Quantitative Trading

Synopsis

Master the lucrative discipline of quantitative trading with this insightful handbook from a master in the field

In the newly revised Second Edition of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, quant trading expert Dr. Ernest P. Chan shows you how to apply both time-tested and novel quantitative trading strategies to develop or improve your own trading firm.

You'll discover new case studies and updated information on the application of cutting-edge machine learning investment techniques, as well as:

• Updated back tests on a variety of trading strategies, with included Python and R code examples
• A new technique on optimizing parameters with changing market regimes using machine learning.
• A guide to selecting the best traders and advisors to manage your money

Perfect for independent retail traders seeking to start their own quantitative trading business, or investors looking to invest in such traders, this new edition of Quantitative Trading will also earn a place in the libraries of individual investors interested in exploring a career at a major financial institution.

Summary

Chapter 1: Introduction to Quantitative Trading

* Definition and history of quantitative trading (quant trading).
* Role of mathematics, statistics, and computer science in quant trading.
* Key concepts: backtesting, alpha, and risk management.

Chapter 2: Data Preparation and Analysis

* Sources of financial data, such as exchanges and data vendors.
* Cleaning and preprocessing raw data to remove errors and outliers.
* Use of statistical techniques to analyze data, identify patterns, and develop trading signals.

Example: Analyzing historical stock prices to identify trends and correlations that could predict future price movements.

Chapter 3: Model Development and Backtesting

* Overview of different quantitative trading models, including statistical arbitrage, machine learning, and deep learning.
* Process of model development, training, and evaluation.
* Importance of backtesting to validate model performance on historical data.

Example: Developing a regression model to predict the future price of a stock based on a set of input variables, such as historical prices and economic indicators.

Chapter 4: Risk Management

* Types of financial risks in quant trading, such as market risk, operational risk, and liquidity risk.
* Methods for measuring and managing risk, including statistical analysis, stress testing, and portfolio optimization.
* Importance of setting clear risk limits and monitoring risk levels.

Example: Calculating the Value at Risk (VaR) for a trading portfolio to estimate the potential maximum loss over a given time horizon at a specified confidence level.

Chapter 5: Trading Execution and Evaluation

* Techniques for executing trades in financial markets, including algorithmic trading and direct market access (DMA).
* Evaluation of trading performance using metrics such as return on investment (ROI), Sharpe ratio, and drawdown.
* Continuous monitoring and improvement of trading strategies.

Example: Implementing an algorithmic trading strategy that automatically adjusts trading orders based on pre-defined criteria, such as price thresholds or technical indicators.

Chapter 6: Technological Infrastructure

* Role of technology in quant trading, including data storage, computation, and visualization.
* Importance of high-performance computing (HPC) and cloud computing.
* Best practices for data management, model deployment, and risk monitoring.

Example: Utilizing cloud computing to scale computing resources on demand for large-scale backtesting or model training.

Chapter 7: Challenges and Future Developments

* Challenges in quant trading, such as data quality, market volatility, and regulatory compliance.
* Emerging trends and future developments in quant trading, including the use of artificial intelligence (AI) and machine learning.
* Ethical considerations in quant trading.