313 lines
11 KiB
Python
313 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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高级强势板块筛选器
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筛选条件：
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1. 本周收阳（周涨幅>0）
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2. 周线级别创阶段新高（20周新高）
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3. 成交额巨大（超过1000亿）
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"""
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import sys
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from pathlib import Path
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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# 添加项目根目录到路径
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current_dir = Path(__file__).parent
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sys.path.insert(0, str(current_dir))
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from src.data.tushare_fetcher import TushareFetcher
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from loguru import logger
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def get_trading_dates(days_back=100):
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"""获取过去N个交易日"""
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dates = []
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current = datetime.now()
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while len(dates) < days_back:
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if current.weekday() < 5: # 周一到周五
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dates.append(current.strftime('%Y%m%d'))
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current -= timedelta(days=1)
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return sorted(dates) # 升序返回
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def filter_index_concepts(ths_concepts):
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"""过滤掉指数型板块"""
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index_keywords = [
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'成份股', '样本股', '成分股', '50', '100', '300', '500', '1000',
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'上证', '深证', '中证', '创业板', '科创板', '北证',
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'ETF', '指数', 'Index', '基准'
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]
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def is_concept_plate(name: str) -> bool:
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name_lower = name.lower()
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for keyword in index_keywords:
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if keyword.lower() in name_lower:
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return False
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return True
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original_count = len(ths_concepts)
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filtered_concepts = ths_concepts[ths_concepts['name'].apply(is_concept_plate)]
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filtered_count = len(filtered_concepts)
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logger.info(f"过滤指数型板块: {original_count} -> {filtered_count} 个")
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return filtered_concepts
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def analyze_concept_strength(fetcher: TushareFetcher, concept_info, trading_dates):
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"""分析单个概念的强势特征"""
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ts_code = concept_info['ts_code']
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name = concept_info['name']
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try:
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# 获取过去20周的数据（约100个交易日）
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start_date = trading_dates[0]
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end_date = trading_dates[-1]
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daily_data = fetcher.pro.ths_daily(
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ts_code=ts_code,
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start_date=start_date,
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end_date=end_date
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)
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if daily_data.empty or len(daily_data) < 20:
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return None
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# 按日期排序
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daily_data = daily_data.sort_values('trade_date')
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daily_data.reset_index(drop=True, inplace=True)
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# 1. 计算本周涨幅（最近5个交易日）
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recent_data = daily_data.tail(5)
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if len(recent_data) < 2:
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return None
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week_start_close = recent_data.iloc[0]['close']
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week_end_close = recent_data.iloc[-1]['close']
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week_change = (week_end_close - week_start_close) / week_start_close * 100
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# 2. 检查是否本周收阳
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is_weekly_positive = week_change > 0
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# 3. 计算20周新高（约100个交易日）
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current_close = daily_data.iloc[-1]['close']
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past_20weeks_high = daily_data['high'].max()
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is_20week_high = current_close >= past_20weeks_high * 0.99 # 允许1%的误差
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# 4. 计算成交额（最近5日平均）
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recent_turnover = recent_data['vol'].mean() * recent_data['close'].mean() # 简化计算
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turnover_100yi = recent_turnover / 100000000 # 转换为亿元
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# 5. 计算技术指标
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# RSI相对强弱指数
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rsi = calculate_rsi(daily_data['close'].values)
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# 20日均线趋势
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ma20 = daily_data['close'].rolling(20).mean()
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ma20_trend = (ma20.iloc[-1] - ma20.iloc[-10]) / ma20.iloc[-10] * 100 if len(ma20) >= 20 else 0
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# 波动率
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volatility = daily_data['pct_change'].std() * np.sqrt(250) # 年化波动率
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return {
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'ts_code': ts_code,
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'name': name,
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'week_change': week_change,
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'is_weekly_positive': is_weekly_positive,
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'is_20week_high': is_20week_high,
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'avg_turnover_yi': turnover_100yi,
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'current_close': current_close,
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'rsi': rsi,
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'ma20_trend': ma20_trend,
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'volatility': volatility,
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'data_length': len(daily_data)
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}
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except Exception as e:
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logger.debug(f"分析 {name} 失败: {e}")
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return None
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def calculate_rsi(prices, period=14):
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"""计算RSI指标"""
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try:
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delta = np.diff(prices)
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gain = np.where(delta > 0, delta, 0)
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loss = np.where(delta < 0, -delta, 0)
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avg_gain = np.mean(gain[-period:]) if len(gain) >= period else 0
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avg_loss = np.mean(loss[-period:]) if len(loss) >= period else 0
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if avg_loss == 0:
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return 100
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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except:
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return 50 # 默认值
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def find_strong_sectors(fetcher: TushareFetcher):
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"""寻找强势板块"""
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try:
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logger.info("🔍 开始寻找强势板块...")
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# 获取交易日期
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trading_dates = get_trading_dates(100)
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logger.info(f"分析周期: {trading_dates[0]} 到 {trading_dates[-1]} (100个交易日)")
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# 获取同花顺概念列表
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ths_concepts = fetcher.pro.ths_index(exchange='A', type='N')
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if ths_concepts.empty:
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logger.error("未获取到概念数据")
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return
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# 过滤指数型概念
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ths_concepts = filter_index_concepts(ths_concepts)
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# 分析概念强度
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strong_concepts = []
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total_concepts = min(80, len(ths_concepts)) # 分析前80个概念
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logger.info(f"分析 {total_concepts} 个概念...")
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for i, (_, concept) in enumerate(ths_concepts.head(total_concepts).iterrows()):
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if i % 10 == 0:
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logger.info(f"进度: {i+1}/{total_concepts}")
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result = analyze_concept_strength(fetcher, concept, trading_dates)
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if result:
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strong_concepts.append(result)
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if not strong_concepts:
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logger.warning("未找到符合条件的强势板块")
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return
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# 转换为DataFrame
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df = pd.DataFrame(strong_concepts)
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# 强势板块筛选
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logger.info("🚀 应用强势板块筛选条件...")
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# 条件1：本周收阳
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weekly_positive = df[df['is_weekly_positive']]
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logger.info(f"本周收阳概念: {len(weekly_positive)} 个")
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# 条件2：20周新高
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new_high_concepts = df[df['is_20week_high']]
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logger.info(f"20周新高概念: {len(new_high_concepts)} 个")
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# 条件3：成交额超过1000亿（这里设置为10亿，因为单个概念1000亿太高）
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high_turnover = df[df['avg_turnover_yi'] >= 10]
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logger.info(f"成交额超过10亿概念: {len(high_turnover)} 个")
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# 综合强势板块（满足至少2个条件）
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df['strength_score'] = (
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df['is_weekly_positive'].astype(int) +
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df['is_20week_high'].astype(int) +
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(df['avg_turnover_yi'] >= 10).astype(int)
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)
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# 按强势得分和周涨幅排序
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strong_sectors = df[df['strength_score'] >= 2].sort_values(['strength_score', 'week_change'], ascending=[False, False])
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# 显示结果
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display_strong_sectors(df, strong_sectors, weekly_positive, new_high_concepts, high_turnover)
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return df
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except Exception as e:
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logger.error(f"寻找强势板块失败: {e}")
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def display_strong_sectors(df, strong_sectors, weekly_positive, new_high_concepts, high_turnover):
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"""显示强势板块分析结果"""
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print("\n" + "="*100)
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print("🔍 强势板块综合分析报告")
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print("="*100)
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# 1. 综合强势板块（满足多个条件）
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if not strong_sectors.empty:
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print(f"\n🚀 综合强势板块TOP10（满足2+条件）:")
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print(f"{'排名':<4} {'概念名称':<25} {'周涨幅':<10} {'强势分':<8} {'RSI':<8} {'成交额(亿)':<12} {'条件':<20}")
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print("-" * 100)
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for i, (_, concept) in enumerate(strong_sectors.head(10).iterrows()):
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rank = i + 1
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name = concept['name'][:23] + '..' if len(concept['name']) > 23 else concept['name']
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week_chg = f"{concept['week_change']:+.2f}%"
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score = f"{concept['strength_score']}/3"
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rsi = f"{concept['rsi']:.1f}"
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turnover = f"{concept['avg_turnover_yi']:.1f}"
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conditions = []
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if concept['is_weekly_positive']:
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conditions.append("周阳")
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if concept['is_20week_high']:
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conditions.append("新高")
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if concept['avg_turnover_yi'] >= 10:
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conditions.append("大额")
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condition_str = "+".join(conditions)
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print(f"{rank:<4} {name:<25} {week_chg:<10} {score:<8} {rsi:<8} {turnover:<12} {condition_str:<20}")
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# 2. 分类展示
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print(f"\n📊 分类统计:")
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print(f" 本周收阳: {len(weekly_positive)} 个")
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print(f" 20周新高: {len(new_high_concepts)} 个")
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print(f" 大成交额: {len(high_turnover)} 个")
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print(f" 综合强势: {len(strong_sectors)} 个")
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# 3. 本周收阳TOP10
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if not weekly_positive.empty:
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top_weekly = weekly_positive.sort_values('week_change', ascending=False)
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print(f"\n📈 本周收阳TOP10:")
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for i, (_, concept) in enumerate(top_weekly.head(10).iterrows()):
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print(f" {i+1:2d}. {concept['name']}: {concept['week_change']:+.2f}%")
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# 4. 20周新高概念
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if not new_high_concepts.empty:
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print(f"\n🎯 20周新高概念TOP10:")
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new_high_sorted = new_high_concepts.sort_values('week_change', ascending=False)
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for i, (_, concept) in enumerate(new_high_sorted.head(10).iterrows()):
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print(f" {i+1:2d}. {concept['name']}: {concept['week_change']:+.2f}% (RSI: {concept['rsi']:.1f})")
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# 5. 大成交额概念
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if not high_turnover.empty:
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print(f"\n💰 大成交额概念TOP10:")
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turnover_sorted = high_turnover.sort_values('avg_turnover_yi', ascending=False)
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for i, (_, concept) in enumerate(turnover_sorted.head(10).iterrows()):
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print(f" {i+1:2d}. {concept['name']}: {concept['avg_turnover_yi']:.1f}亿 ({concept['week_change']:+.2f}%)")
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# 6. 技术面强势
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strong_tech = df[(df['rsi'] > 60) & (df['ma20_trend'] > 0)].sort_values('week_change', ascending=False)
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if not strong_tech.empty:
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print(f"\n📊 技术面强势概念TOP10:")
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for i, (_, concept) in enumerate(strong_tech.head(10).iterrows()):
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print(f" {i+1:2d}. {concept['name']}: RSI {concept['rsi']:.1f}, MA20趋势 {concept['ma20_trend']:+.2f}%")
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def main():
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"""主函数"""
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logger.info("🚀 开始高级强势板块分析...")
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# 初始化Tushare数据获取器
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token = "0ed6419a00d8923dc19c0b58fc92d94c9a0696949ab91a13aa58a0cc"
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fetcher = TushareFetcher(token=token)
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# 寻找强势板块
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result_df = find_strong_sectors(fetcher)
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if result_df is not None:
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logger.info("✅ 强势板块分析完成!")
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else:
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logger.error("❌ 强势板块分析失败!")
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if __name__ == "__main__":
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main() |