What you will learn
- Returns versus trades
- Skew and tails
- Serial dependence
- Selection bias
Returns versus trades
A trade sample must define its unit: completed trade, daily return or another observation. Mixing units changes the meaning of averages and uncertainty. State inclusion rules and the treatment of overlapping positions before calculation.
Skew and tails
Returns can be skewed and heavy-tailed relative to simple symmetric models. A few large observations may dominate the mean. Inspect the distribution and extreme cases rather than relying only on average and standard deviation.
Serial dependence
Serial dependence means nearby observations can contain related information. Ten overlapping trades are not necessarily ten independent experiments. An uncertainty method that assumes independence may be inappropriate without examining the sample structure.
Selection bias
Stationarity assumptions concern whether the process is sufficiently stable for the intended inference. Market behavior can change. A historical estimate is conditional evidence, not a permanent property of a strategy.
Worked example
A strategy produces ten entries during one event and all move together. Treating them as ten independent confirmations overstates how much distinct evidence the event provides.
Try it yourself
Choose a sampling unit for that case and explain how you would retain the underlying trades without claiming independence.
Show the worked solution
An event-level or time-block analysis may be useful depending on the question. Preserve individual trades for accounting, identify their shared event and use an uncertainty method that respects clustering rather than simply inflating the observation count.
Apply this to your course project
Write a performance report with confidence intervals and dependence caveats.
Keep the calculation inputs, assumptions and decisions with your work. Practical exercises are self-reviewed; the scored knowledge checks assess the questions shown, not an independent certification of practical competence.