These chapters cover the foundations of modern financial markets and quantitative trading using QuantConnect. Chapter 1 explains market mechanics, participants (liquidity traders, market makers, informed traders), data feeds, and various asset classes including equities, options, futures, and cryptocurrency. Chapter 2 details the quantitative trading process, including research, backtesting, parameter optimization, debugging tools, strategy development, and asset selection methods. It emphasizes avoiding overfitting and maintaining robust trading strategies through proper hypothesis testing and data analysis.
These chapters cover the foundations of modern financial markets and quantitative trading using QuantConnect. Chapter 1 explains market mechanics, participants (liquidity traders, market makers, informed traders), data feeds, and various asset classes including equities, options, futures, and cryptocurrency. Chapter 2 details the quantitative trading process, including research, backtesting, parameter optimization, debugging tools, strategy development, and asset selection methods. It emphasizes avoiding overfitting and maintaining robust trading strategies through proper hypothesis testing and data analysis.
The chapters cover essential steps in preparing datasets for algorithmic trading. Chapter 3 focuses on problem definition, including identifying financial objectives, target variables, scope, and constraints. Three case studies illustrate different trading scenarios: forecasting stock trends, portfolio rebalancing, and trade execution optimization. Chapter 4 details dataset preparation through data collection, exploratory analysis, preprocessing (handling missing data and outliers), feature engineering, and dataset splitting for model training and testing. The chapters emphasize proper data handling for effective model development.
Finally chapter 5 discusses various machine learning models and techniques for financial trading. It covers supervised learning methods like linear regression and random forests, unsupervised learning like clustering, and semi-supervised approaches including large language models. Key models include LASSO and ridge regression for handling overfitting, Markov switching for regime detection, and specialized models like FinBERT for financial sentiment analysis. Each model is explained with Python implementation examples, use cases in finance, and requirements for feature normalization.
The chapters cover essential steps in preparing datasets for algorithmic trading. Chapter 3 focuses on problem definition, including identifying financial objectives, target variables, scope, and constraints. Three case studies illustrate different trading scenarios: forecasting stock trends, portfolio rebalancing, and trade execution optimization. Chapter 4 details dataset preparation through data collection, exploratory analysis, preprocessing (handling missing data and outliers), feature engineering, and dataset splitting for model training and testing. The chapters emphasize proper data handling for effective model development.
Finally chapter 5 discusses various machine learning models and techniques for financial trading. It covers supervised learning methods like linear regression and random forests, unsupervised learning like clustering, and semi-supervised approaches including large language models. Key models include LASSO and ridge regression for handling overfitting, Markov switching for regime detection, and specialized models like FinBERT for financial sentiment analysis. Each model is explained with Python implementation examples, use cases in finance, and requirements for feature normalization.
Part three focuses on tangible examples of machine learning and AI applied to trading. Chapter 6 demonstrates 19 practical applications of machine learning in algorithmic trading, from basic trend detection to advanced deep learning models. Examples include trend scanning with MLFinLab, factor preprocessing techniques, regime detection using classification, Hidden Markov Models for volatility prediction, and sentiment analysis using FinBERT and GPT models. Each example provides detailed implementation code, performance metrics, and practical trading insights.
Chapter 7 discusses using reinforcement learning for options hedging, presenting a two-stage approach: first training a neural network to mimic Black-Scholes delta hedging using simulated data, then refining it with real market data to account for practical trading conditions and costs. Finally chapters 8, and 9 cover using AI for financial optimization and risk management, focusing on two key approaches: Corrective AI to enhance existing trading strategies and Conditional Parameter/Portfolio Optimization (CPO) for dynamic asset allocation based on market conditions, along with LLM technology applied to financial research.
Part three focuses on tangible examples of machine learning and AI applied to trading. Chapter 6 demonstrates 19 practical applications of machine learning in algorithmic trading, from basic trend detection to advanced deep learning models. Examples include trend scanning with MLFinLab, factor preprocessing techniques, regime detection using classification, Hidden Markov Models for volatility prediction, and sentiment analysis using FinBERT and GPT models. Each example provides detailed implementation code, performance metrics, and practical trading insights.
Chapter 7 discusses using reinforcement learning for options hedging, presenting a two-stage approach: first training a neural network to mimic Black-Scholes delta hedging using simulated data, then refining it with real market data to account for practical trading conditions and costs. Finally chapters 8, and 9 cover using AI for financial optimization and risk management, focusing on two key approaches: Corrective AI to enhance existing trading strategies and Conditional Parameter/Portfolio Optimization (CPO) for dynamic asset allocation based on market conditions, along with LLM technology applied to financial research.
