def ret_annual(ticker, begdate, enddte):
x = getData(ticker, begdate, enddate, asobject=True, adjusted=True)
logret = sp.log(x.aclose[1:] / x.aclose[:-1])
date = []
d0 = x.date
for i in range(0, sp.size(logret)):
date.append(d0[i].strftime("%Y"))
y = pd.DataFrame(logret, date, columns=[ticker])
return sp.exp(y.groupby(y.index).sum()) - 1
def ret_monthly(ticker): # function 1
x = getData(ticker, (begYear, 1, 1), (endYear, 12, 31),
asobject=True,
adjusted=True)
logret = np.log(x.aclose[1:] / x.aclose[:-1])
date = []
d0 = x.date
for i in range(0, np.size(logret)):
date.append(''.join([d0[i].strftime("%Y"), d0[i].strftime("%m")]))
y = pd.DataFrame(logret, date, columns=[ticker])
return y.groupby(y.index).sum()
def sharpeRatio(ticker, begdate=(2012, 1, 1), enddate=(2016, 12, 31)):
"""Objective: estimate Sharpe ratio for stock
ticker : stock symbol
begdate : beginning date
enddate : ending date
Example #1: sharpeRatio("ibm")
0.0068655583807256159
Example #2: date1=(1990,1,1)
date2=(2015,12,23)
sharpeRatio("ibm",date1,date2)
0.027831010497755326
"""
import scipy as sp
from matplotlib.finance import quotes_historical_yahoo_ochl as getData
p = getData(ticker, begdate, enddate, asobject=True, adjusted=True)
ret = p.aclose[1:] / p.aclose[:-1] - 1
return sp.mean(ret) / sp.std(ret)
""" Name : c11_09_normality_test_MSFT.py Book : Python for Finance (2nd ed.) Publisher: Packt Publishing Ltd. Author : Yuxing Yan Date : 6/6/2017 email : [email protected] [email protected] """ import numpy as np from scipy import stats from matplotlib.finance import quotes_historical_yahoo_ochl as getData # ticker = 'MSFT' begdate = (2012, 1, 1) enddate = (2016, 12, 31) # p = getData(ticker, begdate, enddate, asobject=True, adjusted=True) ret = (p.aclose[1:] - p.aclose[:-1]) / p.aclose[1:] print 'ticker=', ticker, 'W-test, and P-value' print(stats.shapiro(ret)) print(stats.anderson(ret))
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, WeekdayLocator
from matplotlib.dates import HourLocator, DayLocator, MONDAY
from matplotlib.finance import candlestick_ohlc, plot_day_summary_oclh
from matplotlib.finance import quotes_historical_yahoo_ochl as getData
#
date1 = (2013, 10, 20)
date2 = (2013, 11, 10)
ticker = 'IBM'
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
quotes = getData(ticker, date1, date2)
if len(quotes) == 0:
raise SystemExit
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
ax.xaxis.set_minor_formatter(dayFormatter)
plot_day_summary_oclh(ax, quotes, ticksize=3)
candlestick_ohlc(ax, quotes, width=0.6)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=80, horizontalalignment='right')
plt.figtext(0.35, 0.45, '10/29: Open, High, Low, Close')
plt.figtext(0.35, 0.42, ' 177.62, 182.32, 177.50, 182.12')
""" Name : c8_04_retrieve_daily_from_Yahoo.py Book : Python for Finance (2nd ed.) Publisher: Packt Publishing Ltd. Author : Yuxing Yan Date : 6/6/2017 email : [email protected] [email protected] """ from matplotlib.finance import quotes_historical_yahoo_ochl as getData x = getData("IBM", (2016, 1, 1), (2016, 1, 21), asobject=True, adjusted=True) print(x[0:4])
def ret_f(ticker, begdate, enddte):
x = getData(ticker, begdate, enddate, asobject=True, adjusted=True)
ret = x.aclose[1:] / x.aclose[:-1] - 1
return ret
email : [email protected] [email protected] """ import numpy as np import scipy as sp import pandas as pd import matplotlib.pyplot as plt from matplotlib.finance import quotes_historical_yahoo_ochl as getData # Step 1: input area ticker = 'MSFT' # input value 1 begdate = (1926, 1, 1) # input value 2 enddate = (2013, 12, 31) # input value 3 n_simulation = 5000 # input value 4 # Step 2: retrieve price data and estimate log returns x = getData(ticker, begdate, enddate, asobject=True) logret = sp.log(x.aclose[1:] / x.aclose[:-1]) # Step 3: estimate annual returns date = [] d0 = x.date for i in range(0, sp.size(logret)): date.append(d0[i].strftime("%Y")) y = pd.DataFrame(logret, date, columns=['logret']) ret_annual = sp.exp(y.groupby(y.index).sum()) - 1 ret_annual.columns = ['ret_annual'] n_obs = len(ret_annual) # Step 4: estimate distribution with replacement sp.random.seed(123577)
def dailyRet(ticker, begdate, enddate):
p = getData(ticker, begdate, enddate, asobject=True, adjusted=True)
return p.aclose[1:] / p.aclose[:-1] - 1
def ret_f(ticker): # function 1
x = getData(ticker, begdate, enddate, asobject=True, adjusted=True)
ret = x.aclose[1:] / x.aclose[:-1] - 1
ddate = x['date'][1:]
y = pd.DataFrame(ret, columns=[ticker], index=ddate)
return y.groupby(y.index).sum()
def ret_f(ticker, begdate, enddate):
p = getData(ticker, begdate, enddate, asobject=True, adjusted=True)
ret = p.aclose[1:] / p.aclose[:-1] - 1
date_ = p.date
return pd.DataFrame(data=ret, index=date_[1:], columns=['ret'])
# -*- coding: utf-8 -*- """ Name : c4_07_first_one.py Book : Python for Finance (2nd ed.) Publisher: Packt Publishing Ltd. Author : Yuxing Yan Date : 6/6/2017 email : [email protected] [email protected] """ from matplotlib.finance import quotes_historical_yahoo_ochl as getData p = getData("IBM", (2015, 1, 1), (2015, 12, 31), asobject=True, adjusted=True) print(p[0:5])
def ret_f(ticker, begdate, enddte):
x = getData(ticker, begdate, enddate, asobject=True, adjusted=True)
ret = x.aclose[1:] / x.aclose[:-1] - 1
d0 = x.date[1:]
return pd.DataFrame(ret, index=d0, columns=[ticker])
def ret_f(ticker, begdate, enddate):
p = getData(ticker, begdate, enddate, asobject=True, adjusted=True)
ret = p.aclose[1:]
ret = p.aclose[1:] / p.aclose[:-1] - 1
return (ret)
