# TradingGym **Repository Path**: ma-yongfan/TradingGym ## Basic Information - **Project Name**: TradingGym - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-02-16 - **Last Updated**: 2024-02-16 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # TradingGym [![Build Status](https://travis-ci.org/Yvictor/TradingGym.svg?branch=master)](https://travis-ci.org/Yvictor/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 = 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](fig/render.gif) ### Training #### simple dqn - WIP #### policy gradient - WIP #### actor-critic - WIP #### A3C with RNN - WIP ### Backtesting - loading env just like training ``` python 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 ``` 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 ```
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 ``` python 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 ```