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import pandas as pd
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import pymysql
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from sqlalchemy import create_engine
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import matplotlib.pyplot as plt
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# # 创建数据库连接
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# engine = create_engine('mysql+pymysql://root:1qazse42W3@192.168.0.222:3306/ry')
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# # 执行SQL查询并将结果存储在pandas DataFrame中
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# df = pd.read_sql_query('select code,trade_day as date ,open as open_price,close as close_price from stocks where trade_day between "2023-06-01" and "2023-06-30" ORDER BY trade_day', engine)
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# # 将date字段转换为日期类型，并设置为索引
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# df['date'] = pd.to_datetime(df['date'])
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# df.set_index('date', inplace=True)
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# # 对每个股票计算涨跌幅
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# df['change'] = df.groupby('code')['open_price'].shift(-1) - df['close_price']
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# # 计算涨跌幅分布
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# distribution = df['change'].value_counts(bins=10, normalize=True)
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# # 显示涨跌幅分布的图表
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# distribution.plot(kind='bar')
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# plt.title('Distribution of Price Changes')
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# plt.xlabel('Price Change')
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# plt.ylabel('Frequency')
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# plt.show()
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# 创建数据库连接
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engine = create_engine('mysql+pymysql://root:1qazse42W3@192.168.0.222:3306/ry')
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# 执行SQL查询来获取日期列表
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# 假设你的数据表中有一个date字段表示日期
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date_df = pd.read_sql_query('select date from trade_dates where trade = "trading" and date between "2023-06-01" and "2023-06-30" order by date', engine)
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# 将date字段转换为日期类型
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date_df['date'] = pd.to_datetime(date_df['date'])
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# 获取日期列表
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date_list = date_df['date'].tolist()
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total_count = 0
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# 低于-3%的开盘价计数
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low_open_count = 0
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# 正常开盘，介于-3%和3%之间的计数
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normal_open_count = 0
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# 高于3%的开盘价计数
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high_open_count = 0
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# 低于-3%的收盘价计数
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low_close_count = 0
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# 正常收盘，介于-3%和3%之间的计数
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normal_close_count = 0
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# 高于3%的收盘价计数
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high_close_count = 0
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# 收盘低于-3% 且开盘低于-3%的计数
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low_open_low_close_count = 0
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# 收盘价低于-3% 且开盘价介于-3%和3%之间的计数
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low_open_normal_close_count = 0
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# 收盘低于-3% 且开盘高于3%的计数
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low_open_high_close_count = 0
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# 收盘价介于-3%和3%之间 且开盘低于-3%的计数
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normal_open_low_close_count = 0
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# 收盘价介于-3%和3%之间 且开盘介于-3%和3%之间的计数
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normal_open_normal_close_count = 0
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# 收盘价介于-3%和3%之间 且开盘高于3%的计数
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normal_open_high_close_count = 0
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# 收盘高于3% 且开盘低于-3%的计数
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high_open_low_close_count = 0
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# 收盘高于3% 且开盘介于-3%和3%之间的计数
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high_open_normal_close_count = 0
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# 收盘高于3% 且开盘高于3%的计数
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high_open_high_close_count = 0
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# 遍历日期列表
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for i, date in enumerate(date_list):
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if i+1 < len(date_list):
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# 将日期转换为字符串格式，以便在SQL查询中使用
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date_str = date.strftime('%Y-%m-%d')
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nextdate_str = date_list[i+1].strftime('%Y-%m-%d')
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print(f'正在处理{date_str}的数据')
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# 执行SQL查询（这只是一个示例，你需要根据你的实际需求和数据库结构进行修改）
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query = f'select code,open,close,differrange from stocks where islimit = "是" and trade_day = "{date_str}"'
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df = pd.read_sql_query(query, engine)
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limit_list = df['code'].tolist()
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for code in limit_list:
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# 总统计数
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total_count += 1
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print(f'正在处理{date_str} - {code}的数据')
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query = f'select code,open,close,differrange from stocks where trade_day = "{nextdate_str}" and code = "{code}"'
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dfnext = pd.read_sql_query(query,engine)
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# 处理数据...
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df['next_open'] = dfnext['open']
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df['next_open_diff'] = 100 * (dfnext['open'] - df['open'])/df['open']
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print(df['next_open_diff'][0])
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df['next_close'] = dfnext['close']
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df['next_close_differrange'] = dfnext['differrange']
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print(df['next_close_differrange'][0])
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if df['next_open_diff'][0] < -3:
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low_open_count += 1
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elif df['next_open_diff'][0] > 3:
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high_open_count += 1
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else:
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normal_open_count += 1
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if df['next_close_differrange'][0] < -3:
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low_close_count += 1
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elif df['next_close_differrange'][0] > 3:
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high_close_count += 1
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else:
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normal_close_count += 1
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if df['next_open_diff'][0] < -3 and df['next_close_differrange'][0] < -3:
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low_open_low_close_count += 1
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elif df['next_open_diff'][0] < -3 and df['next_close_differrange'][0] > -3 and df['next_close_differrange'][0] < 3:
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low_open_normal_close_count += 1
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elif df['next_open_diff'][0] < -3 and df['next_close_differrange'][0] > 3:
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low_open_high_close_count += 1
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elif df['next_open_diff'][0] > -3 and df['next_open_diff'][0] < 3 and df['next_close_differrange'][0] < -3:
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normal_open_low_close_count += 1
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elif df['next_open_diff'][0] > -3 and df['next_open_diff'][0] < 3 and df['next_close_differrange'][0] > -3 and df['next_close_differrange'][0] < 3:
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normal_open_normal_close_count += 1
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elif df['next_open_diff'][0] > -3 and df['next_open_diff'][0] < 3 and df['next_close_differrange'][0] > 3:
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normal_open_high_close_count += 1
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elif df['next_open_diff'][0] > 3 and df['next_close_differrange'][0] < -3:
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high_open_low_close_count += 1
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elif df['next_open_diff'][0] > 3 and df['next_close_differrange'][0] > -3 and df['next_close_differrange'][0] < 3:
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high_open_normal_close_count += 1
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elif df['next_open_diff'][0] > 3 and df['next_close_differrange'][0] > 3:
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high_open_high_close_count += 1
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# 将数据框保存到磁盘
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df.to_csv(f'./data/{date_str}_stock.csv', index=False)
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# # 从磁盘加载数据框
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# df = pd.read_csv('df_distribution.csv')
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print(f'总统计数：{total_count}')
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print(f'低于-3%的开盘价计数：{low_open_count}')
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print(f'正常开盘，介于-3%和3%之间的计数：{normal_open_count}')
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print(f'高于3%的开盘价计数：{high_open_count}')
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print(f'低于-3%的收盘价计数：{low_close_count}')
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print(f'正常收盘，介于-3%和3%之间的计数：{normal_close_count}')
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print(f'高于3%的收盘价计数：{high_close_count}')
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print(f'收盘低于-3% 且开盘低于-3%的计数：{low_open_low_close_count}')
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print(f'收盘价低于-3% 且开盘价介于-3%和3%之间的计数：{low_open_normal_close_count}')
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print(f'收盘低于-3% 且开盘高于3%的计数：{low_open_high_close_count}')
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print(f'收盘价介于-3%和3%之间 且开盘低于-3%的计数：{normal_open_low_close_count}')
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print(f'收盘价介于-3%和3%之间 且开盘介于-3%和3%之间的计数：{normal_open_normal_close_count}')
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print(f'收盘价介于-3%和3%之间 且开盘高于3%的计数：{normal_open_high_close_count}')
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print(f'收盘高于3% 且开盘低于-3%的计数：{high_open_low_close_count}')
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print(f'收盘高于3% 且开盘介于-3%和3%之间的计数：{high_open_normal_close_count}')
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print(f'收盘高于3% 且开盘高于3%的计数：{high_open_high_close_count}')
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df_distribution = pd.DataFrame({'test':[1]})
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df_distribution['total_count'] = total_count
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df_distribution['low_open_count'] = low_open_count
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df_distribution['normal_open_count'] = normal_open_count
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df_distribution['high_open_count'] = high_open_count
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df_distribution['low_close_count'] = low_close_count
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df_distribution['normal_close_count'] = normal_close_count
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df_distribution['high_close_count'] = high_close_count
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df_distribution['low_open_low_close_count'] = low_open_low_close_count
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df_distribution['low_open_normal_close_count'] = low_open_normal_close_count
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df_distribution['low_open_high_close_count'] = low_open_high_close_count
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df_distribution['normal_open_low_close_count'] = normal_open_low_close_count
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df_distribution['normal_open_normal_close_count'] = normal_open_normal_close_count
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df_distribution['normal_open_high_close_count'] = normal_open_high_close_count
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df_distribution['high_open_low_close_count'] = high_open_low_close_count
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df_distribution['high_open_normal_close_count'] = high_open_normal_close_count
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df_distribution['high_open_high_close_count'] = high_open_high_close_count
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# df_distribution["low_open_low_close_rate"] = low_open_low_close_count / total_count
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# df_distribution["low_open_normal_close_rate"] = low_open_normal_close_count / total_count
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# df_distribution["low_open_high_close_rate"] = low_open_high_close_count / total_count
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# df_distribution["normal_open_low_close_rate"] = normal_open_low_close_count / total_count
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# df_distribution["normal_open_normal_close_rate"] = normal_open_normal_close_count / total_count
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# df_distribution["normal_open_high_close_rate"] = normal_open_high_close_count / total_count
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# df_distribution["high_open_low_close_rate"] = high_open_low_close_count / total_count
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# df_distribution["high_open_normal_close_rate"] = high_open_normal_close_count / total_count
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# df_distribution["high_open_high_close_rate"] = high_open_high_close_count / total_count
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# df_distribution["low_open_rate"] = low_open_count / total_count
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# df_distribution["normal_open_rate"] = normal_open_count / total_count
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# df_distribution["high_open_rate"] = high_open_count / total_count
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# df_distribution["low_close_rate"] = low_close_count / total_count
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# df_distribution["normal_close_rate"] = normal_close_count / total_count
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# df_distribution["high_close_rate"] = high_close_count / total_count
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# 删除'test'列
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df_distribution = df_distribution.drop(columns=['test'])
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# 创建一个新的图形
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fig, ax = plt.subplots()
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# 创建一个条形图
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df_distribution.plot(kind='bar', ax=ax)
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# 设置图形的标题和轴标签
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ax.set_title('Distribution')
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ax.set_xlabel('Category')
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ax.set_ylabel('Count')
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# 显示图形
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plt.show() |
