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>

backtest/gpu_backtest.py

@@ -19,7 +19,9 @@ Key properties:
 - Trade simulation is full candle walk on the primary timeframe using
   High/Low; if both SL and TP1 hit in the same bar, SL is assumed first
   (conservative).
-- Ships with CuPy fallback to NumPy so the script runs anywhere.
+- Ships with CuPy fallback to NumPy so the script runs anywhere. The CUDA
+  kernels (backtest/gpu_kernels.py) are bit-for-bit replicas of the NumPy
+  path, so both backends produce the same book; --backend cpu forces NumPy.
 
 Usage:
     python backtest/gpu_backtest.py --pair XAUUSD --days 365
@@ -64,36 +66,22 @@ from engine import IndicatorSignal, TechnicalAnalysis, TechnicalEngine  # noqa:
 from signal_aggregator import SignalAggregator, SignalReport, TradeSetup  # noqa: E402
 import position_sizing  # noqa: E402
 from backtest import replay_record  # noqa: E402
+from backtest import gpu_kernels as _gk  # noqa: E402
 
 # ─────────────────────────────────────────────────────────────────────
-#  GPU shim — CuPy if available AND CUDA runtime works, else NumPy.
-#  An importable CuPy can still fail at first kernel launch if CUDA libs
-#  are missing (containers, headless boxes), so verify with a tiny op.
+#  GPU backend — backtest/gpu_kernels.py owns the CuPy state (probed
+#  lazily and never silently: a fallback carries its reason into the
+#  "Backend:" line) and the CUDA kernels. Every kernel is a bit-for-bit
+#  replica of the NumPy/pandas code kept in this file as the CPU path, so
+#  `--backend cpu` and the GPU produce the same book (proof: the five
+#  reference MD5s, CLAUDE.md section 9). The forked replay workers never
+#  touch the device: every precomputed series is moved to host first.
 # ─────────────────────────────────────────────────────────────────────
-def _detect_gpu():
-    try:
-        import cupy as cp_mod  # type: ignore
-        # Force runtime init: a kernel launch will surface missing libs.
-        _ = cp_mod.asarray(np.zeros(2, dtype=np.float64))
-        _ = cp_mod.cumsum(_).get()
-        return cp_mod, True
-    except Exception:
-        return np, False
+BACKEND = _gk.BACKEND
 
 
-cp, _GPU_AVAILABLE = _detect_gpu()
-
-
-def _to_gpu(arr: np.ndarray):
-    """Move a numpy array to GPU (or no-op if CuPy unavailable)."""
-    return cp.asarray(arr) if _GPU_AVAILABLE else arr
-
-
-def _to_cpu(arr) -> np.ndarray:
-    """Pull array back to host."""
-    if _GPU_AVAILABLE and hasattr(arr, "get"):
-        return arr.get()
-    return np.asarray(arr)
+def _gpu_on() -> bool:
+    return BACKEND.available
 
 
 logger = logging.getLogger("gpu_backtest")
@@ -215,18 +203,29 @@ class IndicatorCache:
 def precompute_indicators(
     df: pd.DataFrame, tech_cfg, gpu_log: bool = False
 ) -> IndicatorCache:
-    """Compute every causal indicator series once on the full df."""
+    """Compute every causal indicator series once on the full df.
+
+    Dispatches to the CUDA kernels when the backend is available and to
+    the NumPy/pandas code otherwise. The two are bit-identical (Module 36
+    and the 42-window check in backtest/research/gpu_backend_2026-09-18.md).
+    """
+    if gpu_log:
+        logger.info(
+            f"Pre-computing indicators on {len(df)} bars "
+            f"({'CuPy GPU' if _gpu_on() else 'NumPy CPU'} backend)"
+        )
+    if _gpu_on():
+        return _precompute_indicators_gpu(df, tech_cfg)
+    return _precompute_indicators_cpu(df, tech_cfg)
+
+
+def _precompute_indicators_cpu(df: pd.DataFrame, tech_cfg) -> IndicatorCache:
+    """The NumPy/pandas path — the reference the CUDA twin replicates."""
     close = df["Close"].to_numpy(dtype=np.float64)
     high = df["High"].to_numpy(dtype=np.float64)
     low = df["Low"].to_numpy(dtype=np.float64)
     n = len(close)
 
-    if gpu_log:
-        logger.info(
-            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()) ─
     ema_fast = _gpu_ewm_span(close, tech_cfg.ema_fast)
     ema_medium = _gpu_ewm_span(close, tech_cfg.ema_medium)
@@ -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]
+    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}"
     )