Machine trading : deploying computer algorithms to conquer the markets

Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1: The Basics of Algorithmic Trading -- Historical Market Data -- Live Market Data -- Backtesting and Trading Platforms -- Brokers -- Performance Metrics -- Portfolio Optimization -- Summary -- Exercises -- Chapter 2: Factor Models -- Time-series Factors -- Cross-sectional Factors -- A Two-Factor Model -- Using Option Prices to Predict Stock Returns -- 1) Implied Moments -- 2) Monthly Change in Implied Volatilities -- 3) Put-call Implied Volatilities -- 4) OTM Put-ATM Call Implied Volatilities -- 5) Daily Change in Implied Market Volatility -- Short Interest -- Liquidity -- Statistical Factors -- Putting Them All Together -- Summary -- Exercises -- Chapter 3: Time-Series Analysis -- AR(p) -- ARMA(p, q) -- VAR(p) -- State Space Models -- Summary -- Exercises -- Chapter 4: Artificial Intelligence Techniques -- Stepwise Regression -- Regression Tree -- Cross Validation -- Bagging -- Random Subspace and Random Forest -- Boosting -- Classification Tree -- Support Vector Machine -- Hidden Markov Model -- Neural Network -- Data Aggregation and Normalization -- Application to Stocks Selection -- Summary -- Exercises -- Chapter 5: Options Strategies -- Trading Volatility without Options -- Predicting Volatility -- Event-Driven Strategies -- Gamma Scalping -- Dispersion Trading -- Cross-Sectional Mean Reversion of Implied Volatility -- Summary -- Exercises -- Chapter 6: Intraday Trading and Market Microstructure -- Latency Reduction -- Order Type and Routing Optimization -- Adding Liquidity -- Taking Liquidity -- Routing to Dark Pools -- Adverse Selection Reduction -- Backtesting Intraday Strategies -- Order Flow -- Order Book Imbalance -- Summary -- Exercises -- Chapter 7: Bitcoins -- Bitcoin Facts -- Time-Series Techniques -- Mean Reversion Strategy -- Artificial Intelligence Techniques.