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>
Live signals.db review (2026-09-04 → 09-25, 100 unique setups replayed on the
scans' stored candles): the stop-hunt claim is refuted (71% of stop-outs ran
≥ 1R past the stop, the same as the books' 70–72%); −25.1R of the −37.8R raw
live book were consecutive-bar re-entries the backtester's 5-bar cooldown never
takes; the cooldown view sits at the 3rd–5th percentile of the books' three-week
windows.
Delivery (book-neutral):
- telegram_bot: 5-bar alert cooldown per timeframe (_ALERT_COOLDOWN_BARS =
backtest.cooldown_bars); setup block prints the trail first as the tested
exit, TP1/TP2 as guides with their R, "Planned TP1 RR", and one validated-book
line per setup class (agent_panel_base_rates.json); the informational 100-pip
TP line is removed (owner request).
- AI block rewritten for a new trader: "AI check: the odds look better than /
worse than / about usual", the chance "in 100" against the usual odds, one
reason for and one against, no verdict word or confidence figure; rendered
whenever the verdict carries a probability. agent_panel: persona prompts ban
trading jargon (PANEL_PROMPT_VERSION v2.1-2026-09-26); the macro-source
verdict now carries its persona's p_win (every live v2 row had
panel_probability NULL). Dashboard and guides mirror the wording.
- telegram_bot: level-breach rescans skip the settlement-pause window (the
20:45 M15 bar was re-logged at 22:01/22:06/22:11 every evening).
Signal (pre-registered rule-1 campaign, CUDA twin paired with gpu_backtest.py):
- M15 grade-A SELLs blocked (sell_min_quality ["A+","B"], M15 override 6):
W2 813/+154.15R PF 1.53 vs 931/+146.14R PF 1.43, OOS +68.5 vs +60.67,
W3 max DD 20.9 vs 19.3R. PASSED a–f.
- Printed target 1 floored at 1R in every generator (signals.tp_print_floor_rr),
applied AFTER the min-RR and spread gates so no trade changes; mirrored in
cuda_signal_kernels.py K2. Label-only re-pin of all four timeframes (only
tp1/rr_planned columns move). A floor applied before the gate cost −24.6R and
was rejected.
- FAILED: M15 min_rr_ratio 1.0 (trade-identical, −2.0R). Owner's call: M5
tp_floor_rr 1.0 (+48.9R W2, max DD 10.1 → 15.9R), not shipped.
New pins: M5 W2 e599c8a8…, M15 W2 5b65ecf6…, M15 OOS 09b8a8e7…, H1 W3
a44fd19b…, H4 W3 f3587bae…; twin and reference MD5s equal on every window.
Base-rate table rebuilt from the new pins. Suite 558 passed.
Evidence: backtest/research/rr_and_sell_campaign_2026-09-26.md (plus the
2026-09-18 loss-anatomy and wrong-direction reports it builds on).
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Owner decisions: research only (live scans stay on the CPU code), gpu_backtest.py
stays the base reference, every research flag supported, the one exemption from
instructions item 6 recorded in backtest/instructions_for_backtest.txt.
- backtest/cuda_signal_kernels.py: the CUDA decision stage. K1 = engine.py + the
GPUTechnicalEngine overrides (one thread per bar), K2 = analyze_from_dataframes
end to end incl. gates 6b-6i, the four setup generators and the regime filter
(one thread per primary bar), K3 = position_sizing.build_features, K4 cooldown,
K5 prefix median, K6 recorder bar rows. Config packer with generated #defines;
leaf module; --fmad=false; Python evaluation order kept (ternary min/max,
pairwise sums, Neumaier sum(), exact round(x, 2)).
- backtest/cudaBasedBacktesting.py: same CLI, cache files and writers as the
reference; the host turns kernel codes into the exact strings; no CPU path.
- test_all.py Module 37 (16 tests): every stage pinned against the live code;
suite 552 / 552 on the GPU, without CUDA and with FB2_BACKEND=cpu.
- Proof: five reference MD5s, four 6-month acceptance books (M5/M15, $0.65/$0.30),
M15 OOS --export-features and --record --record-bars tables, ten research-flag
variants, and the owner's five-year M5 and M15 books, all byte-identical to
gpu_backtest.py. Wall time 0.8-6.6 s per book against 13-353 s.
- CLAUDE.md sections 1, 3, 6, 7, 9, 10, 11 and the instructions addendum: any
signal change is now a parity event for the twin.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Harwinder Sekhon