Learn Algorithmic Trading

Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis

Sebastien Donadio, Sourav Ghosh

• Publisher: Packt Publishing
• Year: 2019
• ISBN:
  9781789342147
• Edition: 1
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading strategies

Key Features

• 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

Book 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.

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

Who this book is 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.

Section 1: Introduction and Environment Setup

In this section, you will be introduced to algorithmic trading and setting up the environment required to perform tasks throughout the book. You will learn the key components of trading and the questions you need to ask before embarking on a robot trading project.

This section comprises the following chapter:

• Chapter 1, Algorithmic Trading Fundamentals

Algorithmic Trading Fundamentals

Algorithmic trading, or automated trading, works with a program that contains a set of instructions for trading purposes. Compared to a human trader, this trade can generate profits and losses at a higher speed. In this chapter, this will be your first time being exposed to trading automation. We will walk you through the different steps to implement your first trading robot. You will learn the trading world and the technical trading components behind it. We will also go into detail about the tools that you will use and, by the end of this chapter, you will be capable of coding your first native trading strategy in Python. We will cover the following topics in this chapter:

• Why are we trading?
• Introducing algorithm trading and automation
• What the main trading components are
• Setting up your first programming environment
• Implementing your first native strategy

Why are we trading?

From the Roman era through to the present day, trading is an inherent part of humankind. Buying raw materials when the price is low to resell it when the price is high has been a part of many cultures. In ancient Rome, the rich Romans used the Roman Forum to exchange currencies, bonds, and investments. In the 14th century, traders negotiated government debts in Venice. The earliest form of the stock exchange was created in Antwerp, Belgium, in 1531. Traders used to meet regularly to exchange promissory notes and bonds. The conquests of new worlds entailed a high cost, but also a good return. The Dutch East India Company in 1602 opened their capital for investors to participate in this costly project with a high potential return. During the same time period, a well-known tulip was sold everywhere in the world, creating a profitable market for investors and sellers. A future contract was created for this reason, since many people speculated regarding the price of this flower.

A hundred years later, a French expedition to Louisiana was also attracting many investors, creating the dream of making a lot of money. The Mississippi Company was created to handle all the investments based on potential wealth in Louisiana. Many other investment opportunities arose across the centuries, including the British railroad and the conquest of Latin America.

All these events had a common root: wealthy people willing to make more money. If we want to answer the question Why are we trading?, the answer is to potentially make more money. However, all the previous historical examples ended pretty badly. Investments turned out to be bad investments or, most of the time, the value was over-estimated and traders ended up losing their money. This is actually a good lesson for the readers of this book. Even if trading can sound a profitable business, always keep in mind the ephemeral part of profitability (it can work sometimes, but not always) and also taking into account the inherent risk that goes with investment.

Basic concepts regarding the modern trading setup

This section will cover the basics of trading and what drives market prices, as well as supply and demand.

As we touched upon in the previous section, trading has been around since the beginning of time, when people wanted to exchange goods between one another and make profits while doing so. Modern markets are still driven by basic economic principles of supply and demand. When demand outweighs supply, prices of a commodity or service are likely to rise higher to reflect the relative shortage of the commodity or service in relation to the demand for it. Conversely, if the market is flooded with a lot of sellers for a particular product, prices are likely to drop. Hence, the market is always trying to reflect the equilibrium price between available supply and demand for a particular product. We will see later how this is the fundamental driver of price discovery in today's markets. With the evolution of modern markets and available technology, price discovery becomes increasingly efficient.

Intuitively, you may draw a parallel with the fact that with the advances in online retail businesses, prices of products have become increasingly efficient across all sellers, and the best offers are always the ones that customers are buying because the information (price discovery) is so easily accessible. The same is true for modern trading. With advances in technology and regulations, more and more market participants have access to complete market data that makes price discovery much more efficient than in the past. Of course, the speed at which participants receive information, the speed at which they react, the granularity of the data that they can receive and handle, and the sophistication with which each participant draws trading insights from the data they receive, is where the competition lies in modern trading, and we will go over these in the subsequent sections. But first, let's introduce some basic concepts regarding the modern trading setup.

Market sectors

In this section, we will briefly introduce the concepts of what different types of market sectors are and how they differ from the concept of asset classes.

Market sectors are the different kinds of underlying products that can be traded. The most popular market sectors are commodities (metals, agricultural produce), energy (oil, gas), equities (stocks of different companies), interest rate bonds (coupons you get in exchange for debt, which accrues interest, hence the name), and foreign exchange (cash exchange rates between currencies for different countries):

Asset classes

Asset classes are the different kinds of actual vehicles that are available for trading at different exchanges. For example, cash interest rate bonds, cash foreign exchange, and cash stock shares are what we described in the previous section, but we can have financial instruments that are derivatives of these underlying products. Derivatives are instruments that are built on top of other instruments and have some additional constraints, which we will explore in this section. The two most popular derivatives are futures and options, and are heavily traded across all derivatives electronic exchanges.

We can have future contracts pertaining to underlying commodities, energy, equities, interest rate bonds, and foreign exchanges that are tied to the prices of the underlying instruments, but have different characteristics and rules. A simple way to think of a future contract is that it is a contract between a buyer and a seller in which the seller promises to sell a certain amount of the underlying product at a certain date in the future (also known as the expiry date), and where the buyer agrees to accept the agreed-upon amount at the specific date at the specific price.

For example, a producer of butter might want to protect themselves from a potential future spike in the price of milk, on which the production costs of butter directly depend, in which case, the butter producer can enter into an agreement with a milk producer to provide them with enough milk in the future at a certain price. Conversely, a milk producer may worry about possible buyers of milk in the future and may want to reduce the risk by making an agreement with butter producers to buy at least a certain amount of milk in the future at a certain price, since milk is perishable and a lack of supply would mean a total loss for a milk producer. This is a very simple example of a future contract trade; modern future contracts are much more complex than this.

Similar to future contracts, we can have options contracts for underlying commodities, energy, equities, interest rate bonds, and foreign exchanges that are tied to the prices of the underlying instruments, but have different characteristics and rules. The difference in an options contract compared to a futures contract is that the buyer and seller of an options contract have the option of refusing to buy or sell at the specific amount, at the specific date, and at the specific price. To safeguard both counterparties involved in an options trade, we have the concept of a premium, which is the minimum amount of money that has been paid upfront to buy/sell an options contract.

A call option, or the right to buy, but not an obligation to buy at expiration, makes money if the price of the underlying product increases prior to expiration because now, such a party can exercise their option at expiration and buy the underlying product at a price lower than the current market price. Conversely, if the price of the underlying product goes down prior to expiration, such a party now has the option of backing out of exercising their option and thus, only losing the premium that they paid for. Put options are analogous, but they give the holder of a put contract the right to sell, but not an obligation to sell, at expiration.

We will not delve too deeply into different financial products and derivatives since that is not the focus of this book, but this brief introduction was meant to introduce the idea that there are a lot of different tradeable financial products out there and that they vary significantly in terms of their rules and complexity.

Basics of what a modern trading exchange looks like

Since this book is primarily designed to introduce what modern algorithmic trading looks like, we will focus on trying to understand how a modern electronic trading exchange appears. Gone are the days of people yelling at one another in the trading pits and making hand signals to convey their intentions to buy and sell products at certain prices. These remain amusing ideas for movies, but modern trading looks significantly different.

Today, most of the trading is done electronically through different software applications. Market data feed handlers process and understand market data disseminated by the trading exchanges to reflect the true state of the limit book and market prices (bids and offers). The market data is published in a specific market data protocol previously agreed upon by the exchange and the market participants (FIX/FAST, ITCH, and HSVF). Then, the same software applications can relay that information back to humans or make decisions themselves algorithmically. Those d...