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