Book Image

Learn Algorithmic Trading

By : Sebastien Donadio, Sourav Ghosh
Book Image

Learn Algorithmic Trading

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.

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Table of Contents (16 chapters)
Title Page
Title Page
Copyright and Credits
Copyright and Credits
Learn Algorithmic Trading
About Packt
About Packt
Why subscribe?
Contributors
Contributors
About the authors
About the reviewers
Packt is searching for authors like you
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Algorithmic Trading Fundamentals
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
2
Deciphering the Markets with Technical Analysis
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
3
Predicting the Markets with Basic Machine Learning
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
4
Classical Trading Strategies Driven by Human Intuition
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
5
Sophisticated Algorithmic Strategies
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
6
Managing the Risk of Algorithmic Strategies
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 
7
Building a Trading System in Python
Building a Trading System in Python
Understanding the trading system
Building a trading system in Python
Designing a limit order book
Summary
8
Connecting to Trading Exchanges
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
9
Creating a Backtester in Python
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
10
Adapting to Market Participants and Conditions
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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Making a risk management algorithm

By now, we're aware of the different types of risks and factors, including the risks in a trading strategy and the most common risk metrics for algorithmic trading strategies. Now, let's have a look at incorporating these risk measures into our volatility adjusted mean reversion trading strategy to make it safer before deploying it into live markets. We will set the risk limits to 150% of the maximum achieved historically. We are doing this because it is possible that there is a day in the future that is very different from what we've seen historically. Let's get started:

  1. Let's define our risk limits, which we are not allowed to breach. As we discussed previously, it will be set to 150% of the historically observed maximums:
# Risk limits
RISK_LIMIT_WEEKLY_STOP_LOSS = -12000 * 1.5
RISK_LIMIT_MONTHLY_STOP_LOSS = -14000...
Previous Section
End of Section 4
Next Section