backtest: real CuPy GPU backend (kernels are bit-identical CPU twins); books byte-identical on both backends (2026-09-18)
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). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
This commit is contained in:
Harwinder Sekhon
@@ -20,8 +20,10 @@ you must follow before changing anything that affects signals. Section 10 lists
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python -m venv venv && venv/bin/pip install -r requirements.txt # Python 3.12; a venv/ already exists on the dev box
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cp config.json.example config.json # then fill in the keys (see section 5)
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venv/bin/pip install cupy-cuda12x "nvidia-cuda-runtime-cu12==12.9.*" # optional, dev box: backtester GPU backend
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# (CuPy from pip needs that header wheel to compile kernels)
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venv/bin/python test_all.py # QA suite, no network; prints its own count
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# (529 tests / 34 modules as of 2026-09-17)
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# (536 tests / 35 modules as of 2026-09-18)
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venv/bin/python main.py --pair XAUUSD --timeframe H1 --detail # one signal report in the terminal
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# --json full dump, --panel adds the advisory LLM panel,
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# --timeframe M5 | M15 | H1 | H4
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@@ -41,7 +43,10 @@ realistic Oanda figure is 0.65 and must be passed explicitly), `--quality A+,A,B
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`--export-csv out.csv`, `--detail`, `--set PATH=JSON` (research override of any config value; an
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unknown path is a hard exit), `--export-features` (appends the 65 decision-time columns; never use
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it on a parity run because it changes the file), `--fixed-rr-tp N` (research only, not shipped),
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`--record DIR [--record-bars]` (research recorder, see section 9).
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`--record DIR [--record-bars]` (research recorder, see section 9), `--backend auto|gpu|cpu` (`auto`
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uses the CuPy GPU backend when CUDA works and falls back to NumPy with the reason in the `Backend:`
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line; `gpu` refuses to fall back; `cpu` forces the NumPy twins for a parity run; the env var
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`FB2_BACKEND` sets the default). Both backends produce byte-identical books (section 9).
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`test_all.py` is **not pytest**. It uses its own `@test("name")` decorator that runs each test the
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moment the file is imported. Run the whole file; it prints per-module results and exits non-zero
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@@ -266,7 +271,8 @@ Arrows show who imports whom (generated from the code on 2026-09-15).
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| File | Role |
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|---|---|
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| `backtest/gpu_backtest.py` | The backtester (CuPy on a GPU, NumPy otherwise; check the `Backend:` log line) |
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| `backtest/gpu_backtest.py` | The backtester. The indicator precompute over the full history and the resolution of every trade run on the GPU when CUDA works (`--backend`, `Backend:` log line names the device or the fallback reason); the per-bar replay through `analyze_from_dataframes` is the live Python signal code in forked CPU workers by design and is 80–95% of wall time |
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| `backtest/gpu_kernels.py` | CuPy backend state (lazy probe, never silent, sets `CUDA_PATH` to the pip header wheel) and the three CUDA kernels: EWM batch, pandas rolling mean/var replica, trade resolver. Each is a bit-for-bit twin of the NumPy/pandas code kept in `gpu_backtest.py` (`--fmad=false`, one thread per series or trade); the twins are pinned by Module 36 and by the reference MD5s |
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| `backtest/replay_record.py` | Opt-in `--record` recorder: why every accepted trade was taken, plus calendar events. Book-neutral by contract |
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| `backtest/replay_scorecard.py` | Read-only reader of recorded runs (`DIR --check` verifies its invariants) |
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| `scripts/ff_calendar_scraper.py` | Builds `backtest/cache/calendar.parquet` (Forex Factory events) for the recorder |
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@@ -281,7 +287,7 @@ Arrows show who imports whom (generated from the code on 2026-09-15).
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| `app_config.py` | Loads `config.json` once per process; env var → config.json → refuse; validates keys |
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| `config.json.example` | The schema of `config.json` (which is gitignored) |
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| `deploy/README-dashboard.md`, `deploy/gold-dashboard.service` | Live-server checklist and systemd unit for the dashboard daemon |
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| `test_all.py` | The QA suite (34 modules; Module 35 is the agent-panel v2 module) |
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| `test_all.py` | The QA suite (35 modules; Module 36 is the GPU backend parity module) |
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---
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@@ -359,7 +365,9 @@ day, 30 kept), `logs/dashboard.log`. Read the databases with `mode=ro`, never `i
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## 6. Testing
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- `venv/bin/python test_all.py` runs everything with mocked network. Expect
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`RESULTS: 529 passed, 0 failed, 529 total` (2026-09-17). The suite's hermetic app config points
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`RESULTS: 536 passed, 0 failed, 536 total` (2026-09-18). On a box with a visible CUDA device
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Module 36 fails if the GPU backend falls back (the reason is in the message); with
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`FB2_BACKEND=cpu` or no CUDA the GPU-path tests print a notice and the NumPy twins are tested. The suite's hermetic app config points
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both database writers at a suite-private temp dir; before 2026-09-17 the RSS fixture tests wrote
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fixture headlines into the real `logs/newsdata.db`, and a Module 32 test now pins the isolation. One Twelve Data interval-map test
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reaches the network and can time out on a slow connection; a rerun clears it.
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@@ -406,8 +414,9 @@ day, 30 kept), `logs/dashboard.log`. Read the databases with `mode=ro`, never `i
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Process rules that go with them: every backtest report follows
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`backtest/instructions_for_backtest.txt`; every candidate is compared with a control run on the
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same code and cache, never with an old book directly; the `Backend:` line and the "GPU cache
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fallbacks: 0" line are part of the evidence; keep `docs/` in sync when behaviour changes.
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same code and cache, never with an old book directly; the `Backend:` line (since 2026-09-18 it
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names the CUDA device or the fallback reason) and the "GPU cache fallbacks: 0" line are part of
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the evidence; keep `docs/` in sync when behaviour changes.
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---
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@@ -446,7 +455,9 @@ trade-level diff is needed; each run takes seconds to a few minutes.
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Harness for every reference book: `venv/bin/python backtest/gpu_backtest.py --pair XAUUSD
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--spread 0.65 --workers 12 --primary-tf <tf> --start <start> --end <end> --export-csv <out>`,
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factory config, no `--set`, `--quality`, `--strategies`, `--confirm-tfs` or `--export-features`.
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All of these run offline from the kept cache windows (NumPy CPU backend, 0 cache fallbacks).
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All of these run offline from the kept cache windows (0 cache fallbacks). Since 2026-09-18 the
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CuPy GPU backend and `--backend cpu` both reproduce every MD5 below; earlier reports say
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`NumPy CPU fallback` because CuPy could not compile kernels on the dev box until then.
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| Book | Window | Trades / R / PF | MD5 of the CSV |
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|---|---|---|---|
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@@ -466,7 +477,8 @@ live names (`XAU` still means `XAU_ATR`).
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Other current-HEAD figures at $0.65: M5 W3 (3-year) 870 / +153.6R / PF 1.50; M15 W3 1273 /
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+186.6R / PF 1.39; M5 12-month (2025-08-14 → 2026-08-14) 560 / +108.81R / PF 1.54;
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H1 12-month (2025-08-04 → 2026-08-04) 91 / +22.2R / PF 1.79. All five MD5s above were
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reproduced on 2026-09-15 after the cache prune.
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reproduced on 2026-09-15 after the cache prune and on 2026-09-18 on both backends
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(`backtest/research/gpu_backend_2026-09-18.md`).
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Kept cache windows (`backtest/cache/oanda_XAU_USD_<tf>_<paddedStart>_<end>.parquet`; the padded
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start is the window start minus 62 days for M5/M15/H1, 234 for H4 and 1,400 for D1, so the D1
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@@ -515,6 +527,20 @@ MD5s. Scorecards from it are in-book statistics, never decisions.
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`NewsEngine`.
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- Twelve Data candles are not a drop-in replacement for Oanda (section 4).
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- `pyarrow` (Parquet) is installed in the dev venv only; live code never touches Parquet.
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- CuPy from pip (`cupy-cuda12x`) cannot compile a kernel without the CUDA headers; the
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`nvidia-cuda-runtime-cu12` wheel provides them and `backtest/gpu_kernels.py` points `CUDA_PATH`
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at it. Until 2026-09-18 that failure was swallowed: every book was built on NumPy while the file
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was called a GPU backtester. Now the fallback reason is in the `Backend:` line and Module 36
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fails on a box that has a visible GPU but no working backend. `cupy` is banned from the live
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roots by the same AST guard as scikit-learn.
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- The CUDA kernels are bit-for-bit replicas of the CPU code: one thread per series (EWM, rolling)
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or per trade, compiled with `--fmad=false`. Do not "optimise" them with a parallel scan or a
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two-pass variance: the last bits change and a threshold can flip somewhere in 500k scans. A
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single sequential series is not faster on the GPU than pandas' C loop; the win is the batch
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across series and trades, and the replay itself stays on the CPU (same code as live).
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- The replay workers are forked after the CUDA context exists and never touch the device (every
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cache array is host NumPy, asserted before the fork). Python 3.12 may warn about forking a
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multi-threaded process under `-X dev`; that is expected.
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- The dashboard's due-timeframe check uses the raw wake time; flooring `wake − buffer` skipped
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every H1 and H4 scan once (2026-08-02).
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- The agent panel's price context reads the frames `analyze_pair` stashes on `report._frames`.
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@@ -535,6 +561,7 @@ One line per validated change or verdict, newest first. Full evidence in the nam
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| Date | Decision | Where the evidence is |
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|---|---|---|
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| 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` |
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| 2026-09-17 | Local news scorer evaluated (the owner's scikit TF-IDF model in `~/Development/ML-NEWS`, trained on 606 DeepSeek gold labels): golden set 39/78 vs DeepSeek 78/78 vs keywords 24/78; out-of-sample agreement with DeepSeek 64% (DeepSeek agrees with itself 90%); cleaning / negation marking / label weighting move nothing; backfilling the 961 missing gold labels lifts macro-headline agreement 38% → 52% but only reshuffles golden-set errors. Verdict: a fallback, not a replacement. SHIPPED to grow a complete corpus: `xau_score` archived for every headline, no-currency headlines scored and archived (never shown), every new headline sent in its cycle (25 per call). Dashboard process made database-read-only (`set_db_writes_for_process`); the bot is the single writer. Books unaffected (news never enters signals) | `~/Development/ML-NEWS/eval/RESULTS_2026-09-17.md` (evaluation scripts live outside the repo by owner instruction) |
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| 2026-09-17 | Live review of two weeks of `signals.db`: live == backtest bar for bar (26/29 trades matched, R within 0.14); the raw advisory −7.15R is cluster re-entries the validated books never take (cooldown view −1.6R, backtester +1.1R on the window, an ordinary fortnight); composite score inversely related to the 24 h move on all timeframes (two-week bear reading, test in-book with `--record-bars`); big-candle gate re-tested and REJECTED again (removes +32R / +60R of net-positive trades). Agent panel v2 SHIPPED: probabilities anchored to book base rates, code-computed decision context, no news doctrine, mean aggregator (`verdict_source: "mean"`), prompt version logged, offline replay harness; owner decision the same day: only the macro persona runs live (`personas: ["macro"]`, 1 call per panel; technical / risk / contrarian carried no information in either evaluation, kept for research); backtester `strategy` tag fixed (label-only, books re-pinned). Delivery findings: dashboard daemon on stale code since Sep 10, bot outage Sep 9 | `backtest/research/signal_log_review_2026-09-17.md`, `backtest/research/panel_v2_2026-09-17.md` |
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| 2026-09-15 | Backtest folder cleanup: `diagnose_filters.py`, `shadow_scan.py`, and the two one-time news scripts removed (last commit with them `931b265`); every pinned CSV, the research output folders, the BTC/ETH cache snapshots, the Tiingo caches and 13 uncited Oanda windows moved to `~/Development/ForexBot2_trash_2026-09-15/` (346 MB) pending the owner's delete; all five reference MD5s reproduced afterwards. Suite 506 / 33 | commit `b0b71e1` |
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+307
-60
@@ -19,7 +19,9 @@ Key properties:
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- Trade simulation is full candle walk on the primary timeframe using
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High/Low; if both SL and TP1 hit in the same bar, SL is assumed first
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(conservative).
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- Ships with CuPy fallback to NumPy so the script runs anywhere.
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- Ships with CuPy fallback to NumPy so the script runs anywhere. The CUDA
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kernels (backtest/gpu_kernels.py) are bit-for-bit replicas of the NumPy
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path, so both backends produce the same book; --backend cpu forces NumPy.
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Usage:
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python backtest/gpu_backtest.py --pair XAUUSD --days 365
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@@ -64,36 +66,22 @@ from engine import IndicatorSignal, TechnicalAnalysis, TechnicalEngine # noqa:
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from signal_aggregator import SignalAggregator, SignalReport, TradeSetup # noqa: E402
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import position_sizing # noqa: E402
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from backtest import replay_record # noqa: E402
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from backtest import gpu_kernels as _gk # noqa: E402
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# ─────────────────────────────────────────────────────────────────────
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# GPU shim — CuPy if available AND CUDA runtime works, else NumPy.
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# An importable CuPy can still fail at first kernel launch if CUDA libs
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# are missing (containers, headless boxes), so verify with a tiny op.
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# GPU backend — backtest/gpu_kernels.py owns the CuPy state (probed
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# lazily and never silently: a fallback carries its reason into the
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# "Backend:" line) and the CUDA kernels. Every kernel is a bit-for-bit
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# replica of the NumPy/pandas code kept in this file as the CPU path, so
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# `--backend cpu` and the GPU produce the same book (proof: the five
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# reference MD5s, CLAUDE.md section 9). The forked replay workers never
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# touch the device: every precomputed series is moved to host first.
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# ─────────────────────────────────────────────────────────────────────
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def _detect_gpu():
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try:
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import cupy as cp_mod # type: ignore
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# Force runtime init: a kernel launch will surface missing libs.
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_ = cp_mod.asarray(np.zeros(2, dtype=np.float64))
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_ = cp_mod.cumsum(_).get()
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return cp_mod, True
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except Exception:
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return np, False
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BACKEND = _gk.BACKEND
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|
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|
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cp, _GPU_AVAILABLE = _detect_gpu()
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|
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|
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def _to_gpu(arr: np.ndarray):
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"""Move a numpy array to GPU (or no-op if CuPy unavailable)."""
|
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return cp.asarray(arr) if _GPU_AVAILABLE else arr
|
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|
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|
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def _to_cpu(arr) -> np.ndarray:
|
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"""Pull array back to host."""
|
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if _GPU_AVAILABLE and hasattr(arr, "get"):
|
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return arr.get()
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return np.asarray(arr)
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def _gpu_on() -> bool:
|
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return BACKEND.available
|
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|
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|
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logger = logging.getLogger("gpu_backtest")
|
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@@ -215,18 +203,29 @@ class IndicatorCache:
|
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def precompute_indicators(
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df: pd.DataFrame, tech_cfg, gpu_log: bool = False
|
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) -> IndicatorCache:
|
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"""Compute every causal indicator series once on the full df."""
|
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"""Compute every causal indicator series once on the full df.
|
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|
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Dispatches to the CUDA kernels when the backend is available and to
|
||||
the NumPy/pandas code otherwise. The two are bit-identical (Module 36
|
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and the 42-window check in backtest/research/gpu_backend_2026-09-18.md).
|
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"""
|
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if gpu_log:
|
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logger.info(
|
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f"Pre-computing indicators on {len(df)} bars "
|
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f"({'CuPy GPU' if _gpu_on() else 'NumPy CPU'} backend)"
|
||||
)
|
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if _gpu_on():
|
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return _precompute_indicators_gpu(df, tech_cfg)
|
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return _precompute_indicators_cpu(df, tech_cfg)
|
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|
||||
|
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def _precompute_indicators_cpu(df: pd.DataFrame, tech_cfg) -> IndicatorCache:
|
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"""The NumPy/pandas path — the reference the CUDA twin replicates."""
|
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close = df["Close"].to_numpy(dtype=np.float64)
|
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high = df["High"].to_numpy(dtype=np.float64)
|
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low = df["Low"].to_numpy(dtype=np.float64)
|
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n = len(close)
|
||||
|
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if gpu_log:
|
||||
logger.info(
|
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f"Pre-computing indicators on {n} bars "
|
||||
f"({'GPU' if _GPU_AVAILABLE else 'CPU'} backend)"
|
||||
)
|
||||
|
||||
# ── EMAs (use ewm span semantics, identical to pandas .ewm(span=, adjust=False).mean()) ─
|
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ema_fast = _gpu_ewm_span(close, tech_cfg.ema_fast)
|
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ema_medium = _gpu_ewm_span(close, tech_cfg.ema_medium)
|
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@@ -388,6 +387,187 @@ def precompute_indicators(
|
||||
)
|
||||
|
||||
|
||||
def _assert_host_cache(cache: IndicatorCache) -> None:
|
||||
"""Every array the forked workers will read must be plain NumPy."""
|
||||
for name, val in vars(cache).items():
|
||||
if name in ("df", "index_pos", "rsi_series", "macd_hist_series") or val is None:
|
||||
continue
|
||||
if type(val) is not np.ndarray:
|
||||
raise TypeError(f"IndicatorCache.{name} is {type(val).__name__}, not np.ndarray")
|
||||
|
||||
|
||||
def _precompute_indicators_gpu(df: pd.DataFrame, tech_cfg) -> IndicatorCache:
|
||||
"""CUDA twin of `_precompute_indicators_cpu`.
|
||||
|
||||
Same expressions in the same order (single-op CuPy ufuncs cannot fuse
|
||||
into an FMA and double add/mul/div/sqrt are correctly rounded on both
|
||||
sides); the recurrences and the pandas rolling statistics run in the
|
||||
kernels of backtest/gpu_kernels.py, one thread per series. Everything
|
||||
comes back to the host once at the end: the replay workers read NumPy.
|
||||
"""
|
||||
cp = BACKEND.cp
|
||||
nan = float("nan")
|
||||
close_h = df["Close"].to_numpy(dtype=np.float64)
|
||||
n = len(close_h)
|
||||
close = cp.asarray(close_h)
|
||||
high = cp.asarray(df["High"].to_numpy(dtype=np.float64))
|
||||
low = cp.asarray(df["Low"].to_numpy(dtype=np.float64))
|
||||
|
||||
# ── RSI inputs (delta[0] is NaN; NaN > 0 is False so gain[0] = 0.0) ──
|
||||
delta = cp.empty(n, dtype=cp.float64)
|
||||
delta[0] = nan
|
||||
delta[1:] = close[1:] - close[:-1]
|
||||
gain = cp.where(delta > 0, delta, 0.0)
|
||||
loss = cp.where(delta < 0, -delta, 0.0)
|
||||
|
||||
# ── True range (fmax ignores NaN, like the CPU path) ──
|
||||
prev_close = cp.empty(n, dtype=cp.float64)
|
||||
prev_close[0] = nan
|
||||
prev_close[1:] = close[:-1]
|
||||
tr1 = high - low
|
||||
tr2 = cp.abs(high - prev_close)
|
||||
tr3 = cp.abs(low - prev_close)
|
||||
tr2[0] = nan
|
||||
tr3[0] = nan
|
||||
tr = cp.fmax(cp.fmax(tr1, tr2), tr3)
|
||||
|
||||
# ── Directional movement ──
|
||||
plus_dm_raw = cp.empty(n, dtype=cp.float64)
|
||||
minus_dm_raw = cp.empty(n, dtype=cp.float64)
|
||||
plus_dm_raw[0] = nan
|
||||
minus_dm_raw[0] = nan
|
||||
plus_dm_raw[1:] = high[1:] - high[:-1]
|
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minus_dm_raw[1:] = -(low[1:] - low[:-1])
|
||||
cond_plus = (plus_dm_raw > minus_dm_raw) & (plus_dm_raw > 0)
|
||||
plus_dm = cp.where(cond_plus, plus_dm_raw, 0.0)
|
||||
cond_minus = (minus_dm_raw > plus_dm_raw) & (minus_dm_raw > 0)
|
||||
minus_dm = cp.where(cond_minus, minus_dm_raw, 0.0)
|
||||
|
||||
# ── Stage 1: every recurrence that depends only on price, one launch ──
|
||||
def span_alpha(span: int) -> float:
|
||||
return 2.0 / (span + 1.0) # identical to _gpu_ewm_span
|
||||
|
||||
rsi_p = tech_cfg.rsi_period
|
||||
atr_p = tech_cfg.atr_period
|
||||
adx_p = tech_cfg.adx_period
|
||||
kc_period = getattr(tech_cfg, 'kc_period', 20)
|
||||
kc_atr_period = getattr(tech_cfg, 'kc_atr_period', 10)
|
||||
kc_multiplier = getattr(tech_cfg, 'kc_multiplier', 1.5)
|
||||
have_trend = n >= tech_cfg.ema_trend
|
||||
jobs = [
|
||||
(close, span_alpha(tech_cfg.ema_fast), 0),
|
||||
(close, span_alpha(tech_cfg.ema_medium), 0),
|
||||
(close, span_alpha(tech_cfg.ema_slow), 0),
|
||||
(close, span_alpha(tech_cfg.macd_fast), 0),
|
||||
(close, span_alpha(tech_cfg.macd_slow), 0),
|
||||
(close, span_alpha(kc_period), 0),
|
||||
(gain, 1.0 / rsi_p, rsi_p),
|
||||
(loss, 1.0 / rsi_p, rsi_p),
|
||||
(tr, 1.0 / atr_p, atr_p),
|
||||
(tr, 1.0 / adx_p, adx_p),
|
||||
(plus_dm, 1.0 / adx_p, adx_p),
|
||||
(minus_dm, 1.0 / adx_p, adx_p),
|
||||
(tr, 1.0 / kc_atr_period, kc_atr_period),
|
||||
]
|
||||
if have_trend:
|
||||
jobs.append((close, span_alpha(tech_cfg.ema_trend), 0))
|
||||
out = _gk.ewm_batch_dev([j[0] for j in jobs], [j[1] for j in jobs],
|
||||
[j[2] for j in jobs])
|
||||
(ema_fast, ema_medium, ema_slow, ema_macd_fast, ema_macd_slow, kc_mid,
|
||||
avg_gain, avg_loss, atr, atr_adx, plus_dm_smooth, minus_dm_smooth,
|
||||
kc_atr) = out[:13]
|
||||
ema_trend = out[13] if have_trend else None
|
||||
|
||||
# ── RSI ──
|
||||
avg_loss_safe = cp.where(avg_loss == 0, 1e-10, avg_loss)
|
||||
rs = avg_gain / avg_loss_safe
|
||||
rsi = 100.0 - (100.0 / (1.0 + rs))
|
||||
|
||||
# ── MACD line; DI / DX ──
|
||||
macd_line = ema_macd_fast - ema_macd_slow
|
||||
atr_adx_safe = cp.where(atr_adx == 0, nan, atr_adx)
|
||||
plus_di = 100.0 * plus_dm_smooth / atr_adx_safe
|
||||
minus_di = 100.0 * minus_dm_smooth / atr_adx_safe
|
||||
di_sum = plus_di + minus_di
|
||||
di_sum_safe = cp.where(di_sum == 0, nan, di_sum)
|
||||
dx = 100.0 * cp.abs(plus_di - minus_di) / di_sum_safe
|
||||
|
||||
# ── Stage 2: recurrences on stage-1 outputs, one launch ──
|
||||
signal_line, adx = _gk.ewm_batch_dev(
|
||||
[macd_line, dx], [span_alpha(tech_cfg.macd_signal), 1.0 / adx_p], [0, adx_p])
|
||||
macd_hist = macd_line - signal_line
|
||||
|
||||
# ── Bollinger: the pandas rolling replica (mean + var, then zsqrt) ──
|
||||
(bb_mid,), (bb_var,) = _gk.roll_mean_var_dev([close], tech_cfg.bb_period, ddof=0)
|
||||
bb_std = _gk.roll_std_from_var_dev(bb_var)
|
||||
bb_upper = bb_mid + tech_cfg.bb_std * bb_std
|
||||
bb_lower = bb_mid - tech_cfg.bb_std * bb_std
|
||||
bb_range = bb_upper - bb_lower
|
||||
bb_range_safe = cp.where(bb_range == 0, nan, bb_range)
|
||||
bb_position = (close - bb_lower) / bb_range_safe
|
||||
bb_position = cp.where(cp.isnan(bb_position), 0.5, bb_position)
|
||||
bb_mid_safe = cp.where(bb_mid == 0, nan, bb_mid)
|
||||
bb_band_width = (bb_upper - bb_lower) / bb_mid_safe
|
||||
# 20-bar mean of the band width: the CPU path uses the literal 20.
|
||||
(bb_avg_width,), _unused = _gk.roll_mean_var_dev([bb_band_width], 20, ddof=0)
|
||||
|
||||
# ── ADX slope ──
|
||||
adx_slope_lookback = getattr(tech_cfg, 'adx_slope_lookback', 5)
|
||||
adx_slope = cp.full(n, nan, dtype=cp.float64)
|
||||
if n > adx_slope_lookback:
|
||||
adx_slope[adx_slope_lookback:] = adx[adx_slope_lookback:] - adx[:-adx_slope_lookback]
|
||||
|
||||
# ── ROC ──
|
||||
roc_period = getattr(tech_cfg, 'roc_period', 10)
|
||||
roc = cp.full(n, nan, dtype=cp.float64)
|
||||
if n > roc_period:
|
||||
prev_close = close[:-roc_period]
|
||||
prev_safe = cp.where(prev_close == 0, nan, prev_close)
|
||||
roc[roc_period:] = ((close[roc_period:] - prev_close) / prev_safe) * 100.0
|
||||
|
||||
# ── Keltner ──
|
||||
kc_upper = kc_mid + kc_multiplier * kc_atr
|
||||
kc_lower = kc_mid - kc_multiplier * kc_atr
|
||||
kc_squeeze = ((bb_lower > kc_lower) & (bb_upper < kc_upper)).astype(cp.float64)
|
||||
|
||||
# ── One transfer to the host; the workers read NumPy only ──
|
||||
def h(a):
|
||||
return a.get()
|
||||
|
||||
rsi_h = h(rsi)
|
||||
macd_hist_h = h(macd_hist)
|
||||
cache = IndicatorCache(
|
||||
df=df,
|
||||
index_pos={ts: i for i, ts in enumerate(df.index)},
|
||||
ema_fast=h(ema_fast),
|
||||
ema_medium=h(ema_medium),
|
||||
ema_slow=h(ema_slow),
|
||||
ema_trend=h(ema_trend) if ema_trend is not None else None,
|
||||
rsi=rsi_h,
|
||||
rsi_series=pd.Series(rsi_h, index=df.index, name="rsi"),
|
||||
macd_hist=macd_hist_h,
|
||||
macd_hist_series=pd.Series(macd_hist_h, index=df.index, name="macd_hist"),
|
||||
bb_mid=h(bb_mid),
|
||||
bb_upper=h(bb_upper),
|
||||
bb_lower=h(bb_lower),
|
||||
bb_position=h(bb_position),
|
||||
bb_band_width=h(bb_band_width),
|
||||
bb_avg_width=h(bb_avg_width),
|
||||
atr=h(atr),
|
||||
adx=h(adx),
|
||||
plus_di=h(plus_di),
|
||||
minus_di=h(minus_di),
|
||||
adx_slope=h(adx_slope),
|
||||
roc=h(roc),
|
||||
kc_mid=h(kc_mid),
|
||||
kc_upper=h(kc_upper),
|
||||
kc_lower=h(kc_lower),
|
||||
kc_squeeze=h(kc_squeeze),
|
||||
)
|
||||
_assert_host_cache(cache)
|
||||
return cache
|
||||
|
||||
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
# GPU-AWARE TECHNICAL ENGINE (subclasses live engine, lookup-only)
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
@@ -1330,6 +1510,15 @@ def simulate_trades(
|
||||
closes_arr[i, :n] = sim_close[entry_pos:end_pos]
|
||||
exit_times.append(list(sim_index[entry_pos:end_pos]))
|
||||
|
||||
if _gpu_on():
|
||||
# One CUDA thread per trade, both exit modes (bit-identical twin of
|
||||
# the two CPU paths below; Module 36 and the reference MD5s).
|
||||
_resolve_trades_gpu(
|
||||
trades, opens_arr, highs_arr, lows_arr, closes_arr,
|
||||
valid_lens, exit_times, spread, trail_arm_r, trail_dist_r,
|
||||
)
|
||||
return
|
||||
|
||||
if trail_arm_r > 0:
|
||||
_simulate_trailing(
|
||||
trades, opens_arr, highs_arr, lows_arr, closes_arr,
|
||||
@@ -1337,42 +1526,33 @@ def simulate_trades(
|
||||
)
|
||||
return
|
||||
|
||||
# GPU vectorized: for each trade, scan its window for first SL/TP hit.
|
||||
# CPU static path, vectorized: for each trade, scan its window for the
|
||||
# first SL/TP hit.
|
||||
sls = np.array([t.sl for t in trades], dtype=np.float64)[:, None]
|
||||
tps = np.array([t.tp1 for t in trades], dtype=np.float64)[:, None]
|
||||
is_buy = np.array([t.direction == "BUY" for t in trades], dtype=bool)[:, None]
|
||||
|
||||
h_g = _to_gpu(highs_arr)
|
||||
l_g = _to_gpu(lows_arr)
|
||||
c_g = _to_gpu(closes_arr)
|
||||
sls_g = _to_gpu(sls)
|
||||
tps_g = _to_gpu(tps)
|
||||
is_buy_g = _to_gpu(is_buy)
|
||||
|
||||
# Hit masks. For BUY: SL when low<=sl, TP when high>=tp.
|
||||
# For SELL: SL when high>=sl, TP when low<=tp.
|
||||
sl_hit_buy = l_g <= sls_g
|
||||
tp_hit_buy = h_g >= tps_g
|
||||
sl_hit_sell = h_g >= sls_g
|
||||
tp_hit_sell = l_g <= tps_g
|
||||
sl_hit_buy = lows_arr <= sls
|
||||
tp_hit_buy = highs_arr >= tps
|
||||
sl_hit_sell = highs_arr >= sls
|
||||
tp_hit_sell = lows_arr <= tps
|
||||
|
||||
sl_hit = cp.where(is_buy_g, sl_hit_buy, sl_hit_sell)
|
||||
tp_hit = cp.where(is_buy_g, tp_hit_buy, tp_hit_sell)
|
||||
sl_hit = np.where(is_buy, sl_hit_buy, sl_hit_sell)
|
||||
tp_hit = np.where(is_buy, tp_hit_buy, tp_hit_sell)
|
||||
|
||||
# Mask out NaN bars (no data past valid_lens).
|
||||
valid_mask = ~cp.isnan(h_g)
|
||||
valid_mask = ~np.isnan(highs_arr)
|
||||
sl_hit = sl_hit & valid_mask
|
||||
tp_hit = tp_hit & valid_mask
|
||||
|
||||
# First-hit indices (or max_bars if never).
|
||||
idx_template = cp.arange(max_bars)[None, :]
|
||||
sl_idx = cp.where(sl_hit, idx_template, max_bars)
|
||||
tp_idx = cp.where(tp_hit, idx_template, max_bars)
|
||||
first_sl = cp.min(sl_idx, axis=1)
|
||||
first_tp = cp.min(tp_idx, axis=1)
|
||||
|
||||
sl_first_arr = _to_cpu(first_sl)
|
||||
tp_first_arr = _to_cpu(first_tp)
|
||||
idx_template = np.arange(max_bars)[None, :]
|
||||
sl_idx = np.where(sl_hit, idx_template, max_bars)
|
||||
tp_idx = np.where(tp_hit, idx_template, max_bars)
|
||||
sl_first_arr = np.min(sl_idx, axis=1)
|
||||
tp_first_arr = np.min(tp_idx, axis=1)
|
||||
|
||||
# Resolve each trade and compute realized MFE/MAE *up to exit bar*
|
||||
# (a trade closed at bar 5 cannot have meaningful excursions at bar 12).
|
||||
@@ -1447,6 +1627,60 @@ def simulate_trades(
|
||||
t.mae_r = float(max(0.0, mae))
|
||||
|
||||
|
||||
def _resolve_trades_gpu(
|
||||
trades: List[Trade],
|
||||
opens_arr: np.ndarray,
|
||||
highs_arr: np.ndarray,
|
||||
lows_arr: np.ndarray,
|
||||
closes_arr: np.ndarray,
|
||||
valid_lens: np.ndarray,
|
||||
exit_times: List[List[pd.Timestamp]],
|
||||
spread: float,
|
||||
trail_arm_r: float,
|
||||
trail_dist_r: float,
|
||||
) -> None:
|
||||
"""CUDA twin of `_simulate_trailing` (trail_arm_r > 0) and of the static
|
||||
SL/TP1 branch of `simulate_trades`: the kernel walks every trade's window
|
||||
in its own thread and returns exit index/price/reason, bars held, R and
|
||||
the excursions; this host loop only writes them onto the Trade objects,
|
||||
with the same 'not set' cases as the CPU code."""
|
||||
trailing = trail_arm_r > 0
|
||||
entries = np.array([t.entry_price for t in trades], dtype=np.float64)
|
||||
sls = np.array([t.sl for t in trades], dtype=np.float64)
|
||||
tps = np.array([t.tp1 for t in trades], dtype=np.float64)
|
||||
is_buy = np.array([t.direction == "BUY" for t in trades], dtype=np.int8)
|
||||
arms = np.array([t.trail_arm_r if t.trail_arm_r > 0 else trail_arm_r
|
||||
for t in trades], dtype=np.float64)
|
||||
dists = np.array([t.trail_dist_r if t.trail_dist_r > 0 else trail_dist_r
|
||||
for t in trades], dtype=np.float64)
|
||||
res = _gk.resolve_trades(
|
||||
opens_arr, highs_arr, lows_arr, closes_arr, valid_lens,
|
||||
entries, sls, tps, is_buy, arms, dists, spread, trailing)
|
||||
for i, t in enumerate(trades):
|
||||
code = int(res["reason"][i])
|
||||
if code == _gk.REASON_NO_DATA:
|
||||
t.exit_reason = "NO_DATA"
|
||||
continue
|
||||
bars = int(res["bars_held"][i])
|
||||
exit_idx = int(res["exit_idx"][i])
|
||||
if code == _gk.REASON_NO_DATA_AFTER_TIMEOUT:
|
||||
# Static path: the TIMEOUT bookkeeping ran before the NaN close
|
||||
# was seen, so bars_held and exit_time are kept and the price
|
||||
# is None (never a NaN R in the book).
|
||||
t.exit_reason = "NO_DATA"
|
||||
t.bars_held = bars
|
||||
t.exit_price = None
|
||||
t.exit_time = exit_times[i][exit_idx]
|
||||
continue
|
||||
t.exit_reason = _gk.REASON_NAMES[code]
|
||||
t.bars_held = bars
|
||||
t.exit_price = float(res["exit_price"][i])
|
||||
t.exit_time = exit_times[i][exit_idx]
|
||||
t.r_multiple = float(res["r"][i])
|
||||
t.mfe_r = float(res["mfe"][i])
|
||||
t.mae_r = float(res["mae"][i])
|
||||
|
||||
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
# BACKTEST RUNNER
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
@@ -1878,7 +2112,12 @@ def run_backtest(
|
||||
tf.value: precompute_indicators(dfs_full[tf], platform_cfg.technical, gpu_log=True)
|
||||
for tf in timeframes
|
||||
}
|
||||
logger.info(f"Indicator pre-compute took {time.perf_counter() - t0:.2f}s")
|
||||
logger.info(
|
||||
f"Indicator pre-compute took {time.perf_counter() - t0:.2f}s "
|
||||
f"({'CuPy GPU' if _gpu_on() else 'NumPy CPU'})")
|
||||
# The replay workers are forked below and must never touch the device:
|
||||
# every cache array is already host NumPy; hand pooled memory back too.
|
||||
BACKEND.release_device_memory()
|
||||
|
||||
# ── Inject GPU engine into a fresh aggregator ──
|
||||
aggregator = SignalAggregator(platform_cfg)
|
||||
@@ -1998,7 +2237,7 @@ def run_backtest(
|
||||
trail_dist_r=platform_cfg.signals.trail_dist_r)
|
||||
logger.info(
|
||||
f"Trade simulation took {time.perf_counter() - t_sim:.2f}s "
|
||||
f"(GPU={'yes' if _GPU_AVAILABLE else 'no'})"
|
||||
f"({'CuPy GPU' if _gpu_on() else 'NumPy CPU'})"
|
||||
)
|
||||
|
||||
if bt_cfg.record_dir:
|
||||
@@ -2009,7 +2248,7 @@ def run_backtest(
|
||||
n_primary_bars=len(primary_df), n_bars_in_window=n_bars,
|
||||
skipped=skipped_reasons, n_candidates=len(candidates),
|
||||
workers=workers,
|
||||
backend="CuPy GPU" if _GPU_AVAILABLE else "NumPy CPU fallback",
|
||||
backend=BACKEND.describe(),
|
||||
cache_files=cache_names, replay_seconds=replay_seconds,
|
||||
cache_fallbacks=cache_fallbacks),
|
||||
sim_df=sim_df, bar_chunks=bar_chunks or None)
|
||||
@@ -2421,6 +2660,12 @@ def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
|
||||
help="Print full per-trade reasoning (indicators, signals, agreement, why entered)")
|
||||
p.add_argument("--workers", type=int, default=0,
|
||||
help="Replay worker processes (0=auto: cpu-2 capped at 12, 1=sequential)")
|
||||
p.add_argument("--backend", choices=("auto", "gpu", "cpu"), default=None,
|
||||
help="Compute backend for the indicator pre-compute and the "
|
||||
"trade resolution: auto = CuPy when CUDA works, else "
|
||||
"NumPy (the reason is logged); gpu = fail loudly without "
|
||||
"CUDA; cpu = force the NumPy twins (parity runs). The "
|
||||
"env var FB2_BACKEND sets the default.")
|
||||
p.add_argument("--reasoning-samples", type=int, default=3,
|
||||
help="If --detail not set, print this many sample trades' full reasoning")
|
||||
p.add_argument("--log-level", default="INFO")
|
||||
@@ -2486,6 +2731,8 @@ def main(argv: Optional[List[str]] = None) -> int:
|
||||
level=args.log_level.upper(),
|
||||
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
|
||||
)
|
||||
if args.backend:
|
||||
BACKEND.set_mode(args.backend)
|
||||
|
||||
pair = _resolve_pair(args.pair)
|
||||
primary_tf = _resolve_tf(args.primary_tf)
|
||||
@@ -2592,7 +2839,7 @@ def main(argv: Optional[List[str]] = None) -> int:
|
||||
)
|
||||
|
||||
logger.info(
|
||||
f"Backend: {'CuPy GPU' if _GPU_AVAILABLE else 'NumPy CPU fallback'} | "
|
||||
f"Backend: {BACKEND.describe()} | "
|
||||
f"Pair: {PAIR_INFO[pair]['display']} | Window: {start.date()} → {end.date()} | "
|
||||
f"Confirm TFs: {','.join(tf.value for tf in confirm_tfs)} | Spread: {spread}"
|
||||
)
|
||||
|
||||
@@ -0,0 +1,699 @@
|
||||
"""
|
||||
backtest/gpu_kernels.py — the CUDA side of the backtester.
|
||||
|
||||
Owns two things:
|
||||
|
||||
1. ``BACKEND`` — the one CuPy/CUDA state object the backtester consults.
|
||||
It is probed lazily (never at import: test_all.py, analyze_signal_log.py
|
||||
and replay_record import the backtester without wanting a CUDA context)
|
||||
and it never fails silently: when CUDA is unusable the reason is kept in
|
||||
``BACKEND.reason`` and printed in the ``Backend:`` log line. Before
|
||||
2026-09-18 the probe swallowed "Failed to auto-detect CUDA root
|
||||
directory" and every book was built on the NumPy path while the file
|
||||
was called a GPU backtester.
|
||||
|
||||
CuPy from pip (cupy-cuda12x) needs the CUDA headers to JIT kernels. They
|
||||
come from the ``nvidia-cuda-runtime-cu12`` wheel, whose splayed layout
|
||||
has no single root, so CuPy cannot find them on its own: this module
|
||||
points ``CUDA_PATH`` at that wheel when the variable is unset.
|
||||
|
||||
2. Three CUDA kernels, each a bit-for-bit replica of the CPU code it
|
||||
replaces (proven on every cached window, see
|
||||
backtest/research/gpu_backend_2026-09-18.md):
|
||||
|
||||
- ``ewm_batch`` — pandas ``ewm(alpha, min_periods, adjust=False)``
|
||||
as implemented by ``gpu_backtest._gpu_ewm_alpha``;
|
||||
one CUDA thread per series (the recurrence is
|
||||
order-dependent, so a parallel scan would change
|
||||
the last bits — that is not acceptable here).
|
||||
- ``roll_mean_var`` — pandas 3.0.2 ``rolling(window).mean()`` and
|
||||
``.var(ddof)`` (Kahan-compensated add/remove and
|
||||
Welford with the numerically-unstable recompute),
|
||||
one thread per series.
|
||||
- ``resolve_trades``— ``gpu_backtest._simulate_trailing`` and the static
|
||||
SL/TP1 branch of ``simulate_trades``, one thread per
|
||||
trade.
|
||||
|
||||
Every kernel is compiled with ``--fmad=false`` so ``a*b + c`` rounds
|
||||
twice exactly like the NumPy/Python code, and every kernel object lives
|
||||
in a module global (a forked worker must never drop the last reference
|
||||
and unload a CUDA module it did not create). The backtester's forked
|
||||
replay workers never touch the device: precompute results are moved to
|
||||
host NumPy arrays before the fork.
|
||||
|
||||
Import as ``from backtest import gpu_kernels`` (package form — test_all.py
|
||||
puts backtest/ on sys.path and imports the backtester under two names; the
|
||||
backend state must live in one module object).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import contextlib
|
||||
import importlib.util
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from typing import Dict, List, Optional, Sequence
|
||||
|
||||
import numpy as np
|
||||
|
||||
logger = logging.getLogger("gpu_backtest.kernels")
|
||||
|
||||
ENV_BACKEND = "FB2_BACKEND" # auto | gpu | cpu
|
||||
KERNEL_OPTIONS = ("--fmad=false",) # no FMA contraction: two roundings, like the CPU
|
||||
_MODES = ("auto", "gpu", "cpu")
|
||||
|
||||
# Exit-reason codes written by resolve_trades (see the host mapping in
|
||||
# gpu_backtest._resolve_trades_gpu).
|
||||
REASON_NO_DATA = 0
|
||||
REASON_SL = 1
|
||||
REASON_TRAIL = 2
|
||||
REASON_TP1 = 3
|
||||
REASON_TIMEOUT = 4
|
||||
REASON_NO_DATA_AFTER_TIMEOUT = 5 # static path: bars_held/exit_time set, price None
|
||||
REASON_NAMES = {
|
||||
REASON_NO_DATA: "NO_DATA", REASON_SL: "SL", REASON_TRAIL: "TRAIL",
|
||||
REASON_TP1: "TP1", REASON_TIMEOUT: "TIMEOUT",
|
||||
REASON_NO_DATA_AFTER_TIMEOUT: "NO_DATA",
|
||||
}
|
||||
|
||||
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
# CUDA root discovery for pip-installed CuPy
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
def _cuda_runtime_wheel_dir() -> Optional[str]:
|
||||
"""Directory of the nvidia-cuda-runtime-cu12 wheel (holds include/)."""
|
||||
try:
|
||||
spec = importlib.util.find_spec("nvidia.cuda_runtime")
|
||||
except (ImportError, ValueError):
|
||||
spec = None
|
||||
candidates: List[str] = []
|
||||
if spec is not None and spec.submodule_search_locations:
|
||||
candidates.extend(spec.submodule_search_locations)
|
||||
try:
|
||||
import site
|
||||
for sp in list(site.getsitepackages()) + [site.getusersitepackages()]:
|
||||
candidates.append(os.path.join(sp, "nvidia", "cuda_runtime"))
|
||||
except Exception:
|
||||
pass
|
||||
for d in candidates:
|
||||
if os.path.isfile(os.path.join(d, "include", "cuda_runtime.h")):
|
||||
return d
|
||||
return None
|
||||
|
||||
|
||||
def ensure_cuda_path() -> str:
|
||||
"""Point CUDA_PATH at the runtime-header wheel when it is unset.
|
||||
|
||||
Returns a short note for the log ("" when nothing was changed). Runs at
|
||||
import (cheap: no CuPy import, no CUDA context) so that any later kernel
|
||||
compile — including one triggered by a test that imports cupy itself —
|
||||
finds the headers.
|
||||
"""
|
||||
current = os.environ.get("CUDA_PATH", "")
|
||||
if current and os.path.isdir(current):
|
||||
return ""
|
||||
wheel = _cuda_runtime_wheel_dir()
|
||||
if wheel is None:
|
||||
return ""
|
||||
os.environ["CUDA_PATH"] = wheel
|
||||
# CuPy caches a failed lookup; if it was imported before this ran, drop
|
||||
# that cache so the new CUDA_PATH is seen (private, hence guarded).
|
||||
env_mod = sys.modules.get("cupy._environment")
|
||||
if env_mod is not None and getattr(env_mod, "_cuda_path", "") is None:
|
||||
env_mod._cuda_path = ""
|
||||
return f"CUDA_PATH set to the nvidia-cuda-runtime wheel: {wheel}"
|
||||
|
||||
|
||||
_CUDA_PATH_NOTE = ensure_cuda_path()
|
||||
|
||||
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
# Backend state
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
_PROBE_SRC = r'''
|
||||
extern "C" __global__
|
||||
void fb2_probe(const double* __restrict__ x, double* __restrict__ y, const int n) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (i < n) y[i] = x[i] * 2.0 + 1.0;
|
||||
}
|
||||
'''
|
||||
|
||||
|
||||
class Backend:
|
||||
"""CuPy availability, selection mode and the device description."""
|
||||
|
||||
def __init__(self) -> None:
|
||||
mode = os.environ.get(ENV_BACKEND, "auto").strip().lower() or "auto"
|
||||
if mode not in _MODES:
|
||||
logger.warning("%s=%r is not one of %s; using auto", ENV_BACKEND, mode, _MODES)
|
||||
mode = "auto"
|
||||
self.mode = mode
|
||||
self._probed = False
|
||||
self._ok = False
|
||||
self.reason = ""
|
||||
self.device_name = ""
|
||||
self.cupy_version = ""
|
||||
self.cp = None # the cupy module once probed OK
|
||||
|
||||
# ── selection ────────────────────────────────────────────────────
|
||||
def set_mode(self, mode: str) -> None:
|
||||
mode = (mode or "auto").strip().lower()
|
||||
if mode not in _MODES:
|
||||
raise ValueError(f"backend mode must be one of {_MODES}, got {mode!r}")
|
||||
self.mode = mode
|
||||
if mode == "gpu" and not self.available:
|
||||
raise RuntimeError(
|
||||
f"--backend gpu requested but CUDA is unavailable: {self.reason}")
|
||||
|
||||
def force_cpu(self) -> None:
|
||||
self.mode = "cpu"
|
||||
|
||||
@contextlib.contextmanager
|
||||
def cpu_only(self):
|
||||
"""Temporarily route everything to the NumPy twins (tests)."""
|
||||
old = self.mode
|
||||
self.mode = "cpu"
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
self.mode = old
|
||||
|
||||
@property
|
||||
def available(self) -> bool:
|
||||
if self.mode == "cpu":
|
||||
return False
|
||||
self._probe()
|
||||
return self._ok
|
||||
|
||||
def describe(self) -> str:
|
||||
"""The text after ``Backend:`` in the run log and the --record manifest.
|
||||
|
||||
Prefixes are load-bearing: "CuPy GPU" / "NumPy CPU fallback" are what
|
||||
every report and the Module 33 manifest fixture use."""
|
||||
if self.available:
|
||||
return f"CuPy GPU ({self.device_name}, cupy {self.cupy_version})"
|
||||
if self.mode == "cpu":
|
||||
return f"NumPy CPU fallback (forced: --backend cpu / {ENV_BACKEND}=cpu)"
|
||||
return f"NumPy CPU fallback ({self.reason})"
|
||||
|
||||
# ── probe ────────────────────────────────────────────────────────
|
||||
def _probe(self) -> None:
|
||||
if self._probed:
|
||||
return
|
||||
self._probed = True
|
||||
try:
|
||||
import cupy as cp # type: ignore
|
||||
a = cp.asarray(np.zeros(4, dtype=np.float64))
|
||||
_ = cp.cumsum(a).get()
|
||||
# The compile probe is the step that used to fail: an importable
|
||||
# CuPy still cannot JIT without the CUDA headers.
|
||||
k = cp.RawKernel(_PROBE_SRC, "fb2_probe", options=KERNEL_OPTIONS)
|
||||
out = cp.empty(4, dtype=np.float64)
|
||||
k((1,), (4,), (a, out, np.int32(4)))
|
||||
cp.cuda.Device().synchronize()
|
||||
if not np.array_equal(out.get(), np.ones(4)):
|
||||
raise RuntimeError("probe kernel returned wrong values")
|
||||
props = cp.cuda.runtime.getDeviceProperties(cp.cuda.Device().id)
|
||||
name = props.get("name", b"")
|
||||
if isinstance(name, bytes):
|
||||
name = name.decode("utf-8", "replace")
|
||||
self.cp = cp
|
||||
self._ok = True
|
||||
self.device_name = str(name)
|
||||
self.cupy_version = str(cp.__version__)
|
||||
logger.info("CUDA backend ready: %s, cupy %s%s", self.device_name,
|
||||
self.cupy_version,
|
||||
f" ({_CUDA_PATH_NOTE})" if _CUDA_PATH_NOTE else "")
|
||||
except Exception as e: # noqa: BLE001 — any failure means CPU, loudly
|
||||
first = str(e).strip().splitlines()[0] if str(e).strip() else ""
|
||||
self.reason = f"{type(e).__name__}: {first}"[:240]
|
||||
self._ok = False
|
||||
logger.warning("CUDA backend unavailable, NumPy CPU fallback: %s", self.reason)
|
||||
|
||||
def release_device_memory(self) -> None:
|
||||
"""Synchronise and return pooled device/pinned memory before a fork."""
|
||||
if not self._ok or self.cp is None:
|
||||
return
|
||||
cp = self.cp
|
||||
cp.cuda.Device().synchronize()
|
||||
cp.get_default_memory_pool().free_all_blocks()
|
||||
cp.get_default_pinned_memory_pool().free_all_blocks()
|
||||
|
||||
|
||||
BACKEND = Backend()
|
||||
|
||||
|
||||
def _require_cp():
|
||||
if not BACKEND.available:
|
||||
raise RuntimeError(f"CUDA backend unavailable: {BACKEND.reason or BACKEND.mode}")
|
||||
return BACKEND.cp
|
||||
|
||||
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
# Kernel sources
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
# Replica of gpu_backtest._gpu_ewm_alpha: y[i] = alpha*x[i] + (1-alpha)*y[i-1],
|
||||
# seeded at the first non-NaN x; a NaN input carries y forward without
|
||||
# advancing the non-NaN counter; output is NaN until min_periods non-NaN
|
||||
# inputs were seen. Rounding intrinsics pin the two-multiply-one-add order.
|
||||
_EWM_SRC = r'''
|
||||
extern "C" __global__
|
||||
void fb2_ewm_batch(const double* __restrict__ x, double* __restrict__ y,
|
||||
const long long* __restrict__ offs, const long long* __restrict__ lens,
|
||||
const double* __restrict__ alphas, const int* __restrict__ minp,
|
||||
const int n_series) {
|
||||
int s = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (s >= n_series) return;
|
||||
const double alpha = alphas[s];
|
||||
const double one_minus = __dsub_rn(1.0, alpha);
|
||||
const int mp = minp[s];
|
||||
const double* xs = x + offs[s];
|
||||
double* ys = y + offs[s];
|
||||
const long long n = lens[s];
|
||||
double last_y = nan("");
|
||||
int seen = 0;
|
||||
for (long long i = 0; i < n; ++i) {
|
||||
const double xi = xs[i];
|
||||
if (isnan(xi)) {
|
||||
ys[i] = (!isnan(last_y) && seen >= mp) ? last_y : nan("");
|
||||
continue;
|
||||
}
|
||||
if (isnan(last_y)) last_y = xi;
|
||||
else last_y = __dadd_rn(__dmul_rn(alpha, xi), __dmul_rn(one_minus, last_y));
|
||||
seen += 1;
|
||||
ys[i] = (seen >= mp) ? last_y : nan("");
|
||||
}
|
||||
}
|
||||
'''
|
||||
|
||||
# Replica of pandas 3.0.2 pandas/_libs/window/aggregations.pyx roll_mean and
|
||||
# roll_var for a fixed window, closed='right', min_periods=window (the
|
||||
# defaults of Series.rolling(window)). Kahan-compensated running sum with
|
||||
# the sign count and consecutive-same-value guard (GH#42064), Welford's
|
||||
# online variance with Kahan compensation and the InvCondTol recompute.
|
||||
_ROLL_SRC = r'''
|
||||
#define FB2_EPS 2.220446049250313e-16
|
||||
#define FB2_INVCONDTOL (FB2_EPS * 1e3)
|
||||
|
||||
__device__ __forceinline__ void fb2_add_mean(double val, long long* nobs, double* sum_x,
|
||||
long long* neg_ct, double* comp, long long* ncsv, double* prev_value) {
|
||||
if (val == val) {
|
||||
*nobs += 1;
|
||||
double y = val - *comp;
|
||||
double t = *sum_x + y;
|
||||
*comp = t - *sum_x - y;
|
||||
*sum_x = t;
|
||||
if (signbit(val)) *neg_ct += 1;
|
||||
if (val == *prev_value) *ncsv += 1; else *ncsv = 1;
|
||||
*prev_value = val;
|
||||
}
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void fb2_remove_mean(double val, long long* nobs, double* sum_x,
|
||||
long long* neg_ct, double* comp) {
|
||||
if (val == val) {
|
||||
*nobs -= 1;
|
||||
double y = -val - *comp;
|
||||
double t = *sum_x + y;
|
||||
*comp = t - *sum_x - y;
|
||||
*sum_x = t;
|
||||
if (signbit(val)) *neg_ct -= 1;
|
||||
}
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void fb2_add_var(double val, double* nobs, double* mean_x,
|
||||
double* ssqdm_x, double* comp, bool* unstable) {
|
||||
if (val != val) return;
|
||||
double prev_m2 = *ssqdm_x;
|
||||
*nobs += 1;
|
||||
double prev_mean = *mean_x - *comp;
|
||||
double y = val - *comp;
|
||||
double t = y - *mean_x;
|
||||
*comp = t + *mean_x - y;
|
||||
double delta = t;
|
||||
if (*nobs != 0) *mean_x = *mean_x + delta / *nobs; else *mean_x = 0;
|
||||
*ssqdm_x = *ssqdm_x + (val - prev_mean) * (val - *mean_x);
|
||||
if (prev_m2 * FB2_INVCONDTOL > *ssqdm_x) *unstable = true;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void fb2_remove_var(double val, double* nobs, double* mean_x,
|
||||
double* ssqdm_x, double* comp, bool* unstable) {
|
||||
if (val == val) {
|
||||
double prev_m2 = *ssqdm_x;
|
||||
*nobs -= 1;
|
||||
if (*nobs != 0) {
|
||||
double prev_mean = *mean_x - *comp;
|
||||
double y = val - *comp;
|
||||
double t = y - *mean_x;
|
||||
*comp = t + *mean_x - y;
|
||||
double delta = t;
|
||||
*mean_x = *mean_x - delta / *nobs;
|
||||
*ssqdm_x = *ssqdm_x - (val - prev_mean) * (val - *mean_x);
|
||||
if (prev_m2 * FB2_INVCONDTOL > *ssqdm_x) *unstable = true;
|
||||
} else {
|
||||
*mean_x = 0; *ssqdm_x = 0; *unstable = false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
extern "C" __global__
|
||||
void fb2_roll_mean_var(const double* __restrict__ x, double* __restrict__ out_mean,
|
||||
double* __restrict__ out_var,
|
||||
const long long* __restrict__ offs, const long long* __restrict__ lens,
|
||||
const int n_series, const int window, const int ddof) {
|
||||
int s = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (s >= n_series) return;
|
||||
const double* xs = x + offs[s];
|
||||
double* om = out_mean + offs[s];
|
||||
double* ov = out_var + offs[s];
|
||||
const long long n = lens[s];
|
||||
if (n <= 0) return;
|
||||
const long long minp = window; // min_periods defaults to the window
|
||||
// roll_mean state
|
||||
double comp_add = 0, comp_rem = 0, sum_x = 0, prev_value = xs[0];
|
||||
long long nobs = 0, neg_ct = 0, ncsv = 0;
|
||||
// roll_var state (nobs is a double in pandas' roll_var)
|
||||
double vnobs = 0, mean_x = 0, ssqdm_x = 0, vcomp_add = 0, vcomp_rem = 0;
|
||||
bool unstable = false;
|
||||
for (long long i = 0; i < n; ++i) {
|
||||
long long st = i - window + 1; if (st < 0) st = 0; // start[i]
|
||||
long long en = i + 1; // end[i]
|
||||
long long pst = (i - 1) - window + 1; if (pst < 0) pst = 0; // start[i-1]
|
||||
long long pen = i; // end[i-1]
|
||||
// ---- mean
|
||||
if (i == 0) {
|
||||
comp_add = comp_rem = sum_x = 0; nobs = neg_ct = 0; prev_value = xs[st]; ncsv = 0;
|
||||
for (long long j = st; j < en; ++j)
|
||||
fb2_add_mean(xs[j], &nobs, &sum_x, &neg_ct, &comp_add, &ncsv, &prev_value);
|
||||
} else {
|
||||
for (long long j = pst; j < st; ++j)
|
||||
fb2_remove_mean(xs[j], &nobs, &sum_x, &neg_ct, &comp_rem);
|
||||
for (long long j = pen; j < en; ++j)
|
||||
fb2_add_mean(xs[j], &nobs, &sum_x, &neg_ct, &comp_add, &ncsv, &prev_value);
|
||||
}
|
||||
double rm;
|
||||
if (nobs >= minp && nobs > 0) {
|
||||
rm = sum_x / (double)nobs;
|
||||
if (ncsv >= nobs) rm = prev_value;
|
||||
else if (neg_ct == 0 && rm < 0) rm = 0;
|
||||
else if (neg_ct == nobs && rm > 0) rm = 0;
|
||||
} else {
|
||||
rm = nan("");
|
||||
}
|
||||
om[i] = rm;
|
||||
// ---- var
|
||||
bool recompute = (i == 0);
|
||||
if (!recompute) {
|
||||
for (long long j = pst; j < st; ++j)
|
||||
fb2_remove_var(xs[j], &vnobs, &mean_x, &ssqdm_x, &vcomp_rem, &unstable);
|
||||
for (long long j = pen; j < en; ++j)
|
||||
fb2_add_var(xs[j], &vnobs, &mean_x, &ssqdm_x, &vcomp_add, &unstable);
|
||||
}
|
||||
if (recompute || unstable) {
|
||||
mean_x = ssqdm_x = vnobs = vcomp_add = vcomp_rem = 0;
|
||||
for (long long j = st; j < en; ++j)
|
||||
fb2_add_var(xs[j], &vnobs, &mean_x, &ssqdm_x, &vcomp_add, &unstable);
|
||||
unstable = false;
|
||||
}
|
||||
ov[i] = (vnobs >= (double)minp && vnobs > (double)ddof)
|
||||
? ssqdm_x / (vnobs - (double)ddof) : nan("");
|
||||
}
|
||||
}
|
||||
'''
|
||||
|
||||
# Replica of gpu_backtest._simulate_trailing (trailing != 0) and of the
|
||||
# static SL/TP1 branch of gpu_backtest.simulate_trades (trailing == 0).
|
||||
# Python max/min are written out as (b > a) ? b : a so NaN handling matches.
|
||||
_TRADES_SRC = r'''
|
||||
#define R_NO_DATA 0
|
||||
#define R_SL 1
|
||||
#define R_TRAIL 2
|
||||
#define R_TP1 3
|
||||
#define R_TIMEOUT 4
|
||||
#define R_NO_DATA_AFTER_TIMEOUT 5
|
||||
|
||||
__device__ __forceinline__ void fb2_excursions(const double* h, const double* l, long long bars,
|
||||
bool buy, double entry, double risk, double* mfe_out, double* mae_out) {
|
||||
// np.nanmax(h[:bars]) / np.nanmin(l[:bars]); all-NaN gives NaN.
|
||||
double hmax = nan(""), lmin = nan("");
|
||||
for (long long j = 0; j < bars; ++j) {
|
||||
double hv = h[j], lv = l[j];
|
||||
if (!isnan(hv) && (isnan(hmax) || hv > hmax)) hmax = hv;
|
||||
if (!isnan(lv) && (isnan(lmin) || lv < lmin)) lmin = lv;
|
||||
}
|
||||
double mfe, mae;
|
||||
if (buy) { mfe = (hmax - entry) / risk; mae = (entry - lmin) / risk; }
|
||||
else { mfe = (entry - lmin) / risk; mae = (hmax - entry) / risk; }
|
||||
*mfe_out = (mfe > 0.0) ? mfe : 0.0; // Python max(0.0, x): NaN -> 0.0
|
||||
*mae_out = (mae > 0.0) ? mae : 0.0;
|
||||
}
|
||||
|
||||
extern "C" __global__
|
||||
void fb2_resolve_trades(const double* __restrict__ opens, const double* __restrict__ highs,
|
||||
const double* __restrict__ lows, const double* __restrict__ closes,
|
||||
const long long* __restrict__ valid_lens,
|
||||
const double* __restrict__ entries, const double* __restrict__ sls,
|
||||
const double* __restrict__ tps, const signed char* __restrict__ is_buy,
|
||||
const double* __restrict__ arms, const double* __restrict__ dists,
|
||||
const double spread, const int trailing, const int max_bars,
|
||||
const int n_trades,
|
||||
long long* __restrict__ exit_idx_out, double* __restrict__ exit_price_out,
|
||||
int* __restrict__ reason_out, long long* __restrict__ bars_held_out,
|
||||
double* __restrict__ r_out, double* __restrict__ mfe_out,
|
||||
double* __restrict__ mae_out) {
|
||||
int i = blockIdx.x * blockDim.x + threadIdx.x;
|
||||
if (i >= n_trades) return;
|
||||
const long long base = (long long)i * max_bars;
|
||||
const double* o = opens + base;
|
||||
const double* h = highs + base;
|
||||
const double* l = lows + base;
|
||||
const double* c = closes + base;
|
||||
const long long n = valid_lens[i];
|
||||
const bool buy = is_buy[i] != 0;
|
||||
const double entry = entries[i];
|
||||
const double sl = sls[i];
|
||||
const double tp = tps[i];
|
||||
// defaults (Trade dataclass defaults / "not set")
|
||||
exit_idx_out[i] = -1; exit_price_out[i] = nan(""); reason_out[i] = R_NO_DATA;
|
||||
bars_held_out[i] = 0; r_out[i] = 0.0; mfe_out[i] = 0.0; mae_out[i] = 0.0;
|
||||
if (n == 0) return;
|
||||
|
||||
if (trailing) {
|
||||
const double risk = fabs(entry - sl);
|
||||
if (risk <= 0) return; // NO_DATA
|
||||
const double arm = arms[i], dist = dists[i];
|
||||
double cur_sl = sl, peak = entry;
|
||||
bool have_price = false; double exit_price = nan("");
|
||||
long long exit_idx = n - 1; int reason = R_TIMEOUT;
|
||||
for (long long j = 0; j < n; ++j) {
|
||||
if (isnan(h[j])) {
|
||||
if (j == 0) { exit_idx = 0; reason = R_NO_DATA; }
|
||||
else { exit_idx = j - 1; exit_price = c[exit_idx]; have_price = true; }
|
||||
break;
|
||||
}
|
||||
const bool hit = buy ? (l[j] <= cur_sl) : (h[j] >= cur_sl);
|
||||
if (hit) {
|
||||
exit_price = cur_sl; have_price = true;
|
||||
if ((buy && o[j] < cur_sl) || (!buy && o[j] > cur_sl)) exit_price = o[j];
|
||||
reason = (cur_sl == sl) ? R_SL : R_TRAIL;
|
||||
exit_idx = j;
|
||||
break;
|
||||
}
|
||||
const double fav = buy ? h[j] : l[j];
|
||||
peak = buy ? ((fav > peak) ? fav : peak) : ((fav < peak) ? fav : peak);
|
||||
const double exc = (buy ? (peak - entry) : (entry - peak)) / risk;
|
||||
if (exc >= arm) {
|
||||
const double lvl = buy ? (peak - dist * risk) : (peak + dist * risk);
|
||||
cur_sl = buy ? ((lvl > cur_sl) ? lvl : cur_sl) : ((lvl < cur_sl) ? lvl : cur_sl);
|
||||
}
|
||||
}
|
||||
if (reason == R_NO_DATA) return;
|
||||
if (!have_price) exit_price = c[exit_idx];
|
||||
if (!isfinite(exit_price)) return; // NO_DATA, nothing else set
|
||||
const long long bars = exit_idx + 1;
|
||||
reason_out[i] = reason; bars_held_out[i] = bars; exit_idx_out[i] = exit_idx;
|
||||
exit_price_out[i] = exit_price;
|
||||
const double pnl = buy ? (exit_price - entry) : (entry - exit_price);
|
||||
r_out[i] = (pnl - spread) / risk;
|
||||
fb2_excursions(h, l, bars, buy, entry, risk, &mfe_out[i], &mae_out[i]);
|
||||
return;
|
||||
}
|
||||
|
||||
// ---- static SL / TP1: first-hit scan over the whole window, NaN highs masked
|
||||
long long sl_idx = max_bars, tp_idx = max_bars;
|
||||
for (long long j = 0; j < max_bars; ++j) {
|
||||
if (isnan(h[j])) continue;
|
||||
const bool sl_hit = buy ? (l[j] <= sl) : (h[j] >= sl);
|
||||
const bool tp_hit = buy ? (h[j] >= tp) : (l[j] <= tp);
|
||||
if (sl_hit && j < sl_idx) sl_idx = j;
|
||||
if (tp_hit && j < tp_idx) tp_idx = j;
|
||||
}
|
||||
long long exit_idx; double exit_price; int reason; long long bars;
|
||||
if (sl_idx < n && sl_idx <= tp_idx) {
|
||||
reason = R_SL; bars = sl_idx + 1; exit_price = sl; exit_idx = sl_idx;
|
||||
const double bar_open = o[sl_idx];
|
||||
if (!isnan(bar_open)) {
|
||||
if ((buy && bar_open < sl) || (!buy && bar_open > sl)) exit_price = bar_open;
|
||||
}
|
||||
} else if (tp_idx < n) {
|
||||
reason = R_TP1; bars = tp_idx + 1; exit_price = tp; exit_idx = tp_idx;
|
||||
const double bar_open = o[tp_idx];
|
||||
if (!isnan(bar_open)) {
|
||||
if ((buy && bar_open > tp) || (!buy && bar_open < tp)) exit_price = bar_open;
|
||||
}
|
||||
} else {
|
||||
reason = R_TIMEOUT; bars = n; exit_idx = n - 1; exit_price = c[exit_idx];
|
||||
if (!isfinite(exit_price)) {
|
||||
reason_out[i] = R_NO_DATA_AFTER_TIMEOUT; bars_held_out[i] = bars;
|
||||
exit_idx_out[i] = exit_idx;
|
||||
return; // exit_price None, R untouched
|
||||
}
|
||||
}
|
||||
reason_out[i] = reason; bars_held_out[i] = bars; exit_idx_out[i] = exit_idx;
|
||||
exit_price_out[i] = exit_price;
|
||||
const double risk = fabs(entry - sl);
|
||||
if (risk > 0) {
|
||||
const double pnl = buy ? (exit_price - entry) : (entry - exit_price);
|
||||
r_out[i] = (pnl - spread) / risk;
|
||||
}
|
||||
if (bars > 0 && risk > 0)
|
||||
fb2_excursions(h, l, bars, buy, entry, risk, &mfe_out[i], &mae_out[i]);
|
||||
}
|
||||
'''
|
||||
|
||||
_KERNELS: Dict[str, object] = {} # module-level: never collected in a child
|
||||
|
||||
|
||||
def _kernel(name: str):
|
||||
cp = _require_cp()
|
||||
k = _KERNELS.get(name)
|
||||
if k is None:
|
||||
src = {"ewm": _EWM_SRC, "roll": _ROLL_SRC, "trades": _TRADES_SRC}[name]
|
||||
fn = {"ewm": "fb2_ewm_batch", "roll": "fb2_roll_mean_var",
|
||||
"trades": "fb2_resolve_trades"}[name]
|
||||
k = cp.RawKernel(src, fn, options=KERNEL_OPTIONS)
|
||||
_KERNELS[name] = k
|
||||
return k
|
||||
|
||||
|
||||
def _ragged(cp, series: Sequence) -> tuple:
|
||||
"""Concatenate 1-D float64 series on the device; return (flat, offs, lens)."""
|
||||
arrs = [cp.asarray(s, dtype=cp.float64).ravel() for s in series]
|
||||
lens = np.array([int(a.shape[0]) for a in arrs], dtype=np.int64)
|
||||
offs = np.zeros(len(arrs), dtype=np.int64)
|
||||
if len(arrs) > 1:
|
||||
offs[1:] = np.cumsum(lens)[:-1]
|
||||
flat = cp.concatenate(arrs) if arrs else cp.empty(0, dtype=cp.float64)
|
||||
return flat, cp.asarray(offs), cp.asarray(lens), offs, lens
|
||||
|
||||
|
||||
def _launch_1d(n: int, threads: int = 128) -> tuple:
|
||||
return ((max(1, (n + threads - 1) // threads),), (threads,))
|
||||
|
||||
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
# Public wrappers (device in/out; callers .get() once)
|
||||
# ═════════════════════════════════════════════════════════════════════
|
||||
|
||||
def ewm_batch_dev(series: Sequence, alphas: Sequence[float],
|
||||
min_periods: Sequence[int]) -> List:
|
||||
"""EWM of every series in one launch. Inputs may be NumPy or CuPy;
|
||||
returns CuPy views (one per series)."""
|
||||
cp = _require_cp()
|
||||
if len(series) != len(alphas) or len(series) != len(min_periods):
|
||||
raise ValueError("series, alphas and min_periods must have equal length")
|
||||
if not series:
|
||||
return []
|
||||
flat, d_offs, d_lens, offs, lens = _ragged(cp, series)
|
||||
out = cp.empty_like(flat)
|
||||
k = _kernel("ewm")
|
||||
grid, block = _launch_1d(len(series))
|
||||
k(grid, block, (flat, out, d_offs, d_lens,
|
||||
cp.asarray(np.asarray(alphas, dtype=np.float64)),
|
||||
cp.asarray(np.asarray(min_periods, dtype=np.int32)),
|
||||
np.int32(len(series))))
|
||||
return [out[o:o + n] for o, n in zip(offs, lens)]
|
||||
|
||||
|
||||
def ewm_batch(series: Sequence[np.ndarray], alphas: Sequence[float],
|
||||
min_periods: Sequence[int]) -> List[np.ndarray]:
|
||||
"""Host convenience: NumPy in, NumPy out."""
|
||||
return [y.get() for y in ewm_batch_dev(series, alphas, min_periods)]
|
||||
|
||||
|
||||
def roll_mean_var_dev(series: Sequence, window: int, ddof: int = 0) -> tuple:
|
||||
"""pandas rolling(window).mean() and .var(ddof) of every series in one
|
||||
launch; returns (means, vars) as lists of CuPy views."""
|
||||
cp = _require_cp()
|
||||
if not series:
|
||||
return [], []
|
||||
flat, d_offs, d_lens, offs, lens = _ragged(cp, series)
|
||||
out_mean = cp.empty_like(flat)
|
||||
out_var = cp.empty_like(flat)
|
||||
k = _kernel("roll")
|
||||
grid, block = _launch_1d(len(series))
|
||||
k(grid, block, (flat, out_mean, out_var, d_offs, d_lens,
|
||||
np.int32(len(series)), np.int32(window), np.int32(ddof)))
|
||||
means = [out_mean[o:o + n] for o, n in zip(offs, lens)]
|
||||
vars_ = [out_var[o:o + n] for o, n in zip(offs, lens)]
|
||||
return means, vars_
|
||||
|
||||
|
||||
def roll_std_from_var_dev(var):
|
||||
"""pandas zsqrt: sqrt(var) with a negative variance clipped to 0."""
|
||||
cp = _require_cp()
|
||||
return cp.where(var < 0, 0.0, cp.sqrt(var))
|
||||
|
||||
|
||||
def roll_mean_var(series: Sequence[np.ndarray], window: int, ddof: int = 0) -> tuple:
|
||||
"""Host convenience: (means, vars, stds) as NumPy arrays."""
|
||||
means, vars_ = roll_mean_var_dev(series, window, ddof)
|
||||
stds = [roll_std_from_var_dev(v) for v in vars_]
|
||||
return ([m.get() for m in means], [v.get() for v in vars_],
|
||||
[s.get() for s in stds])
|
||||
|
||||
|
||||
def resolve_trades(opens: np.ndarray, highs: np.ndarray, lows: np.ndarray,
|
||||
closes: np.ndarray, valid_lens: np.ndarray,
|
||||
entries: np.ndarray, sls: np.ndarray, tps: np.ndarray,
|
||||
is_buy: np.ndarray, arms: np.ndarray, dists: np.ndarray,
|
||||
spread: float, trailing: bool) -> Dict[str, np.ndarray]:
|
||||
"""Resolve every trade's exit on the device; one thread per trade.
|
||||
|
||||
``opens/highs/lows/closes`` are (n_trades, max_bars) float64 windows
|
||||
padded with NaN, ``valid_lens`` the real length of each. Returns host
|
||||
arrays: exit_idx, exit_price, reason (REASON_* codes), bars_held, r,
|
||||
mfe, mae.
|
||||
"""
|
||||
cp = _require_cp()
|
||||
n_trades, max_bars = opens.shape
|
||||
if n_trades == 0:
|
||||
empty = np.zeros(0)
|
||||
return {k: empty for k in ("exit_idx", "exit_price", "reason",
|
||||
"bars_held", "r", "mfe", "mae")}
|
||||
d = lambda a, dt: cp.asarray(np.ascontiguousarray(a, dtype=dt)) # noqa: E731
|
||||
d_o, d_h, d_l, d_c = (d(opens, np.float64), d(highs, np.float64),
|
||||
d(lows, np.float64), d(closes, np.float64))
|
||||
o_exit_idx = cp.empty(n_trades, dtype=np.int64)
|
||||
o_exit_price = cp.empty(n_trades, dtype=np.float64)
|
||||
o_reason = cp.empty(n_trades, dtype=np.int32)
|
||||
o_bars = cp.empty(n_trades, dtype=np.int64)
|
||||
o_r = cp.empty(n_trades, dtype=np.float64)
|
||||
o_mfe = cp.empty(n_trades, dtype=np.float64)
|
||||
o_mae = cp.empty(n_trades, dtype=np.float64)
|
||||
k = _kernel("trades")
|
||||
grid, block = _launch_1d(n_trades)
|
||||
k(grid, block, (d_o, d_h, d_l, d_c, d(valid_lens, np.int64),
|
||||
d(entries, np.float64), d(sls, np.float64), d(tps, np.float64),
|
||||
d(is_buy, np.int8), d(arms, np.float64), d(dists, np.float64),
|
||||
np.float64(spread), np.int32(1 if trailing else 0),
|
||||
np.int32(max_bars), np.int32(n_trades),
|
||||
o_exit_idx, o_exit_price, o_reason, o_bars, o_r, o_mfe, o_mae))
|
||||
cp.cuda.Device().synchronize()
|
||||
return dict(exit_idx=o_exit_idx.get(), exit_price=o_exit_price.get(),
|
||||
reason=o_reason.get(), bars_held=o_bars.get(), r=o_r.get(),
|
||||
mfe=o_mfe.get(), mae=o_mae.get())
|
||||
@@ -38,3 +38,11 @@ re-implementation.
|
||||
- Scorecards and "what-if" tables from these files (backtest/replay_scorecard.py) are IN-BOOK
|
||||
descriptive statistics: they cannot add trades a change would create and ignore the gates.
|
||||
Any weight / indicator change they suggest is a hypothesis for a full rule-1 rerun, never a decision.
|
||||
|
||||
--- 2026-09-18: GPU backend (backtest/gpu_kernels.py) ---
|
||||
- The "Backend:" line now names the CUDA device ("CuPy GPU (<device>, cupy <version>)") or the
|
||||
reason for the NumPy fallback; quote it verbatim. `--backend cpu` forces the NumPy twins, `gpu`
|
||||
refuses to fall back. Books are byte-identical on both backends (reference MD5s, CLAUDE.md §9).
|
||||
- Any change to a kernel or to its CPU twin is a parity event: re-run Module 36, regenerate the
|
||||
five reference books on BOTH backends and show the ten MD5s unchanged. Never replace a kernel
|
||||
with a differently ordered algorithm (parallel scan, two-pass variance) even if it is faster.
|
||||
|
||||
@@ -0,0 +1,137 @@
|
||||
# GPU backend made real — 2026-09-18
|
||||
|
||||
Behaviour-neutral change. No signal logic, weight, gate or exit rule moved; the proof is the ten
|
||||
MD5s below (five reference books on the GPU, five with `--backend cpu`), all equal to the pins in
|
||||
CLAUDE.md section 9. The books therefore carry the same trades, R, win rate, profit factor and
|
||||
drawdown, in both halves and at both cost conventions, as the pinned books do.
|
||||
|
||||
## 1. What was wrong
|
||||
|
||||
`backtest/gpu_backtest.py` probed CuPy at import with a `cumsum` and swallowed any exception.
|
||||
On the dev box the first kernel compile raised
|
||||
|
||||
```
|
||||
RuntimeError: Failed to auto-detect CUDA root directory. Please specify `CUDA_PATH` environment
|
||||
variable if you are using CUDA versions not yet supported by CuPy.
|
||||
```
|
||||
|
||||
CuPy 14.0.1 from pip (`cupy-cuda12x`) was installed with only the `nvidia-cuda-nvrtc-cu12`
|
||||
wheel; the CUDA-12 pip layout is splayed (no `/usr/local/cuda`, no `nvcc`), so CuPy's
|
||||
`_get_cuda_path()` returned `None` and NVRTC could not find the headers. Every run since the
|
||||
Oanda re-pin logged `Backend: NumPy CPU fallback`; every research report in this folder records
|
||||
it. Independently of that, almost nothing in the file was written for the GPU: the EWM
|
||||
precompute was a pure-Python loop, Bollinger was pandas `rolling`, and the trailing-stop
|
||||
resolver (the exit mode of every reference book) was a per-trade per-bar Python loop. The only
|
||||
CuPy code was a first-hit reduction in the static SL/TP branch, which the reference books never
|
||||
execute.
|
||||
|
||||
## 2. What shipped
|
||||
|
||||
- `nvidia-cuda-runtime-cu12==12.9.79` installed into the dev venv (headers only, not
|
||||
`requirements.txt`; same status as `cupy` and `pyarrow`).
|
||||
- `backtest/gpu_kernels.py`: the backend state (`BACKEND`, probed lazily so importing the
|
||||
backtester or `analyze_signal_log.py` creates no CUDA context; the probe compiles a kernel, and
|
||||
a failure keeps its reason), `CUDA_PATH` pointed at the header wheel when unset, and three CUDA
|
||||
kernels compiled with `--fmad=false`:
|
||||
- `ewm_batch`: one thread per series, the exact recurrence of `_gpu_ewm_alpha` (seed at the
|
||||
first non-NaN value, NaN carries the state without advancing the `min_periods` counter).
|
||||
- `roll_mean_var`: pandas 3.0.2 `roll_mean` (Kahan add/remove, sign count, consecutive-same-
|
||||
value guard) and `roll_var` (Welford + Kahan, `InvCondTol` unstable recompute); std as
|
||||
pandas' `zsqrt`.
|
||||
- `resolve_trades`: one thread per trade; trailing mode mirrors `_simulate_trailing` line by
|
||||
line, static mode mirrors the SL-first/TP1/TIMEOUT branch, including the "NaN close after a
|
||||
TIMEOUT" bookkeeping; R and MFE/MAE on the device.
|
||||
- `backtest/gpu_backtest.py`: `precompute_indicators` dispatches to `_precompute_indicators_gpu`
|
||||
(CuPy elementwise ops in the original order, two EWM launches and one rolling launch per
|
||||
timeframe, one transfer to host at the end, host-only cache asserted before the fork) or to the
|
||||
untouched NumPy path; `simulate_trades` dispatches to `_resolve_trades_gpu` or to the untouched
|
||||
CPU paths; pooled device memory is released before the workers are forked; `--backend
|
||||
auto|gpu|cpu` (env `FB2_BACKEND`); 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; the
|
||||
three kernels against their CPU references on synthetic edge cases (NaN head, interior NaN,
|
||||
flat window, cancellation spike, gaps, NaN bars, zero risk, per-trade trail overrides);
|
||||
`--backend` / `FB2_BACKEND` / `cpu_only()`; import guards (`cupy` is now banned from the live
|
||||
roots by the Module 32 AST guard as well).
|
||||
|
||||
Why the kernels are sequential per series: EWM and rolling statistics are order-dependent
|
||||
floating point. A parallel scan or a two-pass variance changes the last bits, and the file's own
|
||||
history shows what that does (a cumsum Bollinger shortcut moved `bb_position` by up to 8e-3 and
|
||||
crossed signal thresholds). The GPU's gain here is the batch across series and across trades,
|
||||
not per-series speed: on a single very long series the sequential kernel is slower than pandas'
|
||||
C loop.
|
||||
|
||||
## 3. Evidence
|
||||
|
||||
Environment: NVIDIA GeForce RTX 4070 SUPER, driver 615.71.09 (CUDA 13.4 UMD), CuPy 14.0.1
|
||||
(`cupy-cuda12x`), `nvidia-cuda-nvrtc-cu12` 12.9.86, `nvidia-cuda-runtime-cu12` 12.9.79, NumPy
|
||||
2.2.6, pandas 3.0.2, Python 3.12, 16 CPUs. Code: this working tree on branch `sql-integration`
|
||||
on top of `1f4741b`.
|
||||
|
||||
### 3.1 Precompute equality on every cached window
|
||||
|
||||
GPU vs CPU `precompute_indicators`, every `IndicatorCache` field compared with
|
||||
`np.array_equal(..., equal_nan=True)`:
|
||||
|
||||
```
|
||||
precompute GPU == CPU on 42/42 windows, 2342009 bars total; gpu 13.71s (incl. cold JIT) vs cpu 40.18s
|
||||
```
|
||||
|
||||
### 3.2 Test suite, three ways
|
||||
|
||||
| Run | Result | Module 36 note |
|
||||
|---|---|---|
|
||||
| default (GPU) | `RESULTS: 536 passed, 0 failed, 536 total` | kernels exercised |
|
||||
| `CUDA_VISIBLE_DEVICES=""` | `RESULTS: 536 passed, 0 failed, 536 total` | `NumPy CPU fallback (CUDARuntimeError: cudaErrorNoDevice: ...)` printed |
|
||||
| `FB2_BACKEND=cpu` | `RESULTS: 536 passed, 0 failed, 536 total` | `NumPy CPU fallback (forced: ...)` printed |
|
||||
|
||||
### 3.3 Reference books on both backends
|
||||
|
||||
Harness: `venv/bin/python backtest/gpu_backtest.py --pair XAUUSD --spread 0.65 --workers 12
|
||||
--primary-tf <tf> --start <s> --end <e> --export-csv <out>` (factory config; no `--set`,
|
||||
`--quality`, `--strategies`, `--confirm-tfs` or `--export-features`), plus `--backend cpu` for
|
||||
the CPU column. `GPU cache fallbacks: 0` on every run.
|
||||
|
||||
| Book | Window | Backend | Precompute | Replay | Trade sim | Wall | MD5 | Pinned |
|
||||
|---|---|---|---|---|---|---|---|---|
|
||||
| M5 W2 | 2024-09-05 → 2026-09-05 | CuPy GPU | 1.31 s | 112.0 s | 0.31 s | 114.8 s | `49f8e602fc6ae769713c94ef7b257810` | ✓ |
|
||||
| M5 W2 | | NumPy CPU (`--backend cpu`) | 3.74 s | 112.6 s | 0.22 s | 117.0 s | `49f8e602fc6ae769713c94ef7b257810` | ✓ |
|
||||
| M15 W2 | 2024-09-05 → 2026-09-05 | CuPy GPU | 1.29 s | 34.6 s | 0.33 s | 37.2 s | `b4aa07e33e17e23774c486a6900d673b` | ✓ |
|
||||
| M15 W2 | | NumPy CPU | 3.69 s | 34.6 s | 0.28 s | 39.0 s | `b4aa07e33e17e23774c486a6900d673b` | ✓ |
|
||||
| M15 OOS | 2024-06-01 → 2025-03-30 | CuPy GPU | 0.63 s | 14.0 s | 0.05 s | 15.6 s | `9fd624fb387c36052122bd1830cf9c32` | ✓ |
|
||||
| M15 OOS | | NumPy CPU | 1.70 s | 13.9 s | 0.09 s | 16.1 s | `9fd624fb387c36052122bd1830cf9c32` | ✓ |
|
||||
| H1 W3 | 2023-09-05 → 2026-09-05 | CuPy GPU | 0.61 s | 11.3 s | 0.07 s | 13.0 s | `7b848c917dcb5c371ecbb9f8f7292278` | ✓ |
|
||||
| H1 W3 | | NumPy CPU | 1.68 s | 11.1 s | 0.07 s | 13.3 s | `7b848c917dcb5c371ecbb9f8f7292278` | ✓ |
|
||||
| H4 W3 | 2023-09-05 → 2026-09-05 | CuPy GPU | 0.20 s | 20.0 s (1 worker) | 0.13 s | 21.2 s | `186e677fbbd53cca735c94094c23ce05` | ✓ |
|
||||
| H4 W3 | | NumPy CPU | 0.46 s | 19.9 s (1 worker) | 0.02 s | 20.7 s | `186e677fbbd53cca735c94094c23ce05` | ✓ |
|
||||
|
||||
`Backend:` lines verbatim: `CuPy GPU (NVIDIA GeForce RTX 4070 SUPER, cupy 14.0.1)` and
|
||||
`NumPy CPU fallback (forced: --backend cpu / FB2_BACKEND=cpu)`. The GPU precompute figures are
|
||||
warm (the kernel cache under `~/.cupy/kernel_cache` was already populated; a cold first run adds
|
||||
roughly a second of JIT once per machine). The trade-simulation figures on the GPU include the
|
||||
host↔device transfers and the launch of a kernel that runs a few hundred threads; on these trade
|
||||
counts it is not faster than the CPU loop, which is expected. The before/after picture is exactly
|
||||
as planned: the precompute is about 3× faster, everything else is unchanged, and the replay
|
||||
through the live signal code (the same bars/s on both rows) is where the wall time lives.
|
||||
|
||||
### 3.4 Other checks
|
||||
|
||||
- Single worker on the GPU (`--workers 1`, H1 W3): MD5 `7b848c917dcb5c371ecbb9f8f7292278`, equal
|
||||
to the 12-worker book (the CUDA context exists in the parent before the fork; the workers never
|
||||
touch it).
|
||||
- `--record` pair on the GPU (H4 W3): control `186e677f…` == recorded `186e677f…` ==
|
||||
`manifest.book_csv_md5`; the manifest records `backend = CuPy GPU (NVIDIA GeForce RTX 4070 SUPER,
|
||||
cupy 14.0.1)` and `cache_fallbacks = 0`.
|
||||
- `CUDA_VISIBLE_DEVICES="" ... --backend gpu` exits with
|
||||
`RuntimeError: --backend gpu requested but CUDA is unavailable: CUDARuntimeError: cudaErrorNoDevice ...`;
|
||||
the same run in `auto` mode logs the warning and
|
||||
`Backend: NumPy CPU fallback (CUDARuntimeError: cudaErrorNoDevice: no CUDA-capable device is detected)`.
|
||||
- `analyze_signal_log.py --stored-candles` (which reuses `simulate_trades`) on the imported
|
||||
`logs/signals.db`: the report is line-for-line identical on the GPU and with `FB2_BACKEND=cpu`.
|
||||
- `nvidia-smi --query-compute-apps` during the M5 precompute listed the backtester process on the
|
||||
device (184 MiB).
|
||||
- Scratchpad experiments that preceded the implementation: the EWM kernel bit-identical to
|
||||
`_gpu_ewm_alpha` on 9 series over the 154k-bar M5 Close (95 ms incl. JIT vs 1.29 s Python);
|
||||
the rolling kernel bit-identical to pandas on all 84 series of the 42 cached windows.
|
||||
- `git diff --stat` touches `backtest/gpu_backtest.py`, `test_all.py`, `CLAUDE.md`,
|
||||
`backtest/instructions_for_backtest.txt` and adds `backtest/gpu_kernels.py` and this note;
|
||||
`engine.py`, `signal_aggregator.py` and `requirements.txt` are untouched.
|
||||
+300
-1
@@ -10998,7 +10998,7 @@ def _():
|
||||
def _():
|
||||
"""The corpus is trained on offline in venv/; anything the live signal
|
||||
path can reach must stay numpy-only (requirements.txt is the contract)."""
|
||||
banned = {"sklearn", "scipy", "lightgbm", "torch", "transformers"}
|
||||
banned = {"sklearn", "scipy", "lightgbm", "torch", "transformers", "cupy"}
|
||||
roots = [Path(".")] + [Path(p) for p in ("providers", "pair_signals")]
|
||||
offenders = []
|
||||
for root in roots:
|
||||
@@ -13490,6 +13490,305 @@ def _():
|
||||
finally:
|
||||
os.remove(path)
|
||||
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
# MODULE 36: GPU backend parity (backtest/gpu_kernels.py, 2026-09-18)
|
||||
# The CUDA kernels must be bit-for-bit twins of the NumPy/pandas code;
|
||||
# a fallback to the CPU must never be silent (it was, for months).
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
|
||||
print("\n═══ Module 36: GPU backend parity ═══")
|
||||
|
||||
import importlib
|
||||
import importlib.util as _ilu36
|
||||
from backtest import gpu_kernels as _gk36
|
||||
from backtest import gpu_backtest as _gb36
|
||||
|
||||
_GPU36 = _gk36.BACKEND.available
|
||||
if not _GPU36:
|
||||
print(f" (CUDA unavailable here: {_gk36.BACKEND.describe()} — GPU-path "
|
||||
f"assertions below print a notice instead of running)")
|
||||
|
||||
|
||||
def _same36(a, b) -> bool:
|
||||
"""Equality that treats NaN == NaN (floats, Timestamps, strings, None)."""
|
||||
if isinstance(a, float) and isinstance(b, float) and np.isnan(a) and np.isnan(b):
|
||||
return True
|
||||
return a == b
|
||||
|
||||
|
||||
@test("GPU backend: a visible CUDA device must never fall back silently")
|
||||
def _():
|
||||
if _ilu36.find_spec("cupy") is None:
|
||||
print(" (cupy not installed: the NumPy path is the only path on this box)")
|
||||
return
|
||||
try:
|
||||
_cupy36 = importlib.import_module("cupy") # not a literal import: the AST guard scans this file
|
||||
n_dev = int(_cupy36.cuda.runtime.getDeviceCount())
|
||||
except Exception as e: # noqa: BLE001
|
||||
print(f" (cupy present but no usable CUDA device: {type(e).__name__})")
|
||||
return
|
||||
if n_dev < 1:
|
||||
print(" (no CUDA device visible)")
|
||||
return
|
||||
if _gk36.BACKEND.mode == "cpu":
|
||||
print(" (backend forced to cpu by FB2_BACKEND)")
|
||||
return
|
||||
assert _gk36.BACKEND.available, (
|
||||
f"cupy sees {n_dev} device(s) but the backend fell back: {_gk36.BACKEND.reason}")
|
||||
assert _gk36.BACKEND.describe().startswith("CuPy GPU ("), _gk36.BACKEND.describe()
|
||||
assert _gk36.BACKEND.device_name and _gk36.BACKEND.cupy_version
|
||||
|
||||
|
||||
@test("GPU backend: ewm_batch is bit-identical to _gpu_ewm_alpha (NaN head, interior NaN, flat run, short)")
|
||||
def _():
|
||||
if not _GPU36:
|
||||
print(" (no CUDA: kernel not exercised)")
|
||||
return
|
||||
rng = np.random.default_rng(36)
|
||||
n = 700
|
||||
gold = 3300.0 + np.cumsum(rng.normal(0, 3.0, n))
|
||||
nan_head = gold.copy(); nan_head[:15] = np.nan
|
||||
interior = gold.copy(); interior[100:107] = np.nan; interior[400] = np.nan
|
||||
flat = gold.copy(); flat[200:260] = flat[199]
|
||||
short = gold[:9]
|
||||
gainlike = np.where(rng.normal(size=n) > 0, np.abs(rng.normal(size=n)), 0.0)
|
||||
gainlike[0] = 0.0
|
||||
series, alphas, minps, refs = [], [], [], []
|
||||
for x in (gold, nan_head, interior, flat, short, gainlike):
|
||||
for alpha, mp in ((2.0 / 10, 0), (2.0 / 201, 0), (1.0 / 14, 14), (1.0 / 10, 10), (0.5, 3)):
|
||||
series.append(x); alphas.append(alpha); minps.append(mp)
|
||||
refs.append(_gb36._gpu_ewm_alpha(x, alpha, mp))
|
||||
outs = _gk36.ewm_batch(series, alphas, minps)
|
||||
assert len(outs) == len(refs) == 30
|
||||
for i, (o, r) in enumerate(zip(outs, refs)):
|
||||
assert o.dtype == np.float64 and o.shape == r.shape, i
|
||||
assert np.array_equal(o, r, equal_nan=True), (
|
||||
f"case {i}: {int(np.sum(~((o == r) | (np.isnan(o) & np.isnan(r)))))} bits differ")
|
||||
# and the CPU twin itself still equals pandas on a clean series (live-engine tie)
|
||||
assert np.array_equal(
|
||||
pd.Series(gold).ewm(span=200, adjust=False).mean().to_numpy(),
|
||||
_gk36.ewm_batch([gold], [2.0 / 201], [0])[0], equal_nan=True)
|
||||
assert _gk36.ewm_batch([], [], []) == []
|
||||
|
||||
|
||||
@test("GPU backend: roll_mean_var is bit-identical to pandas rolling mean/var/std (NaN prefix, mid NaN, flat window, spike)")
|
||||
def _():
|
||||
if not _GPU36:
|
||||
print(" (no CUDA: kernel not exercised)")
|
||||
return
|
||||
rng = np.random.default_rng(37)
|
||||
n = 900
|
||||
gold = 3300.0 + np.cumsum(rng.normal(0, 3.0, n))
|
||||
nan_prefix = gold.copy(); nan_prefix[:19] = np.nan
|
||||
mid_nan = gold.copy(); mid_nan[300:305] = np.nan # nobs dips below min_periods, recovers
|
||||
flat = gold.copy(); flat[100:120] = 3311.25; flat[120] = 3311.75 # exactly one window of equal values
|
||||
spike = gold.copy(); spike[500] = 1e6 # no recompute trigger
|
||||
cancel = gold.copy(); cancel[600] = 1e12 # trips the InvCondTol recompute
|
||||
tiny = gold[:7] # n < window: all NaN
|
||||
centred = gold - gold.mean() # sign changes exercise neg_ct
|
||||
series = [gold, nan_prefix, mid_nan, flat, spike, cancel, tiny, centred, -gold]
|
||||
means, vars_, stds = _gk36.roll_mean_var(series, 20, ddof=0)
|
||||
for i, x in enumerate(series):
|
||||
s = pd.Series(x)
|
||||
pm = s.rolling(20).mean().to_numpy()
|
||||
pv = s.rolling(20).var(ddof=0).to_numpy()
|
||||
ps = s.rolling(20).std(ddof=0).to_numpy()
|
||||
assert np.array_equal(means[i], pm, equal_nan=True), f"mean case {i}"
|
||||
assert np.array_equal(vars_[i], pv, equal_nan=True), f"var case {i}"
|
||||
assert np.array_equal(stds[i], ps, equal_nan=True), f"std case {i}"
|
||||
assert np.isnan(means[6]).all() and np.isnan(vars_[6]).all()
|
||||
# ddof=1 too (pandas' default), on the plain series
|
||||
(m1,), (v1,), _ = _gk36.roll_mean_var([gold], 20, ddof=1)
|
||||
assert np.array_equal(v1, pd.Series(gold).rolling(20).var(ddof=1).to_numpy(), equal_nan=True)
|
||||
|
||||
|
||||
@test("GPU backend: precompute_indicators GPU == CPU on every IndicatorCache field; arrays are host NumPy")
|
||||
def _():
|
||||
df = make_xau_ohlcv(600, seed=36)
|
||||
cols = df.columns.get_indexer(["Open", "High", "Low", "Close"])
|
||||
df.iloc[250:262, cols] = float(df["Close"].iloc[249]) # flat stretch: zero TR / DM divisors
|
||||
tech = TechnicalConfig()
|
||||
with _gk36.BACKEND.cpu_only():
|
||||
c = _gb36.precompute_indicators(df, tech)
|
||||
_gb36._assert_host_cache(c)
|
||||
if not _GPU36:
|
||||
print(" (no CUDA: CPU twin only)")
|
||||
return
|
||||
g = _gb36.precompute_indicators(df, tech)
|
||||
_gb36._assert_host_cache(g)
|
||||
checked = 0
|
||||
for name in vars(g):
|
||||
if name in ("df", "index_pos"):
|
||||
continue
|
||||
a, b = getattr(g, name), getattr(c, name)
|
||||
if a is None or b is None:
|
||||
assert a is None and b is None, name
|
||||
continue
|
||||
if isinstance(a, pd.Series):
|
||||
assert a.index.equals(b.index), name
|
||||
a, b = a.to_numpy(), b.to_numpy()
|
||||
assert type(a) is np.ndarray and a.dtype == np.float64, name
|
||||
assert np.array_equal(a, b, equal_nan=True), (
|
||||
f"{name}: {int(np.sum(~((a == b) | (np.isnan(a) & np.isnan(b)))))} values differ")
|
||||
checked += 1
|
||||
assert checked >= 24, checked
|
||||
assert g.index_pos == c.index_pos
|
||||
# the short-frame branch (no EMA200) is identical too
|
||||
short = make_xau_ohlcv(150, seed=5)
|
||||
gs = _gb36.precompute_indicators(short, tech)
|
||||
with _gk36.BACKEND.cpu_only():
|
||||
cs = _gb36.precompute_indicators(short, tech)
|
||||
assert gs.ema_trend is None and cs.ema_trend is None
|
||||
assert np.array_equal(gs.adx, cs.adx, equal_nan=True)
|
||||
|
||||
|
||||
def _sim_fixture36():
|
||||
rng = np.random.default_rng(38)
|
||||
n = 1500
|
||||
idx = pd.date_range("2025-01-06", periods=n, freq="15min", tz="UTC")
|
||||
close = 3000 + np.cumsum(rng.normal(0, 2.0, n))
|
||||
op = close + rng.normal(0, 0.5, n)
|
||||
hi = np.maximum(op, close) + np.abs(rng.normal(0, 1.5, n))
|
||||
lo = np.minimum(op, close) - np.abs(rng.normal(0, 1.5, n))
|
||||
for j in (500,): # a NaN bar
|
||||
op[j] = hi[j] = lo[j] = close[j] = np.nan
|
||||
op[900] = close[899] - 25.0; lo[900] = min(lo[900], op[900]) # gap through a BUY stop
|
||||
op[950] = close[949] + 25.0; hi[950] = max(hi[950], op[950]) # gap through a SELL stop / beyond a BUY target
|
||||
close[n - 1] = np.nan # NaN close on a final window bar
|
||||
sim = pd.DataFrame({"Open": op, "High": hi, "Low": lo, "Close": close}, index=idx)
|
||||
|
||||
def mk(i, buy, arm=0.0, dist=0.0, risk=6.0, tp_dist=9.0):
|
||||
e = float(op[i]) if not np.isnan(op[i]) else float(close[i - 1])
|
||||
sl = e - risk if buy else e + risk
|
||||
tp = e + tp_dist if buy else e - tp_dist
|
||||
return _gb36.Trade(pair=XAUUSD, entry_time=idx[i], entry_price=e, sl=sl, tp1=tp,
|
||||
tp2=tp + 3, direction="BUY" if buy else "SELL", pip_value=0.1,
|
||||
risk_pips=60, reward_pips=90, rr_planned=1.5, quality="A",
|
||||
final_score=40, final_confidence=60, market_regime="trending_up",
|
||||
trail_arm_r=arm, trail_dist_r=dist)
|
||||
|
||||
trades = [mk(int(i), bool(rng.integers(0, 2)), *((0.7, 0.85) if k % 5 == 0 else (0.0, 0.0)))
|
||||
for k, i in enumerate(rng.integers(1, n - 1, 300))]
|
||||
trades += [mk(n - 1, True), mk(n - 1, False)] # window of one NaN-close bar
|
||||
trades += [mk(n - 3, True, risk=500.0, tp_dist=500.0),
|
||||
mk(n - 3, False, risk=500.0, tp_dist=500.0)] # TIMEOUT onto the NaN close -> NO_DATA
|
||||
trades += [mk(n - 400, True, risk=500.0, tp_dist=500.0),
|
||||
mk(n - 400, False, risk=500.0, tp_dist=500.0)] # a clean TIMEOUT at max_bars
|
||||
trades += [mk(500, True), mk(500, False)] # NaN bar first (NO_DATA)
|
||||
trades += [mk(499, True), mk(499, False)] # NaN bar second
|
||||
trades += [mk(899, True), mk(949, False), mk(949, True)] # gap fills
|
||||
trades += [mk(200, True, risk=0.0), mk(200, False, risk=0.0)] # zero risk
|
||||
return sim, trades
|
||||
|
||||
|
||||
@test("GPU backend: simulate_trades GPU == CPU (trailing and static; NaN windows, gaps, zero risk, per-trade trail)")
|
||||
def _():
|
||||
if not _GPU36:
|
||||
print(" (no CUDA: kernel not exercised)")
|
||||
return
|
||||
sim, trades = _sim_fixture36()
|
||||
fields = ("exit_reason", "exit_price", "exit_time", "bars_held", "r_multiple", "mfe_r", "mae_r")
|
||||
for arm in (0.6, 0.0):
|
||||
a = copy.deepcopy(trades)
|
||||
b = copy.deepcopy(trades)
|
||||
_gb36.simulate_trades(a, sim, max_bars=300, spread=0.65, trail_arm_r=arm, trail_dist_r=0.75)
|
||||
with _gk36.BACKEND.cpu_only():
|
||||
_gb36.simulate_trades(b, sim, max_bars=300, spread=0.65, trail_arm_r=arm, trail_dist_r=0.75)
|
||||
for i, (x, y) in enumerate(zip(a, b)):
|
||||
for f in fields:
|
||||
assert _same36(getattr(x, f), getattr(y, f)), (
|
||||
f"arm={arm} trade {i} {f}: gpu={getattr(x, f)!r} cpu={getattr(y, f)!r}")
|
||||
reasons = {t.exit_reason for t in a}
|
||||
assert "NO_DATA" in reasons and "TIMEOUT" in reasons, reasons
|
||||
assert ("TRAIL" in reasons) == (arm > 0)
|
||||
assert ("TP1" in reasons) == (arm == 0)
|
||||
# the NaN-close TIMEOUT never leaks a NaN R into the book
|
||||
assert all(np.isfinite(t.r_multiple) for t in a)
|
||||
# empty and no-data-after-entry cases return without touching the kernel
|
||||
_gb36.simulate_trades([], sim, max_bars=300)
|
||||
|
||||
|
||||
@test("GPU backend: --backend parses, FB2_BACKEND is honoured, cpu_only() routes to the NumPy twins, gpu fails loudly")
|
||||
def _():
|
||||
assert _gb36._parse_args(["--pair", "XAUUSD", "--backend", "cpu"]).backend == "cpu"
|
||||
assert _gb36._parse_args(["--pair", "XAUUSD"]).backend is None
|
||||
try:
|
||||
_gb36._parse_args(["--pair", "XAUUSD", "--backend", "tpu"])
|
||||
assert False, "invalid backend must be rejected"
|
||||
except SystemExit:
|
||||
pass
|
||||
with patch.dict(os.environ, {_gk36.ENV_BACKEND: "cpu"}):
|
||||
b = _gk36.Backend()
|
||||
assert b.mode == "cpu" and b.available is False
|
||||
assert b.describe().startswith("NumPy CPU fallback (forced")
|
||||
with patch.dict(os.environ, {_gk36.ENV_BACKEND: "nonsense"}):
|
||||
assert _gk36.Backend().mode == "auto"
|
||||
# cpu_only(): the GPU twin must not be called
|
||||
with _gk36.BACKEND.cpu_only():
|
||||
assert _gk36.BACKEND.available is False
|
||||
with patch.object(_gb36, "_precompute_indicators_gpu",
|
||||
side_effect=AssertionError("GPU twin called under cpu_only")):
|
||||
_gb36.precompute_indicators(make_xau_ohlcv(120, seed=1), TechnicalConfig())
|
||||
with patch.object(_gb36, "_resolve_trades_gpu",
|
||||
side_effect=AssertionError("GPU resolver called under cpu_only")):
|
||||
sim, trades = _sim_fixture36()
|
||||
_gb36.simulate_trades(trades[:5], sim, max_bars=50, trail_arm_r=0.6)
|
||||
assert _gk36.BACKEND.describe().startswith(("CuPy GPU (", "NumPy CPU fallback ("))
|
||||
# --backend gpu without CUDA raises instead of falling back
|
||||
b3 = _gk36.Backend()
|
||||
b3._probed, b3._ok, b3.reason = True, False, "synthetic probe failure"
|
||||
try:
|
||||
b3.set_mode("gpu")
|
||||
assert False, "must raise"
|
||||
except RuntimeError as e:
|
||||
assert "synthetic probe failure" in str(e)
|
||||
assert b3.describe() == "NumPy CPU fallback (synthetic probe failure)"
|
||||
try:
|
||||
b3.set_mode("tpu")
|
||||
assert False
|
||||
except ValueError:
|
||||
pass
|
||||
# release_device_memory is a no-op without CUDA
|
||||
b3.release_device_memory()
|
||||
|
||||
|
||||
@test("GPU backend: gpu_kernels imports none of the live writers; cupy never enters the live roots or the backtester")
|
||||
def _():
|
||||
banned = {"signal_log", "news_engine", "providers", "telegram_bot", "news_archive",
|
||||
"agent_panel", "violent_watch", "gpu_backtest", "engine", "signal_aggregator",
|
||||
"replay_record", "sklearn", "scipy", "lightgbm", "torch"}
|
||||
kpath = Path("backtest/gpu_kernels.py")
|
||||
seen = set()
|
||||
for node in ast.walk(ast.parse(kpath.read_text(encoding="utf-8"), str(kpath))):
|
||||
if isinstance(node, ast.Import):
|
||||
seen.update(a.name.split(".")[0] for a in node.names)
|
||||
elif isinstance(node, ast.ImportFrom):
|
||||
seen.add((node.module or "").split(".")[0])
|
||||
assert not (seen & banned), seen & banned
|
||||
assert "numpy" in seen and "cupy" in seen # cupy only inside the probe / kernels
|
||||
ksrc = kpath.read_text(encoding="utf-8")
|
||||
assert 'KERNEL_OPTIONS = ("--fmad=false",)' in ksrc # no FMA contraction, ever
|
||||
assert "options=KERNEL_OPTIONS" in ksrc
|
||||
# the backtester never imports cupy itself; it reads backend state through gpu_kernels
|
||||
bsrc = Path("backtest/gpu_backtest.py").read_text(encoding="utf-8")
|
||||
assert "import cupy" not in bsrc and "_GPU_AVAILABLE" not in bsrc
|
||||
assert "from backtest import gpu_kernels" in bsrc
|
||||
assert "BACKEND.release_device_memory()" in bsrc # before the fork
|
||||
assert bsrc.index("BACKEND.release_device_memory()") < bsrc.index('mp.get_context("fork")')
|
||||
# live roots stay free of cupy (Module 32's guard bans it too)
|
||||
offenders = []
|
||||
for root in (Path("."), Path("providers"), Path("pair_signals")):
|
||||
for path in sorted(root.glob("*.py")):
|
||||
if "venv" in path.parts:
|
||||
continue
|
||||
for node in ast.walk(ast.parse(path.read_text(encoding="utf-8"), str(path))):
|
||||
names = ([a.name for a in node.names] if isinstance(node, ast.Import)
|
||||
else [node.module or ""] if isinstance(node, ast.ImportFrom) else [])
|
||||
offenders += [f"{path}:{node.lineno}" for nm in names if nm.split(".")[0] == "cupy"]
|
||||
assert not offenders, offenders
|
||||
assert "cupy" not in open("requirements.txt").read()
|
||||
|
||||
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
# SUMMARY
|
||||
# ══════════════════════════════════════════════════════════════════════
|
||||
|
||||
Reference in New Issue
Block a user