Esempio n. 1
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def get_history_data(date):
    df = pd.read_csv(HISTORY_FILE)
    data = df.loc[df[STOCK_NAMES[0]] == date_utils.lxrDate2csvDate(date)]
    result = data.values
    if len(result) > 0:
        return result[0]
    else:
        return None


# result = get_history_data('2012')
# print(result)
# y_axises = []
# generate_history_csv()

# import wendu_trend_chart
# wendu_trend_chart.generate(STOCK_NAMES[1:], date_index, y_axises)
def get_result(date):
    result = {}
    # 遍历指数集合,计算每一个指数的温度及50日均线信号(指定日期50均值与收盘价比较)
    for index, stock in enumerate(constants.STOCK_CODES):
        # 读取数据目录下的指数csv文件,获取当前指数数据
        df = read_data(stock)

        # 取得指定日期行的数据
        if date is None:
            date = date_utils.csvDate2lxrDate(df[constants.DATE][0])
        # 取得指定日期之前最新一天的数据
        data_result = df.loc[
            df[constants.DATE].map(lambda x: date_utils.csvDate2lxrDate(x)) <=
            date_utils.lxrDate2csvDate(date)]
        if len(data_result.values) == 0:
            print("指定日期不存在", stock, date)
            return date, None

        # 买点1:判断50日均线突破,当天收盘价是否突破50日均线
        # 买点2:判断100日均线突破,当天收盘价是否突破100日均线
        # 卖点1,卖点2,同上述相反
        # 连续突破情况,首日突破表示买入,后续情况多仓、持仓

        # 上一交易日50日均线信号
        data_index = data_result.index.values[0]
        data = data_result.loc[data_index]
        data_before = data_result.loc[data_index + 1]

        fifty_signal = dp.check_signal(data_before, data,
                                       constants.FIFTY_MEDIAN, 1)
        hundred_signal = dp.check_signal(data_before, data,
                                         constants.HUNDRED_MEDIAN, 2)
        result_stock = {
            'name': constants.STOCK_NAMES[index],
            constants.DATE: data[constants.DATE],
            constants.PB_PERCENTILE: data[constants.PB_PERCENTILE],
            constants.PE_PERCENTILE: data[constants.PE_PERCENTILE],
            constants.FIFTY_SIGNAL: fifty_signal,
            constants.HUNDRED_SIGNAL: hundred_signal
        }
        print('指数数据:', date, result_stock)
        result[stock] = result_stock
    return date, result
Esempio n. 3
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def data_download_stock(stock, url):
    # 请求数据
    request_data = {
        "token": constants.TOKEN,
        "stockCodes": [stock],
        "metricsList": ["pb", "pe_ttm", "sp"],
        "startDate": "2000-01-01"
    }
    result = requests.post(url, json=request_data)
    pbs = []
    pes = []
    cps = []
    date = []
    if result.status_code == 200 and result.json()['message'] == 'success':
        for data in result.json()['data']:
            if 'sp' in data.keys():
                cp = data['sp']
                cps.append(cp)
            else:
                continue

            split_date = data['date'].split('T')
            date.append(dateutils.lxrDate2csvDate(split_date[0]))

            pb = ''
            if 'pb' in data.keys():
                pb = data['pb']
            pbs.append(pb)

            pe = ''
            if 'pe_ttm' in data.keys():
                pe = data['pe_ttm']
            pes.append(pe)
    df = pd.DataFrame()
    df.insert(0, constants.DATE, date)
    df.insert(1, constants.CP, cps)
    df.insert(2, constants.PB, pbs)
    df.insert(3, constants.PE, pes)
    return df
Esempio n. 4
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def generate(stock, input_date, show_signal):
    stock_name, df = download(stock)
    if stock_name is None or df is None:
        print("没有获取到数据,请确认代码输入是否正确")
        return

    print('生成股票温度图:', stock_name)
    date = input_date
    # 取得指定日期行的数据
    if date is None:
        date = date_utils.csvDate2lxrDate(df[constants.DATE][0])
    data = df.loc[df[constants.DATE] == date_utils.lxrDate2csvDate(date)]
    if len(data.values) == 0:
        print("指定日期不存在", stock, date)
        return

    # 买点1:判断50日均线突破,当天收盘价是否突破50日均线
    # 买点2:判断100日均线突破,当天收盘价是否突破100日均线
    # 卖点1,卖点2,同上述相反
    # 连续突破情况,首日突破表示买入,后续情况多仓、持仓

    # 上一交易日50日均线信号
    data_index = data.index.values[0]
    data = df.loc[data_index]
    data_before = df.loc[data_index + 1]

    fifty_signal = dp.check_signal(data_before, data, constants.FIFTY_MEDIAN,
                                   1)
    hundred_signal = dp.check_signal(data_before, data,
                                     constants.HUNDRED_MEDIAN, 2)
    result_stock = {
        constants.PB_PERCENTILE: data[constants.PB_PERCENTILE],
        constants.PE_PERCENTILE: data[constants.PE_PERCENTILE],
        constants.FIFTY_SIGNAL: fifty_signal,
        constants.HUNDRED_SIGNAL: hundred_signal
    }

    generate_chart(stock, stock_name, date, result_stock, show_signal)
def get_new_data(stock, start_date_fix):
    new_data = pd.DataFrame()
    result_data = download_data(stock, start_date_fix)

    dates = []
    pbs = []
    pes = []
    cps = []
    for data in result_data:
        if start_date_fix == data[constants.DATE]:
            # 开始日期的数据在csv中已存在,不需要再放入新的data frame
            continue
        pes.append(data[constants.PE])
        pbs.append(data[constants.PB])
        cps.append(data[constants.CP])
        dates.append(date_utils.lxrDate2csvDate(data[constants.DATE]))

    # 将新的数据插入到data frame,然后返回
    new_data.insert(0, constants.DATE, dates)
    new_data.insert(1, constants.CP, cps)
    new_data.insert(2, constants.PB, pbs)
    new_data.insert(3, constants.PE, pes)
    return new_data