Systematic-trading — 指標和策略

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'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.

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 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.