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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
X-Trend reversal (PRO) 👑 X-TREND REVERSAL (PRO): Institutional Order Flow Analysis Look at the results above. This is not luck. This is mathematics. X-Trend PRO is the flagship algorithm of our ecosystem, engineered for traders who have outgrown standard technical analysis. While the Lite version identifies trends, the PRO version sees where the money is. Unlike 99% of indicators that focus solely on price action, X-Trend PRO analyzes Market Liquidity and Volume Delta (CVD). We have fused market structure with an X-ray view of institutional activity. 💎 EXCLUSIVE PRO FEATURES: 📊 Volume & CVD Fusion: The indicator detects Cumulative Volume Delta divergences. It warns you of reversals when price is still rising, but buyers have already left the market. 📍 Dynamic POC (Point of Control): Automatically identifies "fair price" levels and institutional accumulation zones. You trade from levels defended by market makers. 🛡 Zero Repaint Guarantee: The signal is locked permanently upon bar close. The history you see is the actual real-time performance of the algorithm. 🧠 Smart Filtering: The system filters out "noise" in ranging markets, generating signals only when Trend, Momentum, and Volume align perfectly. ⚠️ IMPORTANT: SYSTEM SYNERGY While X-Trend PRO is a powerful standalone tool, the author does not recommend using it in isolation. To achieve the "Sniper Efficiency" shown in the screenshots and to ensure deep market confluence, this tool is engineered to work ideally in synergy with the X-Trend Dashboard PRO. Combining these modules provides the ultimate confirmation system. 🔒 HOW TO GET ACCESS (INVITE-ONLY) This script is a closed-source proprietary tool. Access is granted exclusively through our official channels. ⛔ NOTE: We do NOT respond to access requests in the comment section below. Please use the links above for instant support and license activation.
Indicateur Pine Script®
par Dev0880
AlphaSignals Pro🟠OVERVIEW AlphaSignals Pro™ is a comprehensive trading indicator suite developed by A1TradingHub. The system combines trend analysis, momentum indicators, and proprietary signal algorithms to identify high-probability trading opportunities across all market conditions and timeframes. Core Components ■ Long/Short Signals — Clear entry markers based on trend reversals and momentum shifts ■ Golden Call/Put — Premium signals for high-conviction directional trades ■ Support & Resistance — Dynamic price zones with visual highlighting ■ Moving Averages — Trend direction with color-coded crossover signals ■ Keltner Channels — Volatility bands for breakout and mean-reversion setups ■ Signal Dashboard — Multi-symbol overview with trend and momentum status 🟠Long & Short Entry Signals The core signal system identifies optimal entry points by analyzing price action, momentum, and trend structure. LONG signals appear at potential reversal points for upward moves, while SHORT signals indicate bearish opportunities at resistance. ◆ Green 'LONG' labels mark buy zones at support levels ◆ Red 'SHORT' labels indicate sell zones at resistance ◆ Momentum confirmation filters out false signals ◆ Works across all timeframes (1min to daily) 🟠Trend Direction Arrows Small directional arrows provide real-time trend feedback. ▲ Green arrows confirm bullish momentum, ▼ red arrows signal bearish pressure. These micro-signals help traders stay aligned with the dominant trend. ◆ Green up arrows during bullish momentum phases ◆ Red down arrows indicate bearish momentum shifts ◆ Confirms entry signals and aids trade management ◆ Minimalist design preserves chart clarity 🟠 Golden Put Signal The Golden Put identifies high-probability short opportunities. Triggers when multiple bearish factors align: price rejection at resistance, momentum divergence, and volume confirmation. These premium signals represent the highest-conviction bearish trades. ◆ Triggered at major resistance with momentum divergence ◆ Red 'GOLDEN PUT' label marks premium short entries ◆ Designed for options traders seeking directional plays ◆ Lower frequency, higher accuracy than standard signals 🟠 Golden Call Signal The Golden Call is the bullish counterpart, identifying premium long opportunities. Appears when price finds strong support with bullish momentum divergence and institutional volume patterns. Marks optimal entry points for aggressive upside plays. ◆ Yellow 'GOLDEN CALL' highlights premium buy setups ◆ Triggers at key support with momentum confirmation ◆ Ideal for call options or leveraged positions ◆ Signals typically precede significant advances 🟠 Moving Average System The dual moving average system provides trend context and dynamic support/resistance. The red line is the fast MA (shorter period), the green line is the slow MA. Color transitions indicate trend changes and crossover signals. ◆ Red MA: Fast period for short-term direction ◆ Green MA: Slow period for primary trend ◆ Bullish crossover: Fast crosses above slow ◆ Bearish crossover: Fast crosses below slow ◆ MAs act as dynamic support/resistance 🟠 Keltner Channel Bands Volatility-based price envelopes that adapt to market conditions. Bands expand during high volatility, contract during consolidation. Price at outer bands signals mean-reversion opportunities; breakouts beyond bands may indicate trend continuation. ◆ Upper/lower bands adapt to current volatility ◆ Pink outer bands mark extreme price zones ◆ Cyan middle band shows baseline trend ◆ Band touches signal reversal or breakout setups ◆ Shaded fill visualizes the volatility range 🟠 Support & Resistance Zones Automatic detection of key price levels with visual highlighting. Green zones mark support where buying pressure emerges, red zones highlight resistance with selling pressure. Dashed lines show precise price levels. ◆ Green shaded areas: Support zones (buy interest) ◆ Red shaded areas: Resistance zones (sell interest) ◆ Horizontal dashed lines mark exact levels ◆ Zones update dynamically as new levels form ◆ Essential for targets and stop-loss placement 🟠 Golden Signal Dashboard Multi-symbol dashboard provides comprehensive market overview. Track multiple instruments with real-time trend status, momentum readings, active signals, and freshness indicators. Perfect for scanning opportunities across your watchlist. ◆ SYMBOL: Tracked instruments (SPY, QQQ, IWM, TSLA) ◆ PRICE & CHG%: Current price and daily change ◆ MOMENTUM: Bullish/Bearish classification ◆ SIGNAL: Active Golden Call or Put signals ◆ BARS: Bars since last signal ◆ STATUS: Signal temperature (HOT/WARM/COLD) 🟠 Best Practices ✓ Confirm signals with dominant trend (use MAs) ✓ Prioritize Golden signals — higher probability ✓ Use S/R zones for stop-loss placement ✓ Check dashboard for multi-timeframe confluence ✓ Combine with your own analysis
Indicateur Pine Script®
par A1TradingHub
Triple VWAP: Anchored# Triple VWAP: Anchored + Last 2 Sessions ## 📊 Overview **Triple VWAP** is a professional-grade Volume Weighted Average Price (VWAP) indicator designed for serious traders who need multi-timeframe volume analysis on a single chart. This indicator combines three powerful VWAP calculations: a custom **Anchored VWAP** from any historical date, a real-time **Current Session VWAP**, and **Previous Session VWAP** reference levels—all with configurable standard deviation bands. Perfect for intraday traders, swing traders, and institutional-level analysis on Indian markets (Nifty, BankNifty) and global instruments. *** ## ✨ Key Features ### 🎯 Three Independent VWAP Calculations **📍 Anchored VWAP (Long-term Reference)** - Anchor VWAP from any specific date and time (year, month, day, hour, minute) - Ideal for tracking price action from significant market events, earnings, or trend reversals - Maintains accumulation from your chosen anchor point throughout the entire timeframe **📅 Current Session VWAP (Real-Time)** - Automatically resets at the start of each trading day - Calculates dynamically as new bars form during the session - Shows institutional trading levels and intraday fair value **📆 Previous Session VWAP (Reference Levels)** - Displays yesterday's final VWAP as horizontal reference lines - Helps identify support/resistance zones from prior session - Useful for gap analysis and opening range strategies ### 📐 Standard Deviation Bands Each VWAP calculation includes optional standard deviation bands: - **±1 SD** - Identifies normal price deviation zones - **±2 SD** - Marks extreme price movements (high probability reversal zones) - **±3 SD** - Ultra-extreme levels (rare price extensions) Configure which SD levels to display for each VWAP type independently. ### 🎨 Customizable Visual Settings - **Independent Color Schemes**: Assign unique colors to each VWAP type and its SD bands - **Adjustable Line Widths**: Configure VWAP and SD line thickness (1-5 for VWAP, 1-3 for SD) - **Plot Styles**: Previous session plots use cross-style for clear differentiation from real-time plots - **Background Zones**: Optional colored zones when price extends beyond ±2 SD levels - **Session Labels**: Automatic date labels marking each VWAP anchor point ### 📊 Information Dashboard Real-time statistics table displaying: - Current VWAP values for all three calculations - Live standard deviation values - ±1 SD levels for quick reference - Distance from price to each VWAP (percentage format) - Session tracking counter - Color-coded proximity indicators *** ## 🔧 How to Use ### For Intraday Trading 1. **Current Session VWAP** acts as dynamic support/resistance—price above VWAP indicates bullish bias, below indicates bearish bias 2. Use **±1 SD bands** for profit targets and stop-loss placement 3. **±2 SD touches** often signal mean reversion opportunities 4. **Previous Session VWAP** provides overnight gap reference levels ### For Swing Trading 1. **Anchored VWAP** from significant events (breakouts, earnings, trend changes) shows institutional cost basis 2. Price trading consistently above/below anchored VWAP confirms trend strength 3. SD band rejections provide high-probability entry zones ### For Multi-Timeframe Analysis 1. Combine all three VWAPs to identify confluence zones 2. When current session VWAP aligns with previous session and anchored VWAP, expect strong support/resistance 3. Monitor distance percentage to gauge overextension *** ## ⚙️ Input Parameters **📍 Anchored VWAP Configuration** - `Enable Anchored VWAP` - Toggle on/off - `Year, Month, Day, Hour, Minute` - Set your custom anchor point (requires confirmation) **📅 Session VWAP Toggles** - `Enable Current Session VWAP` - Toggle real-time daily VWAP - `Enable Previous Session VWAP` - Toggle yesterday's reference VWAP **General Settings** - `VWAP Source` - Choose calculation source (default: HLC3) - `Show ±1/2/3 SD` - Enable/disable each standard deviation level **Visual Customization** - Individual color pickers for each VWAP type and SD bands - Line width adjustments - `Show VWAP Labels` - Date labels at anchor points - `Show Info Table` - Statistics dashboard toggle - `Show SD Zones` - Background coloring beyond ±2 SD *** ## 💡 Trading Applications ✅ **Mean Reversion Trades**: Enter when price touches ±2 SD with reversal confirmation ✅ **Trend Following**: Trade in direction when price holds above/below VWAP ✅ **Institutional Levels**: Anchored VWAP reveals where large players accumulated positions ✅ **Gap Trading**: Use previous session VWAP to gauge gap fill probability ✅ **Volume Profile Analysis**: VWAP represents fair value based on actual volume traded ✅ **Support/Resistance Confluence**: Multiple VWAP alignment creates high-probability zones *** ## 🎓 Understanding VWAP VWAP is the average price weighted by volume—representing the true average price paid by all market participants during a specific period. Unlike simple moving averages, VWAP gives more weight to price levels with higher trading volume, making it particularly valuable for: - Institutional traders executing large orders - Algorithmic trading systems - Identifying fair value and overextended prices - Minimizing market impact on large trades *** ## 📈 Chart Setup Recommendations **Best Timeframes**: 1-minute to 15-minute charts for intraday; 1-hour to 4-hour for swing trading **Compatible Markets**: Stocks, Indices (Nifty 50, Bank Nifty), Futures, Forex, Crypto **Session Settings**: Works with standard daily sessions; automatically detects trading days (not calendar days) *** ## 🚀 What Makes This Indicator Unique Unlike standard VWAP indicators that only show current session data, **Triple VWAP** provides: - **Multi-session analysis** on a single chart without clutter - **Historical anchoring** capability for event-driven analysis - **Professional-grade statistics** dashboard - **Independent customization** for each VWAP type - **Optimized calculations** for real-time performance *** ## ⚠️ Important Notes - **Anchored VWAP** requires manual date input (use significant market events like breakouts, earnings, trend reversals) - **Session detection** based on daily timeframe changes—works on all chart timeframes - **Previous Session VWAP** displays as static horizontal lines (reference only, not recalculated) - **Standard Deviations** calculated using volume-weighted variance formula - Works best on instruments with **consistent volume data** *** ## 📝 Credits & License This indicator is published under **TradingView House Rules**. Code is original and follows Pine Script v6 best practices for performance and maintainability. *** ## 🤝 Support & Feedback If you find this indicator valuable, please: - ⭐ **Like** this publication - 💬 **Comment** with your feedback or questions - 🔔 **Follow** for updates and new releases *** **Disclaimer**: This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always use proper risk management and combine with your own analysis before making trading decisions. ***
Indicateur Pine Script®
par shivkumar503244
STM APEX Pro v2.0**STM APEX Pro v2.0 | Mobile-Optimized SMC & Volatility System** **STM APEX Pro** is a comprehensive technical analysis tool designed for modern traders who require precision on both desktop and mobile devices. This script combines **Smart Money Concepts (SMC)**, **Trend Structure**, and **Volatility Modeling** to assist traders in identifying high-probability market contexts without chart clutter. This indicator does not provide financial advice but offers objective technical levels based on statistical volatility (ATR) and market structure pivots. --- ### 🛠️ Key Features & Methodology **1. Market Structure & Trend Bias** The system automatically detects the underlying market flow using a dual-layer approach: * **Structure Mapping:** Identifies Break of Structure (BOS) and Change of Character (CHoCH) based on adjustable pivot lookbacks to highlight potential reversals or trend continuations. * **Market Bias:** Utilizes a dynamic EMA relationship (Fast 50 / Slow 200) to categorize the market state as BULLISH, BEARISH, or NEUTRAL. **2. Supply & Demand Zones** Algorithmically identifies significant buying and selling zones based on pivot strength. These zones are extended forward to serve as potential reaction areas for future price action. **3. Setup Detection (Confluence Logic)** The script highlights potential trade setups (marked with ●) when multiple technical factors align: * Alignment of Market Structure (BOS/CHoCH). * Agreement with the dominant Trend Bias (EMA Cloud). * (Optional) Confirmation from Momentum (MACD) and proximity to Supply/Demand zones. **4. Dynamic Reference Levels (4-Line System)** Instead of static targets, the system projects dynamic volatility bands to assist with risk management: * **Entry Level:** Based on the close or wick of the setup candle. * **Invalidation Level (Line 2):** Calculated using an ATR multiplier (Average True Range) to determine where the structural setup becomes invalid. This adapts to current market volatility. * **Projected Levels (P1, P2, P3):** These are expansion levels calculated derived from the Risk-to-Reward (R:R) ratio relative to the Invalidation distance. They serve as objective technical references for volatility expansion. **5. Mobile-First Design** Recognizing that many traders operate via mobile apps, this script features a "Clean UI" mode: * Minimalist markers to prevent chart obstruction. * Concise data tables showing only essential values (Entry, Invalidation, Risk Context). * Option to toggle off text labels for a clutter-free experience. --- ### ⚙️ Settings Overview * **Supply & Demand:** Toggle zones and adjust strength sensitivity. * **Market Structure:** Customize lookback periods for BOS/CHoCH detection. * **Reference Levels:** * *Invalidation Distance (ATR):* Adjust how wide the structure validation room should be. * *Projected Level R:R:* Define the multipliers for P1, P2, and P3 expansion levels. * **Signal Sensitivity:** Choose between Low (more aggressive) or High (filtered) detection modes. --- ### ⚠️ Disclaimer This script is for educational and technical analysis purposes only. The levels provided (Entry, Invalidation, Projected) are mathematical calculations based on past price action and volatility; they do not guarantee future performance. Trading carries significant risk. Always use proper risk management.
Indicateur Pine Script®
par stm29112025
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.
Indicateur Pine Script®
par DskyzInvestments
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.
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