Learn Algorithmic Trading

By : Sebastien Donadio, Sourav Ghosh

3.8 (10)

Buy this Book

Learn Algorithmic Trading

3.8 (10)

By: Sebastien Donadio, Sourav Ghosh

Buy this Book

Overview of this book

It’s now harder than ever to get a significant edge over competitors in terms of speed and efficiency when it comes to algorithmic trading. Relying on sophisticated trading signals, predictive models and strategies can make all the difference. This book will guide you through these aspects, giving you insights into how modern electronic trading markets and participants operate. You’ll start with an introduction to algorithmic trading, along with setting up the environment required to perform the tasks in the book. You’ll explore the key components of an algorithmic trading business and aspects you’ll need to take into account before starting an automated trading project. Next, you’ll focus on designing, building and operating the components required for developing a practical and profitable algorithmic trading business. Later, you’ll learn how quantitative trading signals and strategies are developed, and also implement and analyze sophisticated trading strategies such as volatility strategies, economic release strategies, and statistical arbitrage. Finally, you’ll create a trading bot from scratch using the algorithms built in the previous sections. By the end of this book, you’ll be well-versed with electronic trading markets and have learned to implement, evaluate and safely operate algorithmic trading strategies in live markets.

Preface

Who this book is for

What this book covers

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Free Chapter

Section 1: Introduction and Environment Setup

Algorithmic Trading Fundamentals

Why are we trading?

Basic concepts regarding the modern trading setup

Understanding algorithmic trading concepts

From intuition to algorithmic trading

Components of an algorithmic trading system

Why Python?

Summary

Section 2: Trading Signal Generation and Strategies

Deciphering the Markets with Technical Analysis

Designing a trading strategy based on trend- and momentum-based indicators

Creating trading signals based on fundamental technical analysis

Implementing advanced concepts, such as seasonality, in trading instruments

Summary

Predicting the Markets with Basic Machine Learning

Understanding the terminology and notations

Creating predictive models using linear regression methods

Creating predictive models using linear classification methods

Summary

Section 3: Algorithmic Trading Strategies

Classical Trading Strategies Driven by Human Intuition

Creating a trading strategy based on momentum and trend following

Creating a trading strategy that works for markets with reversion behavior

Creating trading strategies that operate on linearly correlated groups of trading instruments

Sophisticated Algorithmic Strategies

Creating a trading strategy that adjusts for trading instrument volatility

Creating a trading strategy for economic events

Understanding and implementing basic statistical arbitrage trading strategies

Summary

Managing the Risk of Algorithmic Strategies

Differentiating between the types of risk and risk factors

Differentiating the measures of risk

Making a risk management algorithm

Summary

Section 4: Building a Trading System

Building a Trading System in Python

Understanding the trading system

Building a trading system in Python

Designing a limit order book

Summary

Connecting to Trading Exchanges

Making a trading system trade with exchanges

Reviewing the Communication API

Receiving price updates

Sending orders and receiving a market response

Summary

Creating a Backtester in Python

Learning how to build a backtester

Learning how to choose the correct assumptions

Evaluating what the value of time is

Backtesting the dual-moving average trading strategy

Summary

Section 5: Challenges in Algorithmic Trading

Adapting to Market Participants and Conditions

Strategy performance in backtester versus live markets

Continued profitability in algorithmic trading

Summary

Final words

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Components of an algorithmic trading system

In an earlier section, we provided a top-level view of the entire algorithmic trading setup and many of the different components involved. In practice, a complete algorithmic trading setup is divided into two sections, as shown in the following diagram:

Core infrastructure deals with exchange-facing market data protocol integration, market data feed handlers, internal market data format normalization, historical data recording, instrument definition recording and dissemination, exchange order entry protocols, exchange order entry gateways, core side risk systems, broker-facing applications, back office reconciliation applications, addressing compliance requirements, and others.
Algorithmic trading strategy components deal with using normalized market data, building order books, generating signals from incoming market data and order...