CuPy from pip had never compiled a kernel on the dev box (no CUDA_PATH for the
splayed CUDA-12 wheel layout); the probe swallowed the error and every book to
date was built on the NumPy path under a "GPU backtester" name.
- backtest/gpu_kernels.py (new): lazily probed, never-silent BACKEND state
(CUDA_PATH pointed at the nvidia-cuda-runtime-cu12 header wheel; the probe
compiles a kernel and keeps the failure reason), --backend auto|gpu|cpu and
FB2_BACKEND, and three CUDA kernels compiled with --fmad=false: ewm_batch
(the _gpu_ewm_alpha recurrence, one thread per series), roll_mean_var
(pandas 3.0.2 roll_mean/roll_var replica, Kahan + Welford + unstable
recompute), resolve_trades (trailing and static exits, one thread per trade).
- gpu_backtest.py: precompute_indicators and simulate_trades dispatch to the
GPU twins or to the untouched NumPy paths; caches are host NumPy and device
memory is released before the replay workers fork; the Backend: line names
the device or the fallback reason.
- test_all.py Module 36 (7 tests): a visible CUDA device must never fall back
silently; kernels vs their CPU references on synthetic edge cases; backend
selection; import guards (cupy banned from the live roots). Suite 536.
- CLAUDE.md, instructions_for_backtest.txt, research note with the evidence.
Verification: all five reference MD5s reproduced on the GPU and with
--backend cpu (ten runs, GPU cache fallbacks 0); precompute GPU == CPU on all
42 cached windows (2.34 M bars); suite green with GPU, with
CUDA_VISIBLE_DEVICES="" and with FB2_BACKEND=cpu; --workers 1 and --record
pairs equal. The per-bar replay stays the live Python signal code on CPU
workers by design; wall time is roughly unchanged (precompute ~3x faster).
Adds an opt-in recorder to the backtester so a replay keeps the evidence each
trade setup was decided on: every indicator's vote, value, confidence, base
weight, regime multiplier, effective weight and contribution on the primary and
both confirmation timeframes, penalties, the setup and outcome, and the Forex
Factory calendar events around the trade (4 h before entry to exit, all-day rows
on the New-York date). --record-bars adds one float32 row per analyzed bar with
the same votes plus 1 h/4 h/24 h forward moves in ATRs. Output is Parquet in a
folder per run with a manifest carrying the engine/gate fingerprints, the regime
weight table in force and the book MD5.
Nothing in the signal core changes: the trade book is byte-identical with
recording on or off (control-vs-recorded MD5 equal on both 2-year runs, M5
781 / +146.07R / PF 1.52, M15 793 / +117.62R / PF 1.41 at $0.65). Module 33
adds 12 tests (498 total): snapshot reproduces the engine's composite, absence
is no vote, calendar join edges/phases/holiday, write/load round-trip,
_replay_span parity with recording on, CLI flags, import and source guards.
Also: backtest/replay_scorecard.py (first-look scorecards, calendar buckets,
in-book and per-bar what-ifs, --check invariants), scripts/ff_calendar_scraper.py
restored from git history (the only way to regenerate the gitignored calendar
file), report in backtest/research/replay_recording_2026-09-09.md.
Claude-Session: https://claude.ai/code/session_01Nc9Ninzyi18QkWezrxeBvB
Owner directive 2026-09-06: Dukascopy and similar research material is irrelevant to
the running system; keep only code and files with a role in system functioning.
Deleted (all in git history before this commit):
- backtest/research/ tooling and data: book_report, book_diff, burst_map,
burst_precursors, burst_indicator_map, open_trade_bursts, calendar_impact,
watch_rule_eval, sizing_model, commit_ladder, llm_news_eval + llm_news_golden.csv,
ff_calendar_scraper, duka_spread + duka_spread_by_hour.csv (the Dukascopy spread
study), feed_divergence_oanda_vs_tiingo.csv, the panel dry-test JSONL, __init__.py
- scripts/feed_divergence_study.py, scripts/signal_parity_study.py (completed studies;
their reports keep the results)
- docs/architecture.png + architecture_diagram.py (developer illustration)
- docs/data_source_change_test_plan.md (Phase 4 shadow scan stays noted in CLAUDE.md)
Kept: the campaign .md reports in backtest/research/ as the decision ledger CLAUDE.md
cites (rule 1 evidence); scripts/shadow_scan.py (pending operational step);
backtest/diagnose_filters.py (test-locked backtester diagnostic).
Local, untracked material removed on this box: .superpowers/, walkforward/, baseline/,
the commit-ladder and LLM-eval result folders, the Dukascopy boundary CSV; the
sizing_2026-09 folder is pruned to the four 3-year baseline books CLAUDE.md names.
.gitignore no longer lists the removed research outputs; the backtest instructions
and CLAUDE.md no longer point at deleted tools.
Suite: 461 passed, 0 failed (nothing imported the removed files).
Claude-Session: https://claude.ai/code/session_0156YhogLytzj9P2iH1skGbL
Removed (all recoverable from git history):
- llm_provider_switch_plan.txt: 2026-08-08 DeepSeek/Anthropic multi-provider plan that
was never implemented (llm_client.py is DeepSeek-only, no `anthropic` anywhere)
- docs/superpowers/specs/2026-07-21-centralized-config-design.md: spec of the
config.json migration that merged the same day; its plan sibling was already
deleted in b936d44. app_config's docstring pointer goes with it.
- scripts/compare_oanda_tiingo.py: Phase 0 recent-tail smoke test, superseded by the
Phase 1/2 study scripts (result recorded in the test plan)
- backtest/research/fetch_oanda_m15.py + oanda_td_divergence.py + its by-hour CSV:
the 2026-07-24 Oanda-vs-Twelve-Data study, superseded by the 2026-08-14 feed
divergence report (which keeps a one-line citation)
- backtest/research/burst_profile.py and hour_analysis.py: superseded by burst_map.py
and book_report.py
- backtest/research/panel_payload_drytest_2026-08-15.py: one-off live DeepSeek probe;
its .jsonl evidence stays
Added to git: the three 2026-08-14 research reports (feed divergence, re-pin
verification, signal parity), their CSV, and docs/data_source_change_test_plan.md -
CLAUDE.md cites them as standing evidence and their driver scripts were already
committed. The test plan no longer refers to auto-execution.
Hygiene:
- .gitignore: drop oanda_state.json (daemon removed 2026-09-03) and the vanished
backtest/trailgrid dirs; ignore .coverage
- docs/architecture_diagram.py only writes the PNG when run as a script (it used
to plot on import); test box refreshed to 461 tests / 31 modules; PNG regenerated
(the committed one still drew the removed daemon)
- backtest/instructions_for_backtest.txt: manual-journal head replaced by the
actual reporting checklist; the 2026-09-06 addendum is unchanged
- doc drift: README/publisher list all six published files; news_summary.php says
4 h (not 24 h) and drops the pre-id sha1 fallback whose window closed;
analyze_signal_log lists six lenses and --scan-outcomes; agent_panel cost model
says 4 requests by default; tiingo comment cites the NY-anchored weekend;
Module 21 title no longer says "execution lifecycle"
Suite: 461 passed, 0 failed.
Claude-Session: https://claude.ai/code/session_0156YhogLytzj9P2iH1skGbL
Walk-forward "second opinion" stake multiplier (meta-labelling on 65 decision-time
features, 3yr Oanda books @ $0.65) FAILED the pre-registered rule on M5 (clean null)
and M15 (lift inside the label-shuffle null, explained by BUY-vs-SELL). Nothing that
changes numbers ships; the 12-month M5 book re-run is byte-identical to the pinned one.
Behaviour-neutral plumbing:
- position_sizing.build_features (65 features; multiplier path returns 1.0, no artifact)
- SignalReport.penalty_tags / penalty_total / momentum_fading (record-keeping)
- TradeSetup.stake_multiplier = None (reserved)
- gpu_backtest --export-features (default export stays the pinned 63 columns; extras
forced to the tail even when the first trade lacks an indicator)
Research tools (backtest/research): book_report, burst_map, burst_precursors,
open_trade_bursts, calendar_impact, burst_indicator_map, sizing_model, watch_rule_eval.
Findings recorded in backtest/research/sizing_campaign_2026-09.md: M5 stretch measures
are positive in both halves; every open-trade protection against violent bars loses on
both TFs; high-impact USD releases cause 20-31% of violent bars but trades held through
them do better; violent bars come when price is already at an extreme, not from calm.
Module 29 tests (13). Suite: 435 passed.
Claude-Session: https://claude.ai/code/session_01RWV2yLt3YXHHGVYHSQU2EE
Harwinder Sekhon