TradingGym

TradingGym is a toolkit for training and backtesting the reinforcement learning algorithms. This was inspired by OpenAI Gym and imitated the framework form. Not only traning env but also has backtesting and in the future will implement realtime trading env with Interactivate Broker API and so on.

This training env originally design for tickdata, but also support for ohlc data format. WIP.

Installation

git clone https://github.com/Yvictor/TradingGym.git
cd TradingGym
python setup.py install

Getting Started

python
import random
import numpy as np
import pandas as pd
import trading_env
df = pd.read_hdf('dataset/SGXTW.h5', 'STW')
env = tradingenv.make(envid='trainingv1', obsdatalen=256, steplen=128,                        df=df, fee=0.1, maxposition=5, dealcol_name='Price',                         feature_names=['Price', 'Volume',                                        'Askprice','Bidprice',                                        'Askdealvol','Biddealvol',                                       'Bid/Ask_deal', 'Updown'])
env.reset() env.render()
state, reward, done, info = env.step(random.randrange(3))
randow choice action and show the transaction detail
for i in range(500):
    print(i)
    state, reward, done, info = env.step(random.randrange(3))
    print(state, reward)
    env.render()
    if done:
        break
env.transaction_details

• obsdatalen: observation data length
• steplen: when call step rolling windows will + steplen
• df exmaple

>|index|datetime|bid|ask|price|volume|serial_number|dealin| >|-----|--------|---|---|-----|------|-------------|------| >|0|2010-05-25 08:45:00|7188.0|7188.0|7188.0|527.0|0.0|0.0| >|1|2010-05-25 08:45:00|7188.0|7189.0|7189.0|1.0|1.0|1.0| >|2|2010-05-25 08:45:00|7188.0|7189.0|7188.0|1.0|2.0|-1.0| >|3|2010-05-25 08:45:00|7188.0|7189.0|7188.0|4.0|3.0|-1.0| >|4|2010-05-25 08:45:00|7188.0|7189.0|7188.0|2.0|4.0|-1.0|

• df: dataframe that contain data for trading

serial_number -> serial num of deal at each day recalculating

• fee: when each deal will pay the fee, set with your product.
• max_position: the max market position for you trading share.
• dealcolname: the column name for cucalate reward used.
• feature_names: list contain the feature columns to use in trading status.

Training

simple dqn

• WIP

policy gradient

• WIP

actor-critic

• WIP

A3C with RNN

• WIP

Backtesting

- loading env just like training

python env = tradingenv.make(envid='backtestv1', obsdatalen=1024, steplen=512,                        df=df, fee=0.1, maxposition=5, dealcol_name='Price',                          feature_names=['Price', 'Volume',                                         'Askprice','Bidprice',                                         'Askdealvol','Biddealvol',                                        'Bid/Ask_deal', 'Updown'])

• load your own agent

python  class YourAgent:     def init(self):         # build your network and so on         pass     def choice_action(self, state):         ## your rule base conditon or your max Qvalue action or Policy Gradient action          # action=0 -> do nothing          # action=1 -> buy 1 share          # action=2 -> sell 1 share         ## in this testing case we just build a simple random policy          return np.random.randint(3)

• start to backtest

python agent = YourAgent()

transactions = [] while not env.backtest_done:     state = env.backtest()     done = False     while not done:         state, reward, done, info = env.step(agent.choice_action(state))         #print(state, reward)         #env.render()         if done:             transactions.append(info)             break transaction = pd.concate(transactions) transaction

exmaple of rule base usage

• ma crossover and crossunder

python
env = tradingenv.make(envid='backtestv1', obsdatalen=10, steplen=1,
                       df=df, fee=0.1, maxposition=5, dealcol_name='Price', 
                       feature_names=['Price', 'MA'])
class MaAgent:
    def init(self):
        pass
        
    def choice_action(self, state):
        if state[-1][0] > state[-1][1] and state[-2][0] <= state[-2][1]:
            return 1
        elif state[-1][0] < state[-1][1] and state[-2][0] >= state[-2][1]:
            return 2
        else:
            return 0
then same as above