update

StockAnalyze.py

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+'''
+@author:rocky
+@email:weigesysu@qq.com
+@feature: 收盘事后分析
+'''
+
+from settings import get_engine
+import pandas as pd
+from scipy import stats
+import tushare as ts
+import datetime
+import os
+import matplotlib.pyplot as plt
+
+pd.set_option('display.max_rows', None)
+
+
+def volume_calculation(code, start, end):
+    '''
+    计算某个股票的某个时间段的累计成交量
+
+    :param start: 开始日期
+    :param end: 结束日期
+    :return: 成交量，占每天比例
+    '''
+
+    df = ts.get_today_ticks(code)
+
+    # 转换str为时间格式，便于下面用来比较时间的大小
+    df['time'] = df['time'].map(lambda x: datetime.datetime.strptime(str(x), '%H:%M:%S'))
+    total = df['volume'].sum()
+    start = datetime.datetime.strptime(start, '%H:%M:%S')
+    end = datetime.datetime.strptime(end, '%H:%M:%S')
+    new_df = df[(df['time'] >= start) & (df['time'] < end)]
+
+    volume = new_df['volume'].sum()
+    rate = round(volume * 1.00 / total * 100, 2)
+
+    return volume, rate
+
+
+def today_statistics(today):
+    '''
+    :help: 今天涨跌幅的统计分析： 中位数，均值等数据
+    :param today: 日期 2019-01-01
+    :return:None
+    '''
+
+    engine = get_engine('db_daily')
+    df = pd.read_sql(today, engine, index_col='index')
+    # 去除停牌的 成交量=0
+
+    df = df[df['volume'] != 0]
+    median = round(df['changepercent'].median(), 2)
+    mean = round(df['changepercent'].mean(), 2)
+    std = round(df['changepercent'].std(), 2)
+    p_25 = round(stats.scoreatpercentile(df['changepercent'], 25), 2)
+    p_50 = round(stats.scoreatpercentile(df['changepercent'], 50), 2)
+    p_75 = round(stats.scoreatpercentile(df['changepercent'], 75), 2)
+
+    print('中位数: {}'.format(median))
+    print('平均数: {}'.format(mean))
+    print('方差: {}'.format(std))
+    print('25%: {}'.format(p_25))
+    print('50%: {}'.format(p_50))
+    print('75%: {}'.format(p_75))
+
+
+def zt_location(date):
+    '''
+    :help: 分析涨停的区域分布
+    :param date:日期格式 20180404
+    :return:
+    '''
+    engine_zdt = get_engine('db_zdt')
+    engine_basic = get_engine('db_stock')
+
+    df = pd.read_sql(date + 'zdt', engine_zdt, index_col='index')
+    df_basic = pd.read_sql('tb_basic_info', engine_basic, index_col='index')
+    result = {}
+
+    for code in df['代码'].values:
+        try:
+            area = df_basic[df_basic['code'] == code]['area'].values[0]
+            result.setdefault(area, 0)
+            result[area] += 1
+
+        except Exception as e:
+            print(e)
+
+    new_result = sorted(result.items(), key=lambda x: x[1], reverse=True)
+    for k, v in new_result:
+        print(k, v)
+
+
+def show_percentage(price):
+    '''
+    :help: 根据收盘价计算每个百分比的价格
+    :param open_price: 开盘价
+    :return:
+    '''
+
+    for i in range(1, 11):
+        print('{}\t+{}% -> {}'.format(price, i, round(price * (1 + 0.01 * i), 2)))
+
+    for i in range(1, 11):
+        print('{}\t-{}% -> {}'.format(price, i, round(price * (1 - 0.01 * i), 2)))
+
+
+def stock_profit(code, start, end):
+    '''
+    :help: 计算某个时间段的收益率
+    :param code: 股票代码
+    :param start: 开始时间
+    :param end: 结束时间
+    :return:
+    '''
+
+    k_data = ts.get_k_data(start=start, end=end, code=code)
+
+    start_price = k_data['close'].values[0]
+    print("Start price: ", start_price)
+
+    end_price = k_data['close'].values[-1]
+
+    print("End price: ", end_price)
+    earn_profit = (end_price - start_price) / start_price * 100
+    print("Profit: ", round(earn_profit, 2))
+
+def exclude_kcb(df):
+    '''
+    :help: 去除科创板
+    :param df:
+    :return:
+    '''
+    non_kcb = df[~df['code'].map(lambda x:True if x.startswith('688') else False)]
+    return non_kcb
+
+
+def plot_percent_distribution(date):
+    '''
+    :help:图形显示某一天的涨跌幅分布
+    :param date:
+    :return:
+    '''
+
+    total = []
+    engine = get_engine('db_daily')
+    df = pd.read_sql(date,con=engine)
+    df = exclude_kcb(df)
+
+    count = len(df[(df['changepercent'] >= -11) & (df['changepercent'] <= -9.5)])
+    total.append(count)
+
+    for i in range(-9, 9, 1):
+        count = len(df[(df['changepercent'] >= i * 1.00) & (df['changepercent'] < ((i + 1)) * 1.00)])
+        total.append(count)
+
+    count = len(df[(df['changepercent'] >= 9)])
+    total.append(count)
+    # print(total)
+    df_figure = pd.Series(total)
+    plt.figure(figsize=(16,10))
+    X = range(-10, 10)
+    plt.bar(X, height=total,color='y')
+    for x, y in zip(X, total):
+        plt.text(x, y + 0.05, y, ha='center', va='bottom')
+    plt.grid()
+    plt.xticks(range(-10,11))
+    plt.show()
+
+
+def main():
+
+    ## 某个股票某个时间段的成交量 ####
+    # code = '000069'
+    # v, ratio = volume_calculation(code,'09:30:00', '10:00:00')
+    # print('\n')
+    # print(v, ratio)
+
+    ## 涨跌幅分布 #####
+    # today=datetime.datetime.now().strftime("%Y-%m-%d")
+    # today_tendency(today)
+
+    ## 分析涨停的区域分布 ####
+    # today = datetime.datetime.now().strftime("%Y%m%d")
+    # zt_location(today)
+
+    ## 显示百分比价格
+    # show_percentage(121)
+
+    ## 计算某个个股某段时间的收益率
+    # stock_profit('300333','2019-01-01','2020-02-03')
+
+    ## 显示价格分布
+    date='2020-02-07'
+    plot_percent_distribution(date)
+
+if __name__ == '__main__':
+    main()