Trading gym rl env

trading-gym-rl-env

USE FOR:

• "gym-style trading environment for RL"
• "tick-level RL trading simulation"
• "backtesting RL agent on OHLC data"
• "custom RL trading environment setup"
• "discrete action space trading (hold/buy/sell)" tags: [RL, gym, trading-environment, tick-data, OHLC, backtesting, reinforcement-learning] kind: framework category: quant-ml-trading

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What Is TradingGym?

OpenAI Gym-inspired RL trading environment toolkit.

• Repo: https://github.com/Yvictor/TradingGym
• Focus: Tick-level data (also OHLC)
• Use cases: RL training, backtesting, future live trading (IB API)

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Environment Design

Action Space

0 → Hold (do nothing)
1 → Buy one unit
2 → Sell one unit

Observation Space

State = selected features over window of N steps
Features: price, volume, bid/ask, custom columns
Window size: configurable (default: 30 ticks)

Reward

Reward = price_delta × position - transaction_fee

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Installation & Setup

import trading_gym
import pandas as pd

# Load your market data (any asset, any timeframe)
data = pd.read_hdf("market_data.h5")

# Create environment
env = trading_gym.TradingEnv(
    data=data,
    obs_len=30,           # Observation window
    step_len=1,           # Steps per action
    fee=0.001,            # Transaction fee (0.1%)
    deal_col_name="close",# Column for P&L calculation
)

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Usage Patterns

Random Agent (Baseline)

obs = env.reset()
done = False
while not done:
    action = env.action_space.sample()  # Random: 0, 1, or 2
    obs, reward, done, info = env.step(action)

Custom RL Agent

class MyAgent:
    def predict(self, obs):
        # Your RL policy here (DQN, PPO, etc.)
        return action  # 0, 1, or 2

agent = MyAgent()
obs = env.reset()
done = False
while not done:
    action = agent.predict(obs)
    obs, reward, done, info = env.step(action)

Rule-Based Strategy (MA Crossover)

class MACrossoverAgent:
    def predict(self, obs):
        fast_ma = obs[-5:, price_col].mean()
        slow_ma = obs[-20:, price_col].mean()
        if fast_ma > slow_ma:
            return 1  # Buy
        elif fast_ma < slow_ma:
            return 2  # Sell
        return 0      # Hold

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Comparison: TradingGym vs TensorTrade

Feature	TradingGym	TensorTrade
Data focus	Tick-level primary	OHLCV + feeds
Action space	Simple discrete (3)	Configurable
Reward	Price delta - fee	Multiple schemes
RL integration	Any gym-compatible	Ray RLlib built-in
Portfolio management	Basic	Full wallets/positions
Complexity	Lightweight	Full framework
Best for	Quick RL experiments	Production RL systems

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