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TradingGym

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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

import random
import numpy as np
import pandas as pd
import trading_env

df = pd.read_hdf('dataset/SGXTW.h5', 'STW')

env = trading_env.make(env_id='training_v1', obs_data_len=256, step_len=128,
                       df=df, fee=0.1, max_position=5, deal_col_name='Price', 
                       feature_names=['Price', 'Volume', 
                                      'Ask_price','Bid_price', 
                                      'Ask_deal_vol','Bid_deal_vol',
                                      '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
  • obs_data_len: observation data length
  • step_len: when call step rolling windows will + step_len
  • 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.
  • deal_col_name: the column name for cucalate reward used.
  • feature_names: list contain the feature columns to use in trading status.

gif

Training

simple dqn

  • WIP

policy gradient

  • WIP

actor-critic

  • WIP

A3C with RNN

  • WIP

Backtesting

  • loading env just like training
env = trading_env.make(env_id='backtest_v1', obs_data_len=1024, step_len=512,
                       df=df, fee=0.1, max_position=5, deal_col_name='Price', 
                        feature_names=['Price', 'Volume', 
                                       'Ask_price','Bid_price', 
                                       'Ask_deal_vol','Bid_deal_vol',
                                       'Bid/Ask_deal', 'Updown'])
  • load your own agent
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
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
step datetime transact transact_type price share price_mean position reward_fluc reward reward_sum color rotation
2 1537 2013-04-09 10:58:45 Buy new 277.1 1.0 277.100000 1.0 0.000000e+00 0.000000e+00 0.000000 1 1
5 3073 2013-04-09 11:47:26 Sell cover 276.8 -1.0 277.100000 0.0 -4.000000e-01 -4.000000e-01 -0.400000 2 2
10 5633 2013-04-09 13:23:40 Sell new 276.9 -1.0 276.900000 -1.0 0.000000e+00 0.000000e+00 -0.400000 2 1
11 6145 2013-04-09 13:30:36 Sell new 276.7 -1.0 276.800000 -2.0 1.000000e-01 0.000000e+00 -0.400000 2 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
211 108545 2013-04-19 13:18:32 Sell new 286.7 -1.0 286.525000 -2.0 -4.500000e-01 0.000000e+00 30.650000 2 1
216 111105 2013-04-19 16:02:01 Sell new 289.2 -1.0 287.416667 -3.0 -5.550000e+00 0.000000e+00 30.650000 2 1
217 111617 2013-04-19 17:54:29 Sell new 289.2 -1.0 287.862500 -4.0 -5.650000e+00 0.000000e+00 30.650000 2 1
218 112129 2013-04-19 21:36:21 Sell new 288.0 -1.0 287.890000 -5.0 -9.500000e-01 0.000000e+00 30.650000 2 1
219 112129 2013-04-19 21:36:21 Buy cover 288.0 5.0 287.890000 0.0 0.000000e+00 -1.050000e+00 29.600000 1 2

128 rows × 13 columns

exmaple of rule base usage

  • ma crossover and crossunder
env = trading_env.make(env_id='backtest_v1', obs_data_len=10, step_len=1,
                       df=df, fee=0.1, max_position=5, deal_col_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

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