def bond_var(period, percent, day):
df_nav = base_ra_index_nav.load_series('120000011')
df_inc = df_nav.pct_change(period)
df_inc = df_inc.dropna()
df_inc = df_inc[df_inc.index < day]
df_inc = df_inc.iloc[-365*5:]
var = np.percentile(df_inc, percent)
#ib_inc = Asset('PO.IB0010').nav().pct_change()
#lrb_inc = Asset('PO.LRB010').nav().pct_change()
#dates = ib_inc.index & lrb_inc.index
#ib_inc = ib_inc.loc[dates]
#lrb_inc = lrb_inc.loc[dates]
#ib_lrb_inc = ib_inc * 0.2 + lrb_inc * 0.8
#ib_lrb_inc = ib_lrb_inc.reindex(df_inc.index).fillna(0.0)
#ib_lrb_inc[ib_lrb_inc == 0] = df_inc[ib_lrb_inc == 0]
#df_inc = ib_lrb_inc
#df_nav = (df_inc + 1).cumprod()
#df_inc = df_nav.pct_change(period).fillna(0.0)
#df_inc = df_inc[df_inc.index < day]
#df_inc = df_inc.iloc[-365*5:]
#var = np.percentile(df_inc, percent)
return var
def macroview_retcompare(ctx, st_date, ed_date):
startDate = parse(st_date)
endDate = parse(ed_date)
assetsID = {
'120000001': '沪深300',
'120000002': '中证500',
'120000013': '标普500',
'120000015': '恒生指数',
'120000014': '沪金指数',
'120000010': '中证国债',
'120000011': '中证信用债'
}
#assetsRet = {'120000001':0.2743,'120000002':0.3576,'120000013':0.0953,'120000015':0.0997,'120000014':-0.0029,'120000010':0.0009,'120000011':0.0032}
assets = dict([(asset_id, base_ra_index_nav.load_series(asset_id))
for asset_id in list(assetsID.keys())])
df_assets = pd.DataFrame(assets).loc[startDate:endDate, ].fillna(
method='pad')
assetsRet = dict([(
asset_id,
df_assets.loc[endDate, asset_id] / df_assets.loc[startDate, asset_id] -
1.0) for asset_id in list(assetsID.keys())])
# #计算给定指数和日期的持有期收益
df_assets = pd.DataFrame(assets)
df_assets = df_assets.rolling(365).apply(lambda x: x[-1] / x[0] - 1,
raw=True)
df = df_assets.reset_index()
tradedaysDiff = df[df.date == endDate].index.tolist()[0] - df[
df.date == startDate].index.tolist()[0]
annualMulty = 365 / tradedaysDiff
MacroCompare = []
for key, value in assetsRet.items():
values = value * annualMulty
ser = df_assets[key].dropna()
ser = ser.sort_values()
if len(ser) == 0:
continue
MacroCompare.append(
(assetsID[key], 1 - len(ser[ser < values]) / len(ser)))
df_MacroCompare = pd.DataFrame(MacroCompare, columns=['ra_index', '分位数'])
#print(df_MacroCompare)
ser = pd.Series(assetsRet)
ret_df = pd.DataFrame(columns=ser.index)
ret_df.loc[endDate] = ser
ret_df = ret_df.rename(columns=assetsID)
print(ret_df)
df_MacroCompare['date'] = endDate
df_MacroCompare = df_MacroCompare.set_index(['date', 'ra_index']).unstack()
df_MacroCompare.columns = df_MacroCompare.columns.get_level_values(1)
df_MacroCompare = df_MacroCompare[ret_df.columns]
df_MacroCompare.columns = df_MacroCompare.columns + ' 分位数'
df = pd.concat([ret_df, df_MacroCompare], axis=1)
print(df)
df.to_csv('宏观观点数据.csv', encoding='gbk')
def load_nav_series(asset_id,
reindex=None,
begin_date=None,
end_date=None):
prefix = asset_id[0:2]
if prefix.isdigit():
xtype = int(asset_id) // 10000000
if xtype == 1:
#
# 基金池资产
#
asset_id = int(asset_id) % 10000000
(pool_id, category) = (asset_id // 100, asset_id % 100)
ttype = pool_id // 10000
sr = asset_ra_pool_nav.load_series(pool_id,
category,
ttype,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif xtype == 3:
#
# 基金池资产
#
sr = base_ra_fund_nav.load_series(asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif xtype == 4:
#
# 修型资产
#
sr = asset_rs_reshape_nav.load_series(asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif xtype == 12:
#
# 指数资产
#
sr = base_ra_index_nav.load_series(asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
else:
sr = pd.Series()
else:
if prefix == 'AP':
#
# 基金池资产
#
sr = asset_ra_pool_nav.load_series(asset_id,
0,
9,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif prefix == 'FD':
#
# 基金资产
#
sr = base_ra_fund_nav.load_series(asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif prefix == 'RS':
#
# 修型资产
#
sr = asset_rs_reshape_nav.load_series(asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif prefix == 'IX':
#
# 指数资产
#
sr = base_ra_index_nav.load_series(asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif prefix == 'ER':
sr = base_exchange_rate_index_nav.load_series(
asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif prefix == 'SK':
#
# 股票资产
#
sr = db.asset_stock.load_stock_nav_series(
asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif prefix == 'MZ':
#
# Markowitz asset
#
sr = db.asset_mz_markowitz_nav.load_series(
asset_id,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
elif prefix == 'PO':
#
# Markowitz asset
#
sr = db.asset_ra_portfolio_nav.load_series(
asset_id,
9,
reindex=reindex,
begin_date=begin_date,
end_date=end_date)
else:
sr = pd.Series()
return sr
def allocate_benchmark_comp(ctx):
index_ids = ['120000016', '120000010']
data = {}
for _id in index_ids:
data[_id] = base_ra_index_nav.load_series(_id)
df = pd.DataFrame(data)
composite_asset_ids = [
'20201', '20202', '20203', '20204', '20205', '20206', '20207', '20208'
]
data = {}
for _id in composite_asset_ids:
nav = asset_ra_composite_asset_nav.load_nav(_id)
nav = nav.reset_index()
nav = nav[['ra_date', 'ra_nav']]
nav = nav.set_index(['ra_date'])
data[_id] = nav.ra_nav
bench_df = pd.DataFrame(data)
benchmark_df = pd.concat([bench_df, df],
axis=1,
join_axes=[bench_df.index])
conn = MySQLdb.connect(**config.db_asset)
conn.autocommit(True)
dfs = []
for i in range(0, 10):
sql = 'select on_date as date, on_nav as nav from on_online_nav where on_online_id = 80000%d and on_type = 8' % i
df = pd.read_sql(sql, conn, index_col=['date'], parse_dates=['date'])
df.columns = ['risk_' + str(i)]
dfs.append(df)
df = pd.concat(dfs, axis=1)
conn.close()
df = pd.concat([df, benchmark_df], axis=1, join_axes=[df.index])
df = df.fillna(method='pad')
df = df.rename(
columns={
'risk_0': '风险10',
'risk_1': '风险1',
'risk_2': '风险2',
'risk_3': '风险3',
'risk_4': '风险4',
'risk_5': '风险5',
'risk_6': '风险6',
'risk_7': '风险7',
'risk_8': '风险8',
'risk_9': '风险9',
'20201': '风险2比较基准',
'20202': '风险3比较基准',
'20203': '风险4比较基准',
'20204': '风险5比较基准',
'20205': '风险6比较基准',
'20206': '风险7比较基准',
'20207': '风险8比较基准',
'20208': '风险9比较基准',
'120000016': '风险10比较基准',
'120000010': '风险1比较基准'
})
cols = [
'风险1', '风险2', '风险3', '风险4', '风险5', '风险6', '风险7', '风险8', '风险9', '风险10',
'风险1比较基准', '风险2比较基准', '风险3比较基准', '风险4比较基准', '风险5比较基准', '风险6比较基准',
'风险7比较基准', '风险8比较基准', '风险9比较基准', '风险10比较基准'
]
df = df[cols]
#df.to_csv('online_benchmark.csv', encoding='gbk')
result_df = pd.DataFrame(columns=df.columns)
last_day = df.index[-1]
#print(last_day)
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 当日'] = df.pct_change().iloc[-1]
result_df.loc[
df.index[-1].strftime('%Y-%m-%d') +
' 过去一周'] = df.loc[last_day] / df.loc[last_day - timedelta(weeks=1)] - 1
result_df.loc[
df.index[-1].strftime('%Y-%m-%d') +
' 过去一月'] = df.loc[last_day] / df.loc[last_day - timedelta(days=31)] - 1
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 过去三个月'] = df.loc[last_day] / df.loc[last_day -
timedelta(days=91)] - 1
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 过去六个月'] = df.loc[last_day] / df.loc[last_day - timedelta(
days=182)] - 1
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 过去一年'] = df.loc[last_day] / df.loc[last_day -
timedelta(days=365)] - 1
#result_df.to_csv('智能组合收益与比较基准收益比较.csv', encoding='gbk')
df = result_df
df = df.reset_index()
df['date'] = pd.Series(None)
df['statistics_type'] = pd.Series(None)
df = df.apply(split_ct, axis=1)
df.pop('index')
df.set_index('date', inplace=True)
cols_list = list(df.columns)
cols_list.insert(0, cols_list.pop(-1))
df = df[cols_list]
if len(df) < 1:
print("无数据需要插入")
return
# 开始注入数据
engine = database.connection('asset')
Session = sessionmaker(bind=engine)
session = Session()
for i in range(len(df)):
ins = asset_allocate.smart_risk(sir_date=df.index[i],
sir_statistics_type=df.iloc[i, 0],
sir_risk1=df.iloc[i, 1],
sir_risk2=df.iloc[i, 2],
sir_risk3=df.iloc[i, 3],
sir_risk4=df.iloc[i, 4],
sir_risk5=df.iloc[i, 5],
sir_risk6=df.iloc[i, 6],
sir_risk7=df.iloc[i, 7],
sir_risk8=df.iloc[i, 8],
sir_risk9=df.iloc[i, 9],
sir_risk10=df.iloc[i, 10],
sir_risk1_standard=df.iloc[i, 11],
sir_risk2_standard=df.iloc[i, 12],
sir_risk3_standard=df.iloc[i, 13],
sir_risk4_standard=df.iloc[i, 14],
sir_risk5_standard=df.iloc[i, 15],
sir_risk6_standard=df.iloc[i, 16],
sir_risk7_standard=df.iloc[i, 17],
sir_risk8_standard=df.iloc[i, 18],
sir_risk9_standard=df.iloc[i, 19],
sir_risk10_standard=df.iloc[i, 20])
session.add(ins)
session.commit()
print("成功插入了 %d 条数据;" % (i + 1))
def macroview_retcompare(ctx, st_date, ed_date):
startDate = parse(st_date)
endDate = parse(ed_date)
assetsID = {
'120000001': '沪深300',
'120000002': '中证500',
'120000013': '标普500',
'120000015': '恒生指数',
'120000014': '沪金指数',
'120000010': '中证国债',
'120000011': '中证信用债'
}
#assetsRet = {'120000001':0.2743,'120000002':0.3576,'120000013':0.0953,'120000015':0.0997,'120000014':-0.0029,'120000010':0.0009,'120000011':0.0032}
assets = dict([(asset_id, base_ra_index_nav.load_series(asset_id))
for asset_id in list(assetsID.keys())])
df_assets = pd.DataFrame(assets).loc[startDate:endDate, ].fillna(
method='pad')
assetsRet = dict([(
asset_id,
df_assets.loc[endDate, asset_id] / df_assets.loc[startDate, asset_id] -
1.0) for asset_id in list(assetsID.keys())])
# #计算给定指数和日期的持有期收益
df_assets = pd.DataFrame(assets)
df_assets = df_assets.rolling(365).apply(lambda x: x[-1] / x[0] - 1,
raw=True)
df = df_assets.reset_index()
tradedaysDiff = df[df.date == endDate].index.tolist()[0] - df[
df.date == startDate].index.tolist()[0]
annualMulty = 365 / tradedaysDiff
MacroCompare = []
for key, value in assetsRet.items():
values = value * annualMulty
ser = df_assets[key].dropna()
ser = ser.sort_values()
if len(ser) == 0:
continue
MacroCompare.append(
(assetsID[key], 1 - len(ser[ser < values]) / len(ser)))
df_MacroCompare = pd.DataFrame(MacroCompare, columns=['ra_index', '分位数'])
#print(df_MacroCompare)
ser = pd.Series(assetsRet)
ret_df = pd.DataFrame(columns=ser.index)
ret_df.loc[endDate] = ser
ret_df = ret_df.rename(columns=assetsID)
print(ret_df)
df_MacroCompare['date'] = endDate
df_MacroCompare = df_MacroCompare.set_index(['date', 'ra_index']).unstack()
df_MacroCompare.columns = df_MacroCompare.columns.get_level_values(1)
df_MacroCompare = df_MacroCompare[ret_df.columns]
df_MacroCompare.columns = df_MacroCompare.columns + ' 分位数'
df = pd.concat([ret_df, df_MacroCompare], axis=1)
print(df)
# 开始注入数据
if len(df) < 1:
print("无数据需要插入")
return
engine = database.connection('asset')
Session = sessionmaker(bind=engine)
session = Session()
for count, index in enumerate(df.index):
ins = asset_allocate.opinion(
#id = None,
oi_date=index,
oi_return_CSI300=df.loc[index, df.columns[0]],
oi_return_IC500=df.loc[index, df.columns[1]],
oi_return_SP500=df.loc[index, df.columns[2]],
oi_return_HSI=df.loc[index, df.columns[3]],
oi_return_SQau=df.loc[index, df.columns[4]],
oi_return_CSI_TB=df.loc[index, df.columns[5]],
oi_return_CSI_DB=df.loc[index, df.columns[6]],
#oi_comment = None
)
session.add(ins)
session.commit()
print("成功插入 %d 条数据;" % (count + 1))
session.close()
def allocate_benchmark_comp(ctx):
index_ids = ['120000016', '120000010']
data = {}
for _id in index_ids:
data[_id] = base_ra_index_nav.load_series(_id)
df = pd.DataFrame(data)
composite_asset_ids = [
'20201', '20202', '20203', '20204', '20205', '20206', '20207', '20208'
]
data = {}
for _id in composite_asset_ids:
nav = asset_ra_composite_asset_nav.load_nav(_id)
nav = nav.reset_index()
nav = nav[['ra_date', 'ra_nav']]
nav = nav.set_index(['ra_date'])
data[_id] = nav.ra_nav
bench_df = pd.DataFrame(data)
benchmark_df = pd.concat([bench_df, df],
axis=1,
join_axes=[bench_df.index])
conn = MySQLdb.connect(**config.db_asset)
conn.autocommit(True)
dfs = []
for i in range(0, 10):
sql = 'select on_date as date, on_nav as nav from on_online_nav where on_online_id = 80000%d and on_type = 8' % i
df = pd.read_sql(sql, conn, index_col=['date'], parse_dates=['date'])
df.columns = ['risk_' + str(i)]
dfs.append(df)
df = pd.concat(dfs, axis=1)
conn.close()
df = pd.concat([df, benchmark_df], axis=1, join_axes=[df.index])
df = df.fillna(method='pad')
df = df.rename(
columns={
'risk_0': '风险10',
'risk_1': '风险1',
'risk_2': '风险2',
'risk_3': '风险3',
'risk_4': '风险4',
'risk_5': '风险5',
'risk_6': '风险6',
'risk_7': '风险7',
'risk_8': '风险8',
'risk_9': '风险9',
'20201': '风险2比较基准',
'20202': '风险3比较基准',
'20203': '风险4比较基准',
'20204': '风险5比较基准',
'20205': '风险6比较基准',
'20206': '风险7比较基准',
'20207': '风险8比较基准',
'20208': '风险9比较基准',
'120000016': '风险10比较基准',
'120000010': '风险1比较基准'
})
cols = [
'风险1', '风险2', '风险3', '风险4', '风险5', '风险6', '风险7', '风险8', '风险9', '风险10',
'风险1比较基准', '风险2比较基准', '风险3比较基准', '风险4比较基准', '风险5比较基准', '风险6比较基准',
'风险7比较基准', '风险8比较基准', '风险9比较基准', '风险10比较基准'
]
df = df[cols]
result_df = pd.DataFrame(columns=df.columns)
last_day = df.index[-1]
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 当日'] = df.pct_change().iloc[-1]
result_df.loc[
df.index[-1].strftime('%Y-%m-%d') +
' 过去一周'] = df.loc[last_day] / df.loc[last_day - timedelta(weeks=1)] - 1
result_df.loc[
df.index[-1].strftime('%Y-%m-%d') +
' 过去一月'] = df.loc[last_day] / df.loc[last_day - timedelta(days=31)] - 1
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 过去三个月'] = df.loc[last_day] / df.loc[last_day -
timedelta(days=91)] - 1
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 过去六个月'] = df.loc[last_day] / df.loc[last_day - timedelta(
days=182)] - 1
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 过去一年'] = df.loc[last_day] / df.loc[last_day -
timedelta(days=365)] - 1
result_df.to_csv('智能组合收益与比较基准收益比较.csv', encoding='gbk')
def cal_layer_type():
engine = database.connection('asset')
Session = sessionmaker(bind=engine)
session = Session()
sql1 = session.query(factor_cluster_asset.fc_asset_id).filter(
factor_cluster_asset.globalid == 'FC.000004')
asset_ids = [asset[0] for asset in sql1.all()]
assets = {}
for asset_id in asset_ids:
assets[asset_id] = load_nav_series(asset_id)
trade_dates = DBData.trade_dates(start_date='2010-06-01',
end_date='2018-12-01')
df_assets = pd.DataFrame(assets)
# df_assets = df_assets[df_assets.index >= start_date]
# df_assets = df_assets[df_assets.index <= end_date]
df_assets = df_assets.reindex(trade_dates).dropna()
df_ret = df_assets.pct_change().dropna()
layer_info = pd.read_csv('data/df_result.csv',
index_col=0,
parse_dates=True)
dates = layer_info.index.unique()
sh300 = base_ra_index_nav.load_series('120000001')
sh300 = sh300.reindex(trade_dates).pct_change().dropna()
zz500 = base_ra_index_nav.load_series('120000002')
zz500 = zz500.reindex(trade_dates).pct_change().dropna()
cyb = base_ra_index_nav.load_series('120000018')
cyb = cyb.reindex(trade_dates).pct_change().dropna()
for i in range(12, len(dates)):
start_date = dates[i - 12]
end_date = dates[i]
layers = layer_info.loc[end_date].layer.unique()
tmp_sh300 = sh300.loc[start_date.date():end_date.date()]
tmp_zz500 = zz500.loc[start_date.date():end_date.date()]
tmp_cyb = cyb.loc[start_date.date():end_date.date()]
for layer in layers:
layer_factor_id = layer_info[layer_info.layer ==
layer].loc[end_date].factor_id
if type(layer_factor_id) == str:
layer_factor_id = np.array([layer_factor_id])
else:
layer_factor_id = layer_factor_id.values
layer_ret = df_ret.loc[start_date.date():end_date.date(),
layer_factor_id]
layer_ret = layer_ret.mean(1)
# layer_nav = (1+layer_ret.mean(1)).cumprod()
sh300_corr = np.corrcoef(layer_ret, tmp_sh300)[0][1]
zz500_corr = np.corrcoef(layer_ret, tmp_zz500)[0][1]
cyb_corr = np.corrcoef(layer_ret, tmp_cyb)[0][1]
corr_list = [sh300_corr, zz500_corr, cyb_corr]
max_corr = max(corr_list)
if max_corr < 0.85:
sign = 'unknown'
elif sh300_corr == max_corr:
sign = 'sh300'
elif zz500_corr == max_corr:
sign = 'zz500'
elif cyb_corr == max_corr:
sign = 'cybzs'
print end_date, layer, sign, sh300_corr, zz500_corr, cyb_corr
def allocate_benchmark_comp():
index_ids = ['120000016', '120000010']
data = {}
for _id in index_ids:
data[_id] = base_ra_index_nav.load_series(_id)
df = pd.DataFrame(data)
composite_asset_ids = [
'20201', '20202', '20203', '20204', '20205', '20206', '20207', '20208'
]
data = {}
for _id in composite_asset_ids:
nav = asset_ra_composite_asset_nav.load_nav(_id)
nav = nav.reset_index()
nav = nav[['ra_date', 'ra_nav']]
nav = nav.set_index(['ra_date'])
data[_id] = nav.ra_nav
bench_df = pd.DataFrame(data)
benchmark_df = pd.concat([bench_df, df],
axis=1,
join_axes=[bench_df.index])
conn = MySQLdb.connect(**config.db_asset)
conn.autocommit(True)
dfs = []
for i in range(0, 10):
sql = 'select on_date as date, on_nav as nav from on_online_nav where on_online_id = 80000%d and on_type = 8' % i
df = pd.read_sql(sql, conn, index_col=['date'], parse_dates=['date'])
df.columns = ['risk_' + str(i)]
dfs.append(df)
df = pd.concat(dfs, axis=1)
conn.close()
df = pd.concat([df, benchmark_df], axis=1, join_axes=[df.index])
df = df.fillna(method='pad')
df = df.rename(
columns={
'risk_0': '风险10',
'risk_1': '风险1',
'risk_2': '风险2',
'risk_3': '风险3',
'risk_4': '风险4',
'risk_5': '风险5',
'risk_6': '风险6',
'risk_7': '风险7',
'risk_8': '风险8',
'risk_9': '风险9',
'20201': '风险2比较基准',
'20202': '风险3比较基准',
'20203': '风险4比较基准',
'20204': '风险5比较基准',
'20205': '风险6比较基准',
'20206': '风险7比较基准',
'20207': '风险8比较基准',
'20208': '风险9比较基准',
'120000016': '风险10比较基准',
'120000010': '风险1比较基准'
})
cols = [
'风险1', '风险2', '风险3', '风险4', '风险5', '风险6', '风险7', '风险8', '风险9', '风险10',
'风险1比较基准', '风险2比较基准', '风险3比较基准', '风险4比较基准', '风险5比较基准', '风险6比较基准',
'风险7比较基准', '风险8比较基准', '风险9比较基准', '风险10比较基准'
]
df = df[cols]
result_df = pd.DataFrame(columns=df.columns)
last_day = df.index[-1]
print(last_day)
first_day = df.index[0]
print(first_day)
#result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 当日'] = df.pct_change().iloc[-1]
#result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去一周'] = df.loc[last_day] / df.loc[last_day - timedelta(weeks = 1)] - 1
#result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去一月'] = df.loc[last_day] / df.loc[last_day - timedelta(days = 31)] - 1
#result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去三个月'] = df.loc[last_day] / df.loc[last_day - timedelta(days = 91)] - 1
#result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去六个月'] = df.loc[last_day] / df.loc[last_day - timedelta(days = 182)] - 1
#result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去一年'] = df.loc[last_day] / df.loc[last_day - timedelta(days = 365)] - 1
# 非滚动战胜概率
length = len(df) // 7
# length = len(df)//31
# length = len(df)//91
# length = len(df)//182
# length = len(df)//365
k1 = 0
k2 = 0
k3 = 0
k4 = 0
k5 = 0
k6 = 0
k7 = 0
k8 = 0
k9 = 0
k10 = 0
days = df.index.values
for i in range(length):
num = -1 - i * 7
# num = -1 - i*31
# num = -1 - i*91
# num = -1 - i*182
# num = -1 - i*365
last_day = pd.to_datetime(str(days[num]))
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 过去一周'] = df.loc[last_day] / df.loc[
last_day - timedelta(weeks=1)] - 1
# result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去一月'] = df.loc[last_day] / df.loc[last_day - timedelta(days = 31)] - 1
# result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去三个月'] = df.loc[last_day] / df.loc[last_day - timedelta(days = 91)] - 1
# result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去六个月'] = df.loc[last_day] / df.loc[last_day - timedelta(days = 182)] - 1
# result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去一年'] = df.loc[last_day] / df.loc[last_day - timedelta(days = 365)] - 1
if result_df['风险1'][0] > result_df['风险1比较基准'][0]:
k1 += 1
if result_df['风险2'][0] > result_df['风险2比较基准'][0]:
k2 += 1
if result_df['风险3'][0] > result_df['风险3比较基准'][0]:
k3 += 1
if result_df['风险4'][0] > result_df['风险4比较基准'][0]:
k4 += 1
if result_df['风险5'][0] > result_df['风险5比较基准'][0]:
k5 += 1
if result_df['风险6'][0] > result_df['风险6比较基准'][0]:
k6 += 1
if result_df['风险7'][0] > result_df['风险7比较基准'][0]:
k7 += 1
if result_df['风险8'][0] > result_df['风险8比较基准'][0]:
k8 += 1
if result_df['风险9'][0] > result_df['风险9比较基准'][0]:
k9 += 1
if result_df['风险10'][0] > result_df['风险10比较基准'][0]:
k10 += 1
# 滚动战胜概率
# length = len(df)-7
# length = len(df)//7
length = len(df) // 30
k1 = 0
k2 = 0
k3 = 0
k4 = 0
k5 = 0
k6 = 0
k7 = 0
k8 = 0
k9 = 0
k10 = 0
days = df.index.values
for i in range(length):
# num = -1 - i
# num = -1 - i*7
# num = -1 - i*30
last_day = pd.to_datetime(str(days[num]))
delta_day = last_day - timedelta(days=31)
choose_dates = df[df.index <= delta_day].index.values
if len(choose_dates) == 0:
break
delta_day = pd.to_datetime(str(choose_dates.max()))
# result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去一周'] = df.loc[last_day] / df.loc[delta_day] - 1
result_df.loc[df.index[-1].strftime('%Y-%m-%d') +
' 过去一月'] = df.loc[last_day] / df.loc[delta_day] - 1
# result_df.loc[df.index[-1].strftime('%Y-%m-%d') + ' 过去三个月'] = df.loc[last_day] / df.loc[delta_day] - 1
if result_df['风险1'][0] > result_df['风险1比较基准'][0]:
k1 += 1
if result_df['风险2'][0] > result_df['风险2比较基准'][0]:
k2 += 1
if result_df['风险3'][0] > result_df['风险3比较基准'][0]:
k3 += 1
if result_df['风险4'][0] > result_df['风险4比较基准'][0]:
k4 += 1
if result_df['风险5'][0] > result_df['风险5比较基准'][0]:
k5 += 1
if result_df['风险6'][0] > result_df['风险6比较基准'][0]:
k6 += 1
if result_df['风险7'][0] > result_df['风险7比较基准'][0]:
k7 += 1
if result_df['风险8'][0] > result_df['风险8比较基准'][0]:
k8 += 1
if result_df['风险9'][0] > result_df['风险9比较基准'][0]:
k9 += 1
if result_df['风险10'][0] > result_df['风险10比较基准'][0]:
k10 += 1
r1 = k1 / length
r2 = k2 / length
r3 = k3 / length
r4 = k4 / length
r5 = k5 / length
r6 = k6 / length
r7 = k7 / length
r8 = k8 / length
r9 = k9 / length
r10 = k10 / length
print([r1, r2, r3, r4, r5, r6, r7, r8, r9, r10])