/TradzQAI

Trading environnement for RL agents, backtesting and training.

Primary LanguagePythonOtherNOASSERTION

TradzQAI

Trading environnement for RL agents, backtesting and training.

  • Available sessions:

    • Local
      • 1M bar datasets any size.
        • header should be ['Time', 'Open', 'High', 'Low', 'Close', 'Volume'] with ';' as separator
      • Tick datasets any size.
        • header should be ['Time', 'BID', 'ASK', 'VOL'] or ['Time', 'Price', 'Volume'] with ',' as separator
    • Live
      • Gdax API

  • Available agents:

    • DDPG
    • DQFD
    • DQN
    • DQNN
    • NAF
    • PPO
    • TRPO
    • VPG

  • Available contract type:

    • CFD
    • Classic

TradzQAI has been inspired by q-trader.

Indicators lib come from pyti

More datasets available here

Status

Alpha in development
    It should be ready soon for live trading with gdax API

Getting Started

  • Dependencies :

    Installation :

    pip install -r requirements.txt

  • Running the project

    Usage:
  python run.py -h (Show usage)
  python run.py -b BUILD (to manually build config file)
  python run.py -g on (Display interface, it does not support live session)
  python run.py -s live (for live session) # Not fully fonctionnal
  python run.py -m eval (for eval mode)
  python run.py -c config_dir/ # load config from directory, make sure you have agent, env and network json files in it
  python run.py (Run as default)

    When you run it for the first time, a config directory is created, you can change it to changes environnement settings and some agents settings. It save settings (env, agent, network) in a save directory, and create a new directory if make any changes.

    You can also do your own runner.

    from core import Local_session as Session
session = Session() # Run with default values
session.loadSession() # loading environnement, worker and agent
session.start() # Start the session thread

    Also, you are able to use the environnement only.

    from core import Local_env
env = Local_env() # run with default values
for e in episode:
  state = env.reset()
  for s in step:
    action = agent.act(state, ...)
    next_state, terminal, reward = env.execute(action)
    agent.observe(reward, terminal)
    if terminal or env.stop:
      break
  if env.stop or e == episode - 1:
    env.logger._running = False #Close the logger thread
    break

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