Reinforcement Learning Trading Bot in Python | Train an AI Agent on Forex (EURUSD)
In this video, we build a reinforcement learning trading bot in Python and train an AI agent on historical EUR/USD Forex data using an hourly timeframe. You’ll see how a model-free reinforcement learning approach allows an AI trading agent to:
- Read historical price data
- Take long and short trading decisions
- Learn from winning and losing trades
- Improve its trading policy through rewards
We cover the core reinforcement learning concepts applied to trading:
Agent, Environment, Actions, Rewards, and Policy.
This is a realistic example of how AI learns trading strategies through trial and error, very similar to how a human trader improves with experience.
⚠️ This video is educational and focuses on algorithmic trading, AI, and reinforcement learning, not financial advice.
○○○ Free Python Script Included, download it below and customize it for your own assets!
╔═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦══╦═╦═╦══╦═╦═╦══╦═╦═╦
🔗 Resources & Links:
--------------------------------------
• 📚 My Algorithmic Trading Courses → https://codetradingcafe.com
• 📘 My Book: “Algorithmic Trading with Python” → https://a.co/d/6woMBHt
• 💻 Free Python Code (GitHub) → https://github.com/ZiadFrancis/ReinforcementTrading_Part_1
Happy learning, happy coding ☕
╚═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩
This educational video focuses on the following topics: reinforcement learning trading, AI trading bot, Forex AI, Python trading bot, reinforcement learning Python, algorithmic trading AI, EURUSD trading strategy, machine learning trading.
In this video, we build a reinforcement learning trading bot in Python and train an AI agent on historical EUR/USD Forex data using an hourly timeframe. You’ll see how a model-free reinforcement learning approach allows an AI trading agent to:
– Read historical price data
– Take long and short trading decisions
– Learn from winning and losing trades
– Improve its trading policy through rewards
We cover the core reinforcement learning concepts applied to trading:
Agent, Environment, Actions, Rewards, and Policy.
This is a realistic example of how AI learns trading strategies through trial and error, very similar to how a human trader improves with experience.
⚠️ This video is educational and focuses on algorithmic trading, AI, and reinforcement learning, not financial advice.
○○○ Free Python Script Included, download it below and customize it for your own assets!
╔═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦═╦══╦═╦═╦══╦═╦═╦══╦═╦═╦
🔗 Resources & Links:
————————————–
• 📚 My Algorithmic Trading Courses → https://codetradingcafe.com
• 📘 My Book: “Algorithmic Trading with Python” → https://a.co/d/6woMBHt
• 💻 Free Python Code (GitHub) → https://github.com/ZiadFrancis/ReinforcementTrading_Part_1
Happy learning, happy coding ☕
╚═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩═╩
This educational video focuses on the following topics: reinforcement learning trading, AI trading bot, Forex AI, Python trading bot, reinforcement learning Python, algorithmic trading AI, EURUSD trading strategy, machine learning trading.
