def get_vol( self, com1, com2, date1, date2 ):
begin = datetime.strptime( date1, '%Y-%m-%d' )
end = datetime.strptime( date2, '%Y-%m-%d' )
filepath = retrieve.adjustedClosesFilepath( filename = 'cleanSP.csv' )
dailyPrices = matrices.getEmpiricalDataFrame( [ com1, com2 ], begin, end, csvPath = filepath )
p1 = dailyPrices[ com1 ] # ystockquote.get_historical_prices( [com1], '20' + date1, '20' + date2 )
p2 = dailyPrices[ com2 ] # ystockquote.get_historical_prices( [com2], '20' + date1, '20' + date2 )
n1 = p1 / p1.iat[ 0 ]
n2 = p2 / p2.iat[ 0 ]
diff = n1 - n2
return numpy.std( diff, axis = 0 )
import afp.settings as settings
import afp.strategies as strats
import cleaner.retrieve as retrieve
import numpy as np
import pandas as pd
import datetime
import os
if __name__ == '__main__':
begin = datetime.date( 2011, 1, 3 )
end = datetime.date( 2013, 11, 27 )
tickerList = keywords.getTickerList()
keywordsMap = keywords.getKeywordToIndexMap()
sentCounter = count.SentimentWordCounter( keywordsMap, sentiment.classifier() )
mentionCounter = count.WordCounter( keywordsMap )
empiricalDf = matrices.getEmpiricalDataFrame( tickerList, begin, end )
constrained = False
minVarBenchmark = { True : 'minvarConstrained.csv', False : 'minvarAnalytical.csv' }
maxDivBenchmark = { True : 'maxDivConstrained.csv', False : 'maxDivAnalytical.csv' }
minvarBenchmarkDf = matrices.getEmpiricalDataFrame( [ strats.MinimumVariance().getName() ], begin, end, retrieve.adjustedClosesFilepath( filename = minVarBenchmark[ constrained ] ) )
maxDivBenchmarkDf = matrices.getEmpiricalDataFrame( [ strats.MaximumDiversification().getName() ], begin, end, retrieve.adjustedClosesFilepath( filename = maxDivBenchmark[ constrained ] ) )
riskParityDf = matrices.getEmpiricalDataFrame( [ strats.RiskParity().getName() ], begin, end, retrieve.adjustedClosesFilepath( filename = 'riskParity.csv' ) )
benchmarkDf = matrices.getEmpiricalDataFrame( [ 'OEF', 'SPY' ], begin, end, retrieve.benchmarkFilepath() )
summedSentDf = matrices.getCountDataFrame( tickerList, sentCounter, empiricalDf.index, aggregator = np.sum )
articleSentDf = matrices.getCountDataFrame( tickerList, sentCounter, empiricalDf.index )
summedMentionDf = matrices.getCountDataFrame( tickerList, mentionCounter, empiricalDf.index, aggregator = np.sum )
articleMentionDf = matrices.getCountDataFrame( tickerList, mentionCounter, empiricalDf.index )
empiricalDf = empiricalDf.ix[:, summedMentionDf.columns ]
empiricalCov = strats.EmpiricalCovariance( empiricalDf )
saveBenchmarks = False
"""
Created on Mar 13, 2014
@author: curly
"""
import afp.keywords as keywords
import afp.matrices as matrices
import afp.settings as settings
import cleaner.retrieve as retrieve
import pandas as pd
import datetime
import os
if __name__ == "__main__":
begin = datetime.date(2011, 1, 3)
end = datetime.date(2013, 11, 27)
tickerList = keywords.getTickerList(os.path.join(settings.KEYWORDS_DIR, "splist.csv"))
filepath = retrieve.adjustedClosesFilepath(filename="cleanSP.csv")
dailyPrices = matrices.getEmpiricalDataFrame(tickerList, begin, end, csvPath=filepath)
normalizedPrices = dailyPrices / dailyPrices.ix[0]
pairs = dict(
(first + "|" + second, sum((normalizedPrices[first] - normalizedPrices[second]) ** 2))
for first in normalizedPrices.columns
for second in normalizedPrices.columns
if first < second
)
pairDf = pd.DataFrame(pairs, index=["Pairs"]).T
pairDf.to_csv(os.path.join(settings.RESULTS_DIR, "leastSqPairs.csv"))
