You're reading from Learn Algorithmic Trading

Product typeBook

Published inNov 2019

Reading LevelIntermediate

PublisherPackt

ISBN-139781789348347

Edition1st Edition

Languages

Python

Concepts

Financial Technology

Authors (2):

Sebastien Donadio

Sourav Ghosh

View More author details

Sophisticated Algorithmic Strategies

In this chapter, we will explore more sophisticated trading strategies employed by leading market participants in the algorithmic trading business. We will build on top of the basic algorithmic strategies and learn about more advanced approaches (such as statistical arbitrage and pair correlation) and their advantages and disadvantages. We will learn how to create a trading strategy that adjusts for trading instrument volatility. We will also learn how to create a trading strategy for economic events and understand and implement the basics of statistical arbitrage trading strategies.

This chapter will cover the following topics:

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

...

Creating a trading strategy that adjusts for trading instrument volatility

An intuitive way to think about price volatility is investor confidence in the specific instrument, that is, how willing the investors are to invest money into the specific instrument and how long they are willing to hold on to a position in that instrument. As price volatility goes up, because prices make bigger swings at faster paces, investor confidence drops. Conversely, as price volatility goes down, investors are more willing to have bigger positions and hold those positions for longer periods of time. Volatility in a few asset classes often spills over into other asset classes, thus slowly spreading volatility over to all economic fields, housing costs, consumer costs, and so on. Obviously, sophisticated strategies need to dynamically adjust to changing volatility in trading instruments by following...

Creating a trading strategy for economic events

In this section, we will explore a new class of trading strategies that is different from what we've seen before. Instead of using technical indicators, we can research economic releases and use various economic releases to estimate/predict the impact on the trading instruments and trade them accordingly. Let's first take a look at what economic releases are and how instrument pricing is influenced by releases.

Economic releases

Economic indicators are a measure of economic activity for a certain country or region or asset classes. These indicators are measured, researched, and released by different entities. Some of these entities are government agencies and some are private...

Understanding and implementing basic statistical arbitrage trading strategies

Statistical arbitrage trading strategies (StatArb) first became popular in the 1980s, delivering many firms double-digit returns. It is a class of strategies that tries to capture relationships between short-term price movements in many correlated products. Then it uses relationships that have been found to be statistically significant in the past research to make predictions in the instrument being traded based on price movements in a large group of correlated products.

Basics of StatArb

Statistical arbitrage or StatArb is in some way similar to pairs trading that takes offsetting positions in co-linearly related products that we explored in Chapter...

Summary

This chapter made use of some of the trading signals we've seen in the previous chapters to build realistic and robust trend-following and mean reversion trading strategies. In addition, we went another step further and made those basic strategies more sophisticated by adding a volatility measure trading signal to make it more dynamic and adaptive to different market conditions. We also looked at a completely new form of trading strategy in the form of trading strategies dealing with economic releases and how to carry out the analysis for that flavor of trading strategies for our sample Non Farm Payroll data. Finally, we looked at our most sophisticated and complex trading strategy so far, which was the statistical arbitrage strategy, and applied it to CAD/USD with the major currency pairs as leading trading signals. We investigated in great detail how to quantify...

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Authors (2)

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.
Read more about 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.
Read more about Sourav Ghosh

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