def printSampleDataFrame(ticker):
start_date="2009-01-01"
end_date="2009-01-03"
options={'qtype':'adjclose',
'tables':['intStocks','itradingDays']}
tickerDataRaw=getRawData(ticker,start_date,end_date,options)
print(tickerDataRaw.head())
#print("\n Size => " + tickerDataRaw.size)
for idx, row in tickerDataRaw.iteritems():
print(idx)
def printFeatureData(ticker):
options = {'qtype':'adjclose',
'tables':["intStocks","itradingDays"],
'freq':0, # The frequency of trading, daily=0, monthly=1,weekly=2
'offset':1, # The offset if the period > 1day, ie which trading day in the month/week the strategy will be executed
'pure':0, # from here we have the features , the returns as is
'cal':1, # Calendar features
'history':0, # last 3 periods returns
'momentum':1, # momentum features
'jump':0, # jump features
'value':0, # long term reversal features
'prevWeeks':1,# Now by turning this to 1 we can run a model which includes previous weeks
'algo':KNeighborsRegressor,
'algo_params':{'n_neighbors':5}
}
supportTickers= None
# [("BANKNIFTY",{'pure':0,'momentum':1,'jump':0,'prevWeeks':0})]
trainStart="2006-06-01"
#testPeriod=["2013-06-01","2016-04-01"]
testPeriod=["2017-05-01", "2017-05-22",]
start_date = "2017-05-01"
end_date = "2017-05-22"
algo = options['algo']
algo_params = options['algo_params']
buffer = 0
tickerDataRaw = getRawData(ticker, start_date, end_date, options, buffer)
tickerDataRaw["Return"] = getReturn(tickerDataRaw["Price"])
print()
printTickerDataFrame(tickerDataRaw)
testData = getFeatures(ticker, testPeriod[0], testPeriod[1], options, supportTickers)[0]
print(testData.head())
pure = tickerDataRaw["Price"][1:]
pureIndex = tickerDataRaw.index[0:-1]
pure.index = pureIndex
print("pure => " + str(pure.tolist()))
print("pureIndex => " + str(pureIndex.tolist()))
print(pure.head(2))
print(pure.tail(2))
print("testData count => " + str(len(testData.index)))
print("Done")
return testData
import plotly.plotly as py
import plotly.graph_objs as go
import pandas_datareader.data as web
from datetime import datetime
from fetchData import getRawData
#df = web.DataReader("^vix", 'yahoo', datetime(2007, 10, 1), datetime(2009, 4, 1))
ticker, start_date, end_date = "VIX", datetime(2007, 10,
1), datetime(2009, 4, 1)
df = getRawData(ticker, start_date, end_date)
print(df.head())
trace = go.Candlestick(x=df.index,
open=df.Open,
high=df.High,
low=df.Low,
close=df.AdjClose)
data = [trace]
py.iplot(data, filename='simple_candlestick')
ticker = "NIFTY"
start_date = "2009-01-01"
end_date = "2010-01-01"
options = {'qtype': 'close', 'tables': ['cm_adjPrice', 'tradingDays']}
from fetchData import getRawData
tickerDataRaw = getRawData(ticker, start_date, end_date, options)
print tickerDataRaw.head()
ticker = "AAPL"
start_date="2015-01-01"
END_date="2017-02-17"
options={
'qtype':'close',
'tables':['adjPrice','tradingdate']
}
from fetchData import getRawData
ticketDataRaw=getRawData(ticker,start_date,end_date,options)
print(ticketDataRaw.head())