def main():
print "Getting Symbols"
# symbols = np.loadtxt('sp5002012.txt',dtype='S10',comments='#', skiprows=1)
symbols = ['GOOG', 'CVX', 'KIM', 'SPY', 'DELL', 'CAT' ]
startday = datetime.date(2012, 1 , 1)
endday = datetime.date(2012, 12, 31)
#Get index
index = web.get_data_yahoo("SPY", startday, endday).index
print "Getting Data"
dataAll = {}
for symbol in symbols:
try:
dataAll[symbol] = web.get_data_yahoo(symbol, startday, endday)
if len(dataAll[symbol].index) != 250:
print symbol, len(dataAll[symbol].index)
except:
print "Could not obtain data for: " + symbol
print "Analyzing Signals"
signals = []
for symbol in dataAll.keys():
generateSignals(symbol,dataAll, signals)
signals2 = defaultdict(list)
for timestamp, symbol, rating in signals:
signals2[timestamp].extend([[symbol, rating]])
return (dataAll.keys(), signals2, dataAll, index)
def get_stocks(tickers, market, start_date, end_date, frequency):
#Set Frequency for resampling
FREQ_DICT = {
'Weekly': 'W-FRI',
'Monthly': 'M',
}
start_yahoo = datetime.datetime.strptime(start_date, '%d/%m/%Y')
end_yahoo = datetime.datetime.strptime(end_date, '%d/%m/%Y')
#Set market portfolio
if (market != 'TA100') and (market != 'TA25'):
if (market == 'SP500'):
prices = DataFrame(web.get_data_yahoo('VFINX', start_yahoo, end_yahoo)['Adj Close'].resample(FREQ_DICT[frequency], how='last', fill_method='ffill'), columns=['SP500'])
else:
prices = DataFrame(web.get_data_yahoo(market, start_yahoo, end_yahoo)['Adj Close'].resample(FREQ_DICT[frequency], how='last', fill_method='ffill'), columns=[market])
else:
prices = get_index_price(index_id = market, start_date = start_date, end_date = end_date, frequency = frequency).resample(FREQ_DICT[frequency], how = 'last')
#Set Stocks Prices
i = 0
while (i < len(tickers)):
get_df_ticker = DataFrame(web.get_data_yahoo(tickers[i], start_yahoo, end_yahoo)['Adj Close'].resample(FREQ_DICT[frequency], how = 'last'), columns=[tickers[i]])
prices = pd.concat([prices, get_df_ticker], join='outer', axis = 1)
i += 1
changes = prices.pct_change()
return prices, changes[1:]
def test_get_data(self):
#single symbol
#http://finance.yahoo.com/q/hp?s=GOOG&a=09&b=08&c=2010&d=09&e=10&f=2010&g=d
df = web.get_data_yahoo('GOOG')
assert df.Volume.ix['OCT-08-2010'] == 2859200
sl = ['AAPL', 'AMZN', 'GOOG']
pan = web.get_data_yahoo(sl, '2012')
ts = pan.Close.GOOG.index[pan.Close.AAPL > pan.Close.GOOG]
assert ts[0].dayofyear == 96
dfi = web.get_components_yahoo('^DJI')
pan = web.get_data_yahoo(dfi, 'JAN-01-12', 'JAN-31-12')
expected = [19.02, 28.23, 25.39]
result = pan.Close.ix['01-18-12'][['GE', 'MSFT', 'INTC']].tolist()
assert result == expected
pan = web.get_data_yahoo(dfi, 'JAN-01-12', 'JAN-31-12',
adjust_price=True)
expected = [18.38, 27.45, 24.54]
result = pan.Close.ix['01-18-12'][['GE', 'MSFT', 'INTC']].tolist()
assert result == expected
pan = web.get_data_yahoo(dfi, '2011', ret_index=True)
d = [[ 1.01757469, 1.01130524, 1.02414183],
[ 1.00292912, 1.00770812, 1.01735194],
[ 1.00820152, 1.00462487, 1.01320257],
[ 1.08025776, 0.99845838, 1.00113165]]
expected = pd.DataFrame(d)
result = pan.Ret_Index.ix['01-18-11':'01-21-11'][['GE', 'INTC', 'MSFT']]
assert_almost_equal(result.values, expected.values)
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 get_px(rorStyle=0):
getTicker = raw_input("Provide mutual fund tickers seperated by commas (no error catching here so be exact!): ").split(",")
cleanTickers = [x.strip() for x in getTicker]
print cleanTickers
if rorStyle == 0:
px = DataFrame({n: web.get_data_yahoo(n, start='1980-01-01')['Adj Close'].pct_change() for n in cleanTickers}).dropna()
elif rorStyle == 1:
px = np.log(DataFrame({n: web.get_data_yahoo(n, start='1980-01-01')['Adj Close'].pct_change() for n in cleanTickers}).dropna() + 1)
return px
def get_bench(rorStyle=0):
getTicker = raw_input("Provide mutual fund or index ticker to be used as benchmark (^gspc is sp500): ").split(",")
cleanTicker = [x.strip() for x in getTicker]
print "Benchmark is: ", cleanTicker
if rorStyle == 0:
ror = web.get_data_yahoo(cleanTicker, start='1980-01-01')['Adj Close'].pct_change().dropna()
elif rorStyle == 1:
px = web.get_data_yahoo(cleanTicker, start='1980-01-01')['Adj Close']
ror = np.log(px / px.shift(1))
return ror
def get_historical_data(ticker, start_date, end_date):
daily_data = web.get_data_yahoo(ticker, start=start_date, end=end_date)
num_days = len(daily_data)
timeseries = range(0, num_days)
values = [daily_data['Adj Close'][i] for i in xrange(num_days)]
datetimes = map(lambda tm: datetime.datetime(tm.year, tm.month, tm.day), daily_data.index.tolist())
return [timeseries, values, datetimes]
def longTerm(symbol):
a=web.get_data_yahoo(symbol, (datetime.datetime.now() - datetime.timedelta(days = 100)), datetime.datetime.now())['Adj Close']
# here are the given number of M, alpha, and beta
xa=[]
for i in range(0,len(a)):
xa.append(i)
ya=a
plt.figure(1)
value = 11
ss = s2 (value,xa)
mm = mx(value,xa,ya)
s = ss[0][0]
m = mm[0] #print the mean and variance
b = len(ya)
print b
index = m - ya[b-1]
if(index < -m*0.005):
return "Sell"
elif(index < m*0.005):
return "Hold"
else:
return "Buy More"
def main():
for ticker in ['AAPL','IBM','MSFT','GOOG']:
all_data[ticker]=web.get_data_yahoo(ticker,'1/3/200','12/31/2009')
price=DataFrame({tic: data['Adj Close'] for tic, data in all_data.iteritems()})
volume=DataFrame({tic: data['Volume'] for tic, data in all_data.iteritems()})
returns=(price-price.shift(1))/price
def finance():
chart = 'CRME'
if request.method == "POST":
chart = request.form.get('chart')
start = datetime.datetime(2010, 3, 1)
# =========== Yahoo API ======================
# df = web.get_data_yahoo( 'TSLA', start, end, interval='w' )
df = web.get_data_yahoo(chart, start)
# convert the dates for the hover tool
dates = pd.Series(df.index)
dates = dates.dt.strftime('%d-%m-%Y').tolist()
# ========== Hover tools configuration ========
# Make a list of strings for every value(the hover tool accepts only str)
# open_p and close the _p is beqause open and close is funcs in python
open_p = [str(i) for i in df.Open]
high = [str(i) for i in df.High]
low = [str(i) for i in df.Low]
close_p = [str(i) for i in df.Close]
vol = [str(i) for i in df.Volume]
adj = [str(i) for i in df['Adj Close']]
TOOLS = 'pan,wheel_zoom,box_zoom,hover,crosshair,resize,reset'
source1 = cds({
"Date": dates, "Open": open_p, "High": high,
"Low": low, "Close": close_p, "Volume": vol, "Adj": adj
})
source2 = cds({
"Date": dates, "Open": open_p, "High": high,
"Low": low, "Close": close_p, "Volume": vol, "Adj": adj
})
TOOLTIPS = [("Date", "@Date"), ("Open", "@Open"), ("High", "@High"),
("Low", "@Low"), ("Close", "@Close"), ("Volume", "@Volume"), ("Adj Close*", "@Adj")]
# Make the figure configuration
f = figure(height = 270, x_axis_type = "datetime", tools = TOOLS, responsive = True)
# Add title and label
f.title = 'Historical Prices for ' + chart + " from 1.03.2010 until yesterday"
f.xaxis.axis_label = 'Date'
f.yaxis.axis_label = 'Open Prices'
# make line and circle plots
f.line(df.index, df.Open, source = source2, color = 'blue')
# f.circle(df.index, df.Open, source = source1, color = 'navy', size = 0.5, alpha = 0.8)
# other hover tool conf
p_hover = f.select(HoverTool)
p_hover.tooltips = TOOLTIPS
return f
def get_symbol_from_yahoo(symbol, start=None, end=None):
px = web.get_data_yahoo(symbol, start=start, end=end)
px = pd.DataFrame.rename(px, columns={'Adj Close': 'adj_close'})
px.columns.name = symbol
rets = px.adj_close.pct_change().dropna()
rets.index = rets.index.tz_localize("UTC")
return rets
def vendor_query(vendor, symbol, from_date, to_date):
"""
Make a web query to data vendor
:param vendor: 'QUANDL', 'YAHOO'
:param symbol:
:param from_date:
:param to_date:
"""
try:
if vendor == "QUANDL":
web_qry_result = Quandl.get(symbol,
trim_start=from_date,
trim_end=to_date,
authtoken=QUANDL_TOKEN,
verbose=False)
elif vendor == "YAHOO":
web_qry_result = web.get_data_yahoo(symbol,
start=from_date,
end=to_date)
elif vendor == 'CSV':
raw_read = pd.read_csv(symbol, index_col='Date', parse_dates=True)
web_qry_result = raw_read[from_date:to_date]
return web_qry_result
except:
print('Error querying the vendor')
def load_yahoo_stock(sids, start=None, end=None, dvds=True):
if hasattr(sids, '__iter__') and not isinstance(sids, basestring):
return Instruments([load_yahoo_stock(sid, start=start, end=end, dvds=dvds) for sid in sids])
else:
sid = sids
end = end and pd.to_datetime(end) or pd.datetime.now()
start = start and pd.to_datetime(start) or end + pd.datetools.relativedelta(years=-1)
data = get_data_yahoo(sid, start=start, end=end)
data = data.rename(columns=lambda c: c.lower())
if dvds:
d = get_dividends_yahoo(sid, start, end)
d.columns = ['dvds']
if not d.empty:
# sanity check - not expected currently
# missing = d.index.difference(data.index)
missing = d.index - data.index
if len(missing) > 0:
raise Exception('dividends occur on non-business day, not expecting this')
# another sanity check to ensure yahoo rolls dividends up, in case a special occurs on same day
if not d.index.is_unique:
d = d.groupby(lambda x: x).sum()
data = data.join(d)
else:
data['dvds'] = np.nan
pxs = InstrumentPrices(data)
return Instrument(sid, pxs, multiplier=1.)
def load_indices(self, tickers, startdate, lags):
self.tickers = tickers
self.filename = "DATA.csv"
self.startdate = startdate
self.enddate = datetime.date.today().strftime("%Y%m%d")
if os.path.isfile(self.filename):
data = pan.DataFrame.from_csv(self.filename)
self.dataframe = data
else:
for ticker in tickers:
data = web.get_data_yahoo(ticker, self.startdate, self.enddate)
index = ticker + '1change'
data[index] = data['Adj Close'].pct_change(1)
#remove unused columns and nan row
data = data[[index]]
data = data[1:]
#filter out middle threshold noise
#data = data[np.logical_or(data[index] >= threshold, data[index] <= -threshold)]
#preprocess data
data = data.apply(preprocess)
#lag data
for i in range(1, lags + 1):
label = ticker + "%dlag" % i
data[label] = data[index].shift(i)
#remove rows used for change calculation
data = data[lags + 1:]
print data.head(10)
if ticker == "%5EGSPC":
self.sp = data
else:
self.sp = merge(self.sp, data, left_index=True, right_index=True)
self.dataframe = self.sp
self.dataframe.to_csv(self.filename)
def select_stocks(self, buy_label, sell_label):
if self.start_date.weekday() == 5 or self.start_date.weekday() == 6:
self.start_date = datetime.datetime(self.start_date.year, self.start_date.month, self.start_date.day - 2)
print((self.start_date.date(), self.end_date.date()))
for ticker in self.tickers:
try:
data = pid.get_data_yahoo(ticker, start=self.start_date, end=self.end_date)["Close"]
dates = data.index
last_price = data[len(dates) - 1]
end_term_ret = (last_price - data[self.start_date]) / data[self.start_date]
print((ticker, end_term_ret))
self.stocks.append((ticker, end_term_ret, last_price))
except (KeyError, IOError) as e:
raise e
self.stocks.sort(key=lambda t: t[1])
utils.print_stocks("Stocks sorted on return", self.stocks);
decile = int(len(self.stocks) * 0.10)
sell_stocks = self.stocks[:decile]
buy_stocks = self.stocks[decile * 9:]
buy_stocks.reverse()
utils.print_stocks("Sell stocks", sell_stocks)
utils.print_stocks("Buy stocks", buy_stocks)
utils.save_portfolio(sell_label, sell_stocks)
utils.save_portfolio(buy_label, buy_stocks)
def get_history(symbols, start, end, data_path, visible=False):
""" to get Yahoo data from saved csv files. If the file does not exist for the symbol,
data is read from Yahoo finance and the csv saved.
symbols: symbol list
start, end : datetime start/end dates
data_path : datapath for csv files - use double \\ and terminate path with \\
"""
symbols_ls = list(symbols)
for ticker in symbols:
print (ticker,' ')
try:
#see if csv data available
data = pd.read_csv(data_path + ticker + '.csv', index_col='Date', parse_dates=True)
except:
#if no csv data, create an empty dataframe
data = pd.DataFrame(data=None, index=[start])
#check if there is data for the start-end data range
if start.toordinal() < data.index[0].toordinal() \
or end.toordinal() > data.index[-1].toordinal():
if visible:
print ('Refresh data.. ',)
try:
new_data = web.get_data_yahoo(ticker, start, end)
if new_data.empty==False:
if data.empty==False:
try:
ticker_data = data.append(new_data).groupby(level=0, by=['rownum']).last()
except:
print ('Merge failed.. ')
else:
ticker_data = new_data
try:
ticker_data.to_csv(data_path + ticker + '.csv')
if visible:
print (' UPDATED.. ')
except:
print ('Save failed.. ')
else:
if visible:
print ('No new data.. ')
except:
print ('Download failed.. ')
# remove symbol from list
symbols_ls.remove(ticker)
else:
if visible:
print ('OK.. ')
pass
pdata = pd.Panel(dict((symbols_ls[i], pd.read_csv(data_path + symbols_ls[i] + '.csv',\
index_col='Date', parse_dates=True).sort(ascending=True)) for i in range(len(symbols_ls))) )
return pdata.ix[:, start:end, :]
def _wrapped_get_data_yahoo(symbol, start, end):
'''Handle the various exceptions that downloading from yahoo raises.'''
try:
return get_data_yahoo(symbols=symbol, start=start, end=end)
except IOError as e:
if re.match(r'after \d tries, Yahoo! did not return a 200 for url',
e.message):
raise ExternalRequestFailed(e.message)
def get_prices_from_yahoo(required_data, type_of_price='Adj Close'):
'''Yields date, value pairs.'''
# Yahoo errors out if you only ask for recent dates.
start, end = required_data.index[0], required_data.index[-1]
symbols = list(required_data.columns)
new_data = get_data_yahoo(symbols=symbols, start=start, end=end)
required_data.update(new_data.Close)
return required_data
def get_daily_data_yahoo(tickers,start_date='1/1/2010',end_date='5/1/2015'):
daily_data={}
for ticker in tickers:
try:
daily_data[ticker[0]]=web.get_data_yahoo(ticker[1],start_date,end_date)
except:
print("cannot download %s." % ticker[1])
return daily_data
def setup_class(self):
"""Bearcart test data and template setup"""
import pandas.io.data as web
all_data = {}
for ticker in ["AAPL", "GOOG"]:
all_data[ticker] = web.get_data_yahoo(ticker, "4/1/2013", "5/1/2013")
self.price = pd.DataFrame({tic: data["Adj Close"] for tic, data in all_data.iteritems()})
self.templates = Environment(loader=FileSystemLoader("templates"))
def get_history(symbols, start, end, data_path):
symbols_ls = list(symbols)
for ticker in symbols:
print ticker,
try:
# see if csv data available
data = pd.read_csv(data_path + ticker + ".csv", index_col="Date", parse_dates=True)
except:
# if no csv data, create an empty dataframe
data = pd.DataFrame(data=None, index=[start])
# check if there is data for the start-end data range
if data.index[-1].toordinal() < end.toordinal() - 3:
print "Refresh data.. ",
try:
new_data = web.get_data_yahoo(ticker, start, end)
if new_data.empty == False:
if data.empty == False:
try:
ticker_data = data.append(new_data).groupby(level=0, by=["rownum"]).last()
except:
print "Merge failed.. "
else:
ticker_data = new_data
try:
ticker_data.to_csv(data_path + ticker + ".csv")
print " UPDATED.. "
except:
print "Save failed.. "
else:
print "No new data.. "
except:
print "Download failed.. "
# remove symbol from list
symbols_ls.remove(ticker)
else:
print "OK.. "
pass
pdata = pd.Panel(
dict(
(
symbols_ls[i],
pd.read_csv(data_path + symbols_ls[i] + ".csv", index_col="Date", parse_dates=True).sort(
ascending=True
),
)
for i in range(len(symbols_ls))
)
)
return pdata.ix[:, start:end, :]
def getStock(symbol):
all_data = web.get_data_yahoo(symbol,'1/1/2012','12/20/2014')
close_px = all_data['Adj Close']
mavg = pandas.rolling_mean(close_px, 50)
#print mavg
all_data['mvg50'] = pandas.Series(mavg,index=all_data.index)
print all_data
validma50(all_data)
drawChart(all_data)
def test_get_date_ret_index(self):
pan = web.get_data_yahoo(["GE", "INTC", "IBM"], "1977", "1987", ret_index=True)
self.assert_(hasattr(pan, "Ret_Index"))
if hasattr(pan, "Ret_Index") and hasattr(pan.Ret_Index, "INTC"):
tstamp = pan.Ret_Index.INTC.first_valid_index()
result = pan.Ret_Index.ix[tstamp]["INTC"]
self.assertEqual(result, 1.0)
# sanity checking
assert np.issubdtype(pan.values.dtype, np.floating)
def test_get_data_interval(self):
# daily interval data
pan = web.get_data_yahoo('XOM', '2013-01-01', '2013-12-31', interval='d')
self.assertEqual(len(pan), 252)
# weekly interval data
pan = web.get_data_yahoo('XOM', '2013-01-01', '2013-12-31', interval='w')
self.assertEqual(len(pan), 53)
# montly interval data
pan = web.get_data_yahoo('XOM', '2013-01-01', '2013-12-31', interval='m')
self.assertEqual(len(pan), 12)
# dividend data
pan = web.get_data_yahoo('XOM', '2013-01-01', '2013-12-31', interval='v')
self.assertEqual(len(pan), 4)
# test fail on invalid interval
self.assertRaises(ValueError, web.get_data_yahoo, 'XOM', interval='NOT VALID')
def test_get_data(self):
import numpy as np
#single symbol
#http://finance.yahoo.com/q/hp?s=GOOG&a=09&b=08&c=2010&d=09&e=10&f=2010&g=d
df = web.get_data_yahoo('GOOG')
assert df.Volume.ix['OCT-08-2010'] == 2859200
sl = ['AAPL', 'AMZN', 'GOOG']
pan = web.get_data_yahoo(sl, '2012')
ts = pan.Close.GOOG.index[pan.Close.AAPL > pan.Close.GOOG]
assert ts[0].dayofyear == 96
#dfi = web.get_components_yahoo('^DJI')
#pan = web.get_data_yahoo(dfi, 'JAN-01-12', 'JAN-31-12')
pan = web.get_data_yahoo(['GE', 'MSFT', 'INTC'], 'JAN-01-12', 'JAN-31-12')
expected = [19.02, 28.23, 25.39]
result = pan.Close.ix['01-18-12'][['GE', 'MSFT', 'INTC']].tolist()
assert result == expected
# sanity checking
t= np.array(result)
assert np.issubdtype(t.dtype, np.floating)
assert t.shape == (3,)
expected = [[ 18.99, 28.4 , 25.18],
[ 18.58, 28.31, 25.13],
[ 19.03, 28.16, 25.52],
[ 18.81, 28.82, 25.87]]
result = pan.Open.ix['Jan-15-12':'Jan-20-12'][['GE', 'MSFT', 'INTC']].values
assert (result == expected).all()
#Check ret_index
pan = web.get_data_yahoo(['GE', 'INTC', 'IBM'], '1977', '1987',
ret_index=True)
tstamp = pan.Ret_Index.INTC.first_valid_index()
result = pan.Ret_Index.ix[tstamp]['INTC']
expected = 1.0
assert result == expected
# sanity checking
t= np.array(pan)
assert np.issubdtype(t.dtype, np.floating)
def get_px(stock, start, end):
'''
Takes a stock ticker, start and end date and will return time delimited
price data.
Parameters:
stock- Stock listing, e.g MSFT
start- Start date for data collection
end -End date for data collection
'''
return web.get_data_yahoo(stock,start,end)['Adj Close']
def test_get_date_ret_index(self):
pan = web.get_data_yahoo(['GE', 'INTC', 'IBM'], '1977', '1987',
ret_index=True)
self.assertTrue(hasattr(pan, 'Ret_Index'))
if hasattr(pan, 'Ret_Index') and hasattr(pan.Ret_Index, 'INTC'):
tstamp = pan.Ret_Index.INTC.first_valid_index()
result = pan.Ret_Index.ix[tstamp]['INTC']
self.assertEqual(result, 1.0)
# sanity checking
self.assertTrue(np.issubdtype(pan.values.dtype, np.floating))
def _remote_fetch(self, start, end):
assert (end - start).days <= self.api_limit, (
'Range is greater than the Yahoo finanace api '
'limit of %s.' % self.api_limit)
def _yahoo_date(date):
assert isinstance(date, datetime)
return date.strftime('%d/%m/%Y')
return get_data_yahoo(self.symbol, start=_yahoo_date(start),
end=_yahoo_date(end))
def test_get_data_multiple_symbols_two_dates(self):
pan = web.get_data_yahoo(["GE", "MSFT", "INTC"], "JAN-01-12", "JAN-31-12")
result = pan.Close.ix["01-18-12"]
self.assertEqual(len(result), 3)
# sanity checking
assert np.issubdtype(result.dtype, np.floating)
expected = np.array([[18.99, 28.4, 25.18], [18.58, 28.31, 25.13], [19.03, 28.16, 25.52], [18.81, 28.82, 25.87]])
result = pan.Open.ix["Jan-15-12":"Jan-20-12"]
self.assertEqual(expected.shape, result.shape)
def setup_class(self):
'''Bearcart test data and template setup'''
import pandas.io.data as web
all_data = {}
for ticker in ['AAPL', 'GOOG']:
all_data[ticker] = web.get_data_yahoo(ticker,
'4/1/2013', '5/1/2013')
self.price = pd.DataFrame({tic: data['Adj Close']
for tic, data in all_data.iteritems()})
self.templates = Environment(loader=FileSystemLoader('templates'))
import pandas as pd
import matplotlib.pyplot as plt
from pandas.io.data import get_data_yahoo
##### download data from Yahoo #####
# Download the S&P 500
SP500 = get_data_yahoo('^GSPC', start='1950-01-03')
##### plot the data #####
# plot the S&P 500 index
ax1 = SP500['Close'].plot()
# add labels, axes, title, etc
ax1.set_ylabel('Close')
ax1.set_yscale('log')
ax1.set_title('Historical S&P 500 Index', weight='bold')
# load the NBER recession dates
NBER_Dates = pd.read_csv('NBER Dates.txt')
# for loop generates recession bands!
for i in range(NBER_Dates.shape[0]):
ax1.axvspan(NBER_Dates['Peak'][i], NBER_Dates['Trough'][i], facecolor='grey', alpha=0.5)
# save the figure and display
plt.savefig('2012-12-21-SP500.png')
plt.show()
covariance:
-measure of the strength of the correlation between two or more sets of random variates
-positive, negative, zero
-http://mathworld.wolfram.com/Covariance.html
-http://stats.stackexchange.com/questions/18058/how-would-you-explain-covariance-to-someone-who-understands-only-the-mean
-http://math.tutorvista.com/statistics/covariance.html
"""
import numpy as np
import pandas as pd
import pandas.io.data as web
alldata = {}
for ticker in ["AAPL", "IBM", "MSFT", "GOOG"]:
alldata[ticker] = web.get_data_yahoo(ticker, "1/1/2010", "1/1/2015")
alldata["AAPL"].keys()
# Index([u'Open', u'High', u'Low', u'Close', u'Volume', u'Adj Close'], dtype='object')
price = pd.DataFrame(
{tic: data["Adj Close"]
for tic, data in alldata.iteritems()}) # [1258 rows x 3 columns]
volume = pd.DataFrame(
{tic: data["Volume"]
for tic, data in alldata.iteritems()}) # [1258 rows x 3 columns]
# compute changes in price
returns = price.pct_change()
returns.tail()
'''
AAPL GOOG IBM MSFT
Date
import datetime as dt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
import pandas.io.data as web
# step 1: Range Selection
st = dt.datetime(2000,12,1)
en = dt.datetime(2015,1,1)
sp500_tickers_lil = ['AA','AAPL','ABC','ABT','ADBE','ADI','ADM','ADP','ADSK','AEE']
sp500_tickers_joey = ['AA','AAPL','ABC','ABT','ADBE','ADI','ADM','ADP','ADSK','AEE','AEP','AES','AET','AFL','AGN','AIG','AIV','AIZ','AKAM','ALL','AMAT','AMGN','AMP','AMT','AMZN','AN','ANTM','AON','APA','APC','APD','AVB','AVY','AXP','AZO','BA','BAC','BAX','BBBY','BBT','BBY','BCR','BDX','BEN',,'BHI','BIIB','BK','BLL','BMY','BRCM','BSX','BXP','C','CA','CAG','CAH','CAT','CB','CBG','CBS','CCE','CCL','CELG','CHK','CHRW','CI','CINF','CL','CLX','CMA','CMCSA','CME','CMI','CMS','CNP','CNX','COF','COH','COL','COP','COST','CPB','CSCO','CSX','CTAS','CTL','CTSH','CTXS','CVS','CVX','D','DD','DE','DFS','DGX','DHI','DHR','DIS','DOV','DOW','DRI','DTE','DUK','DVN','EA','EBAY','ECL','ED','EFX','EIX','EL','EMC','EMN','EMR','EOG','EQR','ESRX','ETFC','ETN','ETR','EXC','EXPD','EXPE','F','FCX','FDX','FE','FIS','FISV','FITB','FLR','FOXA','FTR','GAS','GD','GE','GILD','GIS','GLW','GME','GOOGL','GPC','GPS','GS','GT','GWW','HAL','HAR','HAS','HBAN','HD','HES','HIG','HOG','HON','HOT','HPQ','HRB','HST','HSY','HUM','IBM','ICE','IFF','INTC','INTU','IP','IPG','ITW','JCI','JEC','JNJ','JNPR','JPM','JWN','K','KEY','KIM','KLAC','KMB','KO','KR','KSS','L','LB','LEG','LEN','LH','LLL','LLTC','LLY','LM','LMT','LNC','LOW','LUK','LUV','M','MAR','MAS','MAT','MCD','MCHP','MCK','MCO','MDLZ','MDT','MET','MHFI','MKC','MMC','MMM','MO','MON','MRK','MRO','MS','MSFT','MSI','MTB','MU','MUR','MYL','NBL','NEE','NEM','NI','NKE','NOC','NOV','NSC','NTAP','NTRS','NUE','NVDA','NWL','OMC','ORCL','OXY','PAYX','PBI','PCAR','PCG','PCL','PCP','PDCO','PEG','PEP','PFE','PFG','PG','PGR','PH','PHM','PKI','PLD','PNC','PNW','POM','PPG','PPL','PRU','PSA','PX','QCOM','R','RAI','RF','RHI','RL','ROK','RRC','RTN','SBUX','SCHW','SE','SEE','SHW','SLB','SNA','SNDK','SO','SPG','SPLS','SRE','STI','STJ','STT','STZ','SWK','SYK','SYMC','SYY','T','TAP','TDC','TE','TGT','THC','TIF','TJX','TMK','TMO','TROW','TRV','TSN','TSO','TSS','TWX','TXN','TXT','UNH','UNM','UNP','UPS','USB','UTX','VAR','VFC','VIAB','VLO','VMC','VNO','VRSN','VZ','WAT','WBA','WFC','WFM','WHR','WM','WMB','WMT','WU','WY','WYN','XEL','XL','XLNX','XOM','XRX','YHOO','YUM','ZBH','ZION']
spy = web.get_data_yahoo('SPY',start = st,end = en)['Adj Close']
spy_data = spy.resample("M",how = 'last').pct_change()
regr_coefs = []
dataset = sp500_tickers_joey
#dataset = ['AA']
for ticker in dataset:
try:
data = web.get_data_yahoo(ticker, start=st, end=en).resample("M",how = 'last')['Adj Close']
except:
print(ticker, "error")
continue
data_pct = data.pct_change().dropna()
#returns = pd.DataFrame(index = data.index,columns = [1,2,3,4,5,6,7,8,9,10,11,12,'spy'])
import pandas as pd
import matplotlib.pyplot as plt
from pandas.io.data import get_data_yahoo, get_data_fred
##### download data #####
# Download the S&P 500
SP500 = get_data_yahoo('^GSPC', start='1950-01-03', end='2012-11-30')
# Download the CPI data
CPIAUCSL = get_data_fred('CPIAUCSL', start='1950-01-01')
##### resample S&P 500 data #####
# Need S&P 500 data to be monthly...note I am taking monthly averages
monthly_avg_SP500 = SP500.resample('MS', how='mean')
# Add the CPI data as a column to the monthly DataFrame
monthly_avg_SP500['CPIAUCSL'] = CPIAUCSL
##### Convert nominal values to real values #####
# express all prices in terms of the price level in Nov. 2012...
monthly_avg_SP500['Price Deflator'] = (
monthly_avg_SP500['CPIAUCSL']['2012-11-01'] /
monthly_avg_SP500['CPIAUCSL'])
monthly_avg_SP500['Close (Real)'] = monthly_avg_SP500[
'Close'] * monthly_avg_SP500['Price Deflator']
##### Nominal S&P 500 #####
# IPython log file
plt(np.random.randn(100).cumsum())
plot(np.random.randn(100).cumsum())
plt.figure()
from pandas.io.data import get_data_yahoo
spy_close = get_data_yahoo('SPY'['Adj Close']
asdfas
from pandas.io.data import get_data_yahoo
spy_close = get_data_yahoo('SPY')['Adj Close']
spy_close
spy_close.plot()
_
__
a
a = 1
b = 2
c = 3
__
_
a
_
__
_
____
_ix
_i19
_i15
exec _i15
c
c = 4
def test_get_data_single_symbol(self):
#single symbol
#http://finance.yahoo.com/q/hp?s=GOOG&a=09&b=08&c=2010&d=09&e=10&f=2010&g=d
# just test that we succeed
web.get_data_yahoo('GOOG')
import numpy as np
import pandas as pd
import pandas.io.data as web
#goog=web.DataReader('GOOG',data_source='google',start='1/21/2013',end='4/6/2015')
goog=web.get_data_yahoo('000009.sz',start='1/21/2013',end='4/4/2015')
print goog.head()
def test_get_data_multiple_symbols(self):
# just test that we succeed
sl = ['AAPL', 'AMZN', 'GOOG']
web.get_data_yahoo(sl, '2012')
# -*- coding: utf-8 -*-
"""
Vincent Area Examples
"""
#Build an Area Chart from scratch
from vincent import *
import pandas.io.data as web
all_data = {}
for ticker in ['AAPL', 'GOOG', 'IBM', 'YHOO', 'MSFT']:
all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2010', '1/1/2013')
price = pd.DataFrame(
{tic: data['Adj Close']
for tic, data in all_data.items()})
vis = Visualization(width=500, height=300)
vis.padding = {'top': 10, 'left': 50, 'bottom': 50, 'right': 100}
vis.scales['x'] = Scale(name='x',
type='time',
range='width',
domain=DataRef(data='table', field="data.idx"))
vis.scales['y'] = Scale(name='y',
range='height',
type='linear',
nice=True,
domain=DataRef(data='table', field="data.val"))
vis.scales['color'] = Scale(name='color',
import pandas.io.data as web
import pandas as pd
import numpy as np
import talib as ta
import matplotlib.pyplot as plt
from matplotlib.dates import date2num
from matplotlib.finance import candlestick
# Download sample data
spy = web.get_data_yahoo('SPY', '2010-01-01')
# Data for matplotlib finance plot
spy_ochl = np.array(
pd.DataFrame({
'0': date2num(spy.index),
'1': spy.Open,
'2': spy.Close,
'3': spy.High,
'4': spy.Low
}))
# Technical Analysis
SMA_FAST = 34
SMA_SLOW = 144
RSI_PERIOD = 14
analysis = pd.DataFrame(index=spy.index)
analysis['sma_f'] = pd.rolling_mean(spy.Close, SMA_FAST)
analysis['sma_s'] = pd.rolling_mean(spy.Close, SMA_SLOW)
analysis['rsi'] = ta.RSI(spy.Close, RSI_PERIOD)
# Record signals (open position after crossover)
# -*- coding: utf-8 -*-
import numpy as np
import pandas.io.data as web
from pandas import DataFrame
print(
'相关性与协方差') # 协方差：https://zh.wikipedia.org/wiki/%E5%8D%8F%E6%96%B9%E5%B7%AE
all_data = {}
for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']:
all_data[ticker] = web.get_data_yahoo(ticker, '4/1/2016', '7/15/2015')
price = DataFrame(
{tic: data['Adj Close']
for tic, data in all_data.items()})
volume = DataFrame({tic: data['Volume'] for tic, data in all_data.items()})
returns = price.pct_change()
print(returns.tail())
print(returns.MSFT.corr(returns.IBM))
print(returns.corr()) # 相关性，自己和自己的相关性总是1
print(returns.cov()) # 协方差
print(returns.corrwith(returns.IBM))
print(returns.corrwith(returns.volume))
multi_iter2 = {'index': index_2}
for cat in cat_2:
multi_iter2[cat] = [random.randint(10, 100) for x in index_2]
farm_1 = {'apples': 10, 'berries': 32, 'squash': 21, 'melons': 13, 'corn': 18}
farm_2 = {'apples': 15, 'berries': 43, 'squash': 17, 'melons': 10, 'corn': 22}
farm_3 = {'apples': 6, 'berries': 24, 'squash': 22, 'melons': 16, 'corn': 30}
farm_4 = {'apples': 12, 'berries': 30, 'squash': 15, 'melons': 9, 'corn': 15}
farm_data = [farm_1, farm_2, farm_3, farm_4]
farm_index = ['Farm 1', 'Farm 2', 'Farm 3', 'Farm 4']
df_farm = pd.DataFrame(farm_data, index=farm_index)
#As DataFrames
index_3 = multi_iter2.pop('index')
df_1 = pd.DataFrame(multi_iter2, index=index_3)
df_1 = df_1.reindex(columns=sorted(df_1.columns))
cat_4 = ['Metric_' + str(x) for x in range(0, 10, 1)]
index_4 = ['Data 1', 'Data 2', 'Data 3', 'Data 4']
data_3 = {}
for cat in cat_4:
data_3[cat] = [random.randint(10, 100) for x in index_4]
df_2 = pd.DataFrame(data_3, index=index_4)
import pandas.io.data as web
all_data = {}
for ticker in ['AAPL', 'IBM', 'YHOO', 'MSFT']:
all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2012', '1/1/2014')
price = pd.DataFrame({tic: data['Adj Close'] for tic, data in all_data.iteritems()})
import numpy as np
import pandas as pd
import pandas.io.data as web
goog = web.get_data_yahoo('GOOG', '3/14/2009', '2/14/2014')
goog['Log_Ret'] = np.log(goog['Close'] / goog['Close'].shift(1))
goog['Volatility'] = pd.rolling_std(goog['Log_Ret'], window=252) * np.sqrt(252)
#%matplotlib inline
#goog[['Close','Volatility']].plot(subplots=True,color='blue',figsize=(20,12))
print goog
raw_input('hello')
print('Hello World')
import matplotlib.pyplot as plt
from matplotlib import rcParams
import pandas as pd
from pandas.io.data import get_data_yahoo
plt.ioff()
c1, c2 = rcParams['axes.color_cycle'][:2]
stocks = ['LINE', 'LNCO']
prices = get_data_yahoo(stocks, start='1/1/2000')['Adj Close'].dropna()
day1 = str(prices.index.min().date())
day2 = str(prices.index.max().date())
fig = plt.figure()
ax = fig.add_subplot(111)
prices.plot(ax=ax)
ax.fill_between(prices.index,
prices['LINE'],
prices['LNCO'],
alpha=0.5,
where=prices['LINE'] >= prices['LNCO'],
facecolor=c1)
ax.fill_between(prices.index,
prices['LINE'],
prices['LNCO'],
alpha=0.5,
where=prices['LINE'] < prices['LNCO'],
facecolor=c2)
ax.set_title("LINE and LNCO: %s to %s" % (day1, day2))
import numpy as np
import pandas as ps
import pandas.io.data as pdweb
import datetime
price = pdweb.get_data_yahoo(['CVX', 'XOM', 'BP'],
start=datetime.datetime(2010, 1, 1),
end=datetime.datetime(2013, 1, 1))['Adj Close']
print(price.head())
'''\
Rev: 1
Author: silentshadow
Description: feed investment portfolio data into pandas
Reference: http://www.ibm.com/developerworks/cloud/library/cl-datascienceincloud/
'''
import pandas.io.data as web
import matplotlib.pyplot as plt
from pandas import DataFrame
data_feed = {}
symbols = ['AAPL', 'FB', 'TSLA', 'GOOG']
for ticker in symbols:
data_feed[ticker] = web.get_data_yahoo(ticker, '03/1/2016', '04/1/2016')
price = DataFrame({tic: data['Adj Close'] for tic, data in data_feed.items()})
volume = DataFrame({tic: data['Volume'] for tic, data in data_feed.items()})
returns = price.pct_change()
returns.sum().plot(kind='bar', title="% Return For Year")
plt.show()
#! /usr/bin/env python # -*- coding:utf-8 -*- """ @author : MG @Time : 19-4-11 上午9:11 @File : candle_demo2.py.py @contact : [email protected] @desc : """ #The following example, downloads stock data from Yahoo and plots it. from pandas.io.data import get_data_yahoo import matplotlib.pyplot as plt from matplotlib.pyplot import subplots, draw from matplotlib.finance import candlestick symbol = "GOOG" data = get_data_yahoo(symbol, start = '2013-9-01', end = '2013-10-23')[['Open','Close','High','Low','Volume']] ax = subplots() candlestick(ax,data['Open'],data['High'],data['Low'],data['Close']) if __name__ == "__main__": pass
df.sum()
df.sum(axis=1)
df.mean(axis=1, skipna=False)
df.idxmax()
df.cumsum()
df.describe()
obj = Series(['a', 'a', 'b', 'c'] * 4)
obj.describe()
#相关系数
import pandas.io.data as web
all_data = {}
for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']:
all_data[ticker] = web.get_data_yahoo(ticker)
price = DataFrame({tic: data['Adj Close']
for tic, data in all_data.iteritems()})
volume = DataFrame({tic: data['Volume']
for tic, data in all_data.iteritems()})
returns = price.pct_change()
returns.tail()
returns.MSFT.corr(returns.IBM)
returns.MSFT.cov(returns.IBM)
returns.corr()
returns.cov()
returns.corrwith(returns.IBM)
returns.corrwith(volume)
############################################################################## # # An example of converting a Pandas dataframe with stock data taken from the # web to an xlsx file with a line chart using Pandas and XlsxWriter. # # Copyright 2013-2020, John McNamara, [email protected] # import pandas as pd import pandas.io.data as web # Create some sample data to plot. all_data = {} for ticker in ['AAPL', 'GOOGL', 'IBM', 'YHOO', 'MSFT']: all_data[ticker] = web.get_data_yahoo(ticker, '5/1/2014', '5/1/2015') # Create a Pandas dataframe from the data. df = pd.DataFrame({tic: data['Adj Close'] for tic, data in all_data.items()}) # Create a Pandas Excel writer using XlsxWriter as the engine. sheet_name = 'Sheet1' writer = pd.ExcelWriter('pandas_chart_stock.xlsx', engine='xlsxwriter') df.to_excel(writer, sheet_name=sheet_name) # Access the XlsxWriter workbook and worksheet objects from the dataframe. workbook = writer.book worksheet = writer.sheets[sheet_name] # Adjust the width of the first column to make the date values clearer. worksheet.set_column('A:A', 20)
#!/bin/env python
#
import pandas as pd
import pandas.io.data as web
from qrzigzag import peak_valley_pivots, max_drawdown, compute_segment_returns, pivots_to_modes
X = web.get_data_yahoo('GOOG')['Adj Close']
pivots = peak_valley_pivots(X, 0.2, -0.2)
ts_pivots = pd.Series(X, index=X.index)
ts_pivots = ts_pivots[pivots != 0]
X.plot()
ts_pivots.plot(style='g-o')
import numpy as np
import pandas as pd
from pandas import DataFrame,Series
import matplotlib.pyplot as plt
import pandas.io.data as pdweb
import datetime
prices=pdweb.get_data_yahoo(["CVX","XOM","BP"],start=datetime.datetime(2010,1,1),
end=datetime.datetime(2013,1,1))["Adj Close"]
print(prices.head())
print("="*50)
volume=pdweb.get_data_yahoo(["CVX","XOM","BP"],start=datetime.datetime(2010,1,1),
end=datetime.datetime(2013,1,1))["Volume"]
print(volume.head())
print("="*50)
rets=prices.pct_change() #call percentage change
#get correlation
corr=rets.corr
#plotting it
prices.plot()
plt.show()
import seaborn as sns
sns.corrplot(rets,annot=False,diag_names=False)
def get_benchmark_series(benchmark):
return web.get_data_yahoo([benchmark], fromDate)["Close"][benchmark]
def get_px(stock, start, end): return web.get_data_yahoo(stock, start, end)['Adj Close']
def test_get_data_single_symbol(self):
#single symbol
#http://finance.yahoo.com/q/hp?s=GOOG&a=09&b=08&c=2010&d=09&e=10&f=2010&g=d
df = web.get_data_yahoo('GOOG')
self.assertEqual(df.Volume.ix['OCT-08-2010'], 2859200)
def getPerf(self, symbol, param, strategy, enddatestr=""):
ret = {}
self.setDateRange(enddatestr)
try:
ohlc = web.get_data_yahoo(symbol, self.startdate, self.enddate)
except:
# IO error
print "System/Network Error when retrieving ", symbol, " skip it"
return ret
# calculate perf
px = ohlc['Adj Close']
#additional indicator
for gy in strategy:
if gy not in self.stgyInx:
self.stgyInx[gy] = self.loadStrategy(gy)
self.stgyInx[gy].runIndicator(px, strategy[gy])
p1d = 0
p4w = 0
p12w = 0
p24w = 0
pmax = 0
plen = len(px)
if plen >= 2:
p1d = round((px[-1] / px[-2] - 1) * 100, 2)
if plen >= 4 * 7:
p4w = round((px[-1] / px[-4 * 7] - 1) * 100, 2)
if plen >= 12 * 7:
p12w = round((px[-1] / px[-12 * 7] - 1) * 100, 2)
if plen >= 24 * 7:
p24w = round((px[-1] / px[-24 * 7] - 1) * 100, 2)
if len(px) >= 1 * 7:
p1w = round((px[-1] / px[-1 * 7] - 1) * 100, 2)
pmax = round((px[-1] / px[0] - 1) * 100, 2)
if 'vol20' in param:
sma20vol = pandas.stats.moments.rolling_mean(ohlc['Volume'], 20)
ret['vol20'] = sma20vol[-1]
if 'vol' in param:
ret['vol'] = ohlc['Volume'][-1]
if 'px' in param:
ret['px'] = ohlc['Adj Close'][-1]
if 'ma10' in param:
sma10s = pandas.stats.moments.rolling_mean(px, 10)
ret['ma10'] = round(sma10s[-1], 2)
if 'ma50' in param:
sma50s = pandas.stats.moments.rolling_mean(px, 50)
ret['ma50'] = round(sma50s[-1], 2)
if 'ma200' in param:
sma200s = pandas.stats.moments.rolling_mean(px, 200)
ret['ma200'] = round(sma200s[-1], 2)
ret['p1d'] = p1d
ret['p1w'] = p1w
ret['p4w'] = p4w
ret['p12w'] = p12w
ret['p24w'] = p24w
return ret
df.idxmax()
df.idxmin(axis=1)
df.cumsum()
df.describe()
obj = Series(['a', 'a', 'b', 'c'] * 4)
obj.describe()
df['three'] = ['a', 'b', 'c', 'a']
df.describe()
df['three'].describe()
"""
Correlation and Covariance
"""
import pandas.io.data as web
all_data = {}
for ticker in ['AAPL', 'IBM', 'MSFT', 'GOOG']:
all_data[ticker] = web.get_data_yahoo(ticker, '1/1/2000', '1/1/2010')
price = DataFrame(
{tic: data['Adj Close']
for tic, data in all_data.iteritems()})
volume = DataFrame({tic: data['Volume'] for tic, data in all_data.iteritems()})
returns = price.pct_change()
returns.tail()
returns.MSFT.corr(returns.IBM)
returns.MSFT.cov(returns.IBM)
returns.corr()
returns.cov()
returns.corrwith(returns.IBM)
returns.corrwith(volume)
"""
Unique Values, Value Count, and Membership
"""