def findEvents(symbols, startday,endday,verbose=False): timeofday=dt.timedelta(hours=16) timestamps = du.getNYSEdays(startday,endday,timeofday) dataobj = da.DataAccess('Norgate') if verbose: print __name__ + " reading data" adjclose = dataobj.get_data(timestamps, symbols, closefield) adjclose = (adjclose.fillna()).fillna(method='backfill') if verbose: print __name__ + " finding events" # for symbol in symbols: # close[symbol][close[symbol]>= 1.0] = np.NAN # for i in range(1,len(close[symbol])): # if np.isnan(close[symbol][i-1]) and close[symbol][i] < 1.0 :#(i-1)th was > $1, and (i)th is <$1 # close[symbol][i] = 1.0 #overwriting the price by the bit # close[symbol][close[symbol]< 1.0] = np.NAN #print adjclose # Get the 20 day moving avg and moving stddev movavg = pa.rolling_mean(adjclose,20,min_periods=20) movstddev = pa.rolling_std(adjclose, 20, min_periods=20) # Compute the upper and lower bollinger bands upperband = movavg + 2*movstddev lowerband = movavg - 2*movstddev #bandwidth = (upperband - lowerband)/movavg #print bandwidth #print upperband # Compute the event matrix as follows: # Set periods of low volatility to 1 # In from the period of low volatility to the period of say, 15 days, following low volatility # if the stock price breaks above the upper band, there is a surge. this is a positive event. Set this event to 2 # Finally, set all events other than 2 to NaN. Then, set all 2's to 1 lookaheadperiod = 15 eventMatrix = adjclose.copy() for symbol in symbols: for row in range(len(adjclose[:][symbol])): eventMatrix[symbol][row] = np.NAN if upperband[symbol][row] > 0 and lowerband[symbol][row] > 0 and movavg[symbol][row] > 0: if (upperband[symbol][row] - lowerband[symbol][row])/movavg[symbol][row] < 0.10: eventMatrix[symbol][row] = 1 else: currow = row - 1 numOnes = 0 while currow > row - lookaheadperiod and currow >= 0: if eventMatrix[symbol][currow] != 1: break if eventMatrix[symbol][currow] == 1 and adjclose[symbol][row] > upperband[symbol][row]: numOnes = numOnes + 1 currow = currow - 1 if numOnes >= 5: eventMatrix[symbol][row] = 2 eventMatrix[symbol][eventMatrix[symbol]!= 2] = np.NAN eventMatrix[symbol][eventMatrix[symbol]== 2] = 1 return eventMatrix
def log500( sLog ): ''' @summary: Loads cached features. @param sLog: Filename of features. @return: Nothing, logs features to desired location ''' lsSym = ['A', 'AA', 'AAPL', 'ABC', 'ABT', 'ACE', 'ACN', 'ADBE', 'ADI', 'ADM', 'ADP', 'ADSK', 'AEE', 'AEP', 'AES', 'AET', 'AFL', 'AGN', 'AIG', 'AIV', 'AIZ', 'AKAM', 'AKS', 'ALL', 'ALTR', 'AMAT', 'AMD', 'AMGN', 'AMP', 'AMT', 'AMZN', 'AN', 'ANF', 'ANR', 'AON', 'APA', 'APC', 'APD', 'APH', 'APOL', 'ARG', 'ATI', 'AVB', 'AVP', 'AVY', 'AXP', 'AZO', 'BA', 'BAC', 'BAX', 'BBBY', 'BBT', 'BBY', 'BCR', 'BDX', 'BEN', 'BF.B', 'BHI', 'BIG', 'BIIB', 'BK', 'BLK', 'BLL', 'BMC', 'BMS', 'BMY', 'BRCM', 'BRK.B', 'BSX', 'BTU', 'BXP', 'C', 'CA', 'CAG', 'CAH', 'CAM', 'CAT', 'CB', 'CBG', 'CBS', 'CCE', 'CCL', 'CEG', 'CELG', 'CERN', 'CF', 'CFN', 'CHK', 'CHRW', 'CI', 'CINF', 'CL', 'CLF', 'CLX', 'CMA', 'CMCSA', 'CME', 'CMG', 'CMI', 'CMS', 'CNP', 'CNX', 'COF', 'COG', 'COH', 'COL', 'COP', 'COST', 'COV', 'CPB', 'CPWR', 'CRM', 'CSC', 'CSCO', 'CSX', 'CTAS', 'CTL', 'CTSH', 'CTXS', 'CVC', 'CVH', 'CVS', 'CVX', 'D', 'DD', 'DE', 'DELL', 'DF', 'DFS', 'DGX', 'DHI', 'DHR', 'DIS', 'DISCA', 'DNB', 'DNR', 'DO', 'DOV', 'DOW', 'DPS', 'DRI', 'DTE', 'DTV', 'DUK', 'DV', 'DVA', 'DVN', 'EBAY', 'ECL', 'ED', 'EFX', 'EIX', 'EL', 'EMC', 'EMN', 'EMR', 'EOG', 'EP', 'EQR', 'EQT', 'ERTS', 'ESRX', 'ETFC', 'ETN', 'ETR', 'EW', 'EXC', 'EXPD', 'EXPE', 'F', 'FAST', 'FCX', 'FDO', 'FDX', 'FE', 'FFIV', 'FHN', 'FII', 'FIS', 'FISV', 'FITB', 'FLIR', 'FLR', 'FLS', 'FMC', 'FO', 'FRX', 'FSLR', 'FTI', 'FTR', 'GAS', 'GCI', 'GD', 'GE', 'GILD', 'GIS', 'GLW', 'GME', 'GNW', 'GOOG', 'GPC', 'GPS', 'GR', 'GS', 'GT', 'GWW', 'HAL', 'HAR', 'HAS', 'HBAN', 'HCBK', 'HCN', 'HCP', 'HD', 'HES', 'HIG', 'HNZ', 'HOG', 'HON', 'HOT', 'HP', 'HPQ', 'HRB', 'HRL', 'HRS', 'HSP', 'HST', 'HSY', 'HUM', 'IBM', 'ICE', 'IFF', 'IGT', 'INTC', 'INTU', 'IP', 'IPG', 'IR', 'IRM', 'ISRG', 'ITT', 'ITW', 'IVZ', 'JBL', 'JCI', 'JCP', 'JDSU', 'JEC', 'JNJ', 'JNPR', 'JNS', 'JOYG', 'JPM', 'JWN', 'K', 'KEY', 'KFT', 'KIM', 'KLAC', 'KMB', 'KMX', 'KO', 'KR', 'KSS', 'L', 'LEG', 'LEN', 'LH', 'LIFE', 'LLL', 'LLTC', 'LLY', 'LM', 'LMT', 'LNC', 'LO', 'LOW', 'LSI', 'LTD', 'LUK', 'LUV', 'LXK', 'M', 'MA', 'MAR', 'MAS', 'MAT', 'MCD', 'MCHP', 'MCK', 'MCO', 'MDT', 'MET', 'MHP', 'MHS', 'MJN', 'MKC', 'MMC', 'MMI', 'MMM', 'MO', 'MOLX', 'MON', 'MOS', 'MPC', 'MRK', 'MRO', 'MS', 'MSFT', 'MSI', 'MTB', 'MU', 'MUR', 'MWV', 'MWW', 'MYL', 'NBL', 'NBR', 'NDAQ', 'NE', 'NEE', 'NEM', 'NFLX', 'NFX', 'NI', 'NKE', 'NOC', 'NOV', 'NRG', 'NSC', 'NTAP', 'NTRS', 'NU', 'NUE', 'NVDA', 'NVLS', 'NWL', 'NWSA', 'NYX', 'OI', 'OKE', 'OMC', 'ORCL', 'ORLY', 'OXY', 'PAYX', 'PBCT', 'PBI', 'PCAR', 'PCG', 'PCL', 'PCLN', 'PCP', 'PCS', 'PDCO', 'PEG', 'PEP', 'PFE', 'PFG', 'PG', 'PGN', 'PGR', 'PH', 'PHM', 'PKI', 'PLD', 'PLL', 'PM', 'PNC', 'PNW', 'POM', 'PPG', 'PPL', 'PRU', 'PSA', 'PWR', 'PX', 'PXD', 'QCOM', 'QEP', 'R', 'RAI', 'RDC', 'RF', 'RHI', 'RHT', 'RL', 'ROK', 'ROP', 'ROST', 'RRC', 'RRD', 'RSG', 'RTN', 'S', 'SAI', 'SBUX', 'SCG', 'SCHW', 'SE', 'SEE', 'SHLD', 'SHW', 'SIAL', 'SJM', 'SLB', 'SLE', 'SLM', 'SNA', 'SNDK', 'SNI', 'SO', 'SPG', 'SPLS', 'SRCL', 'SRE', 'STI', 'STJ', 'STT', 'STZ', 'SUN', 'SVU', 'SWK', 'SWN', 'SWY', 'SYK', 'SYMC', 'SYY', 'T', 'TAP', 'TDC', 'TE', 'TEG', 'TEL', 'TER', 'TGT', 'THC', 'TIE', 'TIF', 'TJX', 'TLAB', 'TMK', 'TMO', 'TROW', 'TRV', 'TSN', 'TSO', 'TSS', 'TWC', 'TWX', 'TXN', 'TXT', 'TYC', 'UNH', 'UNM', 'UNP', 'UPS', 'URBN', 'USB', 'UTX', 'V', 'VAR', 'VFC', 'VIA.B', 'VLO', 'VMC', 'VNO', 'VRSN', 'VTR', 'VZ', 'WAG', 'WAT', 'WDC', 'WEC', 'WFC', 'WFM', 'WFR', 'WHR', 'WIN', 'WLP', 'WM', 'WMB', 'WMT', 'WPI', 'WPO', 'WU', 'WY', 'WYN', 'WYNN', 'X', 'XEL', 'XL', 'XLNX', 'XOM', 'XRAY', 'XRX', 'YHOO', 'YUM', 'ZION', 'ZMH'] lsSym.append('SPY') lsSym.sort() ''' Max lookback is 6 months ''' dtEnd = dt.datetime.now() dtEnd = dtEnd.replace(hour=16, minute=0, second=0, microsecond=0) dtStart = dtEnd - relativedelta(months=6) ''' Pull in current data ''' norObj = da.DataAccess('Norgate') ''' Get 2 extra months for moving averages and future returns ''' ldtTimestamps = du.getNYSEdays( dtStart - relativedelta(months=2), \ dtEnd + relativedelta(months=2), dt.timedelta(hours=16) ) dfPrice = norObj.get_data( ldtTimestamps, lsSym, 'close' ) dfVolume = norObj.get_data( ldtTimestamps, lsSym, 'volume' ) ''' Imported functions from qstkfeat.features, NOTE: last function is classification ''' lfcFeatures, ldArgs, lsNames = getFeatureFuncs() ''' Generate a list of DataFrames, one for each feature, with the same index/column structure as price data ''' applyFeatures( dfPrice, dfVolume, lfcFeatures, ldArgs, sLog=sLog )
def findEvents(symbols, startday,endday,verbose=False): timeofday=dt.timedelta(hours=16) timestamps = du.getNYSEdays(startday,endday,timeofday) dataobj = da.DataAccess('Norgate') if verbose: print __name__ + " reading data" adjclose = dataobj.get_data(timestamps, symbols, closefield) adjclose = (adjclose.fillna()).fillna(method='backfill') adjcloseSPY = dataobj.get_data(timestamps, ['SPY'], closefield) adjcloseSPY = (adjcloseSPY.fillna()).fillna(method='backfill') if verbose: print __name__ + " finding events" # for symbol in symbols: # close[symbol][close[symbol]>= 1.0] = np.NAN # for i in range(1,len(close[symbol])): # if np.isnan(close[symbol][i-1]) and close[symbol][i] < 1.0 :#(i-1)th was > $1, and (i)th is <$1 # close[symbol][i] = 1.0 #overwriting the price by the bit # close[symbol][close[symbol]< 1.0] = np.NAN #print adjclose # Get the 20 day moving avg and moving stddev movavg = pa.rolling_mean(adjclose,20,min_periods=20) movavgSPY = pa.rolling_mean(adjcloseSPY,20,min_periods=20) movstddev = pa.rolling_std(adjclose, 20, min_periods=20) movstddevSPY = pa.rolling_std(adjcloseSPY, 20, min_periods=20) upperband = movavg + 2*movstddev upperbandSPY = movavgSPY + 2*movstddevSPY lowerband = movavg - 2*movstddev lowerbandSPY = movavgSPY - 2*movstddevSPY # Compute the bollinger %b indicator for all stocks normalizedindicator = 2*(adjclose - movavg)/(upperband - lowerband) #print normalizedindicator normalizedindicatorSPY = 2*(adjcloseSPY - movavgSPY)/(upperbandSPY - lowerbandSPY) #print normalizedindicatorSPY #bandwidth = (upperband - lowerband)/movavg #print bandwidth #print upperband # Compute the event matrix as follows: # Set periods of low volatility to 1 # In from the period of low volatility to the period of say, 15 days, following low volatility # if the stock price breaks above the upper band, there is a surge. this is a positive event. Set this event to 2 # Finally, set all events other than 2 to NaN. Then, set all 2's to 1 eventMatrix = adjclose.copy() for symbol in symbols: for row in range(len(adjclose[:][symbol])): eventMatrix[symbol][row] = np.NAN if normalizedindicator[symbol][row] - normalizedindicatorSPY['SPY'][row] >= 0.5: eventMatrix[symbol][row] = 1 return eventMatrix
def findEvents(symbols, startday,endday,verbose=False): timeofday=dt.timedelta(hours=16) timestamps = du.getNYSEdays(startday,endday,timeofday) dataobj = da.DataAccess('Norgate') if verbose: print __name__ + " reading data" close = dataobj.get_data(timestamps, symbols, closefield) close = (close.fillna()).fillna(method='backfill') if verbose: print __name__ + " finding events" for symbol in symbols: close[symbol][close[symbol]>= 1.0] = np.NAN for i in range(1,len(close[symbol])): if np.isnan(close[symbol][i-1]) and close[symbol][i] < 1.0 :#(i-1)th was > $1, and (i)th is <$1 close[symbol][i] = 1.0 #overwriting the price by the bit close[symbol][close[symbol]< 1.0] = np.NAN return close
def findEvents(symbols, startday, endday, verbose=False): timeofday = dt.timedelta(hours=16) timestamps = du.getNYSEdays(startday, endday, timeofday) dataobj = da.DataAccess('Norgate') if verbose: print __name__ + " reading data" close = dataobj.get_data(timestamps, symbols, closefield) close = (close.fillna()).fillna(method='backfill') if verbose: print __name__ + " finding events" for symbol in symbols: close[symbol][close[symbol] >= 1.0] = np.NAN for i in range(1, len(close[symbol])): if np.isnan( close[symbol][i - 1] ) and close[symbol][i] < 1.0: #(i-1)th was > $1, and (i)th is <$1 close[symbol][i] = 1.0 #overwriting the price by the bit close[symbol][close[symbol] < 1.0] = np.NAN return close
def __init__(self,eventMatrix,startday,endday,\ lookback_days = 20, lookforward_days =20,\ verbose=False): """ Event Profiler class construtor Parameters : evenMatrix : startday : endday (optional) : lookback_days ( default = 20) (optional) : lookforward_days( default = 20) eventMatrix is a pandas DataMatrix eventMatrix must have the following structure: |IBM |GOOG|XOM |MSFT| GS | JP | (d1)|nan |nan | 1 |nan |nan | 1 | (d2)|nan | 1 |nan |nan |nan |nan | (d3)| 1 |nan | 1 |nan | 1 |nan | (d4)|nan | 1 |nan | 1 |nan |nan | ................................... ................................... Also, d1 = start date nan = no information about any event. = status bit(positively confirms the event occurence) """ self.eventMatrix = eventMatrix self.startday = startday self.endday = endday self.symbols = eventMatrix.columns self.lookback_days = lookback_days self.lookforward_days = lookforward_days self.total_days = lookback_days + lookforward_days + 1 self.dataobj = da.DataAccess('Norgate') self.timeofday = dt.timedelta(hours=16) self.timestamps = du.getNYSEdays(startday,endday,self.timeofday) self.verbose = verbose if verbose: print __name__ + " reading historical data" self.close = self.dataobj.get_data(self.timestamps,\ self.symbols, "close", verbose=self.verbose) self.close = (self.close.fillna()).fillna(method='backfill')
if __name__ == '__main__': ''' Use Dow 30 ''' lsSym = ['AA', 'AXP', 'BA', 'BAC', 'CAT', 'CSCO', 'CVX', 'DD', 'DIS', 'GE', 'HD', 'HPQ', 'IBM', 'INTC', 'JNJ', \ 'JPM', 'KFT', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'PFE', 'PG', 'T', 'TRV', 'UTX', 'WMT', 'XOM' ] #lsSym = ['XOM'] ''' Get data for 2009-2010 ''' dtStart = dt.datetime(2010,8,01) dtEnd = dt.datetime(2010,12,31) norObj = da.DataAccess('Norgate') ldtTimestamps = du.getNYSEdays( dtStart, dtEnd, dt.timedelta(hours=16) ) dfPrice = norObj.get_data( ldtTimestamps, lsSym, 'close' ) dfVolume = norObj.get_data( ldtTimestamps, lsSym, 'volume' ) ''' Imported functions from qstkfeat.features, NOTE: last function is classification ''' lfcFeatures = [ featMA, featRSI, classFutRet ] ''' Default Arguments ''' #ldArgs = [{}] * len(lfcFeatures) ''' Custom Arguments ''' ldArgs = [ {'lLookback':30, 'bRel':True},\ {},\ {}]
def main(): # symbols = np.loadtxt('./Examples/Features/symbols.txt',dtype='S10',comments='#') symbols = [ "AA", "AXP", "BA", "BAC", "CAT", "CSCO", "CVX", "DD", "DIS", "GE", "HD", "HPQ", "IBM", "INTC", "JNJ", "JPM", "KFT", "KO", "MCD", "MMM", "MRK", "MSFT", "PFE", "PG", "T", "TRV", "UTX", "VZ", "WMT", "XOM", ] # symbols = ['XOM'] # This is the start and end dates for the entire train and test data combined alldatastartday = dt.datetime(2007, 1, 1) alldataendday = dt.datetime(2010, 6, 30) timeofday = dt.timedelta(hours=16) timestamps = du.getNYSEdays(alldatastartday, alldataendday, timeofday) dataobj = da.DataAccess("Norgate") voldata = dataobj.get_data(timestamps, symbols, "volume", verbose=True) voldata = (voldata.fillna()).fillna(method="backfill") close = dataobj.get_data(timestamps, symbols, "close", verbose=True) close = (close.fillna()).fillna(method="backfill") featureList = [ featMA, featMA, featRSI, featRSI, featDrawDown, featRunUp, featVolumeDelta, featVolumeDelta, featAroon, classFutRet, ] featureListArgs = [ {"lLookback": 10, "bRel": True}, {"lLookback": 20}, {"lLookback": 10}, {"lLookback": 20}, {}, {}, {"lLookback": 10}, {"lLookback": 20}, {"bDown": False}, {"lLookforward": 5}, ] # print 'Applying Features' # # John Cornwell's featuretest.py was consulted for figuring out the syntax of ftu.applyFeatures() methods and ftu.stackSyms() methods # allfeatureValues = ftu.applyFeatures(close, voldata, featureList, featureListArgs) trainstartday = dt.datetime(2007, 1, 1) trainendday = dt.datetime(2009, 12, 31) traintimestamps = du.getNYSEdays(trainstartday, trainendday, timeofday) # print 'Stack Syms for Training' trainingData = ftu.stackSyms(allfeatureValues, traintimestamps[0], traintimestamps[-1]) # print 'Norm Features for Training' scaleshiftvalues = ftu.normFeatures(trainingData, -1.0, 1.0, False) teststartday = dt.datetime(2010, 1, 1) testendday = dt.datetime(2010, 6, 30) testtimestamps = du.getNYSEdays(teststartday, testendday, timeofday) # print 'Stack Syms for Test' testData = ftu.stackSyms(allfeatureValues, testtimestamps[0], testtimestamps[-1]) # print 'Norm Features for Test' ftu.normQuery(testData[:, :-1], scaleshiftvalues) NUMFEATURES = 9 bestFeatureIndices = [] bestCorrelation = 0.0 fid = open("output.txt", "w") for iteration in range(NUMFEATURES): nextFeatureIndexToAdd = -1 for featureIndex in range(NUMFEATURES): if featureIndex not in bestFeatureIndices: bestFeatureIndices.append(featureIndex) fid.write("testing feature set " + str(bestFeatureIndices) + "\n") print("testing feature set " + str(bestFeatureIndices)) bestFeatureIndices.append(9) curTrainingData = trainingData[:, bestFeatureIndices] curTestData = testData[:, bestFeatureIndices] bestFeatureIndices.remove(9) kdtlearner = knn.kdtknn(5, "mean", leafsize=100) kdtlearner.addEvidence(curTrainingData[:, :-1], curTrainingData[:, -1]) testEstimatedValues = kdtlearner.query(curTestData[:, :-1]) testcorrelation = np.corrcoef(testEstimatedValues.T, curTestData[:, -1].T) curCorrelation = testcorrelation[0, 1] fid.write("corr coef = %.4f\n" % (curCorrelation)) print("corr coef = %.4f" % (curCorrelation)) if curCorrelation > bestCorrelation: nextFeatureIndexToAdd = featureIndex bestCorrelation = curCorrelation bestFeatureIndices.remove(featureIndex) if nextFeatureIndexToAdd >= 0: bestFeatureIndices.append(nextFeatureIndexToAdd) else: break fid.write("best feature set is " + str(bestFeatureIndices) + "\n") print("best feature set is " + str(bestFeatureIndices)) fid.write("corr coef = %.4f" % (bestCorrelation) + "\n") print("corr coef = %.4f" % (bestCorrelation)) fid.close()
def getRandPort( lNum, dtStart=None, dtEnd=None, lsStocks=None, dmPrice=None, dmVolume=None, bFilter=True, fNonNan=0.95, fPriceVolume=100 * 1000, lSeed=None, ): """ @summary Returns a random portfolio based on certain criteria. @param lNum: Number of stocks to be included @param dtStart: Start date for portfolio @param dtEnd: End date for portfolio @param lsStocks: Optional list of ticker symbols, if not provided all symbols will be used @param bFilter: If False, stocks are not filtered by price or volume data, simply return random Portfolio. @param dmPrice: Optional price data, if not provided, data access will be queried @param dmVolume: Optional volume data, if not provided, data access will be queried @param fNonNan: Optional non-nan percent for filter, default is .95 @param fPriceVolume: Optional price*volume for filter, default is 100,000 @warning: Does not work for all sets of optional inputs, e.g. if you don't include dtStart, dtEnd, you need to include dmPrice/dmVolume @return list of stocks which meet the criteria """ if lsStocks is None: if dmPrice is None and dmVolume is None: norObj = da.DataAccess("Norgate") lsStocks = norObj.get_all_symbols() elif not dmPrice is None: lsStocks = list(dmPrice.columns) else: lsStocks = list(dmVolume.columns) if dmPrice is None and dmVolume is None and bFilter == True: norObj = da.DataAccess("Norgate") ldtTimestamps = du.getNYSEdays(dtStart, dtEnd, dt.timedelta(hours=16)) """ if dmPrice and dmVol are provided then we don't query it every time """ bPullPrice = False bPullVol = False if dmPrice is None: bPullPrice = True if dmVolume is None: bPullVol = True """ Default seed (none) uses system clock """ rand.seed(lSeed) lsRetStocks = [] """ Loop until we have enough randomly selected stocks """ llRemainingIndexes = range(0, len(lsStocks)) lsValid = None while len(lsRetStocks) != lNum: lsCheckStocks = [] for i in range(lNum - len(lsRetStocks)): lRemaining = len(llRemainingIndexes) if lRemaining == 0: print "Error in getRandPort: ran out of stocks" return lsRetStocks """ Pick a stock and remove it from the list of remaining stocks """ lPicked = rand.randint(0, lRemaining - 1) lsCheckStocks.append(lsStocks[llRemainingIndexes.pop(lPicked)]) """ If bFilter is false, simply return our first list of stocks, don't check prive/vol """ if not bFilter: return sorted(lsCheckStocks) """ Get data if needed """ if bPullPrice: dmPrice = norObj.get_data(ldtTimestamps, lsCheckStocks, "close") """ Get data if needed """ if bPullVol: dmVolume = norObj.get_data(ldtTimestamps, lsCheckStocks, "volume") """ Only query this once if data is provided, else query every time with new data """ if lsValid is None or bPullVol or bPullPrice: lsValid = stockFilter(dmPrice, dmVolume, fNonNan, fPriceVolume) for sAdd in lsValid: if sAdd in lsCheckStocks: lsRetStocks.append(sAdd) return sorted(lsRetStocks)
global dtStartTest global dtEndTest #reffered to john cornwell's code if __name__ == '__main__': lsSym = ['AA', 'AXP', 'BA', 'BAC', 'CAT', 'CSCO', 'CVX', 'DD', 'DIS', 'GE', 'HD', 'HPQ', 'IBM', 'INTC', 'JNJ', \ 'JPM', 'KFT', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'PFE', 'PG', 'T', 'TRV', 'UTX', 'WMT', 'XOM','VZ' ] dtStarttrain = dt.datetime(2007,01,01) dtEndtrain = dt.datetime(2009,12,31) dtStartTest = dt.datetime(2010,01,01) dtEndTest = dt.datetime(2010,06,30) norObj = da.DataAccess('Norgate') lfdTimestamps = du.getNYSEdays(dtStarttrain, dtEndtrain, dt.timedelta(hours=16)) ldtTimestamps = du.getNYSEdays( dtStartTest, dtEndTest, dt.timedelta(hours=16) ) lTimestamps = du.getNYSEdays(dtStarttrain , dtEndTest , dt.timedelta(hours=16)) global dfPrice global dfVolume dfPrice = norObj.get_data( lTimestamps, lsSym, 'close' ) dfVolume = norObj.get_data( lTimestamps, lsSym, 'volume' ) lfcFeatures = [ featMA,featMA, featRSI,featRSI,featDrawDown,featRunUp,featVolumeDelta,featVolumeDelta,featAroon, classFutRet ] global copyfeatures copyfeatures = lfcFeatures #ldArgs = [{}] * len(lfcFeatures) ''' Custom Arguments ''' ldArgs = [ {'lLookback':10, 'bRel':True},\
# # Prepare to read the data # symbols = ["AAPL", "GLD", "GOOG", "SPY", "XOM"] # #for testing wth the graphs on wiki, uncomment the below two datetimes #and comment the two lines following these two #for actual submission, do the reverse # #startday = dt.datetime(2008,1,1) #endday = dt.datetime(2009,12,31) startday = dt.datetime(2007, 1, 1) endday = dt.datetime(2010, 12, 31) timeofday = dt.timedelta(hours=16) timestamps = du.getNYSEdays(startday, endday, timeofday) dataobj = da.DataAccess('Norgate') voldata = dataobj.get_data(timestamps, symbols, "volume", verbose=True) close = dataobj.get_data(timestamps, symbols, "close", verbose=True) actualclose = dataobj.get_data(timestamps, symbols, "actual_close", verbose=True) # # Plot the adjusted close data # plt.clf() newtimestamps = close.index pricedat = close.values # pull the 2D ndarray out of the pandas object
def getRandPort(lNum, dtStart=None, dtEnd=None, lsStocks=None, dmPrice=None, dmVolume=None, bFilter=True, fNonNan=0.95, fPriceVolume=100 * 1000, lSeed=None): """ @summary Returns a random portfolio based on certain criteria. @param lNum: Number of stocks to be included @param dtStart: Start date for portfolio @param dtEnd: End date for portfolio @param lsStocks: Optional list of ticker symbols, if not provided all symbols will be used @param bFilter: If False, stocks are not filtered by price or volume data, simply return random Portfolio. @param dmPrice: Optional price data, if not provided, data access will be queried @param dmVolume: Optional volume data, if not provided, data access will be queried @param fNonNan: Optional non-nan percent for filter, default is .95 @param fPriceVolume: Optional price*volume for filter, default is 100,000 @warning: Does not work for all sets of optional inputs, e.g. if you don't include dtStart, dtEnd, you need to include dmPrice/dmVolume @return list of stocks which meet the criteria """ if (lsStocks is None): if (dmPrice is None and dmVolume is None): norObj = da.DataAccess('Norgate') lsStocks = norObj.get_all_symbols() elif (not dmPrice is None): lsStocks = list(dmPrice.columns) else: lsStocks = list(dmVolume.columns) if (dmPrice is None and dmVolume is None and bFilter == True): norObj = da.DataAccess('Norgate') ldtTimestamps = du.getNYSEdays(dtStart, dtEnd, dt.timedelta(hours=16)) ''' if dmPrice and dmVol are provided then we don't query it every time ''' bPullPrice = False bPullVol = False if (dmPrice is None): bPullPrice = True if (dmVolume is None): bPullVol = True ''' Default seed (none) uses system clock ''' rand.seed(lSeed) lsRetStocks = [] ''' Loop until we have enough randomly selected stocks ''' llRemainingIndexes = range(0, len(lsStocks)) lsValid = None while (len(lsRetStocks) != lNum): lsCheckStocks = [] for i in range(lNum - len(lsRetStocks)): lRemaining = len(llRemainingIndexes) if (lRemaining == 0): print 'Error in getRandPort: ran out of stocks' return lsRetStocks ''' Pick a stock and remove it from the list of remaining stocks ''' lPicked = rand.randint(0, lRemaining - 1) lsCheckStocks.append(lsStocks[llRemainingIndexes.pop(lPicked)]) ''' If bFilter is false, simply return our first list of stocks, don't check prive/vol ''' if (not bFilter): return sorted(lsCheckStocks) ''' Get data if needed ''' if (bPullPrice): dmPrice = norObj.get_data(ldtTimestamps, lsCheckStocks, 'close') ''' Get data if needed ''' if (bPullVol): dmVolume = norObj.get_data(ldtTimestamps, lsCheckStocks, 'volume') ''' Only query this once if data is provided, else query every time with new data ''' if (lsValid is None or bPullVol or bPullPrice): lsValid = stockFilter(dmPrice, dmVolume, fNonNan, fPriceVolume) for sAdd in lsValid: if sAdd in lsCheckStocks: lsRetStocks.append(sAdd) return sorted(lsRetStocks)
# Prepare to read the data # # #Uncomment this to test with the picture on the wiki # #startday = dt.datetime(2010,1,1) #endday = dt.datetime(2010,10,1) #stock='VZ' startday = dt.datetime(2009,1,1) endday = dt.datetime(2010,1,1) stock='IBM' symbols = [stock] timeofday=dt.timedelta(hours=16) timestamps = du.getNYSEdays(startday,endday,timeofday) dataobj = da.DataAccess('Norgate') adjclose = dataobj.get_data(timestamps, symbols, "close") adjclose = adjclose.fillna() adjclose = adjclose.fillna(method='backfill') # Get the 20 day moving avg and moving stddev movavg = pa.rolling_mean(adjclose,20,min_periods=20) movstddev = pa.rolling_std(adjclose, 20, min_periods=20) # Compute the upper and lower bollinger bands upperband = movavg + 2*movstddev lowerband = movavg - 2*movstddev
plt.ylabel('Error') #plt.show() plt.savefig('FeatureTest.png', format='png') if __name__ == '__main__': ''' Use Dow 30 ''' lsSym = ['AA', 'AXP', 'BA', 'BAC', 'CAT', 'CSCO', 'CVX', 'DD', 'DIS', 'GE', 'HD', 'HPQ', 'IBM', 'INTC', 'JNJ', \ 'JPM', 'KFT', 'KO', 'MCD', 'MMM', 'MRK', 'MSFT', 'PFE', 'PG', 'T', 'TRV', 'UTX', 'WMT', 'XOM' ] #lsSym = ['XOM'] ''' Get data for 2009-2010 ''' dtStart = dt.datetime(2010, 8, 01) dtEnd = dt.datetime(2010, 12, 31) norObj = da.DataAccess('Norgate') ldtTimestamps = du.getNYSEdays(dtStart, dtEnd, dt.timedelta(hours=16)) dfPrice = norObj.get_data(ldtTimestamps, lsSym, 'close') dfVolume = norObj.get_data(ldtTimestamps, lsSym, 'volume') ''' Imported functions from qstkfeat.features, NOTE: last function is classification ''' lfcFeatures = [featMA, featRSI, classFutRet] ''' Default Arguments ''' #ldArgs = [{}] * len(lfcFeatures) ''' Custom Arguments ''' ldArgs = [ {'lLookback':30, 'bRel':True},\ {},\ {}] ''' Generate a list of DataFrames, one for each feature, with the same index/column structure as price data ''' ldfFeatures = ftu.applyFeatures(dfPrice, dfVolume, lfcFeatures, ldArgs) bPlot = False