mymusise / trading-gym

A Trading environment base on Gym

Trading-Gym

Trading-Gym is a trading environment base on Gym. For those who want to custom everything.

install

$ pip install trading-gym

Creating features with ta-lib is suggested, that will improve the performance of agent and make it easy to learn. You should install ta-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-Lib

See more.

Examples

quick start

from trading_gym.env import TradeEnv
import random


env = TradeEnv(data_path='./data/test_exchange.json')
done = False
obs = 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 TradeEnv
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import DQN
from stable_baselines.deepq.policies import MlpPolicy


data_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

Action	Value
PUT	0
HOLD	1
PUSH	2

observation

• 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']

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 close


env = 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.profit


env = TradeEnv(data_path=data_path,
               get_reward_func=custom_reward_func)

Param exchange is entity of Exchange

Reward

• 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])

trading-gym's Issues

ImportError: cannot import name 'GoalEnv' from 'gym.core'

请问这个repo有功能介绍吗？博主能否抽出时间介绍一下这个project

如题。
因为看了example不是很能理解这个repo的内容和功能。

ImportError: cannot import name 'GoalEnv' from 'gym.core'

I get this from the code:

>>> from trading_gym.env import TradeEnv

What should I do?

ValueError: could not broadcast input array from shape (51,6) into shape (50,6)

How to fix this? I run the Example(train_fake.py) file with parameter of True & True

Traceback (most recent call last):
File "C:/Users/amit/PycharmProjects/dqnTrading/train_fake.py", line 40, in
test_fake(True, True)
File "C:/Users/amit/PycharmProjects/dqnTrading/train_fake.py", line 30, in test_fake
'learning_rate': 5e-5
File "C:\Users\amit\PycharmProjects\dqnTrading\base.py", line 111, in train
model.learn(total_timesteps=train_steps, callback=self.callback)
File "C:\Users\amit\venv\new\lib\site-packages\stable_baselines\deepq\dqn.py", line 180, in learn
obs = self.env.reset()
File "C:\Users\amit\venv\new\lib\site-packages\stable_baselines\common\base_class.py", line 765, in reset
return self.unvec_obs(self.venv.reset())
File "C:\Users\amit\venv\new\lib\site-packages\stable_baselines\common\vec_env\dummy_vec_env.py", line 50, in reset
self._save_obs(env_idx, obs)
File "C:\Users\amit\venv\new\lib\site-packages\stable_baselines\common\vec_env\dummy_vec_env.py", line 69, in _save_obs
self.buf_obs[key][env_idx] = obs
ValueError: could not broadcast input array from shape (51,6) into shape (50,6)