Adapting to Market Participants and Conditions

So far, we've gone over all the concepts and ideas involved in algorithmic trading. We went from introducing the different components and players of an algorithmic trading ecosystem to going over practical examples of trading signals, adding predictive analytics into algorithmic trading strategies, and actually building several commonly used basic, as well as sophisticated, trading strategies. We also developed ideas and a system to control risk and manage it over the evolution of a trading strategy. And finally, we went over the infrastructure components required to run these trading strategies as well as the simulator/backtesting research environment required to analyze trading strategy behavior. At this point in the book, you should be able to successfully develop a deep understanding of all the components and sophistication...

Strategy performance in backtester versus live markets

In this section, let's first tackle a very common problem encountered by a lot of algorithmic trading participants that lack sophistication in their backtesters/simulators. Since backtesters are a cornerstone in building, analyzing, and comparing algorithmic trading strategies irrespective of position holding times, if backtested results are not realized in live trading markets, it's difficult to get off the ground or continue trading. Typically, the shorter the position holding period and the larger the trading sizes, the greater the chance that simulation results are different from results actually achieved in live trading markets. Backtesters are often the most complex software component in a lot of high frequency trading (HFT) business because of the need to simulate very accurately. Also, the more complex or...

Continued profitability in algorithmic trading

In the first half of this chapter, we looked at what common issues you can expect when deploying algorithmic trading strategies that have been built and calibrated in simulations and appear to be profitable. We discussed the impact and common causes of simulation dislocation, which cause deviation in trading strategy performance when deployed to live trading markets. We then explored possible solutions to dealing with those problems and how to get algorithmic trading strategies off the ground and start scaling up safely to build a profitable algorithmic trading business. Now, let's look at the next steps after getting up and running with the algorithmic trading strategies in live trading markets. As we mentioned before, live trading markets are in a constant state of evolution, as participants enter and exit markets and adapt...

Summary

This chapter explored what happens when algorithmic trading system and algorithmic trading strategies are deployed to live markets after months, and often years, of development and research. Many common issues with live trading strategies, such as not behaving or performing according to expectations, were discussed and we provided common causes and possible solutions or approaches to remedy these. This should help to prepare anyone looking to build and deploy algorithmic trading strategies to live markets, and equip them with the knowledge to improve trading strategy components when things don't go as expected.

Once the initial trading strategies are deployed and running in live markets as per expectations, we discussed the evolving nature of the algorithmic trading business and global markets in general. We covered a lot of different factors that cause profitable...

Final words

At this point, you have learned about all the components involved in a modern algorithmic trading business. You should be well-versed in all the different components involved in an end-to-end algorithmic trading setup between the trading exchange, as well as the interactions between the trading exchange and the different market participants. In addition, you should be able to understand how market participants interact with each other via the exchange matching engine and available market data.

We looked at all the different methods of incorporating intelligence into our trading signals using conventional technical analysis as well as advanced machine learning methods. We discussed the details of trading strategies and how they convert intelligence from trading signals into the order flow to manage positions and risk such that they are profitable, and then looked...