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Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis

Sebastien Donadio

Sourav Ghosh

€18.99 per month

3.8 (10 Ratings)

Paperback Nov 2019 394 pages 1st Edition

Sebastien Donadio

Sourav Ghosh

€18.99 per month

3.8 (10 Ratings)

Paperback Nov 2019 394 pages 1st Edition

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Key benefits

Understand the power of algorithmic trading in financial markets with real-world examples
Get up and running with the algorithms used to carry out algorithmic trading
Learn to build your own algorithmic trading robots which require no human intervention

Description

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.

Who is this book for?

This book is for software engineers, financial traders, data analysts, and entrepreneurs. Anyone who wants to get started with algorithmic trading and understand how it works; and learn the components of a trading system, protocols and algorithms required for black box and gray box trading, and techniques for building a completely automated and profitable trading business will also find this book useful.

What you will learn

Understand the components of modern algorithmic trading systems and strategies
Apply machine learning in algorithmic trading signals and strategies using Python
Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more
Quantify and build a risk management system for Python trading strategies
Build a backtester to run simulated trading strategies for improving the performance of your trading bot
Deploy and incorporate trading strategies in the live market to maintain and improve profitability

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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

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Product Details

Publication date : Nov 07, 2019

Length: 394 pages

Edition : 1st

Language : English

ISBN-13 : 9781789348347

Category :

Data

Languages :

Python

Concepts :

Data Analysis

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16 Chapters

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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About the authors

Sebastien Donadio

Sebastien Donadio is the Chief Technology Officer at Tradair, responsible for leading the technology. He has a wide variety of professional experience, including being head of software engineering at HC Technologies, partner and technical director of a high-frequency FX firm, a quantitative trading strategy software developer at Sun Trading, working as project lead for the Department of Defense. He also has research experience with Bull SAS, and an IT Credit Risk Manager with Socit Gnrale while in France. He has taught various computer science courses for the past ten years in the University of Chicago, NYU and Columbia University. His main passion is technology but he is also a scuba diving instructor and an experienced rock-climber.

See other products by Sebastien Donadio

Sourav Ghosh

Sourav Ghosh has worked in several proprietary, high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high-throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making and pairs trading strategies with the most liquid global futures contracts. He is currently the vice president at an investment bank based in São Paulo, Brazil. He holds a master's in computer science from the University of Southern California. His areas of interest include computer architecture, FinTech, probability theory and stochastic processes, statistical learning and inference methods, and natural language processing.

See other products by Sourav Ghosh

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Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis

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