褚宏光
0f8b9d836b
- Introduced a new "tilt" parameter to the strength scoring system, allowing for the assessment of triangle slope directionality. - Renamed existing parameters: "拟合贴合度" to "形态规则度" and "边界利用率" to "价格活跃度" for improved clarity. - Updated normalization methods for all strength components to ensure they remain within the [0, 1] range, facilitating LLM tuning. - Enhanced documentation to reflect changes in parameter names and scoring logic, including detailed explanations of the new tilt parameter. - Modified multiple source files and scripts to accommodate the new scoring structure and ensure backward compatibility. Files modified: - `src/converging_triangle.py`, `src/converging_triangle_optimized.py`, `src/triangle_detector_api.py`: Updated parameter names and scoring logic. - `scripts/plot_converging_triangles.py`, `scripts/generate_stock_viewer.py`: Adjusted for new scoring parameters in output. - New documentation files created to explain the renaming and new scoring system in detail.
385 lines
12 KiB
Python
385 lines
12 KiB
Python
"""
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完整流水线性能测试 - 验证Numba优化效果
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此脚本模拟完整的批量检测流程，对比原版和优化版的性能。
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"""
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import os
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import sys
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import pickle
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import time
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import cProfile
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import pstats
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from io import StringIO
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import numpy as np
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import pandas as pd
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# 添加 src 路径
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sys.path.append(os.path.join(os.path.dirname(__file__), "..", "src"))
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from converging_triangle import (
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ConvergingTriangleParams,
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detect_converging_triangle_batch as detect_batch_original,
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)
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class FakeModule:
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"""空壳模块，绕过 model 依赖"""
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ndarray = np.ndarray
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def load_pkl(pkl_path: str) -> dict:
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"""加载 pkl 文件"""
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sys.modules['model'] = FakeModule()
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sys.modules['model.index_info'] = FakeModule()
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with open(pkl_path, 'rb') as f:
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data = pickle.load(f)
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return data
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def load_test_data(data_dir: str, n_stocks: int = None, n_days: int = None):
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"""加载测试数据"""
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print(f"加载数据...")
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open_data = load_pkl(os.path.join(data_dir, "open.pkl"))
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high_data = load_pkl(os.path.join(data_dir, "high.pkl"))
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low_data = load_pkl(os.path.join(data_dir, "low.pkl"))
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close_data = load_pkl(os.path.join(data_dir, "close.pkl"))
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volume_data = load_pkl(os.path.join(data_dir, "volume.pkl"))
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# 截取子集（如果指定）
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if n_stocks and n_days:
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open_mtx = open_data["mtx"][:n_stocks, -n_days:]
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high_mtx = high_data["mtx"][:n_stocks, -n_days:]
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low_mtx = low_data["mtx"][:n_stocks, -n_days:]
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close_mtx = close_data["mtx"][:n_stocks, -n_days:]
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volume_mtx = volume_data["mtx"][:n_stocks, -n_days:]
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dates = close_data["dtes"][-n_days:]
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tkrs = close_data["tkrs"][:n_stocks]
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tkrs_name = close_data["tkrs_name"][:n_stocks]
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else:
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# 全量数据
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open_mtx = open_data["mtx"]
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high_mtx = high_data["mtx"]
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low_mtx = low_data["mtx"]
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close_mtx = close_data["mtx"]
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volume_mtx = volume_data["mtx"]
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dates = close_data["dtes"]
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tkrs = close_data["tkrs"]
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tkrs_name = close_data["tkrs_name"]
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print(f" 数据形状: {close_mtx.shape}")
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print(f" 日期范围: {dates[0]} ~ {dates[-1]}")
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return open_mtx, high_mtx, low_mtx, close_mtx, volume_mtx, dates, tkrs, tkrs_name
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def test_pipeline(
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open_mtx, high_mtx, low_mtx, close_mtx, volume_mtx,
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params: ConvergingTriangleParams,
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use_optimized: bool = False,
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profile: bool = False
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):
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"""
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测试完整流水线
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Args:
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use_optimized: 是否使用优化版本
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profile: 是否生成profile
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"""
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print(f"\n{'='*80}")
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print(f"测试: {'Numba优化版' if use_optimized else '原版'}")
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print(f"{'='*80}")
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n_stocks, n_days = close_mtx.shape
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window = params.window
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# 计算测试范围
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start_day = window - 1
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# 找到最后有效的数据日
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any_valid = np.any(~np.isnan(close_mtx), axis=0)
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valid_day_idx = np.where(any_valid)[0]
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end_day = valid_day_idx[-1] if len(valid_day_idx) > 0 else n_days - 1
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total_points = n_stocks * (end_day - start_day + 1)
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print(f"\n配置:")
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print(f" 股票数: {n_stocks}")
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print(f" 交易日: {n_days}")
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print(f" 窗口大小: {window}")
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print(f" 检测点数: {total_points}")
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print(f" 使用优化: {'是' if use_optimized else '否'}")
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# 如果使用优化版，导入优化模块并替换函数
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if use_optimized:
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try:
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print("\n导入Numba优化模块...")
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import converging_triangle
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from converging_triangle_optimized import (
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pivots_fractal_optimized,
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pivots_fractal_hybrid_optimized,
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fit_boundary_anchor_optimized,
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calc_geometry_score_optimized,
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calc_activity_score_optimized,
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calc_breakout_strength_optimized,
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)
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# 猴子补丁替换
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converging_triangle.pivots_fractal = pivots_fractal_optimized
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converging_triangle.pivots_fractal_hybrid = pivots_fractal_hybrid_optimized
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converging_triangle.fit_boundary_anchor = fit_boundary_anchor_optimized
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converging_triangle.calc_geometry_score = calc_geometry_score_optimized
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converging_triangle.calc_activity_score = calc_activity_score_optimized
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converging_triangle.calc_breakout_strength = calc_breakout_strength_optimized
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print(" [OK] Numba优化已启用")
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# 预热编译
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print("\n预热Numba编译...")
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sample_high = high_mtx[0, :window]
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sample_low = low_mtx[0, :window]
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valid_mask = ~(np.isnan(sample_high) | np.isnan(sample_low))
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if np.sum(valid_mask) >= window:
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sample_high = sample_high[valid_mask]
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sample_low = sample_low[valid_mask]
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_ = pivots_fractal_optimized(sample_high, sample_low, k=params.pivot_k)
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print(" [OK] 预热完成")
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except Exception as e:
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print(f" [ERROR] 无法启用优化: {e}")
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return None, 0
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# 运行检测
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print("\n开始批量检测...")
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if profile:
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profiler = cProfile.Profile()
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profiler.enable()
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start_time = time.time()
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df = detect_batch_original(
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open_mtx=open_mtx,
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high_mtx=high_mtx,
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low_mtx=low_mtx,
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close_mtx=close_mtx,
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volume_mtx=volume_mtx,
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params=params,
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start_day=start_day,
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end_day=end_day,
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only_valid=True,
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verbose=False,
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)
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elapsed = time.time() - start_time
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if profile:
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profiler.disable()
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# 打印profile
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print("\n" + "-" * 80)
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print("Profile Top 20:")
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print("-" * 80)
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s = StringIO()
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ps = pstats.Stats(profiler, stream=s).sort_stats('cumulative')
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ps.print_stats(20)
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print(s.getvalue())
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# 统计结果
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print(f"\n{'='*80}")
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print("性能统计")
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print(f"{'='*80}")
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print(f"\n总耗时: {elapsed:.2f} 秒 ({elapsed/60:.2f} 分钟)")
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print(f"处理点数: {total_points}")
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print(f"平均速度: {total_points/elapsed:.1f} 点/秒")
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print(f"单点耗时: {elapsed/total_points*1000:.3f} 毫秒/点")
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if len(df) > 0:
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valid_count = len(df[df['is_valid'] == True])
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print(f"\n检测结果:")
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print(f" 有效三角形: {valid_count}")
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print(f" 检出率: {valid_count/total_points*100:.2f}%")
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if 'breakout_strength_up' in df.columns:
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strong_up = (df['breakout_strength_up'] > 0.3).sum()
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strong_down = (df['breakout_strength_down'] > 0.3).sum()
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print(f" 高强度向上突破 (>0.3): {strong_up}")
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print(f" 高强度向下突破 (>0.3): {strong_down}")
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return df, elapsed
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def compare_results(df_original, df_optimized):
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"""对比两个版本的输出结果"""
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print(f"\n{'='*80}")
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print("结果一致性验证")
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print(f"{'='*80}")
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if df_original is None or df_optimized is None:
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print("\n[ERROR] 无法对比：某个版本未成功运行")
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return False
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# 检查记录数
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if len(df_original) != len(df_optimized):
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print(f"\n[WARNING] 记录数不一致:")
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print(f" 原版: {len(df_original)}")
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print(f" 优化: {len(df_optimized)}")
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return False
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print(f"\n记录数: {len(df_original)} (一致 [OK])")
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# 检查数值列
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numeric_cols = [
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'breakout_strength_up', 'breakout_strength_down',
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'price_score_up', 'price_score_down',
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'convergence_score', 'volume_score', 'geometry_score',
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'upper_slope', 'lower_slope', 'width_ratio',
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'touches_upper', 'touches_lower', 'apex_x'
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]
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numeric_cols = [c for c in numeric_cols if c in df_original.columns]
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print(f"\n数值列对比:")
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print(f"{'列名':<30} {'最大差异':<15} {'平均差异':<15} {'状态':<10}")
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print("-" * 70)
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all_match = True
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for col in numeric_cols:
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diff = (df_original[col] - df_optimized[col]).abs()
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max_diff = diff.max()
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mean_diff = diff.mean()
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# 判断标准：最大差异 < 1e-6
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match = max_diff < 1e-6
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status = "[OK]" if match else "[ERR]"
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print(f"{col:<30} {max_diff:<15.10f} {mean_diff:<15.10f} {status:<10}")
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if not match:
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all_match = False
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print("-" * 70)
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if all_match:
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print("\n[结论] 所有数值列完全一致 (误差 < 1e-6) ✓")
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else:
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print("\n[结论] 发现不一致的列，请检查优化实现")
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return all_match
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def main():
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"""主测试流程"""
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print("=" * 80)
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print("收敛三角形检测 - 完整流水线性能测试")
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print("=" * 80)
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# 配置
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DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "data")
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# 检测参数
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params = ConvergingTriangleParams(
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window=240,
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pivot_k=15,
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boundary_n_segments=2,
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boundary_source="full",
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fitting_method="anchor",
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upper_slope_max=0,
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lower_slope_min=0,
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touch_tol=0.10,
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touch_loss_max=0.10,
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shrink_ratio=0.45,
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break_tol=0.005,
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vol_window=20,
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vol_k=1.5,
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false_break_m=5,
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)
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# 加载数据（全量）
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open_mtx, high_mtx, low_mtx, close_mtx, volume_mtx, dates, tkrs, tkrs_name = \
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load_test_data(DATA_DIR)
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# 测试1: 原版
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print("\n" + "=" * 80)
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print("阶段 1/2: 测试原版")
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print("=" * 80)
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df_original, time_original = test_pipeline(
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open_mtx, high_mtx, low_mtx, close_mtx, volume_mtx,
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params,
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use_optimized=False,
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profile=False
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)
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# 测试2: 优化版
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print("\n" + "=" * 80)
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print("阶段 2/2: 测试优化版")
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print("=" * 80)
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df_optimized, time_optimized = test_pipeline(
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open_mtx, high_mtx, low_mtx, close_mtx, volume_mtx,
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params,
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use_optimized=True,
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profile=False
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)
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# 对比结果
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if df_original is not None and df_optimized is not None:
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results_match = compare_results(df_original, df_optimized)
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# 性能对比
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print(f"\n{'='*80}")
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print("性能对比总结")
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print(f"{'='*80}")
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speedup = time_original / time_optimized if time_optimized > 0 else 0
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improvement = ((time_original - time_optimized) / time_original * 100) if time_original > 0 else 0
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time_saved = time_original - time_optimized
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print(f"\n{'指标':<20} {'原版':<20} {'优化版':<20} {'改善':<20}")
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print("-" * 80)
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print(f"{'总耗时':<20} {time_original:.2f}秒 ({time_original/60:.2f}分) "
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f"{time_optimized:.2f}秒 ({time_optimized/60:.2f}分) "
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f"-{time_saved:.2f}秒")
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print(f"{'加速比':<20} {'1.00x':<20} {f'{speedup:.2f}x':<20} {f'+{speedup-1:.2f}x':<20}")
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print(f"{'性能提升':<20} {'0%':<20} {f'{improvement:.1f}%':<20} {f'+{improvement:.1f}%':<20}")
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n_stocks, n_days = close_mtx.shape
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window = params.window
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end_day_idx = np.where(np.any(~np.isnan(close_mtx), axis=0))[0][-1]
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total_points = n_stocks * (end_day_idx - window + 2)
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speed_original = total_points / time_original
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speed_optimized = total_points / time_optimized
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print(f"{'处理速度':<20} {f'{speed_original:.0f}点/秒':<20} {f'{speed_optimized:.0f}点/秒':<20} {f'+{speed_optimized-speed_original:.0f}点/秒':<20}")
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print("\n" + "=" * 80)
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print("最终结论")
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print("=" * 80)
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if results_match:
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print("\n[OK] 输出结果完全一致 ✓")
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else:
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print("\n[WARNING] 输出结果存在差异，请检查")
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print(f"\n性能提升: {speedup:.1f}x ({improvement:.1f}%)")
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print(f"时间节省: {time_saved:.2f}秒 ({time_saved/60:.2f}分钟)")
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if speedup > 100:
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print("\n[推荐] 性能提升巨大 (>100x)，强烈建议部署优化版本！")
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elif speedup > 10:
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print("\n[推荐] 性能提升显著 (>10x)，建议部署优化版本")
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elif speedup > 2:
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print("\n[推荐] 性能有提升 (>2x)，可以考虑部署优化版本")
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else:
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print("\n[提示] 性能提升不明显，可能不需要部署")
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print("\n" + "=" * 80)
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if __name__ == "__main__":
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main()
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