GPU parallel EMA + fix backtest/live parity bugs
- Add CUDA parallel prefix scan (Blelloch) for EMA: 16 EMAs in 3 batched kernel launches - Eliminate CPU↔GPU array bouncing; keep arrays on-device until final cache build - Auto-detect pip-installed CUDA libs for CuPy kernel compilation - Add --gpu-only and --quick (30-day) CLI flags - Fix missing _adx_slope in GPUTechnicalEngine (was suppressing transitioning regime) - Fix ROC early-entry: guard was pos < n+1, should be pos < n+4 to match live engine - Fix weekend filter: use proper forex weekend (Fri 22:00–Sun 22:00 UTC) instead of weekday >= 5 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
harwinder
+536
-193
@@ -65,6 +65,22 @@ from pair_signals import PAIR_PRODUCERS # noqa: E402
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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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# ─────────────────────────────────────────────────────────────────────
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def _auto_detect_cuda_path():
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"""Set CUDA_PATH from pip-installed nvidia packages if not already set."""
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if os.environ.get("CUDA_PATH"):
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return
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for p in sys.path:
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nvrtc_lib = os.path.join(p, "nvidia", "cuda_nvrtc", "lib")
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if os.path.isdir(nvrtc_lib):
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os.environ["CUDA_PATH"] = os.path.join(p, "nvidia")
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ld = os.environ.get("LD_LIBRARY_PATH", "")
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if nvrtc_lib not in ld:
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os.environ["LD_LIBRARY_PATH"] = nvrtc_lib + (":" + ld if ld else "")
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return
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_auto_detect_cuda_path()
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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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@@ -93,6 +109,147 @@ def _to_cpu(arr) -> np.ndarray:
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logger = logging.getLogger("gpu_backtest")
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# ─────────────────────────────────────────────────────────────────────
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# CUDA kernels for parallel EMA (prefix scan on affine transforms)
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#
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# EMA recurrence: y[i] = α·x[i] + β·y[i-1], β = 1-α
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# Represented as affine pair (a, b) where y = a·seed + b.
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# Non-NaN element: (β, α·x[i])
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# First non-NaN: (0, x[i]) ← resets/seeds the state
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# NaN element: (1, 0) ← carries previous value
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# Associative combine: (a1,b1)·(a2,b2) = (a1·a2, a2·b1 + b2)
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# After inclusive scan, the b component at position i equals y[i].
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#
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# NaN handling & min_periods masking done as CuPy pre/post-processing
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# (build a/b arrays before kernel, mask output after). The kernel
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# itself is a pure prefix scan on (a, b) pairs — no NaN logic inside.
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# ─────────────────────────────────────────────────────────────────────
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_EWM_BLOCK_SIZE = 512
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_ewm_scan_kernel = None
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_ewm_batch_kernel = None
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if _GPU_AVAILABLE:
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_ewm_scan_kernel = cp.RawKernel(r'''
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extern "C" __global__
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void ewm_scan(const double* __restrict__ a_in,
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const double* __restrict__ b_in,
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double* __restrict__ out,
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const int n) {
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extern __shared__ double sdata[];
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double* s_a = sdata;
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double* s_b = sdata + blockDim.x;
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const int tid = threadIdx.x;
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double carry_a = 1.0;
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double carry_b = 0.0;
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for (int tile = 0; tile < n; tile += blockDim.x) {
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int idx = tile + tid;
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s_a[tid] = (idx < n) ? a_in[idx] : 1.0;
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s_b[tid] = (idx < n) ? b_in[idx] : 0.0;
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__syncthreads();
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for (int stride = 1; stride < blockDim.x; stride <<= 1) {
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double pa = 1.0, pb = 0.0;
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if (tid >= stride) {
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pa = s_a[tid - stride];
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pb = s_b[tid - stride];
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}
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__syncthreads();
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if (tid >= stride) {
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s_b[tid] = s_a[tid] * pb + s_b[tid];
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s_a[tid] = pa * s_a[tid];
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}
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__syncthreads();
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}
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if (idx < n)
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out[idx] = s_a[tid] * carry_b + s_b[tid];
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__syncthreads();
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if (tid == 0) {
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int last = min(blockDim.x, n - tile) - 1;
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double la = s_a[last], lb = s_b[last];
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double nb = la * carry_b + lb;
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carry_a = la * carry_a;
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carry_b = nb;
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}
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__syncthreads();
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if (tid == 0) { s_a[0] = carry_a; s_b[0] = carry_b; }
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__syncthreads();
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carry_a = s_a[0];
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carry_b = s_b[0];
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}
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}
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''', 'ewm_scan')
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_ewm_batch_kernel = cp.RawKernel(r'''
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extern "C" __global__
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void ewm_batch(const double* __restrict__ a_in,
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const double* __restrict__ b_in,
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double* __restrict__ out,
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const int* __restrict__ offsets,
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const int* __restrict__ lengths,
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const int num_emas) {
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int ema_idx = blockIdx.x;
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if (ema_idx >= num_emas) return;
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const int off = offsets[ema_idx];
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const int n = lengths[ema_idx];
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const int tid = threadIdx.x;
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extern __shared__ double sdata[];
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double* s_a = sdata;
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double* s_b = sdata + blockDim.x;
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const double* my_a = a_in + off;
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const double* my_b = b_in + off;
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double* my_out = out + off;
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double carry_a = 1.0;
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double carry_b = 0.0;
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for (int tile = 0; tile < n; tile += blockDim.x) {
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int idx = tile + tid;
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s_a[tid] = (idx < n) ? my_a[idx] : 1.0;
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s_b[tid] = (idx < n) ? my_b[idx] : 0.0;
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__syncthreads();
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for (int stride = 1; stride < blockDim.x; stride <<= 1) {
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double pa = 1.0, pb = 0.0;
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if (tid >= stride) {
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pa = s_a[tid - stride];
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pb = s_b[tid - stride];
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}
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__syncthreads();
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if (tid >= stride) {
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s_b[tid] = s_a[tid] * pb + s_b[tid];
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s_a[tid] = pa * s_a[tid];
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}
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__syncthreads();
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}
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if (idx < n)
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my_out[idx] = s_a[tid] * carry_b + s_b[tid];
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__syncthreads();
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if (tid == 0) {
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int last = min(blockDim.x, n - tile) - 1;
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double la = s_a[last], lb = s_b[last];
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double nb = la * carry_b + lb;
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carry_a = la * carry_a;
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carry_b = nb;
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}
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__syncthreads();
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if (tid == 0) { s_a[0] = carry_a; s_b[0] = carry_b; }
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__syncthreads();
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carry_a = s_a[0];
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carry_b = s_b[0];
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}
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}
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''', 'ewm_batch')
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PAIR_ALIASES = {
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"XAUUSD": XAUUSD,
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@@ -115,94 +272,167 @@ BAR_SECONDS = {
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# GPU INDICATOR PRE-COMPUTATION
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# ═════════════════════════════════════════════════════════════════════
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def _gpu_ewm_alpha(x: np.ndarray, alpha: float, min_periods: int = 0) -> np.ndarray:
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"""
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Exponential weighted mean matching pandas
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`ewm(alpha=alpha, min_periods=p, adjust=False).mean()` exactly.
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def _build_ewm_pairs(xg, alpha):
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"""Build (a, b) affine-transform pairs for EMA prefix scan on GPU.
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Recursion: y[i] = alpha * x[i] + (1-alpha) * y[i-1], seeded at the
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first non-NaN x. NaN inputs carry the previous y forward without
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advancing the non-NaN counter (pandas' default `ignore_na=False`).
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Output positions before min_periods non-NaN observations are NaN.
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Sequential CPU pass — EMA is recursive so true GPU parallelism
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requires a scan kernel, which is overkill for a one-shot pre-compute.
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Returns (a, b, non_nan_cum) — all CuPy arrays.
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"""
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n = x.shape[0]
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out = np.full(n, np.nan, dtype=np.float64)
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if n == 0:
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beta = 1.0 - alpha
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nan_mask = cp.isnan(xg)
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non_nan_cum = cp.cumsum((~nan_mask).astype(cp.int32))
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a = cp.where(nan_mask, 1.0, beta)
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b = cp.where(nan_mask, 0.0, alpha * xg)
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first_mask = (non_nan_cum == 1) & (~nan_mask)
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a = cp.where(first_mask, 0.0, a)
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b = cp.where(first_mask, xg, b)
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return a, b, non_nan_cum
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def _gpu_ewm_alpha(x, alpha: float, min_periods: int = 0):
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"""EMA via parallel prefix scan (GPU) or sequential loop (CPU).
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Returns CuPy array when GPU available, numpy array otherwise.
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NaN positions after the first valid value carry the last computed
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EMA value (differs from pandas which outputs NaN at those positions).
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"""
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if _GPU_AVAILABLE:
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xg = cp.asarray(x, dtype=cp.float64)
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n = xg.shape[0]
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if n == 0:
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return cp.empty(0, dtype=cp.float64)
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a, b, non_nan_cum = _build_ewm_pairs(xg, alpha)
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out = cp.empty(n, dtype=cp.float64)
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shared = 2 * _EWM_BLOCK_SIZE * 8
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_ewm_scan_kernel((1,), (_EWM_BLOCK_SIZE,),
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(a, b, out, np.int32(n)), shared_mem=shared)
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if min_periods > 0:
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out = cp.where(non_nan_cum < min_periods, cp.nan, out)
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out = cp.where(non_nan_cum == 0, cp.nan, out)
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return out
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else:
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arr = np.asarray(x, dtype=np.float64)
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n = arr.shape[0]
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out = np.full(n, np.nan, dtype=np.float64)
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if n == 0:
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return out
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one_minus = 1.0 - alpha
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seen = 0
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last_y = np.nan
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for i in range(n):
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xi = arr[i]
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if np.isnan(xi):
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if not np.isnan(last_y) and seen >= min_periods:
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out[i] = last_y
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continue
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last_y = xi if np.isnan(last_y) else alpha * xi + one_minus * last_y
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seen += 1
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if seen >= min_periods:
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out[i] = last_y
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return out
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one_minus = 1.0 - alpha
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seen = 0
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last_y = np.nan
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for i in range(n):
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xi = x[i]
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if np.isnan(xi):
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# NaN input: y stays at last_y, don't advance counter.
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if not np.isnan(last_y) and seen >= min_periods:
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out[i] = last_y
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continue
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last_y = xi if np.isnan(last_y) else alpha * xi + one_minus * last_y
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seen += 1
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if seen >= min_periods:
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out[i] = last_y
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return out
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def _gpu_ewm_span(x: np.ndarray, span: int) -> np.ndarray:
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"""ewm with span= semantics (alpha = 2/(span+1), no min_periods, seeded at x[0])."""
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def _gpu_ewm_span(x, span: int):
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"""ewm with span= semantics (alpha = 2/(span+1), no min_periods)."""
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alpha = 2.0 / (span + 1.0)
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return _gpu_ewm_alpha(x, alpha, min_periods=0)
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def _gpu_rolling_mean(x: np.ndarray, window: int) -> np.ndarray:
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"""Rolling mean via cumsum-diff, NaN-aware (matches pandas
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`.rolling(window).mean()` default min_periods=window — returns NaN
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if any value in the window is NaN)."""
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n = x.shape[0]
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out = np.full(n, np.nan, dtype=np.float64)
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def _gpu_ewm_alpha_batch(items: list):
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"""Batch multiple EMA computations into a single kernel launch.
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items: list of (x_gpu: cp.ndarray, alpha: float, min_periods: int)
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Returns: list of CuPy arrays (one per input EMA).
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"""
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if not items:
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return []
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all_a, all_b, all_nnc = [], [], []
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offsets, lengths = [], []
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offset = 0
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for xg, alpha, mp in items:
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n = xg.shape[0]
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a, b, nnc = _build_ewm_pairs(xg, alpha)
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all_a.append(a)
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all_b.append(b)
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all_nnc.append((nnc, mp))
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offsets.append(offset)
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lengths.append(n)
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offset += n
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cat_a = cp.concatenate(all_a)
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cat_b = cp.concatenate(all_b)
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cat_out = cp.empty(offset, dtype=cp.float64)
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d_offsets = cp.array(offsets, dtype=cp.int32)
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d_lengths = cp.array(lengths, dtype=cp.int32)
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num = len(items)
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shared = 2 * _EWM_BLOCK_SIZE * 8
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_ewm_batch_kernel((num,), (_EWM_BLOCK_SIZE,),
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(cat_a, cat_b, cat_out, d_offsets, d_lengths,
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np.int32(num)),
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shared_mem=shared)
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results = []
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for i, (nnc, mp) in enumerate(all_nnc):
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o, n = offsets[i], lengths[i]
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out = cat_out[o:o + n].copy()
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if mp > 0:
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out = cp.where(nnc < mp, cp.nan, out)
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out = cp.where(nnc == 0, cp.nan, out)
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results.append(out)
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return results
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def _gpu_rolling_mean(x, window: int):
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"""Rolling mean via cumsum-diff, NaN-aware. Returns CuPy array on GPU."""
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xp = cp if _GPU_AVAILABLE else np
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xg = xp.asarray(x, dtype=xp.float64)
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n = xg.shape[0]
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out = xp.full(n, xp.nan, dtype=xp.float64)
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if n < window:
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return out
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xg = _to_gpu(x.astype(np.float64))
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nan_mask = cp.isnan(xg)
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x_filled = cp.where(nan_mask, 0.0, xg)
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cs = cp.cumsum(x_filled)
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valid_cnt = cp.cumsum((~nan_mask).astype(cp.int64))
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nan_mask = xp.isnan(xg)
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x_filled = xp.where(nan_mask, 0.0, xg)
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cs = xp.cumsum(x_filled)
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valid_cnt = xp.cumsum((~nan_mask).astype(xp.int64))
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sum_w = cp.empty(n - window + 1, dtype=cp.float64)
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cnt_w = cp.empty(n - window + 1, dtype=cp.int64)
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sum_w = xp.empty(n - window + 1, dtype=xp.float64)
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cnt_w = xp.empty(n - window + 1, dtype=xp.int64)
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sum_w[0] = cs[window - 1]
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sum_w[1:] = cs[window:] - cs[:-window]
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cnt_w[0] = valid_cnt[window - 1]
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cnt_w[1:] = valid_cnt[window:] - valid_cnt[:-window]
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# pandas: NaN if any window value is NaN (cnt < window).
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rolling = cp.where(cnt_w < window, cp.nan, sum_w / window)
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out[window - 1:] = _to_cpu(rolling)
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rolling = xp.where(cnt_w < window, xp.nan, sum_w / window)
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out[window - 1:] = rolling
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return out
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def _gpu_rolling_std(x: np.ndarray, window: int, ddof: int = 0) -> np.ndarray:
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"""Rolling std via cumsum of x and x^2, NaN-aware. ddof=0 matches
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pandas std(ddof=0). NaN if any window value is NaN."""
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n = x.shape[0]
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out = np.full(n, np.nan, dtype=np.float64)
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def _gpu_rolling_std(x, window: int, ddof: int = 0):
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"""Rolling std via cumsum of x and x^2, NaN-aware. Returns CuPy array on GPU."""
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xp = cp if _GPU_AVAILABLE else np
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xg = xp.asarray(x, dtype=xp.float64)
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n = xg.shape[0]
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out = xp.full(n, xp.nan, dtype=xp.float64)
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if n < window:
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return out
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xg = _to_gpu(x.astype(np.float64))
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nan_mask = cp.isnan(xg)
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x_filled = cp.where(nan_mask, 0.0, xg)
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nan_mask = xp.isnan(xg)
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x_filled = xp.where(nan_mask, 0.0, xg)
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x2_filled = x_filled * x_filled
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cs = cp.cumsum(x_filled)
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cs2 = cp.cumsum(x2_filled)
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valid_cnt = cp.cumsum((~nan_mask).astype(cp.int64))
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cs = xp.cumsum(x_filled)
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cs2 = xp.cumsum(x2_filled)
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valid_cnt = xp.cumsum((~nan_mask).astype(xp.int64))
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sum_x = cp.empty(n - window + 1, dtype=cp.float64)
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sum_x2 = cp.empty(n - window + 1, dtype=cp.float64)
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cnt_w = cp.empty(n - window + 1, dtype=cp.int64)
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sum_x = xp.empty(n - window + 1, dtype=xp.float64)
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sum_x2 = xp.empty(n - window + 1, dtype=xp.float64)
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cnt_w = xp.empty(n - window + 1, dtype=xp.int64)
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sum_x[0] = cs[window - 1]
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sum_x[1:] = cs[window:] - cs[:-window]
|
||||
sum_x2[0] = cs2[window - 1]
|
||||
@@ -212,12 +442,12 @@ def _gpu_rolling_std(x: np.ndarray, window: int, ddof: int = 0) -> np.ndarray:
|
||||
|
||||
mean = sum_x / window
|
||||
var = sum_x2 / window - mean * mean
|
||||
var = cp.maximum(var, 0.0) # numerical safety against tiny negatives
|
||||
var = xp.maximum(var, 0.0)
|
||||
if ddof:
|
||||
var = var * window / max(window - ddof, 1)
|
||||
std = cp.sqrt(var)
|
||||
std = cp.where(cnt_w < window, cp.nan, std)
|
||||
out[window - 1:] = _to_cpu(std)
|
||||
std = xp.sqrt(var)
|
||||
std = xp.where(cnt_w < window, xp.nan, std)
|
||||
out[window - 1:] = std
|
||||
return out
|
||||
|
||||
|
||||
@@ -275,11 +505,21 @@ 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."""
|
||||
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)
|
||||
"""Compute every causal indicator series once on the full df.
|
||||
|
||||
When GPU available: all intermediate arrays stay on-device (CuPy).
|
||||
Only converted to numpy at the end for IndicatorCache (scalar lookups).
|
||||
EMA calls are batched into 3 kernel launches by dependency group.
|
||||
"""
|
||||
xp = cp if _GPU_AVAILABLE else np
|
||||
close_np = df["Close"].to_numpy(dtype=np.float64)
|
||||
high_np = df["High"].to_numpy(dtype=np.float64)
|
||||
low_np = df["Low"].to_numpy(dtype=np.float64)
|
||||
n = len(close_np)
|
||||
|
||||
close = xp.asarray(close_np)
|
||||
high = xp.asarray(high_np)
|
||||
low = xp.asarray(low_np)
|
||||
|
||||
if gpu_log:
|
||||
logger.info(
|
||||
@@ -287,35 +527,138 @@ def precompute_indicators(
|
||||
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)
|
||||
ema_slow = _gpu_ewm_span(close, tech_cfg.ema_slow)
|
||||
ema_trend = _gpu_ewm_span(close, tech_cfg.ema_trend) if n >= tech_cfg.ema_trend else None
|
||||
# ── True Range (shared by ATR, ADX, Keltner) ──
|
||||
prev_c = xp.empty(n, dtype=xp.float64)
|
||||
prev_c[0] = xp.nan
|
||||
prev_c[1:] = close[:-1]
|
||||
tr1 = high - low
|
||||
tr2 = xp.abs(high - prev_c)
|
||||
tr3 = xp.abs(low - prev_c)
|
||||
tr2[0] = xp.nan
|
||||
tr3[0] = xp.nan
|
||||
tr = xp.fmax(xp.fmax(tr1, tr2), tr3)
|
||||
|
||||
# ── RSI (Wilder via ewm alpha=1/period, min_periods=period) ──
|
||||
# pandas: delta = close.diff() → delta[0] is NaN; gain = delta.where(delta > 0, 0.0)
|
||||
# evaluates NaN > 0 as False so gain[0] becomes 0.0 (NOT NaN). Same for loss.
|
||||
delta = np.empty(n, dtype=np.float64)
|
||||
delta[0] = np.nan
|
||||
# ── RSI delta / gain / loss ──
|
||||
delta = xp.empty(n, dtype=xp.float64)
|
||||
delta[0] = xp.nan
|
||||
delta[1:] = close[1:] - close[:-1]
|
||||
with np.errstate(invalid="ignore"):
|
||||
gain = np.where(delta > 0, delta, 0.0)
|
||||
loss = np.where(delta < 0, -delta, 0.0)
|
||||
avg_gain = _gpu_ewm_alpha(gain, 1.0 / tech_cfg.rsi_period, min_periods=tech_cfg.rsi_period)
|
||||
avg_loss = _gpu_ewm_alpha(loss, 1.0 / tech_cfg.rsi_period, min_periods=tech_cfg.rsi_period)
|
||||
avg_loss_safe = np.where(avg_loss == 0, 1e-10, avg_loss)
|
||||
gain = xp.where(delta > 0, delta, 0.0)
|
||||
loss = xp.where(delta < 0, -delta, 0.0)
|
||||
|
||||
# ── ADX directional movement ──
|
||||
plus_dm_raw = xp.empty(n, dtype=xp.float64)
|
||||
minus_dm_raw = xp.empty(n, dtype=xp.float64)
|
||||
plus_dm_raw[0] = xp.nan
|
||||
minus_dm_raw[0] = xp.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 = xp.where(cond_plus, plus_dm_raw, 0.0)
|
||||
cond_minus = (minus_dm_raw > plus_dm_raw) & (minus_dm_raw > 0)
|
||||
minus_dm = xp.where(cond_minus, minus_dm_raw, 0.0)
|
||||
|
||||
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)
|
||||
rsi_alpha = 1.0 / tech_cfg.rsi_period
|
||||
atr_alpha = 1.0 / tech_cfg.atr_period
|
||||
adx_alpha = 1.0 / tech_cfg.adx_period
|
||||
kc_atr_alpha = 1.0 / kc_atr_period
|
||||
|
||||
if _GPU_AVAILABLE:
|
||||
t_ema = time.perf_counter()
|
||||
# ── BATCH 1: 10 independent EMAs ──
|
||||
batch1_items = [
|
||||
(close, 2.0 / (tech_cfg.ema_fast + 1), 0), # 0: ema_fast
|
||||
(close, 2.0 / (tech_cfg.ema_medium + 1), 0), # 1: ema_medium
|
||||
(close, 2.0 / (tech_cfg.ema_slow + 1), 0), # 2: ema_slow
|
||||
(gain, rsi_alpha, tech_cfg.rsi_period), # 3: avg_gain
|
||||
(loss, rsi_alpha, tech_cfg.rsi_period), # 4: avg_loss
|
||||
(close, 2.0 / (tech_cfg.macd_fast + 1), 0), # 5: ema_macd_fast
|
||||
(close, 2.0 / (tech_cfg.macd_slow + 1), 0), # 6: ema_macd_slow
|
||||
(tr, atr_alpha, tech_cfg.atr_period), # 7: atr
|
||||
(close, 2.0 / (kc_period + 1), 0), # 8: kc_mid
|
||||
]
|
||||
if n >= tech_cfg.ema_trend:
|
||||
batch1_items.append(
|
||||
(close, 2.0 / (tech_cfg.ema_trend + 1), 0) # 9: ema_trend
|
||||
)
|
||||
b1 = _gpu_ewm_alpha_batch(batch1_items)
|
||||
ema_fast = b1[0]
|
||||
ema_medium = b1[1]
|
||||
ema_slow = b1[2]
|
||||
avg_gain = b1[3]
|
||||
avg_loss = b1[4]
|
||||
ema_macd_fast = b1[5]
|
||||
ema_macd_slow = b1[6]
|
||||
atr = b1[7]
|
||||
kc_mid = b1[8]
|
||||
ema_trend = b1[9] if n >= tech_cfg.ema_trend else None
|
||||
|
||||
# ── Intermediate: MACD line, DM inputs ──
|
||||
macd_line = ema_macd_fast - ema_macd_slow
|
||||
|
||||
# ── BATCH 2: 5 EMAs depending on Batch 1 outputs ──
|
||||
batch2_items = [
|
||||
(macd_line, 2.0 / (tech_cfg.macd_signal + 1), 0), # 0: signal_line
|
||||
(tr, adx_alpha, tech_cfg.adx_period), # 1: atr_adx
|
||||
(plus_dm, adx_alpha, tech_cfg.adx_period), # 2: plus_dm_smooth
|
||||
(minus_dm, adx_alpha, tech_cfg.adx_period), # 3: minus_dm_smooth
|
||||
(tr, kc_atr_alpha, kc_atr_period), # 4: kc_atr
|
||||
]
|
||||
b2 = _gpu_ewm_alpha_batch(batch2_items)
|
||||
signal_line = b2[0]
|
||||
atr_adx = b2[1]
|
||||
plus_dm_smooth = b2[2]
|
||||
minus_dm_smooth = b2[3]
|
||||
kc_atr = b2[4]
|
||||
|
||||
# ── Intermediate: DX for ADX ──
|
||||
atr_adx_safe = cp.where(atr_adx == 0, cp.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, cp.nan, di_sum)
|
||||
dx = 100.0 * cp.abs(plus_di - minus_di) / di_sum_safe
|
||||
|
||||
# ── BATCH 3: 1 EMA depending on Batch 2 ──
|
||||
b3 = _gpu_ewm_alpha_batch([(dx, adx_alpha, tech_cfg.adx_period)])
|
||||
adx = b3[0]
|
||||
|
||||
logger.info(f" EMA batches (3 launches, 16 EMAs): {time.perf_counter() - t_ema:.4f}s")
|
||||
else:
|
||||
# CPU path — sequential EMA calls (no batching)
|
||||
ema_fast = _gpu_ewm_span(close, tech_cfg.ema_fast)
|
||||
ema_medium = _gpu_ewm_span(close, tech_cfg.ema_medium)
|
||||
ema_slow = _gpu_ewm_span(close, tech_cfg.ema_slow)
|
||||
ema_trend = _gpu_ewm_span(close, tech_cfg.ema_trend) if n >= tech_cfg.ema_trend else None
|
||||
avg_gain = _gpu_ewm_alpha(gain, rsi_alpha, min_periods=tech_cfg.rsi_period)
|
||||
avg_loss = _gpu_ewm_alpha(loss, rsi_alpha, min_periods=tech_cfg.rsi_period)
|
||||
ema_macd_fast = _gpu_ewm_span(close, tech_cfg.macd_fast)
|
||||
ema_macd_slow = _gpu_ewm_span(close, tech_cfg.macd_slow)
|
||||
macd_line = ema_macd_fast - ema_macd_slow
|
||||
signal_line = _gpu_ewm_span(macd_line, tech_cfg.macd_signal)
|
||||
atr = _gpu_ewm_alpha(tr, atr_alpha, min_periods=tech_cfg.atr_period)
|
||||
kc_mid = _gpu_ewm_span(close, kc_period)
|
||||
atr_adx = _gpu_ewm_alpha(tr, adx_alpha, min_periods=tech_cfg.adx_period)
|
||||
plus_dm_smooth = _gpu_ewm_alpha(plus_dm, adx_alpha, min_periods=tech_cfg.adx_period)
|
||||
minus_dm_smooth = _gpu_ewm_alpha(minus_dm, adx_alpha, min_periods=tech_cfg.adx_period)
|
||||
atr_adx_safe = xp.where(atr_adx == 0, xp.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 = xp.where(di_sum == 0, xp.nan, di_sum)
|
||||
dx = 100.0 * xp.abs(plus_di - minus_di) / di_sum_safe
|
||||
adx = _gpu_ewm_alpha(dx, adx_alpha, min_periods=tech_cfg.adx_period)
|
||||
kc_atr = _gpu_ewm_alpha(tr, kc_atr_alpha, min_periods=kc_atr_period)
|
||||
|
||||
# ── RSI ──
|
||||
avg_loss_safe = xp.where(avg_loss == 0, 1e-10, avg_loss)
|
||||
rs = avg_gain / avg_loss_safe
|
||||
rsi = 100.0 - (100.0 / (1.0 + rs))
|
||||
rsi_series = pd.Series(rsi, index=df.index, name="rsi")
|
||||
|
||||
# ── MACD (12/26 EMA spans, signal 9 EMA span) ──
|
||||
ema_macd_fast = _gpu_ewm_span(close, tech_cfg.macd_fast)
|
||||
ema_macd_slow = _gpu_ewm_span(close, tech_cfg.macd_slow)
|
||||
macd_line = ema_macd_fast - ema_macd_slow
|
||||
signal_line = _gpu_ewm_span(macd_line, tech_cfg.macd_signal)
|
||||
# ── MACD histogram ──
|
||||
macd_hist = macd_line - signal_line
|
||||
macd_hist_series = pd.Series(macd_hist, index=df.index, name="macd_hist")
|
||||
|
||||
# ── Bollinger ──
|
||||
bb_mid = _gpu_rolling_mean(close, tech_cfg.bb_period)
|
||||
@@ -323,115 +666,67 @@ def precompute_indicators(
|
||||
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 = np.where(bb_range == 0, np.nan, bb_range)
|
||||
bb_range_safe = xp.where(bb_range == 0, xp.nan, bb_range)
|
||||
bb_position = (close - bb_lower) / bb_range_safe
|
||||
# pandas behavior: when range is 0 it returns 0.5 (handled by engine fallback)
|
||||
bb_position = np.where(np.isnan(bb_position), 0.5, bb_position)
|
||||
bb_mid_safe = np.where(bb_mid == 0, np.nan, bb_mid)
|
||||
bb_position = xp.where(xp.isnan(bb_position), 0.5, bb_position)
|
||||
bb_mid_safe = xp.where(bb_mid == 0, xp.nan, bb_mid)
|
||||
bb_band_width = (bb_upper - bb_lower) / bb_mid_safe
|
||||
bb_avg_width = _gpu_rolling_mean(bb_band_width, 20)
|
||||
|
||||
# ── ATR ──
|
||||
prev_close = np.empty(n, dtype=np.float64)
|
||||
prev_close[0] = np.nan
|
||||
prev_close[1:] = close[:-1]
|
||||
tr1 = high - low
|
||||
tr2 = np.abs(high - prev_close)
|
||||
tr3 = np.abs(low - prev_close)
|
||||
# NaN handling at index 0: pandas concat+max returns max ignoring NaN
|
||||
# but for our purposes the engine takes the latest value, so propagate NaN here
|
||||
tr2[0] = np.nan
|
||||
tr3[0] = np.nan
|
||||
tr = np.fmax(np.fmax(tr1, tr2), tr3) # fmax ignores NaN — matches pandas behavior
|
||||
# ewm: pandas uses the first non-NaN value to seed
|
||||
atr = _gpu_ewm_alpha(tr, 1.0 / tech_cfg.atr_period, min_periods=tech_cfg.atr_period)
|
||||
|
||||
# ── ADX ──
|
||||
# pandas: plus_dm = high.diff(); minus_dm = -low.diff(); both NaN at index 0.
|
||||
# plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0.0) — NaN comparisons
|
||||
# are False, so plus_dm[0] becomes 0.0 (NOT NaN). Same for minus_dm.
|
||||
plus_dm_raw = np.empty(n, dtype=np.float64)
|
||||
minus_dm_raw = np.empty(n, dtype=np.float64)
|
||||
plus_dm_raw[0] = np.nan
|
||||
minus_dm_raw[0] = np.nan
|
||||
plus_dm_raw[1:] = high[1:] - high[:-1]
|
||||
minus_dm_raw[1:] = -(low[1:] - low[:-1])
|
||||
with np.errstate(invalid="ignore"):
|
||||
cond_plus = (plus_dm_raw > minus_dm_raw) & (plus_dm_raw > 0)
|
||||
plus_dm = np.where(cond_plus, plus_dm_raw, 0.0)
|
||||
cond_minus = (minus_dm_raw > plus_dm_raw) & (minus_dm_raw > 0)
|
||||
minus_dm = np.where(cond_minus, minus_dm_raw, 0.0)
|
||||
|
||||
atr_adx = _gpu_ewm_alpha(tr, 1.0 / tech_cfg.adx_period, min_periods=tech_cfg.adx_period)
|
||||
plus_dm_smooth = _gpu_ewm_alpha(
|
||||
plus_dm, 1.0 / tech_cfg.adx_period, min_periods=tech_cfg.adx_period
|
||||
)
|
||||
minus_dm_smooth = _gpu_ewm_alpha(
|
||||
minus_dm, 1.0 / tech_cfg.adx_period, min_periods=tech_cfg.adx_period
|
||||
)
|
||||
atr_adx_safe = np.where(atr_adx == 0, np.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 = np.where(di_sum == 0, np.nan, di_sum)
|
||||
dx = 100.0 * np.abs(plus_di - minus_di) / di_sum_safe
|
||||
adx = _gpu_ewm_alpha(dx, 1.0 / tech_cfg.adx_period, min_periods=tech_cfg.adx_period)
|
||||
# ── Keltner Channels ──
|
||||
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(xp.float64)
|
||||
|
||||
# ── ADX Slope ──
|
||||
adx_slope_lookback = getattr(tech_cfg, 'adx_slope_lookback', 5)
|
||||
adx_slope = np.full(n, np.nan, dtype=np.float64)
|
||||
adx_slope = xp.full(n, xp.nan, dtype=xp.float64)
|
||||
if n > adx_slope_lookback:
|
||||
adx_slope[adx_slope_lookback:] = adx[adx_slope_lookback:] - adx[:-adx_slope_lookback]
|
||||
|
||||
# ── ROC (Rate of Change) ──
|
||||
roc_period = getattr(tech_cfg, 'roc_period', 10)
|
||||
roc = np.full(n, np.nan, dtype=np.float64)
|
||||
roc = xp.full(n, xp.nan, dtype=xp.float64)
|
||||
if n > roc_period:
|
||||
prev_close = close[:-roc_period]
|
||||
prev_safe = np.where(prev_close == 0, np.nan, prev_close)
|
||||
roc[roc_period:] = ((close[roc_period:] - prev_close) / prev_safe) * 100.0
|
||||
prev_roc = close[:-roc_period]
|
||||
prev_roc_safe = xp.where(prev_roc == 0, xp.nan, prev_roc)
|
||||
roc[roc_period:] = ((close[roc_period:] - prev_roc) / prev_roc_safe) * 100.0
|
||||
|
||||
# ── Keltner Channels ──
|
||||
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)
|
||||
kc_mid = _gpu_ewm_span(close, kc_period)
|
||||
kc_atr = _gpu_ewm_alpha(tr, 1.0 / kc_atr_period, min_periods=kc_atr_period)
|
||||
kc_upper = kc_mid + kc_multiplier * kc_atr
|
||||
kc_lower = kc_mid - kc_multiplier * kc_atr
|
||||
with np.errstate(invalid="ignore"):
|
||||
kc_squeeze = (bb_lower > kc_lower) & (bb_upper < kc_upper)
|
||||
kc_squeeze = kc_squeeze.astype(np.float64)
|
||||
# ── Transfer to CPU for IndicatorCache (scalar lookups in GPUTechnicalEngine) ──
|
||||
def cpu(arr):
|
||||
if arr is None:
|
||||
return None
|
||||
return _to_cpu(arr)
|
||||
|
||||
index_pos = {ts: i for i, ts in enumerate(df.index)}
|
||||
|
||||
return IndicatorCache(
|
||||
df=df,
|
||||
index_pos=index_pos,
|
||||
ema_fast=ema_fast,
|
||||
ema_medium=ema_medium,
|
||||
ema_slow=ema_slow,
|
||||
ema_trend=ema_trend,
|
||||
rsi=rsi,
|
||||
rsi_series=rsi_series,
|
||||
macd_hist=macd_hist,
|
||||
macd_hist_series=macd_hist_series,
|
||||
bb_mid=bb_mid,
|
||||
bb_upper=bb_upper,
|
||||
bb_lower=bb_lower,
|
||||
bb_position=bb_position,
|
||||
bb_band_width=bb_band_width,
|
||||
bb_avg_width=bb_avg_width,
|
||||
atr=atr,
|
||||
adx=adx,
|
||||
plus_di=plus_di,
|
||||
minus_di=minus_di,
|
||||
adx_slope=adx_slope,
|
||||
roc=roc,
|
||||
kc_mid=kc_mid,
|
||||
kc_upper=kc_upper,
|
||||
kc_lower=kc_lower,
|
||||
kc_squeeze=kc_squeeze,
|
||||
ema_fast=cpu(ema_fast),
|
||||
ema_medium=cpu(ema_medium),
|
||||
ema_slow=cpu(ema_slow),
|
||||
ema_trend=cpu(ema_trend),
|
||||
rsi=cpu(rsi),
|
||||
rsi_series=pd.Series(cpu(rsi), index=df.index, name="rsi"),
|
||||
macd_hist=cpu(macd_hist),
|
||||
macd_hist_series=pd.Series(cpu(macd_hist), index=df.index, name="macd_hist"),
|
||||
bb_mid=cpu(bb_mid),
|
||||
bb_upper=cpu(bb_upper),
|
||||
bb_lower=cpu(bb_lower),
|
||||
bb_position=cpu(bb_position),
|
||||
bb_band_width=cpu(bb_band_width),
|
||||
bb_avg_width=cpu(bb_avg_width),
|
||||
atr=cpu(atr),
|
||||
adx=cpu(adx),
|
||||
plus_di=cpu(plus_di),
|
||||
minus_di=cpu(minus_di),
|
||||
adx_slope=cpu(adx_slope),
|
||||
roc=cpu(roc),
|
||||
kc_mid=cpu(kc_mid),
|
||||
kc_upper=cpu(kc_upper),
|
||||
kc_lower=cpu(kc_lower),
|
||||
kc_squeeze=cpu(kc_squeeze),
|
||||
)
|
||||
|
||||
|
||||
@@ -784,11 +1079,26 @@ class GPUTechnicalEngine(TechnicalEngine):
|
||||
signal = SignalStrength.SELL if adx_val >= c.adx_strong_trend else SignalStrength.NEUTRAL
|
||||
direction = "bearish"
|
||||
|
||||
desc = f"ADX {adx_val:.1f} ({trend_strength} {direction}) | +DI={plus_di_val:.1f} -DI={minus_di_val:.1f}"
|
||||
adx_slope_val = float(cache.adx_slope[pos]) if not np.isnan(cache.adx_slope[pos]) else 0.0
|
||||
|
||||
slope_desc = ""
|
||||
emerging_threshold = c.adx_slope_emerging_threshold
|
||||
if adx_val < c.adx_strong_trend and adx_slope_val > emerging_threshold:
|
||||
slope_desc = " | TREND EMERGING (ADX rising fast)"
|
||||
conf = min(conf + 15, 85)
|
||||
elif adx_val >= c.adx_strong_trend and adx_slope_val > 0:
|
||||
slope_desc = f" | Trend strengthening (slope +{adx_slope_val:.1f})"
|
||||
conf = min(conf + 5, 85)
|
||||
elif adx_val >= c.adx_strong_trend and adx_slope_val < -emerging_threshold:
|
||||
slope_desc = f" | Trend weakening (slope {adx_slope_val:.1f})"
|
||||
conf = max(conf - 10, 30)
|
||||
|
||||
desc = f"ADX {adx_val:.1f} ({trend_strength} {direction}) | +DI={plus_di_val:.1f} -DI={minus_di_val:.1f}{slope_desc}"
|
||||
result.indicators["adx"] = IndicatorSignal(
|
||||
name="ADX", value=adx_val, signal=signal, confidence=conf,
|
||||
description=desc, weight=0.5,
|
||||
)
|
||||
result._adx_slope = adx_slope_val
|
||||
|
||||
# ── ATR override ──────────────────────────────────────────────
|
||||
|
||||
@@ -839,7 +1149,7 @@ class GPUTechnicalEngine(TechnicalEngine):
|
||||
c = self.config
|
||||
n = c.roc_period
|
||||
|
||||
if pos < n + 1:
|
||||
if pos < n + 4:
|
||||
return
|
||||
|
||||
roc_val = float(cache.roc[pos])
|
||||
@@ -1373,20 +1683,25 @@ class BacktestConfig:
|
||||
|
||||
def _is_xau_blocked_by_calendar(ts: pd.Timestamp, cfg: PlatformConfig) -> Optional[str]:
|
||||
"""Apply XAU weekend + allowed-weekday filter (live `analyze_pair` does this;
|
||||
`analyze_from_dataframes` does not, so we replicate here)."""
|
||||
`analyze_from_dataframes` does not, so we replicate here).
|
||||
Matches signal_aggregator._is_forex_weekend: Fri 22:00 → Sun 22:00 UTC."""
|
||||
xau = cfg.xau_strategy
|
||||
if xau.weekend_filter and ts.weekday() >= 5:
|
||||
return "WEEKEND_FILTER"
|
||||
if xau.weekend_filter:
|
||||
wd, hour = ts.weekday(), ts.hour
|
||||
if (wd == 4 and hour >= 22) or wd == 5 or (wd == 6 and hour < 22):
|
||||
return "WEEKEND_FILTER"
|
||||
if xau.allowed_weekdays and ts.weekday() not in xau.allowed_weekdays:
|
||||
return "DAY_FILTER"
|
||||
return None
|
||||
|
||||
|
||||
def _is_pair_blocked_by_calendar(ts: pd.Timestamp, cfg: PlatformConfig) -> Optional[str]:
|
||||
"""Generic weekend + weekday filter using FilterConfig (backtest field)."""
|
||||
bt = cfg.backtest
|
||||
if ts.weekday() >= 5:
|
||||
"""Generic weekend + weekday filter. Matches signal_aggregator._is_forex_weekend:
|
||||
Fri 22:00 → Sun 22:00 UTC."""
|
||||
wd, hour = ts.weekday(), ts.hour
|
||||
if (wd == 4 and hour >= 22) or wd == 5 or (wd == 6 and hour < 22):
|
||||
return "WEEKEND_FILTER"
|
||||
bt = cfg.backtest
|
||||
if bt.allowed_weekdays and ts.weekday() not in bt.allowed_weekdays:
|
||||
return "DAY_FILTER"
|
||||
return None
|
||||
@@ -1917,6 +2232,10 @@ def _parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
|
||||
help="Print full per-trade reasoning (indicators, signals, agreement, why entered)")
|
||||
p.add_argument("--reasoning-samples", type=int, default=3,
|
||||
help="If --detail not set, print this many sample trades' full reasoning")
|
||||
p.add_argument("--gpu-only", action="store_true",
|
||||
help="Require GPU (CuPy/CUDA). Exit with error if unavailable.")
|
||||
p.add_argument("--quick", action="store_true",
|
||||
help="Quick test mode: 30-day window for speed verification")
|
||||
p.add_argument("--log-level", default="INFO")
|
||||
return p.parse_args(argv)
|
||||
|
||||
@@ -1945,6 +2264,18 @@ def main(argv: Optional[List[str]] = None) -> int:
|
||||
format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
|
||||
)
|
||||
|
||||
if args.gpu_only and not _GPU_AVAILABLE:
|
||||
logger.error("--gpu-only specified but CuPy/CUDA is not available")
|
||||
return 1
|
||||
|
||||
if args.quick:
|
||||
if not args.start:
|
||||
args.days = min(args.days, 30)
|
||||
args.warmup_bars = min(args.warmup_bars, 200)
|
||||
logger.info("Quick mode: 30-day window, warmup=200 bars")
|
||||
|
||||
t_total_start = time.perf_counter()
|
||||
|
||||
pair = _resolve_pair(args.pair)
|
||||
primary_tf = _resolve_tf(args.primary_tf)
|
||||
confirm_tfs = [_resolve_tf(s.strip()) for s in args.confirm_tfs.split(",") if s.strip()]
|
||||
@@ -1976,6 +2307,14 @@ def main(argv: Optional[List[str]] = None) -> int:
|
||||
f"Backend: {'CuPy GPU' if _GPU_AVAILABLE else 'NumPy CPU fallback'} | "
|
||||
f"Pair: {PAIR_INFO[pair]['display']} | Window: {start.date()} → {end.date()}"
|
||||
)
|
||||
if _GPU_AVAILABLE:
|
||||
import cupy as _cp_info
|
||||
_dev = _cp_info.cuda.Device()
|
||||
_mem_free, _mem_total = _dev.mem_info
|
||||
logger.info(
|
||||
f"GPU {_dev.id}: compute capability {_dev.compute_capability} | "
|
||||
f"Memory: {_mem_free / 1e9:.1f} GB free / {_mem_total / 1e9:.1f} GB total"
|
||||
)
|
||||
|
||||
if pair in PAIR_PRODUCERS:
|
||||
platform_cfg = PAIR_PRODUCERS[pair](primary_tf, confirm_tfs)
|
||||
@@ -2000,6 +2339,10 @@ def main(argv: Optional[List[str]] = None) -> int:
|
||||
df.to_csv(args.export_csv, index=False)
|
||||
logger.info(f"Exported {len(df)} trades → {args.export_csv}")
|
||||
|
||||
logger.info(
|
||||
f"Total backtest time: {time.perf_counter() - t_total_start:.2f}s "
|
||||
f"(GPU={'yes' if _GPU_AVAILABLE else 'no'})"
|
||||
)
|
||||
return 0
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user