https://github.com/Yvictor/TradingGym/blob/8af2bb02250797c580b37c7ef43aab21b2d968c7/trading_env/envs/training_v1.py#L183-L207

Maybe line 207 should be changed from

 if self.step_st+self.obs_len+self.step_len >= len(self.price):

to

 if self.step_st+self.obs_len >= len(self.price):

• separate training and backtesting mode and work in process realtime mode.
• origin env as v0 and add new env v1
• v1 has tick level transactions detail and support to return the transactions and fluctuant reward in observation state.

enter_price = self.chg_price[0] -> enter_price = self.chg_price[-1]

Since actual trading occurs at the last time step, the enter_price should point to the last entry of the price array

Hey! Great project, really excited about it but having a small problem.

Can't run the agent, env.backtest is causing a problem, so in the trading_env.make function I added the parameter backtest=1, and now the backtest_done is popping up as having to attribute.

This seems to be a problem with threading, from what I can read online.

Has anyone had success beyond the point of this error? Is it a package version I can downgrade?

Attached my notebook. trading-gym-andy.zip

no matter what I add to trading_env.make, including backtest_done=0 or 1, it fails to see it as an attribute... wtf!

Thank you for this I was looking for something like this. when I am trying out this I am getting a assertion error like this

self.ax.lines.remove(self.price_plot[0]) AttributeError: 'trading_env' object has no attribute 'ax'

data frame sample

>>> df.head()
   serial_number        Date   Open   High    Low  Close      Volume
0              0  1998-01-02  13.63  16.25  13.50  16.25   6411700.0
1              1  1998-01-05  16.50  16.56  15.19  15.88   5820300.0
2              2  1998-01-06  15.94  20.00  14.75  18.94  16182800.0
3              3  1998-01-07  18.81  19.00  17.31  17.50   9300200.0
4              4  1998-01-08  17.44  18.62  16.94  18.19   6910900.0

I want to use Volume column alone as the my observation state. This is the whole code

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

df = pd.read_csv('./dataset/AAPL.csv')

df.rename(columns={'Unnamed: 0':'serial_number'},inplace=True)

env = trading_env.make(env_id='training_v1', obs_data_len=50, step_len=14,
                       df=df, fee=0.1, max_position=5, deal_col_name='Close', 
                       feature_names=['Volume'])


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

IMPORTANT: Can you please tell me what is the action space index for buy, sell and hold. I can see action space consists of three integers but can't find where its mapped to.

• abandon v0
• v1 support for customize return transaction into state
• support render with show(bool) and directly save or get the fig to apply other action like use ipywidgets to show realtime update in jupyter notebook

todo

• [ ] support with ohlc data with action(buy or sell) in next bar open

Hi, thanks for another new great gym environment!

Its not actual issue, more like question: I am curious about actual RL trained strategies examples (not necessary profitable), because I didn't find one and OpenAI didn't pay much attention to financial gyms. It will be much easier to learn on existed examples, how tune architecture or parameters. I would be very grateful if you point me out useful links if you know some. Thanks