Building a Modular EMA Trend Trading Framework with Risk Controls

Summary

The document describes a Python framework for a futures trend strategy, organized as a class with separate modules for market decisions, order handling, account calculations, risk controls, and persistent data. Its trend signal uses price relative to an EMA and a standard-deviation filter to classify conditions as long, short, or sideways. The author presents the signal module as replaceable, suggesting alternatives such as RSI, MACD, or combinations of indicators.

The framework also covers position opening and closing, stop-loss and fixed or trailing take-profit checks, order sizing, available-funds checks, liquidation-price estimates, profit statistics, and recovery of saved state after a restart. A status display reports runtime, account assets, and position details. The article provides architectural descriptions and code excerpts, but no backtest, live trading results, or validation of its calculations. Its flexibility and safeguards are design claims; users would need to check exchange-specific behavior, assumptions, and execution edge cases before relying on it.

Key ideas

• The strategy organizes trading, account, and persistence functions into a modular class.
• Its example trend filter combines EMA price relationships with standard deviation.
• The signal module is designed to support replacement or combination of indicators.
• Order sizing, stop-losses, take-profit rules, and account checks are handled separately from signal generation.
• The document describes implementation features but provides no performance validation.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.