Python for trade logging: small script, big payoff

[Shadow Trader]

Logging doesn't need to be fancy. A script that reads the platform's history export and calculates a handful of metrics gives a far better picture than eyeballing the account balance.

A starter list of columns worth computing: net pips per trade, net result in R, spread at entry, slippage, holding time, hour of day, day of week, pair, and setup tag. From those, the script can print win rate by hour, average R by setup, and the worst losing streaks.

Libraries like pandas make the work easy, even for beginners. A few dozen lines can read a CSV, group by hour, and output a table.

The real value comes from running it weekly. Patterns that stay invisible in the daily noise show up in a table of two hundred trades.

A starter sketch of the logic, in plain language: read the exported CSV, convert times to UTC, compute net pips per trade after costs, group by hour and by setup, print a table, save a chart of the equity curve. Fifty lines, maybe a hundred. After that, weekly updates take one command.

Ready-made libraries handle most of the heavy lifting. The hard part is deciding which numbers deserve attention, and that comes from asking a specific question each week, such as which hour lost the most.

For anyone who has automated their trade analysis, what metrics ended up being the most useful, and which ones turned out to be a waste of time?