コード例 #1
0
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)
コード例 #2
0
ファイル: fincompare.py プロジェクト: PhiTheta/scripts
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)
コード例 #3
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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)
    
        
コード例 #4
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"""

# 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)
コード例 #5
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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]
コード例 #6
0
"""

# 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)