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

TL;DR

This paper introduces constraint-based heuristics for Forex trading that are dataset-agnostic and optimized through machine learning, resulting in reproducible parameters and an average daily profit of 118 pips.

Contribution

It presents novel, dataset-agnostic Forex trading heuristics and a machine learning method to optimize their parameters across multiple instruments and granularities.

Findings

High rate of trading signals from the heuristics

Reproducible optimal parameters for each instrument-granularity pair

Average daily profit of 118 pips for optimized configurations

Abstract

The Foreign Exchange (Forex) is a large decentralized market, on which trading analysis and algorithmic trading are popular. Research efforts have been focusing on proof of efficiency of certain technical indicators. We demonstrate, however, that the values of indicator functions are not reproducible and often 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 different OHLC granularities. As a result, we develop a specific and reproducible list of most optimal trade parameters found for each instrument-granularity pair, with 118 pips of average daily profit for the optimized configuration.