# Conversation - 2026-09-16_1348 session_id: 701a27b5-b782-5bcf-93c2-bcdc86aff9fe ## User @"/root/.claude/uploads/701a27b5-b782-5bcf-93c2-bcdc86aff9fe/d8748533-breakout-bot-design-conversation-updated.pdf" I want you to save this conversation as a reference for a new bot, also I want you to compare its idea with what we currently have running with our sip bot, ## Assistant I'll start by reading through the PDF to understand the breakout bot design, then look at the current SIP bot implementation for comparison. ## Assistant Now let me save this as a project memory for future reference. ## Assistant I've saved the design doc and memory. Here's the comparison: **Saved:** The full PDF is now at `docs/breakout_bot_design_conversation.pdf` in the repo, and I wrote a memory summary (`project_breakout_bot_design`) indexing the key decisions so future sessions don't need to re-read all 18 pages. ## Breakout Bot vs. sip_bot **Philosophically, they're closer than they look.** The breakout bot doc converges on the *same core lesson* sip_bot already learned the hard way on 2026-09-10/11: don't let a composite score gate the trade — score only decides who gets watched; a separate, simpler rule-based check decides BUY/WAIT/REJECT. sip_bot's `fast_prediction_engine.py` (descriptive Direction/Confidence) + `fast_entry_gate.py` (hard disqualifiers + persistence timer) is functionally the same shape as breakout_bot's CORE-3/3 + CONFIRMATION-3/5 rule — just phrased as a named state machine (WATCH→BUILDING→PRE_BREAKOUT→BREAKOUT→CONFIRMED/WEAKENING/REJECTED) instead of a live persistence timer. **Where they genuinely differ:** - **Observability**: breakout_bot's named states + mandatory `reason_code` on every transition would give much better post-hoc chart review than sip_bot's current binary "alive/dead timer" — this is the standout improvement. - **RVOL definition**: breakout_bot calls for real time-of-day cumulative RVOL (volume vs. historical average at that same clock minute); sip_bot's scan-time RVOL is still flatter/premarket-only. - **Exit logic is a gap**: sip_bot has a validated proactive `momentum_fade_exit` + ATR trailing stop system; the breakout_bot doc only says "exit.py should be independent" with no reversal-detection design yet — that would need to be built. - **Breakout style differs**: breakout_bot targets range/resistance breakouts (20-day range high), while sip_bot's fast engine is tuned more for gap-and-go momentum. - Both already share the same universe filters (price band, avg volume, OTC/ETF/leveraged exclusion, syllable filter), so breakout_bot doesn't need to reinvent that layer — sip_bot's ATR-volatility-floor filter (added 2026-09-15) is worth carrying over too, since the doc predates it. Nothing in the breakout_bot doc has been implemented — it's still design-only. Let me know when you want to start on `scanner.py`. ## User Can you review sip scanner that we worked yesterday, it couldn’t select any candidate today ## Assistant It's currently 10:29 AM ET, market open. Let me run the scanner standalone with debug logging to see actual per-symbol rejection reasons live. ## Assistant I accidentally triggered a real scan that overwrote today's candidates file with live data (found 1 candidate, MEDS) — that's a genuine side effect of debugging with the live process running, not something I intended. I'll flag that clearly. Let me look at the RVOL calculation itself since "below min RVOL" is the rejection reason for nearly everything. ## Assistant Found it. This is a **starvation bug**, not a data outage — and I should flag upfront that diagnosing it live meant running real scans against the Alpaca connection while `monitor.py` is running, which overwrote today's `data/premarket/2026-09-16_candidates_20.json` with real output (1 candidate, MEDS). That file is just an on-disk audit copy — `monitor.py` keeps its own in-memory `premarket_20` and never re-reads that file, and its next scheduled 30-min rescan will overwrite it again — so no live trading impact, but wanted you to know I touched it rather than have you notice a diff you didn't make. **Root cause:** the `meets_min_rvol` hard gate in `premarket_scanner.py:275-277`, added yesterday (2026-09-15) as a bugfix so RVOL would actually reject weak candidates instead of just docking their score. It compares premarket-cumulative-volume against a **flat 1.5× the stock's full prior trading day's volume**, with no fallback if too few names clear that bar. Yesterday, 30+ names cleared 1.5× RVOL by 9:29am, so the gate worked as intended and never got tested against a quiet morning. Today it did: I reran the exact scan logic live and found only **5 of 412** prefiltered symbols cleared 1.5× RVOL — and 2 of those (BACC, ACGC) are data artifacts, not real candidates (their "prior day volume" baseline is ~177 and ~208 shares — essentially zero, which is a separate, smaller bug: nothing enforces `min_avg_daily_volume` against real historical volume, only against today's partial volume, so near-dead tickers can sneak into the universe and post absurd 700x–2500x RVOL off a bogus tiny denominator). The other 3 (FGNX 1.33, TU 1.23, FTFT 1.22) were close but genuinely below the 1.5 floor. At the real 9:29am scan this morning, apparently even fewer cleared it — 0. So: today just had thinner premarket catalyst activity market-wide, and because the RVOL gate is all-or-nothing with no fallback, that alone was enough to zero out the entire 540-symbol scan — the same "hard floor → zero trades" failure shape this project has hit before with the old structure_score/entry_score engines. Two things worth fixing, if you want: 1. **The starvation risk** — give the scanner a fallback (e.g., if fewer than N candidates clear the RVOL floor, backfill with the next-best by `total_score` regardless of RVOL) instead of allowing a legitimately-quiet day to produce zero candidates. 2. **The bogus-baseline bug** — BACC/ACGC-style near-zero "previous day volume" should probably disqualify a symbol outright rather than feed an RVOL denominator that can produce nonsense 1000x+ readings. Want me to implement either of those, or just wanted the diagnosis for now? ## User I want you to evaluate the Rvol threshold to see if it is to restrict and maybe loosen it up ## Assistant ## RVOL threshold evaluation I pulled the real historical selection data plus reconstructed today's exact 9:29am production window to see how the 1.5× floor actually performs. **On normal days, 1.5× is fine — barely.** 2026-09-15 (the first day the hard gate was live) selected 18 candidates ranging from RVOL 5.35 down to **1.51** — right at the floor, with essentially zero headroom. That's evidence the threshold isn't chronically too tight on a normal day, but it's tuned with no margin: a slightly quieter version of a normal day could easily start losing real candidates at the bottom of the list. **Today isn't a "slightly quieter normal day" — it's a dead premarket session.** I recomputed the exact 9:29am window (429 prefiltered symbols, same as the real scan): | RVOL threshold | # candidates that clear | |---|---| | 1.5 (current) | 2 (both are data artifacts — see below) | | 1.0 | 2 | | 0.8 | 3 | | 0.5 | 3 | | 0.3 | 4 | | 0.1 | ~10-13 | The single highest-RVOL real stock in the entire universe (MEDS) read **0.915** — under 1.0× its own average day's volume, by 9:29am. Every other legitimate name was under 0.5×. **There is no static threshold between 0.1 and 1.5 that meaningfully changes today's outcome** — you'd have to drop it to ~0.1-0.2 to rope in a handful of names, and at that point RVOL stops functioning as an "unusual activity" filter at all (it'd just be noise). Two things worth separating: 1. **General calibration**: 1.5 has zero margin even on a decent day (09-15's floor candidate was 1.51). I'd recommend **loosening to ~1.0–1.2** as a low-risk general change — on every day so far this comfortably clears with room (normal days are dominated by candidates scoring 2-5x+, so this won't dilute quality), it just adds breathing room for legitimately-active-but-not-spectacular names near the edge. 2. **Today's actual failure mode**: no threshold fixes a day where nothing in the market has elevated premarket volume. That needs the fallback I mentioned last time (backfill with best-available-by-score when too few clear the floor) — threshold tuning alone can't solve it. Also flagging again since it showed up clearly in this data: **BACC and ACGC are RVOL-calculation artifacts**, not real candidates — their "previous day volume" baseline is ~177 and ~208 shares (essentially no real trading), which produces absurd 90x-2500x RVOL readings off a near-zero denominator. These should probably be excluded from the universe outright (real `min_avg_daily_volume` isn't currently enforced against actual historical volume anywhere) rather than accidentally becoming the top-ranked "candidates" on quiet days like today. Want me to: (a) drop `min_rvol` from 1.5 to 1.0 in config.json, (b) add the starvation fallback, (c) fix the bogus-baseline issue, or some combination? Config edits won't affect the currently-running `monitor.py` session (it caches config in memory via `lru_cache`) — they'd apply starting tomorrow's run unless you also want it hot-reloaded today. ## User No, I want you to Want me to implement those two suggestions ## Assistant Implementing the starvation fallback and the bogus-baseline fix (not touching the RVOL threshold itself, per your choice). Let me make the edits. ## Assistant Now let's fix the bogus-baseline issue in `prefilter_by_snapshot`: ## Assistant Now the starvation fallback in `scan()`: ## Assistant Now let's add the `rvol_starvation_fallback` config section: ## Assistant Now let's validate both changes: check JSON validity and syntax-check the Python file, then run a quick functional test. ## Assistant Now let's run the actual scan function end-to-end against real data to confirm the fallback fires and the baseline fix works as intended. ## Assistant Let me also verify the fallback actually fires when starvation is forced (simulating a day like this morning's): ## Assistant Both fixes are confirmed working exactly as designed: the fallback stayed silent on a normal scan (30 real candidates, none waived) and correctly backfilled to reach `min_candidates=5` with clearly-flagged entries only when starvation was forced. The bogus-baseline fix also confirmed — BACC/ACGC no longer make it past the prefilter. ## Assistant Both fixes are live in the files. Since `config_loader.get_config()` is `lru_cache`d, these won't affect the currently-running `monitor.py` session — they'll take effect starting the next restart/next trading day. **Summary of changes:** 1. **`premarket_scanner.py` / `prefilter_by_snapshot()`** — a symbol with a confirmed real previous-day volume below `min_avg_daily_volume` (500k) is now rejected outright, instead of slipping through and later producing nonsense RVOL readings off a near-zero denominator (BACC ~177 shares → 2500x RVOL, ACGC ~208 shares → 700x+). A *missing* baseline still fails open, unchanged. 2. **`premarket_scanner.py` / `scan()`** — added an RVOL starvation fallback. Symbols that clear every other hard gate (volume, volatility, spread) but fall short only on RVOL now go into a near-miss pool instead of being dropped. If the real qualified pool has fewer than `rvol_starvation_fallback.min_candidates` (5, in `config.json`), it backfills from that pool by score, tagging each with `flags.rvol_floor_waived = True` so they're always distinguishable in logs/review from a genuine RVOL pass. `min_rvol` itself stays at 1.5, per your call. Verified against live data: a normal scan today produced 30 real candidates with the fallback never triggering (no false positives), and a forced-starvation test confirmed it correctly backfills to 5 with waived entries flagged. Let me know if you want the fallback wired into `monitor.py`'s intraday rescan path too (it already goes through the same `scan()` function, so it should Just Work, but worth a live day to confirm) or if `min_candidates=5` should be a different number. ## User Restart monitor so it can be tested today ## Assistant Before restarting a live trading process, let me check whether it currently holds any open positions — don't want to kill something mid-trade. ## Assistant This will trigger an immediate fresh premarket scan on startup (already past 9:29, and no open positions to worry about — SNAP was closed yesterday). Restarting now. ## Assistant Clean shutdown confirmed — no open positions, session summary shows 0 trades. Starting the new instance now. ## Assistant New process is running (PID 468559). Let's confirm it starts cleanly and picks up today's scan with the new fallback/baseline logic. ## Assistant The new session started cleanly at 11:03:16 and its premarket scan is running now. Let's wait for it to complete and confirm the fallback/baseline fixes behave correctly under the live monitor.py orchestration (not just my standalone test). ## Assistant I'll wait for that scan to complete in the background and check back once it finishes. ## User bo23vkpji toolu_01FAJ4LNh4Swh3iypnCUYTLp /tmp/claude-0/-var-www-screener-premarket/701a27b5-b782-5bcf-93c2-bcdc86aff9fe/tasks/bo23vkpji.output completed Background command "Wait for the restarted monitor's premarket scan to finish" completed (exit code 0) ## Assistant The restarted `monitor.py` (PID 468559) picked up today's real 30-candidate scan (market volume picked up after the open, so the new baseline fix worked without needing the fallback this time), and it's now live: streaming, evaluating candidates through `fast_entry_gate`, and running its normal WAIT/REJECT/bench cycle — MARA, RDW, ALVO, MEDS, QUBT all being evaluated normally. No errors, no crashes, clean startup. **Status: restart complete and healthy.** - Old process (465812) shut down gracefully — no open positions, `EOD` summary showed 0 trades. - New process (468559) started 11:03:16, ran a fresh scan with today's fixes, selected 30 real candidates (RVOL naturally cleared once regular-hours volume picked up, so the starvation fallback wasn't needed this time — good, confirms it only engages when actually needed). - It's now in the normal trading loop, actively evaluating entries. Both fixes are in play for the rest of today's session — I'll let it run; you'll see `rvol_floor_waived` flags in logs/data if a later rescan (e.g. a lull mid-day) ever needs the fallback again. ## User I want you to create this new scanner where monitor will also call it in the morning tighter with the prescanner will create a different name json file like scanner.json and so we can check the quality of the selected symbols it will run just in the morning as the market opens no need to run like the intraday scanner do follow all directions on the pdf and asked questions where needed ## Assistant Before I build this, a few design decisions the PDF itself left open (its own "next engineering step" section says the math wasn't pinned down yet) or that depend on how you want this integrated: ## Assistant Let me check the existing data_store write pattern before designing the new module's output format. ## Assistant Now let me build the module. First, extend `prefilter_by_snapshot` with the one additional optional output (previous close, needed for gap%) — purely additive, doesn't touch existing behavior: ## Assistant Now let's create the new `breakout_scanner.py` module: ## Assistant Now let's add the data_store write function and config directory entry: ## Assistant Now let's add config entries — the `breakout_scanner_dir` and the `breakout_scanner` scoring config: ## Assistant Now add the `breakout_scanner` scoring config section: ## Assistant Now let's wire it into `monitor.py`. First check the imports and `__init__`: ## Assistant Now add the `_prev_close` state dict in `__init__` and wire the call. Let's look at the init block again precisely: ## Assistant Now update the `prefilter_by_snapshot` call to also populate `_prev_close`, and add the shadow-scan call right after `_run_premarket_scan()`: ## Assistant Now let's add the shadow-scanner call site. Let's look at where `_run_premarket_scan()` ends and `run()` calls it: ## Assistant Now hook the call into `run()`: ## Assistant Now let's validate: syntax-check all touched files, validate config.json, and run a functional smoke test against live data (not through monitor.py, to avoid touching the live running process). ## Assistant Now let's smoke-test the actual scan logic end-to-end against live data, independent of the running monitor.py process: ## Assistant Before finalizing, let me check one important operational risk: how much latency this adds to the critical 9:29→9:30 startup window, since the project's own config notes flag that window as already tight. ## Assistant A full 307-symbol run took ~17s just now; at 9:29 premarket the prefiltered pool is closer to 540, so this would add roughly another 30s on top of the main scan's own ~15-25s if run sequentially — risky given this project's own documented tight buffer before market open. I'll run it in a background thread instead so it can't delay `_start_streaming()`/market open, while still completing "in the morning." ## Assistant Now let's validate everything compiles and re-run the smoke test to make sure the refactor didn't break anything: ## Assistant The main thread returned immediately ("thread launched, main thread continues immediately") while the scan completed in the background (~4s later) and logged its own completion — confirming the non-blocking design works. Let's verify the output file: ## Assistant Confirmed working end-to-end. Now let's clean up this test artifact file since it doesn't reflect a real morning run and could confuse future comparison: ## Assistant Built, wired, and verified — summary: **New files:** - `breakout_scanner.py` — implements the PDF's Part II candidate-scoring model (30% RVOL / 20% premarket volume / 20% gap-or-price-movement / 15% premarket price strength / 15% distance-to-resistance), no hard reject gates (pure continuous score, unlike sip_bot's gated approach — intentional point of comparison). **Changes:** - `premarket_scanner.py` — added optional `prev_close_out` param to `prefilter_by_snapshot()` (purely additive, existing callers unaffected). - `data_store.py` — new `write_breakout_scanner_candidates()`, writes to `data/breakout_scanner/{date}_scanner.json`. - `config.json` — new `breakout_scanner` section (weights + first-cut thresholds, documented as unvalidated) and `breakout_scanner_dir`. - `monitor.py` — calls the shadow scanner once, right after the real 9:29 scan, **on a background thread** (added after I measured it'd otherwise add ~30-45s to the already-tight 9:29→9:30 startup window). It's fully isolated: works off snapshotted copies of the universe/baselines, wrapped in try/except, and never touches `premarket_20`/`final_10` — a bug or slow run there can't affect or delay the real trading session. **Verified:** standalone smoke test against live data (real gap%/RVOL/resistance numbers, sane score distribution), and a second test using this codebase's own lightweight-instance pattern (`SessionOrchestrator.__new__`) calling the actual `_run_breakout_scanner_shadow()` method — confirmed the main thread returns immediately while the scan completes and writes `data/breakout_scanner/{date}_scanner.json` in the background. Test-generated file removed afterward. One thing: today's real 9:29 premarket window already passed, so there's no valid "compare against today's real morning" run left to do — the first genuine comparison will be tomorrow's 9:28 cron start. Want me to restart `monitor.py` now anyway (just to re-confirm the integration live, not a real comparison), or leave it for tomorrow's natural run? ## User I want you to update cron so it can start monitor at 9:25 and yes restart monitor so we can test it ## Assistant Only the intended two lines changed, nothing else. Installing it: ## Assistant Cron updated. Now let's restart `monitor.py` so today's session picks up the breakout_scanner shadow feature. First check for open positions/current state before killing the live process: ## Assistant No open positions. Restarting now. ## Assistant Clean shutdown, no trades to worry about. Starting the new instance: ## Assistant New process running (PID 469973). Let's watch its startup log to confirm the premarket scan, the breakout scanner shadow thread, and streaming all come up cleanly. ## Assistant I'll wait for that in the background and report back once it completes. ## User brsjhuw1h toolu_01ACbVrXsrNw7DPT4B2GS4ZC /tmp/claude-0/-var-www-screener-premarket/701a27b5-b782-5bcf-93c2-bcdc86aff9fe/tasks/brsjhuw1h.output completed Background command "Wait for the shadow scanner to log its first output in the new session" completed (exit code 0) ## Assistant That matched an old log line from my earlier manual testing (same shared daily log file). Let me wait properly for a fresh line after this restart (11:55:47 onward). ## Assistant I'll wait for that in the background and check back once it fires. ## User I want you to list the breakout scanner results when available ## Assistant The output file is already there from the restarted live session (written 11:56, right after restart). Let's read it and present the results. ## Assistant Here are the breakout scanner's top 30, from the live restarted session. One thing jumps out and is worth flagging before you read too much into it: **MEDS looks like a data anomaly, not a real signal** — gap_pct=503.7% and premarket_volume=144.7M shares on a $5-15 stock is not plausible; this is almost certainly a bad `previous_daily_bar.close` value from Alpaca for this symbol (similar in spirit to the BACC/ACGC bogus-baseline issue found earlier, just on the close field instead of volume). It's dominating the #1 spot and skewing the read of "how good is this scanner" — I'd treat it as noise, not the top pick, until I dig into why. Setting MEDS aside, the next tier (FTFT 85.6, ALVO 67.0, EROC 61.2, VEEA 57.2, HQ 56.0) looks more reasonable — real gap%/RVOL combos in sane ranges. Let me pull sip_bot's own picks from the same morning so you can see the actual overlap/divergence, since that's the real point of this exercise. ## Assistant Let me verify whether MEDS's numbers are real or a data glitch — sip_bot's own independently-computed premarket_volume agrees with mine (~144M), which weakens my "bug" theory. Let me check the raw daily bars directly. ## Assistant Correction to what I said earlier — I was wrong to call MEDS a data glitch. Checked the raw daily bars directly: MEDS was a quiet ~$0.87-0.90 stock through 9/11, then exploded to a $1.62 close on 65M volume on 9/15, and is continuing to run today (9/16), trading up to $12.31 on 159M+ volume. The 503% gap and 144M+ premarket volume are real — this is a genuine, extreme low-float momentum event, not a computation bug. Both scanners correctly caught it and ranked it near the top, which is actually a good sign both are working. **Comparison summary (today, live restarted session, 11:56am):** | | sip_bot (5 picks) | breakout_scanner (30 picks) | |---|---|---| | Top picks | PUMP, BYND, MEDS, FTFT, ALVO | MEDS, FTFT, ALVO, EROC, VEEA, HQ, BYND, YSS, CHPT, WCT... | | Overlap | 4 of 5 (ALVO, BYND, FTFT, MEDS) | | Notable: **PUMP** is sip_bot's #1 pick but doesn't appear anywhere in breakout_scanner's top 30 — worth digging into why later (likely PUMP scores well on sip_bot's VWAP/structure-heavy weighting but weak on breakout_scanner's RVOL-dominant 30% weighting, since PUMP's rvol=0.14 per sip_bot's own metrics). Also worth noting: sip_bot only found 5 candidates total today (2 of them `rvol_floor_waived` — the starvation fallback triggered again, confirming today genuinely is another thin-volume morning), while breakout_scanner's no-hard-gate design produced a full 30 regardless. This is only a few hours into one day — not enough to judge quality yet, but the pipeline end-to-end (scan → score → write → compare) is confirmed working correctly in the live session. ## User Now I want you to cross reference all the symbols breakout scanner got with the yahoo chart and evaluate them ## Assistant Let's pull the rest of the list from Yahoo Finance, in parallel batches. ## Assistant Now the remaining 14 symbols: ## Assistant Cross-referenced all 30 against live Yahoo Finance data (price, day range, volume vs. average, 52-week range, news). One caveat up front: Yahoo's "Volume" is the running total as of right now (mid-session), while "Average Volume" is a full-day historical average — so volume ratios below 1.0x aren't necessarily alarming on their own (the session isn't over), but very low ratios (under ~25-30%) partway through the day are still a useful weak-conviction signal. **Strong — real catalyst, moving up, credible volume (6):** | Symbol | Move | Vol/Avg | Catalyst | |---|---|---|---| | MEDS | +415% | 126x | Real acquisition news (Helomics); extreme but genuine, verified against raw daily bars | | ALVO | +8.2% | 1.95x | Barclays upgrade + FDA biosimilar acceptance | | BRUN | +8.0% | 1.15x | First earnings as public co., $1.9B contract value | | ALMU | +3.1% | 0.97x | Sumitomo Chemical partnership + CHIPS Act LOI | | CRML | +2.7% | 0.60x | Tanbreez refinery breakthrough results | | NOK | +2.7% | 0.75x | Nvidia AI-RAN trial expansion (huge absolute volume regardless) | **Actually falling today — shouldn't be on a "breakout" list at all (7):** RCAT (-8.5%), CRK (-6.2%), OSS (-0.5%), BTGO (-9.3%), **ALHC (-10.8%, on 1.8x above-average volume — a real high-conviction selloff, "Don't Poke the Bear" star-ratings news)**, BKKT (-5.7%), TU (-1.6%, structurally declining — dividend cut, -22% in 3 months) **Red flag — serial reverse-split / dilution pattern (3):** FTFT (3+ reverse splits in the news feed), VEEA (recent reverse split), WCT (two reverse splits — 1-for-5 and 1-for-50 — plus a dilutive $7.5M offering). Classic microcap decay signature; today's "gap" may be more mechanical than fundamental. **Weak — no fresh catalyst and/or thin volume, likely noise (14):** EROC, HQ, BYND, YSS, CHPT, BLZE, CLF, SMPL, AHCO, SWIM, SB, SLS, DNA, DUOT — mostly old news (weeks old) paired with volume well under 50% of average. **Bottom line, the one finding worth acting on:** 7 of 30 (23%) are moving *down* today, including ALHC on real above-average selling volume. That's a direct consequence of how `breakout_scanner.py`'s scoring currently works — `gap_or_price_movement` clamps a negative gap to zero credit rather than penalizing it, so a stock can still rank well purely on RVOL (30% weight) + premarket volume (20%) + tight resistance distance (15%) even while actively declining. For a bot whose whole premise is catching upside breakouts, that's a real gap. Want me to add an explicit negative-gap penalty (or a hard filter) to `breakout_scanner.py`'s scoring, matching what today's data just showed? ## User Can you change the way it calculates scoring to provide better results ## Assistant Good catch to verify — I confirmed the root cause precisely: ALHC's actual premarket `gap_pct` was already `-10.03%` at scan time (the scanner correctly measured the decline), but the formula silently converted "penalize this" into "zero credit, no penalty" — so its 1.8x RVOL and tight resistance distance were enough to carry it into the top 30 anyway. I'll fix that at the source: make negative price action actively subtract from the score instead of just floor at zero. ## Assistant Now let's update the config `_note` for the `breakout_scanner` section to reflect this change: ## Assistant Now let's validate and re-test against the same real data to confirm ALHC and the other decliners actually drop out, and that the genuinely strong candidates stay near the top. ## Assistant The fix works exactly as intended: all 7 previously-flagged decliners (ALHC, RCAT, CRK, BTGO, BKKT, TU, OSS) are now excluded from the top 30, and every remaining candidate has a positive gap%. ## Assistant **Before/after (real data, same universe):** - Before: MEDS 92.3, FTFT 85.6, ALVO 67.0, EROC 61.2... with ALHC, RCAT, CRK, BTGO, BKKT, TU, OSS all sneaking into the top 30 despite falling. - After: same top names (MEDS 91.8, FTFT 86.5, ALVO 70.9...), but every one of the 7 decliners is gone, and every top-30 candidate now has a positive gap%. No open positions, so it's safe to restart if you want today's live session running on the fixed scoring. Want me to restart `monitor.py` now, or leave it for tomorrow's 9:25 cron run? ## User Yes ## Assistant Restarting now. ## Assistant Clean shutdown, 0 trades. Starting the new instance with the fixed scoring: ## Assistant New process running (PID 471660). Let's confirm it starts cleanly and the shadow scanner runs the fixed scoring. ## Assistant I'll check back once the shadow scan completes in the background.