Chapter 3 of 4
Keeping a learning journal
Build a record that separates observations from hindsight and includes rejected ideas, costs and process failures.
In this chapter
Record the decision before the outcome #
A journal is useful when it preserves what was actually known and believed at the time. Include the hypothesis, observable conditions, timeframe, uncertainty and what would invalidate the explanation. Adding a compelling narrative only after the outcome invites hindsight bias.
For a simulation, also state the data source, timestamp convention, entry and exit assumptions, quantity, estimated costs and any restriction on execution. An experiment without reproducible assumptions cannot be assessed from its favorable results alone.
Keep the uncomfortable observations #
Record losses, missed fills, rejected setups, rule changes, outages and operational errors. If an example was excluded from a study, preserve the reason. Selective records make a process appear more reliable than it was.
Distinguish a process error from an adverse result. A correctly followed experimental rule can lose, while an accidental rule violation can make money. Rewarding only the result can teach the wrong behavior.
Review aggregates without overclaiming #
Periodically group observations by pre-defined conditions rather than searching unlimited categories until a favorable one appears. Use net results and include uncertainty around the average. Changes in market regime or execution can reduce the relevance of older observations.
A journal is a learning and audit tool, not a certificate of future profitability. Do not publish selected screenshots or a short favorable streak as an assured-income claim. Personal information and account credentials should not be included in a shared journal.
Decision lab: Your journal must preserve the rejected trade #
A ledger containing only executed trades cannot explain whether selectivity improved results. An entry rejected by the cash or risk gate also carries information about how the process behaves. Avoid retrospectively logging only the skipped winners.
Keep original plan, version, eligible opportunities, skips, attempted orders, actual fills and rule breaches. Score process compliance separately from outcome. Compare the reason written before the decision with the later explanation without rewriting the historical note.
Hypothetical worked example
A result and a process score are different columns
A fictional paper experiment records a ₹500 gross gain. The simulated entry, however, used a price that was not available for the assumed quantity. Another observation loses ₹200 despite following the stated rule and conservative fill assumptions.
Calling the first one a success and discarding the second one would reward an unrealistic model. A useful record marks the first result as invalid for performance analysis while retaining it as a data-quality error.
At review time, compare all valid observations, not the best screenshots. If the rule was changed halfway through the sample, identify the versions separately rather than presenting a single clean historical track record.
Common mistakes
- Recording only completed profitable examples.
- Rewriting the original hypothesis after seeing what happened.
Which journal entry is most useful?
Make this chapter useful
Can you defend the reasoning?
Before moving on, answer the self-check in your own words and write why each alternative fails. Use the worked example to test that explanation. Then apply it in the module research task; keep the assumptions and the evidence that could change your answer.
Sources and further reading
Further-reading references from the curriculum source list. Contract sizes, expiry calendars, settlement references, margin parameters and tax treatment can change; verify the current operative documents for the actual product. Worked figures are illustrative, not current quotes.
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