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The Foreign Exchange (Forex) is a large decentralized market, on whichtrading analysis and algorithmic trading are popular. Research efforts havebeen focusing on proof of efficiency of certain technical indicators. Wedemonstrate, however, that the values of indicator functions are notreproducible and often reduce the number of trade opportunities, compared toprice-action trading.  In this work, we develop two dataset-agnostic Forex trading heuristictemplates with high rate of trading signals. In order to determine most optimalparameters for the given heuristic prototypes, we perform a machine learningsimulation of 10 years of Forex price data over three low-margin instrumentsand 6 different OHLC granularities. As a result, we develop a specific andreproducible list of most optimal trade parameters found for eachinstrument-granularity pair, with 118 pips of average daily profit for theoptimized configuration.

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