ThePickLog · Exit-Rule Scenario Calculator

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"What could the +10%-target exit (H-EX1) earn if it works?" Every input below is recomputed live in your browser from the raw log — outcomes.csv — using the same plug-in convention as the Bayesian read-out. This is a scenario tool, not a forecast: the honest OOS test lives in the weekly report §4d. Click any for the exact definition, formula, and data lineage of that number.
⚠️ Read before dreaming: fills are assumed exactly at +10% (net +8% after the 2% cost haircut). On thin low-float names, limit orders gap through and fill quality is poor, so real-world results will be worse than every number on this page. The all-time window includes the in-sample picks that suggested H-EX1, so it flatters the rule. No compounding, taxes, borrowing, or liquidity limits are modeled. Nothing here is investment advice — it's arithmetic on a public log (HYPOTHESES.md §H-EX1).
Full methodology — every formula, assumption, and data source on this page

Where the data comes from (lineage)

1. The scanner stamps each pick into picks.csv before the outcome is known (append-only, never edited). 2. Five trading days later the grader writes one row per pick to outcomes.csv with the entry open, same-day close, 5-day close, and the 5-day max-favorable / max-adverse excursions (mfe_5d / mae_5d), all net of a 2% round-trip cost haircut on return columns. 3. This page fetches outcomes.csv raw and recomputes everything in your browser — download it and check any number yourself.

The exit rule being modeled (H-EX1, frozen 2026-06-23)

Rest a +10% limit order over the 5-trading-day hold. If the 5-day high (mfe_5d) reaches +10%, the order is assumed to fill → realized +8% net (10% minus the 2% haircut). Otherwise exit at the 5-day close (ret_open_5dclose_net). Deterministic from the log; no discretion.

The three numbers that determine everything

p = touch rate = share of evaluable picks with mfe_5d ≥ 10 m = miss average = mean ret_open_5dclose_net of picks with mfe_5d < 10 EV = expectancy = p × (+8%) + (1 − p) × m ["plug-in" convention]

"Evaluable" = graded rows where both mfe_5d and ret_open_5dclose_net are numeric (voided/pending rows are excluded). "Plug-in" means point estimates are plugged into the formula — the uncertainty of m and the baseline is not propagated (same convention, and same limitation, as bayes_h_ex1.py, roadmap R3).

Breakeven touch rates

p*(threshold) = (threshold − m) / (8 − m) beats-baseline p* uses threshold = baseline EV (mean same-day open→close net) absolute p* uses threshold = 0

The optional −20% disaster stop (H-EX2 convention)

Adds a −20% stop alongside the target → a stopped miss realizes −22% net. Modeled here mechanically: each miss's 5-day-close return is floored at −22%. This is optimistic — it ignores touch order within the window and gap-through on the stop itself. The registered H-EX2 test (weekly report §4e) replays the actual daily path from paths.csv with a conservative same-day-collision rule; that is the honest version.

Dollar scaling

position size = bankroll ÷ (picks/day × 5) [5-day hold ⇒ that many open at once] $ per trade = position size × EV $ per year = $ per trade × picks/day × 250 annual return % = $ per year ÷ bankroll

This is expectancy × turnover, not a compounded equity curve. It ignores taxes, borrowing, partial fills, liquidity limits (these are thin low-float names — size moves them), and sequencing risk: a losing streak that forces you to stop is not modeled. A negative annual figure below −100% simply means the expected bleed exceeds the bankroll within the year — you'd be broke before December.

Known biases, in the honest direction

• Fill assumption (+10% exactly) is optimistic on thin floats → real results worse.
• All-time window contains the picks that inspired the rule → flatters it (selection-on-the-data).
• The mechanical stop floor is optimistic vs the path-replay version.
• The only unbiased read is the post-2026-06-23 OOS column as n grows — judged in weekly report §4d, with a peeking-immune Bayesian read-out in bayes-h-ex1-LATEST.md.

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