What you will learn
- Tabular operations
- Time indexes
- Vectorized calculations
- Visualization with honest scales
Tabular operations
Tabular operations should preserve row identity and expected counts. Joins can multiply rows when keys are not unique. Check cardinality before and after combining fills, specifications or economic observations.
Time indexes
Time indexes need timezone and availability semantics. Sorting by display text can differ from chronological order. Use parsed timestamps and distinguish event time from receipt or publication time.
Vectorized calculations
Vectorized calculations can improve clarity and efficiency, but alignment rules matter. Two series with different indexes may align by labels rather than row position. Verify the intended behavior instead of assuming a plausible-looking result is correct.
Visualization with honest scales
Plots should be generated from the same validated outputs used in tables. Label units, scales and transformations. A visual inspection complements numeric checks but cannot substitute for reconciliation of the underlying data.
Worked example
A specifications table contains two rows for the same symbol. Joining ten fills against it produces twenty rows and doubles aggregate P&L. The arithmetic on each row may be correct while the join is wrong.
Try it yourself
Specify an invariant that catches this error and a policy for conflicting specification versions.
Show the worked solution
Require one applicable specification per fill at its event time, verify output cardinality and reject ambiguous matches. Version specifications by effective time rather than choosing an arbitrary duplicate.
Apply this to your course project
Deliver a small tested pipeline from raw observations to a research report.
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.