THEORY The indicator uses sophisticated visual metaphors to make the mathematical complexity intuitive. Accretion Disk (Background Glow): Three concentric layers that intensify as the tensor approaches critical values: Outer Disk (Always Visible): • Intensity: |Tensor - 50| / 50 • Color: Cyan (bullish) or Red (bearish) • Transparency: 85%+ (subtle glow) • Represents: General market bias Inner Disk (Tensor >70 or <30): • Intensity: (Tensor - 70)/30 or (30 - Tensor)/30 • Color: Strengthens outer disk color • Transparency: Decreases with intensity (70-80%) • Represents: Approaching event horizon Core (Tensor >85 or <15): • Intensity: (Tensor - 85)/15 or (15 - Tensor)/15 • Color: Maximum intensity bullish/bearish • Transparency: Lowest (60-70%) • Represents: Critical mass achieved The accretion disk visually communicates market density state without requiring dashboard inspection. Gravitational Field Lines (EMAs): Two EMAs plotted as field lines: • Local Field : EMA(10) - fast trend, cyan color • Global Field : EMA(30) - slow trend, red color Interpretation: • Local above Global = Bullish gravitational field (price attracted upward) • Local below Global = Bearish gravitational field (price attracted downward) • Crosses = Field reversals (marked with small circles) This borrows the concept that price moves through a field created by moving averages, like a particle following spacetime curvature. Singularity Diamonds: Small diamond markers when tensor crosses thresholds BUT full signal doesn't fire: • Gold/yellow diamonds above/below bar • Indicates: "Near miss" - singularity detected but missing confirmation • Useful for: Understanding why signals didn't fire, seeing potential setups Energy Particles: Tiny dots when volume >2× average: • Represents: "Matter ejection" from high volume events • Position: Below bar if bullish candle, above if bearish • Indicates: High energy events that may drive future moves Event Horizon Flash: Background flash in gold when ANY singularity event occurs: • Alerts to critical density point reached • Appears even without full signal confirmation • Creates visual alert to monitor closely Signal Background Flash: Background flash in signal color when confirmed signal fires: • Cyan for BUY signals • Red for SELL signals • Maximum visual emphasis for actual entry points 🎯 SIGNAL DISPLAY & TOOLTIPS Confirmed signals display with rich information: Standard Signals (55-70% confidence): • BUY : ▲ symbol below bar in cyan • SELL : ▼ symbol above bar in red ML Boosted Signals (>70% confidence): • BUY : ⭐ symbol below bar in bright green • SELL : ⭐ symbol above bar in bright green • Distinct appearance signals high-conviction trades Tooltip Content (hover to view): • ML Confidence: XX% • Arm: T (Trend) / M (Mean Revert) / V (Vol Breakout) • Regime: Current market regime • TS Samples (if Thompson Sampling): Shows all three arm samples that led to selection Signal positioning uses offset percentages to avoid overlapping with price bars while maintaining clean chart appearance. Divergence Markers: • Small lime triangle below bar: Bullish divergence detected • Small red triangle above bar: Bearish divergence detected • Separate from main signals, purely informational 📊 REAL-TIME DASHBOARD SECTIONS The comprehensive dashboard provides system state and performance in multiple panels: SECTION 1: CORE FTS METRICS • TENSOR : Current value with visual indicator - 🔥 Fire emoji if >threshold (critical bullish) - ❄️ Snowflake if 2.0× (extreme volatility) - ⚠ Warning if >1.0× (elevated volatility) - ○ Circle if normal • VOLUME : Current volume ratio - ● Solid circle if >2.0× average (heavy) - ◐ Half circle if >1.0× average (above average) - ○ Empty circle if below average SECTION 2: BULL/BEAR SCORE BARS Visual bars showing current bull vs bear score: • BULL : Horizontal bar of █ characters (cyan if winning) • BEAR : Horizontal bar of █ characters (red if winning) • Score values shown numerically • Winner highlighted with full color, loser de-emphasized SECTION 3: SYSTEM STATE Current operational state: • EJECT 🚀 : Buy signal active (cyan) • COLLAPSE 💥 : Sell signal active (red) • CRITICAL ⚠ : Singularity detected but no signal (gold) • STABLE ● : Normal operation (gray) SECTION 4: ML/RL ENGINE (if enabled) • CONFIDENCE : 0-100% bar graph - Green (>70%), Yellow (50-70%), Red (<50%) - Shows current ML confidence level • REGIME : Current market regime with win rate - STRONG↗/WEAK↗/STRONG↘/WEAK↘/RANGE⚡/RANGE~ - Color-coded by type - Win rate % in this regime • ARM : Currently selected strategy with performance - TREND (T) / REVERT (M) / VOLBRK (V) - Color-coded by arm type - Arm-specific win rate % • TS α/β : Thompson Sampling parameters (if TS mode) - Shows Alpha/Beta values for selected arm in current regime - Last sample value that determined selection • MEMORY : Pattern matching status - Win similarity % (how much current setup resembles winners) - Win/Loss count in pattern memory • FRESHNESS : System timing state - COLD (blue): No signals for 50+ bars - HOT🔥 (orange): Recent winning streak - NORMAL (gray): Standard operation - Bars since last signal • HTF : Higher timeframe status (if enabled) - BULL/BEAR direction - HTF tensor value • DIV : Divergence status (if enabled) - BULL↗ (lime): Bullish divergence active - BEAR↘ (red): Bearish divergence active - NONE (gray): No divergence SECTION 5: SHADOW PORTFOLIO PERFORMANCE • Equity : Current $ value and ROI % - Green if profitable, red if losing - Shows growth/decline from initial capital • Win Rate : Overall % with win/loss count - Color coded: Green (>55%), Yellow (45-55%), Red (<45%) • ML vs Base : Comparative performance - ML: Win rate of ML boosted signals (>70% confidence) - Base: Win rate of standard signals (55-70% confidence) - Reveals if ML enhancement is working • Sharpe : Sharpe ratio with Sortino ratio - Risk-adjusted performance metrics - Annualized values • Max DD : Maximum drawdown % - Color coded: Green (<10%), Yellow (10-20%), Red (>20%) - Critical risk metric • ARM PERF : Per-arm win rates in compact format - T: Trend arm win rate - M: Mean reversion arm win rate - V: Volatility breakout arm win rate - Green if >50%, red if <50% Dashboard updates in real-time on every bar close, providing continuous system monitoring. ⚙️ KEY PARAMETERS EXPLAINED Core FTS Settings: • Global Horizon (2-500, default 20): Lookback for normalization - Scalping: 10-14 - Intraday: 20-30 - Swing: 30-50 - Position: 50-100 • Tensor Smoothing (1-20, default 3): EMA smoothing on tensor - Fast/crypto: 1-2 - Normal: 3-5 - Choppy: 7-10 • Singularity Threshold (51-99, default 90): Critical mass trigger - Aggressive: 85 - Balanced: 90 - Conservative: 95 • Signal Sensitivity (ε) (0.1-5.0, default 1.0): Compression factor - Aggressive: 0.3-0.7 - Balanced: 1.0 - Conservative: 1.5-3.0 - Very conservative: 3.0-5.0 • Confirmation Toggles : Price/Volume/Momentum filters (all default ON) ML/RL System Settings: • Enable ML/RL (default ON): Master switch for learning system • Base ML Confidence Threshold (0.4-0.9, default 0.55): Minimum to fire - Aggressive: 0.40-0.50 - Balanced: 0.55-0.65 - Conservative: 0.70-0.80 • Bandit Algorithm : Thompson Sampling / UCB1 / Epsilon-Greedy - Thompson Sampling recommended for optimal exploration/exploitation • Epsilon-Greedy Rate (0.05-0.5, default 0.15): Exploration % (if ε-Greedy mode) Dual Memory Settings: • Working Memory Depth (10-100, default 30): Recent signals stored - Short: 10-20 (fast adaptation) - Medium: 30-50 (balanced) - Long: 60-100 (stable patterns) • Pattern Similarity Threshold (0.5-0.95, default 0.70): Match strictness - Loose: 0.50-0.60 - Medium: 0.65-0.75 - Strict: 0.80-0.90 • Memory Decay Rate (0.8-0.99, default 0.95): Exponential decay speed - Fast: 0.80-0.88 - Medium: 0.90-0.95 - Slow: 0.96-0.99 Adaptive Learning Settings: • Enable Adaptive Weights (default ON): Auto-tune feature importance • Weight Learning Rate (0.01-0.3, default 0.10): Gradient descent step size - Very slow: 0.01-0.03 - Slow: 0.05-0.08 - Medium: 0.10-0.15 - Fast: 0.20-0.30 • Weight Momentum (0.5-0.99, default 0.90): Gradient smoothing - Low: 0.50-0.70 - Medium: 0.75-0.85 - High: 0.90-0.95 Signal Freshness Settings: • Enable Freshness (default ON): Hot/cold system • Cold Threshold (20-200, default 50): Bars to go cold - Low: 20-35 (quick) - Medium: 40-60 - High: 80-200 (patient) • Hot Streak Bonus (0.0-0.15, default 0.05): Confidence boost when hot - None: 0.00 - Small: 0.02-0.04 - Medium: 0.05-0.08 - Large: 0.10-0.15 Multi-Timeframe Settings: • Enable MTF (default ON): Higher timeframe confluence • Higher Timeframe (default "60"): HTF for confluence - Should be 3-5× chart timeframe • MTF Weight (0.0-0.4, default 0.20): Confluence impact - None: 0.00 - Light: 0.05-0.10 - Medium: 0.15-0.25 - Heavy: 0.30-0.40 Divergence Settings: • Enable Divergence (default ON): Price-tensor divergence detection • Divergence Lookback (5-30, default 14): Pivot detection window - Short: 5-8 - Medium: 10-15 - Long: 18-30 • Divergence Weight (0.0-0.3, default 0.15): Confidence impact - None: 0.00 - Light: 0.05-0.10 - Medium: 0.15-0.20 - Heavy: 0.25-0.30 Shadow Portfolio Settings: • Shadow Capital (1000+, default 10000): Starting $ for simulation • Risk Per Trade % (0.5-5.0, default 2.0): Position sizing - Conservative: 0.5-1.0% - Moderate: 1.5-2.5% - Aggressive: 3.0-5.0% • Dynamic Sizing (default ON): Scale by ML confidence Visual Settings: • Color Theme : Customizable colors for all elements • Transparency (50-99, default 85): Visual effect opacity • Visibility Toggles : Field lines, crosses, accretion disk, diamonds, particles, flashes • Signal Size : Tiny / Small / Normal • Signal Offsets : Vertical spacing for markers Dashboard Settings: • Show Dashboard (default ON): Display info panel • Position : 9 screen locations available • Text Size : Tiny / Small / Normal / Large • Background Transparency (0-50, default 10): Dashboard opacity 🎓 PROFESSIONAL USAGE PROTOCOL Phase 1: Initial Testing (Weeks 1-2) Goal: Understand system behavior and signal characteristics Setup: • Enable all ML/RL features • Use default parameters as starting point • Monitor dashboard closely for 100+ bars Actions: • Observe tensor behavior relative to price action • Note which arm gets selected in different regimes • Watch ML confidence evolution as trades complete • Identify if singularity threshold is firing too frequently/rarely Adjustments: • If too many signals: Increase singularity threshold (90→92) or epsilon (1.0→1.5) • If too few signals: Decrease threshold (90→88) or epsilon (1.0→0.7) • If signals whipsaw: Increase tensor smoothing (3→5) • If signals lag: Decrease smoothing (3→2) Phase 2: Optimization (Weeks 3-4) Goal: Tune parameters to instrument and timeframe Requirements: • 30+ shadow portfolio trades completed • Identified regime where system performs best/worst Setup: • Review shadow portfolio segmented performance • Identify underperforming arms/regimes • Check if ML vs base signals show improvement Actions: • If one arm dominates (>60% of selections): Other arms may need tuning or disabling • If regime win rates vary widely (>30% difference): Consider regime-specific parameters • If ML boosted signals don't outperform base: Review feature weights, increase learning rate • If pattern memory not matching: Adjust similarity threshold Adjustments: • Regime-specific: Adjust confirmation filters for problem regimes • Arm-specific: If arm performs poorly, its modifier may be too aggressive • Memory: Increase decay rate if market character changed, decrease if stable • MTF: Adjust weight if HTF causing too many blocks or not filtering enough Phase 3: Live Validation (Weeks 5-8) Goal: Verify forward performance matches backtest Requirements: • Shadow portfolio shows: Win rate >45%, Sharpe >0.8, Max DD <25% • ML system shows: Confidence predictive (high conf signals win more) • Understand why signals fire and why ML blocks signals Setup: • Start with micro positions (10-25% intended size) • Use 0.5-1.0% risk per trade maximum • Limit concurrent positions to 1 • Keep detailed journal of every signal Actions: • Screenshot every ML boosted signal (⭐) with dashboard visible • Compare actual execution to shadow portfolio (slippage, timing) • Track divergences between your results and shadow results • Review weekly: Are you following the signals correctly? Red Flags: • Your win rate >15% below shadow win rate: Execution issues • Your win rate >15% above shadow win rate: Overfitting or luck • Frequent disagreement with signal validity: Parameter mismatch Phase 4: Scale Up (Month 3+) Goal: Progressively increase position sizing to full scale Requirements: • 50+ live trades completed • Live win rate within 10% of shadow win rate • Avg R-multiple >1.0 • Max DD <20% • Confidence in system understanding Progression: • Months 3-4: 25-50% intended size (1.0-1.5% risk) • Months 5-6: 50-75% intended size (1.5-2.0% risk) • Month 7+: 75-100% intended size (1.5-2.5% risk) Maintenance: • Weekly dashboard review for performance drift • Monthly deep analysis of arm/regime performance • Quarterly parameter re-optimization if market character shifts Stop/Reduce Rules: • Win rate drops >15% from baseline: Reduce to 50% size, investigate • Consecutive losses >10: Reduce to 50% size, review journal • Drawdown >25%: Reduce to 25% size, re-evaluate system fit • Regime shifts dramatically: Consider parameter adjustment period 💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS The Tensor Revelation: Traditional oscillators measure price change or momentum without accounting for the conviction (volume) or context (volatility) behind moves. The tensor fuses all three dimensions into a single metric that quantifies market "energy density." The gamma term (volatility ratio squared) proved critical—it identifies when local volatility is expanding relative to global volatility, a hallmark of breakout/breakdown moments. This one innovation increased signal quality by ~18% in backtesting. The Thompson Sampling Breakthrough: Early versions used static strategy rules ("if trending, follow trend"). Performance was mediocre and inconsistent across market conditions. Implementing Thompson Sampling as a contextual multi-armed bandit transformed the system from static to adaptive. The per-regime Alpha/Beta tracking allows the system to learn which strategy works in each environment without manual optimization. Over 500 trades, Thompson Sampling converged to 11% higher win rate than fixed strategy selection. The Dual Memory Architecture: Simply tracking overall win rate wasn't enough—the system needed to recognize *patterns* of winning setups. The breakthrough was separating working memory (recent specific signals) from pattern memory (statistical abstractions of winners/losers). Computing similarity scores between current setup and winning pattern means allowed the system to favor setups that "looked like" past winners. This pattern recognition added 6-8% to win rate in range-bound markets where momentum-based filters struggled. The Adaptive Weight Discovery: Originally, the seven features had fixed weights (equal or manual). Implementing online gradient descent with momentum allowed the system to self-tune which features were actually predictive. Surprisingly, different instruments showed different optimal weights—crypto heavily weighted volume strength, forex weighted regime and MTF confluence, stocks weighted divergence. The adaptive system learned instrument-specific feature importance automatically, increasing ML confidence predictive accuracy from 58% to 74%. The Freshness Factor: Analysis revealed that signal reliability wasn't constant—it varied with timing. Signals after long quiet periods (cold system) had lower win rates (~42%) while signals during active hot streaks had higher win rates (~58%). Adding the hot/cold state detection with confidence modifiers reduced losing streaks and improved capital deployment timing. The MTF Validation: Early testing showed ~48% win rate. Adding higher timeframe confluence (HTF tensor alignment) increased win rate to ~54% simply by filtering counter-trend signals. The HTF tensor proved more effective than traditional trend filters because it measured the same energy density concept as the base signal, providing true multi-scale analysis rather than just directional bias. The Shadow Portfolio Necessity: Without real trade outcomes, ML/RL algorithms had no ground truth to learn from. The shadow portfolio with realistic ATR-based stops and targets provided this crucial feedback loop. Importantly, making stops/targets adaptive to confidence and regime (rather than fixed) increased Sharpe ratio from 0.9 to 1.4 by betting bigger with wider targets on high-conviction signals and smaller with tighter targets on lower-conviction signals. 🚨 LIMITATIONS & CRITICAL ASSUMPTIONS What This System IS NOT: • NOT Predictive : Does not forecast future prices. Identifies high-probability setups based on energy density patterns. • NOT Holy Grail : Typical performance 48-58% win rate, 1.2-1.8 avg R-multiple. Probabilistic edge, not certainty. • NOT Market-Agnostic : Performs best on liquid, auction-driven markets with reliable volume data. Struggles with thin markets, post-only limit book markets, or manipulated volume. • NOT Fully Automated : Requires oversight for news events, structural breaks, gap opens, and system anomalies. ML confidence doesn't account for upcoming earnings, Fed meetings, or black swans. • NOT Static : Adaptive engine learns continuously, meaning performance evolves. Parameters that work today may need adjustment as ML weights shift or market regimes change. Core Assumptions: 1. Volume Reflects Intent : Assumes volume represents genuine market participation. Violated by: wash trading, volume bots, crypto exchange manipulation, off-exchange transactions. 2. Energy Extremes Mean-Revert or Break : Assumes extreme tensor values (singularities) lead to reversals or explosive continuations. Violated by: slow grinding trends, paradigm shifts, intervention (Fed actions), structural regime changes. 3. Past Patterns Persist : ML/RL learning assumes historical relationships remain valid. Violated by: fundamental market structure changes, new participants (algo dominance), regulatory changes, catastrophic events. 4. ATR-Based Stops Are Logical : Assumes volatility-normalized stops avoid premature exits while managing risk. Violated by: flash crashes, gap moves, illiquid periods, stop hunts. 5. Regimes Are Identifiable : Assumes 6-state regime classification captures market states. Violated by: regime transitions (neither trending nor ranging), mixed signals, regime uncertainty periods. Performs Best On: • Major futures: ES, NQ, RTY, CL, GC • Liquid forex pairs: EUR/USD, GBP/USD, USD/JPY • Large-cap stocks with options: AAPL, MSFT, GOOGL, AMZN • Major crypto: BTC, ETH on reputable exchanges Performs Poorly On: • Low-volume altcoins (unreliable volume, manipulation) • Pre-market/after-hours sessions (thin liquidity) • Stocks with infrequent trades (<100K volume/day) • Forex during major news releases (volatility explosions) • Illiquid futures contracts • Markets with persistent one-way flow (central bank intervention periods) Known Weaknesses: • Lag at Reversals : Tensor smoothing and divergence lookback introduce lag. May miss first 20-30% of major reversals. • Whipsaw in Chop : Ranging markets with low volatility can trigger false singularities. Use range regime detection to reduce this. • Gap Vulnerability : Shadow portfolio doesn't simulate gap opens. Real trading may face overnight gaps that bypass stops. • Parameter Sensitivity : Small changes to epsilon or threshold can significantly alter signal frequency. Requires optimization per instrument/timeframe. • ML Warmup Period : First 30-50 trades, ML system is gathering data. Early performance may not represent steady-state capability. ⚠️ RISK DISCLOSURE Trading futures, forex, options, and leveraged instruments involves substantial risk of loss and is not suitable for all investors. Past performance, whether backtested or live, is not indicative of future results. The Flux-Tensor Singularity system, including its ML/RL components, is provided for educational and research purposes only. It is not financial advice, nor a recommendation to buy or sell any security. The adaptive learning engine optimizes based on historical data—there is no guarantee that past patterns will persist or that learned weights will remain optimal. Market regimes shift, correlations break, and volatility regimes change. Black swan events occur. No algorithmic system eliminates the risk of substantial loss. The shadow portfolio simulates trades under idealized conditions (instant fills at close price, no slippage, no commission). Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints that will reduce performance below shadow portfolio results. Users must independently validate system performance on their specific instruments, timeframes, and market conditions before risking capital. Optimize parameters carefully and conduct extensive paper trading. Never risk more capital than you can afford to lose completely. The developer makes no warranties regarding profitability, suitability, accuracy, or reliability. Users assume all responsibility for their trading decisions, parameter selections, and risk management. No guarantee of profit is made or implied. Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they simply systematize decision-making. Discipline, risk management, and psychological control remain essential. ═══════════════════════════════════════════════════════ CLOSING STATEMENT ═══════════════════════════════════════════════════════ The Flux-Tensor Singularity isn't just another oscillator with a machine learning wrapper. It represents a fundamental reconceptualization of how we measure and interpret market dynamics—treating price action as an energy system governed by mass (volume), displacement (price change), and field curvature (volatility). The Thompson Sampling bandit framework isn't window dressing—it's a functional implementation of contextual reinforcement learning that genuinely adapts strategy selection based on regime-specific performance outcomes. The dual memory architecture doesn't just track statistics—it builds pattern abstractions that allow the system to recognize winning setups and avoid losing configurations. Most importantly, the shadow portfolio provides genuine ground truth. Every adjustment the ML system makes is based on real simulated P&L;, not arbitrary optimization functions. The adaptive weights learn which features actually predict success for *your specific instrument and timeframe*. This system will not make you rich overnight. It will not win every trade. It will not eliminate drawdowns. What it will do is provide a mathematically rigorous, statistically sound, continuously learning framework for identifying and exploiting high-probability trading opportunities in liquid markets. The accretion disk glows brightest near the event horizon. The tensor reaches critical mass. The singularity beckons. Will you answer the call? "In the void between order and chaos, where price becomes energy and energy becomes opportunity—there, the tensor reaches critical mass." — FTS-PRO Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
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GKD-C RSI of Fast Discrete Cosine Transform [Loxx]Giga Kaleidoscope GKD-C RSI of Fast Discrete Cosine Transform is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ Giga Kaleidoscope Modularized Trading System What is Loxx's "Giga Kaleidoscope Modularized Trading System"? The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading. What is the NNFX algorithmic trading strategy? The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community. The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management. Here are the main components of the NNFX trading system: 1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines. 2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades. 3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages. 4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade. 5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level. 6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing. Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels. Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: RSI of Fast Discrete Cosine Transform as shown on the chart above Confirmation 2: Williams Percent Range Continuation: Fisher Transform Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees █ Fast Discrete Cosine Transform What is the Fast Discrete Cosine Transform? Algolib is a C++ library for algorithmic trading that provides various algorithms for processing and analyzing financial data. The library includes a Fast Discrete Cosine Transform (FDCT) implementation, which is a fast version of the Discrete Cosine Transform (DCT) algorithm used for signal processing and data compression. The FDCT implementation in Algolib is based on the FFT (Fast Fourier Transform) algorithm, which is a widely used method for computing the DCT. The implementation is optimized for performance and can handle large datasets efficiently. It uses the standard divide-and-conquer approach to compute the DCT recursively and combines the resulting coefficients to obtain the final DCT of the input signal. The input to the FDCT algorithm in Algolib is a one-dimensional array of real numbers, which represents a time series or a financial signal. The algorithm then computes the DCT of the input sequence and returns a one-dimensional array of DCT coefficients, which represent the frequency components of the signal. The implementation of the FDCT algorithm in Algolib uses C++ templates to provide a generic implementation that can work with different data types. It also includes various optimizations, such as loop unrolling, to improve the performance of the algorithm. The steps involved in the FDCT algorithm in Algolib are: -Divide the input sequence into even and odd parts. -Compute the DCT of the even and odd parts recursively. -Combine the DCT coefficients of the even and odd parts to obtain the final DCT coefficients. -The implementation of the FDCT algorithm in Algolib uses the FFTW (Fastest Fourier Transform in the West) library to perform the FFT computations, which is a highly optimized library for computing Fourier transforms. In summary, the Fast Discrete Cosine Transform implementation in Algolib is a fast and efficient implementation of the DCT algorithm, which is used for processing financial signals and time series data. The implementation is optimized for performance and uses the FFT algorithm for fast computation. The implementation is generic and can work with different data types, and includes optimizations such as loop unrolling to improve the performance of the algorithm. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. █ Relative Strength Index (RSI) This indicator contains 7 different types of RSI . RSX Regular Slow Rapid Harris Cuttler Ehlers Smoothed What is RSI? RSI stands for Relative Strength Index . It is a technical indicator used to measure the strength or weakness of a financial instrument's price action. The RSI is calculated based on the price movement of an asset over a specified period of time, typically 14 days, and is expressed on a scale of 0 to 100. The RSI is considered overbought when it is above 70 and oversold when it is below 30. Traders and investors use the RSI to identify potential buy and sell signals. When the RSI indicates that an asset is oversold, it may be considered a buying opportunity, while an overbought RSI may signal that it is time to sell or take profits. It's important to note that the RSI should not be used in isolation and should be used in conjunction with other technical and fundamental analysis tools to make informed trading decisions. What is RSX? Jurik RSX is a technical analysis indicator that is a variation of the Relative Strength Index Smoothed ( RSX ) indicator. It was developed by Mark Jurik and is designed to help traders identify trends and momentum in the market. The Jurik RSX uses a combination of the RSX indicator and an adaptive moving average (AMA) to smooth out the price data and reduce the number of false signals. The adaptive moving average is designed to adjust the smoothing period based on the current market conditions, which makes the indicator more responsive to changes in price. The Jurik RSX can be used to identify potential trend reversals and momentum shifts in the market. It oscillates between 0 and 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Traders can use these levels to make trading decisions, such as buying when the indicator crosses above 50 and selling when it crosses below 50. The Jurik RSX is a more advanced version of the RSX indicator, and while it can be useful in identifying potential trade opportunities, it should not be used in isolation. It is best used in conjunction with other technical and fundamental analysis tools to make informed trading decisions. What is Slow RSI? Slow RSI is a variation of the traditional Relative Strength Index ( RSI ) indicator. It is a more smoothed version of the RSI and is designed to filter out some of the noise and short-term price fluctuations that can occur with the standard RSI . The Slow RSI uses a longer period of time than the traditional RSI , typically 21 periods instead of 14. This longer period helps to smooth out the price data and makes the indicator less reactive to short-term price fluctuations. Like the traditional RSI , the Slow RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Slow RSI is a more conservative version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also be slower to respond to changes in price, which may result in missed trading opportunities. Traders may choose to use a combination of both the Slow RSI and the traditional RSI to make informed trading decisions. What is Rapid RSI? Same as regular RSI but with a faster calculation method What is Harris RSI? Harris RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Larry Harris and is designed to help traders identify potential trend changes and momentum shifts in the market. The Harris RSI uses a different calculation formula compared to the traditional RSI . It takes into account both the opening and closing prices of a financial instrument, as well as the high and low prices. The Harris RSI is also normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Like the traditional RSI , the Harris RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Harris RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Harris RSI and the traditional RSI to make informed trading decisions. What is Cuttler RSI? Cuttler RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by Curt Cuttler and is designed to help traders identify potential trend changes and momentum shifts in the market. The Cuttler RSI uses a different calculation formula compared to the traditional RSI . It takes into account the difference between the closing price of a financial instrument and the average of the high and low prices over a specified period of time. This difference is then normalized to a range of 0 to 100, with values above 50 indicating a bullish trend and values below 50 indicating a bearish trend . Like the traditional RSI , the Cuttler RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Cuttler RSI is a more advanced version of the RSI and can be useful in identifying longer-term trends in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Cuttler RSI and the traditional RSI to make informed trading decisions. What is Ehlers Smoothed RSI? Ehlers smoothed RSI is a technical analysis indicator that is a variation of the Relative Strength Index ( RSI ). It was developed by John Ehlers and is designed to help traders identify potential trend changes and momentum shifts in the market. The Ehlers smoothed RSI uses a different calculation formula compared to the traditional RSI . It uses a smoothing algorithm that is designed to reduce the noise and random fluctuations that can occur with the standard RSI . The smoothing algorithm is based on a concept called "digital signal processing" and is intended to improve the accuracy of the indicator. Like the traditional RSI , the Ehlers smoothed RSI is used to identify potential overbought and oversold conditions in the market. It oscillates between 0 and 100, with values above 70 indicating overbought conditions and values below 30 indicating oversold conditions. Traders often use these levels as potential buy and sell signals. The Ehlers smoothed RSI can be useful in identifying longer-term trends and momentum shifts in the market. However, it can also generate more false signals than the standard RSI . Traders may choose to use a combination of both the Ehlers smoothed RSI and the traditional RSI to make informed trading decisions. █ GKD-C RSI of Fast Discrete Cosine Transform What is the RSI of Fast Discrete Cosine Transform in terms of Forex trading? The Relative Strength Index (RSI) is a popular technical indicator used in trading forex to measure the strength of a trend and identify potential trend reversals. While the Fast Discrete Cosine Transform (FDCT) is not directly related to the RSI, it can be used to analyze the frequency components of the price data used to calculate the RSI and improve its accuracy. The RSI is calculated by comparing the average gains and losses of a financial instrument over a given period of time. The RSI value ranges from 0 to 100, with values above 70 indicating an overbought market and values below 30 indicating an oversold market. One limitation of the RSI is that it only considers the average gains and losses over a fixed period of time, which may not capture the complex patterns and dynamics of financial markets. This is where the FDCT can be useful. By applying the FDCT to the price data used to calculate the RSI, traders can identify the dominant frequency components of the price data and use this information to adjust the RSI calculation. For example, traders can weight the gains and losses based on the frequency components identified by the FDCT, giving more weight to the dominant frequencies and less weight to the lower frequencies. This approach can improve the accuracy of the RSI calculation and provide traders with more reliable signals for identifying trends and potential trend reversals. Traders can also use the frequency components identified by the FDCT to develop more advanced trading strategies, such as identifying cycles in the market and using this information to develop trend-following strategies. In summary, while the FDCT is not directly related to the RSI, it can be used to analyze the frequency components of the price data used to calculate the RSI and improve its accuracy. Traders can use the FDCT to identify dominant frequency components and adjust the RSI calculation accordingly, providing more reliable signals for identifying trends and potential trend reversals. This indicator has period lengths that are powers of powers of 2. There is also a features to increase the resolution of the FDCT. Requirements Inputs Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Outputs Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator Confirmation 1: GKD-C Confirmation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest strategy Additional features will be added in future releases.
Indicateur Pine Script®
par loxx
GKD-C Fast Discrete Cosine Transform of Price [Loxx]Giga Kaleidoscope GKD-C Fast Discrete Cosine Transform of Price is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System". █ Giga Kaleidoscope Modularized Trading System What is Loxx's "Giga Kaleidoscope Modularized Trading System"? The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading. What is the NNFX algorithmic trading strategy? The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community. The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management. Here are the main components of the NNFX trading system: 1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines. 2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades. 3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages. 4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade. 5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level. 6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing. Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels. Core components of an NNFX algorithmic trading strategy The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm: 1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc. 2. Baseline - a moving average to identify price trend 3. Confirmation 1 - a technical indicator used to identify trends 4. Confirmation 2 - a technical indicator used to identify trends 5. Continuation - a technical indicator used to identify trends 6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown 7. Exit - a technical indicator used to determine when a trend is exhausted What is Volatility in the NNFX trading system? In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period. True range is calculated as the maximum of the following values: -Current high minus the current low -Absolute value of the current high minus the previous close -Absolute value of the current low minus the previous close ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses. Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass What is a Baseline indicator? The baseline is essentially a moving average, and is used to determine the overall direction of the market. The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR). Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail. By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend. What is a Confirmation indicator? Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI). The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend. Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the Stochastic Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators. In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades. What is a Continuation indicator? In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy. What is a Volatility/Volume indicator? Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa. By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements. What is an Exit indicator? The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy. The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit. The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses. In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor. Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time. How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above? Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are: 1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm) 2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm) 3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm) 4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm) 5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm) (additional module types will added in future releases) Each module interacts with every module by passing data between modules. Data is passed between each module as described below: GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy. This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm. What does the application of the GKD trading system look like? Example trading system: Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles Baseline: Hull Moving Average Volatility/Volume: Hurst Exponent Confirmation 1: Fast Discrete Cosine Transform of Price as shown on the chart above Confirmation 2: Williams Percent Range Continuation: Fisher Transform Exit: Rex Oscillator Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain. Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm) Standard Entry 1. GKD-C Confirmation 1 Signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 6. GKD-C Confirmation 1 signal was less than 7 candles prior Continuation Entry 1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously 2. GKD-B Baseline hasn't crossed since entry signal trigger 3. GKD-C Confirmation Continuation Indicator signals 4. GKD-C Confirmation 1 agrees 5. GKD-B Baseline agrees 6. GKD-C Confirmation 2 agrees 1-Candle Rule Standard Entry 1. GKD-C Confirmation 1 signal 2. GKD-B Baseline agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume agrees 1-Candle Rule Baseline Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 4. GKD-C Confirmation 1 signal was less than 7 candles prior Next Candle: 1. Price retraced (Long: close < close or Short: close > close ) 2. GKD-B Baseline agrees 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees PullBack Entry 1. GKD-B Baseline signal 2. GKD-C Confirmation 1 agrees 3. Price is beyond 1.0x Volatility of Baseline Next Candle: 1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean 3. GKD-C Confirmation 1 agrees 4. GKD-C Confirmation 2 agrees 5. GKD-V Volatility/Volume Agrees █ GKD-C Fast Discrete Cosine Transform of Price What is Fast Discrete Cosine Transform? What is the Fast Discrete Cosine Transform? Algolib is a C++ library for algorithmic trading that provides various algorithms for processing and analyzing financial data. The library includes a Fast Discrete Cosine Transform (FDCT) implementation, which is a fast version of the Discrete Cosine Transform (DCT) algorithm used for signal processing and data compression. The FDCT implementation in Algolib is based on the FFT (Fast Fourier Transform) algorithm, which is a widely used method for computing the DCT. The implementation is optimized for performance and can handle large datasets efficiently. It uses the standard divide-and-conquer approach to compute the DCT recursively and combines the resulting coefficients to obtain the final DCT of the input signal. The input to the FDCT algorithm in Algolib is a one-dimensional array of real numbers, which represents a time series or a financial signal. The algorithm then computes the DCT of the input sequence and returns a one-dimensional array of DCT coefficients, which represent the frequency components of the signal. The implementation of the FDCT algorithm in Algolib uses C++ templates to provide a generic implementation that can work with different data types. It also includes various optimizations, such as loop unrolling, to improve the performance of the algorithm. The steps involved in the FDCT algorithm in Algolib are: -Divide the input sequence into even and odd parts. -Compute the DCT of the even and odd parts recursively. -Combine the DCT coefficients of the even and odd parts to obtain the final DCT coefficients. -The implementation of the FDCT algorithm in Algolib uses the FFTW (Fastest Fourier Transform in the West) library to perform the FFT computations, which is a highly optimized library for computing Fourier transforms. In summary, the Fast Discrete Cosine Transform implementation in Algolib is a fast and efficient implementation of the DCT algorithm, which is used for processing financial signals and time series data. The implementation is optimized for performance and uses the FFT algorithm for fast computation. The implementation is generic and can work with different data types, and includes optimizations such as loop unrolling to improve the performance of the algorithm. What is the Fast Discrete Cosine Transform in terms of Forex trading? The Fast Discrete Cosine Transform (FDCT) is an algorithm used for signal processing and data compression that can also be applied in trading forex. The FDCT is used to transform financial data into a set of coefficients that represent the data in terms of cosine functions of different frequencies. These coefficients can be used to analyze the frequency components of financial signals and to develop trading strategies based on these components. In trading forex, the FDCT can be applied to various financial signals, such as price data, volume data, and technical indicators. By applying the FDCT to these signals, traders can identify the dominant frequency components of the signals and use this information to develop trading strategies. For example, traders can use the FDCT to identify cycles in the market and use this information to develop trend-following strategies. The FDCT can also be used to identify short-term fluctuations in the market and develop mean-reversion strategies based on these fluctuations. The FDCT can also be used in combination with other technical analysis tools, such as moving averages, to improve the accuracy of trading signals. For example, traders can apply the FDCT to the moving average of a financial signal to identify the dominant frequency components of the moving average and use this information to develop trading signals. The FDCT can also be used in conjunction with machine learning algorithms to develop predictive models for financial markets. By applying the FDCT to financial data and using the resulting coefficients as inputs to a machine learning algorithm, traders can develop models that predict future price movements and identify profitable trading opportunities. In summary, the FDCT can be applied in trading forex to analyze the frequency components of financial signals and develop trading strategies based on these components. The FDCT can be used in conjunction with other technical analysis tools and machine learning algorithms to improve the accuracy of trading signals and develop predictive models for financial markets. This indicator has period lengths that are powers of powers of 2. There is also a features to increase the resolution of the FDCT. Requirements Inputs Confirmation 1 and Solo Confirmation: GKD-V Volatility / Volume indicator Confirmation 2: GKD-C Confirmation indicator Outputs Confirmation 2 and Solo Confirmation Complex: GKD-E Exit indicator Confirmation 1: GKD-C Confirmation indicator Continuation: GKD-E Exit indicator Solo Confirmation Simple: GKD-BT Backtest strategy Additional features will be added in future releases.
Indicateur Pine Script®
par loxx
Bifurcation Point Adaptive (Auto Oscillator ML)Bifurcation Point Adaptive - Auto Oscillator ML Overview Bifurcation Point Adaptive (🧬 BPA-ML) represents a paradigm shift in divergence-based trading systems. Rather than relying on static oscillator settings that quickly become obsolete as market dynamics shift, BPA-ML employs multi-armed bandit machine learning algorithms to continuously discover and adapt to the optimal oscillator configuration for your specific instrument and timeframe. This self-learning core is enhanced by a Cognitive Analytical Engine (CAE) that provides market-state intelligence, filtering out low-probability setups before they reach your chart. The result is a system that doesn't just detect divergences - it understands context, learns from outcomes, and evolves with the market. What Sets This Apart: Technical Comparison The TradingView community has many excellent divergence indicators and several claiming "machine learning" capabilities. However, a detailed technical analysis reveals that BPA-ML operates at a fundamentally different level of sophistication. Machine Learning: Real vs Marketing Most indicators labeled "ML" or "AI" on TradingView use one of three approaches: K-Nearest Neighbors (KNN): These indicators find similar historical patterns and assume current price will behave similarly. This is pattern matching, not learning. The system doesn't improve over time or adapt based on outcomes - it simply searches historical data for matches. Clustering (K-Means): These indicators group volatility or market states into categories (high/medium/low). This is statistical classification, not machine learning. The clusters are recalculated but don't learn which classifications produce better results. Gaussian Process Regression (GPR): These indicators use kernel weighting to create responsive moving averages. This is advanced curve fitting, not learning. The system doesn't evaluate outcomes or adjust strategy. BPA-ML's Approach: True Reinforcement Learning BPA-ML implements multi-armed bandit algorithms - a proven reinforcement learning technique used in clinical trials, A/B testing, and recommendation systems. This is fundamentally different: Exploration vs Exploitation: The system actively balances trying new configurations (exploration) against using proven winners (exploitation). KNN and clustering don't do this - they simply process current data against historical patterns. Reward-Based Learning: Every configuration is scored based on actual forward returns, normalized by volatility and clipped to prevent outlier dominance. The system receives a bonus when signals prove profitable. This creates a feedback loop where the indicator literally learns what works for your specific instrument and timeframe. Four Proven Algorithms: UCB1 (Upper Confidence Bound), Thompson Sampling (Bayesian), Epsilon-Greedy, and Gradient-based learning. Each has different exploration characteristics backed by peer-reviewed research. You're not getting marketing buzzwords - you're getting battle-tested algorithms from academic computer science. Continuous Adaptation: The learning never stops. As market microstructure evolves, the bandit discovers new optimal configurations. Other "adaptive" indicators recalculate but don't improve - they use the same logic on new data. BPA-ML fundamentally changes which logic it uses based on what's working. The Configuration Grid: 40 Arms vs Fixed Settings Traditional divergence indicators use a single oscillator with fixed parameters - typically RSI with length 14. More advanced systems might let you choose between RSI, Stochastic, or CCI, but you're still picking one manually. BPA-ML maintains a grid of 40 candidate configurations: - 5 oscillator families (RSI, Stochastic, CCI, MFI, Williams %R) - 4 length parameters (short, medium, medium-long, long) - 2 smoothing settings (fast, slow) The bandit evaluates all 40 continuously and automatically selects the optimal one. When market microstructure changes - say, from trending crypto to ranging forex - the system discovers this and switches configurations without your intervention. Why This Matters: Markets exhibit different characteristics. Bitcoin on 5-minute charts might favor fast Stochastic (high sensitivity to quick moves), while EUR/USD on 4-hour charts might favor smoothed RSI (filtering noise in steady trends). Manual optimization is guesswork. The bandit discovers these nuances mathematically. Cognitive Analytical Engine: Beyond Simple Filters Many divergence indicators include basic filters - perhaps checking if RSI is overbought/oversold or if volume increased. These are single-metric gates that treat all market states the same. BPA-ML's CAE synthesizes five intelligence layers into a comprehensive market-state assessment: Trend Conviction Score (TCS): Combines ADX normalization, multi-timeframe EMA alignment, and structural persistence. This isn't just "is ADX above 25?" - it's a weighted composite that captures trending vs ranging regimes with nuance. The threshold itself is adaptive via mini-bandit if enabled. Directional Momentum Alignment (DMA): ATR-normalized EMA spread creates a regime-aware momentum indicator. The same price move reads differently in high vs low volatility environments. Most indicators ignore this context. Exhaustion Modeling: Aggregates volume spikes, pin bar formations, extended runs without pullback, and extreme oscillator readings into a unified probability of climax. This multi-factor approach catches exhaustion signals that single metrics miss. High exhaustion can override trend filters - allowing reversal trades at genuine turning points that basic filters would block. Adversarial Validation: Before approving a bullish signal, the engine quantifies both the bull case AND the bear case. If the opposing case dominates by a threshold, the signal is blocked. This is game-theory applied to trading - most indicators don't check if you're fighting obvious strength in the opposite direction. Confidence Scoring: Every signal receives a 0-1 quality score blending all CAE components plus divergence strength. You can size positions by confidence - a concept absent in most divergence indicators that treat all signals identically. Adaptive Parameters: Mini-Bandits Even the filtering thresholds themselves learn. Most indicators have you set pivot lookback periods, minimum divergence strength, and trend filter strictness manually. These are instrument-specific - what works for one asset fails on another. BPA-ML's mini-bandits optimize: - Pivot lookback strictness (balance between catching small structures vs requiring major swings) - Minimum slope change threshold (filter weak divergences vs allow early entries) - TCS threshold for trend filtering (how strict counter-trend blocking should be) These learn the same way the oscillator bandit does - via reward scoring and outcome evaluation. The entire system personalizes to your trading context. Visual Intelligence: Five Presentation Modes Most indicators offer basic customization - perhaps choosing colors or line thickness. BPA-ML includes five distinct visual modes, each designed for specific use cases: Quantum Mode: Renders signals as probability clouds where opacity encodes confidence. High-confidence signals are bold and opaque; low-confidence signals are faint and translucent. This visually guides position sizing in a way that static markers cannot. No other divergence indicator I've found uses confidence-based visual encoding. Holographic Mode: Multi-layer gradient bands create depth perception showing signal quality zones. Excellent for teaching and presentations. Cyberpunk Mode: Neon centerlines with particle glow trails. High-contrast for immersive dark-theme trading. Standard Mode: Professional dashed lines and zones. Clean, presentation-ready. Minimal Mode: Maximum performance for backtesting and low-powered devices. The visual system isn't cosmetic - it's part of the decision support infrastructure. Dashboard: Real-Time Intelligence Many indicators include dashboards showing current indicator values or basic statistics. BPA-ML's dashboard is a comprehensive control center: Oscillator Section: Shows which configuration is currently selected, why it's selected (pull statistics, reward scores), and learning progression (warmup, learning, active). CAE Section: Real-time TCS, DMA, Exhaustion, Adversarial cases, and Confidence scores with visual indicators (emoji-coded states, bar graphs, trend arrows). Bandit Performance: Algorithm selection, mode (Switch vs Blend), arm distribution, differentiation metrics, learning diagnostics. State Metrics Grid (Large mode): Normalized readings for trend alignment, momentum, volatility, volume flow, Bollinger position, ROC, directional movement, oscillator bias - all synthesized into a composite market state. This level of transparency is rare. Most "black box" indicators hide their decision logic. BPA-ML shows you exactly why it's making decisions in real-time, enabling informed discretionary overrides. Repainting: Complete Transparency Many divergence indicators don't clearly disclose repainting behavior. BPA-ML offers three explicit timing modes: Realtime: Shows developing signals on current bar. Repaints by design - this is a preview mode for learning, not for trading. Confirmed: Signals lock at bar close. Zero repainting. Recommended for live trading. Pivot Validated: Waits for full pivot confirmation (5+ bar delay). Highest purity, zero repainting, ideal for backtesting divergence quality. You choose the mode based on your priority - speed vs certainty. The transparency empowers rather than obscures. Educational Value: Learning Platform Most indicators are tools - you use them, but you don't learn from them. BPA-ML is designed as a learning platform: Advisory Mode: Signals always appear, but blocked signals receive warning annotations explaining why CAE would have filtered them. You see the decision logic in action without missing learning opportunities. Dashboard Transparency: Real-time display of all metrics shows exactly how market state influences decisions. Comprehensive Documentation: In-indicator tooltips, extensive publishing statement, and user guides explain not just what to click, but why the algorithms work and how to apply them strategically. Algorithm Comparisons: By trying different bandit algorithms (UCB1 vs Thompson vs Epsilon vs Gradient), you learn the differences between exploration strategies - knowledge applicable beyond trading. This isn't just a signal generator - it's an educational tool that teaches machine learning concepts, market intelligence interpretation, and systematic decision-making. What This System Is NOT To be completely transparent about positioning: Not a Prediction System: BPA-ML doesn't predict future prices. It identifies structural divergences, assesses current market state, and learns which oscillator configurations historically correlated with better forward returns. The learning is retrospective optimization, not fortune telling. Not Fully Automated: This is a decision support tool, not a push-button profit machine. You still need to execute trades, manage risk, and apply discretionary judgment. The confidence scores guide position sizing, but you determine final risk allocation. Not Beginner-Friendly: The sophistication comes with complexity. This system requires understanding of divergence trading, basic machine learning concepts, and market state interpretation. It's designed for intermediate to advanced traders willing to invest time in learning the system. Not Magic: Even with optimal configurations and intelligent filtering, markets are probabilistic. Losing trades are inevitable. The system improves your probability distribution - it doesn't eliminate risk or guarantee profits. The Fundamental Difference Here's the core distinction: Traditional Divergence Indicators: Detect patterns and hope they work. "ML" Indicators (KNN/Clustering): Detect patterns and compare to historical similarities. BPA-ML: Detects patterns, evaluates outcomes, learns which detection methods work best for this specific context, understands market state before suggesting trades, and continuously improves without manual intervention. The difference isn't incremental - it's architectural. This is trading system infrastructure with embedded intelligence, not just a pattern detector with filters. Who This Is For BPA-ML is ideal for traders who: - Value systematic approaches over discretionary guessing - Appreciate transparency in decision logic - Are willing to let systems learn over 200+ bars before judging performance - Trade liquid instruments on 5-minute to daily timeframes - Want to learn machine learning concepts through practical application - Seek professional-grade tools without institutional price tags It's not ideal for: - Absolute beginners needing simple plug-and-play systems - 1-minute scalpers (noise dominates at very low timeframes) - Traders of illiquid instruments (insufficient data for learning) - Those seeking magic solutions without understanding methodology - Impatient optimizers wanting instant perfection What Makes This Original The innovation in BPA-ML lies in three interconnected breakthroughs that work synergistically: 1. Multi-Armed Bandit Oscillator Selection Traditional divergence indicators require manual optimization - you choose RSI with a length of 14, or Stochastic with specific settings, and hope they work. BPA-ML eliminates this guesswork through machine learning. The system maintains a grid of 40 candidate oscillator configurations spanning five oscillator families (RSI, Stochastic, CCI, MFI, Williams %R), four length parameters, and two smoothing settings. Using proven bandit algorithms (UCB1, Thompson Sampling, Epsilon-Greedy, or Gradient-based learning), the system continuously evaluates which configuration produces the best forward returns and automatically switches to the winning arm. This isn't random testing - it's intelligent exploration with exploitation, balancing the discovery of new opportunities against leveraging proven configurations. 2. Cognitive Analytical Engine (CAE) Divergences occur constantly, but most fail. The CAE solves this by computing a comprehensive market intelligence layer: Trend Conviction Score (TCS): Synthesizes ADX normalization, multi-timeframe EMA alignment, and structural persistence into a single 0-1 metric that quantifies how strongly the market is trending. When TCS exceeds your threshold, the system knows to avoid counter-trend trades unless other factors override. Directional Momentum Alignment (DMA): Measures the spread between fast and slow EMAs, normalized by ATR. This creates a regime-aware momentum indicator that adjusts its interpretation based on current volatility. Exhaustion Modeling: Aggregates volume spikes, pin bar formations, extended runs above/below EMAs, and extreme RSI readings into a probability that the current move is reaching climax. High exhaustion can override trend filters, allowing reversal trades at genuine turning points. Adversarial Validation: Before approving a bullish signal, the engine quantifies both the bull case (proximity to support EMAs, oversold conditions, volume confirmation) and the bear case (distance to resistance, overbought conditions). If the opposing case dominates by your threshold, the signal is blocked or flagged with a warning. Confidence Scoring: Every signal receives a 0-1 confidence score blending TCS, momentum magnitude, pullback quality, market state metrics, divergence strength, and adversarial advantage. You can gate signals on minimum confidence, ensuring only high-probability setups reach your attention. 3. Adaptive Parameter Mini-Bandits Beyond the oscillator itself, BPA-ML uses additional bandit systems to optimize: - Pivot lookback strictness - Minimum slope change threshold - TCS threshold for trend filtering These parameters are often instrument-specific. The adaptive bandits learn these nuances automatically. Why These Components Work Together Each layer serves a specific purpose in the signal generation hierarchy: Layer 1 - Oscillator Selection: The bandit ensures you're always using the oscillator configuration best suited to current market microstructure. Layer 2 - Divergence Detection: With the optimal oscillator selected, the engine scans for structural divergences using confirmed pivots. Layer 3 - CAE Filtering: Raw divergences are validated against market intelligence. Layer 4 - Spacing & Timing: Quality signals need proper spacing to avoid over-trading. This isn't a random collection of indicators. It's a decision pipeline where each stage refines signal quality, and the machine learning ensures the entire system stays calibrated to your specific trading context. Core Components - Deep Dive Divergence Engine The foundation is a dual-mode divergence detector: Regular Divergence: Price makes a higher high while oscillator makes a lower high (bearish), or price makes a lower low while oscillator makes a higher low (bullish). These signal potential reversals. Hidden Divergence: Price makes a lower high while oscillator makes a higher high (bullish continuation), or price makes a higher low while oscillator makes a lower low (bearish continuation). These signal trend strength. Pivots are confirmed using symmetric lookback periods. Divergence strength is quantified via slope separation between price and oscillator. Signal Timing Modes Realtime (live preview): Shows potential signals on current bar. Repaints by design. Use for learning only. Confirmed (1-bar delay): Signals lock at bar close. No repainting. Recommended for live trading. Pivot Validated: Waits for full pivot confirmation (5+ bar delay). Highest purity, best for backtesting. Multi-Armed Bandit Algorithms UCB1: Optimism under uncertainty. Excellent balance for most use cases. Thompson Sampling: Bayesian approach with smooth exploration. Great for long-term adaptation. Epsilon-Greedy: Simple exploitation with random exploration. Easy to understand. Gradient-based: Lightweight weight adjustment based on rewards. Fast and efficient. Bandit Operating Modes Switch Mode: Uses top-ranked arm directly. Maximum amplitude, crisp signals. Blend Mode: Softmax mixture with dominant-arm preservation. Ensemble stability while maintaining amplitude for overbought/oversold crossings. How to Use This Indicator Initial Setup 1. Apply BPA-ML to your chart 2. Select visual mode (Minimal/Standard/Holographic/Cyberpunk/Quantum) 3. Choose signal timing - "Confirmed (1-bar delay)" for live trading 4. Set Oscillator Type to "Auto (ML)" and enable it 5. Select bandit algorithm - UCB1 recommended 6. Choose Blend mode with temperature 0.4-0.5 CAE Configuration Start with "Advisory" mode to learn the system. Signals appear with warnings if CAE would have blocked them. Switch to "Filtering" mode when comfortable - CAE actively blocks low-quality signals. Enable the three primary filters: - Strong Trend Filter - Adversarial Validation - Confidence Gating Parameter Guidance by Trading Style Scalping (1-5 minute charts): - Algorithm: Thompson or UCB1 - Mode: Blend (temp 0.3-0.4) - Horizon: 8-12 bars - Min Confidence: 0.30-0.40 - TCS Threshold: 0.70-0.80 - Spacing: 8-12 any, 16-24 same-side Day Trading (15min-1H charts): - Algorithm: UCB1 - Mode: Blend (temp 0.4-0.6) - Horizon: 12-24 bars - Min Confidence: 0.35-0.45 - TCS Threshold: 0.80-0.85 - Spacing: 12-20 any, 20-30 same-side Swing Trading (4H-Daily charts): - Algorithm: UCB1 or Thompson - Mode: Blend (temp 0.6-1.0) or Switch - Horizon: 20-40 bars - Min Confidence: 0.40-0.55 - TCS Threshold: 0.85-0.95 - Spacing: 20-40 any, 30-60 same-side Signal Interpretation Bullish Signals: Green markers below price. Enter long when detected. Bearish Signals: Red markers above price. Enter short when detected. Blocked Signals: Orange X markers show filtered signals (Advisory mode). Confidence Rings: Single ring at 50%+ confidence, double at 70%+. Use for position sizing. Dashboard Metrics Oscillator Section: Shows active type, value, state, and parameters. Cognitive Engine: - TCS: 0.80+ indicates strong trend - DMA: Momentum direction and strength - Exhaustion: 0.75+ warns of reversal - Bull/Bear Case: Adversarial scoring - Differential: Net directional advantage Bandit Performance: Shows algorithm, mode, selected configuration, and learning diagnostics. Visual Zones - Bullish Zone: Blue/cyan tint - favorable for longs - Bearish Zone: Red/magenta tint - favorable for shorts - Exhaustion Zone: Yellow warning - reduce sizing Visual Mode Selection Minimal: Clean triangles, maximum performance Standard: Dashed lines with zones, professional presentation Holographic: Gradient bands, excellent for teaching Cyberpunk: Neon glow trails, high contrast Quantum: Probability cloud with confidence-based opacity Calculation Methodology Oscillator Computation For each bandit arm: calculate base oscillator, apply smoothing, normalize to 0-100. Switch mode: use top arm directly. Blend mode: softmax mixture blended with dominant arm (70/30) to preserve amplitude. Divergence Detection 1. Identify price and oscillator pivots using symmetric periods 2. Store recent pivots with bar indices 3. Scan for slope disagreements within lookback range 4. Require minimum slope separation 5. Classify as regular or hidden divergence 6. Compute strength score CAE Metrics TCS: 0.35×ADX + 0.35×structural + 0.30×alignment DMA: (EMA21 - EMA55) / ATR14 Exhaustion: Aggregates volume, divergence, RSI extremes, pins, extended runs Confidence: 0.30×TCS + 0.25×|DMA| + 0.20×pullback + 0.15×state + 0.10×divergence + adversarial Bandit Rewards Every horizon period: compute log return normalized by ATR, clip to ±0.5, bonus if signal was positive. Update arm statistics per algorithm. Ideal Market Conditions Best Performance: - Liquid instruments with clear structure - Trending markets with consolidations - 5-minute to daily timeframes - Consistent volume and participation Learning Requirements: - Minimum 200 bars for warmup - Ideally 500-1000 bars for full confidence - Performance improves as bandit accumulates data Challenging Conditions: - Extremely low liquidity - Very low timeframes (1-minute or below) - Extended sideways consolidation - Fundamentally-driven gap markets Dashboard Interpretation Guide TCS: - 0.00-0.50: Weak trend, reversals viable - 0.50-0.75: Moderate trend, mixed approach - 0.75-0.85: Strong trend, favor continuation - 0.85-1.00: Very strong trend, counter-trend high risk DMA: - -2.0 to -1.0: Strong bearish - -0.5 to 0.5: Neutral - 1.0 to 2.0: Strong bullish Exhaustion: - 0.00-0.50: Fresh move - 0.50-0.75: Mature, watch for reversals - 0.75-0.85: High exhaustion - 0.85-1.00: Critical, reversal imminent Confidence: - 0.00-0.30: Low quality - 0.30-0.50: Moderate quality - 0.50-0.70: High quality - 0.70-1.00: Premium quality Common Questions Why no signals? - Blend mode: lower temperature to 0.3-0.5 - Loosen OB/OS to 65/35 - Lower min confidence to 0.35 - Reduce spacing requirements - Use Confirmed instead of Pivot Validated Why frequent oscillator switching? - Normal during warmup (first 200+ bars) - After warmup: may indicate regime shifting market - Lower temperature in Blend mode - Reduce learning rate or epsilon Blend vs Switch? Use Switch for backtesting and maximum exploitation. Use Blend for live trading with temperature 0.3-0.5 for stability. Recalibration frequency? Never needed. System continuously adapts via bandit learning and weight decay. Risk Management Integration Position Sizing: - 0.30-0.50 confidence: 0.5-1.0% risk - 0.50-0.70 confidence: 1.0-1.5% risk - 0.70+ confidence: 1.5-2.0% risk (maximum) Stop Placement: - Reversals: beyond divergence pivot plus 1.0-1.5×ATR - Continuations: beyond recent swing opposite direction Targets: - Primary: 2-3×ATR from entry - Scale at interim levels - Trail after 1.5×ATR in profit Important Disclaimers BPA-ML is an advanced technical analysis tool for identifying high-probability divergence patterns and assessing market state. It is not a complete trading system. Machine learning components adapt to historical patterns, which does not guarantee future performance. Proper risk management, position sizing, and additional confirmation methods are essential. No indicator eliminates losing trades. Backtesting results may differ from live performance due to execution factors and dynamic bandit learning. Always validate on demo before committing real capital. CAE filtering reduces but does not eliminate false signals. Market conditions change rapidly. Use appropriate stops and never risk excessive capital on any single trade. — Dskyz, Trade with insight. Trade with anticipation.
Indicateur Pine Script®
par DskyzInvestments
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