Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization
This work addresses inefficiencies in algorithmic Forex trading for traders by providing reproducible parameters, though it is incremental as it builds on existing heuristic optimization methods.
The paper tackled the problem of unreliable technical indicators in Forex trading by developing dataset-agnostic heuristic templates, resulting in an optimized configuration that achieved 118 pips of average daily profit across multiple instruments and granularities.
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.