Thread by @quantscience_

🚨BREAKING: A new Python library for algorithmic trading.

Introducing TensorTrade: An open-source Python framework for trading using Reinforcement Learning (AI)

TensorTrade is an open source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning leveraging:

- numpy
- pandas
- gym
- keras
- tensorflow

Example: Using TensorTrade to Train and Evaluate with Reinforcement Learning

Step 1: Create training and evaluation sets

We'll start by creating a training and evaluation set as CSV files.

Step 2: Create a Configuration

Here we are using the config dictionary to store the CSV filename that we need to read.

Step 3: Initialize and run with Ray

Now it’s time to initialize and run Ray, passing all the parameters necessary, including the name of the environment creator function (create_env defined above).

Next Steps: Reward Agents

Reward Agents (AI) bring a new capability to allow Reinforcement Learning to integrate a reward system that allows the Agent to optimize the strategy to a reward (e.g. profit).

Performance:

Here's a performance chart of a reward-optimized example.

🚨 NEW WORKSHOP: How I built an automated algorithmic trading system with Python.

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That ends on August 7th.

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