Pratik Bhadane
71b6343e92
Refresh the benchmark and performance surface across the repo. This updates the benchmark wrappers and helper scripts, regenerates the checked-in benchmark and perf-contract artifacts, and folds in the related roadmap, compatibility, and example notebook changes that belong with this performance-focused pass. Harden the Python CI and local pre-push flow so the same checks pass reliably in both places. The workflow and pre-push script now use module-safe uv typecheck invocations, the Python test environment installs the optional MCP dependency needed by the MCP server tests, and one-off root benchmark outputs are ignored to keep the repo clean. Align local tooling with the current project configuration by updating the Ruff pre-commit hook, tightening the API typing and MCP server helpers, and refreshing the lockfile to pick up the audited PyJWT fix while preserving the rest of the staged source changes.
228 lines
7.2 KiB
Python
228 lines
7.2 KiB
Python
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 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
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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_fn(fn, *args, rounds: int = 5, **kwargs) -> float:
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fn(*args, **kwargs)
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times: list[float] = []
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for _ in range(rounds):
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t0 = time.perf_counter()
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fn(*args, **kwargs)
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times.append(time.perf_counter() - t0)
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return min(times)
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def run_batch_benchmark(
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*,
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n_samples: int = 100_000,
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n_series: int = 100,
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seed: int = 42,
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) -> dict[str, Any]:
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rng = np.random.default_rng(seed)
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close2d = rng.uniform(100.0, 200.0, (n_samples, n_series))
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high2d = close2d + rng.uniform(0.1, 2.0, (n_samples, n_series))
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low2d = close2d - rng.uniform(0.1, 2.0, (n_samples, n_series))
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close1d = close2d[:, 0]
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high1d = high2d[:, 0]
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low1d = low2d[:, 0]
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batch_rows: list[dict[str, Any]] = []
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grouped_rows: list[dict[str, Any]] = []
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indicators = [
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(
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"SMA",
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lambda: ferro_ta.batch.batch_sma(close2d, timeperiod=14, parallel=True),
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lambda: ferro_ta.batch.batch_sma(close2d, timeperiod=14, parallel=False),
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lambda: [
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ferro_ta.SMA(close2d[:, j], timeperiod=14) for j in range(n_series)
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],
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),
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(
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"RSI",
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lambda: ferro_ta.batch.batch_rsi(close2d, timeperiod=14, parallel=True),
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lambda: ferro_ta.batch.batch_rsi(close2d, timeperiod=14, parallel=False),
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lambda: [
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ferro_ta.RSI(close2d[:, j], timeperiod=14) for j in range(n_series)
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],
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),
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(
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"ATR",
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lambda: ferro_ta.batch.batch_atr(
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high2d, low2d, close2d, timeperiod=14, parallel=True
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),
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lambda: ferro_ta.batch.batch_atr(
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high2d, low2d, close2d, timeperiod=14, parallel=False
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),
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lambda: [
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ferro_ta.ATR(high2d[:, j], low2d[:, j], close2d[:, j], timeperiod=14)
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for j in range(n_series)
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],
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),
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(
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"ADX",
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lambda: ferro_ta.batch.batch_adx(
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high2d, low2d, close2d, timeperiod=14, parallel=True
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),
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lambda: ferro_ta.batch.batch_adx(
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high2d, low2d, close2d, timeperiod=14, parallel=False
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),
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lambda: [
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ferro_ta.ADX(high2d[:, j], low2d[:, j], close2d[:, j], timeperiod=14)
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for j in range(n_series)
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],
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),
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]
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for name, parallel_fn, sequential_fn, loop_fn in indicators:
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batch_parallel_s = _time_fn(parallel_fn)
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batch_sequential_s = _time_fn(sequential_fn)
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loop_s = _time_fn(loop_fn)
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batch_rows.append(
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{
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"indicator": name,
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"parallel_ms": round(batch_parallel_s * 1000, 4),
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"sequential_ms": round(batch_sequential_s * 1000, 4),
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"loop_ms": round(loop_s * 1000, 4),
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"parallel_speedup_vs_loop": round(loop_s / batch_parallel_s, 4),
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"sequential_speedup_vs_loop": round(loop_s / batch_sequential_s, 4),
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}
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)
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grouped_cases = [
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(
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"close_bundle_3",
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lambda: ferro_ta.batch.compute_many(
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[
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("SMA", {"timeperiod": 10}),
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("EMA", {"timeperiod": 12}),
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("RSI", {"timeperiod": 14}),
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],
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close=close1d,
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),
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lambda: (
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ferro_ta.SMA(close1d, timeperiod=10),
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ferro_ta.EMA(close1d, timeperiod=12),
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ferro_ta.RSI(close1d, timeperiod=14),
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),
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),
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(
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"hlc_bundle_3",
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lambda: ferro_ta.batch.compute_many(
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[
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("ATR", {"timeperiod": 14}),
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("ADX", {"timeperiod": 14}),
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("CCI", {"timeperiod": 14}),
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],
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close=close1d,
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high=high1d,
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low=low1d,
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),
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lambda: (
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ferro_ta.ATR(high1d, low1d, close1d, timeperiod=14),
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ferro_ta.ADX(high1d, low1d, close1d, timeperiod=14),
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ferro_ta.CCI(high1d, low1d, close1d, timeperiod=14),
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),
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),
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]
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for name, grouped_fn, separate_fn in grouped_cases:
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grouped_s = _time_fn(grouped_fn)
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separate_s = _time_fn(separate_fn)
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grouped_rows.append(
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{
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"case": name,
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"grouped_ms": round(grouped_s * 1000, 4),
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"separate_ms": round(separate_s * 1000, 4),
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"speedup_vs_separate": round(separate_s / grouped_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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"batch",
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extra={
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"dataset": {
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"n_samples": n_samples,
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"n_series": n_series,
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"total_bars": n_samples * n_series,
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"seed": seed,
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}
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},
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),
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"results": batch_rows,
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"grouped_results": grouped_rows,
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}
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def main() -> int:
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parser = argparse.ArgumentParser(description="Benchmark batch indicator execution.")
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parser.add_argument("--samples", type=int, default=100_000)
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parser.add_argument("--series", type=int, default=100)
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parser.add_argument("--seed", type=int, default=42)
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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_batch_benchmark(
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n_samples=args.samples,
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n_series=args.series,
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seed=args.seed,
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)
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dataset = payload["metadata"]["dataset"]
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print(
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"Batch Benchmark: "
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f"{dataset['n_samples']} bars, {dataset['n_series']} series "
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f"(Total: {dataset['total_bars'] / 1e6:.1f} M bars)"
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)
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print("-" * 74)
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print(
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f"{'Indicator':<12} {'Parallel (ms)':>14} {'Sequential (ms)':>16} "
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f"{'Loop (ms)':>12} {'P speedup':>10}"
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)
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print("-" * 74)
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for row in payload["results"]:
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print(
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f"{row['indicator']:<12} {row['parallel_ms']:14.1f} "
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f"{row['sequential_ms']:16.1f} {row['loop_ms']:12.1f} "
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f"{row['parallel_speedup_vs_loop']:10.2f}x"
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)
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if payload["grouped_results"]:
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print("\nGrouped Multi-Indicator Calls")
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print("-" * 64)
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print(
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f"{'Case':<18} {'Grouped (ms)':>14} {'Separate (ms)':>16} {'Speedup':>12}"
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)
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print("-" * 64)
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for row in payload["grouped_results"]:
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print(
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f"{row['case']:<18} {row['grouped_ms']:14.1f} "
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f"{row['separate_ms']:16.1f} {row['speedup_vs_separate']:12.2f}x"
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)
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if args.json_path:
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json_path = Path(args.json_path)
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json_path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
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print(f"\nWrote JSON results to {json_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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