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 and change their trading strategies.

In addition to the market participants themselves, there are numerous global economic and political conditions that can influence price movements in global and/or local asset classes and trading instruments. It is not enough to just be able to set up an algorithmic trading business; it is also mandatory to be able to adapt to all of these possible changing conditions and market risk and continue to stay profitable. That is an extremely tough goal; over time, previously profitable and sophisticated market participants have had to shut down their trading businesses and exit the market, making algorithmic and quantitative trading one of the most challenging businesses out there. In this section, let's explore what causes winning trading strategies to die out after first being profitable.

We will explore solutions that can help us to maintain and improve trading strategy profitability after the initial deployment to live trading markets. Finally, we will wrap up this chapter by discussing adapting to changing market conditions and market participants, that is, dealing with the ever-evolving nature of the algorithmic trading business and how to work on building an algorithmic trading business that survives for a very long time.

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