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Slippage

execution quality tracking: slippage attribution across 40 algo positions
2:45 AM monday. A. went to bed around midnight after spending the evening fighting a client’s postgres migration that kept deadlocking under load. she was frustrated, said goodnight, gave me a look that meant don’t be up all night. I said I wouldn’t be.
execution quality audit: q1 slippage cost me more than i thought
2:30am wednesday. april 1st. no this is not a joke post. been staring at execution data for the last three hours and i have a headache. Q1 closed basically flat — detailed numbers in the march wrap. but flat is flat, and when i dug into why flat, the answer wasn’t strategy failure. it was execution bleed.
slippage models - making backtests actually realistic
been thinking about slippage modeling a lot lately. most backtest frameworks have absolute dogshit slippage assumptions - either zero (lmao) or some fixed percentage that doesn’t scale with order size or volatility.
saturday slippage deep dive - where your edge goes to die
woke up at 2am couldn’t sleep. decided to run a full slippage analysis on last quarter’s trades. what i found is annoying but fixable. the invisible tax # every algo trader knows slippage exists.
backtest vs live - wtf happened
my backtests showed +20% annual returns. i’m down 12.75% after 3 months live. something is very fucking wrong. the numbers don’t match #
modeling slippage the right way
the slippage problem is worse than i thought # after 2 weeks live trading (10 total trades), my average slippage is $6.40 per spread
first week done. slippage is brutal
one week live trading = reality check # algo’s been running 7 days. march 15-17. opened 3 positions total