Trading Strategies and Their Core Elements

In previous lessons, we considered algorithmic (algo) and systematic trading from two standpoints: we learned about the market itself, its participants, the way it operates, and how all this is reflected in the pricing; on the other hand, we did some preparation work in programming, so now we can retrieve and process market data, build technical indicators, and do some charts and plots. In other words, we have the heart and the bones of the body of our future trading application, and now it’s time to add brains and limbs: the trading logic that generates orders, and the order execution control mechanism that finally connects the app with the final destination – an exchange, a broker, or an electronic communication network (ECN).

In this lesson, we will consider the most important classes of trading strategies that are typically used to trade the FX markets. We will learn about the sources of profit generation, consider typical trading ideas and their practical implementations, and understand their technical requirements.

Note that we are not going to develop actual codes implementing strategies of all these types. Some of them may be available only to institutional traders while others require sophisticated and expensive infrastructure, such as the colocation of trading servers directly with the exchange or ECN servers, special hardware such as field programmable gate arrays (FPGAs), and so on. There are two reasons why we mention them in this book. First, it’s definitely useful to know alternative approaches to algo trading. Second, you never know where you will find yourself in the future: for example, as a bank dealer or researcher at a hedge fund, and, in that case, this knowledge, even quite surface-level, will be very helpful.

In this lesson, you will learn about the following topics:

• Alpha and beta as sources of profit generation
• Options pricing – the most science-consuming risk models
• Alpha classics – trend-following, mean reversion, and breakout
• Arbitrage – let’s earn from others’ mistakes
• Statistical arbitrage
• Event-driven trading strategies
• Market making – profiting from liquidity provision and associated risks
• High frequency, low latency – where Python fails