def importData(self):
"""
Import (New) Data from Yahoo.
"""
start = self._getLatestDate()
end = self._getTodaysDate()
Logger.log(logging.INFO, "Loading Data", {"scope":__name__, "tickerCode":self._tickerCode, "start":str(start), "end":str(end)})
self._data = DataReader(self._tickerCode, "yahoo", start, end)
self._data['Code'] = self._tickerCode
for item in ['Open', 'High', 'Low']:
self._data[item] = self._data[item] * self._data['Adj Close'] / self._data['Close']
self._data.drop('Close', axis=1, inplace=True)
self._data.rename(columns={'Adj Close':'Close'}, inplace=True)
self._data['Volume'] = self._data['Volume'].astype(float)
connection = sqlite3.connect(pyswing.database.pySwingDatabase)
query = "insert or replace into Equities (Date, Open, High, Low, Volume, Close, Code) values (?,?,?,?,?,?,?)"
connection.executemany(query, self._data.to_records(index=True))
connection.commit()
connection.close()
def stockhistorynobackfilltodataframeusingcache(symbol, fromdate, todate):
print('--------------------------')
print('Initialized pullprices.stockhistorydailytodataframeusingcache')
import pandas as pd
#import numpy as np
from pandas.io.data import DataReader
#from datetime import datetime, timedelta
import config
mycachefolder = config.mycachefolder
import mytools
mytools.general().make_sure_path_exists(mycachefolder)
cachedfilepathname = mycachefolder + '\\stockhistorynobackfill ' + symbol + ' ' + fromdate + ' ' + todate + '.csv'
import os
if os.path.isfile(cachedfilepathname):
print(' Found cached file: ' + cachedfilepathname)
df_hist = pd.read_csv(cachedfilepathname, index_col=0)
else:
print(' Getting new file:' + cachedfilepathname)
df_hist = DataReader(symbol, "yahoo", fromdate, todate)
df_hist.to_csv(cachedfilepathname,
columns=('Open', 'High', 'Low', 'Close', 'Volume',
'Adj Close'))
return df_hist
def readData(self, lookupTicker, source, start, end):
'''Read the data - assumes start and end are datetime.date objects'''
try:
lookupTicker = str(lookupTicker)
if source == 'Quandl':
#use Quandl reader
start = str(start)
end = str(end)
data = Quandl.get(lookupTicker,
authtoken = self.quandlAuthToken,
trim_start = start,
trim_end= end)
else:
#use pandas.io DataReader
data = DataReader(lookupTicker, source , start, end)
data = data.reset_index()
logging.info("Read ticker {}".format(lookupTicker))
except:
logging.error("importData: Can't read ticker {}".format(lookupTicker))
raise
else:
return data
def main():
# db = psql.get_db()
# product_codes = db.prepare('''SELECT p.code AS code, p.id as id
# FROM products p LEFT JOIN companies c ON p.company_id = c.id
# WHERE c.sector IS NOT NULL
# and p.id < 9354
# ORDER BY p.id''')()
product_codes = get_components_yahoo('^DJI').index
total = len(product_codes)
cur = 0
products=[]
logger.info('Start downloading of %d products.', len(product_codes))
for code in product_codes:
cur += 1
try:
product = DataReader(code, 'yahoo', start=date_from, end=date_to)
except:
logger.warn('(%d/%d) Fail downloading %s.', cur, total, code)
continue
product.code = code
products.append(product)
logger.info('(%d/%d) Downloaded %s.', cur, total, code)
logger.info('Download complete.')
return opt(products)
def save_data():
start = '1/1/1990'
# Get S&P 500 data from yahoo
sp500 = get_data_yahoo('^GSPC', start=start)['Adj Close']
sp500.name = 'SP500'
vix = get_data_yahoo('^VIX', start=start)['Adj Close']
vix.name = 'VIX'
# Get ten year and 3 month t-bill rates
ten_yr = DataReader('DGS10', 'fred', start=start)
three_mon = DataReader('DGS3MO', 'fred', start=start)
ten_yr = ten_yr.ix[ten_yr.DGS10.str.count(r'^\.') != 1].astype(float)
three_mon = three_mon.ix[three_mon.DGS3MO.str.count(r'^\.') != 1].astype(float)
data = ten_yr.join(three_mon)
data = data.join(sp500)
data = data.join(vix)
# Drop non-like observations (obs on different days)
data = data.dropna()
data.save('SP_YC.db')
data.to_csv('the_data.csv')
def getTimewindowStockPrice(ticker):
timewindowStockPrice = {}#key: year; value: weekly price
stockDF = DataReader(ticker, "google", "2009-01-01", datetime.today().date())
# print stockDF
for idx, row in stockDF.iterrows():
# print row[0], row['Close']
# print datetime.fromtimestamp(idx)
# print str(idx)
dt = dateutil.parser.parse(str(idx)).date()
year = dt.isocalendar()[0]
week = dt.isocalendar()[1]
price = row['Close']
print year, week, price
if year not in timewindowStockPrice:
timewindowStockPrice[year] = {}
if week not in timewindowStockPrice[year]:
timewindowStockPrice[year][week] = []
# print row['Close']
timewindowStockPrice[year][week].append(price)
#normalized weekly price
for year in timewindowStockPrice.keys():
for week in timewindowStockPrice[year].keys():
timewindowStockPrice[year][week] = scipy.mean(timewindowStockPrice[year][week])
# for year in timewindowStockPrice.keys():
# print timewindowStockPrice[year]
return timewindowStockPrice
def pull_stocks_data(retries=2, start_date=None, end_date=None):
"""
Pulling stocks raw data, of the stocks in the symbol list.
:param retries: number of retries for getting each stock's data
:param start_date: the first day of the data (datetime format), default value is 2 years before end_date.
:param end_date: the last day of the data (datetime format), default value is today
"""
symbols = get_stocks_symbols(write_to_files=False)
log.notice("Starting to pull stocks data")
end_date = datetime.today() if end_date is None else end_date
start_date = end_date - timedelta(365*2) # take as a default 2 years backwards
for retry in range(retries):
for symbol in symbols:
filepath = make_filepath(DATA_PATH+"symbols", symbol, 'csv') # optimize by avoiding calling this function every time
try:
data = DataReader(symbol, 'yahoo', start_date, end_date, retry_count=1)
except IOError as e:
log.error("IOError for data query of symbol: {}\n\tError msg: {}".format(symbol, e))
continue
data.to_csv(filepath)
symbols.pop(symbols.index(symbol))
log.warning("Unable to get {} symbols on try #{}".format(len(symbols), retry+1))
log.error("Unable to get {} symbols after {} retries:\n{}".format(len(symbols), retries, symbols))
def refreshYahooDatabase(db):
rs = yahoo.yahooRecordset(db)
ts = timeseries.timeseriesRecordset(db)
# step 1: get the yahoo index end date
idx = rs.select('*')
for i in idx:
try:
r = ts.getLastDate(i['id'])
start = r[0]['max_date']
data = pd.DataFrame()
if start != None:
start = start + dt.timedelta(days=1)
data = DataReader( str(i['key']), 'yahoo', start)
else:
data = DataReader( str(i['key']), 'yahoo')
# insert new data
add = []
for index, row in data.iterrows():
add.append((i['id'], index, row[i['field']]))
ts.insert(add)
except Exception as e:
print('error processing index ' + str(i['key'] \
+ '/' + str(i['field']) + '\n'))
def get_riskfree_rate(self,startdate,enddate,freq="M",maturity='1M'):
"""
Rates from FRED
http://research.stlouisfed.org/fred2/categories/116
"""
rfcache = self.__class__._cacherfrate
grabdata = False
if rfcache == None:
grabdata = True
elif rfcache[0]< startdate or rfcache[1] > enddate:
grabdata = True
if grabdata:
dt = DataReader('DTB4WK',"fred", startdate,enddate)
dt.columns = ['RFRate']
dt.fillna(method='backfill',inplace=True)
rfcache = (startdate,enddate,dt)
self.__class__._cacherfrate= rfcache
else:
dt = rfcache[2]
dsm = dt[startdate:enddate].resample('M')
return dsm
def fetch_timeseries(symbol, dir_name='data', use_cache=True):
"""
Read time series data. Use cached version if it exists and
use_cache is True, otherwise retrive, cache, then read.
"""
base_dir = ''
try:
conf = pf.read_config()
base_dir = conf['base_dir']
except:
pass
finally:
dir_name = os.path.join(base_dir, dir_name)
if not os.path.exists(dir_name):
os.makedirs(dir_name)
timeseries_cache = os.path.join(dir_name, symbol + '.csv')
if os.path.isfile(timeseries_cache) and use_cache:
pass
else:
ts = DataReader(symbol, 'yahoo', start=datetime.datetime(1900, 1, 1))
ts.to_csv(timeseries_cache, encoding='utf-8')
ts = pd.read_csv(timeseries_cache, index_col='Date', parse_dates=True)
ts = _adj_column_names(ts)
return ts
def get_data_from_yahoo(ticker):
# No older computations OK
start_date = date(2010, 01, 01)
end_date = date.today()
df = DataReader(ticker, 'yahoo', start_date, end_date)
df.to_pickle(TMP_dir + ticker)
print "Getting data for " + ticker + " from yahoo"
return df
def Econ_env(YYYY, m, dd):
start_date = datetime.datetime(YYYY, m, dd)
GDP = DataReader('GDP', "fred", start=start_date)
sp500 = DataReader('^GSPC', "yahoo", start=start_date)
Array = DataFrame({'S&P':sp500["Adj Close"]})
return Array
def getPayOff(year,month):
nifty = DataReader("^NSEI","yahoo",datetime(year,month,1),datetime(year,month,25))
bn=DataReader("^NSEBANK","yahoo",datetime(year,month,1),datetime(year,month,25))
nifty_daily=nifty.diff()['Close']
bn_daily=bn.diff()['Close']
nifty_daily_return =50*(nifty_daily)
bn_daily_return =25*(bn_daily)
return -nifty_daily_return.sum()+bn_daily_return.sum()
def hist_vol(sym, days=10):
try:
quotes = DataReader(sym, 'yahoo')['Close'][-days:]
except Exception:
print "Problem getting historical volatility!"
raise SystemExit(code)
return None, None
logreturns = np.log(quotes / quotes.shift(1))
vol = np.sqrt(252*logreturns.var()) #252 trading days in year (annualized volatility)
return float(vol)
def main(symbol):
t1 = datetime.now()
t2 = t1 - timedelta(days=PAST_DAYS)
df = DataReader(symbol, FIN_SERVICE_PROVIDER , t2, t1)
print df.head()
print '...' * 20
print df.tail()
return df
def ts(self, symbol):
parse = self.parsesymbol(symbol)
df = DataReader(parse['eq'], parse['proto'],start=datetime.datetime(1950,1,1))
df = df.rename(columns=lambda x: '_'.join(x.split()).lower()) # Need for Adj Close :(
#print df.columns
ts = df[parse['hlocv']]
ts.index = map(lambda x: x.date(), ts.index)
ts.name = parse['eq']
if '@' in symbol:
ts.name += '@%s' % (parse['hlocv'])
return ts
def getLongShortPayOff(year,month,longSymbol,longQty,shortSymbol,shortQty):
shortStk = DataReader(shortSymbol,"yahoo",datetime(year,month,1),datetime(year,month,25))
longStk=DataReader(longSymbol,"yahoo",datetime(year,month,1),datetime(year,month,25))
short_daily=shortStk.diff()['Close']
long_daily=longStk.diff()['Close']
short_daily_return =shortQty*(short_daily)
long_daily_return =longQty*(long_daily)
#print abs(long_daily_return.sum())-abs(short_daily_return.sum())
return abs(long_daily_return.sum())-abs(short_daily_return.sum())
def _load_raw_yahoo_data(indexes=None, stocks=None, start=None, end=None):
"""Load closing prices from yahoo finance.
:Optional:
indexes : dict (Default: {'SPX': '^GSPC'})
Financial indexes to load.
stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
Stock closing prices to load.
start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
Retrieve prices from start date on.
end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
Retrieve prices until end date.
:Note:
This is based on code presented in a talk by Wes McKinney:
http://wesmckinney.com/files/20111017/notebook_output.pdf
"""
assert indexes is not None or stocks is not None, """
must specify stocks or indexes"""
if start is None:
start = pd.datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
if start is not None and end is not None:
assert start < end, "start date is later than end date."
data = OrderedDict()
if stocks is not None:
for stock in stocks:
print(stock)
stock_pathsafe = stock.replace(os.path.sep, '--')
cache_filename = "{stock}-{start}-{end}.csv".format(
stock=stock_pathsafe,
start=start,
end=end).replace(':', '-')
cache_filepath = get_cache_filepath(cache_filename)
if os.path.exists(cache_filepath):
stkd = pd.DataFrame.from_csv(cache_filepath)
else:
stkd = DataReader(stock, 'yahoo', start, end).sort_index()
stkd.to_csv(cache_filepath)
data[stock] = stkd
if indexes is not None:
for name, ticker in iteritems(indexes):
print(name)
stkd = DataReader(ticker, 'yahoo', start, end).sort_index()
data[name] = stkd
return data
def get_prices_yahoo(self):
"""
It get prices data from yahoo
"""
try:
self.df_prices = DataReader(self.symbol, "yahoo", self.from_date,
self.to_date)
self.df_prices['pct Adj Close'] = self.df_prices.pct_change()['Adj Close']
self.data = True
except Exception, e:
print e
sleep(20)
def importData():
#Start Time
start = datetime(2010,1,1)
end = datetime.date(datetime.now())
data = DataReader(sp500constituents[0], "yahoo", start, end)
en = enumerate(sp500constituents)
[i for i, x in en if x=='WFMI']
sp500constituents[200:len(sp500constituents)]
problems = []
dataImportProblems = []
for series in sp500constituents[485:len(sp500constituents)]:
print series
try:
data = DataReader(series, "yahoo", start, end)
data = data.reset_index()
except:
print "Can't read {}".format(series)
dataImportProblems.append(series)
continue
con = sqlite3.connect("/home/phcostello/Documents/Data/FinanceData.sqlite")
try:
psql.write_frame( data, series, con)
con.commit()
except:
print "Problems with {}".format(series)
problems.append(series)
finally:
con.close()
#changing tables to have date formats so RODBC driver recognizes
#Should check that this is occuring above.
con = sqlite3.connect("/home/phcostello/Documents/Data/FinanceData.sqlite")
for tb in sp500constituents:
if psql.has_table(tb, con):
sqltxt = "SELECT * FROM {}".format(tb)
#print sqltxt
data = psql.read_frame(sqltxt, con)
sqlDropTxt = 'DROP TABLE "main"."{}"'.format(tb)
#print sqlDropTxt
psql.execute(sqlDropTxt, con)
con.commit()
psql.write_frame( data, tb, con)
con.commit()
con.close()
def get_prices_yahoo(self):
"""
It get prices data from yahoo (last year)
"""
try:
self.df_prices = DataReader(self.symbol, "yahoo", self.from_date,
self.to_date)
self.df_prices['pct Adj Close'] = self.df_prices.pct_change()['Adj Close']
except Exception, e:
print e
raise
def run(self):
"""Collects data from Yahoo finance and preprocesses them."""
# Get reference dates from SP500
df_ref = DataReader('SPY', 'yahoo', self.start, self.end)['Adj Close']
df_ref.sort_index(inplace=True)
if 'SPY' in assets:
self.df = df_ref
self.df.rename({'AdjClose': 'SPY'})
else:
self.df = pd.DataFrame(index=df_ref.index)
# Retrieve AdjClose price for other assets
for asset in self.assets:
df_asset = DataReader(asset, 'yahoo', self.start,
self.end)['Adj Close']
df_asset.rename(asset, inplace=True)
self.df = self.df.join(df_asset, how='left')
# Fill NaN
self.df.fillna(method='ffill', inplace=True)
self.df.fillna(method='bfill', inplace=True)
# Compute assets simple returns
self.df = (self.df / self.df.shift(1) - 1.0).ix[1:, :]
# Reset indices
self.df.reset_index(drop=True, inplace=True)
def download_ohlc(sector_tickers, start, end):
sector_ohlc = {}
for sector, tickers in sector_tickers.iteritems():
print 'Downloading data from Yahoo for %s sector' % sector
data = DataReader(tickers, 'yahoo', start, end)
for item in ['Open', 'High', 'Low']:
data[item] = data[item] * data['Adj Close'] / data['Close']
data.rename(items={'Open': 'open', 'High': 'high', 'Low': 'low',
'Adj Close': 'close', 'Volume': 'volume'},
inplace=True)
data.drop(['Close'], inplace=True)
sector_ohlc[sector] = data
print 'Finished downloading data'
return sector_ohlc
def getHistoricalQuotes(self, symbol, index, market=None):
assert (isinstance(index, pd.Index))
source = 'yahoo'
try:
quotes = DataReader(symbol, source, index[0], index[-1])
except:
log.error('** Could not get {} quotes'.format(symbol))
return pd.DataFrame()
if index.freq != pd.datetools.BDay() or index.freq != pd.datetools.Day():
#NOTE reIndexDF has a column arg but here not provided
quotes = utils.reIndexDF(quotes, delta=index.freq, reset_hour=False)
if not quotes.index.tzinfo:
quotes.index = quotes.index.tz_localize(self.tz)
quotes.columns = utils.Fields.QUOTES
return quotes
def getHistoricalQuotes(self, symbol, index, market=None):
assert (isinstance(index, pd.Index))
source = 'yahoo'
try:
quotes = DataReader(symbol, source, index[0], index[-1])
except:
log.error('** Could not get {} quotes'.format(symbol))
return pd.DataFrame()
if index.freq != pd.datetools.BDay() or index.freq != pd.datetools.Day():
#NOTE reIndexDF has a column arg but here not provided
quotes = utils.reIndexDF(quotes, delta=index.freq, reset_hour=False)
if not quotes.index.tzinfo:
quotes.index = quotes.index.tz_localize(self.tz)
quotes.columns = utils.Fields.QUOTES
return quotes
def ADF(ticker, start, end):
print('ADF')
stock = DataReader(ticker, "yahoo", start, end)
result = ts.adfuller(stock['Adj Close'], 1)
print(result)
print('')
test = result[0]
crit = result[4]
one = crit['1%']
five = crit['5%']
ten = crit['10%']
if test < one:
print('Lesser than 1%')
print('-----------------------------------------')
return stock
if test < five:
print('Lesser than 5%')
print('-----------------------------------------')
return stock
if test < ten:
print('Lesser than 10%')
print('-----------------------------------------')
return stock
print('Cannot reject Null Hypothesis')
print('-----------------------------------------')
return stock
def peak_begin_dates(start="01/01/1972", end=datetime.now()):
"""
Use the fred dataset `USRECQ` to determine the beginning of the
peaks before all recessions between dates start and end
Parameters
----------
start : string or datetime.datetime, optional(default='01/01/1972')
A string or other acceptable pandas date identifier that marks
the beginning of the window for which we will search for starts
of peaks
end : string or datetime.datetime, optional(default=datetime.now())
The ending date of the search window
Returns
-------
rec_startind : pd.DatetimeIndex
A pandas DatetimeIndex representing the starting points of each
"peak" from start to end
"""
# Get quarterly recession dates from FRED
rec_dates = DataReader("USRECQ", "fred", start=start)
one_vals = np.where(rec_dates == 1)[0]
rec_start = [one_vals[0]]
# Find the beginning of the recession dates (Don't include ones that
# begin within three years of a previous one -- hence the `+12`)
for d in one_vals:
if d > max(rec_start) + 12:
rec_start.append(d)
rec_startind = rec_dates.index[rec_start]
return rec_startind
def create_lagged_series(symbol, start_date, end_date, lags=5):
"""
这个函数创建一个pandas的DataFrame，存储某个来自于Yahoo财经的股票
的以调整收盘价计算的收益，以及一系列滞后的收益，还包括交易量以及某一天
变动的方向
"""
ts = DataReader(symbol, "yahoo", start_date - datetime.timedelta(days=365),
end_date)
tslag = pd.DataFrame(index=ts.index)
tslag["Today"] = ts["Adj Close"]
tslag["Volume"] = ts["Volume"]
for i in range(0, lags):
tslag["Lag%s" % str(i + 1)] = ts["Adj Close"].shift(i + 1)
tsret = pd.DataFrame(index=tslag.index)
tsret["Volume"] = tslag["Volume"]
tsret["Today"] = tslag["Today"].pct_change() * 100.0
for i, x in enumerate(tsret["Today"]):
if (abs(x) < 0.0001):
tsret["Today"][i] = 0.0001
for i in range(0, lags):
tsret["Lag%s" %
str(i + 1)] = tslag["Lag%s" % str(i + 1)].pct_change() * 100.0
tsret["Direction"] = np.sign(tsret["Today"])
tsret = tsret[tsret.index >= start_date]
return tsret
def stockhistory(symbol, fromdate, todate):
from pandas.io.data import DataReader
#from datetime import datetime
#dfromdate = fromdate.strftime('%b%d')
#datetime(2000,1,1), datetime(2012,1,1)
hist = DataReader(symbol, "yahoo", fromdate, todate)
return hist
def historical_volatility(sym, days):
"Return the annualized stddev of daily log returns of `sym`."
try:
quotes = DataReader(sym, 'yahoo')['Close'][-days:]
except Exception, e:
print "Error getting data for symbol '{}'.\n".format(sym), e
return None, None
class Individual_screener():
"""
This class will store the relevant information and functions for the
signal detection of entry points of one Strategy in one Security
"""
def __init__(self, symbol, to_date=dt.datetime.today()):
self.data = False
self.symbol = symbol
self.to_date = to_date
self.from_date = self.to_date.replace(to_date.year - 1)
self.signal = False
self.get_prices_yahoo()
def get_prices_yahoo(self):
"""
It get prices data from yahoo (last year)
"""
try:
self.df_prices = DataReader(self.symbol, "yahoo", self.from_date,
self.to_date)
self.df_prices['pct Adj Close'] = self.df_prices.pct_change(
)['Adj Close']
self.data = True
except Exception, e:
print e
sleep(5)
class Individual_screener():
"""
This class will store the relevant information and functions for the
signal detection of entry points of one Strategy in one Security
"""
def __init__(self, symbol, to_date=dt.datetime.today()):
self.data = False
self.symbol = symbol
self.to_date = to_date
self.from_date = self.to_date.replace(to_date.year - 1)
self.signal = False
self.get_prices_yahoo()
def get_prices_yahoo(self):
"""
It get prices data from yahoo (last year)
"""
try:
self.df_prices = DataReader(self.symbol, "yahoo", self.from_date,
self.to_date)
self.df_prices['pct Adj Close'] = self.df_prices.pct_change()['Adj Close']
self.data = True
except Exception, e:
print e
sleep(5)
def get_prices_df(self,ticker, date_start, date_end):
try:
cotation_data = DataReader(ticker, "yahoo", date_start, date_end)
cotation_data = cotation_data[cotation_data.Volume != 0] # on ne prend pas les jours feriés p.ex 01/01
except Exception as e:
raise ErrorInternetConnexion('yahoo DataReader', e)
return cotation_data
def main():
'''
1. The data from Yahoo! Finance is not grabbed by calling url apis, is by using Pandas APIs.
2. This program is to get TWSE data only, if wants OTC data, need to modify code.
'''
#Setup figure
stock_fig = plt.figure()
stock_plt = plt.subplot2grid((1, 1), (0, 0), colspan=1)
stock_title = "{} day price".format(stock_num)
plt.suptitle(stock_title)
startday = dtime.date(2000, 1, 1)
# Add ".TW" to tell yahoo!Finance to query TWSE stock data.
# If want to query OTC, please add ".TWO"
stock_str = "{}.TW".format(stock_num)
#print stock_str
#about how the DataReader() works, please refer to data.py from pandas
try:
stock_data = DataReader(stock_str, 'yahoo', startday)
#Clear the current axes
stock_plt.cla()
#Turn the axes grids on
stock_plt.grid(True)
#plot date and price
stock_plt.plot(stock_data.index, stock_data['Close'])
#show
plt.show()
except:
exit("Error happened!!\nTry: python TwanStkEx1.py 2330")
def test_read_famafrench(self):
for name in ("F-F_Research_Data_Factors",
"F-F_Research_Data_Factors_weekly", "6_Portfolios_2x3",
"F-F_ST_Reversal_Factor"):
ff = DataReader(name, "famafrench")
assert ff
assert isinstance(ff, dict)
def set_source(self, source, tickers, start, end):
prices = pd.DataFrame()
counter = 0.
for ticker in tickers:
try:
self._logger.info('Loading ticker %s' %
(counter / len(tickers)))
prices[ticker] = DataReader(ticker, source, start,
end).loc[:, 'Close']
except Exception as e:
self._logger.error(e)
pass
counter += 1
events = []
for row in prices.iterrows():
timestamp = row[0]
series = row[1]
vals = series.values
indx = series.index
for k in np.random.choice(len(vals), replace=False,
size=len(vals)): # Shuffle!
if np.isfinite(vals[k]):
events.append((timestamp, indx[k], vals[k]))
self._source = events
self._logger.info('Loaded data!')
def get_data(stock, starttime, endtime):
ibm = DataReader(stock, 'yahoo', starttime, endtime)
#print(ibm['Adj Close'])
daily_returns = deque(maxlen=c.normalize_std_len)
daily_ret_arr = []
size = len(ibm['Adj Close'])
return_array = []
i=0
lastAc = ibm['Adj Close'][0]
for stock in ibm['Adj Close']:
return_array.append(stock)
i+=1
#for rec_date in (c.start + timedelta(days=n) for n in xrange((c.end-c.start).days)):
#idx = next(i for i,d in enumerate(segment_start_dates) if rec_date >= d)
try:
#d = rec_date.strftime("%Y-%m-%d")
ac = stock
daily_return = (ac - lastAc)/lastAc
#if len(daily_returns) == daily_returns.maxlen:
# seq[idx].append(daily_return/np.std(daily_returns))
daily_returns.append(daily_return*scale)
daily_ret_arr.append(daily_return*scale)
lastAc = ac
#print "---"
#print stock
#print daily_return
except KeyError:
pass
print "Records found:" + str(len(daily_ret_arr))
return daily_ret_arr, return_array
def stockhistorybackfilledtodictionary(symbol, fromdate, todate):
from pandas.io.data import DataReader
from datetime import datetime, timedelta
hist = DataReader(symbol, "yahoo", fromdate, todate)
date_format = "%Y-%m-%d"
d = datetime.strptime(fromdate, date_format)
delta = timedelta(days=1)
last_adjclose = 'NaN'
dictAdjClose = {}
while d <= datetime.strptime(todate, date_format):
#print(d.strftime(date_format))
d_string = d.strftime(date_format)
if d_string in hist.index:
last_adjclose = hist.ix[d_string]['Adj Close']
print(d_string, last_adjclose)
else:
print(d_string, 'nothing', last_adjclose)
dictAdjClose[d_string] = [('AdjClose', last_adjclose)]
d += delta
return dictAdjClose
def test_read_famafrench(self):
for name in ("F-F_Research_Data_Factors",
"F-F_Research_Data_Factors_weekly", "6_Portfolios_2x3",
"F-F_ST_Reversal_Factor", "F-F_Momentum_Factor"):
ff = DataReader(name, "famafrench")
self.assertTrue(ff is not None)
self.assertIsInstance(ff, dict)
def historical_pandas_yahoo(symbol, source='yahoo', start=None, end=None):
'''
Fetch from yahoo! finance historical quotes
'''
#NOTE Panel for multiple symbols ?
#NOTE Adj Close column name not cool (a space)
return DataReader(symbol, source, start=start, end=end)
def DownloadStocks(self, startingDate, endDate):
for stock in self._names:
print("Getting data from {0}...".format(stock))
stockData = DataReader(stock, "google", startingDate, endDate)
self._columns = stockData.columns
print(" Number of lines:{0}".format(stockData.shape[0]))
self._data.append(stockData)
return self._data
def downloadStock(ticker, dataSource, start, end):
gtemp = pd.DataFrame()
try:
gtemp = DataReader(ticker, dataSource, start, end)
print ticker
except:
pass
return gtemp
def load_from_yahoo(indexes=None, stocks=None, start=None, end=None):
"""Load closing prices from yahoo finance.
:Optional:
indexes : dict (Default: {'SPX': '^GSPC'})
Financial indexes to load.
stocks : list (Default: ['AAPL', 'GE', 'IBM', 'MSFT',
'XOM', 'AA', 'JNJ', 'PEP', 'KO'])
Stock closing prices to load.
start : datetime (Default: datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc))
Retrieve prices from start date on.
end : datetime (Default: datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc))
Retrieve prices until end date.
:Note:
This is based on code presented in a talk by Wes McKinney:
http://wesmckinney.com/files/20111017/notebook_output.pdf
"""
if indexes is None:
indexes = {'SPX': '^GSPC'}
if stocks is None:
stocks = ['AAPL', 'GE', 'IBM', 'MSFT', 'XOM', 'AA', 'JNJ', 'PEP', 'KO']
if start is None:
start = pd.datetime(1993, 1, 1, 0, 0, 0, 0, pytz.utc)
if end is None:
end = pd.datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
assert start < end, "start date is later than end date."
data = OrderedDict()
for stock in stocks:
print stock
stkd = DataReader(stock, 'yahoo', start, end).sort_index()
data[stock] = stkd
for name, ticker in indexes.iteritems():
print name
stkd = DataReader(ticker, 'yahoo', start, end).sort_index()
data[name] = stkd
df = pd.DataFrame({key: d['Close'] for key, d in data.iteritems()})
df.index = df.index.tz_localize(pytz.utc)
return df
def get_stock_history(stock):
prices = None
try:
start_date = date.today() - timedelta(days=365)
prices = DataReader(stock, "yahoo", start=start_date)
except (HTTPError, BadStatusLine):
pass
return prices
def test_read_famafrench(self):
raise nose.SkipTest('buggy as of 2/14/16; maybe a data revision?')
for name in ("F-F_Research_Data_Factors",
"F-F_Research_Data_Factors_weekly", "6_Portfolios_2x3",
"F-F_ST_Reversal_Factor", "F-F_Momentum_Factor"):
ff = DataReader(name, "famafrench")
self.assertTrue(ff is not None)
self.assertIsInstance(ff, dict)
def get_history(self, stock):
print "Retrieving data for %s" % stock
prices = None
try:
start_date = datetime.today() - timedelta(days=365)
prices = DataReader(stock, "yahoo", start=start_date)
except (HTTPError, BadStatusLine):
pass
return prices
def data_StockView_import(self):
self.data = DataReader("GOOGL", "google", self.start_date,
self.end_date)
source = urllib2.urlopen(
'http://hopey.netfonds.no/posdump.php?date=20140530&paper=AAPL.O&csv_format=txt'
)
data = pandas.read_table(source)
print data
def stocks():
tickers = ['AAPL', 'GOOG', 'MSFT', 'AMZN']
end = datetime.now()
start = datetime(end.year-1, end.month, end.day)
for ticker in tickers:
globals()[ticker] = DataReader(ticker, 'yahoo', start, end)
build_stock_analyses()
return render_template('stocks.html', AAPL=AAPL, GOOG=GOOG, MSFT=MSFT, AMZN=AMZN)
def fetch_timeseries(symbol, dir_name='data', use_cache=True):
"""
Read time series data. Use cached version if it exists and
use_cache is True, otherwise retrive, cache, then read.
"""
if not os.path.exists(dir_name):
os.makedirs(dir_name)
timeseries_cache = os.path.join(dir_name, symbol + '.csv')
if os.path.isfile(timeseries_cache) and use_cache:
pass
else:
ts = DataReader(symbol, 'yahoo', start=datetime.datetime(1900, 1, 1))
ts.to_csv(timeseries_cache, encoding='utf-8')
ts = pd.read_csv(timeseries_cache, index_col='Date', parse_dates=True)
ts = _adj_column_names(ts)
return ts
def import_data_yahoo_to_files( list_symbols,path,startdate):
list_error=[]
logger.info("importing from "+str(startdate))
for symbol in list_symbols:
try :
prices_df = DataReader(symbol, "yahoo", startdate)
count_newdata = len(prices_df)
print symbol , " ", count_newdata
if(count_newdata <=0):
raise Exception("NO DATA for Dates for %s"%symbol)
prices_df = prices_df.rename(columns={'Date': 'date', 'Open': 'open', 'High': 'high',
'Low': 'low', 'Close': 'actualclose', 'Adj Close': 'close',
'Volume': 'volume', 'Symbol': 'symbol'})
prices_df['symbol'] = symbol
prices_df['symbol'] = prices_df.apply(lambda x: x['symbol'].replace('\r','').upper(), axis=1 )
prices_df.to_csv(path + "/" + symbol + '.csv')
except Exception as ex:
logger.error(ex)
list_error.append(symbol)
logger.error(traceback.format_exc())
def calculateTrend(sym, edate):
DD = datetime.timedelta(days=365*5)
sdate = (datetime.datetime.now() - DD).strftime("%Y-%m-%d")
df = DataReader(sym, 'yahoo', sdate, edate)
dfb = DataReader('^GSPC', 'yahoo', sdate, edate)
# create a time-series of monthly data points
rts = df.resample('M',how='last')
rbts = dfb.resample('M',how='last')
dfsm = pd.DataFrame({'s_adjclose' : rts['Adj Close'],
'b_adjclose' : rbts['Adj Close']},
index=rts.index)
# compute returns
dfsm[['s_returns', 'b_returns']] = dfsm[['s_adjclose', 'b_adjclose']]/\
dfsm[['s_adjclose', 'b_adjclose']].shift(1) -1
dfsm = dfsm.dropna()
covmat = np.cov(dfsm["s_returns"], dfsm["b_returns"])
# calculate measures now
beta = covmat[0,1]/covmat[1,1]
alpha= np.mean(dfsm["s_returns"])-beta*np.mean(dfsm["b_returns"])
# r_squared = 1. - SS_res/SS_tot
ypred = alpha + beta * dfsm["b_returns"]
SS_res = np.sum(np.power(ypred-dfsm["s_returns"],2))
SS_tot = covmat[0,0]*(len(dfsm)-1) # SS_tot is sample_variance*(n-1)
r_squared = 1. - SS_res/SS_tot
# 5- year volatiity and 1-year momentum
volatility = np.sqrt(covmat[0,0])
momentum = np.prod(1+dfsm["s_returns"].tail(12).values) -1
# annualize the numbers
prd = 12. # used monthly returns; 12 periods to annualize
alpha = alpha*prd
volatility = volatility*np.sqrt(prd)
return beta, alpha, r_squared, volatility, momentum
def determine_trend(symbol, trade_date=datetime.datetime.now(),
trend_length=20, trend_end_days_ago=1):
"""
returns a "trend score" derived from performing a linear
regression on the daily closing price of the stock
identified by symbol.
This score is on the following scale:
"Negative Trend" ----- "Positive Trend"
-1.0 -----------0-----------1.0
The score considers both the slope of the linear model and
the "fit" (based on the r^2 output of the ols function)
trade_date -- date used to determine the trend from
symbol -- the stock symbol to determine trend for
trend_length -- the number of days to derive trend for
trend_end_days_ago -- the number of days prior to trend_date to determine
when to end the trend analysis
"""
end_date = datetime.date.today() - datetime.timedelta(days=trend_end_days_ago)
start_date = end_date - datetime.timedelta(days=trend_length)
stock_df = DataReader(symbol, "yahoo", start=start_date, end=end_date)
stock_df = stock_df.reset_index()
result = ols(y=stock_df['Adj Close'], x=Series(stock_df.index))
# This is the formula for the score without adjusting to fit within the
# -1.0 - 1.0 scale. Basically this takes the slope/starting price to get
# the % change per day. This is divided by a somewhat arbitrary value of
score = (result.beta['x']/result.beta['intercept'])/LARGE_DAILY_GAIN * result.r2
# Now adjust the score to keep it in our trend range:
if score > 1.0:
return 1.0
elif score < -1.0:
return -1.0
else:
return score
class Indicator1():
def __init__(self, indicator, stock_name, start_date, end_date, buy_value, sell_value):
if not isinstance(stock_name, str):
raise TypeError("Sorry. 'Stock Name' must be string")
self.stock = DataReader(stock_name, "yahoo", start_date, end_date)
self.stock['returnValue'] = 'NaN'
self.stock['returnValue'] = 'NaN'
high_array = []
for index, row in self.stock.iterrows():
high_array.append(row['High'])
high_nparray = numpy.asarray(high_array)
low_array = []
for index, row in self.stock.iterrows():
low_array.append(row['Low'])
low_nparray = numpy.asarray(low_array)
close_array = []
for index, row in self.stock.iterrows():
close_array.append(row['Close'])
close_nparray = numpy.asarray(close_array)
real = getattr(talib, indicator)(high_nparray, low_nparray, close_nparray, timeperiod=14)
self.stock['indicator'] = real
self.stock['indicator_trigger'] = "NaN"
count = 0
flag = False
for index, row in self.stock.iterrows():
if count < len(self.stock.index):
if float(row['indicator']) < buy_value and (not flag):
self.stock['indicator_trigger'][count] = "Buy"
flag = True
elif float(row['indicator']) > sell_value and flag:
self.stock['indicator_trigger'][count] = "Sell"
flag = False
count += 1
def load(symbol, startDate="19910428", forceDownload=False):
symbol = symbol.lower()
startDate = datetools.to_datetime(startDate) + datetools.bday - datetools.bday
dataPath = "data_from_yhoo"
# first check if data is in directory, or if there is even a directory
if not os.path.exists(dataPath):
print "data path doesn't exist. creating."
os.makedirs(dataPath)
# check if file is even there
# get all stock symbols by reading csv names from data folder
symbols = []
os.chdir(dataPath + "/")
for afile in glob.glob("*.df"):
symbols.append(afile[:-3]) # slice out csv extension
os.chdir("../") # reset dir
# if it is, read data. right now, if you don't download
if symbol in symbols and not forceDownload:
df = DataFrame.load(dataPath + "/" + symbol + ".df")
print "read " + symbol + " data from binary file"
df = df[df.index >= startDate]
return df
else:
# otherwise, redownload data from yahoo
print symbol + " data not downloaded. downloading from yhoo now..."
df = DataReader(symbol, "yahoo", startDate)
# save locally
df.save(dataPath + "/" + symbol + ".df")
# return data df
return df
class Simulation():
"""
Class that will store all data and functions related to a simulation
"""
def __init__(self, symbol, from_date=None, to_date=None):
"""
If dates not entered it will take a default to determine ######### TODO
"""
self.symbol = symbol
self.from_date = from_date
self.to_date = to_date
self.df_prices = pd.DataFrame()
self.open = None # Price of current open trade
self.close = None # Price of current closed trade
self.status = 'out' # 'out' not invested, 'in' invested
self.signal = False # It will get records in which the signal activates
self.max_open = 0.0 # Max individual investment (for % profit calculation)
### Measures
self.nperiods = 0
self.ntrades = 0
self.abs_profit = 0.0 # Accumulated abs_profit ($ value gained/loss)
# self.pct_simple_profit = 0.0 # Profit over max investment (without reinvestment)
self.pct_compound_profit = 1.0 # Profit over max investment (with reinvestment)
# self.pct_annual_simple_profit = 0.0 # Annualized simple profit
# self.pct_annual_compound_profit = 0.0 # Annualized compound profit
self.volatility = 0.0 # Volatility of returns (annualized)
self.sharpe = 0.0 # Sharpe ratio (Rf = 0)
self.drawdown = 0.0 # It will store the worst abs_profit
### Years calculation
d_from_date = date(int(from_date[0:4]), int(from_date[4:6]), int(from_date[6:8]))
d_to_date = date(int(to_date[0:4]), int(to_date[4:6]), int(to_date[6:8]))
self.years = (d_to_date-d_from_date).days/365.0
self.profit_trades = []
def get_prices_yahoo(self):
"""
It get prices data from yahoo
"""
try:
self.df_prices = DataReader(self.symbol, "yahoo", self.from_date,
self.to_date)
self.df_prices['pct Adj Close'] = self.df_prices.pct_change()['Adj Close']
except Exception, e:
print e
raise
def get_historical(self):
"""
If mode = "online", download Yahoo quotes from start to end date in a pandas dataframe
If mode = "disk", the data is extracted from the hard drive
"""
if self._mode == "online":
self._historical = DataReader(self._symbol,'yahoo',self._start,self._end)
else:
# check dictionary of symbols and open the file at the correct location
data = stock_database.extract_series(self._symbol)
df = pd.DataFrame(data,columns = ['date','Open','High','Low','Close','Volume','Adj Close'])
self._historical = df[pd.to_datetime(df['date']) > self._start]
self._historical = self._historical[pd.to_datetime(df['date']) < self._end]
self._historical = self._historical.set_index('date')
return self._historical
# Parameters---------------------------------------------------------
start=dt.datetime(2007, 01, 01)
end=dt.date.today()
LI3=pd.date_range(start, end, freq='D')
curr=['USD','GBP','EUR']
libor_1m=[]
for i in curr:
tick=i+'1MTD156N'
libor_1m.append(tick)
df_libor_1m=pd.DataFrame(index=LI3)
for i in libor_1m:
df2 = DataReader(i, "fred", start,end)
df2=df2.applymap(f)
df2=df2.ffill()
df_libor_1m[i]=df2
df_libor_1m=df_libor_1m.ffill()
df_libor_1m.columns=curr
print df_libor_1m.head(8)
libor_1w=[]
for i in curr:
tick=i+'1WKD156N'
libor_1w.append(tick)
df_libor_1w=pd.DataFrame(index=LI3)
for i in libor_1w: