Trading robot · MT5

RL Agent Trader

by rezakarbasi

RL Agent Trader is an expert advisor for MetaTrader 5 that integrates with a Python-based reinforcement learning environment via network sockets.

GitHub stars12
Forks5
Last update2 years ago20 Mar 2024
LicenceNone stated

Overview

RL Agent Trader implements a breakout strategy based on the highest and lowest prices of previous candles. The core functionality relies on a Deep Q-Network (DQN) in Python to dynamically optimize the lookback period for entry signals and stop-loss placement. The system communicates real-time state data, including multi-timeframe price extremes, to the external agent. It manages trade execution with automated stop-loss adjustments and fixed lot sizing.

Details

Author

rezakarbasi on GitHub

Type

Trading robot

Markets

Any symbol

Timeframes

M15-H1

Risk Profile

No grid, martingale or hedging logic was found in its code or README. That says nothing about how it performs: backtest it first.

Topics

dqnmetatradermqlrlsockettrade

Input Parameters

Read from ContractorAgent.mq5. Defaults are the author's; change them in the EA's Inputs tab.

NameTypeDefaultDescription
Hostnamestring127.0.0.1
ServerPortushort19968
lotdouble0.1

How to Install It

  1. 1Download the sourceUse "Download source (.zip)" above, or clone rezakarbasi/RL-agent-trader from GitHub, and unzip it.
  2. 2Open the data folderIn MetaTrader choose File, then Open Data Folder, and go to MQL5/Experts.
  3. 3Copy the filesCopy the .mq5 file into Experts, and any .mqh files into the Include folder the code expects.
  4. 4Compile in MetaEditorOpen the file in MetaEditor and press Compile. Fix any missing includes the compiler reports.
  5. 5Test before you tradeRun it in the Strategy Tester, then attach it to a demo chart and allow algorithmic trading.
Third-party code. ForexR did not write, test or endorse this EA, and the summary above was generated automatically from its README and code. ForexR lists only repositories with readable source code and never hosts files. Read the code, backtest it and use a demo account first; automated trading can lose money quickly.

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