Making actual trading decisions – trading logic and credit risk

In directional trading, systemic risks are mostly realized when you or your trading strategy supposed the price would go in one direction, but in reality, it went in the opposite direction. Don’t worry, this situation is absolutely normal for systematic trading, and no one can guarantee that any strategy will generate a win rate of 100%.

There are two major types of trading strategies: data-driven and event-driven. Data-driven strategies analyze the price time series (which we discussed in the Retrieving market data – quality and consistency as keys to success section) in order to find a certain pattern or sequence that then triggers an order. Event-driven strategies wait for a certain event to happen – for example, a new tick at a certain price and with a certain volume, or a release of political news or economic indicators. In both cases, a trading app should have rules to not only open positions but also to liquidate them – again, based on price data or events (or both).

In general, if a strategy generates some wins and some losses, there are only two ways for it to make money:

• Generate more wins than losses
• Have the average win greater than the average loss

If your trading algorithm does not have a routine that handles situations when the market goes against the position, then most likely, the average loss across a statistically significant number of trades will be greater than the average win, and it will become really problematic to make money with such a strategy.

Don’t forget that forex is traded using leverage, which means the ability to trade much greater amounts of money than you actually have in your account. In the case that your trading algorithm has poor risk management logic and incorrect position sizing, an open position may quickly drain your account to zero if the price goes the opposite way, and even worse – some brokers will even let you go negative and you will be left with debt instead of profit.

Key takeaway

Systemic risk management and position sizing algorithms are crucial parts of an algo trading application.

Systemic risk is very important, but there’s good news for systematic traders: it is possible to mitigate this risk by carefully testing the strategy before going to production and adjusting it so the systemic risk is minimized. But there is one more risk that is hard to mitigate during the research and development phase: transactional risk.