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. Some brokerage firms may offer an application programming interface (API) as part of their service offering to customers who wish to deploy their own trading algorithms. For developing an algorithmic trading system, it must be highly robust and handle any point of failure during the order execution. Network configuration, hardware, memory management and speed, and user experience are some factors to be considered when designing a system in executing orders. Designing larger systems inevitably add 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. To preserve the trading capital as much as possible, it is important to incorporate risk management measures to the trading system. Perhaps the most common risk measure used in the financial industry is the value-at-risk (VaR) technique. We will discuss the beauty and flaws of VaR, and how it can be incorporated into our trading system that we will develop in this chapter.
In this chapter, we will cover the following topics:
- An overview of algorithmic trading
- List of brokers and system vendors with public API
- Choosing a programming language for a trading system
- Setting up API access on Interactive Brokers (IB) trading platform
- Using the IbPy module to interact with IB Trader WorkStation (TWS)
- Implementing a mean-reverting algorithmic trading strategy
- Setting up API access on OANDA with fxTrade Practice platform
- Using the oandapy module to interact with OANDA's REST API
- Implementing a trend-following algorithmic trading strategy
- Introduction to VaR for risk management in our trading system
- Performing VaR calculation in Python with data from Yahoo! Finance
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 traditional open outcry trading in pits, giving traders 24-hour access to the exchange's risk management tools. With improvements in technology, technology-based trading became less expensive, fueling the growth of trading platforms that are faster and powerful. Higher reliability of order execution and lower rates of message transmission error deepened the reliance of technology by financial institutions. The majority of asset managers, proprietary traders, and market makers have since moved from the trading pits to electronic trading floors.
As systematic or computerized trading became more commonplace, speed became the most important factor in determining the outcome of a trade. Quants utilizing sophisticated fundamental models are able to recompute fair values of trading products on the fly and execute trading decisions, enabling them to reap profits at the expense of fundamental traders using traditional tools. This gave way to the term high-frequency trading (HFT) that relies on fast computers to execute the trading decisions before anyone else can. HFT has evolved into a billion-dollar industry.
Algorithmic trading refers to the automation of the systematic trading process, where the order execution is heavily optimized to give the best price possible. It is not part of the portfolio allocation process.
Banks, hedge funds, brokerage firms, clearing firms, and trading firms typically have their servers placed right next to the electronic exchange to receive the latest market prices and to perform the fastest order execution where possible. They bring enormous trading volumes to the exchange. Anyone who wishes to participate in low-latency, high-volume trading activities, such as complex event processing or capturing fleeting price discrepancies, by acquiring exchange connectivity may do so in the form of co-location, where his or her server hardware can be placed on a rack right next to the exchange for a fee.
The Financial Information Exchange (FIX) protocol is the industry standard for electronic communications with the exchange from the private server for direct market access (DMA) to real-time information. C++ is the common choice of programming language for trading over the FIX protocol, though other languages, such as .NET framework common language and Java can be used. Before creating an algorithmic trading platform, you would need to assess various factors, such as speed and ease of learning before deciding on a specific language for the purpose.
Brokerage firms would provide a trading platform of some sort to their customers for them to execute orders on selected exchanges in return for the commission fees. Some brokerage firms may offer an API as part of their service offering to technically inclined customers who wish to run their own trading algorithms. In most circumstances, customers may also choose from a number of commercial trading platforms offered by third-party vendors. Some of these trading platforms may also offer API access to route orders electronically to the exchange. It is important to read the API documentation beforehand to understand the technical capabilities offered by your broker and to formulate an approach in developing an algorithmic trading system.