Complete Trading Environment for automation with RL (FinRock)
Welcome to a second in-depth tutorial on creating a custom environment for automated financial trading using Python and Reinforcement Learning techniques. In this video, we'll walk you through building a robust trading environment from scratch, designed to work seamlessly with RL algorithms.
Whether you're a seasoned trader looking to automate your strategies or a beginner interested in exploring the exciting world of algorithmic trading, this tutorial will provide you with the fundamental knowledge and practical skills you need.
In this video, we will cover:
Setting up your Python development environment.
Creating a Gym-like structure for your custom trading environment.
Loading historical OHLC (Open, High, Low, Close) data from CSV files using Pandas.
Rendering OHLC candlestick charts to visualize the historical data.
Simulating trading with sinusoid data.
Calculating reward and balance change while trading.
Laying the foundation for applying reinforcement learning techniques to automate financial trading.
No matter your experience level, I'll take you step by step through building a robust trading environment. By the end of this tutorial, you'll be well-prepared to dive deeper into automated financial trading.
Remember to like and subscribe for more tutorials on algorithmic trading, machine learning, and Python development. If you have any questions or need more help, feel free to comment below, and I'll be happy to help.
Let's go ahead and get started on your journey to mastering automated financial trading. Watch the video, and let's build your trading environment together!
GitHub: https://github.com/pythonlessons/FinRock
Tutorial code: https://github.com/pythonlessons/FinR...
Tutorial code download: https://github.com/pythonlessons/FinR...
Tutorial part #1 - • Automated Financial Trading Introduct...
Tutorial part #2 - This video
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Tutorial part #4 - • Supercharging AI Trading: Adding Indi...
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#finance #trading #python