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Learn Algorithmic Trading

Learn Algorithmic Trading

By : Sebastien Donadio, Sourav Ghosh
3.8 (10)
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Learn Algorithmic Trading

Learn Algorithmic Trading

3.8 (10)
By: Sebastien Donadio, Sourav Ghosh

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.
Table of Contents (17 chapters)
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1
Section 1: Introduction and Environment Setup
3
Section 2: Trading Signal Generation and Strategies
6
Section 3: Algorithmic Trading Strategies
10
Section 4: Building a Trading System
14
Section 5: Challenges in Algorithmic Trading

Understanding the terminology and notations

To develop ideas quickly and build an intuition regarding supply and demand, we have a simple and completely hypothetical dataset of height, weight, and race of a few random samples obtained from a survey. Let's have a look at the dataset:

Height (inches)

Weight (lbs)

Race (Asian/African/Caucasian)

72

180

Asian

66

150

Asian

70

190

African

75

210

Caucasian

64

150

Asian

77

220

African

70

200

Caucasian

65

150

African

 

Let's examine the individual fields:

  • Height in inches and weight in lbs are continuous data types because they can take on any values, such as 65, 65.123, and 65.3456667.
  • Race, on the other hand, would be an example of a categorical data type, because there are a finite number of possible values that can go in the field. In this...

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