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.

中文翻译：

---

基于约束的外汇交易模型优化启发式推理

外汇（Forex）是一个大型去中心化市场，交易分析和算法交易盛行。研究工作一直集中在某些技术指标的效率证明上。然而，我们证明，与价格行为交易相比，指标函数的值是不可重复的，并且通常会减少交易机会的数量。在这项工作中，我们开发了两个具有高交易信号率的与数据集无关的外汇交易启发式模板。为了确定给定启发式原型的最佳参数，我们对三种低利润工具和 6 种不同 OHLC 粒度的 10 年外汇价格数据进行了机器学习模拟。因此，