Awesome Open Source
Awesome Open Source


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).


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

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 with your username.

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:

git pull
python3 agents/ ./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.


Get A Weekly Email With Trending Projects For These Topics
No Spam. Unsubscribe easily at any time.
python (54,447
deep-learning (3,985
machine-learning (3,642
blockchain (840
reinforcement-learning (566
p2p (319
distributed (239
decentralized (149
machinelearning (106
timeseries (103
agent (86
openai-gym (71
q-learning (37
evolutionary-algorithms (35
forex (31
deep-q-network (25