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 )
def getEmpiricalDataFrame( tickerList,
fromDate,
toDate,
csvPath = retrieve.adjustedClosesFilepath() ):
"""Returns a :py:class:`pandas.DataFrame` according to selected stocks and dates
:param tickerList: A list of the tickers to be added into the table
:param fromDate: Time from which to begin the table
:type fromDate: :py:class:`datetime.date`
:param toDate: TIme from which to end the table
:type toDate: :py:class:`datetime.date`
:param csvPath: The name of the file within the Empirical file store
"""
df = pd.read_csv( csvPath, index_col = 0, parse_dates = True, na_values = 'NA' )
tickers = set( tickerList )
extraColumns = [ column for column in df.columns if column not in tickers ]
start = df.index.searchsorted( fromDate )
end = df.index.searchsorted( toDate )
return df[ start:end ].drop( extraColumns, 1 )
columns = columns,
default_fill_value = 0 )
def _getCountRows( args ):
timestamp, aggregator, wordCounter = args
try:
date = timestamp.date()
except AttributeError:
date = timestamp
counts = wordCounter( retrieve.getDailyArticles( date ) )
try:
return ( date, aggregator( counts ) )
except TypeError:
return ( date, counts )
if __name__ == "__main__":
begin = datetime.date( 2011, 1, 3 )
end = datetime.date( 2013, 11, 27 )
keywordsFile = join( settings.KEYWORDS_DIR, 'splist.csv' )
tickerList = keywords.getTickerList( keywordsFile )
keywordsMap = keywords.getKeywordToIndexMap( keywordsFile )
empiricalDf = getEmpiricalDataFrame( tickerList, begin, end, retrieve.adjustedClosesFilepath( filename = 'cleanSP.csv' ) )
countDf = getCountDataFrame( tickerList,
count.WordCounter( keywordsMap ),
empiricalDf.index )
tfidf = normalize.TfIdf()( countDf )
empiricalDf = empiricalDf.ix[ tfidf.index ]
tfidf.corr().to_csv( join( settings.RESULTS_DIR, 'hft_CountCorr.csv' ) )
empiricalDf.corr().to_csv( join( settings.RESULTS_DIR, 'hft_EmpCorr.csv' ) )
# corr.to_csv( join( settings.RESULTS_DIR, 'corrtest_withSent_all.csv' ) )
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
if saveBenchmarks:
beginBench = begin + datetime.timedelta( 20 )
for constrained in [ True, False ]:
minvar = strats.Backtest( empiricalDf, empiricalCov, strats.MinimumVariance( constrained = constrained ), beginBench, end ).run().portfolioValues()
"""
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"))
'''
Created on Mar 12, 2014
@author: curly
'''
import datetime
import afp.matrices as matrices
import afp.keywords as keywords
import afp.settings as settings
import cleaner.retrieve as retrieve
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 = 'hftDaily.csv' )
dailyPrices = matrices.getEmpiricalDataFrame( tickerList, begin, end, csvPath = filepath )
n = dailyPrices.shape[ 0 ]
threshold = .01
removedCols = dailyPrices.ix[ :, ( dailyPrices != dailyPrices ).sum() < threshold * n ]
removedCols.to_csv( os.path.join( os.path.join( settings.RESULTS_DIR, 'cleanSP.csv' ) ) )
