class Indicators():
def __init__(self):
self.DC=DataCollect()
#Clean up the format of indicator to every indicator
def Indic_gen(self,histprice,histindic):
histindic=pd.DataFrame(histindic)
histindic['date']=histprice['date']
histindic.columns=(['indic','date'])
histindic=histindic[['date','indic']]
return(histindic)
def Onedayback(self,histindic):
histindic['date']=histindic['date'].shift(-1)
histindic=histindic.loc[histindic['date'].isnull()==False,:].copy()
return(histindic)
#Calculate the RSI of each index
def RSI(self,ticker):
histprice=self.DC.Hist_price(ticker)
histindic=TA.momentum.rsi(histprice['close'],window=14)
histindic=self.Indic_gen(histprice,histindic,ticker)
histindic['indic']=np.nan
histindic.loc[histindic['indic']>=70,['indic']]=0
histindic.loc[histindic['indic']<=50,['indic']]=1
histindic=histindic.fillna(method='ffill')
return(histindic)
def ADX(self,histprice):
ADX=TA.trend.adx(histprice['high'], histprice['low'], histprice['close'],window=14,fillna=False)
ADX_NEG=TA.trend.adx_neg(histprice['high'], histprice['low'], histprice['close'],window=14,fillna=False)
ADX_POS=TA.trend.adx_pos(histprice['high'], histprice['low'], histprice['close'],window=14,fillna=False)
adx=pd.DataFrame([ADX,ADX_NEG,ADX_POS])
adx=adx.transpose()
adx['date']=histprice['date']
adx['adx_status']='NA'
adx.loc[adx['adx_pos']>adx['adx_neg'],'adx_status']='bull'
adx.loc[adx['adx_pos']<adx['adx_neg'],'adx_status']='bear'
return(adx)
def MACD(self,histprice):
mdff=TA.trend.macd_diff(histprice['close'])
masig=TA.trend.macd_signal(histprice['close'])
macd=pd.DataFrame([mdff,masig])
macd=macd.transpose()
macd['date']=histprice['date']
macd['diff']=macd['mdff']-macd['masig']
return(macd)
def Kama(self,histprice):
histindic=TA.momentum.kama(histprice['close'])
return(histindic)
def Ppo(self,histprice):
histindic=TA.momentum.ppo(histprice['close'])
return(histindic)
def ROC(self,histprice):
histindic=TA.momentum.ppo(histprice['close'])
return(histindic)
@author: wudi
"""
import pandas as pd
import numpy as np
from Toolbox import DataCollect
from Toolbox import WeightScheme
from MSSQL import MSSQL
from Toolbox import ReturnCal
from Toolbox import DataStructuring
from Querybase import Query
from scipy import stats
from itertools import chain
from Quant import Optimize
from HotStock import SecR
DC=DataCollect()
RC=ReturnCal()
DS=DataStructuring()
Q=Query()
WS=WeightScheme()
Opt=Optimize()
SR=SecR()
#rebaldaylist=DC.Rebaldaylist(startdate,rebal_period)
class Prep():
def __init__(self):
pass
#use the query in Querybase to download hitorical signal
def Funda_download(self,startdate,signal):
ms = MSSQL(host="GS-UATVDBSRV01\GSUATSQL",user="******",pwd="SASThom111",db="JYDBBAK")
query=getattr(Q,signal)(startdate)
def __init__(self):
self.DC = DataCollect()
class Indicators():
def __init__(self):
self.DC = DataCollect()
#Clean up the format of indicator to every indicator
def Indic_gen(self, histprice, histindic):
histindic = pd.DataFrame(histindic)
histindic['date'] = histprice['date']
histindic.columns = (['indic', 'date'])
histindic = histindic[['date', 'indic']]
return (histindic)
def Onedayback(self, histindic):
histindic['date'] = histindic['date'].shift(-1)
histindic = histindic.loc[histindic['date'].isnull() ==
False, :].copy()
return (histindic)
def MACD(self, ticker):
histprice = self.DC.Hist_price(ticker)
diff = TA.trend.macd_diff(histprice['close'])
diff = self.Indic_gen(histprice, diff)
diff['MACD_sig'] = np.nan
diff.loc[diff['indic'] > 0, ['MACD_sig']] = 1
diff.loc[diff['indic'] <= 0, ['MACD_sig']] = 0
diff['MACD_sig'] = diff['MACD_sig'].fillna(method='ffill')
diff = self.Onedayback(diff)
return (diff)
def SMA(self, ticker):
histprice = self.DC.Hist_price(ticker)
sma50 = TA.trend.sma_indicator(histprice['close'], window=50)
sma50 = self.Indic_gen(histprice, sma50)
sma50 = sma50.rename(columns={'indic': 'sma50'})
sma100 = TA.trend.sma_indicator(histprice['close'], window=100)
sma100 = self.Indic_gen(histprice, sma100)
sma100 = sma100.rename(columns={'indic': 'sma100'})
sma = pd.merge(sma50,
sma100[['date', 'sma100']],
on='date',
how='left')
sma = sma.loc[sma['sma100'].isnull() == False, :].copy()
sma['diff'] = sma['sma50'] - sma['sma100']
sma['SMA_sig'] = np.nan
sma.loc[sma['diff'] > 0, ['SMA_sig']] = 1
sma.loc[sma['diff'] <= 0, ['SMA_sig']] = 0
sma['diff'] = sma['diff'].fillna(method='ffill')
sma = self.Onedayback(sma)
return (sma)
#Calculate the RSI of each index
def RSI(self, ticker):
histprice = self.DC.Hist_price(ticker)
histindic = TA.momentum.rsi(histprice['close'], window=14)
histindic = self.Indic_gen(histprice, histindic, ticker)
histindic['indic'] = np.nan
histindic.loc[histindic['indic'] >= 70, ['indic']] = 0
histindic.loc[histindic['indic'] <= 50, ['indic']] = 1
histindic = histindic.fillna(method='ffill')
return (histindic)
def ADX(self, histprice):
histindic = TA.trend.adx(histprice['high'],
histprice['low'],
histprice['close'],
window=14,
fillna=False)
return (histindic)
def Kama(self, histprice):
histindic = TA.momentum.kama(histprice['close'])
return (histindic)
def Ppo(self, histprice):
histindic = TA.momentum.ppo(histprice['close'])
return (histindic)
def ROC(self, histprice):
histindic = TA.momentum.ppo(histprice['close'])
return (histindic)
def __init__(self):
self.DC = DataCollect()
self.I = Indicators()
class Analysis():
def __init__(self):
self.DC = DataCollect()
self.I = Indicators()
#combine historical price and RSI of each sector
#return each sector's RSI or ROC on rebalday
#each sector's price (every tradingday)
def Single_backtest(self, ticker, indicname):
histindic = getattr(self.I, indicname)(ticker)
histreturn = self.DC.Hist_return(ticker)
histreturn = pd.merge(histreturn, histindic, on='date', how='left')
histreturn = histreturn.sort_values(by=['date'], ascending=[True])
histreturn['return'] = histreturn['dailyreturn'] * histreturn[indicname
+ '_sig']
histreturn = histreturn.loc[histreturn['return'].isnull() ==
False, :].copy()
PNL = histreturn[['date', 'return']].copy()
PNL['stratReturn'] = np.exp(np.log1p(PNL['return']).cumsum())
return (histindic, PNL)
def Combine(self):
rebaldaylist = DC.Rebaldaylist('2008-12-31', 10)
tickerlist = [
'000951', '000952', '000849', '000928', '000929', '000930',
'000931', '000932', '000933', '000935', '000936', '000937'
]
hist_market = self.I.Hist_price('000985')[['date']]
hist_price = self.I.Hist_price('000985')[['date']]
for ticker in tickerlist:
histindic = RSI(ticker)
hist_market = pd.merge(hist_market,
histindic[['date', ticker]],
on='date',
how='left')
hist_price = pd.merge(hist_price,
histprice[['date', ticker]],
on='date',
how='left')
hist_price = hist_price.sort_values(by=['date'], ascending=[True])
hist_market = hist_market.sort_values(by=['date'], ascending=[True])
hist_market_indic = pd.melt(
hist_market,
id_vars='date',
value_vars=list(hist_market.columns[1:len(hist_market.columns) +
1]),
var_name='ticker',
value_name='indic')
hist_market_indic = hist_market_indic.loc[
hist_market_indic['date'].isin(rebaldaylist), :]
hist_market_indic['indic_rank'] = hist_market_indic.groupby(
"date")["indic"].rank("dense", ascending=False)
hist_market_indic = hist_market_indic.sort_values(by=['date'],
ascending=[True])
return (hist_market_indic, hist_price)
#Backtest the strategy of buying top3 highest value of the indicator (whatever indicator)
def Top3_backtest():
indexdaily = Hist_return()
hist_market_indic, hist_price = Combine()
hist_market_indic = hist_market_indic.loc[
hist_market_indic['indic_rank'] <= 3, ].copy(
) #take the 3sectors with the highest RSI on each rebal day
hist_market_indic = hist_market_indic.pivot_table(index='date',
columns='ticker',
values='indic_rank',
aggfunc='first')
hist_market_indic.reset_index(inplace=True)
hist_market_indic = hist_market_indic.sort_values(by=['date'],
ascending=[True])
hist_market_indic = hist_market_indic.fillna(
0) #Fill na with 0, these are the non top3 sectors on rebal day
hist_market_indic = pd.merge(hist_price[['date']],
hist_market_indic,
on='date',
how='left')
hist_market_indic = hist_market_indic.fillna(method='ffill')
hist_market_indic['date'] = hist_market_indic['date'].shift(-1)
hist_market_indic = hist_market_indic.loc[
hist_market_indic['date'].isnull() == False, :].copy()
hist_market_indic = pd.melt(
hist_market_indic,
id_vars='date',
value_vars=list(
hist_market_indic.columns[1:len(hist_market_indic.columns) +
1]),
var_name='ticker',
value_name='indic_rank')
hist_market_indic = hist_market_indic.loc[
hist_market_indic['indic_rank'] > 0, :].copy()
hist_market_indic = hist_market_indic.sort_values(by=['date'],
ascending=[True])
hist_market_indic[
'index'] = hist_market_indic['date'] + hist_market_indic['ticker']
indexdaily['index'] = indexdaily['date'] + indexdaily['ticker']
dailyreturn_select = indexdaily.loc[
indexdaily['index'].isin(hist_market_indic['index']), :].copy(
) #select the dailyreturn of each period's top3 RSI sectors
dailyreturn_select['dailyreturn'] = dailyreturn_select[
'dailyreturn'].astype(float)
dailyreturn_select['return'] = dailyreturn_select.groupby(
'date')['dailyreturn'].transform('mean')
dailyreturn_select = dailyreturn_select.drop_duplicates(
subset=['date'], keep='last')
dailyreturn_select = dailyreturn_select.sort_values(by=['date'],
ascending=[True])
PNL = dailyreturn_select[['date', 'return']].copy()
PNL['stratReturn'] = np.exp(np.log1p(PNL['return']).cumsum())
return (PNL)
# -*- coding: utf-8 -*-
"""
Created on Thu May 28 11:22:16 2020
@author: wudi
"""
import pandas as pd
import numpy as np
from MSSQL import MSSQL
from Toolbox import DataCollect
from Toolbox import ReturnCal
from Toolbox import WeightScheme
DC = DataCollect()
RC = ReturnCal()
dailyreturn = DC.Dailyreturn_retrieve()
WS = WeightScheme()
class Prep():
def __init__(self):
pass
#use the query in Querybase to download hitorical signal
def Bank_download(self):
ms = MSSQL(host="GS-UATVDBSRV01\GSUATSQL",
user="******",
pwd="SASThom111",
db="JYDBBAK")
siglist = [
'23000', '22800', '55000', '26200', '26000', '30240', '21300'
from FundaStock import Funda as FundaStockFund
from FundaStock import Prep as FundaStockPrep
from Toolbox import DataCollect
from Toolbox import WeightScheme
from HotStock import Prep as HSPrep
from HotStock import Review as HSReview
from HotStock import SecR as SR
from AnalystStock import Top_analyst as ASTA
from AnalystStock import Niu2 as ASNiu2
from AnalystStock import Prep as APrep
from BankFunda import Prep as BankFP
from Toolbox import DataStructuring
from scipy import stats
from Quant import Otho
DC = DataCollect()
FP = FundaStockPrep()
FF = FundaStockFund()
HP = HSPrep()
HR = HSReview()
TA = ASTA()
N = ASNiu2()
AP = APrep()
BFP = BankFP()
WS = WeightScheme()
DS = DataStructuring()
OT = Otho()
SRDaily = SR()
N.Analyst_history()
print("analyst_history_updated")
