StableBaselinesTradingStrategy

A trading strategy capable of self tuning, training, and evaluating with stable-baselines.

Class Parameters

  • environment
    • A TradingEnvironment instance for the agent to trade within.
  • agent
    • A Tensorforce agent or agent specification.
  • model
    • The runner will automatically save the best agent
  • policy
    • The RL policy to train the agent’s model with. Defaults to ‘MlpPolicy’.
  • _model_kwargs
    • …

Properties and Setters

  • agent
    • A TradingEnvironment instance for the agent to trade within.
  • max_episode_timesteps
    • The maximum timesteps per episode.

Functions

  • restore_agent
    • Deserialize the strategy’s learning agent from a file.
    • parameters:
      • path
        • The str path of the file the agent specification is stored in.
  • save_agent
    • Serialize the learning agent to a file for restoring later.
    • parameters:
      • path
        • The str path of the file to store the agent specification in.
  • tune
    • Function NotImplemented
  • run
    • Runs all of the episodes specified.
    • parameters:
      • steps
      • episodes
      • episode_callback
      • evaluation

Use Cases

Use Case #1: Run a Strategy

from stable_baselines import PPO2
from tensortrade.strategies import StableBaselinesTradingStrategy
from tensortrade.environments import TradingEnvironment

environment = TradingEnvironment(exchange=exchange,
                                 action_strategy=action_strategy,
                                 reward_strategy=reward_strategy)
b_strategy = StableBaselinesTradingStrategy(model=PPO2)
strategy.environment = environment
stategy.run(episodes=10)

Use Case #2: Run a Live Strategy

import ccxt
from tensortrade.environments import TradingEnvironment
from tensortrade.strategies import StableBaselinesTradingStrategy
from tensortrade.exchanges.live import CCXTExchange
coinbase = ccxt.coinbasepro(...) # your credentials go here in dictionary form
exchange = CCXTExchange(exchange=coinbase,
                        timeframe='1h',
                        base_instrument='USD',
                        feature_pipeline=feature_pipeline)
environment = TradingEnvironment(exchange=exchange,
                                 action_strategy=action_strategy,
                                 reward_strategy=reward_strategy)
strategy.environment = environment
strategy.restore_agent(path="../agents/ppo_btc/1h")
live_performance = strategy.run(steps=0, trade_callback=episode_cb)