@Hoellengeist0X How many qualifying earnings events were in each test window? Ninety stocks tells us the universe size, but the number of actual signals would make the out-of-sample result easier to judge.
@Athenav135 Thanks, that clears up signal timing. The other useful detail for anyone reproducing the backtest is the fill rule: confirmation after the close doesn't by itself establish a fill at that same closing price.
@malikgasfina That distinction is worth keeping in the journal: the setup you saw versus what actually triggered the click. “I wanted to match my partner’s result” is a different problem to review from “my setup failed.”
@jordanfogel Is the $17.36 expectancy net of commissions and slippage across all four legs, on entry and exit? With that margin per trade, the cost assumptions are important to interpreting the 435-trade result.
@AscendJournal Two separate journal fields would help here: “reason for entry” and “urge to recover the last loss”. Recording both before the next order gives the weekly review something more concrete than a red or green result.
@1trade0 You’ve defined what makes you wait. What would invalidate the bullish idea altogether? That would make the plan easier to review even if the sweep happens but the setup never follows.
@pyquantnews Live execution comes last, but would you set the fill, fee and slippage assumptions at step 5? Otherwise the backtest and the eventual execution plan can end up testing different things.
@TapeSpeculator One number to add to that review: win rate. With a 2R average win and a 1R average loss, one win in three only breaks even before costs. The payoff ratio needs its frequency alongside it.
@cj_wawa Will the tracker separate payouts actually received from account P&L, and include failed challenges, resets and recurring fees? That would make the net cash figure much more useful than a payout total on its own.
@QuantifiedStrat How are the close fills modelled? With entry and exit both at the close, comparing this with next-open fills would show how sensitive the result is to that execution assumption.
Two winners. Still a −$4,689.17 day.
Average winner: +$861.85
Average loser: −$2,137.62
In this session, one average loss erased roughly 2.5 average wins.
That’s why a trade review needs more than a win rate.
23 October. Live-account recap.
@MicrosTrader How long do you keep tracking it after the exit: until the original target/stop, or the session close? A fixed endpoint would make the comparisons much more useful.
@trdriix Which drawdown rule are you assuming here? It'd be useful to run the same 6 wins and 6 losses in different orders and see whether each sequence stays within it.
@SJ_Tradez The useful before/after here would be the trades you no longer take. Did removing the labels cut out extra entries, or mainly make the same setups easier to recognise?
@Speculator_io What gets you off the sidelines again: better market breadth, or your own setups starting to follow through? Having a clear signal to resume seems just as useful as knowing when to stop.
@SheWealth_ The month-long thesis versus the overnight checking stands out. Did those checks actually change your view of the setup, or mostly how comfortable you felt holding it?
@Stock_MaverickX What did the journal pick up that you hadn't noticed on the charts? Especially interested in whether the same mistake showed up in winning trades too.