gym-fx
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Forex trading simulator environment for OpenAI Gym, observations contain the order status, performance and timeseries loaded from a CSV file containing rates and indicators. Work In Progress
gym-forex
The Forex environment is a forex trading simulator featuring: configurable initial capital, dynamic or dataset-based spread, CSV history timeseries for trading currencies and observations for the agent, fixed or agent-controlled take-profit, stop-loss and order volume.
The environment features discrete action spaces and optionally continuous action spaces if the orders dont have fixed take-profit/stop-loss and order volume.
Observation Space
A concatenation of num_ticks vectors for the lastest: vector of values from timeseries, equity and its variation, order_status( -1=closed,1=opened),time_opened (normalized with max_order_time), order_profit and its variation, order_drawdown /order_volume_pips, consecutive_drawdown/max_consecutive_dd
Action Space
discrete action 0: 0=nop,1=close,2=buy,3=sell
discrete action 0 parameter: symbol
(optional) continuous action 0 parameter: percent_tp, percent_sl,percent_max
Reward Function
The reward function is the average of the area under the curve of equity and the balance variation.
MQL4 Dataset Generator
The datasets used for the tests were generated with a MQL4 program located in the agents folder (Pending documentation).
Installation
Step 1 - Setup Dependencies
Install Python, pip, OpenAI Gym and other dependencies:
sudo apt-get install -y python3-numpy python3-dev cmake zlib1g-dev libjpeg-dev xvfb ffmpeg libboost-all-dev libsdl2-dev python3-pip git gcc make perl
pip3 install graphviz neat-python gitpython gym neat-python matplotlib
Step 2 - Setup gym-forex from GitHub
git clone https://github.com/harveybc/gym-forex
Step 3 - Configure the NEAT parameters
Set the PYTHONPATH venvironment variable, you may add the following line to the .profile file in your home directory to export on start of sessions. Replace
export PYTHONPATH=/home/username/gym-forex/:${PYTHONPATH}
Step 4 - Configure the NEAT parameters
cd gym-forex
nano agents/config
Configure the population size and other parameters according to your computing capacity or requirements, start with the defaults.
Step 5 - Configure a startup/restart script
nano res
For example:
#!/bin/bash
git pull
python3 agents/agent_NEAT.py ./datasets/ts_5min_1w.CSV ./datasets/vs_5min_1w.CSV config_20
After editing, change the permission of the file to be executable:
chmod 777 res
Step 6 - Start your optimizer that uses the gym-forex environment and an agent.
./res