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This study proposes a new Forex trading method by combining Genetic Algorithms (GAs) and Directional Change (DC) strategies. The synergy improves trading results through better adaptation to market volatility.

youtu.be/47q2hmufyew
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AuroraLiu3230/MSc-Individual-Project

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Contents Backtest/

  • fitness.py: Evaluates trade performance to calculate fitness score for GA
  • trade.py: Executes backtesting for a trading strategy

Data_Process/

  • Data cleaning scripts for preparing FX data

DC/

  • DC_Transformer.py: Transforms time series data to intrinsic time series

GA/

  • Genetic algorithm components, operators, and engine

Strategy/

  • Implementations of various trading strategies, including benchmarks

Experiment/

  • Output logs from experiments

Usage

  1. Use calc_r.py to estimate r ratios for dataset and thresholds
  2. Run engine.py to find optimal parameters for DC strategies via GA
  3. Run backtesting.py to backtest optimized strategies on data

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This study proposes a new Forex trading method by combining Genetic Algorithms (GAs) and Directional Change (DC) strategies. The synergy improves trading results through better adaptation to market volatility.

youtu.be/47q2HMUFyEw

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genetic-algorithm directional-changes

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