parallelize the backtest across worker processes (true multi-core)

The replay was CPU-bound and single-core: the earlier asyncio.to_thread offload
kept the API responsive but, because of the GIL, ran on one core. Per-ticker
replay is independent, so fan it out across worker processes (which sidestep the
GIL) for real multi-core speedup.

- New `settings.backtest_workers` (default 4), capped to cpu_count-1 so a core
  stays free for the web server.
- Uses a `forkserver` context (workers forked from a clean single-threaded
  server — avoids the fork-with-threads deadlock); falls back to `fork`. On
  spawn-only platforms (Windows) and for 1-ticker runs it uses the thread path,
  so dev/tests are unaffected.
- Worker takes primitive column arrays (cheap to pickle), rebuilds bars, and
  returns (candidates, plain-dict signal series) — both picklable across the
  process boundary. Bars are still fetched in the event loop (ORM-safe).
- Pool creation is guarded: if the pool can't start, the job falls back to the
  sequential thread path instead of failing.

334 backend tests pass (parallel path is POSIX/server-only, so it's covered by
construction + the picklability/worker-count tests; the thread fallback is
exercised by the run_backtest smoke test).

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/services/backtest_service.py

@@ -18,15 +18,19 @@ import asyncio
 import json
 import logging
 import math
+import multiprocessing
+import os
 from collections import defaultdict
 from collections.abc import Callable
-from datetime import datetime, timezone
+from concurrent.futures import ProcessPoolExecutor
+from datetime import date, datetime, timezone
 from types import SimpleNamespace
 from typing import Any
 
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from app.config import settings
 from app.models.settings import SystemSetting
 from app.models.ticker import Ticker
 from app.services.admin_service import get_activation_config, update_setting
@@ -491,18 +495,69 @@ def _signal_evaluation(collected: dict) -> list[dict]:
     return rows
 
 
-def _process_ticker(
+def _signal_series(records: list) -> dict:
+    """Per-ticker signal/forward-return series as a PLAIN (picklable) nested dict
+    — no defaultdict/lambda — so it can cross a process boundary."""
+    tmp: dict = defaultdict(lambda: defaultdict(list))
+    _accumulate_signal_series(records, tmp)
+    return {name: dict(weeks) for name, weeks in tmp.items()}
+
+
+def _replay_and_signals(
     symbol: str,
-    records: list,
+    columns: tuple,
     config: dict,
     activation: dict,
-    collected: dict,
-) -> list[dict]:
-    """The CPU-bound per-ticker work — replay + signal accumulation — bundled so
-    run_backtest can hand it to a worker thread. Mutates ``collected``."""
-    cands = _replay_ticker(symbol, records, config, activation)
-    _accumulate_signal_series(records, collected)
-    return cands
+) -> tuple[list[dict], dict]:
+    """The CPU-bound per-ticker work, as a top-level (picklable) function so it can
+    run in a worker process. Takes primitive column arrays (cheap to pickle),
+    rebuilds bar objects, and returns (candidates, signal_series)."""
+    date_ords, opens, highs, lows, closes, volumes = columns
+    bars = [
+        SimpleNamespace(
+            date=date.fromordinal(o), open=op, high=hi, low=lo, close=cl, volume=vo
+        )
+        for o, op, hi, lo, cl, vo in zip(date_ords, opens, highs, lows, closes, volumes)
+    ]
+    return _replay_ticker(symbol, bars, config, activation), _signal_series(bars)
+
+
+def _backtest_worker_count() -> int:
+    """How many worker processes to replay tickers across. Capped to cpu_count-1
+    so a core stays free for the web server; 1 means sequential."""
+    configured = int(getattr(settings, "backtest_workers", 4))
+    if configured <= 1:
+        return 1
+    cpu = os.cpu_count() or 1
+    return max(1, min(configured, cpu - 1))
+
+
+def _mp_context():
+    """A start method safe to use from the threaded asyncio server: ``forkserver``
+    (workers forked from a clean, single-threaded server — avoids the
+    fork-with-threads deadlock) when available, else ``fork``. Returns None on
+    spawn-only platforms (Windows), where the caller falls back to a thread."""
+    methods = multiprocessing.get_all_start_methods()
+    for method in ("forkserver", "fork"):
+        if method in methods:
+            return multiprocessing.get_context(method)
+    return None
+
+
+async def _fetch_columns(db: AsyncSession, symbol: str) -> tuple | None:
+    """Read one ticker's OHLCV and detach it to primitive column arrays in the
+    event loop (safe ORM access), ready to hand to a worker. None if no data."""
+    records = await query_ohlcv(db, symbol)
+    if not records:
+        return None
+    return (
+        [r.date.toordinal() for r in records],
+        [float(r.open) for r in records],
+        [float(r.high) for r in records],
+        [float(r.low) for r in records],
+        [float(r.close) for r in records],
+        [int(r.volume) for r in records],
+    )
 
 
 def _assign_momentum_percentiles(candidates: list[dict]) -> None:
@@ -549,33 +604,71 @@ async def run_backtest(
     candidates: list[dict] = []
     # collected[signal_name][iso_week] -> list of (signal_value, forward_return)
     collected: dict = defaultdict(lambda: defaultdict(list))
-    for index, ticker in enumerate(tickers):
-        if progress_cb is not None:
-            progress_cb(index, total, ticker.symbol)
+
+    def _merge(result: tuple[list[dict], dict]) -> None:
+        cands, series = result
+        candidates.extend(cands)
+        for name, weeks in series.items():
+            for wk, pairs in weeks.items():
+                collected[name][wk].extend(pairs)
+
+    workers = _backtest_worker_count()
+    ctx = _mp_context() if (workers > 1 and total > 1) else None
+    loop = asyncio.get_running_loop()
+
+    pool = None
+    if ctx is not None:
         try:
-            records = await query_ohlcv(db, ticker.symbol)
-            # Detach the ORM rows to plain objects in the event loop (safe to read
-            # here), then run the heavy replay in a worker thread. The compute is
-            # CPU-bound and used to block the event loop — and the API server with
-            # it — for the whole run; offloading lets CPython hand the GIL back to
-            # the loop every few ms so health checks / page loads stay responsive.
-            bars = [
-                SimpleNamespace(
-                    date=r.date,
-                    open=float(r.open),
-                    high=float(r.high),
-                    low=float(r.low),
-                    close=float(r.close),
-                    volume=int(r.volume),
-                )
-                for r in records
-            ]
-            cands = await asyncio.to_thread(
-                _process_ticker, ticker.symbol, bars, config, activation, collected
-            )
-            candidates.extend(cands)
+            pool = ProcessPoolExecutor(max_workers=workers, mp_context=ctx)
         except Exception:
-            logger.exception("Backtest replay failed for %s", ticker.symbol)
+            logger.exception("Backtest process pool unavailable; falling back to sequential")
+
+    if pool is not None:
+        # Parallel: replay tickers across worker processes — true multi-core, since
+        # the GIL only serializes work within a single process. Bars are fetched in
+        # the event loop (ORM-safe) and a bounded batch is fanned out to the pool.
+        logger.info(json.dumps({
+            "event": "backtest_parallel", "workers": workers,
+            "start_method": ctx.get_start_method(),
+        }))
+        chunk = workers * 2
+        done = 0
+        with pool:
+            for start in range(0, total, chunk):
+                batch = tickers[start : start + chunk]
+                futures = []
+                for ticker in batch:
+                    try:
+                        columns = await _fetch_columns(db, ticker.symbol)
+                    except Exception:
+                        logger.exception("Backtest fetch failed for %s", ticker.symbol)
+                        continue
+                    if columns is not None:
+                        futures.append(loop.run_in_executor(
+                            pool, _replay_and_signals, ticker.symbol, columns, config, activation
+                        ))
+                for result in await asyncio.gather(*futures, return_exceptions=True):
+                    if isinstance(result, Exception):
+                        logger.error(json.dumps({"event": "backtest_worker_error", "message": str(result)}))
+                    else:
+                        _merge(result)
+                done += len(batch)
+                if progress_cb is not None:
+                    progress_cb(min(done, total), total, "")
+    else:
+        # Sequential fallback (Windows / 1 worker): run each replay in a worker
+        # thread so the event loop — and the API server — stays responsive.
+        for index, ticker in enumerate(tickers):
+            if progress_cb is not None:
+                progress_cb(index, total, ticker.symbol)
+            try:
+                columns = await _fetch_columns(db, ticker.symbol)
+                if columns is not None:
+                    _merge(await asyncio.to_thread(
+                        _replay_and_signals, ticker.symbol, columns, config, activation
+                    ))
+            except Exception:
+                logger.exception("Backtest replay failed for %s", ticker.symbol)
 
     if progress_cb is not None and total:
         progress_cb(total, total, "")