def plot_funds(tickerlist, initial, start, end):
'''Plot a fund by its ticker symbol,
normalized to a given initial value.
'''
numdays = (end - start).days
daysinyear = 365.0
print '%9s %9s %9s %9s' % ('Ticker', 'daily', 'CC', 'abs')
# For testing, use something like
# FUSVX = yahoo('FUSVX', datetime.datetime(2012, 10, 1),
# datetime.datetime(2013, 4, 1),
# asobject=True)
for i, ticker in enumerate(tickerlist):
# This gives a runtime warning for SCAL, and all the aclose vals
# come out zero. Catching a RuntimeWarning isn't as simple as try;
# http://stackoverflow.com/questions/10519237/python-how-to-avoid-runtimewarning-in-function-definition
# http://stackoverflow.com/questions/9349434/how-do-i-check-for-numeric-overflow-without-getting-a-warning-in-python
try:
fund_data = yahoo(ticker, start, end, asobject=True)
except:
print "Couldn't get data for", ticker
continue
# Guard against failures of quotes_historical_yahoo;
# without this check you'll see more uncatchable RuntimeWarnings.
if fund_data['aclose'][0] == 0:
print ticker, ": First adjusted close is 0!"
continue
# Calculate effective daily-compounded interest rate
fixed_pct = fund_data['aclose'][-1] / fund_data['aclose'][0] - 1.
Rcc = daysinyear / numdays * \
numpy.log(fund_data['aclose'][-1] / fund_data['aclose'][0])
# Convert CC return to daily-compounded return:
Rdaily = daysinyear * (math.exp(Rcc / daysinyear) - 1.)
# Another attempt to compute the daily rate, but it's wrong.
# Reff = daysinyear * (math.exp(math.log(fund_data['aclose'][-1]
# - fund_data['aclose'][0])
# /numdays) - 1)
print "%9s %9.2f %9.2f %9.2f" % (ticker, Rdaily * 100, Rcc * 100,
fixed_pct * 100)
# Normalize to the initial investment:
fund_data['aclose'] *= initial / fund_data['aclose'][0]
# and plot
ax1.plot_date(x=fund_data['date'],
y=fund_data['aclose'],
fmt=pick_color(i),
label=ticker)
def plot_funds(tickerlist, initial, start, end):
'''Plot a fund by its ticker symbol,
normalized to a given initial value.
'''
numdays = (end - start).days
daysinyear = 365.0
print '%9s %9s %9s %9s' % ('Ticker', 'daily', 'CC', 'abs')
# For testing, use something like
# FUSVX = yahoo('FUSVX', datetime.datetime(2012, 10, 1),
# datetime.datetime(2013, 4, 1),
# asobject=True)
for i, ticker in enumerate(tickerlist):
# This gives a runtime warning for SCAL, and all the aclose vals
# come out zero. Catching a RuntimeWarning isn't as simple as try;
# http://stackoverflow.com/questions/10519237/python-how-to-avoid-runtimewarning-in-function-definition
# http://stackoverflow.com/questions/9349434/how-do-i-check-for-numeric-overflow-without-getting-a-warning-in-python
try:
fund_data = yahoo(ticker, start, end, asobject=True)
except:
print "Couldn't get data for", ticker
continue
# Guard against failures of quotes_historical_yahoo;
# without this check you'll see more uncatchable RuntimeWarnings.
if fund_data['aclose'][0] == 0:
print ticker, ": First adjusted close is 0!"
continue
# Calculate effective daily-compounded interest rate
fixed_pct = fund_data['aclose'][-1]/fund_data['aclose'][0] - 1.
Rcc = daysinyear / numdays * \
numpy.log(fund_data['aclose'][-1] / fund_data['aclose'][0])
# Convert CC return to daily-compounded return:
Rdaily = daysinyear * (math.exp(Rcc / daysinyear) - 1.)
# Another attempt to compute the daily rate, but it's wrong.
# Reff = daysinyear * (math.exp(math.log(fund_data['aclose'][-1]
# - fund_data['aclose'][0])
# /numdays) - 1)
print "%9s %9.2f %9.2f %9.2f" % (ticker,
Rdaily*100, Rcc*100, fixed_pct*100)
# Normalize to the initial investment:
fund_data['aclose'] *= initial / fund_data['aclose'][0]
# and plot
ax1.plot_date(x=fund_data['date'], y=fund_data['aclose'],
fmt=pick_color(i), label=ticker)
def Vol_Smile(ticker,expiration,S,K,T,r,call_price):
implied=1.
minimum_value=1000
for i in range(10000):
sigma=0.0001*(1+i)
d1=(sp.log(S/K)+(r+sigma*sigma/2.)*T)/(sigma*sp.sqrt(T))
d2=d1-sigma*sp.sqrt(T)
call_iv=S*sp.stats.norm.cdf(d1)-K*sp.exp(-r*T)*sp.stats.norm.cdf(d2)
if call_iv-call_price < minimum_value:
minimum_value=call_iv-call_price
implied=sigma
i=k
return implied
begdate=datetime.date(2011,1,1)
enddate=datetime.date.today()
option_data=Options(ticker,'yahoo').get_call_data(expiry=expiration)
price_data=yahoo(ticker,begdate,enddate,asobject=True,adjusted=True)
"""
# Lower Partial Standard Deviation
import pandas as pd
import scipy.stats as stats
import numpy as np
import datetime as dt
from matplotlib.finance import quotes_historical_yahoo as yahoo
ticker='IBM'
begdate=(2008,12,31)
enddate=(2009,11,1)
# For the risk free rate, using Fama-French daily dataset. The data already formatted has been downloaded from Yan's website.
price=yahoo(ticker,begdate,enddate,asobject=True,adjusted=True)
returns=(price.aclose[1:]-price.aclose[:-1])/price.aclose[1:]
x=pd.DataFrame(data=returns,index=price.date[:-1],columns=['returns'])
ff=pd.load('ffDaily.pickle')
final_data=pd.merge(x,ff,left_index=True,right_index=True)
premium=final_data.returns-final_data.Rf
LPSD=np.std(premium[premium > 0])*np.sqrt(252)
print "LPSD is " + str(LPSD)
import nltk
from nltk.parse.cpchart import CInsideChartParser
from nltk.grammar import parse_cpcfg, Nonterminal
from scipy.stats import norm
from matplotlib.finance import quotes_historical_yahoo as yahoo
if __name__ == "__main__":
import datetime
import numpy as np
date1 = datetime.date(2010, 10, 13)
date2 = datetime.date(2010, 11, 26)
quotes = yahoo("uup", date1, date2)
if (len(quotes) == 0):
raise SystemExit
test = np.diff([100 * np.log(q[2]) for q in quotes])
test = np.round(test, decimals=3).tolist()
print test
#test = [-0.5,-0.5, 0,0, 0.5, 0.5]
densityEmission = {
Nonterminal('e1') : lambda x : norm.logpdf(x, loc=0, scale=1),
Nonterminal('e2') : lambda x : norm.logpdf(x, loc=0.5, scale=1),
Nonterminal('e3') : lambda x : norm.logpdf(x, loc=-0.5, scale=1),
}
emission = {
Nonterminal('e1') : lambda x : norm.rvs(x, loc=0, scale=1),
Nonterminal('e2') : lambda x : norm.rvs(x, loc=0.5, scale=1),
Nonterminal('e3') : lambda x : norm.rvs(x, loc=-0.5, scale=1),
}
grammar = parse_cpcfg("""
S -> UPTRI [0.5] | BOTRI [0.5]
"""
# Candlesticks indicator and representation
from matplotlib.dates import DateFormatter as df, WeekdayLocator as wl, HourLocator as hl, DayLocator as dl, MONDAY
import matplotlib.pyplot as plt
from matplotlib.finance import quotes_historical_yahoo as yahoo, candlestick as cs, candlestick2 as cs2, plot_day_summary as pds
import numpy as np
from matplotlib import rc, rcParams
begdate=(2014, 9, 27)
enddate=(2014, 10, 27)
ticker='AAPL'
stock_price=yahoo(ticker,begdate,enddate)
if len(stock_price)==0:
raise SystemExit
rcParams['xtick.major.pad']='15'
figure,ax=plt.subplots()
ax.xaxis.set_major_locator(wl(MONDAY))
ax.xaxis.set_major_formatter(df("%b %d"))
ax.xaxis.set_minor_locator(dl())
ax.xaxis.set_minor_formatter(df("%d"))
pds(ax,stock_price,ticksize=1)
cs(ax,stock_price,width=0.5)
rc('xtick', labelsize=8)