Download and Process High Frequency Data from NetFods

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API for trade data

http://hopey.netfonds.no/tradedump.php?date=20150423&paper=AAPL.O&csv_format=csv

API for quota data should be

http://hopey.netfonds.no/posdump.php?date=20150423&paper=AAPL.O&csv_format=txt

but does not work.

Download trading price and volume

import numpy as npimport pandas as pdimport datetime as dtfrom urllib import urlretrieveurl1 = 'http://hopey.netfonds.no/tradedump.php?'url2 = 'date=%s%s%s&paper=AAPL.O&csv_format=csv'url = url1 + url2year = '2015'month = '04'days = ['20', '21', '22', '23', '24']AAPL =  pd.DataFrame()for day in days:    tickdata = pd.read_csv(url % (year, month, day), index_col=0, header=0, parse_dates=True)    AAPL = AAPL.append(tickdata)AAPL = AAPL[['price', 'quantity']]AAPL_resample = AAPL.resample(rule='5min', how='last')

Another piece of code for quota from web

from pylab import *from urllib import urlretrieveimport talibimport matplotlib.pyplot as pltfrom matplotlib.dates import date2numfrom matplotlib.finance import candlestickimport pandas as pdfrom matplotlib.dates import  DateFormatter, WeekdayLocator, HourLocator, \     DayLocator, MONDAYurl='http://hopey.netfonds.no/posdump.php?date=20121130&paper=%s.O&csv_format=csv'urlretrieve(url % 'AAPL', 'AAPL.csv')urlretrieve(url % 'GOOG', 'GOOG.csv')AAPL = pd.read_csv('AAPL.csv')GOOG = pd.read_csv('GOOG.csv')AAPL = AAPL.drop_duplicates(cols='time')GOOG = GOOG.drop_duplicates(cols='time')for i in AAPL.index:    AAPL['time'][i]=pd.datetime.strptime(AAPL['time'][i],'%Y%m%dT%H%M%S')#AAPL.index=AAPL['time']; del AAPL['time']AAPL.index=AAPL['time']for i in GOOG.index:    GOOG['time'][i]=pd.datetime.strptime(GOOG['time'][i],'%Y%m%dT%H%M%S')#GOOG.index=GOOG['time']; del GOOG['time']GOOG.index=GOOG['time']AAPL.time = date2num(AAPL.time)GOOG.time = date2num(GOOG.time) DATA = pd.DataFrame({'AAPL':AAPL['bid'],'GOOG':GOOG['bid']}) DATA = DATA[DATA.index > pd.datetime(2012, 11, 30, 9, 59, 0)]DATA['AAPL'] = (DATA['AAPL'].fillna(method='ffill')).fillna(method='backfill')DATA['GOOG'] = (DATA['GOOG'].fillna(method='ffill')).fillna(method='backfill')DATA['GOOG_SMA'] = talib.SMA(DATA['GOOG'],1000)DATA['GOOG_FMA'] = talib.SMA(DATA['GOOG'],500)DATA['AAPL_SMA'] = talib.SMA(DATA['AAPL'],1000)DATA['AAPL_FMA'] = talib.SMA(DATA['AAPL'],500) print DATA.ix[:20].to_string()DATA = pd.DataFrame({'AAPL':AAPL['bid'], 'GOOG':GOOG['bid']})# Compute OHLC data with pandas from raw tick dataDATA_15MIN = pd.Panel({'AAPL':DATA.AAPL.resample('15min', how='ohlc', fill_method='backfill'),                       'GOOG':DATA.GOOG.resample('15min', how='ohlc', fill_method='backfill')})DATA_15MIN.GOOG['time']=DATA_15MIN.GOOG.indexDATA_15MIN.AAPL['time']=DATA_15MIN.AAPL.index  DATA_15MIN.GOOG = DATA_15MIN.GOOG.reindex(columns= ('time','open', 'close', 'high', 'low'))DATA_15MIN.AAPL = DATA_15MIN.AAPL.reindex(columns= ('time','open', 'close', 'high', 'low'))DATA_15MIN.GOOG['time'] = date2num(DATA_15MIN.GOOG['time'])DATA_15MIN.AAPL['time'] = date2num(DATA_15MIN.AAPL['time'])                       # Technical AnalysisDATA_15MIN.GOOG['SMA'] = talib.MA(DATA_15MIN.GOOG.close, 15)DATA_15MIN.GOOG['FMA'] = talib.MA(DATA_15MIN.GOOG.close, 9)DATA_15MIN.AAPL['SMA'] = talib.MA(DATA_15MIN.AAPL.close, 15)DATA_15MIN.AAPL['FMA'] = talib.MA(DATA_15MIN.AAPL.close, 9)#mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays#alldays    = DayLocator()               # minor ticks on the days#weekFormatter = DateFormatter('%b %d')  # Eg, Jan 12#dayFormatter = DateFormatter('%d')fig = figure()fig.subplots_adjust(bottom=0.2)ax1 = fig.add_subplot(211)ax2 = fig.add_subplot(212)#ax.xaxis.set_major_locator(mondays)#ax.xaxis.set_minor_locator(alldays)#ax.xaxis.set_major_formatter(weekFormatter)#ax1.xaxis.set_minor_formatter(dayFormatter)#ax2.xaxis.set_minor_formatter(dayFormatter)# Plotcandlestick(ax1, np.array(DATA_15MIN.AAPL), width=(1/48), colorup='g', colordown='r')candlestick(ax2, np.array(DATA_15MIN.GOOG), width=(1/48), colorup='g', colordown='r')#fig, (ax1, ax2) = plt.subplots(2, 1, sharex = True)ax1.set_ylabel('AAPL', size=20)ax2.set_ylabel('GOOG', size=20)#DATA_15MIN.AAPL.close.plot(ax=ax1, lw=2)DATA_15MIN.AAPL.SMA.plot(ax=ax1, c = 'g', label='SMA')DATA_15MIN.AAPL.FMA.plot(ax=ax1, c = 'r', label='FMA')ax1.legend(loc='upper left')#DATA_15MIN.GOOG.close.plot(ax=ax2, lw=2)DATA_15MIN.GOOG.SMA.plot(ax=ax2, c = 'g')DATA_15MIN.GOOG.FMA.plot(ax=ax2, c = 'r')ax2.legend(loc='upper left')plt.show()print "Done."

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