Training information: Profound Strength Learning For Computerized Dealing Python

In this tutorial, we’ll see an example of deep reinforcement learning for algorithmic trading using BTGym (OpenAI Gym environment API for backtrader backtesting library) and a DQN algorithm from a medium post (link below) to interact with the environment and does the trading.

Access to the code:

Telegram Group:

OpenAI Gym:
Bitcoin TensorForce Trading Bot:
Self Learning Quant:

15 Antworten auf „Training information: Profound Strength Learning For Computerized Dealing Python“

  1. you're ignoring "replay", that's where the whole learning is happening. In "target_train", you're only updating the target network weights. That's doing no learning whatsoever. Good job /facepalm

  2. Hi thks for the tutorial. I have this error: 'ActionDictSpace' object has no attribute 'n'. any idea ? :). the issue seems to come from : model.add(Dense(self.env.action_space.n))…

  3. I know this is not the place to ask error questions. But, have you ran into this error:

    State observation shape/range mismatch!
    Space set by env:

    This happens in env.reset().
    Appreciate your help.

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