How to Code an AI -- Machine Learning Trading Algorithm

Matt Macarty · Intermediate ·📅 Project Management ·1y ago

About this lesson

@MattMacarty #algotrading #machinelearning #python #tradingbots #algorithmictrading #lumibot **Master Automated Trading with Machine Learning and the LumiBot Framework.** This tutorial provides a comprehensive walkthrough of building a Support Vector Machine (SVM) binary classifier to trade the Gold ETF (GLD). We transition from raw data processing to a live-ready, object-oriented trading strategy that manages risk by closing positions daily. Learn how to build a machine learning trading algorithm to trade the gold ETF. We utilize LumiBot from LumiWealth to implement and manage this algo trading strategy, focusing on practical coding examples. This ml project assumes familiarity with LumiBot basics and covers the implementation of a gold trading bot for Gld. ### ⏱️ Key Technical Milestones 0:00 - Architecture Overview: LumiBot Strategy Design 0:52 - Trading Logic: Implementing Daily Risk Management 3:47 - Model Development: Scikit-learn and SVM Implementation 4:58 - Feature Engineering: Lagged Returns & Data Transformation 8:23 - Classification: Mapping Market Direction to Binary Signals 9:43 - Training & Optimization: Hyperparameter Tuning 12:47 - Signal Forecasting: Real-time Prediction Logic 15:16 - Refactoring: Deploying an Object-Oriented ML Class 19:40 - Performance Analysis: Strategy vs. Buy & Hold Benchmark --- ### 🏛️ Implementation Overview This project utilizes a **Support Vector Machine (SVM)** model to identify pattern-based classification in market direction. By predicting price movement for the GLD ETF, the algorithm automates the decision to go long or short. **1. Environment & Setup:** * Integration with **LumiBot** for strategy execution. * Data sourcing via **yfinance** and **Polygon**. * Environment isolation using virtualenv/conda. **2. Strategy Logic:** * Inherits from `lumibot.strategies.Strategy`. * Signal calculation via lagged returns and technical indicators. * Automated order submission via `self.submit_order()`. **3. Backtest

Original Description

@MattMacarty #algotrading #machinelearning #python #tradingbots #algorithmictrading #lumibot **Master Automated Trading with Machine Learning and the LumiBot Framework.** This tutorial provides a comprehensive walkthrough of building a Support Vector Machine (SVM) binary classifier to trade the Gold ETF (GLD). We transition from raw data processing to a live-ready, object-oriented trading strategy that manages risk by closing positions daily. Learn how to build a machine learning trading algorithm to trade the gold ETF. We utilize LumiBot from LumiWealth to implement and manage this algo trading strategy, focusing on practical coding examples. This ml project assumes familiarity with LumiBot basics and covers the implementation of a gold trading bot for Gld. ### ⏱️ Key Technical Milestones 0:00 - Architecture Overview: LumiBot Strategy Design 0:52 - Trading Logic: Implementing Daily Risk Management 3:47 - Model Development: Scikit-learn and SVM Implementation 4:58 - Feature Engineering: Lagged Returns & Data Transformation 8:23 - Classification: Mapping Market Direction to Binary Signals 9:43 - Training & Optimization: Hyperparameter Tuning 12:47 - Signal Forecasting: Real-time Prediction Logic 15:16 - Refactoring: Deploying an Object-Oriented ML Class 19:40 - Performance Analysis: Strategy vs. Buy & Hold Benchmark --- ### 🏛️ Implementation Overview This project utilizes a **Support Vector Machine (SVM)** model to identify pattern-based classification in market direction. By predicting price movement for the GLD ETF, the algorithm automates the decision to go long or short. **1. Environment & Setup:** * Integration with **LumiBot** for strategy execution. * Data sourcing via **yfinance** and **Polygon**. * Environment isolation using virtualenv/conda. **2. Strategy Logic:** * Inherits from `lumibot.strategies.Strategy`. * Signal calculation via lagged returns and technical indicators. * Automated order submission via `self.submit_order()`. **3. Backtest
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Chapters (9)

Architecture Overview: LumiBot Strategy Design
0:52 Trading Logic: Implementing Daily Risk Management
3:47 Model Development: Scikit-learn and SVM Implementation
4:58 Feature Engineering: Lagged Returns & Data Transformation
8:23 Classification: Mapping Market Direction to Binary Signals
9:43 Training & Optimization: Hyperparameter Tuning
12:47 Signal Forecasting: Real-time Prediction Logic
15:16 Refactoring: Deploying an Object-Oriented ML Class
19:40 Performance Analysis: Strategy vs. Buy & Hold Benchmark
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