Trading performance measurement. The necessary tools and metrics.

Presenting the necessary tools to evaluate your trading.

One particular question arises after applying a trading strategy is, how good is it? The goodness metric is reflected through three characteristics:

  • Stable accuracies through time;
  • Economically intuitive robust features;

Consistent low-volatility returns

A strategy that produces volatile results is not a desirable one. It is preferred that the strategy can handle different market fluctuations and regimes and not provide a 28% return on a period only to give out -54% the next one. The difficulty here is assessing how well in the future will it be consistent, and the most efficient way to address this is through time series cross validation and calculating the standard deviation of these results (when dealing with systematic strategies).

Stable accuracies through time

This concept is extremely important as it shows how well the strategy is doing on average. You can have a fantastic accuracy of 64% but on another back-test you get 31%. With random-like financial time series we are interested on average in a stable accuracy of between 52% — 54% with a risk reward that is at least around 1.8–2.0 as that will also give us a margin for when transaction costs and other fees are processed. Cross validation can also be performed to measure accuracy each time the model is run.

Economically intuitive robust features

If you want to predict housing prices, you will intuitively think of areas, number of bedrooms, parking spaces, region, etc. It is reasonable to assume that these variables impact housing prices. More bedrooms should increase the price of a house, all else equal. Therefore, when creating a model other than an auto-regressive algorithm, variables should be chosen so as to explain the variations in the dependent variable. Chocolate ice cream sales cannot be used to predict default rates on financial institutions, correlation does not mean causality, even though in the field of data science, many ignore this rule. In other words, when developing a fundamental strategy, we have to make sure that the explanatory variables are economically sound.

PERFORMANCE & EFFICIENCY MEASUREMENT

The ultimate goal of speculative trading is alpha generation if it is benchmarked to an index, otherwise a positive net return after transaction costs and various fees, as well as a decent return after tax considerations.

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  • Illiquid holdings tend to bias the ratio to the upside.
  • It is not appropriate for non-normal returns, i.e. skewed.
  • It does not consider correlations among assets.
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STRATEGY AND RISK EVALUATION

Risk exists wherever there is an opportunity and managing it means maximizing your chances for ending up in positive territory. We will now explore risk and various metrics that can offer us more insight into our strategy and help us optimize it.

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Estimating transaction costs

Something you should always be aware of is that back-testing results are false. You are most likely to never get a good estimate of future results except perhaps by luck. You cannot accurately estimate the actual fees, spreads, slippage, and any other unexpected events that will occur during live execution and therefore when including a proxy of these costs in your back-tests, it is always helpful to bias them upwards. For instance, consider that the average historical bid-ask spread on the USDCAD pair given by your broker is 0.6 pips, the best thing to do is to suppose that the actual spread is at least the historical average plus a margin for all the unexpected costs.

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Written by

Institutional FOREX Strategist | Trader | Data Science Enthusiast. Author of the Book of Back-tests: https://www.amazon.com/dp/B089CWQWF8

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