Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization

Abstract

e Foreign Exchange (Forex) is a large decentralized market, on which trading analysis and algorithmic trading are popular. Research e orts have been focusing on proof of e ciency of certain technical indicators. We demonstrate, however, that the values of indicator functions are not reproducible and o en reduce the number of trade opportunities, compared to price-action trading.In this work, we develop two dataset-agnostic Forex trading heuristic templates with high rate of trading signals. In order to determine most optimal parameters for the given heuristic prototypes, we perform a machine learning simulation of 10 years of Forex price data over three low-margin instruments and 6 di erent OHLC granularities. As a result, we develop a speci c and reproducible list of most optimal trade parameters found for each instrument-granularity pair, with 118 pips of average daily pro t for the optimized con guration.