chore: release v1.0.2
This commit is contained in:
Pratik Bhadane
+206
-50
@@ -1,65 +1,221 @@
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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 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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def _time_fn(fn, *args, **kwargs):
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times = []
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# Warmup
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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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for _ in range(5):
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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 main():
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n_samples = 100_000
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n_series = 100
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print(f"Batch Benchmark: {n_samples} bars, {n_series} series (Total: {n_samples*n_series/1e6:.1f} M bars)")
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np.random.seed(42)
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# contiguous array in row-major
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close2d = np.random.uniform(100.0, 200.0, (n_samples, n_series))
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h2d = close2d + np.random.uniform(0.1, 2.0, (n_samples, n_series))
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l2d = close2d - np.random.uniform(0.1, 2.0, (n_samples, n_series))
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print("-" * 50)
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print(f"{'Indicator':<15} {'Batch (ms)':>12} {'Loop (ms)':>12} {'Speedup':>10}")
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print("-" * 50)
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# 1. SMA
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kwargs = {"timeperiod": 14}
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def loop_sma(arr):
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for j in range(arr.shape[1]):
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ferro_ta.SMA(arr[:, j], **kwargs)
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t_batch_sma = _time_fn(ferro_ta.batch.batch_sma, close2d, **kwargs)
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t_loop_sma = _time_fn(loop_sma, close2d)
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print(f"SMA {t_batch_sma*1000:12.1f} {t_loop_sma*1000:12.1f} {t_loop_sma/t_batch_sma:9.1f}x")
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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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# 2. RSI
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def loop_rsi(arr):
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for j in range(arr.shape[1]):
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ferro_ta.RSI(arr[:, j], **kwargs)
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t_batch_rsi = _time_fn(ferro_ta.batch.batch_rsi, close2d, **kwargs)
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t_loop_rsi = _time_fn(loop_rsi, close2d)
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print(f"RSI {t_batch_rsi*1000:12.1f} {t_loop_rsi*1000:12.1f} {t_loop_rsi/t_batch_rsi:9.1f}x")
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batch_rows: list[dict[str, Any]] = []
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grouped_rows: list[dict[str, Any]] = []
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# 3. ATR
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def loop_atr(h, l, c):
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for j in range(h.shape[1]):
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ferro_ta.ATR(h[:, j], l[:, j], c[:, j], **kwargs)
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t_batch_atr = _time_fn(ferro_ta.batch.batch_atr, h2d, l2d, close2d, **kwargs)
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t_loop_atr = _time_fn(loop_atr, h2d, l2d, close2d)
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print(f"ATR {t_batch_atr*1000:12.1f} {t_loop_atr*1000:12.1f} {t_loop_atr/t_batch_atr:9.1f}x")
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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: [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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"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: [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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"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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# 4. ADX
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def loop_adx(h, l, c):
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for j in range(h.shape[1]):
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ferro_ta.ADX(h[:, j], l[:, j], c[:, j], **kwargs)
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t_batch_adx = _time_fn(ferro_ta.batch.batch_adx, h2d, l2d, close2d, **kwargs)
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t_loop_adx = _time_fn(loop_adx, h2d, l2d, close2d)
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print(f"ADX {t_batch_adx*1000:12.1f} {t_loop_adx*1000:12.1f} {t_loop_adx/t_batch_adx:9.1f}x")
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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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if __name__ == '__main__':
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main()
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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(f"{'Case':<18} {'Grouped (ms)':>14} {'Separate (ms)':>16} {'Speedup':>12}")
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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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