Building an Algorithmic Trading Platform

Algorithmic trading automates the systematic trading process, where orders are executed at the best price possible based on a variety of factors, such as pricing, timing, and volume. Brokerage firms may offer an Application Programming Interface (API) as part of their service offering for customers who wish to deploy their own trading algorithms. An algorithmic trading system must be highly robust to handle any point of failure during the order execution. Network configuration, hardware, memory management, speed, and user experience are a number of factors to be considered when designing a system for executing orders. Designing larger systems inevitably adds more complexity to the framework.

As soon as a position in a market is opened, it is subjected to various types of risk, such as market risk, interest rate risk, and liquidity risk...

Introducing algorithmic trading

In the 1990s, exchanges had already begun to use electronic trading systems. By 1997, 44 exchanges worldwide used automated systems for trading futures and options with more exchanges in the process of developing automated technology. Exchanges such as the Chicago Board of Trade (CBOT) and the London International Financial Futures and Options Exchange (LIFFE) used their electronic trading systems as an after-hours complement to the traditional open outcry trading pits, thus giving traders 24-hour access to the exchange's risk management tools. With these improvements in technology, technology-based trading became less expensive, fueling the growth of trading platforms that are faster and more powerful. The higher reliability of order execution and the lower rate of message transmission error has deepened the reliance on technology by financial...

Building an algorithmic trading platform

In this section, we will design and build a live algorithmic trading system in Python. Since developer tools and offerings vary with each broker, it is important to take into consideration the different programming implementation that is required in integrating with our very own trading system. With a good system design, we can build a generic service that allows configurations of different brokers to plug in and play together nicely with our trading system.

Designing a broker interface

When designing a trading platform, the following three functionalities are highly desirable to fulfill any given trading plan:

• Getting prices: Pricing data is the one of the most basic units of information...

Building a mean-reverting algorithmic trading system

With our broker now accepting orders and responding to our requests, we can begin to design a fully-automated trading system. In this section, we will explore how to design and implement a mean-reverting algorithmic trading system.

Designing the mean-reversion algorithm

Suppose we believe that in normal market conditions, prices fluctuate, but tend to revert back to some short-term level, such as the average of the most recent prices. In this example, we assume that the EUR/USD currency pair is exhibiting a mean-reversion property in the near short-term period. First, we resample the raw tick-level data into standard time series intervals, for example, at one-minute intervals...

Building a trend-following trading platform

In the previous section, we followed the steps for building a mean-reverting trading platform. The same functionality can be easily extended to incorporate any other trading strategies. In this section, we will take a look at reusing the MeanReversionTrader class to implement a trend-following trading system.

Designing the trend-following algorithm

Suppose that this time, we believe that the current market conditions exhibit a trend-following pattern, perhaps due to seasonal changes, economic projections, or government policy. As prices fluctuate, and as the short-term average price level crosses the average long-term price level by a certain threshold, we generate a buy or...

VaR for risk management

As soon as we open a position in the market, we are exposed to various types of risks, such as volatility risk and credit risk. To preserve our trading capital as much as possible, it is important to incorporate some form of risk management measures to our trading system.

Perhaps the most common measure of risk used in the financial industry is the VaR technique. It is designed to simply answer the following question: What is the worst expected amount of loss, given a specific probability level, say 95%, over a certain period of time? The beauty of VaR is that it can be applied to multiple levels, from position-specific micro-level to portfolio-based macro-level. For example, a VaR of $1 million with a 95% confidence level for a 1-day time horizon states that, on average, only 1 day out of 20 could you expect to lose more than $1 million due to market...

Summary

In this chapter, we were introduced to the evolution of trading from the pits to the electronic trading platform, and learned how algorithmic trading came about. We looked at some brokers offering API access to their trading service offering. To help us get started on our journey of developing an algorithmic trading system, we used the Oanda v20 library to implement a mean-reversion trading system.

In designing an event-driven broker interface class, we defined event handlers for listening to orders, prices, and position updates. Child classes inheriting the Broker class simply extend this interface class with broker-specific functions, while still keeping the underlying trading functions compatible with our trading system. We successfully tested the connection with our broker by getting market prices, sending a market order, and receiving position updates.

We discussed...