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import time
from datetime import datetime, timedelta
import pandas as pd
from pandas import DataFrame
from tigeropen.common.consts import Market, Valuation, Balance, Income, CashFlow, FinancialReportPeriodType
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 100)
pd.set_option('display.width', 1000)
class IndicatorCalculator:
"""市盈率等指标计算"""
def __init__(self, quote_client, symbols, market=Market.US):
self.quote_client = quote_client
self.symbols = symbols
self.market = market
def get_company_currency(self) -> DataFrame:
"""获取标的公司总市值的所使用的货币单位
"""
# result 示例
# currency company_currency
# symbol
# AAPL USD USD
# BABA USD USD
result: DataFrame = self.quote_client.get_financial_currency(self.symbols, self.market).set_index('symbol')
return result
def get_exchange_rate(self, source_currency, target_currency) -> float:
"""
1 source 可以兑换多少 target， 如 source CNY， target HKD， 则返回 7.82/7.26 = 1.077
"""
if source_currency == target_currency:
return 1
today = int(time.time()) * 1000
# 获取货币和美元兑换的汇率（1美元换多少单位其他货币） result example
# date value
# currency
# HKD 1692028800000 7.81878
# CNY 1692028800000 7.26230
result: DataFrame = self.quote_client.get_financial_exchange_rate([source_currency, target_currency],
today).set_index('currency')
rate = result.loc[target_currency].value / result.loc[source_currency].value
return rate
def get_market_capital(self):
"""获取总市值
"""
end = datetime.today()
begin = end - timedelta(days=1)
# result example:
# field date value
# symbol
# AAPL market_capitalization 1691942400000 2.779610e+12
# BABA market_capitalization 1691942400000 2.437778e+11
result: DataFrame = self.quote_client.get_financial_daily(self.symbols, self.market,
fields=[Valuation.market_capitalization],
begin_date=int(begin.timestamp()) * 1000,
end_date=int(end.timestamp()) * 1000).set_index(
'symbol')
return result['value']
def get_financial(self):
fields = [
# 总普通股本
Balance.total_common_equity,
# 净收入
Income.net_income,
# 总收入
Income.total_revenue,
# 每股净资产
Balance.book_valueshare,
# 经营净额现金流
CashFlow.cash_from_ops,
]
# symbol currency field value period_end_date filing_date
# 0 AAPL USD total_common_equity 5.0672E10 2022-09-24 2022-10-28
# 1 AAPL USD net_income 9.9803E10 2022-09-24 2022-10-28
# 2 AAPL USD total_revenue 3.94328E11 2022-09-24 2022-10-28
# 3 AAPL USD book_valueshare 3.178238 2022-09-24 2022-10-28
# 4 AAPL USD cash_from_ops 1.22151E11 2022-09-24 2022-10-28
# 5 BABA CNY total_common_equity 9.89657E11 2023-03-31 2023-07-21
# 6 BABA CNY net_income 7.2783E10 2023-03-31 2023-07-21
# 7 BABA CNY total_revenue 8.68687E11 2023-03-31 2023-07-21
# 8 BABA CNY book_valueshare 385.718073 2023-03-31 2023-07-21
# 9 BABA CNY cash_from_ops 1.99752E11 2023-03-31 2023-07-21
result = self.quote_client.get_financial_report(self.symbols, self.market, fields,
period_type=FinancialReportPeriodType.LTM,
)
result['value'] = pd.to_numeric(result['value'], errors='coerce')
# field currency period_end_date filing_date book_valueshare cash_from_ops net_income total_common_equity total_revenue
# symbol
# AAPL USD 2022-09-24 2022-10-28 3.178238 1.22151E11 9.9803E10 5.0672E10 3.94328E11
# BABA CNY 2023-03-31 2023-07-21 385.718073 1.99752E11 7.2783E10 9.89657E11 8.68687E11
pivot_df = result.pivot(index=['symbol', 'currency', 'period_end_date', 'filing_date'], columns='field',
values='value').reset_index().set_index('symbol')
company_currency = self.get_company_currency()
pivot_df['company_currency'] = company_currency['company_currency']
pivot_df['exchange_rate'] = pivot_df.apply(
lambda row: self.get_exchange_rate(row['company_currency'], row['currency']), axis=1)
market_cap = self.get_market_capital()
pivot_df['market_capitalization'] = market_cap * pivot_df['exchange_rate']
# print(pivot_df)
# 市净率
pivot_df['pb_rate'] = pivot_df['market_capitalization'] / pivot_df['total_common_equity']
# 市盈率
pivot_df['pe_rate'] = pivot_df['market_capitalization'] / pivot_df['net_income']
# 市销率
pivot_df['ps_rate'] = pivot_df['market_capitalization'] / pivot_df['total_revenue']
# 结果示例
# field currency period_end_date filing_date book_valueshare cash_from_ops net_income total_common_equity total_revenue company_currency exchange_rate market_capitalization pb_rate pe_rate ps_rate
# symbol
# AAPL USD 2023-07-01 2023-08-04 3.851898 1.130720e+11 9.476000e+10 6.027400e+10 3.839330e+11 USD 1.0000 2.779610e+12 46.116238 29.333159 7.239831
# BABA CNY 2023-06-30 2023-08-10 397.452549 2.111890e+11 8.436600e+10 1.013504e+12 8.972880e+11 USD 7.2623 1.770387e+12 1.746798 20.984605 1.973042
print(pivot_df)
# 打印第一个股票的市盈率
print(f'{self.symbols[0]} PE rate: {pivot_df.loc[self.symbols[0]]["pe_rate"]}')
if __name__ == '__main__':
# 此处修改为自己环境对应的 quote_client 实例
from client import quote_client
calculator = IndicatorCalculator(quote_client, symbols=['AAPL', 'BABA'], market=Market.US)
# calculator = IndicatorCalculator(quote_client, symbols=['09988'], market=Market.HK)
calculator.get_financial()