Few books to review on the topic:
- Applied Quantitative Method by Christian L Dunis, Jason Laws, Patrick Naim 2003. regression analysis, Neil Burgess cointegration, interest curve term structure model, recurrent neural model for ccy vol, decision tree and nn for credit risk, volatility regime switch for fx, equity invest with time varying (kalman) factor sensi, stoch vol for options, markowitz portfolio analysis alternatives for many assets, volatility and correl models in excel, optimal allocation of trend follow rules, portfolio mgt for otc options, filling missing data: wheather derivatives
- Statistical Arbitrage by Andrew Poole 2007, covers pair trading, factors/pca, cointegration, a 225 page book that is verbose and uses elaborate prose but drops many insights along the way.
- Quantitative Investment Analysis 3rd ed Richar DeFusco, Dennis W McLeavey, Jerald E Pinto, David E Runkle,2015; Time Value of Money; DCF Applications; Statistical Concepts and Market Returns (sharpe ratio, skew...); Probability Concepts (Bayes); Common Probability Distributions (Uniform,Binomial, Gauss, LogNormal); Sampling and Estimation; Hypothesis testing; Correlation and Regression; Multilple Regression and issues in regression analysis; Time Series Analysis (linear trend, log trend, AR models, Random walks, unit root test, moving average); Introduction to Multifactor models (MPT; APT; returm attribution, risk attribution, portfolio construction, portfolio decision)
- Quantitative Trading Algorithms, Data, Models, Optimizations X Guo (Berkeley), Tze Leung Lai (Stanford), Howard Shek (Tower Research Capital), Samuel Po-Shing Wong (5Lattices Capital HK) 2016: Statistical models and methods for quant trading, active portfolio mgt and investment, econometrics of transactions on electronic platforms, limit orde rbook: data analytics and dynamic models, optimal execution and placement, market making and smart order routing; informatics, regulations and risk mgt.
- Python for Algo Trading Yves Hilpsich 2021: Python and Algo Trading; Python Infrastructure; Working with Financial Data; Mastering Vectorized Backtesting; Predicting Mkt Movement with ML; Building classes for event-based backtesting; Working with RT data and sockets; CFD trading with OANDA; FX trading with FXCM; Automating trading Operations (Kelly Criterion for binomial bets, for stocks and indexes).
- Algorithmic Trading Methods 2nd Ed Robert Kissel 2021: Algorithmic trading; Transaction costs; Market impact model; Probability and Stats; LR models; Probabiltiy Models; Non-linear regression models; ML Techniques; Estimating I-Star Mkt Impact model params; Risk Vol and Factor models; Volume forecast; Algo Decision making framework; Portfolio optim and trade schedule optim; Advanced algo modeling; Decode and reverse engineer broker models with ML techniques; Portfolio construction with transaction cost analysis; Quantitative analysis with TCA; ML and trade schedule optimization