Machine Learning for Trading Specialization Course Syllabus

Overview: This specialization provides a hands-on foundation in machine learning and reinforcement learning applied to trading strategies, combining theoretical concepts with practical implementation in Python and Google Cloud Platform (GCP). Over approximately 38 hours, learners will explore ML-driven financial modeling, backtesting, and RL-based trading agents, with a focus on real-world applications in quantitative finance. The course blends theory and coding exercises, culminating in a final project that integrates learned skills into a deployable trading strategy framework.

Module 1: Introduction to Trading, ML & GCP

Estimated time: 5 hours

• Trading basics: trends, returns, stop-loss, and volatility
• Introduction to quantitative trading strategies (e.g., arbitrage)
• Overview of machine learning applications in finance
• Hands-on setup using Jupyter and Google Cloud Platform

Module 2: Using ML in Trading and Finance

Estimated time: 18 hours

• Exploratory data analysis for financial time series
• Building momentum trading models using supervised learning
• Developing pairs trading strategies with statistical methods
• Implementing Keras and TensorFlow for financial forecasting
• Backtesting ML-based trading strategies in Python

Module 3: Reinforcement Learning for Trading Strategies

Estimated time: 15 hours

• Introduction to reinforcement learning: policy and value functions
• Applying RL to trading decision-making
• LSTM networks for time-series prediction in trading
• Designing and training RL-based trading agents

Module 4: Building Scalable ML Pipelines on GCP

Estimated time: 10 hours

• Designing ML workflows for trading systems
• Deploying models using Google Cloud Platform
• Scaling data processing and model inference pipelines

Module 5: Strategy Evaluation and Risk Management

Estimated time: 8 hours

• Analyzing financial patterns and market regimes
• Evaluating strategy performance using risk-adjusted metrics
• Incorporating transaction costs and slippage in backtests

Module 6: Final Project

Estimated time: 12 hours

• Design and implement a complete ML-driven trading strategy
• Backtest and evaluate performance using real financial data
• Deploy a model pipeline on GCP with documentation

Prerequisites

• Intermediate proficiency in Python programming
• Basic understanding of statistics and probability
• Familiarity with financial markets and trading concepts

What You'll Be Able to Do After

• Apply supervised learning and time-series forecasting to trading problems
• Build and backtest momentum, statistical, and pairs trading models
• Develop reinforcement learning agents for automated trading decisions
• Create scalable ML pipelines using Google Cloud Platform
• Deploy and evaluate ML-driven trading strategies with real-world considerations