Systematic — インジケーターとストラテジー

LBR Cinderella Electric Curfew Session Capital Control Linda Bradford Raschke's Cinderella Electric Curfew – Session Capital Control Overview This strategy demonstrates structured session-based trading with capital protection rules and volatility-adjusted risk management. The core idea is simple: Trade only during a defined exchange session, and automatically close all open positions once the session ends — enforcing a strict “capital curfew.” The script includes configurable session templates for major global markets and applies risk-based position sizing using ATR-derived stop distance. Core Features: Session-Based Trading Users can select a predefined exchange session (e.g., New York, London, Frankfurt, Tokyo, Hong Kong, Sydney). Trades are only allowed during the selected session window. Electric Curfew Logic All open positions are automatically closed when the session ends (with optional grace period in minutes). This prevents overnight exposure and reduces gap risk. Risk-Based Position Sizing Position size is calculated as: Risk Amount ÷ Stop Distance Where: Risk Amount = % of current equity Stop Distance = ATR × Multiplier This ensures consistent capital allocation regardless of volatility regime. ATR-Based Stops & Targets Optional stop-loss and take-profit levels are volatility-adjusted. Example Signal Logic A simple SMA crossover is included as a placeholder. Users are encouraged to replace it with their own entry logic. Intended Use This strategy is designed for: • Intraday traders • Session-based systems • Traders avoiding overnight risk • Prop-style capital discipline models The session control framework can be integrated into any strategy logic. Past performance does not guarantee future results.

LBR's Game Theory EMA Strategy - Alternate Game Theory EMA Strategy Overview: This strategy combines EMA crossover structure with a normalized momentum “utility edge” model to filter low-conviction trades. Instead of trading every EMA crossover, the script evaluates whether directional momentum statistically favors buyers or sellers before entering. Core Logic: EMA Structure Shift: A fast EMA crossing a slow EMA signals a possible momentum transition. Trend Alignment: Trades are only taken in the direction of a higher timeframe trend (Trend EMA). Utility Edge Filter: RSI is normalized around 50 to estimate directional advantage: • RSI > 55 → Buyers show advantage • RSI < 45 → Sellers show advantage • Between 45–55 → Considered equilibrium zone (no trade) Optional ADX Filter Trades can be restricted to environments with expanding trend strength. Risk Management Default Properties: • Initial Capital: 10,000 • Risk per trade: 2% of equity • Risk–Reward Ratio: 1.5:1 • Commission: 0.05% • Slippage: 1 tick • Max Trades per Day: 5 • Daily Loss Limit: 2% Position sizing is volatility-adjusted: Position Size = (2% Equity) ÷ ATR Stop Distance Stops and targets expand or contract automatically based on market volatility. Intended Use Designed for trending instruments. Users are encouraged to test on long historical datasets to ensure sufficient trade sample size. This strategy does not predict price direction. It reacts to structural and momentum shifts. Past performance does not guarantee future results.

LBR Aberration Trend Following Strategy Linda Bradford Raschke's Aberration Trend Following Strategy Overview This strategy implements a directional Bollinger Band breakout model with volatility normalization and trend alignment. Instead of trading every band breakout, the system applies: • Trend bias using EMA • ATR-based volatility regime filtering • Volatility-adjusted stop-loss placement • Fixed risk-to-reward targeting Core Logic: [Bollinger Band Expansion: Entries trigger when price breaks beyond the outer band during acceptable volatility conditions. Trend Confirmation: Long trades require price above the trend EMA. Short trades require price below the trend EMA. Volatility Filter: ATR is compared against its rolling percentile median to avoid extremely low or extreme volatility environments. Risk Management: Stops are ATR-based and targets are defined using configurable risk-reward ratios. This strategy is designed for trending environments and performs best during volatility expansion phases.

LBR The Motley Fool Foolish Four Portfolio Rotation Strategy Linda Bradford Raschkey's Foolish Four Portfolio Rotation Strategy This strategy implements a rules-based portfolio rebalancing framework inspired by systematic dividend-style equity rotation concepts. It is designed to simulate structured portfolio reallocation on a fixed schedule rather than short-term trading. 🔍 What This Strategy Does The strategy: • Rebalances either monthly or yearly • Allocates capital across four positions • Uses fixed percentage allocation • Applies realistic trading conditions (commission + slippage) • Limits per-position risk to sustainable levels (default 5%) This is a capital allocation strategy — not a signal-based indicator. 🧠 Core Concept Instead of attempting to predict price direction, this model: Uses time-based portfolio rotation. Closes all open positions at rebalance. Reallocates capital according to chosen weighting logic. Maintains disciplined exposure caps. Two allocation modes are included: 1️⃣ Equal-Weight Variant Allocates capital evenly (25% per asset). 2️⃣ Weighted Variant Allocates heavier exposure to one asset (40%) and lighter to others (20%), capped by the defined risk % to remain compliant with sustainable equity exposure. ⚙️ Default Strategy Properties To comply with TradingView backtesting standards: Initial Capital: 100,000 Order Size Type: Percent of Equity Default Order Size: 25% Commission: 0.05% Slippage: 2 ticks Pyramiding: 0 Risk Cap Per Position: 5% (user adjustable 1–10%) These settings are used in the published version. If users modify these values, results will change. 📊 Backtesting Notes • Designed for long historical datasets • Works best on equities or ETFs • Monthly rebalance recommended to generate sufficient trade count • Not optimized for short-term scalping • No forward-looking data is used Users should test across multiple assets and timeframes. This script does not guarantee profitability and makes no performance claims. 📈 How To Use Add to chart of a liquid equity or ETF. Select Monthly or Yearly rebalance. Choose allocation variant. Keep realistic commission & slippage. Backtest over 10+ years for meaningful sample size. 📌 Important This is a structural portfolio framework for research purposes.

LBR's Game Theory - EMA Strategy Linda Bradford Raschke's Game Theory EMA Strategy: Overview: This strategy combines EMA crossover structure with a momentum-based utility edge model inspired by basic game theory concepts. The objective is to participate only when directional momentum suggests one side of the market has a measurable advantage. Core Logic: Structure Shift: A fast EMA crossing a slow EMA signals a potential momentum transition. Trend Alignment: Trades are only taken in the direction of a higher timeframe trend (Trend EMA). Utility Edge Model: Momentum is normalized using RSI to approximate directional “utility.” When RSI is above 50, buyer utility is positive. When RSI is below 50, seller utility is positive. If the difference between buyers and sellers is too small (equilibrium zone), trades are avoided. Trend Strength Filter: Optional ADX filter ensures trades occur in expanding trend conditions. Risk Management: Default Properties: • Initial Capital: 10,000 • Risk per trade: 2% of equity • Risk–Reward: 1.8 : 1 • Commission: 0.05% • Slippage: 1 tick • No pyramiding Position size is dynamically calculated: Position Size = (2% Equity) ÷ Stop Distance Stops and targets are ATR-based to adapt to volatility. Intended Use: Best suited for: • Trending instruments (Forex majors, Indices, Gold, Crypto) • Multi-year datasets for proper statistical evaluation This strategy reacts to structural and momentum shifts. It does not predict future price movement. Past performance does not guarantee future results.

Seasonal Strategies V1 Seasonal Strategies V1 is a rule-based futures seasonality framework built around predefined calendar windows per asset. The strategy automatically detects the current symbol and activates long or short trading phases strictly based on historically observed seasonal tendencies. All entries and exits are fully time-based — no indicators, no predictions, no discretionary input. Key Features Asset-specific seasonal windows (MMDD-based) Automatic long and short activation Fully time-based entries and exits One position at a time (no pyramiding) Clean chart visualization using subtle background shading No indicators, no filters, no curve fitting Philosophy: This strategy is designed as a structural trading tool, not a forecasting model. It focuses on when a market historically shows seasonal tendencies — not why or how far price might move. Seasonal Strategies V1 intentionally keeps the chart clean and minimal, making it suitable as a baseline framework for research, portfolio-style seasonal approaches, or further extensions in later versions. Intended Use: Futures and commodity markets Seasonality research and testing Systematic, calendar-driven strategies Educational and analytical purposes Disclaimer This script is provided for educational and research purposes only. Past seasonal tendencies do not guarantee future performance. Risk management, position sizing, and portfolio decisions are the responsibility of the user.