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.
Table of Contents (16 chapters)
Title Page

Backtesting the dual-moving average trading strategy

The dual-moving average trading strategy places a buy order when the short moving average crosses the long moving average in an upward direction and will place a sell order when the cross happens on the other side. This section will present the backtesting implementation of the dual-moving average strategy. We will present the implementation of a for-loop backtester and an event-based backtester.

For-loop backtester

  1. As regards the implementation of this backtester, we will use the GOOG data by retrieving it with the same function we used previously, load_financial_data. We will follow the pseudo code that we proposed during the previous section:
for each price update:
create_metric_out_of_prices...