Deep reinforcement learning multi-agent algorithmic trading framework that learns to trade from experience and then evaluate with brand new data
- Miniconda or Anaconda
An API Key on CryptoCompare
# paste your API Key on .env cp .env.example .env # make sure you have these installed sudo apt-get install gcc g++ build-essential python-dev python3-dev -y # create env conda env create -f t-1000.yml # activate it conda activate t-1000# to see all arguments available # $ python main.py --help # to train python main.py -a btc eth bnb -c usd # to test python main.py / --checkpoint_path results/t-1000/model-hash/checkpoint_750/checkpoint-750# instatiate the environment T_1000 = CreateEnv(assets=['OMG','BTC','ETH'], currency='USDT', granularity='day', datapoints=600) # define the hyperparams to train T_1000.train(timesteps=5e4, checkpoint_freq=10, lr_schedule=[ [ [0, 7e-5], # [timestep, lr] [100, 7e-6], ], [ [0, 6e-5], [100, 6e-6], ] ], algo='PPO')Once you have a sattisfatory reward_mean benchmark you can see how it performs with never seen data
# same environment T_1000 = CreateEnv(assets=['OMG','BTC','ETH'], currency='USDT', granularity='day', datapoints=600) # checkpoint are saved in /results # it will automatically use a different time period from trainnig to backtest T_1000.backtest(checkpoint_path='path/to/checkpoint_file/checkpoint-400')
- state of the art agents
- hyperparam grid search
- multi agent parallelization
- learning rate schedule
- result analysis
"It just needs to touch something to mimic it." - Sarah Connor, about the T-1000
Some nice tools to keep an eye while your agent train are (of course) tensorboard, gpustat and htop
# from the project home folder $ tensorboard --logdir=models # show how your gpu is going $ gpustat -i # show how your cpu and ram are going $ htop- Bind the agent's output with an exchange place order API
