install

$ pip install trading-gym

$ wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz $ tar -zxvf ta-lib-0.4.0-src.tar.gz$ cd ta-lib/$ ./configure --prefix=$PREFIX$ make install$ export TA_LIBRARY_PATH=$PREFIX/lib$ export TA_INCLUDE_PATH=$PREFIX/include$ pip install TA-Lib

Examples

quick start

from trading_gym.env import TradeEnvimport randomenv = TradeEnv(data_path='./data/test_exchange.json')done = Falseobs = env.reset()for i in range(500):    action = random.sample([0, 1, 2], 1)[0]    obs, reward, done, info = env.step(action)    env.render()    if done:        break

A sample train with stable-baselines

from trading_gym.env import TradeEnvfrom stable_baselines.common.vec_env import DummyVecEnvfrom stable_baselines import DQNfrom stable_baselines.deepq.policies import MlpPolicydata_path = './data/fake_sin_data.json'env = TradeEnv(data_path=data_path, unit=50000, data_kwargs={'use_ta': True})env = DummyVecEnv([lambda: env])model = DQN(MlpPolicy, env, verbose=2, learning_rate=1e-5)model.learn(200000)obs = env.reset()for i in range(8000):    action, _states = model.predict(obs)    obs, rewards, done, info = env.step(action)    env.render()    if done:        break

input format

[    {        "open": 10.0,        "close": 10.0,        "high": 10.0,        "low": 10.0,        "volume": 10.0,        "date": "2019-01-01 09:59"    },    {        "open": 10.1,        "close": 10.1,        "high": 10.1,        "low": 10.1,        "volume": 10.1,        "date": "2019-01-01 10:00"    }]

actions

observation

env = TradeEnv(data_path=data_path)

env = TradeEnv(data_path=data_path, data_kwargs={'use_ta': True})

Custom

custom obs

def custom_obs_features_func(history, info):    close = [obs.close for obs in history]    return closeenv = TradeEnv(data_path=data_path,               get_obs_features_func=custom_obs_features_func,               ops_shape=(1))

custom reward

def custom_reward_func(exchange):    return exchange.profitenv = TradeEnv(data_path=data_path,               get_reward_func=custom_reward_func)

Reward