feat: init the repo

benchmarks/bench_batch.py

@@ -0,0 +1,65 @@
+import time
+import numpy as np
+import ferro_ta
+
+def _time_fn(fn, *args, **kwargs):
+    times = []
+    # Warmup
+    fn(*args, **kwargs)
+    for _ in range(5):
+        t0 = time.perf_counter()
+        fn(*args, **kwargs)
+        times.append(time.perf_counter() - t0)
+    return min(times)
+
+def main():
+    n_samples = 100_000
+    n_series = 100
+    print(f"Batch Benchmark: {n_samples} bars, {n_series} series (Total: {n_samples*n_series/1e6:.1f} M bars)")
+    
+    np.random.seed(42)
+    # contiguous array in row-major
+    close2d = np.random.uniform(100.0, 200.0, (n_samples, n_series))
+    h2d = close2d + np.random.uniform(0.1, 2.0, (n_samples, n_series))
+    l2d = close2d - np.random.uniform(0.1, 2.0, (n_samples, n_series))
+    
+    print("-" * 50)
+    print(f"{'Indicator':<15} {'Batch (ms)':>12} {'Loop (ms)':>12} {'Speedup':>10}")
+    print("-" * 50)
+
+    # 1. SMA
+    kwargs = {"timeperiod": 14}
+    def loop_sma(arr):
+        for j in range(arr.shape[1]):
+            ferro_ta.SMA(arr[:, j], **kwargs)
+            
+    t_batch_sma = _time_fn(ferro_ta.batch.batch_sma, close2d, **kwargs)
+    t_loop_sma = _time_fn(loop_sma, close2d)
+    print(f"SMA             {t_batch_sma*1000:12.1f} {t_loop_sma*1000:12.1f} {t_loop_sma/t_batch_sma:9.1f}x")
+
+    # 2. RSI
+    def loop_rsi(arr):
+        for j in range(arr.shape[1]):
+            ferro_ta.RSI(arr[:, j], **kwargs)
+    t_batch_rsi = _time_fn(ferro_ta.batch.batch_rsi, close2d, **kwargs)
+    t_loop_rsi = _time_fn(loop_rsi, close2d)
+    print(f"RSI             {t_batch_rsi*1000:12.1f} {t_loop_rsi*1000:12.1f} {t_loop_rsi/t_batch_rsi:9.1f}x")
+
+    # 3. ATR
+    def loop_atr(h, l, c):
+        for j in range(h.shape[1]):
+            ferro_ta.ATR(h[:, j], l[:, j], c[:, j], **kwargs)
+    t_batch_atr = _time_fn(ferro_ta.batch.batch_atr, h2d, l2d, close2d, **kwargs)
+    t_loop_atr = _time_fn(loop_atr, h2d, l2d, close2d)
+    print(f"ATR             {t_batch_atr*1000:12.1f} {t_loop_atr*1000:12.1f} {t_loop_atr/t_batch_atr:9.1f}x")
+
+    # 4. ADX
+    def loop_adx(h, l, c):
+        for j in range(h.shape[1]):
+            ferro_ta.ADX(h[:, j], l[:, j], c[:, j], **kwargs)
+    t_batch_adx = _time_fn(ferro_ta.batch.batch_adx, h2d, l2d, close2d, **kwargs)
+    t_loop_adx = _time_fn(loop_adx, h2d, l2d, close2d)
+    print(f"ADX             {t_batch_adx*1000:12.1f} {t_loop_adx*1000:12.1f} {t_loop_adx/t_batch_adx:9.1f}x")
+
+if __name__ == '__main__':
+    main()