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Conviction campaign 2026-09-27 (FAILED, research knob kept) + plain-words alert wording

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Owner question: can score and confidence be combined into one number that is
easier to understand and for the system to decide on?

Research (pre-registered, nothing ships to the signal path):
- signals.grading_mode: "pair" (default, the shipped two-floor rule, byte-
  identical: all five reference MD5s reproduce on both scripts), "min"
  (min(|score|, 0.6*conf)), "product" (|score|*conf/100); thresholds fixed from
  the control's corner points, no tuning. Helpers _final_signal_and_grade /
  _transitioning_boost / conviction / CONVICTION_THRESHOLDS in signal_aggregator;
  mirrored in K2 (GRADING_MODE_CODES, PF_conv_* packed from the live module);
  twin refuses other values; M15 OOS MD5 equal on both scripts for both modes.
- Verdict: FAILED on M5 and M15. K-MIN moves 43-54 trades for -1.2R / -2.9R
  (clauses a,b,e / a,b,d); K-PROD -29R on M15 (PF 1.29, W3 DD 28.9R), neutral
  on M5. H1/H4 product +11.9R / +17.0R on W3 from extra low-score trades
  (H1 PF 1.98 -> 1.75, DD doubles): hypothesis only.
- Code review (high) before the runs: 6 findings fixed, 1 recorded
  (engine_config_fingerprint omits the switch under "pair", so live rows keep
  their fingerprint).

Delivery (books untouched): alerts lead with the grade and print the two raw
numbers in words (main.plain_words: "Direction: strong buy · Reliability: good
· timeframes agree", cut-offs = the grade table's own) on Telegram, /status,
the CLI and the dashboard card (plain_words in the snapshot); "MTF" label
replaced by "Other timeframes"; guides updated. Suite 566 passed.

Evidence: backtest/research/conviction_campaign_2026-09-27.md

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
This commit is contained in:
Harwinder Sekhon
2026-09-27 14:54:36 -04:00
parent 1454ea1838
commit 6af1237c2d
13 changed files with 808 additions and 59 deletions
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CLAUDE.md
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@@ -23,7 +23,7 @@ cp config.json.example config.json # then fill i
venv/bin/pip install cupy-cuda12x "nvidia-cuda-runtime-cu12==12.9.*" # optional, dev box: backtester GPU backend
# (CuPy from pip needs that header wheel to compile kernels)
venv/bin/python test_all.py # QA suite, no network; prints its own count
# (558 tests / 37 modules as of 2026-09-26)
# (566 tests / 37 modules as of 2026-09-27)
venv/bin/python main.py --pair XAUUSD --timeframe H1 --detail # one signal report in the terminal
# --json full dump, --panel adds the advisory LLM panel,
# --timeframe M5 | M15 | H1 | H4
@@ -46,7 +46,9 @@ realistic Oanda figure is 0.65 and must be passed explicitly), `--quality A+,A,B
`--export-csv out.csv`, `--detail`, `--set PATH=JSON` (research override of any config value; an
unknown path is a hard exit), `--export-features` (appends the 65 decision-time columns; never use
it on a parity run because it changes the file), `--fixed-rr-tp N` (research only, not shipped),
`--record DIR [--record-bars]` (research recorder, see section 9), `--backend auto|gpu|cpu` (`auto`
`--set signals.grading_mode=min|product` (research only, 2026-09-27: grade on one conviction number
instead of the score + confidence floors; both FAILED rule 1 on M5 and M15, default `pair` is the
shipped rule and byte-identical), `--record DIR [--record-bars]` (research recorder, see section 9), `--backend auto|gpu|cpu` (`auto`
uses the CuPy GPU backend when CUDA works and falls back to NumPy with the reason in the `Backend:`
line; `gpu` refuses to fall back; `cpu` forces the NumPy twins for a parity run; the env var
`FB2_BACKEND` sets the default). Both backends produce byte-identical books (section 9).
@@ -173,6 +175,12 @@ later generator shipped a setup.
timeframe within 5 bars of the last alerted setup's bar (`_ALERT_COOLDOWN_BARS`, the backtester's
entry cooldown; consecutive-bar re-entries were −25.1R of the −37.8R live book of 2026-09-04 →
09-25). After that, a fingerprint (signal + direction) stops repeats; a grade upgrade re-alerts.
Since 2026-09-27 the header leads with the grade (`Grade A · BUY · XAU/USD · M15`) and the raw
score / confidence line is replaced by words from `main.plain_words` (`Direction: strong buy ·
Reliability: good · timeframes agree`; the cut-offs are the grade table's own, so the words never
disagree with the grade; the numbers stay in `--json`, `--detail`, the dashboard meters and the
signal log). The `/status` summary and the dashboard card carry the same words (`plain_words` in
the snapshot).
The setup block prints the trailing rule first as the tested exit, TP1/TP2 as guides with their R,
the "Planned TP1 RR", and one line of validated-book statistics for the setup class
(`_book_rate_line`, from `agent_panel_base_rates.json`). The fingerprint is committed only after a successful send,
@@ -381,7 +389,7 @@ day, 30 kept), `logs/dashboard.log`. Read the databases with `mode=ro`, never `i
## 6. Testing
- `venv/bin/python test_all.py` runs everything with mocked network. Expect
`RESULTS: 558 passed, 0 failed, 558 total` (2026-09-26). On a box with a visible CUDA device
`RESULTS: 566 passed, 0 failed, 566 total` (2026-09-27). On a box with a visible CUDA device
Module 36 fails if the GPU backend falls back (the reason is in the message); with
`FB2_BACKEND=cpu` or no CUDA the GPU-path tests print a notice and the NumPy twins are tested.
Module 37 (the CUDA decision stage) adds about 85 s on the GPU because its references replay
@@ -624,6 +632,7 @@ One line per validated change or verdict, newest first. Full evidence in the nam
| Date | Decision | Where the evidence is |
|---|---|---|
| 2026-09-27 | Single "conviction" number vs the two-number grade (owner question: one number that is easier to understand and to decide on). Pre-registered; switch `signals.grading_mode` (`pair` default = the shipped rule, byte-identical on both scripts, all five pins reproduced; `min` = min(\|score\|, 0.6 × conf); `product` = \|score\| × conf / 100; thresholds from the control's corner points, no tuning; mirrored in K2, twin MD5-equal to the reference on both new paths). FAILED on M5 and M15: K-MIN moves 43–54 trades for −1.2R / −2.9R (a, b, e / a, b, d); K-PROD −29R on M15 with PF 1.29 and W3 DD 28.9R, neutral on M5. H1 / H4 K-PROD +11.9R / +17.0R on W3 by taking 40–100 extra low-score trades (H1 PF 1.98 → 1.75, DD doubles): a hypothesis, not evidence. Nothing ships; the knob stays research-only. Code review (high) before the runs: 6 findings fixed, 1 recorded. SHIPPED the display half of the question (owner request the same day): alerts lead with the grade and print score / confidence as words (`main.plain_words`, same line on Telegram, `/status`, CLI and the dashboard card); books untouched. Suite 566 | `backtest/research/conviction_campaign_2026-09-27.md` |
| 2026-09-26 | Quant review + live log 2026-09-04 → 09-25 (100 setups replayed on the scans' stored candles): stop-hunt claim REFUTED (71% of stop-outs ran ≥ 1R past the stop, same as the books); −25.1R of the −37.8R raw live book were consecutive-bar re-entries the backtester's cooldown never takes; the cooldown view sits at the 3rd–5th percentile of the books' three-week windows. SHIPPED: 5-bar alert cooldown; alert wording (trail first as the tested exit, TP1 a guide with its R, planned RR, validated-book line per class); M15 grade-A SELL block (`sell_min_quality` `A+, B`, PASSED a–f: W2 +154.15R/813 PF 1.53 vs +146.14/931 PF 1.43, OOS +68.5 vs +60.67, W3 DD +8%); 1R printed-target floor in every generator after the gates (`tp_print_floor_rr`, owner rule "TP1 at least 1:1"; label-only re-pin of all four timeframes). FAILED: M15 `min_rr_ratio` 1.0 (trade-identical, −2.0R); a pre-gate target floor (−24.6R). OWNER'S CALL: M5 `tp_floor_rr` 1.0 adds +48.9R on W2 with both halves up but max DD 10.1 → 15.9R (ladder 0.6–1.0 non-monotone); not shipped. Telegram / dashboard AI block rewritten for a new trader (owner rule): one plain headline against the usual odds, the chance "in 100", one reason for and one against, no verdict word or confidence figure; persona prompts ban trading jargon (`PANEL_PROMPT_VERSION` v2.1). Two delivery bugs from the DB checks fixed: the macro-source panel verdict never stored its probability (all v2 rows NULL); the level-breach rescan re-logged the 20:45 M15 bar three times every evening after the settlement pause (guard added to `_handle_breach`). Indicator grid and volume / daily-direction screens (Part C) not run | `backtest/research/rr_and_sell_campaign_2026-09-26.md` |
| 2026-09-18 | Full-GPU research twin SHIPPED: `backtest/cudaBasedBacktesting.py` + `backtest/cuda_signal_kernels.py` (owner decisions: research only, `gpu_backtest.py` stays the base reference, every research flag supported, the one exemption from instructions item 6 recorded there). Proof: the five reference MD5s, four 6-month acceptance books (2026-03-18 → 2026-09-18, M5 and M15, $0.65 and $0.30), the M15 OOS `--export-features` CSV and `--record --record-bars` tables, and ten research-flag variants, all byte-identical to the reference script; Module 37 (16 tests) pins every stage against the live code. Books take 0.8–2.4 s instead of 13–115 s. Rule added: any signal change is a parity event for the twin | `backtest/research/cuda_backtester_2026-09-18.md` |
| 2026-09-18 | GPU backend made real: CuPy had never compiled a kernel on the dev box (`CUDA_PATH` missing for the pip layout), so every book to date was NumPy. SHIPPED: `backtest/gpu_kernels.py` (lazy, loud backend probe; `nvidia-cuda-runtime-cu12` header wheel; CUDA kernels for the EWM batch, the pandas rolling mean/var replica and the trade resolver, each a bit-for-bit twin of the CPU code), `--backend auto/gpu/cpu` + `FB2_BACKEND`, Module 36. All five reference MD5s reproduced on the GPU and with `--backend cpu`; precompute GPU == CPU on all 42 cached windows (2.34 M bars). Wall time roughly unchanged: the replay is CPU by design | `backtest/research/gpu_backend_2026-09-18.md` |