褚宏光
8dea3fbccb
- Updated all_results.csv with additional stock data and breakout strength metrics. - Revised report.md to improve clarity and detail on stock selection criteria and results. - Expanded strong_breakout_down.csv and strong_breakout_up.csv with new entries reflecting recent analysis. - Introduced new chart images for selected stocks to visualize breakout patterns. - Added plot_converging_triangles.py script for generating visualizations of stocks meeting convergence criteria. - Enhanced report_converging_triangles.py to allow for date-specific reporting and improved output formatting. - Optimized run_converging_triangle.py for performance and added execution time logging.
356 lines
12 KiB
Python
356 lines
12 KiB
Python
"""
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为当日满足收敛三角形的个股生成图表
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用法:
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python scripts/plot_converging_triangles.py
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python scripts/plot_converging_triangles.py --date 20260120
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"""
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from __future__ import annotations
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import argparse
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import csv
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import os
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import pickle
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import sys
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from typing import Dict, List, Optional
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import matplotlib.pyplot as plt
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import numpy as np
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# 配置中文字体
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plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS'] # 支持中文
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plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
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# 让脚本能找到 src/ 下的模块
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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,
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line_y,
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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_ohlcv_from_pkl(data_dir: str) -> tuple:
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"""从 pkl 文件加载 OHLCV 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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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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return (
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open_data["mtx"],
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high_data["mtx"],
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low_data["mtx"],
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close_data["mtx"],
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volume_data["mtx"],
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dates,
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tkrs,
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tkrs_name,
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)
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def load_daily_stocks(csv_path: str, target_date: int) -> List[Dict]:
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"""从CSV读取指定日期的股票列表"""
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stocks = []
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with open(csv_path, newline="", encoding="utf-8-sig") as f:
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reader = csv.DictReader(f)
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for row in reader:
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try:
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date = int(row.get("date", "0"))
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if date == target_date:
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stocks.append({
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"stock_idx": int(row.get("stock_idx", "0")),
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"stock_code": row.get("stock_code", ""),
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"stock_name": row.get("stock_name", ""),
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"breakout_dir": row.get("breakout_dir", "none"),
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"breakout_strength_up": float(row.get("breakout_strength_up", "0")),
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"breakout_strength_down": float(row.get("breakout_strength_down", "0")),
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})
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except (ValueError, TypeError):
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continue
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return stocks
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def plot_triangle(
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stock_idx: int,
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stock_code: str,
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stock_name: str,
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date_idx: int,
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high_mtx: np.ndarray,
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low_mtx: np.ndarray,
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close_mtx: np.ndarray,
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volume_mtx: np.ndarray,
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dates: np.ndarray,
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params: ConvergingTriangleParams,
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output_path: str,
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display_window: int = 500, # 新增：显示窗口大小
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) -> None:
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"""绘制单只股票的收敛三角形图"""
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# 提取该股票数据并过滤NaN
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high_stock = high_mtx[stock_idx, :]
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low_stock = low_mtx[stock_idx, :]
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close_stock = close_mtx[stock_idx, :]
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volume_stock = volume_mtx[stock_idx, :]
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valid_mask = ~np.isnan(close_stock)
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valid_indices = np.where(valid_mask)[0]
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if len(valid_indices) < params.window:
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print(f" [跳过] {stock_code} {stock_name}: 有效数据不足")
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return
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# 找到date_idx在有效数据中的位置
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if date_idx not in valid_indices:
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print(f" [跳过] {stock_code} {stock_name}: date_idx不在有效范围")
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return
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valid_end = np.where(valid_indices == date_idx)[0][0]
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if valid_end < params.window - 1:
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print(f" [跳过] {stock_code} {stock_name}: 窗口数据不足")
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return
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# 提取检测窗口数据（用于三角形检测）
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detect_start = valid_end - params.window + 1
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high_win = high_stock[valid_mask][detect_start:valid_end + 1]
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low_win = low_stock[valid_mask][detect_start:valid_end + 1]
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close_win = close_stock[valid_mask][detect_start:valid_end + 1]
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volume_win = volume_stock[valid_mask][detect_start:valid_end + 1]
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# 提取显示窗口数据（用于绘图，更长的历史）
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display_start = max(0, valid_end - display_window + 1)
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display_high = high_stock[valid_mask][display_start:valid_end + 1]
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display_low = low_stock[valid_mask][display_start:valid_end + 1]
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display_close = close_stock[valid_mask][display_start:valid_end + 1]
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display_volume = volume_stock[valid_mask][display_start:valid_end + 1]
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display_dates = dates[valid_indices[display_start:valid_end + 1]]
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# 检测三角形（使用检测窗口数据）
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result = detect_converging_triangle(
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high=high_win,
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low=low_win,
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close=close_win,
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volume=volume_win,
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params=params,
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stock_idx=stock_idx,
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date_idx=date_idx,
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)
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if not result.is_valid:
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print(f" [跳过] {stock_code} {stock_name}: 未识别到有效三角形")
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return
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# 绘图准备
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x_display = np.arange(len(display_close), dtype=float)
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# 计算三角形在显示窗口中的位置偏移
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triangle_offset = len(display_close) - len(close_win)
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# 三角形的上下沿线（相对于检测窗口）
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a_u, a_l = result.upper_slope, result.lower_slope
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start = result.window_start
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end = result.window_end
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# 计算截距（基于检测窗口）
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upper_end_val = high_win[end]
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lower_end_val = low_win[end]
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b_u = upper_end_val - a_u * end
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b_l = lower_end_val - a_l * end
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# 三角形线段在显示窗口中的X坐标
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xw_in_display = np.arange(start + triangle_offset, end + triangle_offset + 1, dtype=float)
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# 但计算Y值时仍使用原始检测窗口的X坐标
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xw_original = np.arange(start, end + 1, dtype=float)
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upper_line = line_y(a_u, b_u, xw_original)
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lower_line = line_y(a_l, b_l, xw_original)
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# 三角形检测窗口的日期范围（用于标题）
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detect_dates = dates[valid_indices[detect_start:valid_end + 1]]
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# 创建图表
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8),
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gridspec_kw={'height_ratios': [3, 1]})
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# 主图：价格和趋势线(使用显示窗口数据)
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ax1.plot(x_display, display_close, linewidth=1.5, label='收盘价', color='black', alpha=0.7)
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ax1.plot(xw_in_display, upper_line, linewidth=2, label='上沿', color='red', linestyle='--')
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ax1.plot(xw_in_display, lower_line, linewidth=2, label='下沿', color='green', linestyle='--')
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ax1.axvline(len(display_close) - 1, color='gray', linestyle=':', linewidth=1, alpha=0.5)
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ax1.set_title(
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f"{stock_code} {stock_name} - 收敛三角形 (检测窗口: {detect_dates[0]} ~ {detect_dates[-1]})\n"
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f"显示范围: {display_dates[0]} ~ {display_dates[-1]} ({len(display_dates)}个交易日) "
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f"突破方向: {result.breakout_dir} 宽度比: {result.width_ratio:.2f} "
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f"触碰: 上{result.touches_upper}/下{result.touches_lower} "
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f"放量确认: {'是' if result.volume_confirmed else '否' if result.volume_confirmed is False else '-'}",
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fontsize=11, pad=10
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)
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ax1.set_ylabel('价格', fontsize=10)
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ax1.legend(loc='best', fontsize=9)
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ax1.grid(True, alpha=0.3)
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# X轴日期标签（稀疏显示，基于显示窗口）
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step = max(1, len(display_dates) // 10)
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tick_indices = np.arange(0, len(display_dates), step)
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ax1.set_xticks(tick_indices)
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ax1.set_xticklabels(display_dates[tick_indices], rotation=45, ha='right', fontsize=8)
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# 副图：成交量（使用显示窗口数据）
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ax2.bar(x_display, display_volume, width=0.8, color='skyblue', alpha=0.6)
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ax2.set_ylabel('成交量', fontsize=10)
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ax2.set_xlabel('交易日', fontsize=10)
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ax2.grid(True, alpha=0.3, axis='y')
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ax2.set_xticks(tick_indices)
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ax2.set_xticklabels(display_dates[tick_indices], rotation=45, ha='right', fontsize=8)
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plt.tight_layout()
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plt.savefig(output_path, dpi=120)
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plt.close()
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print(f" [完成] {stock_code} {stock_name} -> {output_path}")
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def main() -> None:
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parser = argparse.ArgumentParser(description="为当日满足收敛三角形的个股生成图表")
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parser.add_argument(
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"--input",
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default=os.path.join("outputs", "converging_triangles", "all_results.csv"),
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help="输入 CSV 路径",
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)
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parser.add_argument(
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"--date",
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type=int,
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default=None,
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help="指定日期（YYYYMMDD），默认为数据最新日",
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)
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parser.add_argument(
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"--output-dir",
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default=os.path.join("outputs", "converging_triangles", "charts"),
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help="图表输出目录",
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)
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args = parser.parse_args()
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print("=" * 70)
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print("收敛三角形图表生成")
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print("=" * 70)
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# 1. 加载数据
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print("\n[1] 加载 OHLCV 数据...")
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data_dir = os.path.join(os.path.dirname(__file__), "..", "data")
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open_mtx, high_mtx, low_mtx, close_mtx, volume_mtx, dates, tkrs, tkrs_name = load_ohlcv_from_pkl(data_dir)
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print(f" 股票数: {len(tkrs)}, 交易日数: {len(dates)}")
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# 2. 确定目标日期
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if args.date:
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target_date = args.date
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else:
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# 从CSV读取最新日期
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with open(args.input, newline="", encoding="utf-8-sig") as f:
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reader = csv.DictReader(f)
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all_dates = [int(r.get("date", "0")) for r in reader if r.get("date") and r["date"].isdigit()]
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target_date = max(all_dates) if all_dates else 0
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if target_date == 0:
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print("错误: 无法确定目标日期")
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return
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print(f"\n[2] 目标日期: {target_date}")
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# 3. 加载当日股票列表
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stocks = load_daily_stocks(args.input, target_date)
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print(f" 当日满足三角形的股票数: {len(stocks)}")
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if not stocks:
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print("当日无满足条件的股票")
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return
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# 4. 创建输出目录并清空旧图片
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os.makedirs(args.output_dir, exist_ok=True)
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# 清空目录中的旧图片
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print(f"\n[4] 清空输出目录...")
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old_files = [f for f in os.listdir(args.output_dir) if f.endswith('.png')]
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for f in old_files:
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os.remove(os.path.join(args.output_dir, f))
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print(f" 已删除 {len(old_files)} 个旧图片")
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# 5. 检测参数（与 run_converging_triangle.py 保持一致）
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params = ConvergingTriangleParams(
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window=120,
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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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upper_slope_max=0.10,
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lower_slope_min=-0.10,
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touch_tol=0.10,
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touch_loss_max=0.10,
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shrink_ratio=0.8,
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break_tol=0.001,
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vol_window=20,
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vol_k=1.3,
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false_break_m=5,
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)
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# 6. 找到target_date在dates中的索引
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date_idx = np.where(dates == target_date)[0]
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if len(date_idx) == 0:
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print(f"错误: 日期 {target_date} 不在数据范围内")
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return
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date_idx = date_idx[0]
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# 7. 生成图表
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print(f"\n[3] 生成图表...")
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for stock in stocks:
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stock_idx = stock["stock_idx"]
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stock_code = stock["stock_code"]
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stock_name = stock["stock_name"]
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output_filename = f"{target_date}_{stock_code}_{stock_name}.png"
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output_path = os.path.join(args.output_dir, output_filename)
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try:
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plot_triangle(
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stock_idx=stock_idx,
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stock_code=stock_code,
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stock_name=stock_name,
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date_idx=date_idx,
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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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dates=dates,
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params=params,
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output_path=output_path,
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display_window=500, # 显示500个交易日
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)
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except Exception as e:
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print(f" [错误] {stock_code} {stock_name}: {e}")
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print(f"\n完成！图表已保存至: {args.output_dir}")
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if __name__ == "__main__":
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main()
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