Constraint-Based Inference of Heuristics for Foreign Exchange Trade
Model Optimization
- URL: http://arxiv.org/abs/2105.14194v1
- Date: Tue, 11 May 2021 00:36:02 GMT
- Title: Constraint-Based Inference of Heuristics for Foreign Exchange Trade
Model Optimization
- Authors: Nikolay Ivanov and Qiben Yan
- Abstract summary: We develop two datasets with high rate of trading signals.
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.
- Score: 13.26093613374959
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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