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Money Moving PROGuide to Using the "Money Moving PRO" Indicator Important Note: This is a custom indicator compiled from several indicators by other authors. It combines elements from "Money Moving" (based on ATR trailing stop), EMA lines, premium/discount zones with delta volume, and volume profile. I am not the original author of the base components, but the indicator has been adapted for comprehensive analysis. Use it at your own risk as a tool for analysis, not as the sole basis for trading. It is recommended to test it on a demo account. Installing the Indicator Open TradingView (tradingview.com) and log into your account. Go to the chart of any asset (e.g., cryptocurrencies, stocks, or forex). Click on the "Indicators" button (at the top of the chart). Select "Pine Editor" at the bottom of the window (or press Ctrl+E). Copy the entire code from the file "Money Moving PRO (LummiCrypto) alerts.txt" and paste it into the editor. Click "Add to Chart." The indicator will appear on the chart. You can customize its settings in the indicator menu (gear icon next to the indicator name). The indicator operates in overlay mode (overlaid on the price chart), supports up to 500 lines and boxes, and uses up to 5,000 bars of historical data. Indicator Settings The indicator is divided into setting groups. Below are the main parameters and their descriptions: Money Moving Settings MM Key Value (Sensitivity): Sensitivity (default: 1). Higher values widen the ATR-based stop-loss. MM ATR Period: ATR period (default: 3). Affects volatility calculation. MM Use Heiken Ashi Candles: Use Heiken Ashi candles instead of regular ones (default: disabled). Enable for smoother signals. EMA Settings Show Ema 20/50/100/200: Display EMAs with periods 20, 50, 100, 200 (all enabled by default). Ema Colors: Line colors (red for 20, orange for 50, aqua for 100, blue for 200). Can be changed for convenience. Premium & Discount Settings Show Premium & Discount Delta Volume: Display zones with delta volume (default: enabled). Premium & Discount Lookback Period: Lookback period for support/resistance levels (default: 50 bars). Macro Lookback Period: Lookback period for macro levels (default: 200 bars). Discount/Premium Colors: Zone colors (blue for discount, orange for premium). Volume Profile Settings Show Volume Profile: Display volume profile (default: enabled). Fixed Range Lookback Depth: Historical bars to include (10–3000, default: 200). Number of Volume Bars: Number of bars in the profile (10–490, default: 200). Volume Bar Thickness: Bar width (1–30, default: 1). Bar Length Multiplier: Bar length multiplier (1–50, default: 20). Right Offset: Space from the right edge of the chart (0–400, default: 70). Volume Type: Volume type ("Both," "Bullish," or "Bearish"; default: "Both"). Display Point of Control (PoC): Show PoC (default: enabled). Display Value Area: Show value area (default: enabled, 68% by default). Other: Colors and line thicknesses for PoC and Value Area. Customize the settings to match your trading style. For beginners, keep the default values. Buy and Sell Signals The primary signals are generated in the "Money Moving" block based on the ATR trailing stop and EMA(1). This is a simple system similar to SuperTrend but with custom settings. Buy Signal (BUY): Green upward triangle below the bar. Triggered when the price crosses above the trailing stop (src > xATRTrailingStop and EMA(1) crosses above the stop). Indicates a potential upward reversal or trend continuation. Entry Conditions: Confirm the signal with price crossing above EMA 20/50 or in the Discount zone. Volume profile should show accumulation at the lower end. Alert: In TradingView, set up an alert for "BUY Signal" — message "Money Moving: BUY Signal at {{close}}". Sell Signal (SELL): Red downward triangle above the bar. Triggered when the price crosses below the trailing stop (src < xATRTrailingStop and EMA(1) crosses below the stop). Indicates a potential downward reversal or correction. Entry Conditions: Confirm with price crossing below EMA 20/50 or in the Premium zone. Volume profile should show distribution at the upper end. Alert: Set up an alert for "SELL Signal" — message "Money Moving: SELL Signal at {{close}}". Important: Signals are not 100% accurate. Use them in combination with other indicator elements: EMA Lines: Uptrend — price above EMA 200, EMA 20 above EMA 50. For buys, look for bounces off lower EMAs. Premium/Discount Zones: Discount (Blue box at the bottom): Oversold zone with positive delta volume (more buying). Ideal for buys. Premium (Orange box at the top): Overbought zone with negative delta volume (more selling). Ideal for sells. Delta Volume shows balance (in %): positive — bullish, negative — bearish. Volume Profile: PoC (Red line): Level of maximum volume — strong support/resistance. Value Area (Blue bars/lines): 68% of volume — equilibrium zone. Breakout above VAH (top) is bullish, below VAL (bottom) is bearish. Example Strategy: Wait for a BUY signal in the Discount zone, with price above EMA 50 and PoC below the price. Stop-Loss: Below the trailing stop or nearest low. Take-Profit: At the Premium zone or 1–2 ATR distance. Similar for SELL. Additional Features Alert Conditions: The indicator includes built-in alerts for BUY/SELL. In TradingView, set up notifications (SMS, email, popup). Visualization: Boxes for zones, lines for EMAs and volume profile. If the chart is cluttered, disable unnecessary parts (e.g., Volume Profile for short timeframes). Compatibility: Works on any timeframe, but best on H1-D1 for crypto/stocks. Enable Heiken Ashi for noise filtering. Tips for Use Testing: Backtest on historical data in TradingView. Test on different assets (BTC, ETH, etc.). Risk Management: Risk no more than 1–2% of capital per trade. Combine with other indicators (RSI, MACD). Limitations: The indicator relies on volume (better on exchanges with real volume data). For forex/crypto, volumes may be tick-based. Updates: If the code changes, check TradingView forums (e.g., search for "Money Moving" or "Volume Profile"). Warning: Trading carries risks. This is not financial advice. Руководство по использованию индикатора "Money Moving PRO" Важное замечание: Этот индикатор является кастомным, собранным из нескольких индикаторов других авторов. Он сочетает элементы из "Money Moving" (на основе ATR-трейлинг-стопа), EMA-линий, зон премиум/дисконт с дельта-объемом и профиля объема. Я не являюсь оригинальным автором базовых компонентов, но индикатор адаптирован для комплексного анализа. Используйте его на свой риск, как инструмент для анализа, а не как единственную основу для торговли. Рекомендуется тестировать на демо-счете. Установка индикатора Откройте TradingView (tradingview.com) и войдите в аккаунт. Перейдите в график любого актива (например, криптовалюты, акции или форекс). Нажмите на кнопку "Индикаторы" (в верхней панели графика). Выберите "Pine Editor" в нижней части окна (или нажмите Ctrl+E). Скопируйте весь код из файла "Money Moving PRO (LummiCrypto) alerts.txt" и вставьте его в редактор. Нажмите "Add to Chart" (Добавить на график). Индикатор появится на графике. Вы можете настроить его параметры в меню индикатора (шестеренка рядом с названием). Индикатор работает в overlay-режиме (накладывается на график цены), поддерживает до 500 линий и боксов, и использует до 5000 баров истории. Настройки индикатора Индикатор разделен на группы настроек. Вот основные параметры и их описание: Money Moving Settings (Настройки Money Moving) MM Key Value (Sensitivity): Чувствительность (по умолчанию 1). Чем выше значение, тем шире стоп-лосс на основе ATR. MM ATR Period: Период ATR (по умолчанию 3). Влияет на расчет волатильности. MM Use Heiken Ashi Candles: Использовать Heiken Ashi свечи вместо обычных (по умолчанию выключено). Включайте для сглаживания сигналов. EMA Settings (Настройки EMA) Show Ema 20/50/100/200: Показывать EMA с периодами 20, 50, 100, 200 (все по умолчанию включены). Ema Colors: Цвета линий (красный для 20, оранжевый для 50, аква для 100, синий для 200). Можно изменить для удобства. Premium & Discount Settings (Настройки Премиум и Дисконт) Show Premium & Discount Delta Volume: Показывать зоны с дельта-объемом (по умолчанию включено). Premium & Discount Lookback Period: Период обзора для S/R уровней (по умолчанию 50 баров). Macro Lookback Period: Период для макро-уровней (по умолчанию 200 баров). Discount/Premium Colors: Цвета зон (голубой для дисконт, оранжевый для премиум). Volume Profile Settings (Настройки Профиля Объема) Show Volume Profile: Показывать профиль объема (по умолчанию включено). Fixed Range Lookback Depth: Глубина обзора (10–3000 баров, по умолчанию 200). Number of Volume Bars: Количество баров в профиле (10–490, по умолчанию 200). Volume Bar Thickness: Толщина баров (1–30, по умолчанию 1). Bar Length Multiplier: Множитель длины баров (1–50, по умолчанию 20). Right Offset: Отступ справа (0–400, по умолчанию 70). Volume Type: Тип объема ("Both" — оба, "Bullish" — бычий, "Bearish" — медвежий; по умолчанию "Both"). Display Point of Control (PoC): Показывать точку контроля (по умолчанию включено). Display Value Area: Показывать область значения (по умолчанию включено, 68% по умолчанию). Другие: Цвета, толщины линий для PoC и Value Area. Настройте параметры под свой стиль торговли. Для начинающих оставьте значения по умолчанию. Сигналы на покупку и продажу Основные сигналы генерируются в блоке "Money Moving" на основе ATR-трейлинг-стопа и EMA(1). Это простая система, похожая на SuperTrend, но с кастомными настройками. Сигнал на покупку (BUY): Зеленый треугольник вверх под баром. Происходит, когда цена пересекает вверх трейлинг-стоп (src > xATRTrailingStop и кроссовер EMA(1) над стопом). Это указывает на потенциальный разворот вверх или продолжение тренда. Условия для входа: Подтвердите сигнал пересечением цены выше EMA 20/50 или в зоне дисконт (Discount). Объем в профиле должен показывать накопление в нижней части. Алерт: В TradingView настройте алерт на "BUY Signal" — сообщение "Money Moving: BUY Signal at {{close}}". Сигнал на продажу (SELL): Красный треугольник вниз над баром. Происходит, когда цена пересекает вниз трейлинг-стоп (src < xATRTrailingStop и кроссундер EMA(1) под стопом). Указывает на разворот вниз или коррекцию. Условия для входа: Подтвердите пересечением ниже EMA 20/50 или в зоне премиум (Premium). Объем в профиле должен показывать распределение в верхней части. Алерт: Настройте алерт на "SELL Signal" — сообщение "Money Moving: SELL Signal at {{close}}". Важно: Сигналы не 100% точны. Используйте их в комбинации с другими элементами индикатора: EMA-линии: Восходящий тренд — цена выше EMA 200, EMA 20 выше EMA 50. Для покупок ищите отскок от нижних EMA. Премиум/Дисконт зоны: Discount (Дисконт, голубой бокс снизу): Зона перепроданности с положительным дельта-объемом (больше покупок). Идеально для покупок. Premium (Премиум, оранжевый бокс сверху): Зона перекупленности с отрицательным дельта-объемом (больше продаж). Идеально для продаж. Delta Volume показывает баланс (в %): положительный — бычий, отрицательный — медвежий. Профиль объема: PoC (красная линия): Уровень максимального объема — сильная поддержка/сопротивление. Value Area (синие бары/линии): 68% объема — зона равновесия. Пробой VAH (верх) — бычий, VAL (низ) — медвежий. Пример стратегии: Ждите сигнала BUY в зоне Discount, когда цена выше EMA 50 и PoC ниже цены. Стоп-лосс: Ниже трейлинг-стопа или ближайшего минимума. Тейк-профит: У зоны Premium или на расстоянии 1-2 ATR. Аналогично для SELL. Дополнительные функции Алерткондиции: Индикатор имеет встроенные алерты для BUY/SELL. В TradingView настройте уведомления (SMS, email, popup). Визуализация: Боксы для зон, линии для EMA и профиля. Если график перегружен, отключите ненужные части (например, Volume Profile для коротких таймфреймов). Совместимость: Работает на любых таймфреймах, но лучше на H1-D1 для крипты/акций. Для Heiken Ashi включите опцию для фильтрации шума. Советы по использованию Тестирование: Backtest на исторических данных в TradingView. Проверьте на разных активах (BTC, ETH и т.д.). Риск-менеджмент: Не рискуйте более 1-2% капитала на сделку. Комбинируйте с другими индикаторами (RSI, MACD). Ограничения: Индикатор зависит от объема (лучше на биржах с реальными объемами). На форексе/крипте объемы могут быть тиковыми. Обновления: Если код изменится, проверьте на форумах TradingView (например, по ключам "Money Moving" или "Volume Profile"). Предупреждение: Торговля несет риски. Это не финансовый совет.
Pine Script® 인디케이터
theatamanmusic의
Smart MACD Volume Trader# Smart MACD Volume Trader ## Overview Smart MACD Volume Trader is an enhanced momentum indicator that combines the classic MACD (Moving Average Convergence Divergence) oscillator with an intelligent high-volume filter. This combination significantly reduces false signals by ensuring that trading signals are only generated when price momentum is confirmed by substantial volume activity. The indicator supports over 24 different instruments including major and exotic forex pairs, precious metals (gold and silver), energy commodities (crude oil, natural gas), and industrial metals (copper). For forex and commodity traders, the indicator automatically maps to CME and COMEX futures contracts to provide accurate institutional-grade volume data. ## Originality and Core Concept Traditional MACD indicators generate signals based solely on price momentum, which can result in numerous false signals during low-activity periods or ranging markets. This indicator addresses this critical weakness by introducing a volume confirmation layer with automatic institutional volume integration. **What makes this approach original:** - Signals are triggered only when MACD crossovers coincide with elevated volume activity - Implements a lookback mechanism to detect volume spikes within recent bars - Automatically detects and maps 24+ forex pairs and commodities to their corresponding CME and COMEX futures contracts - Provides real institutional volume data for forex pairs where spot volume is unreliable - Combines two independent market dimensions (price momentum and volume) into a single, actionable signal - Includes intelligent asset detection that works across multiple exchanges and ticker formats **The underlying principle:** Volume validates price movement. When institutional money enters the market, it creates volume signatures. By requiring high volume confirmation and using actual institutional volume data from futures markets, this indicator filters out weak price movements and focuses on trades backed by genuine market participation. The automatic futures mapping ensures that forex and commodity traders always have access to the most accurate volume data available, without manual configuration. ## How It Works ### MACD Component The indicator calculates MACD using standard methodology: 1. **Fast EMA (default: 12 periods)** - Tracks short-term price momentum 2. **Slow EMA (default: 26 periods)** - Tracks longer-term price momentum 3. **MACD Line** - Difference between Fast EMA and Slow EMA 4. **Signal Line (default: 9-period SMA)** - Smoothed average of MACD line **Crossover signals:** - **Bullish:** MACD line crosses above Signal line (momentum turning positive) - **Bearish:** MACD line crosses below Signal line (momentum turning negative) ### Volume Filter Component The volume filter adds an essential confirmation layer: 1. **Volume Moving Average** - Calculates exponential MA of volume (default: 20 periods) 2. **High Volume Threshold** - Multiplies MA by ratio (default: 2.0x or 200%) 3. **Volume Detection** - Identifies bars where current volume exceeds threshold 4. **Lookback Period** - Checks if high volume occurred in recent bars (default: 5 bars) **Signal logic:** - Buy/Sell signals only trigger when BOTH conditions are met: - MACD crossover/crossunder occurs - High volume detected within lookback period ### Automatic CME Futures Integration For forex traders, spot FX volume data can be unreliable or non-existent. This indicator solves this problem by automatically detecting forex pairs and mapping them to corresponding CME futures contracts with real institutional volume data. **Supported Major Forex Pairs (7):** - EURUSD → CME:6E1! (Euro FX Futures) - GBPUSD → CME:6B1! (British Pound Futures) - AUDUSD → CME:6A1! (Australian Dollar Futures) - USDJPY → CME:6J1! (Japanese Yen Futures) - USDCAD → CME:6C1! (Canadian Dollar Futures) - USDCHF → CME:6S1! (Swiss Franc Futures) - NZDUSD → CME:6N1! (New Zealand Dollar Futures) **Supported Exotic Forex Pairs (4):** - USDMXN → CME:6M1! (Mexican Peso Futures) - USDRUB → CME:6R1! (Russian Ruble Futures) - USDBRL → CME:6L1! (Brazilian Real Futures) - USDZAR → CME:6Z1! (South African Rand Futures) **Supported Cross Pairs (6):** - EURJPY → CME:6E1! (Uses Euro Futures) - GBPJPY → CME:6B1! (Uses British Pound Futures) - EURGBP → CME:6E1! (Uses Euro Futures) - AUDJPY → CME:6A1! (Uses Australian Dollar Futures) - EURAUD → CME:6E1! (Uses Euro Futures) - GBPAUD → CME:6B1! (Uses British Pound Futures) **Supported Precious Metals (2):** - Gold (XAUUSD, GOLD) → COMEX:GC1! (Gold Futures) - Silver (XAGUSD, SILVER) → COMEX:SI1! (Silver Futures) **Supported Energy Commodities (3):** - WTI Crude Oil (USOIL, WTIUSD) → NYMEX:CL1! (Crude Oil Futures) - Brent Oil (UKOIL) → NYMEX:BZ1! (Brent Crude Futures) - Natural Gas (NATGAS) → NYMEX:NG1! (Natural Gas Futures) **Supported Industrial Metals (1):** - Copper (COPPER) → COMEX:HG1! (Copper Futures) **How the automatic detection works:** The indicator intelligently identifies the asset type by analyzing: 1. Exchange name (FX, OANDA, TVC, COMEX, NYMEX, etc.) 2. Currency pair pattern (6-letter codes like EURUSD, GBPUSD) 3. Commodity identifiers (XAU for gold, XAG for silver, OIL for crude) When a supported instrument is detected, the indicator automatically switches to the corresponding futures contract for volume analysis. For stocks, cryptocurrencies, and other assets, the indicator uses the native volume data from the current chart. **Visual feedback:** An information table appears in the top-right corner of the MACD pane showing: - Current chart symbol - Exchange name - Currency pair or asset name - Volume source being used (highlighted in orange for futures, yellow for native volume) - Current high volume status This provides complete transparency about which data source the indicator is using for its volume analysis. ## How to Use ### Basic Setup 1. Add the indicator to your chart 2. The indicator displays in a separate pane (MACD) and overlay (signals/volume bars) 3. Default settings work well for most assets, but can be customized ### Signal Interpretation ### Visual Signals **Visual Signals:** - **Green "BUY" label** - Bullish MACD crossover confirmed by high volume - **Red "SELL" label** - Bearish MACD crossunder confirmed by high volume - **Green/Red candles** - Highlight bars with volume exceeding the threshold - **Light green/red background** - Emphasizes signal bars on the chart **Information Table:** A detailed information table appears in the top-right corner of the MACD pane, providing real-time transparency about the indicator's operation: - **Chart:** Current symbol being analyzed - **Exchange:** The exchange or data feed being used - **Pair:** The currency pair or asset name extracted from the ticker - **Volume From:** The actual symbol used for volume analysis - Orange color indicates CME or COMEX futures are being used (automatic institutional volume) - Yellow color indicates native volume from the chart symbol is being used - Hover tooltip shows whether automatic futures mapping is active - **High Volume:** Current status showing YES (green) when volume exceeds threshold, NO (gray) otherwise This table ensures complete transparency and allows you to verify that the correct volume source is being used for your analysis. **Volume Analysis:** - Gray histogram bars = Normal volume - Red histogram bars = High volume (exceeds threshold) - Green line = Volume moving average baseline **MACD Analysis:** - Blue line = MACD line (momentum indicator) - Orange line = Signal line (trend confirmation) - Gray dotted line = Zero line (bullish above, bearish below) ### Parameter Customization **MACD Parameters:** - Adjust Fast/Slow EMA lengths for different sensitivities - Shorter periods = More signals, faster response - Longer periods = Fewer signals, less noise **Volume Parameters:** - **Volume MA Period:** Higher values smooth volume analysis - **High Volume Ratio:** Lower values (1.5x) = More signals; Higher values (3.0x) = Fewer, stronger signals - **Volume Lookback Bars:** Controls how recent the volume spike must be **Direction Filters:** - **Only Buy Signals:** Enables long-only strategy mode - **Only Sell Signals:** Enables short-only strategy mode ### Alert Configuration The indicator includes three alert types: 1. **Buy Signal Alert** - Triggers when bullish signal appears 2. **Sell Signal Alert** - Triggers when bearish signal appears 3. **High Volume Alert** - Triggers when volume exceeds threshold To set up alerts: 1. Click the indicator name → "Add alert on Smart MACD Volume Trader" 2. Select desired alert condition 3. Configure notification method (popup, email, webhook, etc.) ## Trading Strategy Guidelines ### Best Practices **Recommended markets:** - Liquid stocks (large-cap, high daily volume) - Major forex pairs (EURUSD, GBPUSD, USDJPY, AUDUSD, USDCAD, USDCHF, NZDUSD) - Exotic forex pairs (USDMXN, USDRUB, USDBRL, USDZAR) - Cross pairs (EURJPY, GBPJPY, EURGBP, AUDJPY, EURAUD, GBPAUD) - Precious metals (Gold, Silver with automatic COMEX futures mapping) - Energy commodities (Crude Oil, Natural Gas with automatic NYMEX futures mapping) - Industrial metals (Copper with automatic COMEX futures mapping) - Major cryptocurrency pairs - Index futures and ETFs **Timeframe recommendations:** - **Day trading:** 5-minute to 15-minute charts - **Swing trading:** 1-hour to 4-hour charts - **Position trading:** Daily charts **Risk management:** - Use signals as entry confirmation, not standalone strategy - Combine with support/resistance levels - Consider overall market trend direction - Always use stop-loss orders ### Strategy Examples **Trend Following Strategy:** 1. Identify overall trend using higher timeframe (e.g., daily chart) 2. Trade only in trend direction 3. Use "Only Buy" filter in uptrends, "Only Sell" in downtrends 4. Enter on signal, exit on opposite signal or at resistance/support **Volume Breakout Strategy:** 1. Wait for consolidation period (low volume, tight MACD range) 2. Enter when signal appears with high volume (confirms breakout) 3. Target previous swing highs/lows 4. Stop loss below/above recent consolidation **Forex Scalping Strategy (with automatic CME futures):** 1. The indicator automatically detects forex pairs and uses CME futures volume 2. Trade during active sessions only (use session filter) 3. Focus on quick profits (10-20 pips) 4. Exit at opposite signal or profit target **Commodities Trading Strategy (Gold, Silver, Oil):** 1. The indicator automatically maps to COMEX and NYMEX futures contracts 2. Trade during high-liquidity sessions (overlap of major markets) 3. Use the high volume confirmation to identify institutional entry points 4. Combine with key support and resistance levels for entries 5. Monitor the information table to confirm futures volume is being used (orange color) 6. Exit on opposite MACD signal or at predefined profit targets ## Why This Combination Works ### The Volume Advantage Studies consistently show that price movements accompanied by high volume are more likely to continue, while low-volume movements often reverse. This indicator leverages this principle by requiring volume confirmation. **Key benefits:** 1. **Reduced False Signals:** Eliminates MACD whipsaws during low-volume consolidation 2. **Confirmation Bias:** Two independent indicators (price momentum + volume) agreeing 3. **Institutional Alignment:** High volume often indicates institutional participation 4. **Trend Validation:** Volume confirms that price momentum has "conviction" ### Statistical Edge By combining two uncorrelated signals (MACD crossovers and volume spikes), the indicator creates a higher-probability setup than either signal alone. The lookback mechanism ensures signals aren't missed if volume spike slightly precedes the MACD cross. ## Supported Exchanges and Automatic Detection The indicator includes intelligent asset detection that works across multiple exchanges and ticker formats: **Forex Exchanges (Automatic CME Mapping):** - FX (TradingView forex feed) - OANDA - FXCM - SAXO - FOREXCOM - PEPPERSTONE - EASYMARKETS - FX_IDC **Commodity Exchanges (Automatic COMEX/NYMEX Mapping):** - TVC (TradingView commodity feed) - COMEX (directly) - NYMEX (directly) - ICEUS **Other Asset Classes (Native Volume):** - Stock exchanges (NASDAQ, NYSE, AMEX, etc.) - Cryptocurrency exchanges (BINANCE, COINBASE, KRAKEN, etc.) - Index providers (SP, DJ, etc.) The detection algorithm analyzes three factors: 1. Exchange prefix in the ticker symbol 2. Pattern matching for currency pairs (6-letter codes) 3. Commodity identifiers in the symbol name This ensures accurate automatic detection regardless of which data feed or exchange you use for charting. The information table in the top-right corner always displays which volume source is being used, providing complete transparency. ## Technical Details **Calculations:** - MACD Fast MA: EMA(close, fastLength) - MACD Slow MA: EMA(close, slowLength) - MACD Line: Fast MA - Slow MA - Signal Line: SMA(MACD Line, signalLength) - Volume MA: Exponential MA of volume - High Volume: Current volume >= Volume MA × Ratio **Signal logic:** ``` Buy Signal = (MACD crosses above Signal) AND (High volume in last N bars) Sell Signal = (MACD crosses below Signal) AND (High volume in last N bars) ``` ## Parameters Reference | Parameter | Default | Description | |-----------|---------|-------------| | Volume Symbol | Blank | Manual override for volume source (leave blank for automatic detection) | | Use CME Futures | False | Legacy option (automatic detection is now built-in) | | Alert Session | 1530-2200 | Active session time range for alerts | | Timezone | UTC+1 | Timezone for alert sessions | | Volume MA Period | 20 | Number of periods for volume moving average | | High Volume Ratio | 2.0 | Volume threshold multiplier (2.0 = 200% of average) | | Volume Lookback | 5 | Number of bars to check for high volume confirmation | | MACD Fast Length | 12 | Fast EMA period for MACD calculation | | MACD Slow Length | 26 | Slow EMA period for MACD calculation | | MACD Signal Length | 9 | Signal line SMA period | | Only Buy | False | Filter to show only bullish signals | | Only Sell | False | Filter to show only bearish signals | | Show Signals | True | Display buy and sell labels on chart | ## Optimization Tips **For volatile markets (crypto, small caps):** - Increase High Volume Ratio to 2.5-3.0 - Reduce Volume Lookback to 3-4 bars - Consider faster MACD settings (8, 17, 9) **For stable markets (large-cap stocks, bonds):** - Decrease High Volume Ratio to 1.5-1.8 - Increase Volume MA Period to 30-50 - Use standard MACD settings **For forex (with automatic CME futures):** - The indicator automatically uses CME futures when forex pairs are detected - Set appropriate trading session based on your timezone - Use Volume Lookback of 5-7 bars - Consider session-based alerts only - Monitor the information table to verify correct futures mapping **For commodities (Gold, Silver, Oil, Copper):** - The indicator automatically maps to COMEX and NYMEX futures - Increase High Volume Ratio to 2.0-2.5 for metals - Use slightly higher Volume MA Period (25-30) for smoother analysis - Trade during active market hours for best volume data - The information table will show the futures contract being used (orange highlight) ## Limitations and Considerations **What this indicator does NOT do:** - Does not predict future price direction - Does not guarantee profitable trades - Does not replace proper risk management - Does not work well in extremely low-volume conditions **Market conditions to avoid:** - Pre-market and after-hours sessions (low volume) - Major news events (volatile, unpredictable volume) - Holidays and low-liquidity periods - Extremely low float stocks ## Conclusion Smart MACD Volume Trader represents a significant evolution of the traditional MACD indicator by combining volume confirmation with automatic institutional volume integration. This dual-confirmation approach significantly improves signal quality by filtering out low-conviction price movements and ensuring traders work with accurate volume data. The indicator's automatic detection and mapping system supports over 24 instruments across forex, commodities, and metals markets. By intelligently switching to CME and COMEX futures contracts when appropriate, the indicator provides forex and commodity traders with the same quality of volume data that stock traders naturally have access to. This indicator is particularly valuable for traders who want to: - Align their entries with institutional money flow - Avoid getting trapped in false breakouts - Trade forex pairs with reliable volume data - Access accurate volume information for gold, silver, and energy commodities - Combine momentum and volume analysis in a single, streamlined tool Whether you are day trading stocks, swing trading forex pairs, or positioning in commodities markets, this indicator provides a robust framework for identifying high-probability momentum trades backed by genuine institutional participation. The automatic futures mapping works seamlessly across all supported instruments, requiring no manual configuration or expertise in futures markets. --- ## Support and Updates This indicator is actively maintained and updated based on user feedback and market conditions. For questions about implementation or custom modifications, please use the comments section below. **Disclaimer:** This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always conduct your own analysis and risk management before trading.
Pine Script® 인디케이터
Smart-Day-Trader의
SMA+デマンド・オファーゾーン最強版（仮）This indicator is designed to help traders visually identify both trend direction and potential reversal zones in one glance. 📊 Key Features: • Displays 4 SMAs (9 / 20 / 75 / 200) simultaneously • Automatically detects and plots Demand Zones (green) and Supply Zones (red) based on recent swing highs and lows • Shows ▲ Golden Cross and ▼ Dead Cross markers when SMA 9 crosses SMA 20 • Built-in alert conditions for SMA crossovers and zone breakouts 💡 How to Use: Use SMA direction and alignment to confirm the trend, and watch for Supply/Demand zones and cross signals as potential reversal or breakout setups. Perfect for spotting pullbacks and identifying confluence zones between trend and structure. ⚙️ Customization: • Adjustable SMA lengths, colors, and line widths • Modify pivot length to fine-tune zone sensitivity ✅ Built with Pine Script v5 ✅ Works on FX, Stocks, Crypto, and Indices Author’s Note: I created this indicator to combine trend-following tools with supply-demand logic in a clean and intuitive way. Even beginners can easily visualize where reactions or breakouts are likely to happen. If you find this script helpful, please give it a ❤️ and follow for more updates!
Pine Script® 인디케이터
kouki_yanagida의
Venza Rocket ScalperVenza Rocket Scalper: Compliant Description (Plaintext) This strategy is a complex, multi-indicator trend-following system designed for intraday scalping on low-timeframe charts. It uses a confluence of four distinct filters to ensure high-conviction entries during optimal momentum and volume. 1. Overview and Core Logic The entry signal requires simultaneous confirmation from the following components: Trend Confirmation (Heikin-Ashi EMAs): The primary trend is established using Heikin-Ashi price action combined with an EMA (Fast=8) crossing and remaining above an EMA (Slow=21). This provides a smoother, momentum-based trend signal. Momentum Strength (ADX/RSI): The trend must be validated by the ADX (default 16) to confirm sufficient directional strength, and the RSI (default 42) to confirm continued positive internal momentum. Volume Validation: A dynamic filter requires the current bar's volume to be greater than the 20-period Volume MA (multiplied by the default 1.0 factor), ensuring trades are executed during periods of active market participation. Session & Volatility Filter: Trades are restricted to a defined trading window (default UTC 12:00 to 20:00). The script also includes an optional Volatility Cap filter based on a long-term ATR to suppress entries during extreme volatility. 2. Trade Management and Realistic Risk This strategy employs a robust, partial-exit risk management plan driven by the Average True Range (ATR) for sustainable risk control. Initial Stop Loss (SL): The initial SL is tight and calculated dynamically using the 14-period ATR multiplied by an adjustable factor (default 0.7). This size is designed for micro-losses appropriate for scalping and is adapted slightly during high volatility. Partial Exits & Profit Taking: The position is split into two equal halves for exit management: 50% Position (TP1): Exited at a 1R profit target, where 1R is defined as the exact value of the initial ATR-based SL. 50% Position (Run): Managed by a Trailing Stop Loss (TSL), with trail points also calculated dynamically using the current ATR. Breakeven (BE) Lock: An optional feature (default: ON) automatically moves the stop loss to Breakeven (entry price plus 1 tick) once the position is 2 ticks in profit, locking in capital protection rapidly. Daily Risk Controls: The strategy includes mandatory daily money management features (default: ON): Max Daily Loss Stop: Stops all trading for the day if the cumulative closed P&L reaches -$500 (default). Profit Protection Floor: If the closed P&L reaches a minimum threshold (default $110), any open position will be closed if the total daily P&L drops back below this floor, locking in minimum daily gains. 3. Strategy Properties & Backtesting Disclosure The default settings are configured for high-liquidity futures or FX markets. Users must ensure their backtesting environment is realistic: Risk Per Trade: The ATR-based SL aims to keep the risk per trade below 5% of a reasonable account size, which is critical for sustainable trading. Contracts/Size: Default quantity is 3 contracts. Commissions/Slippage: Commissions and slippage MUST be configured by the user in the Strategy Properties window to reflect real-world brokerage fees and execution costs. Sample Size: The strategy should be run on a dataset that generates over 100 trades for statistically valid results. MANDATORY DISCLAIMER: Past performance is not necessarily indicative of future results. Trading involves substantial risk. All claims of historical performance are substantiated by the backtesting results on the chart, but these results do not guarantee actual trading outcomes. Keep your language realistic.
Pine Script™ 전략
ngukevin90의
JOPA Channel (Dual-Volumed) v1 [JopAlgo]JOPA Channel (Dual-Volumed) v1 Short title: JOPAV1 • License: MPL-2.0 • Provider: JopAlgo We have developed our own, first channel-based trading indicator and we’re making it available to all traders. The goal was a channel that breathes with the tape—built on a volume-weighted backbone—so the outcome stays lively instead of static. That led to the JOPA Channel. All important features (at a glance) In one line: A Rolling-VWAP channel whose width adapts with two volumes (RVOL + dollar-flow), adds order-flow asymmetry (OBV tilt) and regime awareness (Efficiency Ratio), and frames risk with outer containment bands from residual extremes—so you see fair value, momentum, and exhaustion in one view. Feature list Rolling VWAP centerline: Tracks where volume traded (fair value). Dual-volume width: Bands expand/contract with relative volume and value traded (price×volume). OBV tilt: Upper/lower widths skew toward the side actually pushing. Regime adapter (ER): Tighter in trend, wider in chop—automatically. Outer containment rails: Residual-extreme ceilings/floors, smoothed + margin. 20% / 80% guides: 20% light blue (discount), 80% light red (premium). Squeeze dots (optional): Orange circles below candles during compression. Non-repainting: Uses rolling sums and past-only math; no lookahead. Default visual in this release Containment rails + fill: ON (stepline, medium). Inner Value rails + fill: Rails OFF (stepline, thin), fill ON (drawn only if rails are shown). 20% & 80% guides: ON (dashed, thin; 20% light blue, 80% light red). Squeeze dots: OFF by default (orange circles when enabled). What you see on the chart RVWAP (centerline): Your compass for fair value. Inner Value Bands (optional): Tight rails for breakouts and pullback timing. Outer Containment Bands (default ON): High-confidence ceilings/floors for targets and fades. 20% / 80% guides: Quick read of “where in the channel” price is sitting. Squeeze dots (optional): Volatility compression heads-up (no text labels). Non-repainting note: The indicator does not revise closed bars. Forecast-Lock uses linear regression to extrapolate 1–3 bars ahead without using future data. How to use it Core reads (works on any timeframe) Bias: Above a rising RVWAP → long bias; below a falling RVWAP → short bias. Breakouts (momentum): Close beyond an Inner Value rail with RVOL ≥ threshold (alert provided). Reversions (fades): Tag Outer Containment, stall, then close back inside → expect mean reversion toward RVWAP. 20/80 timing: At/above 80% (light red) → premium/exhaustion risk; trim longs or consider fades if RVOL cools. At/below 20% (light blue) → discount/exhaustion risk; trim shorts or consider longs if RVOL cools. Squeeze clusters: When dots bunch up, expect a range break; use the Breakout alert as confirmation. Playbooks by trading style Day Trading (1–5m) Setup: Keep the chart clean (Containment ON, Value rails OFF). Toggle Inner Value ON when hunting a breakout or timing a pullback. Pullback Long: Dip to RVWAP / Lower Value with sub-threshold RVOL, then a close back above RVWAP → long. Stop: Just beyond Lower Containment or the pullback swing. Targets (1:1:1): ⅓ at RVWAP, ⅓ at Upper Value, ⅓ trail toward Upper Containment. Breakout Long: After a squeeze cluster, take the Breakout Long alert (close > Upper Value, RVOL ≥ min). If no retest, demand the next bar holds outside. Range Fade: Only when RVWAP is flat and dots cluster; short Upper Containment → RVWAP (mirror for longs at the lower rail). Intraday (15m–1H) HTF compass: Take bias from 4H. Pullback Long: “Touch & reclaim” of RVWAP while RVOL cools; enter on the reclaim close or break of that candle’s high. Breakout: Run Inner Value ON; act on Breakout alerts (RVOL gate ≈ 1.10–1.15 typical). Avoid low-probability fades against the 4H slope unless RVWAP is flat. Swing (4H–1D) Continuation: In uptrends, buy pullbacks to RVWAP / Lower Value with sub-threshold RVOL; scale at Upper Containment. Adds: Post-squeeze Breakout Long adds; trail on RVWAP or Lower Value. Fades: Prefer when RVWAP flattens and price oscillates between containments. Position (1D+) Framework: Daily RVWAP slope + position within containment. Add rule: Each reclaim of RVWAP after a dip is an add; trim into Upper Containment or near 80% light red. Sizing: Containment distance is larger—size down and trail on RVWAP. Inputs & Settings (complete) Core Source: Price input for RVWAP. Rolling VWAP Length: Window of the centerline (higher = smoother). Volume Baseline (RVOL): SMA window for relative volume. Inner Value Bands (volatility-based width) k·StdDev(residuals), k·ATR, k·MAD(residuals): Blend three measures into base width. StdDev / ATR / MAD Lengths: Lookbacks for each. Two-Volume Fusion RVOL Exponent: How aggressively width responds to relative volume. Dollar-Flow Gain: Adds push from price×volume (value traded). Dollar-Flow Z-Window: Standardization window for dollar-flow. Asymmetry (Order-Flow Tilt) Enable Tilt (OBV): Lets flow skew upper/lower widths. Tilt Strength (0..1): Gain applied to OBV slope z-score. OBV Slope Z-Window: Window to standardize OBV slope. Regime Adapter Efficiency Ratio Lookback: Measures trend vs chop. ER Width Min/Max: Maps ER into a width factor (tighter in trend, wider in chop). Band Tracking (inner value rails) Tracking Mode: Base: Pure base rails. Parallel-Lock: Smooth RVWAP & width; track in parallel. Slope-Lock: Adds a fraction of recent slope (momentum-friendly). Forecast-Lock: 1–3 bar extrapolation via linreg (non-repainting on closed bars). Attach Strength (0..1): Blend tracked rails vs base rails. Tracking Smooth Length: EMA smoothing of RVWAP and width. Slope Influence / Forecast Lead Bars: Gains for the chosen mode. Outer Containment Bands Show Containment Bands: Master toggle (default ON). Residual Extremes Lookback: Highest/lowest residual window. Extreme Smoothing (EMA): Stability on extreme lines. Margin vs inner width: Extra padding relative to smoothed inner width. Squeeze & Alerts Squeeze Window / Threshold: Width vs average; at/under threshold = dot (when enabled). Min RVOL for Breakout: Required RVOL for breakout alerts. Style (defaults in this release) Inner Value rails: OFF (stepline, thin). Inner & Containment fills: ON. Containment rails: ON (stepline, medium). 20% / 80% guides: ON — 20% light blue, 80% light red, dashed, thin. Squeeze dots: OFF by default (orange circles below candles when enabled). Practical templates (copy/paste into a plan) Momentum Breakout Context: Squeeze cluster near RVWAP; Inner Value ON. Trigger: Breakout Long (close > Upper Value & RVOL ≥ min). Stop: Below Lower Value (tight) or below RVWAP (safer). Targets (1:1:1): ⅓ Value → ⅓ Containment → ⅓ trail on RVWAP. Pullback Continuation Context: Uptrend; dip to RVWAP / Lower Value with cooling RVOL. Trigger: Close back above RVWAP or break of reclaim candle’s high. Stop: Just outside Lower Containment or pullback swing. Targets: RVWAP → Upper Value → Upper Containment. Containment Reversion (range) Context: RVWAP flat; repeated containment tags. Trigger: Stall at containment, then close back inside. Stop: A step beyond that containment. Target: RVWAP; runner only if RVOL stays muted. Alerts included DVWAP Breakout Long / Short (Value Bands) Top Zone / Bottom Zone (20% / 80% guides) Tip: On lower TFs, act on Breakout alerts with higher-TF bias (e.g., trade 5–15m in the direction of 1H/4H RVWAP slope/position). Best practices Let RVWAP be the compass; if unsure, wait until price picks a side. Respect RVOL; low-RVOL breaks are prone to fail. Use guides for timing, not certainty. Pair 20/80 zones with flow context. Start with defaults; change one knob at a time. Common pitfalls Fading every containment touch → only fade when RVWAP is flat or RVOL cools. Over-tuning inputs → the defaults are robust; small tweaks go a long way. Fighting the higher timeframe on low TFs → expensive habit. Footer — License & Publishing License: Mozilla Public License 2.0 (MPL-2.0). You may modify and redistribute; keep this file under MPL and provide source for this file. Originality: © 2025 JopAlgo. No third-party code reused; Pine built-ins and common formulas only. Publishing: Keep this header/description intact when releasing on TradingView. Avoid promotional links in the public script text.
Pine Script® 인디케이터
JopAlgo의
PineConnectorLibrary "PineConnector" This library is a comprehensive alert webhook text generator for PineConnector. It contains every possible alert syntax variation from the documentation, along with some debugging functions. To use it, just import the library (eg. "import ZenAndTheArtOfTrading/PineConnector/1 as pc") and use pc.buy(licenseID) to send an alert off to PineConnector - assuming all your webhooks etc are set up correctly. View the PineConnector documentation for more information on how to send the commands you're looking to send (all of this library's function names match the documentation). all()   Usage: pc.buy(pc_id, freq=pc.all())   Returns: "all" once_per_bar()   Usage: pc.buy(pc_id, freq=pc.once_per_bar())   Returns: "once_per_bar" once_per_bar_close()   Usage: pc.buy(pc_id, freq=pc.once_per_bar_close())   Returns: "once_per_bar_close" na0(value)   Checks if given value is either 'na' or 0. Useful for streamlining scripts with float user setting inputs which default values to 0 since na is unavailable as a user input default.   Parameters:      value (float) : The value to check   Returns: True if the given value is 0 or na getDecimals()   Calculates how many decimals are on the quote price of the current market.   Returns: The current decimal places on the market quote price truncate(number, decimals)   Truncates the given number. Required params: mumber.   Parameters:      number (float) : Number to truncate      decimals (int) : Decimal places to cut down to   Returns: The input number, but as a string truncated to X decimals getPipSize(multiplier)   Calculates the pip size of the current market.   Parameters:      multiplier (int) : The mintick point multiplier (1 by default, 10 for FX/Crypto/CFD but can be used to override when certain markets require)   Returns: The pip size for the current market toWhole(number)   Converts pips into whole numbers. Required params: number.   Parameters:      number (float) : The pip number to convert into a whole number   Returns: The converted number toPips(number)   Converts whole numbers back into pips. Required params: number.   Parameters:      number (float) : The whole number to convert into pips   Returns: The converted number debug(txt, tooltip, displayLabel)   Prints to console and generates a debug label with the given text. Required params: txt.   Parameters:      txt (string) : Text to display      tooltip (string) : Tooltip to display (optional)      displayLabel (bool) : Turns on/off chart label (default: off)   Returns: Nothing order(licenseID, command, symbol, parameters, accfilter, comment, secret, freq, debug)   Generates an alert string. Required params: licenseID, command.   Parameters:      licenseID (string) : Your PC license ID      command (string) : Command to send      symbol (string) : The symbol to trigger this order on      parameters (string) : Other optional parameters to include      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: An alert string with valid PC syntax based on supplied parameters market_order(licenseID, buy, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a market entry alert with relevant syntax commands. Required params: licenseID, buy, risk.   Parameters:      licenseID (string) : Your PC license ID      buy (bool) : true=buy/long, false=sell/short      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A market order alert string with valid PC syntax based on supplied parameters buy(licenseID, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a market buy alert with relevant syntax commands. Required params: licenseID, risk.   Parameters:      licenseID (string) : Your PC license ID      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A market order alert string with valid PC syntax based on supplied parameters sell(licenseID, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a market sell alert with relevant syntax commands. Required params: licenseID, risk.   Parameters:      licenseID (string) : Your PC license ID      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A market order alert string with valid PC syntax based on supplied parameters closeall(licenseID, comment, secret, freq, debug)   Closes all open trades at market regardless of symbol. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closealleaoff(licenseID, comment, secret, freq, debug)   Closes all open trades at market regardless of symbol, and turns the EA off. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closelong(licenseID, symbol, comment, secret, freq, debug)   Closes all long trades at market for the given symbol. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closeshort(licenseID, symbol, comment, secret, freq, debug)   Closes all open short trades at market for the given symbol. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closelongshort(licenseID, symbol, comment, secret, freq, debug)   Closes all open trades at market for the given symbol. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closelongbuy(licenseID, risk, symbol, comment, secret, freq, debug)   Close all long positions and open a new long at market for the given symbol with given risk/contracts. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      risk (float) : Risk or contracts (according to EA settings)      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closeshortsell(licenseID, risk, symbol, comment, secret, freq, debug)   Close all short positions and open a new short at market for the given symbol with given risk/contracts. Required params: licenseID, risk.   Parameters:      licenseID (string) : Your PC license ID      risk (float) : Risk or contracts (according to EA settings)      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string newsltplong(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)   Updates the stop loss and/or take profit of any open long trades on the given symbol with the given values. Required params: licenseID, sl and/or tp.   Parameters:      licenseID (string) : Your PC license ID      sl (float) : Stop loss pips or price (according to EA settings)      tp (float) : Take profit pips or price (according to EA settings)      symbol (string) : Symbol to act on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string newsltpshort(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)   Updates the stop loss and/or take profit of any open short trades on the given symbol with the given values. Required params: licenseID, sl and/or tp.   Parameters:      licenseID (string) : Your PC license ID      sl (float) : Stop loss pips or price (according to EA settings)      tp (float) : Take profit pips or price (according to EA settings)      symbol (string) : Symbol to act on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closelongpct(licenseID, symbol, comment, secret, freq, debug)   Close a percentage of open long positions (according to EA settings). Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closeshortpct(licenseID, symbol, comment, secret, freq, debug)   Close a percentage of open short positions (according to EA settings). Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closelongvol(licenseID, risk, symbol, comment, secret, freq, debug)   Close all open long contracts on the current symbol until the given risk value is remaining. Required params: licenseID, risk.   Parameters:      licenseID (string) : Your PC license ID      risk (float) : The quantity to leave remaining      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string closeshortvol(licenseID, risk, symbol, comment, secret, freq, debug)   Close all open short contracts on the current symbol until the given risk value is remaining. Required params: licenseID, risk.   Parameters:      licenseID (string) : Your PC license ID      risk (float) : The quantity to leave remaining      symbol (string) : Symbol to act on (defaults to current symbol)      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string limit_order(licenseID, buy, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a limit order alert with relevant syntax commands. Required params: licenseID, buy, price, risk.   Parameters:      licenseID (string) : Your PC license ID      buy (bool) : true=buy/long, false=sell/short      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A limit order alert string with valid PC syntax based on supplied parameters buylimit(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a buylimit order alert with relevant syntax commands. Required params: licenseID, price, risk.   Parameters:      licenseID (string) : Your PC license ID      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A limit order alert string with valid PC syntax based on supplied parameters selllimit(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a selllimit order alert with relevant syntax commands. Required params: licenseID, price, risk.   Parameters:      licenseID (string) : Your PC license ID      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A limit order alert string with valid PC syntax based on supplied parameters stop_order(licenseID, buy, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a stop order alert with relevant syntax commands. Required params: licenseID, buy, price, risk.   Parameters:      licenseID (string) : Your PC license ID      buy (bool) : true=buy/long, false=sell/short      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A stop order alert string with valid PC syntax based on supplied parameters buystop(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a buystop order alert with relevant syntax commands. Required params: licenseID, price, risk.   Parameters:      licenseID (string) : Your PC license ID      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A stop order alert string with valid PC syntax based on supplied parameters sellstop(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Generates a sellstop order alert with relevant syntax commands. Required params: licenseID, price, risk.   Parameters:      licenseID (string) : Your PC license ID      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A stop order alert string with valid PC syntax based on supplied parameters cancel_neworder(licenseID, order, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Cancel + place new order template function.   Parameters:      licenseID (string) : Your PC license ID      order (string) : Cancel order type      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A stop order alert string with valid PC syntax based on supplied parameters cancellongbuystop(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Cancels all long orders with the specified symbol and places a new buystop order. Required params: licenseID, price, risk.   Parameters:      licenseID (string) : Your PC license ID      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A stop order alert string with valid PC syntax based on supplied parameters cancellongbuylimit(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Cancels all long orders with the specified symbol and places a new buylimit order. Required params: licenseID, price, risk.   Parameters:      licenseID (string) : Your PC license ID      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A stop order alert string with valid PC syntax based on supplied parameters cancelshortsellstop(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Cancels all short orders with the specified symbol and places a sellstop order. Required params: licenseID, price, risk.   Parameters:      licenseID (string) : Your PC license ID      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A stop order alert string with valid PC syntax based on supplied parameters cancelshortselllimit(licenseID, price, risk, sl, tp, betrigger, beoffset, spread, trailtrig, traildist, trailstep, atrtimeframe, atrperiod, atrmultiplier, atrshift, atrtrigger, symbol, accfilter, comment, secret, freq, debug)   Cancels all short orders with the specified symbol and places a selllimit order. Required params: licenseID, price, risk.   Parameters:      licenseID (string) : Your PC license ID      price (float) : Price or pips to set limit order (according to EA settings)      risk (float) : Risk quantity (according to EA settings)      sl (float) : Stop loss distance in pips or price      tp (float) : Take profit distance in pips or price      betrigger (float) : Breakeven will be activated after the position gains this number of pips      beoffset (float) : Offset from entry price. This is the amount of pips you'd like to protect      spread (float) : Enter the position only if the spread is equal or less than the specified value in pips      trailtrig (float) : Trailing stop-loss will be activated after a trade gains this number of pips      traildist (float) : Distance of the trailing stop-loss from current price      trailstep (float) : Moves trailing stop-loss once price moves to favourable by a specified number of pips      atrtimeframe (int) : ATR Trailing Stop timeframe, only updates once per bar close. Options: 1, 5, 15, 30, 60, 240, 1440      atrperiod (int) : ATR averaging period      atrmultiplier (float) : Multiple of ATR to utilise in the new SL computation, default = 1      atrshift (int) : Relative shift of price information, 0 uses latest candle, 1 uses second last, etc. Default = 0      atrtrigger (int) : Activate the trigger of ATR Trailing after market moves favourably by a number of pips. Default = 0 (instant)      symbol (string) : The symbol to trigger this order on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment (maximum 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A stop order alert string with valid PC syntax based on supplied parameters cancellong(licenseID, symbol, accfilter, comment, secret, freq, debug)   Cancels all pending long orders with the specified symbol. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      symbol (string) : Symbol to act on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A cancel long alert command cancelshort(licenseID, symbol, accfilter, comment, secret, freq, debug)   Cancels all pending short orders with the specified symbol. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      symbol (string) : Symbol to act on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: A cancel short alert command newsltpbuystop(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)   Updates the stop loss and/or take profit of any pending buy stop orders on the given symbol. Required params: licenseID, sl and/or tp.   Parameters:      licenseID (string) : Your PC license ID      sl (float) : Stop loss pips or price (according to EA settings)      tp (float) : Take profit pips or price (according to EA settings)      symbol (string) : Symbol to act on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string newsltpbuylimit(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)   Updates the stop loss and/or take profit of any pending buy limit orders on the given symbol. Required params: licenseID, sl and/or tp.   Parameters:      licenseID (string) : Your PC license ID      sl (float) : Stop loss pips or price (according to EA settings)      tp (float) : Take profit pips or price (according to EA settings)      symbol (string) : Symbol to act on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string newsltpsellstop(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)   Updates the stop loss and/or take profit of any pending sell stop orders on the given symbol. Required params: licenseID, sl and/or tp.   Parameters:      licenseID (string) : Your PC license ID      sl (float) : Stop loss pips or price (according to EA settings)      tp (float) : Take profit pips or price (according to EA settings)      symbol (string) : Symbol to act on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string newsltpselllimit(licenseID, sl, tp, symbol, accfilter, comment, secret, freq, debug)   Updates the stop loss and/or take profit of any pending sell limit orders on the given symbol. Required params: licenseID, sl and/or tp.   Parameters:      licenseID (string) : Your PC license ID      sl (float) : Stop loss pips or price (according to EA settings)      tp (float) : Take profit pips or price (according to EA settings)      symbol (string) : Symbol to act on (defaults to current symbol)      accfilter (float) : Optional minimum account balance filter      comment (string) : Optional comment to include (max 20 characters)      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string eaoff(licenseID, secret, freq, debug)   Turns the EA off. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string eaon(licenseID, secret, freq, debug)   Turns the EA on. Required params: licenseID.   Parameters:      licenseID (string) : Your PC license ID      secret (string) : Optional secret key (must be enabled in dashboard)      freq (string) : Alert frequency. Default = "all", options = "once_per_bar", "once_per_bar_close" and "none"      debug (bool) : Turns on/off debug label   Returns: The required alert syntax as a string
Pine Script® 라이브러리
ZenAndTheArtOfTrading의
2
Rollover LTEThis indicator shows where price needs to be and when in order to cause the 20-sma and 50-sma moving averages to change directions. A change in direction requires the slope of a moving average to change from negative to positive or from positive to negative. When a moving average changes direction, it can be said that it has “rolled over” or “rolled up,” with the latter only applying if slope went from negative to positive. Theory: In order to solve for the price of the current bar that will cause the moving average to roll up, the slope from the previous bar’s average to the current bar’s average must be set equal to zero which is to say that the averages must be the same. For the 20-sma, the equation simply stated in words is as follows: Current MA as a function of current price and previous 19 values = previous MA which is fixed based on previous 20 values The denominators which are both 20 cancel and the previous 19 values cancel. What’s left is current price on the left side and the value from 20 bars ago on the right. Current price = value from 20 bars ago and since the equation was set up for solving for the price of the current bar that will cause the MA to roll over Rollover price = value from 20 bars ago This makes plotting rollover price, both current and forecasted, fairly simple, as it’s merely the closing price plotted with an offset to the right the same distance as the moving average length. Application: The 20-sma and 50-sma rollover prices are plotted because they are considered to be the two most important moving averages for rollover analysis. Moving average lengths can be modified in the indicator settings. The 20-sma and 20-sma rollover price are both plotted in white and the 50-sma and 50-sma rollover price are both plotted in blue. There are two rollover prices because the 20-sma rollover price is the price that will cause the 20-sma to roll over and the 50-sma rollover price is the price that will cause the 50-sma to roll over. The one that's vertically furthest away from the current price is the one that will cause both to rollover, as should become clearer upon reading the explanation below. The distance between the current price and the 20-sma rollover price is referred to as the “rollover strength” of the price relative to the 20-sma. A large disparity between the current price and the rollover price suggests bearishness (negative rollover strength) if the rollover price is overhead because price would need to travel all that distance in order to cause the moving average to roll up. If the rollover price and price are converging, as is often the case, a change in moving average and price direction becomes more plausible. The rollover strengths of the 20-sma and 50-sma are added together to calculate the Rollover Strength and if a negative number is the result then the background color of the plot cloud turns red. If the result is positive, it turns green. Rollover Strength is plotted below price as a separate indicator in this publication for reference only and it's not part of this indicator. It does not look much different from momentum indicators. The code is below if anybody wants to try to use it. The important thing is that the distances between the rollover prices and the price action are kept in mind as having shrinking, growing, or neutral bearish and bullish effects on current and forecasted price direction. Trades should not be entered based on cloud colorization changes alone. If you are about to crash into a wall of the 20-sma rollover price, as is indicated on the chart by the green arrow, you might consider going long so long as the rollover strength, both current and forecasted, of the 50-sma isn’t questionably bearish. This is subject to analysis and interpretation. There was a 20-sma rollover wall as indicated with yellow arrow, but the bearish rollover strength of the 50-sma was growing and forecasted to remain strong for a while at that time so a long entry would have not been suggested by both rollover prices. If you are about to crash into both the 20-sma and 50-sma rollover prices at the same time (not shown on this chart), that’s a good time to place a trade in anticipation of both slopes changing direction. You may, in the case of this chart, see that a 20-sma rollover wall precedes a 50-sma rollover convergence with price and anticipate a cascade which turned out to be the case with this recent NQ rally. Price exiting the cloud entirely to either the upside or downside has strong implications. When exiting to the downside, the 20-sma and 50-sma have both rolled over and price is below both of them. The same is true for upside exits. Re-entering the cloud after a rally may indicate a reversal is near, especially if the forecasted rollover prices, particularly the 50-sma, agree. This indicator should be used in conjunction with other technical analysis tools. Additional Notes: The original version of this script which will not be published was much heavier, cluttered, and is not as useful. This is the light version, hence the “LTE” suffix. LTE stands for “long-term evolution” in telecommunications, not “light.” Bar colorization (red, yellow, and green bars) was added using the MACD Hybrid BSH script which is another script I’ve published. If you’re not sure what a bar is, it’s the same thing as a candle or a data point on a line chart. Every vertical line showing price action on the chart above is a bar and it is a bar chart. sma = simple moving average Rollover Strength Script: // This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org // © Skipper86 //@version=5 indicator(title="Rollover Strength", shorttitle="Rollover Strength", overlay=false) source = input.source(close) length1 = input.int(20, "Length 1", minval=1) length2 = input.int(50, "Length 2", minval=1) RolloverPrice1 = source RolloverPrice2 = source RolloverStrength1 = source-RolloverPrice1 RolloverStrength2 = source-RolloverPrice2 RolloverStrength = RolloverStrength1 + RolloverStrength2 Color1 = color.rgb(155, 155, 155, 0) Color2 = color.rgb(0, 0, 200, 0) Color3 = color.rgb(0, 200, 0, 0) plot(RolloverStrength, title="Rollover Strength", color=Color3) hline(0, "Middle Band", color=Color1) //End of Rollover Strength Script
Pine Script® 인디케이터
Skipper86의
Turtle Trader StrategyTurtle Trader Strategy : Introduction : This strategy is based on the well known « Turtle Trader Strategy », that has proven itself over the years. It sends long and short signals with pyramid orders of up to 5, meaning that the strategy can trigger up to 5 orders in the same direction. Good risk and money management. It's important to note that the strategy combines 2 systems working together (S1 and S2). Let’s describe the specific features of this strategy. 1/ Position size : Position size is very important for turtle traders to manage risk properly. This position sizing strategy adapts to market volatility and to account (gains and losses). It’s based on ATR (Average True Range) which can also be called « N ». Its length is per default 20. ATR(20) = (previous_atr(20)*19 + actual_true_range)/20 The number of units to buy is : Unit = 1% * account/(ATR(20)*dollar_per_point) where account is the actual account value and dollar_per_point is the variation in dollar of the asset with a 1 point move. Depending on your risk aversion, you can increase the percentage of your account, but turtle traders default to 1%. If you trade contracts, units must be rounded down by default. There is also an additional rule to reduce the risk if the value of the account falls below the initial capital : in this case and only in this case, account in the unit formula must be replace by : account = actual_account*actual_account/initial capital 2/ Open a position : 2 systems are working together : System 1 : Entering a new 20 day breakout System 2 : Entering a new 55 day breakout A breakout is a new high or new low. If it’s a new high, we open long position and vice versa if it’s a new low we enter in short position. We add an additional rule : System 1 : Breakout is ignored if last long/short position was a winner System 2 : All signals are taken This additional rule allows the trader to be in the major trends if the system 1 signal has been skipped. If a signal for system 1 has been skipped, and next candle is also a new 20 day breakout, S1 doesn’t give a signal. We have to wait S2 signal or wait for a candle that doesn’t make a new breakout to reactivate S1. 3/ Pyramid orders : Turtle Strategy allows us to add extra units to the position if the price moves in our favor. I've configured the strategy to allow up to 5 orders to be added in the same direction. So if the price varies from 0.5*ATR(20) , we add units with the position size formula. Note that the value of account will be replaced by "remaining_account", i.e. the cash remaining in our account after subtracting the value of open positions. 4/ Stop Loss : We set a stop loss at 1.5*ATR(20) below the entry price for longs and above the entry price for shorts. If pyramid units are added, the stop is increased/decreased by 0.5*ATR(20). Note that if SL is configured for a loss of more than 10%, we set the SL to 10% for the first entry order to avoid big losses. This configuration does not work for pyramid orders as SL moves by 0.5*ATR(20). 5/ Exit signals : System 1 : Exit long on a 10 day low Exit short on a 10 day high System 2 : Exit long on a 20 day low Exit short on a 20 day high 6/ What types of orders are placed ? To enter in a position, stop orders are placed meaning that we place orders that will be automatically triggered by the signal at the exact breakout price. Stop loss and exit signals are also stop orders. Pyramid orders are market orders which will be triggered at the opening of the next candle to avoid repainting. PARAMETERS : Risk % of capital : Percentage used in the position size formula. Default is 1% ATR period : ATR length used to calculate ATR. Default is 20 Stop ATR : Parameters used to fix stop loss. Default is 1.5 meaning that stop loss will be set at : buy_price - 1.5*ATR(20) for long and buy_price + 1.5*ATR(20) for short. Turtle traders default is 2 but 1.5 is better for cryptocurrency as there is a huge volatility. S1 Long : System 1 breakout length for long. Default is 20 S2 Long : System 2 breakout length for long. Default is 55 S1 Long Exit : System 1 breakout length to exit long. Default is 10 S2 Long Exit : System 2 breakout length to exit long. Default is 20 S1 Short : System 1 breakout length for short. Default is 15 S2 Short : System 2 breakout length for short. Default is 55 S1 Short Exit : System 1 breakout length to exit short. Default is 7 S2 Short Exit : System 2 breakout length to exit short. Default is 20 Initial capital : $1000 Fees : Interactive Broker fees apply to this strategy. They are set at 0.18% of the trade value. Slippage : 3 ticks or $0.03 per trade. Corresponds to the latency time between the moment the signal is received and the moment the order is executed by the broker. Pyramiding : Number of orders that can be passed in the same direction. Default is 5. Important : Turtle traders don't trade crypto. For this specific asset type, I modify some parameters such as SL and Short S1 in order to maximize return while limiting drawdown. This strategy is the most optimal on BINANCE:BTCUSD in 1D timeframe with the parameters set per default. If you want to use this strategy for a different crypto please adapt parameters. NOTE : It's important to note that the first entry order (long or short) will be the largest. Subsequent pyramid orders will have fewer units than the first order. We've set a maximum SL for the first order of 10%, meaning that you won't lose more than 10% of the value of your first order. However, it is possible to lose more on your pyramid orders, as the SL is increased/decreased by 0.5*ATR(20), which does not secure a loss of more than 10% on your pyramid orders. The risk remains well managed because the value of these orders is less than the value of the first order. Remain vigilant to this small detail and adjust your risk according to your risk aversion. Enjoy the strategy and don’t forget to take the trade :)
Pine Script™ 전략
geneZ_fi의
14
Keltner Channel Enhanced [DCAUT]█ Keltner Channel Enhanced 📊 ORIGINALITY & INNOVATION The Keltner Channel Enhanced represents an important advancement over standard Keltner Channel implementations by introducing dual flexibility in moving average selection for both the middle band and ATR calculation. While traditional Keltner Channels typically use EMA for the middle band and RMA (Wilder's smoothing) for ATR, this enhanced version provides access to 25+ moving average algorithms for both components, enabling traders to fine-tune the indicator's behavior to match specific market characteristics and trading approaches. Key Advancements: Dual MA Algorithm Flexibility: Independent selection of moving average types for middle band (25+ options) and ATR smoothing (25+ options), allowing optimization of both trend identification and volatility measurement separately Enhanced Trend Sensitivity: Ability to use faster algorithms (HMA, T3) for middle band while maintaining stable volatility measurement with traditional ATR smoothing, or vice versa for different trading strategies Adaptive Volatility Measurement: Choice of ATR smoothing algorithm affects channel responsiveness to volatility changes, from highly reactive (SMA, EMA) to smoothly adaptive (RMA, TEMA) Comprehensive Alert System: Five distinct alert conditions covering breakouts, trend changes, and volatility expansion, enabling automated monitoring without constant chart observation Multi-Timeframe Compatibility: Works effectively across all timeframes from intraday scalping to long-term position trading, with independent optimization of trend and volatility components This implementation addresses key limitations of standard Keltner Channels: fixed EMA/RMA combination may not suit all market conditions or trading styles. By decoupling the trend component from volatility measurement and allowing independent algorithm selection, traders can create highly customized configurations for specific instruments and market phases. 📐 MATHEMATICAL FOUNDATION Keltner Channel Enhanced uses a three-component calculation system that combines a flexible moving average middle band with ATR-based (Average True Range) upper and lower channels, creating volatility-adjusted trend-following bands. Core Calculation Process: 1. Middle Band (Basis) Calculation: The basis line is calculated using the selected moving average algorithm applied to the price source over the specified period: basis = ma(source, length, maType) Supported algorithms include EMA (standard choice, trend-biased), SMA (balanced and symmetric), HMA (reduced lag), WMA, VWMA, TEMA, T3, KAMA, and 17+ others. 2. Average True Range (ATR) Calculation: ATR measures market volatility by calculating the average of true ranges over the specified period: trueRange = max(high - low, abs(high - close ), abs(low - close )) atrValue = ma(trueRange, atrLength, atrMaType) ATR smoothing algorithm significantly affects channel behavior, with options including RMA (standard, very smooth), SMA (moderate smoothness), EMA (fast adaptation), TEMA (smooth yet responsive), and others. 3. Channel Calculation: Upper and lower channels are positioned at specified multiples of ATR from the basis: upperChannel = basis + (multiplier × atrValue) lowerChannel = basis - (multiplier × atrValue) Standard multiplier is 2.0, providing channels that dynamically adjust width based on market volatility. Keltner Channel vs. Bollinger Bands - Key Differences: While both indicators create volatility-based channels, they use fundamentally different volatility measures: Keltner Channel (ATR-based): Uses Average True Range to measure actual price movement volatility Incorporates gaps and limit moves through true range calculation More stable in trending markets, less prone to extreme compression Better reflects intraday volatility and trading range Typically fewer band touches, making touches more significant More suitable for trend-following strategies Bollinger Bands (Standard Deviation-based): Uses statistical standard deviation to measure price dispersion Based on closing prices only, doesn't account for intraday range Can compress significantly during consolidation (squeeze patterns) More touches in ranging markets Better suited for mean-reversion strategies Provides statistical probability framework (95% within 2 standard deviations) Algorithm Combination Effects: The interaction between middle band MA type and ATR MA type creates different indicator characteristics: Trend-Focused Configuration (Fast MA + Slow ATR): Middle band uses HMA/EMA/T3, ATR uses RMA/TEMA, quick trend changes with stable channel width, suitable for trend-following Volatility-Focused Configuration (Slow MA + Fast ATR): Middle band uses SMA/WMA, ATR uses EMA/SMA, stable trend with dynamic channel width, suitable for volatility trading Balanced Configuration (Standard EMA/RMA): Classic Keltner Channel behavior, time-tested combination, suitable for general-purpose trend following Adaptive Configuration (KAMA + KAMA): Self-adjusting indicator responding to efficiency ratio, suitable for markets with varying trend strength and volatility regimes 📊 COMPREHENSIVE SIGNAL ANALYSIS Keltner Channel Enhanced provides multiple signal categories optimized for trend-following and breakout strategies. Channel Position Signals: Upper Channel Interaction: Price Touching Upper Channel: Strong bullish momentum, price moving more than typical volatility range suggests, potential continuation signal in established uptrends Price Breaking Above Upper Channel: Exceptional strength, price exceeding normal volatility expectations, consider adding to long positions or tightening trailing stops Price Riding Upper Channel: Sustained strong uptrend, characteristic of powerful bull moves, stay with trend and avoid premature profit-taking Price Rejection at Upper Channel: Momentum exhaustion signal, consider profit-taking on longs or waiting for pullback to middle band for reentry Lower Channel Interaction: Price Touching Lower Channel: Strong bearish momentum, price moving more than typical volatility range suggests, potential continuation signal in established downtrends Price Breaking Below Lower Channel: Exceptional weakness, price exceeding normal volatility expectations, consider adding to short positions or protecting against further downside Price Riding Lower Channel: Sustained strong downtrend, characteristic of powerful bear moves, stay with trend and avoid premature covering Price Rejection at Lower Channel: Momentum exhaustion signal, consider covering shorts or waiting for bounce to middle band for reentry Middle Band (Basis) Signals: Trend Direction Confirmation: Price Above Basis: Bullish trend bias, middle band acts as dynamic support in uptrends, consider long positions or holding existing longs Price Below Basis: Bearish trend bias, middle band acts as dynamic resistance in downtrends, consider short positions or avoiding longs Price Crossing Above Basis: Potential trend change from bearish to bullish, early signal to establish long positions Price Crossing Below Basis: Potential trend change from bullish to bearish, early signal to establish short positions or exit longs Pullback Trading Strategy: Uptrend Pullback: Price pulls back from upper channel to middle band, finds support, and resumes upward, ideal long entry point Downtrend Bounce: Price bounces from lower channel to middle band, meets resistance, and resumes downward, ideal short entry point Basis Test: Strong trends often show price respecting the middle band as support/resistance on pullbacks Failed Test: Price breaking through middle band against trend direction signals potential reversal Volatility-Based Signals: Narrow Channels (Low Volatility): Consolidation Phase: Channels contract during periods of reduced volatility and directionless price action Breakout Preparation: Narrow channels often precede significant directional moves as volatility cycles Trading Approach: Reduce position sizes, wait for breakout confirmation, avoid range-bound strategies within channels Breakout Direction: Monitor for price breaking decisively outside channel range with expanding width Wide Channels (High Volatility): Trending Phase: Channels expand during strong directional moves and increased volatility Momentum Confirmation: Wide channels confirm genuine trend with substantial volatility backing Trading Approach: Trend-following strategies excel, wider stops necessary, mean-reversion strategies risky Exhaustion Signs: Extreme channel width (historical highs) may signal approaching consolidation or reversal Advanced Pattern Recognition: Channel Walking Pattern: Upper Channel Walk: Price consistently touches or exceeds upper channel while staying above basis, very strong uptrend signal, hold longs aggressively Lower Channel Walk: Price consistently touches or exceeds lower channel while staying below basis, very strong downtrend signal, hold shorts aggressively Basis Support/Resistance: During channel walks, price typically uses middle band as support/resistance on minor pullbacks Pattern Break: Price crossing basis during channel walk signals potential trend exhaustion Squeeze and Release Pattern: Squeeze Phase: Channels narrow significantly, price consolidates near middle band, volatility contracts Direction Clues: Watch for price positioning relative to basis during squeeze (above = bullish bias, below = bearish bias) Release Trigger: Price breaking outside narrow channel range with expanding width confirms breakout Follow-Through: Measure squeeze height and project from breakout point for initial profit targets Channel Expansion Pattern: Breakout Confirmation: Rapid channel widening confirms volatility increase and genuine trend establishment Entry Timing: Enter positions early in expansion phase before trend becomes overextended Risk Management: Use channel width to size stops appropriately, wider channels require wider stops Basis Bounce Pattern: Clean Bounce: Price touches middle band and immediately reverses, confirms trend strength and entry opportunity Multiple Bounces: Repeated basis bounces indicate strong, sustainable trend Bounce Failure: Price penetrating basis signals weakening trend and potential reversal Divergence Analysis: Price/Channel Divergence: Price makes new high/low while staying within channel (not reaching outer band), suggests momentum weakening Width/Price Divergence: Price breaks to new extremes but channel width contracts, suggests move lacks conviction Reversal Signal: Divergences often precede trend reversals or significant consolidation periods Multi-Timeframe Analysis: Keltner Channels work particularly well in multi-timeframe trend-following approaches: Three-Timeframe Alignment: Higher Timeframe (Weekly/Daily): Identify major trend direction, note price position relative to basis and channels Intermediate Timeframe (Daily/4H): Identify pullback opportunities within higher timeframe trend Lower Timeframe (4H/1H): Time precise entries when price touches middle band or lower channel (in uptrends) with rejection Optimal Entry Conditions: Best Long Entries: Higher timeframe in uptrend (price above basis), intermediate timeframe pulls back to basis, lower timeframe shows rejection at middle band or lower channel Best Short Entries: Higher timeframe in downtrend (price below basis), intermediate timeframe bounces to basis, lower timeframe shows rejection at middle band or upper channel Risk Management: Use higher timeframe channel width to set position sizing, stops below/above higher timeframe channels 🎯 STRATEGIC APPLICATIONS Keltner Channel Enhanced excels in trend-following and breakout strategies across different market conditions. Trend Following Strategy: Setup Requirements: Identify established trend with price consistently on one side of basis line Wait for pullback to middle band (basis) or brief penetration through it Confirm trend resumption with price rejection at basis and move back toward outer channel Enter in trend direction with stop beyond basis line Entry Rules: Uptrend Entry: Price pulls back from upper channel to middle band, shows support at basis (bullish candlestick, momentum divergence) Enter long on rejection/bounce from basis with stop 1-2 ATR below basis Aggressive: Enter on first touch; Conservative: Wait for confirmation candle Downtrend Entry: Price bounces from lower channel to middle band, shows resistance at basis (bearish candlestick, momentum divergence) Enter short on rejection/reversal from basis with stop 1-2 ATR above basis Aggressive: Enter on first touch; Conservative: Wait for confirmation candle Trend Management: Trailing Stop: Use basis line as dynamic trailing stop, exit if price closes beyond basis against position Profit Taking: Take partial profits at opposite channel, move stops to basis Position Additions: Add to winners on subsequent basis bounces if trend intact Breakout Strategy: Setup Requirements: Identify consolidation period with contracting channel width Monitor price action near middle band with reduced volatility Wait for decisive breakout beyond channel range with expanding width Enter in breakout direction after confirmation Breakout Confirmation: Price breaks clearly outside channel (upper for longs, lower for shorts), channel width begins expanding from contracted state Volume increases significantly on breakout (if using volume analysis) Price sustains outside channel for multiple bars without immediate reversal Entry Approaches: Aggressive: Enter on initial break with stop at opposite channel or basis, use smaller position size Conservative: Wait for pullback to broken channel level, enter on rejection and resumption, tighter stop Volatility-Based Position Sizing: Adjust position sizing based on channel width (ATR-based volatility): Wide Channels (High ATR): Reduce position size as stops must be wider, calculate position size using ATR-based risk calculation: Risk / (Stop Distance in ATR × ATR Value) Narrow Channels (Low ATR): Increase position size as stops can be tighter, be cautious of impending volatility expansion ATR-Based Risk Management: Use ATR-based risk calculations, position size = 0.01 × Capital / (2 × ATR), use multiples of ATR (1-2 ATR) for adaptive stops Algorithm Selection Guidelines: Different market conditions benefit from different algorithm combinations: Strong Trending Markets: Middle band use EMA or HMA, ATR use RMA, capture trends quickly while maintaining stable channel width Choppy/Ranging Markets: Middle band use SMA or WMA, ATR use SMA or WMA, avoid false trend signals while identifying genuine reversals Volatile Markets: Middle band and ATR both use KAMA or FRAMA, self-adjusting to changing market conditions reduces manual optimization Breakout Trading: Middle band use SMA, ATR use EMA or SMA, stable trend with dynamic channels highlights volatility expansion early Scalping/Day Trading: Middle band use HMA or T3, ATR use EMA or TEMA, both components respond quickly Position Trading: Middle band use EMA/TEMA/T3, ATR use RMA or TEMA, filter out noise for long-term trend-following 📋 DETAILED PARAMETER CONFIGURATION Understanding and optimizing parameters is essential for adapting Keltner Channel Enhanced to specific trading approaches. Source Parameter: Close (Most Common): Uses closing price, reflects daily settlement, best for end-of-day analysis and position trading, standard choice HL2 (Median Price): Smooths out closing bias, better represents full daily range in volatile markets, good for swing trading HLC3 (Typical Price): Gives more weight to close while including full range, popular for intraday applications, slightly more responsive than HL2 OHLC4 (Average Price): Most comprehensive price representation, smoothest option, good for gap-prone markets or highly volatile instruments Length Parameter: Controls the lookback period for middle band (basis) calculation: Short Periods (10-15): Very responsive to price changes, suitable for day trading and scalping, higher false signal rate Standard Period (20 - Default): Represents approximately one month of trading, good balance between responsiveness and stability, suitable for swing and position trading Medium Periods (30-50): Smoother trend identification, fewer false signals, better for position trading and longer holding periods Long Periods (50+): Very smooth, identifies major trends only, minimal false signals but significant lag, suitable for long-term investment Optimization by Timeframe: 1-15 minute charts use 10-20 period, 30-60 minute charts use 20-30 period, 4-hour to daily charts use 20-40 period, weekly charts use 20-30 weeks. ATR Length Parameter: Controls the lookback period for Average True Range calculation, affecting channel width: Short ATR Periods (5-10): Very responsive to recent volatility changes, standard is 10 (Keltner's original specification), may be too reactive in whipsaw conditions Standard ATR Period (10 - Default): Chester Keltner's original specification, good balance between responsiveness and stability, most widely used Medium ATR Periods (14-20): Smoother channel width, ATR 14 aligns with Wilder's original ATR specification, good for position trading Long ATR Periods (20+): Very smooth channel width, suitable for long-term trend-following Length vs. ATR Length Relationship: Equal values (20/20) provide balanced responsiveness, longer ATR (20/14) gives more stable channel width, shorter ATR (20/10) is standard configuration, much shorter ATR (20/5) creates very dynamic channels. Multiplier Parameter: Controls channel width by setting ATR multiples: Lower Values (1.0-1.5): Tighter channels with frequent price touches, more trading signals, higher false signal rate, better for range-bound and mean-reversion strategies Standard Value (2.0 - Default): Chester Keltner's recommended setting, good balance between signal frequency and reliability, suitable for both trending and ranging strategies Higher Values (2.5-3.0): Wider channels with less frequent touches, fewer but potentially higher-quality signals, better for strong trending markets Market-Specific Optimization: High volatility markets (crypto, small-caps) use 2.5-3.0 multiplier, medium volatility markets (major forex, large-caps) use 2.0 multiplier, low volatility markets (bonds, utilities) use 1.5-2.0 multiplier. MA Type Parameter (Middle Band): Critical selection that determines trend identification characteristics: EMA (Exponential Moving Average - Default): Standard Keltner Channel choice, Chester Keltner's original specification, emphasizes recent prices, faster response to trend changes, suitable for all timeframes SMA (Simple Moving Average): Equal weighting of all data points, no directional bias, slower than EMA, better for ranging markets and mean-reversion HMA (Hull Moving Average): Minimal lag with smooth output, excellent for fast trend identification, best for day trading and scalping TEMA (Triple Exponential Moving Average): Advanced smoothing with reduced lag, responsive to trends while filtering noise, suitable for volatile markets T3 (Tillson T3): Very smooth with minimal lag, excellent for established trend identification, suitable for position trading KAMA (Kaufman Adaptive Moving Average): Automatically adjusts speed based on market efficiency, slow in ranging markets, fast in trends, suitable for markets with varying conditions ATR MA Type Parameter: Determines how Average True Range is smoothed, affecting channel width stability: RMA (Wilder's Smoothing - Default): J. Welles Wilder's original ATR smoothing method, very smooth, slow to adapt to volatility changes, provides stable channel width SMA (Simple Moving Average): Equal weighting, moderate smoothness, faster response to volatility changes than RMA, more dynamic channel width EMA (Exponential Moving Average): Emphasizes recent volatility, quick adaptation to new volatility regimes, very responsive channel width changes TEMA (Triple Exponential Moving Average): Smooth yet responsive, good balance for varying volatility, suitable for most trading styles Parameter Combination Strategies: Conservative Trend-Following: Length 30/ATR Length 20/Multiplier 2.5, MA Type EMA or TEMA/ATR MA Type RMA, smooth trend with stable wide channels, suitable for position trading Standard Balanced Approach: Length 20/ATR Length 10/Multiplier 2.0, MA Type EMA/ATR MA Type RMA, classic Keltner Channel configuration, suitable for general purpose swing trading Aggressive Day Trading: Length 10-15/ATR Length 5-7/Multiplier 1.5-2.0, MA Type HMA or EMA/ATR MA Type EMA or SMA, fast trend with dynamic channels, suitable for scalping and day trading Breakout Specialist: Length 20-30/ATR Length 5-10/Multiplier 2.0, MA Type SMA or WMA/ATR MA Type EMA or SMA, stable trend with responsive channel width Adaptive All-Conditions: Length 20/ATR Length 10/Multiplier 2.0, MA Type KAMA or FRAMA/ATR MA Type KAMA or TEMA, self-adjusting to market conditions Offset Parameter: Controls horizontal positioning of channels on chart. Positive values shift channels to the right (future) for visual projection, negative values shift left (past) for historical analysis, zero (default) aligns with current price bars for real-time signal analysis. Offset affects only visual display, not alert conditions or actual calculations. 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES Keltner Channel Enhanced provides improvements over standard implementations while maintaining proven effectiveness. Response Characteristics: Standard EMA/RMA Configuration: Moderate trend lag (approximately 0.4 × length periods), smooth and stable channel width from RMA smoothing, good balance for most market conditions Fast HMA/EMA Configuration: Approximately 60% reduction in trend lag compared to EMA, responsive channel width from EMA ATR smoothing, suitable for quick trend changes and breakouts Adaptive KAMA/KAMA Configuration: Variable lag based on market efficiency, automatic adjustment to trending vs. ranging conditions, self-optimizing behavior reduces manual intervention Comparison with Traditional Keltner Channels: Enhanced Version Advantages: Dual Algorithm Flexibility: Independent MA selection for trend and volatility vs. fixed EMA/RMA, separate tuning of trend responsiveness and channel stability Market Adaptation: Choose configurations optimized for specific instruments and conditions, customize for scalping, swing, or position trading preferences Comprehensive Alerts: Enhanced alert system including channel expansion detection Traditional Version Advantages: Simplicity: Fewer parameters, easier to understand and implement Standardization: Fixed EMA/RMA combination ensures consistency across users Research Base: Decades of backtesting and research on standard configuration When to Use Enhanced Version: Trading multiple instruments with different characteristics, switching between trending and ranging markets, employing different strategies, algorithm-based trading systems requiring customization, seeking optimization for specific trading style and timeframe. When to Use Standard Version: Beginning traders learning Keltner Channel concepts, following published research or trading systems, preferring simplicity and standardization, wanting to avoid optimization and curve-fitting risks. Performance Across Market Conditions: Strong Trending Markets: EMA or HMA basis with RMA or TEMA ATR smoothing provides quicker trend identification, pullbacks to basis offer excellent entry opportunities Choppy/Ranging Markets: SMA or WMA basis with RMA ATR smoothing and lower multipliers, channel bounce strategies work well, avoid false breakouts Volatile Markets: KAMA or FRAMA with EMA or TEMA, adaptive algorithms excel by automatic adjustment, wider multipliers (2.5-3.0) accommodate large price swings Low Volatility/Consolidation: Channels narrow significantly indicating consolidation, algorithm choice less impactful, focus on detecting channel width contraction for breakout preparation Keltner Channel vs. Bollinger Bands - Usage Comparison: Favor Keltner Channels When: Trend-following is primary strategy, trading volatile instruments with gaps, want ATR-based volatility measurement, prefer fewer higher-quality channel touches, seeking stable channel width during trends. Favor Bollinger Bands When: Mean-reversion is primary strategy, trading instruments with limited gaps, want statistical framework based on standard deviation, need squeeze patterns for breakout identification, prefer more frequent trading opportunities. Use Both Together: Bollinger Band squeeze + Keltner Channel breakout is powerful combination, price outside Bollinger Bands but inside Keltner Channels indicates moderate signal, price outside both indicates very strong signal, Bollinger Bands for entries and Keltner Channels for trend confirmation. Limitations and Considerations: General Limitations: Lagging Indicator: All moving averages lag price, even with reduced-lag algorithms Trend-Dependent: Works best in trending markets, less effective in choppy conditions No Direction Prediction: Indicates volatility and deviation, not future direction, requires confirmation Enhanced Version Specific Considerations: Optimization Risk: More parameters increase risk of curve-fitting historical data Complexity: Additional choices may overwhelm beginning traders Backtesting Challenges: Different algorithms produce different historical results Mitigation Strategies: Use Confirmation: Combine with momentum indicators (RSI, MACD), volume, or price action Test Parameter Robustness: Ensure parameters work across range of values, not just optimized ones Multi-Timeframe Analysis: Confirm signals across different timeframes Proper Risk Management: Use appropriate position sizing and stops Start Simple: Begin with standard EMA/RMA before exploring alternatives Optimal Usage Recommendations: For Maximum Effectiveness: Start with standard EMA/RMA configuration to understand classic behavior Experiment with alternatives on demo account or paper trading Match algorithm combination to market condition and trading style Use channel width analysis to identify market phases Combine with complementary indicators for confirmation Implement strict risk management using ATR-based position sizing Focus on high-quality setups rather than trading every signal Respect the trend: trade with basis direction for higher probability Complementary Indicators: RSI or Stochastic: Confirm momentum at channel extremes MACD: Confirm trend direction and momentum shifts Volume: Validate breakouts and trend strength ADX: Measure trend strength, avoid Keltner signals in weak trends Support/Resistance: Combine with traditional levels for high-probability setups Bollinger Bands: Use together for enhanced breakout and volatility analysis USAGE NOTES This indicator is designed for technical analysis and educational purposes. Keltner Channel Enhanced has limitations and should not be used as the sole basis for trading decisions. While the flexible moving average selection for both trend and volatility components provides valuable adaptability across different market conditions, algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Key considerations: Always use multiple forms of analysis and confirmation before entering trades Backtest any parameter combination thoroughly before live trading Be aware that optimization can lead to curve-fitting if not done carefully Start with standard EMA/RMA settings and adjust only when specific conditions warrant Understand that no moving average algorithm can eliminate lag entirely Consider market regime (trending, ranging, volatile) when selecting parameters Use ATR-based position sizing and risk management on every trade Keltner Channels work best in trending markets, less effective in choppy conditions Respect the trend direction indicated by price position relative to basis line The enhanced flexibility of dual algorithm selection provides powerful tools for adaptation but requires responsible use, thorough understanding of how different algorithms behave under various market conditions, and disciplined risk management.
Pine Script® 인디케이터
DCAUT의
Adv EMA Cloud v6 (ADX, Alerts)Summary: This indicator provides a multi-faceted view of market trends using Exponential Moving Averages (EMAs) arranged in visually intuitive clouds, enhanced with an optional ADX-based range filter and configurable alerts for key market conditions. It aims to help traders quickly gauge trend alignment across short, medium, and long timeframes while filtering signals during potentially choppy market conditions. Key Features: Multiple EMAs: Displays 10-period (Fast), 20-period (Mid), and 50-period (Slow) EMAs. Long-Term Trend Filter: Includes a 200-period EMA to provide context for the overall dominant trend direction. Dual EMA Clouds: Fast/Mid Cloud (10/20 EMA): Fills the area between the 10 and 20 EMAs. Defaults to Green when 10 > 20 (bullish short-term momentum) and Red when 10 < 20 (bearish short-term momentum). Mid/Slow Cloud (20/50 EMA): Fills the area between the 20 and 50 EMAs. Defaults to Aqua when 20 > 50 (bullish mid-term trend) and Fuchsia when 20 < 50 (bearish mid-term trend). Optional ADX Range Filter: Uses the Average Directional Index (ADX) to identify potentially non-trending or choppy markets. When enabled and ADX falls below a user-defined threshold, the EMA clouds will turn grey, visually warning that trend-following signals may be less reliable. Configurable Alerts: Provides several built-in alert conditions using Pine Script's alertcondition function: Confluence Condition: Triggers when a 10/20 EMA crossover occurs while both EMA clouds show alignment (both bullish/green/aqua or both bearish/red/fuchsia) and price respects the 200 EMA filter and the ADX filter indicates a trend (if filters are enabled). MA Filter Cross: Triggers when price crosses above or below the 200 EMA filter line. Full Alignment Start: Triggers on the first bar where full bullish or bearish alignment occurs (both clouds aligned + MA filter respected + ADX trending, if filters are enabled). How It Works: EMA Calculation: Standard Exponential Moving Averages are calculated for the 10, 20, 50, and 200 periods based on the closing price. Cloud Creation: The fill() function visually shades the area between the 10 & 20 EMAs and the 20 & 50 EMAs. Cloud Coloring: The color of each cloud is determined by the relationship between the two EMAs that define it (e.g., if EMA 10 is above EMA 20, the first cloud is bullish-colored). ADX Filter Logic: The script calculates the ADX value. If the "Use ADX Trend Filter?" input is checked and the calculated ADX is below the specified "ADX Trend Threshold", the script considers the market potentially ranging. ADX Visual Effect: During detected ranging periods (if the ADX filter is active), the plotCloud12Color and plotCloud23Color variables are assigned a neutral grey color instead of their normal bullish/bearish colors before being passed to the fill() function. Alert Logic: Boolean variables track the specific conditions (crossovers, cloud alignment, filter positions, ADX state). The alertcondition() function creates triggerable alerts based on these pre-defined conditions. Potential Interpretation (Not Financial Advice): Trend Alignment: When both clouds share the same directional color (e.g., both bullish - Green & Aqua) and price is on the corresponding side of the 200 EMA filter, it may suggest a stronger, more aligned trend. Conversely, conflicting cloud colors may indicate indecision or transition. Dynamic Support/Resistance: The EMA lines themselves (especially the 20, 50, and 200) can sometimes act as dynamic levels where price might react. Range Warning: Greyed-out clouds (when ADX filter is enabled) serve as a visual warning that trend-based strategies might face increased difficulty or whipsaws. Confluence Alerts: The specific confluence alerts signal moments where multiple conditions align (crossover + cloud agreement + filters), which some traders might view as higher-probability setups. Customization: All EMA lengths (10, 20, 50, 200) are adjustable via the Inputs menu. The ADX length and threshold are configurable. The MA Trend Filter and ADX Trend Filter can be independently enabled or disabled. Disclaimer: This indicator is provided for informational and educational purposes only. Trading financial markets involves significant risk. Past performance is not indicative of future results. Always conduct your own thorough analysis and consider your risk tolerance before making any trading decisions. This indicator should be used in conjunction with other analysis methods and tools. Do not trade based solely on the signals or visuals provided by this indicator.
Pine Script® 인디케이터
epphllps의
1
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork. Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret? The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously. This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation. Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio. The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally. Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional. The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply. The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach. 1. Introduction and Theoretical Background 1.1 The Limitations of Static Portfolio Allocation Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies. The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework. 1.2 Multi-Factor Approaches in Asset Allocation Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding. 2. Data Foundation and Data Quality 2.1 Data Sources Used The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics. First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures. Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model. Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator. Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data. 2.2 Handling Missing Data A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals. 3. Component 1: Market Regime Detection 3.1 The Concept of Market Regimes The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly. DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis. 3.2 Trend Analysis Through Moving Averages Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs. The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent. 3.3 Volatility Regimes Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986). DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices. The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989). 3.4 Drawdown Analysis A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric. The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%. 3.5 Regime Classification Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations. 4. Component 2: Risk-Based Allocation 4.1 Volatility Targeting as Risk Management Approach The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced. Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility. 4.2 Market Volatility Calculation Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure. Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice. The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation. 4.3 Drawdown-Based Position Size Adjustment In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979). The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25). 4.4 Dynamic Risk Adjustment An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure. Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability. 5. Component 3: Valuation Analysis 5.1 Theoretical Foundations of Fundamental Valuation DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets. Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets. 5.2 Equity Risk Premium as Central Valuation Metric The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation. DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs. The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003). The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone. The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth. The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this. The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10. 5.3 Quality Adjustments to Valuation Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure. Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points. Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies. Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010). 6. Component 4: Sentiment Analysis 6.1 The Role of Sentiment in Financial Markets Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions. Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops. 6.2 VIX-Based Sentiment Assessment DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods. A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities. 6.3 VIX Term Structure as Sentiment Signal The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D). Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines. 6.4 Safe-Haven Flows During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion. The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases. Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection. 7. Component 5: Macroeconomic Analysis 7.1 The Yield Curve as Economic Indicator The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity. DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points. The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006). 7.2 Credit Conditions and Spreads Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns. The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points. Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score. 7.3 Financial Stability at Corporate Level While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health. A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions. Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases. 8. Component 6: Crisis Detection 8.1 The Need for Systematic Crisis Detection Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components. Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators. 8.2 Multi-Signal Crisis Identification The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence. The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility. The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns. The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis. The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points. The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point. A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms. 8.3 Crisis-Based Allocation Override When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007). This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability. 9. Integration and Final Allocation Calculation 9.1 Component Weighting The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results. The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations. The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017). The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988). Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable. 9.2 Model Type Adjustments DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments. Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons. Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets. Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged. 9.3 Smoothing and Stability The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change. The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals. 10. Visualization and Interpretation 10.1 Main Output: Equity Allocation DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash. The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases. 10.2 Dashboard Metrics A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status. Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments. Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down. 10.3 Component Breakdown (Optional) Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning." This feature is disabled by default to keep the chart clean but can be activated for deeper analysis. 10.4 Confidence Bands The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations. This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources. 11. Alert System 11.1 Allocation Alerts DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning. These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts. 11.2 Crisis Alerts Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors). 11.3 Regime Change Alerts An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics. 11.4 Risk Breach Alerts A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions. 12. Practical Application and Limitations 12.1 Portfolio Implementation DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation. A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs. 12.2 Adaptation to Individual Preferences The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight. Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation. Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect. 12.3 Limitations and Disclaimers DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007). The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market. The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination. Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation. The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems. 12.4 Relationship to Other Strategies DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing. The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory. The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio. 13. Scientific Foundation and Evaluation 13.1 Theoretical Consistency DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998). This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990). 13.2 Empirical Validation While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements: In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation. Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance. Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters. 13.3 Comparison with Existing Literature DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources. Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. 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(1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61. Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79. Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49. Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465. Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78. Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720. Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin. Graham, B. and Dodd, D.L. (1934) *Security Analysis*. 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(2001) 'Extreme Correlation of International Equity Markets', *The Journal of Finance*, 56(2), pp. 649-676. Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419. Markowitz, H. (1952) 'Portfolio Selection', *The Journal of Finance*, 7(1), pp. 77-91. Modigliani, F. and Miller, M.H. (1961) 'Dividend Policy, Growth, and the Valuation of Shares', *The Journal of Business*, 34(4), pp. 411-433. Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644. Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250. Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65. Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41. Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press. Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360. Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449. Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153. Sharpe, W.F. (1966) 'Mutual Fund Performance', *The Journal of Business*, 39(1), pp. 119-138. Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58. Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462. Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17. Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105. Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research. Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
Pine Script® 인디케이터
EdgeTools의
12
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer Version: PineScriptv6 📌Description The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not. At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape. The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions. 🚀Points of Innovation Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds 🔧Core Components RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels 🔥Key Features 56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count 🎨Visualization Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers 📖Usage Guidelines RSI Period Default: 14 Range: 1 to unlimited Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications. MACD Fast Length Default: 12 Range: 1 to unlimited Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification. MACD Slow Length Default: 26 Range: 1 to unlimited Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix. MACD Signal Length Default: 9 Range: 1 to unlimited Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts. Volume MA Period Default: 20 Range: 1 to unlimited Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts. Statistics Lookback Period Default: 200 Range: 50 to 500 Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics. Forward Performance Bars Default: 5 Range: 1 to 20 Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation. Color Intensity Sensitivity Default: 2.0 Range: 0.5 to 5.0, step 0.5 Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets. Minimum Occurrences for Coloring Default: 3 Range: 1 to 10 Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis. Table Position Default: top_right Options: top_left, top_right, bottom_left, bottom_right Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement. Show Current State Panel Default: true Options: true, false Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed. Info Panel Position Default: bottom_left Options: top_left, top_right, bottom_left, bottom_right Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout. Win Rate Smoothing Strength Default: 5 Range: 1 to 20 Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates. ✅Best Use Cases Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence ⚠️Limitations Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets 💡What Makes This Unique Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements 🔬How It Works 1. State Classification and Encoding Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80) Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix 2. Historical Data Accumulation As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window This continuous process builds a comprehensive database of past market conditions and their subsequent price movements 3. Forward Return Calculation and Statistics Update On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples 💡Note: The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights.
Pine Script® 인디케이터
PhenLabs의
Stochastic [Paifc0de]Stochastic — clean stochastic oscillator with visual masking, neutral markers, and basic filters What it does This indicator plots a standard stochastic oscillator (%K with smoothing and %D) and adds practical quality-of-life features for lower timeframes: optional visual masking when %K hugs overbought/oversold, neutral K–D cross markers, session-gated edge triangles (K crossing 20/80), and simple filters (minimum %K slope, minimum |K–D| gap, optional %D slope agreement, mid-zone mute, and a cooldown between markers). Display values are clamped to 0–100 to keep the panel scale stable. The tool is for research/education and does not generate entries/exits or financial advice. Default preset: 20 / 10 / 10 K Length = 20 Classic lookback used in many textbooks. On intraday charts it balances responsiveness and stability: short enough to react to momentum shifts, long enough to avoid constant whipsaws. In practice it captures ~the last 20 bars’ position of close within the high–low range. K Smoothing = 10 A 10-period SMA applied to the raw %K moderates the “saw-tooth” effect that raw stochastic can exhibit in choppy phases. The smoothing reduces over-reaction to micro spikes while preserving the main rhythm of swings; visually, %K becomes a continuous path that is easier to read. D Length = 10 %D is the moving average of smoothed %K. With 10, %D becomes a clearly slower guide line. The larger separation between %K(10-SMA) and %D(10-SMA of %K) produces cleaner crosses and fewer spurious toggles than micro settings (e.g., 3/3/3). On M5–M15 this pair often yields readable cross cycles without flooding the chart. How the 20/10/10 trio behaves In persistent trends, %K will spend more time near 20 or 80; the 10-period smoothing delays flips slightly and emphasizes only meaningful turn attempts. In ranges, %K oscillates around mid-zone (40–60). With 10/10 smoothing, cross signals cluster less densely; combining with the |K–D| gap filter helps keep only decisive crosses. If your symbol is unusually volatile or illiquid, reduce K Length (e.g., 14) or reduce K Smoothing (e.g., 7) to keep responsiveness. If crosses feel late, decrease D Length (e.g., 7). If noise is excessive, increase K Smoothing first, then consider raising D Length. Visuals OB/OS lines: default 80/20 reference levels and a midline at 50. Masking near edges: %K can be temporarily hidden when it is pressing an edge, approaching it with low slope, or going nearly flat near the boundary. This keeps the panel readable during “stuck at the edge” phases. Soft glow (optional): highlights %K’s active path; can be turned off. Light/Dark palette: quick toggle to match your chart theme. Scale safety: all plotted values (lines, fills, markers) are clamped to 0–100 to prevent the axis from expanding beyond the stochastic range. Markers and filters Neutral K–D cross markers: circles in the mid-zone when %K crosses %D. Edge triangles: show when %K crosses 20 or 80; can be restricted to a session window (02:00–12:00 ET). Filters (optional): Min %K slope: require a minimum absolute slope so very flat crosses are ignored. Min |K–D| gap: demand separation between lines at the cross moment. %D slope agreement: keep crosses that align with %D’s direction. Mid-zone mute: suppress crosses inside a user-defined 40–60 band (defaults). Cooldown: minimum bars between successive markers. Parameters (quick guide) K Length / K Smoothing / D Length: core stochastic settings. Start with 20/10/10; tune K Smoothing first if you see too much jitter. Overbought / Oversold (80/20): adjust for assets that tend to trend (raise to 85/15) or mean-revert (lower to 75/25). Slope & gap filters: increase on very noisy symbols; reduce if you miss too many crosses. Session window (triangles only): use if you want edge markers only during active hours. Marker size and offset: cosmetic; they do not affect calculations. Alerts K–D Cross Up (filtered) and K–D Cross Down (filtered): fire when a cross passes your filters/cooldown. Edge Up / Edge Down: fire when %K crosses the 20/80 levels. All alerts confirm on bar close. Notes & attribution Original implementation and integration by Paifc0de; no third-party code is copied. This indicator is for research/education and does not provide entries/exits or financial advice.
Pine Script® 인디케이터
paifcode의
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE) ## Advanced Multi-Dimensional Mathematical Analysis System *Where Quantum Mathematics Meets Market Structure* --- ## 🎓 THEORETICAL FOUNDATION The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry. ### 🌊 Multi-Dimensional Volatility with Jump Detection **Hawkes Process Implementation:** The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects. **Mathematical Foundation:** ``` Intensity λ(t) = μ + Σ α(t - Tᵢ) ``` Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99). **Mahalanobis Distance Calculation:** The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions: - **Dimension 1:** Price volatility (standard deviation of returns) - **Dimension 2:** Volume volatility (normalized volume fluctuations) - **Dimension 3:** Range volatility (high-low spread variations) - **Dimension 4:** Correlation volatility (price-volume relationship changes) - **Dimension 5:** Microstructure volatility (intrabar positioning analysis) This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches. ### 📐 Hurst Exponent Regime Detection **Fractal Market Hypothesis Integration:** The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification: - **H > 0.6:** Trending (persistent) markets - momentum strategies optimal - **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal - **H ≈ 0.5:** Random walk markets - breakout strategies preferred **Adaptive R/S Analysis:** Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions: ``` H = log(R/S) / log(n) ``` Where R is the range of cumulative deviations and S is the standard deviation over period n. **Dynamic Regime Classification:** The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods. ### 🔄 Transfer Entropy Analysis **Information Flow Quantification:** Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements: ``` TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log ``` **Causality Detection:** - **Volume → Price:** Indicates accumulation/distribution phases - **Price → Volume:** Suggests retail participation or momentum chasing - **Balanced Flow:** Market equilibrium or transition periods The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows. --- ## 🔧 COMPREHENSIVE INPUT SYSTEM ### Core Parameters Group **Primary Analysis Window (10-100, Default: 50)** The fundamental lookback period affecting all calculations. Optimization by timeframe: - **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements) - **15 minute-1 hour:** 30-50 (balanced responsiveness and stability) - **4 hour-daily:** 50-100 (smooth signals, reduced noise) - **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60 **Signal Sensitivity (0.1-2.0, Default: 0.7)** Master control affecting all threshold calculations: - **Conservative (0.3-0.6):** High-quality signals only, fewer false positives - **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles - **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering **Signal Generation Mode:** - **Aggressive:** Any component signals (highest frequency) - **Confluence:** 2+ components agree (balanced approach) - **Conservative:** All 3 components align (highest quality) ### Volatility Jump Detection Group **Volatility Dimensions (2-5, Default: 3)** Determines the mathematical space complexity: - **2D:** Price + Volume volatility (suitable for clean markets) - **3D:** + Range volatility (optimal for most conditions) - **4D:** + Correlation volatility (advanced multi-asset analysis) - **5D:** + Microstructure volatility (maximum sensitivity) **Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)** Standard deviations required for volatility jump classification: - **Cryptocurrency:** 2.0-2.5σ (naturally volatile) - **Stock Indices:** 2.5-3.0σ (moderate volatility) - **Forex Major Pairs:** 3.0-3.5σ (typically stable) - **Commodities:** 2.0-3.0σ (varies by commodity) **Jump Clustering Decay (0.5-0.99, Default: 0.85)** Hawkes process memory parameter: - **0.5-0.7:** Fast decay (jumps treated as independent) - **0.8-0.9:** Moderate clustering (realistic market behavior) - **0.95-0.99:** Strong clustering (crisis/event-driven markets) ### Hurst Exponent Analysis Group **Calculation Method Options:** - **Classic R/S:** Original Rescaled Range (fast, simple) - **Adaptive R/S:** Dynamic windowing (recommended for trading) - **DFA:** Detrended Fluctuation Analysis (best for noisy data) **Trending Threshold (0.55-0.8, Default: 0.60)** Hurst value defining persistent market behavior: - **0.55-0.60:** Weak trend persistence - **0.65-0.70:** Clear trending behavior - **0.75-0.80:** Strong momentum regimes **Mean Reversion Threshold (0.2-0.45, Default: 0.40)** Hurst value defining anti-persistent behavior: - **0.35-0.45:** Weak mean reversion - **0.25-0.35:** Clear ranging behavior - **0.15-0.25:** Strong reversion tendency ### Transfer Entropy Parameters Group **Information Flow Analysis:** - **Price-Volume:** Classic flow analysis for accumulation/distribution - **Price-Volatility:** Risk flow analysis for sentiment shifts - **Multi-Timeframe:** Cross-timeframe causality detection **Maximum Lag (2-20, Default: 5)** Causality detection window: - **2-5 bars:** Immediate causality (scalping) - **5-10 bars:** Short-term flow (day trading) - **10-20 bars:** Structural flow (swing trading) **Significance Threshold (0.05-0.3, Default: 0.15)** Minimum entropy for signal generation: - **0.05-0.10:** Detect subtle information flows - **0.10-0.20:** Clear causality only - **0.20-0.30:** Very strong flows only --- ## 🎨 ADVANCED VISUAL SYSTEM ### Tensor Volatility Field Visualization **Five-Layer Resonance Bands:** The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength: - **Core Layer (Purple):** Primary tensor field with highest intensity - **Layer 2 (Neutral):** Secondary mathematical resonance - **Layer 3 (Info Blue):** Tertiary harmonic frequencies - **Layer 4 (Warning Gold):** Outer field boundaries - **Layer 5 (Success Green):** Maximum field extension **Field Strength Calculation:** ``` Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity) ``` The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility. **Radiation Line Network:** During active tensor states, the system projects directional radiation lines showing field energy distribution: - **8 Directional Rays:** Complete angular coverage - **Tapering Segments:** Progressive transparency for natural visual flow - **Pulse Effects:** Enhanced visualization during volatility jumps ### Dimensional Portal System **Portal Mathematics:** Dimensional portals visualize regime transitions using category theory principles: - **Green Portals (◉):** Trending regime detection (appear below price for support) - **Red Portals (◎):** Mean-reverting regime (appear above price for resistance) - **Yellow Portals (○):** Random walk regime (neutral positioning) **Tensor Trail Effects:** Each portal generates 8 trailing particles showing mathematical momentum: - **Large Particles (●):** Strong mathematical signal - **Medium Particles (◦):** Moderate signal strength - **Small Particles (·):** Weak signal continuation - **Micro Particles (˙):** Signal dissipation ### Information Flow Streams **Particle Stream Visualization:** Transfer entropy creates flowing particle streams indicating information direction: - **Upward Streams:** Volume leading price (accumulation phases) - **Downward Streams:** Price leading volume (distribution phases) - **Stream Density:** Proportional to information flow strength **15-Particle Evolution:** Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent. ### Fractal Matrix Grid System **Multi-Timeframe Fractal Levels:** The system calculates and displays fractal highs/lows across five Fibonacci periods: - **8-Period:** Short-term fractal structure - **13-Period:** Intermediate-term patterns - **21-Period:** Primary swing levels - **34-Period:** Major structural levels - **55-Period:** Long-term fractal boundaries **Triple-Layer Visualization:** Each fractal level uses three-layer rendering: - **Shadow Layer:** Widest, darkest foundation (width 5) - **Glow Layer:** Medium white core line (width 3) - **Tensor Layer:** Dotted mathematical overlay (width 1) **Intelligent Labeling System:** Smart spacing prevents label overlap using ATR-based minimum distances. Labels include: - **Fractal Period:** Time-based identification - **Topological Class:** Mathematical complexity rating (0, I, II, III) - **Price Level:** Exact fractal price - **Mahalanobis Distance:** Current mathematical field strength - **Hurst Exponent:** Current regime classification - **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡) ### Wick Pressure Analysis **Rejection Level Mathematics:** The system analyzes candle wick patterns to project future pressure zones: - **Upper Wick Analysis:** Identifies selling pressure and resistance zones - **Lower Wick Analysis:** Identifies buying pressure and support zones - **Pressure Projection:** Extends lines forward based on mathematical probability **Multi-Layer Glow Effects:** Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart. ### Enhanced Regime Background **Dynamic Intensity Mapping:** Background colors reflect mathematical regime strength: - **Deep Transparency (98% alpha):** Subtle regime indication - **Pulse Intensity:** Based on regime strength calculation - **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random) **Smoothing Integration:** Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts. ### Color Scheme System **Six Professional Themes:** - **Dark (Default):** Professional trading environment optimization - **Light:** High ambient light conditions - **Classic:** Traditional technical analysis appearance - **Neon:** High-contrast visibility for active trading - **Neutral:** Minimal distraction focus - **Bright:** Maximum visibility for complex setups Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences. --- ## 📊 INSTITUTIONAL-GRADE DASHBOARD ### Tensor Field Status Section **Field Strength Display:** Real-time Mahalanobis distance calculation with dynamic emoji indicators: - **⚡ (Lightning):** Extreme field strength (>1.5× threshold) - **● (Solid Circle):** Strong field activity (>1.0× threshold) - **○ (Open Circle):** Normal field state **Signal Quality Rating:** Democratic algorithm assessment: - **ELITE:** All 3 components aligned (highest probability) - **STRONG:** 2 components aligned (good probability) - **GOOD:** 1 component active (moderate probability) - **WEAK:** No clear component signals **Threshold and Anomaly Monitoring:** - **Threshold Display:** Current mathematical threshold setting - **Anomaly Level (0-100%):** Combined volatility and volume spike measurement - **>70%:** High anomaly (red warning) - **30-70%:** Moderate anomaly (orange caution) - **<30%:** Normal conditions (green confirmation) ### Tensor State Analysis Section **Mathematical State Classification:** - **↑ BULL (Tensor State +1):** Trending regime with bullish bias - **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias - **◈ SUPER (Tensor State 0):** Random walk regime (neutral) **Visual State Gauge:** Five-circle progression showing tensor field polarity: - **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment - **⚪⚪🟡⚪⚪:** Neutral/transitional state - **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment **Trend Direction and Phase Analysis:** - **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification - **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength) - **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength) - **😴 CALM:** Low information flow (<1.0 flow strength) ### Trading Signals Section **Real-Time Signal Status:** - **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability - **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability - **Components (X/3):** Active algorithmic components - **Mode Display:** Current signal generation mode **Signal Strength Visualization:** Color-coded component count: - **Green:** 3/3 components (maximum confidence) - **Aqua:** 2/3 components (good confidence) - **Orange:** 1/3 components (moderate confidence) - **Gray:** 0/3 components (no signals) ### Performance Metrics Section **Win Rate Monitoring:** Estimated win rates based on signal quality with emoji indicators: - **🔥 (Fire):** ≥60% estimated win rate - **👍 (Thumbs Up):** 45-59% estimated win rate - **⚠️ (Warning):** <45% estimated win rate **Mathematical Metrics:** - **Hurst Exponent:** Real-time fractal dimension (0.000-1.000) - **Information Flow:** Volume/price leading indicators - **📊 VOL:** Volume leading price (accumulation/distribution) - **💰 PRICE:** Price leading volume (momentum/speculation) - **➖ NONE:** Balanced information flow - **Volatility Classification:** - **🔥 HIGH:** Above 1.5× jump threshold - **📊 NORM:** Normal volatility range - **😴 LOW:** Below 0.5× jump threshold ### Market Structure Section (Large Dashboard) **Regime Classification:** - **📈 TREND:** Hurst >0.6, momentum strategies optimal - **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal - **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred **Mathematical Field Analysis:** - **Dimensions:** Current volatility space complexity (2D-5D) - **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00) - **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal) ### Settings Summary Section (Large Dashboard) **Active Configuration Display:** - **Sensitivity:** Current master sensitivity setting - **Lookback:** Primary analysis window - **Theme:** Active color scheme - **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA) **Dashboard Sizing Options:** - **Small:** Essential metrics only (mobile/small screens) - **Normal:** Balanced information density (standard desktop) - **Large:** Maximum detail (multi-monitor setups) **Position Options:** - **Top Right:** Standard placement (avoids price action) - **Top Left:** Wide chart optimization - **Bottom Right:** Recent price focus (scalping) - **Bottom Left:** Maximum price visibility (swing trading) --- ## 🎯 SIGNAL GENERATION LOGIC ### Multi-Component Convergence System **Component Signal Architecture:** The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing: **Volatility Component:** - **Jump Detection:** Mahalanobis distance threshold breach - **Hawkes Intensity:** Self-exciting process activation (>0.2) - **Multi-dimensional:** Considers all volatility dimensions simultaneously **Hurst Regime Component:** - **Trending Markets:** Price above SMA-20 with positive momentum - **Mean-Reverting Markets:** Price at Bollinger Band extremes - **Random Markets:** Bollinger squeeze breakouts with directional confirmation **Transfer Entropy Component:** - **Volume Leadership:** Information flow from volume to price - **Volume Spike:** Volume 110%+ above 20-period average - **Flow Significance:** Above entropy threshold with directional bias ### Democratic Signal Weighting **Signal Mode Implementation:** - **Aggressive Mode:** Any single component triggers signal - **Confluence Mode:** Minimum 2 components must agree - **Conservative Mode:** All 3 components must align **Momentum Confirmation:** All signals require momentum confirmation: - **Long Signals:** RSI >50 AND price >EMA-9 - **Short Signals:** RSI <50 AND price 0.6):** - **Increase Sensitivity:** Catch momentum continuation - **Lower Mean Reversion Threshold:** Avoid counter-trend signals - **Emphasize Volume Leadership:** Institutional accumulation/distribution - **Tensor Field Focus:** Use expansion for trend continuation - **Signal Mode:** Aggressive or Confluence for trend following **Range-Bound Markets (Hurst <0.4):** - **Decrease Sensitivity:** Avoid false breakouts - **Lower Trending Threshold:** Quick regime recognition - **Focus on Price Leadership:** Retail sentiment extremes - **Fractal Grid Emphasis:** Support/resistance trading - **Signal Mode:** Conservative for high-probability reversals **Volatile Markets (High Jump Frequency):** - **Increase Hawkes Decay:** Recognize event clustering - **Higher Jump Threshold:** Avoid noise signals - **Maximum Dimensions:** Capture full volatility complexity - **Reduce Position Sizing:** Risk management adaptation - **Enhanced Visuals:** Maximum information for rapid decisions **Low Volatility Markets (Low Jump Frequency):** - **Decrease Jump Threshold:** Capture subtle movements - **Lower Hawkes Decay:** Treat moves as independent - **Reduce Dimensions:** Simplify analysis - **Increase Position Sizing:** Capitalize on compressed volatility - **Minimal Visuals:** Reduce distraction in quiet markets --- ## 🚀 ADVANCED TRADING STRATEGIES ### The Mathematical Convergence Method **Entry Protocol:** 1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels 2. **Tensor Field Confirmation:** Verify field expansion supporting direction 3. **Portal Signal:** Wait for dimensional portal appearance 4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals 5. **Component Consensus:** Confirm 2+ components agree in Confluence mode **Example Implementation:** - Price approaching 21-period fractal high - Tensor field expanding upward (bullish mathematical alignment) - Green portal appears below price (trending regime confirmation) - ELITE quality signal with 3/3 components active - Enter long position with stop below fractal level **Risk Management:** - **Stop Placement:** Below/above fractal level that generated signal - **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size) - **Profit Targets:** Next fractal level or tensor field resistance ### The Regime Transition Strategy **Regime Change Detection:** 1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds 2. **Portal Color Change:** Regime transitions show different portal colors 3. **Background Intensity:** Increasing regime background intensity 4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis) **Trading Implementation:** - **Trending Transitions:** Trade momentum breakouts, follow trend - **Mean Reversion Transitions:** Trade range boundaries, fade extremes - **Random Transitions:** Trade breakouts with tight stops **Advanced Techniques:** - **Multi-Timeframe:** Confirm regime on higher timeframe - **Early Entry:** Enter on regime transition rather than confirmation - **Regime Strength:** Larger positions during strong regime signals ### The Information Flow Momentum Strategy **Flow Detection Protocol:** 1. **Monitor Transfer Entropy:** Watch for significant information flow shifts 2. **Volume Leadership:** Strong edge when volume leads price 3. **Flow Acceleration:** Increasing flow strength indicates momentum 4. **Directional Confirmation:** Ensure flow aligns with intended trade direction **Entry Signals:** - **Volume → Price Flow:** Enter during accumulation/distribution phases - **Price → Volume Flow:** Enter on momentum confirmation breaks - **Flow Reversal:** Counter-trend entries when flow reverses **Optimization:** - **Scalping:** Use immediate flow detection (2-5 bar lag) - **Swing Trading:** Use structural flow (10-20 bar lag) - **Multi-Asset:** Compare flow between correlated assets ### The Tensor Field Expansion Strategy **Field Mathematics:** The tensor field expansion indicates mathematical pressure building in market structure: **Expansion Phases:** 1. **Compression:** Field contracts, volatility decreases 2. **Tension Building:** Mathematical pressure accumulates 3. **Expansion:** Field expands rapidly with directional movement 4. **Resolution:** Field stabilizes at new equilibrium **Trading Applications:** - **Compression Trading:** Prepare for breakout during field contraction - **Expansion Following:** Trade direction of field expansion - **Reversion Trading:** Fade extreme field expansion - **Multi-Dimensional:** Consider all field layers for confirmation ### The Hawkes Process Event Strategy **Self-Exciting Jump Trading:** Understanding that market shocks cluster and create follow-on opportunities: **Jump Sequence Analysis:** 1. **Initial Jump:** First volatility jump detected 2. **Clustering Phase:** Hawkes intensity remains elevated 3. **Follow-On Opportunities:** Additional jumps more likely 4. **Decay Period:** Intensity gradually decreases **Implementation:** - **Jump Confirmation:** Wait for mathematical jump confirmation - **Direction Assessment:** Use other components for direction - **Clustering Trades:** Trade subsequent moves during high intensity - **Decay Exit:** Exit positions as Hawkes intensity decays ### The Fractal Confluence System **Multi-Timeframe Fractal Analysis:** Combining fractal levels across different periods for high-probability zones: **Confluence Zones:** - **Double Confluence:** 2 fractal levels align - **Triple Confluence:** 3+ fractal levels cluster - **Mathematical Confirmation:** Tensor field supports the level - **Information Flow:** Transfer entropy confirms direction **Trading Protocol:** 1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR 2. **Mathematical Support:** Verify tensor field alignment 3. **Signal Quality:** Wait for STRONG or ELITE signal 4. **Risk Definition:** Use fractal level for stop placement 5. **Profit Targeting:** Next major fractal confluence zone --- ## ⚠️ COMPREHENSIVE RISK MANAGEMENT ### Mathematical Position Sizing **Mahalanobis Distance Integration:** Position size should inversely correlate with mathematical field strength: ``` Position Size = Base Size × (Threshold / Mahalanobis Distance) ``` **Risk Scaling Matrix:** - **Low Field Strength (<2.0):** Standard position sizing - **Moderate Field Strength (2.0-3.0):** 75% position sizing - **High Field Strength (3.0-4.0):** 50% position sizing - **Extreme Field Strength (>4.0):** 25% position sizing or no trade ### Signal Quality Risk Adjustment **Quality-Based Position Sizing:** - **ELITE Signals:** 100% of planned position size - **STRONG Signals:** 75% of planned position size - **GOOD Signals:** 50% of planned position size - **WEAK Signals:** No position or paper trading only **Component Agreement Scaling:** - **3/3 Components:** Full position size - **2/3 Components:** 75% position size - **1/3 Components:** 50% position size or skip trade ### Regime-Adaptive Risk Management **Trending Market Risk:** - **Wider Stops:** Allow for trend continuation - **Trend Following:** Trade with regime direction - **Higher Position Size:** Trend probability advantage - **Momentum Stops:** Trail stops based on momentum indicators **Mean-Reverting Market Risk:** - **Tighter Stops:** Quick exits on trend continuation - **Contrarian Positioning:** Trade against extremes - **Smaller Position Size:** Higher reversal failure rate - **Level-Based Stops:** Use fractal levels for stops **Random Market Risk:** - **Breakout Focus:** Trade only clear breakouts - **Tight Initial Stops:** Quick exit if breakout fails - **Reduced Frequency:** Skip marginal setups - **Range-Based Targets:** Profit targets at range boundaries ### Volatility-Adaptive Risk Controls **High Volatility Periods:** - **Reduced Position Size:** Account for wider price swings - **Wider Stops:** Avoid noise-based exits - **Lower Frequency:** Skip marginal setups - **Faster Exits:** Take profits more quickly **Low Volatility Periods:** - **Standard Position Size:** Normal risk parameters - **Tighter Stops:** Take advantage of compressed ranges - **Higher Frequency:** Trade more setups - **Extended Targets:** Allow for compressed volatility expansion ### Multi-Timeframe Risk Alignment **Higher Timeframe Trend:** - **With Trend:** Standard or increased position size - **Against Trend:** Reduced position size or skip - **Neutral Trend:** Standard position size with tight management **Risk Hierarchy:** 1. **Primary:** Current timeframe signal quality 2. **Secondary:** Higher timeframe trend alignment 3. **Tertiary:** Mathematical field strength 4. **Quaternary:** Market regime classification --- ## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS ### Advanced Mathematical Concepts **Tensor Analysis in Markets:** The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture. **Information Theory Applications:** Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior. **Fractal Geometry in Trading:** The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure. **Stochastic Process Theory:** The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects. ### Learning Progressive Complexity **Beginner Mathematical Concepts:** - **Volatility Dimensions:** Understanding multi-dimensional analysis - **Regime Classification:** Learning market personality types - **Signal Democracy:** Algorithmic consensus building - **Visual Mathematics:** Interpreting mathematical concepts visually **Intermediate Mathematical Applications:** - **Mahalanobis Distance:** Statistical distance in multi-dimensional space - **Rescaled Range Analysis:** Fractal dimension measurement - **Information Entropy:** Quantifying uncertainty and causality - **Field Theory:** Understanding mathematical fields in market context **Advanced Mathematical Integration:** - **Tensor Field Dynamics:** Multi-dimensional market force analysis - **Stochastic Self-Excitation:** Event clustering and memory effects - **Categorical Composition:** Mathematical signal combination theory - **Topological Market Analysis:** Understanding market shape and connectivity ### Practical Mathematical Intuition **Developing Market Mathematics Intuition:** The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of: - **How markets exhibit mathematical structure beneath apparent randomness** - **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches** - **How information flows through markets in measurable, predictable ways** - **Why mathematical models provide probabilistic edges rather than certainties** --- ## 🔬 IMPLEMENTATION AND OPTIMIZATION ### Getting Started Protocol **Phase 1: Observation (Week 1)** 1. **Apply with defaults:** Use standard settings on your primary trading timeframe 2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams 3. **Monitor dashboard:** Observe how metrics change with market conditions 4. **No trading:** Focus entirely on pattern recognition and understanding **Phase 2: Pattern Recognition (Week 2-3)** 1. **Identify signal patterns:** Note what market conditions produce different signal qualities 2. **Regime correlation:** Observe how Hurst regimes affect signal performance 3. **Visual confirmation:** Learn to read tensor field expansion and portal signals 4. **Component analysis:** Understand which components drive signals in different markets **Phase 3: Parameter Optimization (Week 4-5)** 1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument 2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe 3. **Sensitivity adjustment:** Balance signal frequency with quality 4. **Visual customization:** Optimize colors and intensity for your trading environment **Phase 4: Live Implementation (Week 6+)** 1. **Paper trading:** Test signals with hypothetical trades 2. **Small position sizing:** Begin with minimal risk during learning phase 3. **Performance tracking:** Monitor actual vs. expected signal performance 4. **Continuous optimization:** Refine settings based on real performance data ### Performance Monitoring System **Signal Quality Tracking:** - **ELITE Signal Win Rate:** Track highest quality signals separately - **Component Performance:** Monitor which components provide best signals - **Regime Performance:** Analyze performance across different market regimes - **Timeframe Analysis:** Compare performance across different session times **Mathematical Metric Correlation:** - **Field Strength vs. Performance:** Higher field strength should correlate with better performance - **Component Agreement vs. Win Rate:** More component agreement should improve win rates - **Regime Alignment vs. Success:** Trading with mathematical regime should outperform ### Continuous Optimization Process **Monthly Review Protocol:** 1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown 2. **Parameter Assessment:** Evaluate if current settings remain optimal 3. **Market Adaptation:** Adjust for changes in market character or volatility 4. **Component Weighting:** Consider if certain components should receive more/less emphasis **Quarterly Deep Analysis:** 1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid 2. **Regime Distribution:** Analyze time spent in different market regimes 3. **Signal Evolution:** Track how signal characteristics change over time 4. **Correlation Analysis:** Monitor correlations between different mathematical components --- ## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS ### Revolutionary Mathematical Integration **First-Ever Implementations:** 1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility 2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes 3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation 4. **Democratic Component Voting:** First algorithmic consensus system for signal generation 5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels ### Advanced Visualization Innovations **Mathematical Visualization Breakthroughs:** - **Tensor Field Radiation:** Visual representation of mathematical field energy - **Dimensional Portal System:** Category theory visualization for regime transitions - **Information Flow Streams:** Real-time visual display of market information transfer - **Multi-Layer Fractal Grid:** Intelligent spacing and projection system - **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength ### Practical Trading Innovations **Trading System Advances:** - **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence - **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality - **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering - **Component Performance Tracking:** Real-time monitoring of algorithmic component success - **Field-Strength Position Sizing:** Mathematical volatility integration for risk management --- ## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS ### Mathematical Model Limitations **Understanding Model Boundaries:** While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations: - **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements - **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely - **Model Risk:** Complex models can fail during unprecedented market conditions - **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions ### Proper Implementation Guidelines **Risk Management Requirements:** - **Never Risk More Than 2% Per Trade:** Regardless of signal quality - **Diversification Mandatory:** Don't rely solely on mathematical signals - **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence - **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters **Realistic Expectations:** - **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes - **Learning Curve Required:** Complex mathematical concepts require time to master - **Market Adaptation Necessary:** Parameters must evolve with changing market conditions - **Continuous Education Important:** Understanding underlying mathematics improves application ### Ethical Trading Considerations **Market Impact Awareness:** - **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants - **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure - **Sharing Knowledge:** Consider educational contributions to trading community - **Fair Market Participation:** Use mathematical advantages responsibly within market framework ### Professional Development Path **Skill Development Sequence:** 1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application 2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals 3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights 4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution --- ## 🔮 CONCLUSION The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability. ### Mathematical Innovation Legacy This indicator establishes new paradigms in technical analysis: - **Tensor analysis for market volatility understanding** - **Stochastic self-excitation for event clustering prediction** - **Information theory for causality-based trade generation** - **Democratic algorithmic consensus for signal quality enhancement** - **Mathematical field visualization for intuitive market understanding** ### Practical Trading Revolution Beyond mathematical innovation, the TMAE transforms practical trading: - **Quality-rated signals replace binary buy/sell decisions** - **Regime-adaptive strategies automatically optimize for market personality** - **Multi-dimensional risk management integrates mathematical volatility measures** - **Visual mathematical concepts make complex analysis immediately interpretable** - **Educational value creates lasting improvement in trading understanding** ### Future-Proof Design The mathematical foundations ensure lasting relevance: - **Universal mathematical principles transcend market evolution** - **Multi-dimensional analysis adapts to new market structures** - **Regime detection automatically adjusts to changing market personalities** - **Component democracy allows for future algorithmic additions** - **Mathematical visualization scales with increasing market complexity** ### Commitment to Excellence The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value. ### Trading with Mathematical Precision In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals. By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics. ### Final Perspective Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure. Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior. Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE. *"In mathematics, you don't understand things. You just get used to them." - John von Neumann* *With the TMAE, mathematical market understanding becomes not just possible, but intuitive.* — Dskyz, Trade with insight. Trade with anticipation.
Pine Script® 인디케이터
DskyzInvestments의
16
Swing EMAWhat is Swing EMA? Swing EMA is an exponential moving average crossover-based indicator used for low-risk directional trading. it's used for different types of Ema 20,50,100 and 200, 3 of them are plotted on chat 20,100,200. 100 and 200 Ema is used for showing support and resistance and it contains highlights area between them and its change color according to market crossover condition. 20 moving average is used for knowing Market Behaviour and changing its color according to crossover conditions of 50 and 20 Ema. How does it work? It contains 4 different types of moving averages 20,50,100, 200 out of 3 are plotted on the chart. 20 Ema is used for knowing current market behavior. Its changes its color based on the crossover of 50 Ema and 20 Ema, if 20 Ema is higher than 50 Ema then it changes its color to green, and its opposites are changed their color to red when 20 Ema is lower than 50 Ema. 100 and 200 Ema used as a support and resistance and is also contain highlighted areas between them its change their color based on the crossover if 100 Ema is higher than 200 Ema a then both of them are going to change color to Green and as an opposite, if 200 Ema is higher then 100 Ema is going to change its color to red. So in simple word 100 and 200 Ema is used as support and resistance zone and 20 Ema is used to know current market behavior. How to use it? It is very easy to understand by looking at the example I gave where are the two different types of phrases. phrase bull phrase and bear phrase so 100 and 200 Ema is used as a support and resistance and to tell you which phrase is currently on the market on example there is a bull phrase on the left side and bear phrase on the right side by using your technical analysis you can find out a really good spot to buy your stocks on a bull phrase and too short on the bear phrase. 20 Ema is used as a knowing the current market behavior it doesn't make any difference on buying or selling as much as 100 Ema and 200 Ema. Tips Don't trade against the market. Try trade on trending stocks rather than sideways stock. The higher the area between 100 Ema and 200 Ema is the stronger the phrase. Do Backtesting before real trading. Enjoy Trading.
Pine Script® 인디케이터
DevikSoni의
1
Candle VolumeScript Based on Volume Based Coloured Bars by KivancOzbilgic ///////////// This indicator turns the candle into a volume-weighted signal, When the price falls, the candle is red, and when the price rises, the candle is green. In addition, we each have two colors Happening: Dark red: It is dark red when the downtrend trading volume is greater than 200% of its average price (default 20 days), which indicates that our price action is supported by strong bearish trading volume Red: When the price drops and the trading volume is between 50% and 200% of its average (default 20 days), in this case, we can think that the trading volume is neither strong nor weak Light red: When the price drops and VOLUME is less than 50% of its average price (default 20 days), the trading volume is weak and there is not much support for price movements Dark green: When the price rises and the trading volume is greater than 200% of its average price (default 20 days), it indicates that our price movement is supported by a strong bullish trading volume Green: When the price rises and the trading volume is between 50% and 200% of its average price (the default is 20 days), in this case, we can think that the trading volume is neither strong nor weak Light green: When the price rises and the trading volume is less than 50% of its average price (default 20 days), the trading volume is weak and does not support the price trend well Default Low Volume is 50% (0.5) and High 200% (2), but if those values don't suit you, you can change them according to your trading personality ////////////////////////// Esse é um indicador que colore a candlera de acordo com o volume baseado na média, quando o volume está acima da média a candlera fica verde, e quando está abaixo, a candlera fica vermelha, e as cores das candleras funcionam dessa forma : Vermelho escuro: fica vermelho escuro quando o preço cai e o volume de negociação é superior a 200% do preço médio (padrão 20 dias), o que indica que nossa ação de preço é suportada por um forte volume de negociação de baixa Vermelho: quando o preço cai e o volume de negociação está entre 50% e 200% de sua média (padrão de 20 dias), nesse caso, podemos pensar que o volume de negociação não é forte nem fraco Vermelho claro: quando o preço cai e VOLUME é inferior a 50% do preço médio (padrão 20 dias), o volume de negociação é fraco e não há muito suporte para movimentos de preço Verde escuro: quando o preço aumenta e o volume de negociação é superior a 200% do preço médio (padrão 20 dias), isso indica que nosso movimento de preço é suportado por um forte volume de negociação de alta Verde: quando o preço aumenta e o volume de negociação está entre 50% e 200% do preço médio (o padrão é 20 dias), nesse caso, podemos pensar que o volume de negociação não é forte nem fraco Verde claro: quando o preço aumenta e o volume de negociação é inferior a 50% do preço médio (padrão 20 dias), o volume de negociação é fraco e não suporta bem a tendência de preço O volume baixo padrão é 50% (0,5) e alto 200% (2), mas se esses valores não forem adequados para você, você poderá alterá-los de acordo com sua personalidade de trading
Pine Script® 인디케이터
Marqx의
4