"""Throughput benchmark for the Wickra Python binding. Measures how many indicator updates per second the binding sustains, both per-tick (streaming ``update``) and bulk (``batch``), over a synthetic OHLCV series. It is the Python counterpart of the Node ``throughput.js`` and the Rust criterion benches: it benchmarks Wickra's own O(1) streaming engine across the Python<->Rust boundary, so the headline number is raw per-binding throughput / FFI overhead, not a cross-library ratio. For the cross-library comparison against TA-Lib, pandas-ta, tulipy and finta, see ``benchmarks/compare_libraries.py`` instead. Three indicators are timed, chosen by call-signature archetype rather than algorithm: SMA (1-in -> 1-out), ATR (multi-in -> 1-out) and MACD (1-in -> multi-out). Streaming is timed for all three; batch only for the single-output SMA and ATR (multi-output batch returns a 2-D array and is not compared here). Install the binding first (``maturin develop --release`` in bindings/python), then run from bindings/python:: python -m benchmarks.throughput # 200k bars (default) python -m benchmarks.throughput --bars 1000000 """ from __future__ import annotations import argparse import time import numpy as np import wickra as ta def _time_ns(fn, reps: int = 3) -> float: """Median elapsed-ns over a few repetitions, after one warmup pass.""" fn() # warmup samples = [] for _ in range(reps): t0 = time.perf_counter_ns() fn() samples.append(time.perf_counter_ns() - t0) samples.sort() return samples[len(samples) // 2] def main() -> None: parser = argparse.ArgumentParser(description="Wickra Python throughput benchmark") parser.add_argument("--bars", type=int, default=200_000, help="number of synthetic bars") args = parser.parse_args() bars = args.bars if args.bars >= 1000 else 200_000 # Deterministic synthetic OHLCV (no RNG, so runs are comparable). idx = np.arange(bars, dtype=np.float64) mid = 100 + np.sin(idx * 0.001) * 20 + idx * 1e-4 close = mid + np.sin(idx * 0.05) * 2 high = np.maximum(close, mid) + 1.5 low = np.minimum(close, mid) - 1.5 open_ = mid volume = 1000 + (idx % 97) * 13 close_list = close.tolist() # ATR streams a 6-tuple (open, high, low, close, volume, timestamp) per tick. candles = list( zip(open_.tolist(), high.tolist(), low.tolist(), close_list, volume.tolist(), range(bars)) ) def mups(ns: float) -> float: return bars / (ns / 1e9) / 1e6 def sma_stream() -> None: ind = ta.SMA(20) for value in close_list: ind.update(value) def sma_batch() -> None: ta.SMA(20).batch(close) def atr_stream() -> None: ind = ta.ATR(14) for candle in candles: ind.update(candle) def atr_batch() -> None: ta.ATR(14).batch(high, low, close) def macd_stream() -> None: ind = ta.MACD(12, 26, 9) for value in close_list: ind.update(value) # SMA (scalar 1-in/1-out), ATR (multi-in/1-out), MACD (1-in/multi-out). indicators = [ ("SMA(20)", sma_stream, sma_batch), ("ATR(14)", atr_stream, atr_batch), ("MACD(12,26,9)", macd_stream, None), # multi-output: streaming only ] print(f"Wickra Python throughput - {bars:,} bars (median of 3 runs)\n") print(f"{'Indicator':<22}{'streaming (Mupd/s)':>20}{'batch (Mupd/s)':>18}") print("-" * 60) for name, stream, batch in indicators: stream_mups = f"{mups(_time_ns(stream)):.1f}" batch_mups = "-" if batch is None else f"{mups(_time_ns(batch)):.1f}" print(f"{name:<22}{stream_mups:>20}{batch_mups:>18}") print( "\nMupd/s = million indicator updates per second. Streaming is the per-tick\n" "`update` path crossing the Python<->Rust boundary once per value; batch is\n" "the bulk numpy path (one boundary crossing). Higher is better. Numbers are\n" "machine-dependent - use them for relative comparison, not as a speed claim." ) if __name__ == "__main__": main()