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wickra/bindings/python/benchmarks/throughput.py
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"""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()