def LoadData(self):
ds = YahooStockDataSource(cachedFolderName=cachedFolderName,
dataSetId=dataSetId,
instrumentIds=self.instrumentIds,
startDateStr=startDateStr,
endDateStr=endDateStr,
event='history')
self.stkData = [ds.getBookDataByFeature()['open'],
ds.getBookDataByFeature()['high'],
ds.getBookDataByFeature()['low'],
ds.getBookDataByFeature()['close'],
ds.getBookDataByFeature()['adjClose'],
ds.getBookDataByFeature()['volume']]
tbRSI = tb.RSI(self.stkData[4][self.instrumentIds[0]],14)
tbChaikin = tb.ADOSC(self.stkData[1][self.instrumentIds[0]],
self.stkData[2][self.instrumentIds[0]],
self.stkData[3][self.instrumentIds[0]],
self.stkData[5][self.instrumentIds[0]])
'''
Task1
Create new dataframe here with 5 columns
column 1 : Date of stock price
column 2 : Day of the week (Monday Tuesday etc)
Columnn 3 : tbRSI
Column 4 : tbChaikin
column 5 : Stock's adjusted price.
Index : column1
'''
dfdata = {'Adj_Close':self.stkData[4][self.instrumentIds[0]],
'RSI':tbRSI,
'Chaikin':tbChaikin}
df = pd.DataFrame(dfdata)
df['Trade_Date'] = pd.to_datetime(df.index)
df['day_of_week'] = df['Trade_Date'].dt.day_name()
dfFinal = df[["Trade_Date", "day_of_week", "RSI", "Chaikin", "Adj_Close"]]
# dfFinal2 = df[["Trade_Date", "day_of_week", "RSI", "Chaikin", "Adj_Close"]]
#df_rsi = pd.DataFrame(dfFinal['RSI'])
#print(df_rsi)
#df_rsi.to_csv('yahooData/weeklyTradeTest/RSI_Value.csv')
'''
Task 2
filter above dataframe using column 2 value (e.g. 'Wednesday') and store in other dataframe
'''
# dfFinal2 = dfFinal.loc[df['day_of_week'] == 'Wednesday']
# dfFinal.to_csv(dir_path)
# print(dfFinal.head(50)
# dfFinal = dfFinal.loc[df['day_of_week'] == 'Friday']
dfFinal.to_csv(dir_path)
print(dfFinal.head(50))
startDateStr = '2007/12/01'
endDateStr = '2017/12/01'
cachedFolderName = 'yahooData/'
dataSetId = 'testPairsTrading'
instrumentIds = [
'SPY', 'AAPL', 'ADBE', 'T', 'EBAY', 'MSFT', 'QCOM', 'HPQ', 'JNPR', 'AMD',
'IBM'
]
ds = YahooStockDataSource(cachedFolderName=cachedFolderName,
dataSetId=dataSetId,
instrumentIds=instrumentIds,
startDateStr=startDateStr,
endDateStr=endDateStr,
event='history')
data = ds.getBookDataByFeature()['adjClose']
# Heatmap to show the p-values of the cointegration test
# between each pair of stocks
scores, pvalues, pairs = find_cointegrated_pairs(data)
import seaborn
m = [0, 0.2, 0.4, 0.6, 0.8, 1]
seaborn.heatmap(pvalues,
xticklabels=instrumentIds,
yticklabels=instrumentIds,
cmap='RdYlGn_r',
mask=(pvalues >= 0.98))
plt.show()
print(pairs)