If you'd like to learn more about how we created this agent, check out the Medium article: https://towardsdatascience.com/creating-bitcoin-trading-bots-that-dont-lose-money-2e7165fb0b29

Later, we optimized this repo using feature engineering, statistical modeling, and Bayesian optimization, check it out: https://towardsdatascience.com/using-reinforcement-learning-to-trade-bitcoin-for-massive-profit-b69d0e8f583b

Discord server: https://discord.gg/ZZ7BGWh

Data sets: https://www.cryptodatadownload.com/data/northamerican/

Linux:

sudo lspci | grep -i --color 'vga\|3d\|2d' | grep -i nvidia

If this returns anything, then you should have an nVIDIA card.

The first thing you will need to do to get started is install the requirements. If your system has an nVIDIA GPU that you should start by using:

cd "path-of-your-cloned-rl-trader-dir"
pip install -r requirements.txt

More information regarding how you can take advantage of your GPU while using docker: https://github.com/NVIDIA/nvidia-docker

If you have another type of GPU or you simply want to use your CPU, use:

pip install -r requirements.no-gpu.txt

Update your current static files, that are used by default:

 python ./cli.py update-static-data

Afterwards you can simply see the currently available options:

python ./cli.py --help

or simply run the project with default options:

python ./cli.py optimize

If you have a standard set of configs you want to run the trader against, you can specify a config file to load configuration from. Rename config/config.ini.dist to config/config.ini and run

python ./cli.py --from-config config/config.ini optimize

python ./cli.py optimize

Start the vagrant box using:

vagrant up

Code will be located at /vagrant. Play and/or test with whatever package you wish. Note: With vagrant you cannot take full advantage of your GPU, so is mainly for testing purposes

If you want to run everything within a docker container, then just use:

./run-with-docker (cpu|gpu) (yes|no) optimize

• cpu - start the container using CPU requirements
• gpu - start the container using GPU requirements
• yes | no - start or not a local postgres container Note: in case using yes as second argument, use

python ./ cli.py --params-db-path "postgres:https://rl_trader:rl_trader@localhost" optimize

The database and it's data are pesisted under data/postgres locally.

If you want to spin a docker test environment:

./run-with-docker (cpu|gpu) (yes|no)

If you want to run existing tests, then just use:

./run-tests-with-docker

./dev-with-docker

conda create --name rltrader python=3.6.8 pip git conda activate rltrader conda install tensorflow-gpu git clone https://github.com/notadamking/RLTrader pip install -r RLTrader/requirements.txt

While you could just let the agent train and run with the default PPO2 hyper-parameters, your agent would likely not be very profitable. The stable-baselines library provides a great set of default parameters that work for most problem domains, but we need to better.

To do this, you will need to run optimize.py.

python ./optimize.py

This can take a while (hours to days depending on your hardware setup), but over time it will print to the console as trials are completed. Once a trial is completed, it will be stored in ./data/params.db, an SQLite database, from which we can pull hyper-parameters to train our agent.

From there, agents will be trained using the best set of hyper-parameters, and later tested on completely new data to verify the generalization of the algorithm.

Feel free to ask any questions in the Discord!

Enter and run the following snippet in the first cell to load RLTrader into a Google Colab environment. Don't forget to set hardware acceleration to GPU to speed up training!

!git init && git remote add origin https://github.com/notadamking/RLTrader.git && git pull origin master
!pip install -r requirements.txt

Normally this is caused by missing mpi module. You should install it according to your platorm.

• Windows: https://docs.microsoft.com/en-us/message-passing-interface/microsoft-mpi
• Linux/MacOS: https://www.mpich.org/downloads/

If you would like to contribute, here is the roadmap for the future of this project. To assign yourself to an item, please create an Issue/PR titled with the item from below and I will add your name to the list.

• Create a generic data loader for inputting multiple data sources (.csv, API, in-memory, etc.) [@sph3rex, @lukeB, @notadamking] ✅
  • Map each data source to OHCLV format w/ same date/time format **[@notadamking] ✅
• Implement live trading capabilities [@notadamking]
  • Allow model/agent to be passed in at run time [@notadamking]
  • Allow live data to be saved in a format that can be later trained on [@notadamking]
  • Enable paper-trading by default [@notadamking]
• Enable complete multi-processing throughout the environment [@notadamking] arunavo4
  • Optionally replace SQLite db with Postgres to enable multi-processed Optuna training
    • This is enabled through Docker, though support for Postgres still needs to be improved
  • Replace DummyVecEnv with SubProcVecEnv everywhere throughout the code [@archenroot, @arunavo4, @notadamking]
• Allow features to be added/removed at runtime
  • Create simple API for turning off default features (e.g. prediction, indicators, etc.)
  • Create simple API for adding new features to observation space

• Add more optional features to the feature space
  • Other exchange pair data (e.g. LTC/USD, ETH/USD, EOS/BTC, etc.)
  • Twitter sentiment analysis
  • Google trends analysis
  • Order book data
  • Market tick data
• Create a generic prediction interface to allow any prediction function to be used
  • Implement SARIMAX using generic interface
  • Implement FB Prophet using generic interface
  • Implement pre-trained LSTM using generic interface
• Allow trained models to be saved to a local database (SQLite/Postgres)
  • Save performance metrics with the model

• Implement a Generative Aderversarial Network (GAN) for accurately simulating asset price fluctuations
  • Implement Monte Carlo rollouts to find the most probabilistic outcomes
• Implement a custom RL agent using ODEs or other state-of-the-art algorithm (relational recurrent networks)
  • Incorporate GAN predictions into model state
• Implement xgboost and Stacked Auto-encoders to improve the feature selection of the model
• Experiment with Auto-decoders to remove noise from the observation space
• Implement self-play in a multi-process environment to improve model exploration
  • Experiment with dueling actors vs tournament of dueling agents
• Find sources of CPU bottlenecks to improve GPU utilization
  • Replace pandas or improve speed of pandas methods by taking advantage of GPU
• Find source of possible memory leak (in RLTrader.optimize) and squash it

Contributions are encouraged and I will always do my best to get them implemented into the library ASAP. This project is meant to grow as the community around it grows. Let me know if there is anything that you would like to see in the future or if there is anything you feel is missing.

Working on your first Pull Request? You can learn how from this free series How to Contribute to an Open Source Project on GitHub

Want to show your support for this project and help it grow?

BTC Address: 1Lc47bhYvdyKGk1qN8oBHdYQTkbFLL3PFw

ETH Address: 0x9907A0cF64Ec9Fbf6Ed8FD4971090DE88222a9aC

Supporters:

• Ap9944
• KILLth
• Nex