wehub-resource-sync
134 lines
3.6 KiB
Python
134 lines
3.6 KiB
Python
"""Performance benchmark: compare old vs new operator/equity paths.
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Development-only script — not included in the package.
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Run: python agent/scripts/bench_performance.py
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"""
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from __future__ import annotations
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import os
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import sys
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import time
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import numpy as np
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import pandas as pd
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
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def bench_operators():
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"""Benchmark factor operators: old pandas vs new fast paths."""
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from src.factors.base import decay_linear, ts_argmax, ts_argmin, ts_rank
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np.random.seed(42)
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df = pd.DataFrame(np.random.randn(5000, 100))
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n = 20
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print("=== Operator Benchmarks (5000 rows × 100 cols, window=20) ===\n")
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ops = [
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("ts_rank", lambda: ts_rank(df, n)),
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("ts_argmax", lambda: ts_argmax(df, n)),
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("ts_argmin", lambda: ts_argmin(df, n)),
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("decay_linear", lambda: decay_linear(df, n)),
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]
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for name, fn in ops:
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_ = fn()
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t0 = time.perf_counter()
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for _ in range(3):
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fn()
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elapsed = (time.perf_counter() - t0) / 3
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print(f" {name:20s}: {elapsed:.3f}s")
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print()
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def bench_equity():
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"""Benchmark _calc_equity: vectorized vs loop."""
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from backtest.engines.base import BaseEngine
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from backtest.models import Position
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class _Stub(BaseEngine):
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def can_execute(self, *a):
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return True
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def round_size(self, s, p):
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return s
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def calc_commission(self, *a):
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return 0.0
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def apply_slippage(self, p, d):
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return p
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np.random.seed(42)
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n_symbols = 50
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n_days = 1000
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symbols = [f"SYM{i:03d}" for i in range(n_symbols)]
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dates = pd.date_range("2020-01-01", periods=n_days, freq="B")
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close_df = pd.DataFrame(
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np.cumsum(np.random.randn(n_days, n_symbols), axis=0) + 100,
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index=dates,
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columns=symbols,
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)
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engine = _Stub({"initial_cash": 10_000_000})
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engine.capital = 5_000_000
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for i, sym in enumerate(symbols):
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engine.positions[sym] = Position(
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symbol=sym,
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direction=1 if i % 2 == 0 else -1,
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size=100.0 + i * 10,
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entry_price=95.0 + i,
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leverage=1.0,
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entry_time=dates[0],
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)
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ts = dates[500]
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_ = engine._calc_equity(close_df, ts)
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t0 = time.perf_counter()
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for _ in range(1000):
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engine._calc_equity(close_df, ts)
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vec_time = (time.perf_counter() - t0) / 1000
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# Force loop path by monkey-patching
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original_pnl = type(engine)._calc_pnl
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def _loop_pnl(self, *a):
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return original_pnl(self, *a)
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type(engine)._calc_pnl = _loop_pnl
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_ = engine._calc_equity(close_df, ts)
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t0 = time.perf_counter()
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for _ in range(1000):
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engine._calc_equity(close_df, ts)
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loop_time = (time.perf_counter() - t0) / 1000
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type(engine)._calc_pnl = original_pnl
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speedup = loop_time / vec_time if vec_time > 0 else float("inf")
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print("=== Equity Calculation (50 positions, 1000 iterations) ===\n")
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print(f" Vectorized: {vec_time * 1e6:.1f} µs/call")
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print(f" Loop: {loop_time * 1e6:.1f} µs/call")
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print(f" Speedup: {speedup:.1f}x")
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print()
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if __name__ == "__main__":
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print("Vibe-Trading Performance Benchmark")
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print("=" * 50)
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print()
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from src.factors._backend import HAS_BOTTLENECK
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print(f"Bottleneck available: {HAS_BOTTLENECK}")
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from src.config.accessor import get_env_config
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print(f"VIBE_TRADING_DISABLE_BOTTLENECK: {get_env_config().agent_tuning.vibe_trading_disable_bottleneck}")
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print()
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bench_operators()
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bench_equity()
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