280 lines
10 KiB
Python
280 lines
10 KiB
Python
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# coding:utf-8
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import requests
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import pandas as pd
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from sqlalchemy import create_engine, text
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from datetime import datetime
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import time
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from config import XUEQIU_HEADERS
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def ensure_stock_price_changes_table_exists(engine):
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"""确保股价变化表存在"""
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try:
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create_table_query = text("""
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CREATE TABLE IF NOT EXISTS stock_price_changes (
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id INT AUTO_INCREMENT PRIMARY KEY,
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symbol VARCHAR(10),
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name VARCHAR(100),
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current FLOAT,
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percent FLOAT,
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time_mark VARCHAR(20), -- '9:24:40' 或 '9:25:00'
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add_time DATETIME,
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UNIQUE KEY unique_stock_time (symbol, time_mark, add_time)
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)
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""")
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create_analysis_table_query = text("""
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CREATE TABLE IF NOT EXISTS rapid_price_changes (
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id INT AUTO_INCREMENT PRIMARY KEY,
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symbol VARCHAR(10),
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name VARCHAR(100),
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price_change_ratio FLOAT, -- 两个时间点之间的涨幅
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sector_count INT, -- 所属板块数量
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sectors TEXT, -- 所属板块名称（逗号分隔）
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add_time DATETIME
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)
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""")
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with engine.connect() as conn:
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conn.execute(create_table_query)
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conn.execute(create_analysis_table_query)
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conn.commit()
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print("股价变化表检查/创建完成")
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except Exception as e:
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print("创建股价变化表时发生错误: {}".format(str(e)))
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def fetch_stock_data(db_url, time_mark):
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"""获取雪球数据并保存到数据库"""
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base_url = 'https://stock.xueqiu.com/v5/stock/screener/quote/list.json'
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types = ['sha', 'sza', 'kcb']
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headers = XUEQIU_HEADERS
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all_data = []
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for stock_type in types:
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# 先获取总数据量
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params = {
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'page': 1,
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'size': 100,
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'order': 'desc',
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'order_by': 'percent',
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'market': 'CN',
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'type': stock_type
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}
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# 初次请求以获取总页数
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response = requests.get(base_url, headers=headers, params=params)
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if response.status_code != 200:
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print(f"请求 {stock_type} 数据失败，状态码：{response.status_code}")
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continue
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data = response.json()
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total_count = data['data']['count']
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total_pages = (total_count // params['size']) + 1
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# 获取所有页面的数据
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for page in range(1, total_pages + 1):
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params['page'] = page
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response = requests.get(base_url, headers=headers, params=params)
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if response.status_code == 200:
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data = response.json()
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all_data.extend(data['data']['list'])
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else:
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print(f"请求 {stock_type} 数据第 {page} 页失败，状态码：{response.status_code}")
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if all_data:
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df = pd.DataFrame(all_data)
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selected_columns = ['symbol', 'name', 'current', 'percent']
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df = df[selected_columns]
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df['symbol'] = df['symbol'].str[2:] # 去掉前缀
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df['time_mark'] = time_mark
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df['add_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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engine = create_engine(db_url)
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ensure_stock_price_changes_table_exists(engine)
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# 先删除同一时间标记的旧数据
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with engine.connect() as conn:
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delete_query = text("""
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DELETE FROM stock_price_changes
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WHERE time_mark = :time_mark
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AND DATE(add_time) = CURDATE()
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""")
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conn.execute(delete_query, {'time_mark': time_mark})
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conn.commit()
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# 保存新数据
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df.to_sql('stock_price_changes', con=engine, if_exists='append', index=False)
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print(f"成功保存{len(df)}条{time_mark}的股价数据")
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return True
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return False
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def analyze_price_changes(db_url):
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"""分析两个时间点之间的板块涨跌情况"""
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engine = create_engine(db_url)
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try:
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# 获取所有股票的涨跌幅数据
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query = text("""
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SELECT
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t1.symbol,
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t1.name,
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((t2.current - t1.current) / t1.current * 100) as price_change_ratio
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FROM
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stock_price_changes t1
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JOIN stock_price_changes t2 ON t1.symbol = t2.symbol
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WHERE
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t1.time_mark = '9:24:00'
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AND t2.time_mark = '9:25:10'
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AND DATE(t1.add_time) = CURDATE()
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AND DATE(t2.add_time) = CURDATE()
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""")
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price_changes_df = pd.read_sql_query(query, engine)
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if not price_changes_df.empty:
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# 获取板块信息
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gp_gnbk_df = pd.read_sql_table('gp_gnbk', con=engine)
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# 合并获取板块信息
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merged_df = price_changes_df.merge(gp_gnbk_df, left_on='symbol', right_on='gp_code')
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# 按板块统计涨幅超过1%的股票数量和总数
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sector_stats = []
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for bk_name in merged_df['bk_name'].unique():
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sector_stocks = merged_df[merged_df['bk_name'] == bk_name]
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total_count = len(sector_stocks)
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up_count = len(sector_stocks[sector_stocks['price_change_ratio'] > 0.7])
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ratio = up_count / total_count if total_count > 0 else 0
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sector_stats.append({
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'sector_name': bk_name,
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'up_count': up_count,
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'total_count': total_count,
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'ratio': ratio,
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'add_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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})
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# 转换为DataFrame并按比例排序
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result_df = pd.DataFrame(sector_stats)
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result_df = result_df.sort_values('ratio', ascending=False)
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# 打印分析结果
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print("\n=== 板块涨幅分析结果 ===")
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print(f"分析时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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print("\n{:<15} {:<8} {:<8} {:<10}".format(
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"板块名称", "上涨家数", "总家数", "占比"
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))
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print("-" * 45)
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# 打印所有板块数据
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for _, row in result_df.iterrows():
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print("{:<15} {:<8d} {:<8d} {:<10.2f}%".format(
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row['sector_name'],
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row['up_count'],
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row['total_count'],
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row['ratio'] * 100
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))
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print("\n总计分析板块数量:", len(result_df))
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else:
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print("未获取到股价变化数据")
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except Exception as e:
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print("分析数据时发生错误: {}".format(str(e)))
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def get_previous_sector_ranks(engine):
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"""获取上一次的板块排名"""
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try:
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query = text("""
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SELECT sector_name, rank_num
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FROM sector_performance
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WHERE DATE(add_time) = CURDATE()
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ORDER BY add_time DESC
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LIMIT 1
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""")
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result = pd.read_sql_query(query, engine)
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if result.empty:
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return {}
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return dict(zip(result['sector_name'], result['rank_num']))
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except Exception as e:
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print("获取上一次排名数据时发生错误: {}".format(str(e)))
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return {}
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def calculate_rank_change(row, previous_ranks):
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"""计算排名变化"""
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previous_rank = previous_ranks.get(row['sector_name'])
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if previous_rank is None:
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return 0
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return previous_rank - row['rank_num']
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def get_cache_mark():
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"""获取当前时间对应的缓存标记"""
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current_minute = datetime.now().minute
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mark = (current_minute % 10) // 2 * 2
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return "{}m".format(mark)
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def save_sector_cache(engine, df_result, cache_mark):
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"""保存板块数据到缓存表"""
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try:
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df_cache = df_result.copy()
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df_cache['cache_mark'] = cache_mark
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with engine.connect() as conn:
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delete_query = text("DELETE FROM sector_performance_cache WHERE cache_mark = :cache_mark")
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conn.execute(delete_query, {'cache_mark': cache_mark})
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conn.commit()
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df_cache.to_sql('sector_performance_cache', con=engine, if_exists='append', index=False)
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print(f"缓存数据已保存，标记: {cache_mark}")
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except Exception as e:
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print("保存缓存数据时发生错误: {}".format(str(e)))
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def main(db_url):
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"""主函数"""
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engine = create_engine(db_url)
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target_time_1 = datetime.now().replace(hour=9, minute=24, second=0, microsecond=0)
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target_time_2 = datetime.now().replace(hour=9, minute=25, second=10, microsecond=0)
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current_time = datetime.now()
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# 如果当前时间已经超过了第二个时间点，直接进行分析
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if current_time > target_time_2:
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print("当前时间已超过9:24:00，直接进行数据分析...")
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analyze_price_changes(db_url)
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return
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# 如果还没到第一个时间点，等待
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if current_time < target_time_1:
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wait_seconds = (target_time_1 - current_time).total_seconds()
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print(f"等待{wait_seconds}9:24:00...")
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time.sleep(wait_seconds)
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# 获取第一次数据
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success_1 = fetch_stock_data(db_url, '9:24:00')
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if success_1:
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# 如果还没到第二个时间点，等待
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current_time = datetime.now()
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if current_time < target_time_2:
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wait_seconds = (target_time_2 - current_time).total_seconds()
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print(f"等待{wait_seconds}9:25:10...")
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time.sleep(wait_seconds)
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# 获取第二次数据
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success_2 = fetch_stock_data(db_url, '9:25:10')
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if success_2:
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# 分析数据
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analyze_price_changes(db_url)
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else:
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print("9:25:10数据失败")
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else:
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print("9:24:00数据失败")
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if __name__ == "__main__":
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db_url = 'mysql+pymysql://root:Chlry$%.8@192.168.1.82:3306/db_gp_cj'
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main(db_url)
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