|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Rl Stock||2,419||6 months ago||31||mit||Jupyter Notebook|
|Awesome Ai In Finance||1,842||16 days ago||1||cc0-1.0|
|🔬 A curated list of awesome machine learning strategies & tools in financial market.|
|Gym Anytrading||1,531||2 months ago||6||mit||Python|
|The most simple, flexible, and comprehensive OpenAI Gym trading environment (Approved by OpenAI Gym)|
|Btgym||825||2 years ago||11||lgpl-3.0||Python|
|Scalable, event-driven, deep-learning-friendly backtesting library|
|Deep_rl_trader||337||6 months ago||33||Python|
|Trading Environment(OpenAI Gym) + DDQN (Keras-RL)|
|Trading Gym||212||2 years ago||15||other||Jupyter Notebook|
|This trading-gym is the first trading for agent to train with episode of short term trading itself.|
|Gym Fx||187||5 months ago||1||Python|
|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|
|Tf_deep_rl_trader||186||a year ago||28||Python|
|Trading Environment(OpenAI Gym) + PPO(TensorForce)|
|Freqtrade Gym||101||2 years ago||6||gpl-3.0||Python|
|A customized gym environment for developing and comparing reinforcement learning algorithms in crypto trading.|
|Gym_trading||89||6 years ago||7||apache-2.0||Jupyter Notebook|
The Forex environment is a forex trading simulator for OpenAI Gym, allowing to test the performace of a custom trading agent. 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.
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 requests
git clone harveybc/gym-fx
Set the PYTHONPATH environment variable, you may add the following line to the .profile file in your home directory to export on start of sessions. Replace
python setup.py install
Configure in the file, the population size and other parameters according to your computing capacity or requirements, start with the defaults.
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
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
The reward function is the average of the area under the curve of equity and the balance variation.
Download and install Metatrader 4.
Library -> Application -> Support -> MetaTrader 4 -> Bottles -> metatrader64 -> drive_c -> Program Files(x86) -> MetaTrader 4 > MQL4.
*.mq4 files from datasets into the
To run these scripts, open MT4 and in the
Navigator pane, run the scripts under the "Scripts" folder. Right click the file and click
Modify. Run, edit, and debug scripts here as you see fit. The
.csv files generated with these scripts will appear in
File-> Open Data folder -> MQL4.