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Trading-Gym is a trading environment base on Gym. For those who want to custom everything.
$ pip install trading-gymCreating features withta-lib is suggested, that will improve the performance of agent and make it easy to learn. You should installta-lib before it. Take Ubuntu x64 for example.
$ 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-Libfrom 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: breakfrom 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
[ { "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" }]| Action | Value |
|---|---|
| PUT | 0 |
| HOLD | 1 |
| PUSH | 2 |
- native obs: shape=(*, 51, 6), return 51 history data with OCHL
env = TradeEnv(data_path=data_path)- obs with ta: shape=(*, 10), return obs using talib.
- default feature:
['ema', 'wma', 'sma', 'sar', 'apo', 'macd', 'macdsignal', 'macdhist', 'adosc', 'obv']
- default feature:
env = TradeEnv(data_path=data_path, data_kwargs={'use_ta': True})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))def custom_reward_func(exchange): return exchange.profitenv = TradeEnv(data_path=data_path, get_reward_func=custom_reward_func)Paramexchange is entity ofExchange
- reward = fixed_profit
- profit = fixed_profit + floating_profit
- floating_profit = (latest_price - avg_price) * unit
- unit = int(nav / buy_in_price)
- avg_price = ((buy_in_price * unit) + charge) / unit
- fixed_profit = SUM([every floating_profit after close position])