How to Code an AI -- Machine Learning Trading Algorithm

​ @MattMacarty
#algotrading #machinelearning #python #tradingbots #algorithmictrading #lumibot

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How to code an AI -- Machine Learning Support Vector Machine in Python

AI (Machine Learning) with Python sklearn


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**This video is for educational purposes only. It should not be construed as investment advice.**

Gold is one of the most popular assets in the financial world, with a rich history of serving as a store of value and a hedge against inflation. However, trading gold can be complex and time-consuming, requiring constant monitoring of market trends and analysis of data.

This example Gold Machine Learning Trading Bot is based on pattern recognition classification using a support vector machine model. SVM aims to identify and profit by predicting market direction of the price movement. This video shows you how to implement and automate trading using a machine learning model to either buy or short the GLD ETF.




1. Project Setup

Install Lumibot:
Bash

pip install lumibot
Create a Python Environment:
This is highly recommended to isolate your project dependencies. Use tools like virtualenv or conda.
Set up Lumibot Configuration:
Create a configuration file (e.g., .env) to store:
API keys for your brokerage (e.g., Alpaca, Interactive Brokers, Tradier)
Data providers (e.g., Yahoo Finance, Polygon)
Logging settings
Backtesting parameters
2. Code Your Algorithm

Define a Strategy Class:
Inherit from the lumibot.strategies.Strategy class.
Implement the on_trading_iteration() method:
This method is called at each trading interval (e.g., daily, hourly).
Within this method:
Fetch Market Data: Use Lumibot's data fetching capabilities to get real-time or historical data (stock prices, option chains, volatility).
Calculate Signals: Implement your trading logic (e.g., option Greeks, technical indicators, machine learning models).
Generate Orders: Create Order objects based on your trading signals.
Submit Orders: Use self.submit_order() to send orders to your brokerage.
Example (Simplified):

Python Pseudocode

from lumibot.strategies import Strategy

class ML(Strategy):
def on_trading_iteration(self):
1. Fetch Market Data

2. Calculate Signals

3. Create and submit orders
order = self.create_order(option.symbol, 1, "buy")
self.submit_order(order)
4. Backtesting

Use Lumibot's Backtesting Framework:
Create a backtesting engine (e.g., PolygonDataBacktesting) to simulate historical market conditions.
Run your strategy on historical data using the backtesting engine.
Analyze Backtesting Results:
Calculate key performance metrics:
Annualized return
Sharpe ratio
Maximum drawdown
Win rate
Visualize results:
Plot equity curves
Analyze trade distributions



5. Refine and Iterate

Analyze backtesting results and identify areas for improvement.
Adjust parameters and refine your trading logic.
Continuously test and iterate on your algorithm.
Important Considerations:

Risk Management: Implement robust risk management rules (e.g., stop-loss orders, position sizing) to protect your capital.
Data Quality: Ensure the accuracy and reliability of the market data you use.
Overfitting: Avoid overfitting your algorithm to historical data. Use techniques like cross-validation to prevent overfitting.
Disclaimer:

This is a simplified example. Real-world options trading algorithms are significantly more complex.

Backtesting results do not guarantee future performance.
This information is for educational purposes only and should not be considered investment advice.
By following these steps and continuously refining your approach, you can effectively code, test, and deploy trading algorithms using Lumibot.​