def prepare_data(self, begin_date, end_date):
shifted_begin_date = shift_date(begin_date, 800)
bs = cp.concat_fund(self.data_source, self.tickers,
'BS').loc[shifted_begin_date:end_date,
['ticker', 86]]
bs['release_date'] = bs.index
bs['report_date'] = bs.index
bs['motherEquity'] = bs[86]
# 归母权益
equity_mean = []
for ticker in bs['ticker'].unique():
try:
tmp_equity = ttmDiscrete(bs[bs['ticker'] == ticker],
'motherEquity', 5)
tmp_equity['ticker'] = ticker
except:
continue
equity_mean.append(tmp_equity)
equity_mean = pd.concat(equity_mean)
inst = cp.concat_fund(self.data_source, self.tickers,
'IS').loc[shifted_begin_date:end_date,
['ticker', 40]]
inst['release_date'] = inst.index
inst['report_date'] = inst.index
inst['motherNetProfit'] = inst[40]
# 归母净利润
net_profit = []
for ticker in inst['ticker'].unique():
try:
tmp_profit = ttmContinues(inst[inst['ticker'] == ticker],
'motherNetProfit')
tmp_profit['ticker'] = ticker
except:
continue
net_profit.append(tmp_profit)
net_profit = pd.concat(net_profit)
# 时间排序处理
equity_mean['report_date'] = equity_mean['report_date'].apply(
lambda x: x.strftime("%Y-%m-%d"))
net_profit['report_date'] = net_profit['report_date'].apply(
lambda x: x.strftime("%Y-%m-%d"))
self.equity_mean = equity_mean.sort_values(
by=['report_date', 'datetime'], ascending=[False, False])
self.net_profit = net_profit.sort_values(
by=['report_date', 'datetime'], ascending=[False, False])
def prepare_data(self, begin_date, end_date):
"""
数据预处理
"""
# 净资产周转率 = 营业收入_TTM / 净资产总计_TTM
# 净资产总计=总资产-负债总额
# 营业收入_TTM为最近4个季度报告期的营业收入之和，
# 净资产总计_TTM为最近5个季度报告期总资产的平均值。
# Net asset turnover ratio = netAssets / totalLiabilities
# 获取财务数据:
shifted_begin_date = shift_date(begin_date, 500)
#117负债, 121资产
netAssets = cp.concat_fund(self.data_source, self.tickers, 'BS').loc[shifted_begin_date:end_date, ['ticker', 117, 121]]
netAssets['netAssets'] = netAssets[121] - netAssets[117]
netAssets.drop([117, 121], axis=1, inplace=True)
netAssets = netAssets[netAssets['netAssets'] :0]
netAssets['report_date'] = netAssets.index
netAssets['release_date'] = netAssets.index
netAssetsTTM_ls = []
for ticker in netAssets['ticker'].unique():
try:
netAssets_df = ttmDiscrete(netAssets[netAssets['ticker'] == ticker], 'netAssets')
netAssets_df['ticker'] = ticker
except:
# print(ticker + ': net asset error')
continue
netAssetsTTM_ls.append(netAssets_df)
#0营业收入
revenue = cp.concat_fund(self.data_source, self.tickers, 'IS').loc[shifted_begin_date:end_date, ['ticker', 0]]
revenue['revenue'] = revenue[0]
revenue.drop([0], axis=1, inplace=True)
revenue['report_date'] = revenue.index
revenue['release_date'] = revenue.index
revenueTTM_ls = []
for ticker in revenue['ticker'].unique():
try: # 财务数据不足4条会有异常
reven_df = ttmContinues(revenue[revenue['ticker'] == ticker], 'revenue')
reven_df['ticker'] = ticker
except:
# print(ticker + ': revenue error')
continue
revenueTTM_ls.append(reven_df)
self.revenueTTM = pd.concat(revenueTTM_ls)
self.netAssetsTTM = pd.concat(netAssetsTTM_ls)
def prepare_data(self, begin_date, end_date):
"""
数据预处理
"""
# 获取财务数据:
# CATurnover = currentAssets 103 / revenue 0
shifted_begin_date = shift_date(begin_date, 500)
bs = cp.concat_fund(self.data_source, self.tickers,
'BS').loc[shifted_begin_date:end_date,
['ticker', 103]]
bs['release_date'] = bs.index
bs['report_date'] = bs.index
bs['currentAssets'] = bs[103]
bs.drop(103, axis=1, inplace=True)
inst = cp.concat_fund(self.data_source, self.tickers,
'IS').loc[shifted_begin_date:end_date,
['ticker', 0]]
inst['release_date'] = inst.index
inst['report_date'] = inst.index
inst['revenue'] = inst[0]
inst.drop([0], axis=1, inplace=True)
# TTM Continues处理
revenueTTM_ls = []
for ticker in inst['ticker'].unique():
try: # 财务数据不足4条会有异常
reven_df = ttmContinues(inst[inst['ticker'] == ticker],
'revenue')
reven_df['ticker'] = ticker
except:
print(ticker + ': revenue error')
continue
revenueTTM_ls.append(reven_df)
# TTM Discrete 取近期平均
currentAssetsTTM_ls = []
for ticker in bs['ticker'].unique():
try:
currentAssets_df = ttmDiscrete(bs[bs['ticker'] == ticker],
'currentAssets')
currentAssets_df['ticker'] = ticker
except:
print(ticker + ': current asset error')
continue
currentAssetsTTM_ls.append(currentAssets_df)
self.revenueTTM = pd.concat(revenueTTM_ls)
self.currentAssetsTTM = pd.concat(currentAssetsTTM_ls)
def prepare_data(self, begin_date, end_date):
shifted_begin_date = shift_date(begin_date, 700)
# totalAssets 121
bs = cp.concat_fund(self.data_source, self.tickers,
'BS').loc[shifted_begin_date:end_date,
['ticker', 121]]
bs['release_date'] = bs.index
bs['report_date'] = bs.index
bs['totalAssets'] = bs[121]
bs.drop(121, axis=1, inplace=True)
# revenue 0, cost 4
inst = cp.concat_fund(self.data_source, self.tickers,
'IS').loc[shifted_begin_date:end_date,
['ticker', 0, 4]]
inst['release_date'] = inst.index
inst['report_date'] = inst.index
inst['revenue'] = inst[0]
inst.drop(0, axis=1, inplace=True)
revenueTTM_ls = []
totalAssetsTTM_ls = []
for ticker in inst['ticker'].unique():
try: # 财务数据不足4条会有异常
reven_df = ttmContinues(inst[inst['ticker'] == ticker],
'revenue')
reven_df['ticker'] = ticker
except:
print(ticker + ': revenue error')
continue
revenueTTM_ls.append(reven_df)
for ticker in bs['ticker'].unique():
try:
total_asset_df = ttmDiscrete(bs[bs['ticker'] == ticker],
'totalAssets')
total_asset_df['ticker'] = ticker
except:
print(ticker + ': total asset error')
continue
totalAssetsTTM_ls.append(total_asset_df)
self.revenueTTM = pd.concat(revenueTTM_ls)
self.totalAssetsTTM = pd.concat(totalAssetsTTM_ls)