An Introduction to Algorithmic Trading: Basic to Advanced Strategies

by Edward Leshik and Jane Cralle

Quick Summary

This Wiley book provides an accessible introduction to algorithmic trading, covering the history of algos, their definition and classification, who uses and provides them, why they became mainstream, currently popular algo types (VWAP, TWAP, implementation shortfall, participation), optimization techniques for individual traders, and the future direction of algorithmic trading. It bridges the gap between institutional algo usage and individual trader implementation.

Detailed Summary

Leshik and Cralle's "An Introduction to Algorithmic Trading" is structured in two parts. Part I provides the conceptual framework, while Part II covers practical implementation.

Part I opens with a history of algorithmic trading (Chapter 1), tracing its evolution from early computerized order routing to the sophisticated execution and alpha-generation algorithms used by modern institutions. Chapter 2 answers the question "what is an algo?" -- defining it as a set of rules that automate trading decisions, from simple execution algorithms that minimize market impact to complex strategies that generate trading signals.

Chapter 3 classifies algorithms into execution algorithms (designed to fill large orders with minimal market impact), alpha-generating algorithms (designed to identify and exploit profitable opportunities), and hybrid approaches. Chapter 4 surveys who uses algos (buy-side institutions, sell-side brokers, proprietary trading firms, individual traders) and who provides them (broker-dealers, independent software vendors, exchanges).

Chapter 5 explains the rapid mainstream adoption of algos: regulatory changes (decimalization, Reg NMS), technology improvements (faster computers, lower-latency networks, electronic exchanges), cost pressures (the need to reduce execution costs), and the availability of historical data for backtesting.

Chapter 6 surveys currently popular algorithms. VWAP (Volume-Weighted Average Price) algorithms slice large orders across the day in proportion to historical volume patterns. TWAP (Time-Weighted Average Price) algorithms distribute orders evenly across a time window. Implementation Shortfall algorithms balance the urgency of execution against market impact. Participation (POV) algorithms maintain a target percentage of market volume. Iceberg algorithms hide large order sizes. Smart order routing algorithms direct orders to the venue with the best available price.

Chapter 7 provides a perspective from a Tier 1 firm on institutional algo development and deployment. Chapters 8-9 focus on individual traders: how to select and use algos, how to optimize parameters, and how to evaluate algo performance. Chapter 10 discusses the future of algorithmic trading, including machine learning, high-frequency trading, and the evolving regulatory landscape.

Part II of the book covers more advanced topics including building trading systems, backtesting methodologies, risk management, and specific strategy implementations.

The book is designed for readers with basic market knowledge who want to understand how algorithmic trading works and how to begin implementing it, without requiring deep programming or mathematical expertise.