Trading environnement for RL agents, backtesting and training.
Trading environnement for RL agents, backtesting and training.
Live session with coinbasepro-python is finaly arrived !
Available sessions:
Available agents:
Available contract type:
TradzQAI has been inspired by q-trader.
Indicators lib come from pyti
More datasets available here
Alpha in development
GUI rework on track
Dependencies :
pip install -r requirements.txt
Running the project
Usage:
python run.py -h (Show usage)
python run.py -b agent_name (to manually build config file, it build config files from agent, default PPO)
python run.py -s live (for live session)
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.
Do you own decision function for maker side. For more info look at this function
from core import Local_session as Session
from mymodule import myfunc
session = Session(mode=args.mode, config=args.config)
session.initApi(key=key, b64=b64, passphrase=passphrase, url=url,
product_id=product_id)
session.getApi().setBestPriceFunc(myfunc)
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
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
You have to define your input to fit with columns name of your datasets, it will automaticaly it grab input from network and compare it with your dataset columns in getState function, it allow you to do complex network like this :
[
[
{"names": ["Price"], "type": "input"},
{"activation": "relu", "size": 8, "type": "dense"},
{"activation": "relu", "size": 8, "type": "dense"},
{"name": "pricenet", "type": "output"}
],
[
{"names": ["Volume"], "type": "input"},
{"activation": "relu", "size": 8, "type": "dense"},
{"activation": "relu", "size": 8, "type": "dense"},
{"name": "volnet", "type": "output"}
],
[
{"names": ["pricenet", "volnet"], "type": "input"},
{"activation": "relu", "size": 64, "type": "dense"},
{"activation": "relu", "size": 32, "type": "dense"},
{"activation": "relu", "size": 8, "type": "dense"},
{"name": "prediction", "type": "output"}
]
]
[
{"activation": "relu", "size": 64, "type": "dense"},
{"activation": "relu", "size": 64, "type": "dense"}
]
py run.py -b DEEP
.
Your model have to be placed in the same directory as the one you use to launch it and have to be called deep_model.h5
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