扩展指标
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from __future__ import annotations
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import argparse
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import json
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import time
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from collections.abc import Callable
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from pathlib import Path
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from typing import Any
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import numpy as np
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import ferro_ta as ft
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try:
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from benchmarks.metadata import benchmark_metadata
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except ModuleNotFoundError: # pragma: no cover - script execution fallback
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from metadata import benchmark_metadata
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def _time_min(fn: Callable[[], object], rounds: int = 5) -> float:
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fn()
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samples: list[float] = []
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for _ in range(rounds):
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t0 = time.perf_counter()
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fn()
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samples.append(time.perf_counter() - t0)
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return min(samples)
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def _stream_close(close: np.ndarray, factory: Callable[[], Any]) -> float:
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streamer = factory()
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last = np.nan
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for value in close:
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last = streamer.update(float(value))
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return float(last) if not np.isnan(last) else np.nan
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def _stream_hlc(
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high: np.ndarray,
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low: np.ndarray,
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close: np.ndarray,
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factory: Callable[[], Any],
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) -> float:
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streamer = factory()
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last = np.nan
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for high_value, low_value, close_value in zip(high, low, close):
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last = streamer.update(float(high_value), float(low_value), float(close_value))
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return float(last) if not np.isnan(last) else np.nan
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def _stream_hlcv(
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high: np.ndarray,
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low: np.ndarray,
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close: np.ndarray,
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volume: np.ndarray,
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factory: Callable[[], Any],
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) -> float:
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streamer = factory()
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last = np.nan
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for high_value, low_value, close_value, volume_value in zip(
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high, low, close, volume
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):
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last = streamer.update(
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float(high_value),
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float(low_value),
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float(close_value),
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float(volume_value),
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)
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return float(last) if not np.isnan(last) else np.nan
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def run_streaming_benchmark(
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*,
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n_bars: int = 100_000,
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seed: int = 2026,
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) -> dict[str, Any]:
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rng = np.random.default_rng(seed)
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close = 100.0 + np.cumsum(rng.normal(0.0, 1.0, n_bars)).astype(np.float64)
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high = close + rng.uniform(0.1, 2.0, n_bars)
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low = close - rng.uniform(0.1, 2.0, n_bars)
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volume = rng.uniform(1_000.0, 100_000.0, n_bars)
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cases = [
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(
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"StreamingSMA",
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"close",
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lambda: _stream_close(close, lambda: ft.StreamingSMA(period=20)),
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lambda: ft.SMA(close, timeperiod=20),
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),
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(
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"StreamingEMA",
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"close",
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lambda: _stream_close(close, lambda: ft.StreamingEMA(period=20)),
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lambda: ft.EMA(close, timeperiod=20),
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),
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(
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"StreamingRSI",
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"close",
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lambda: _stream_close(close, lambda: ft.StreamingRSI(period=14)),
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lambda: ft.RSI(close, timeperiod=14),
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),
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(
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"StreamingATR",
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"hlc",
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lambda: _stream_hlc(
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high,
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low,
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close,
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lambda: ft.StreamingATR(period=14),
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),
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lambda: ft.ATR(high, low, close, timeperiod=14),
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),
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(
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"StreamingVWAP",
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"hlcv",
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lambda: _stream_hlcv(
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high,
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low,
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close,
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volume,
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lambda: ft.StreamingVWAP(),
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),
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lambda: ft.VWAP(high, low, close, volume),
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),
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]
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rows: list[dict[str, Any]] = []
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for name, input_kind, stream_fn, batch_fn in cases:
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stream_s = _time_min(stream_fn)
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batch_s = _time_min(batch_fn)
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rows.append(
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{
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"indicator": name,
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"inputs": input_kind,
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"stream_total_ms": round(stream_s * 1000.0, 4),
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"batch_total_ms": round(batch_s * 1000.0, 4),
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"stream_ns_per_update": round(stream_s * 1e9 / n_bars, 2),
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"batch_ns_per_bar": round(batch_s * 1e9 / n_bars, 2),
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"updates_per_second": round(n_bars / stream_s, 2),
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"stream_over_batch_ratio": round(stream_s / batch_s, 4),
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}
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)
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return {
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"metadata": benchmark_metadata(
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"streaming",
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extra={
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"dataset": {
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"n_bars": n_bars,
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"seed": seed,
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}
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},
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),
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"results": rows,
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}
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def main() -> int:
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parser = argparse.ArgumentParser(
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description="Benchmark streaming indicator execution."
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)
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parser.add_argument("--bars", type=int, default=100_000)
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parser.add_argument("--seed", type=int, default=2026)
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parser.add_argument("--json", dest="json_path")
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args = parser.parse_args()
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payload = run_streaming_benchmark(n_bars=args.bars, seed=args.seed)
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dataset = payload["metadata"]["dataset"]
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print(f"Streaming Benchmark: {dataset['n_bars']} bars")
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print("-" * 86)
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print(
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f"{'Indicator':<16} {'Stream (ms)':>12} {'Batch (ms)':>12} "
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f"{'ns/update':>12} {'upd/s':>12} {'ratio':>10}"
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)
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print("-" * 86)
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for row in payload["results"]:
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print(
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f"{row['indicator']:<16} {row['stream_total_ms']:12.2f} "
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f"{row['batch_total_ms']:12.2f} {row['stream_ns_per_update']:12.2f} "
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f"{row['updates_per_second']:12.1f} {row['stream_over_batch_ratio']:10.2f}"
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)
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if args.json_path:
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path = Path(args.json_path)
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path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
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print(f"\nWrote JSON results to {path}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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