def olsfit(ticker, bench, begdate, enddate):
tickerdata = ts.get_k_data(ticker, start=begdate, end=enddate)
benchdata = ts.get_k_data(bench, start=begdate, end=enddate)
y = DP.closetoret(tickerdata['close'])
x = DP.closetoret(benchdata['close'])
x = sm.add_constant(x)
model = sm.OLS(y, x)
results = model.fit()
return results.summary()
def rollingbeta(ticker, bench, begdate, enddate, window=252):
tickerdata = ts.get_k_data(ticker, start=begdate, end=enddate)
benchdata = ts.get_k_data(bench, start=begdate, end=enddate)
y0 = DP.closetoret(tickerdata['close'])
x0 = DP.closetoret(benchdata['close'])
y0 = pd.Series(y0)
x0 = pd.Series(x0)
model = pd.ols(y=y0, x=x0, window=window)
model.beta.plot()
plt.show()
def adftest(stockcode, begdate, enddate):
'''
example result (0.049177575166452235,
0.96241494632563063,
1,
3771,
{’1%’: -3.4320852842548395,
’10%’: -2.5671781529820348,
’5%’: -2.8623067530084247},
19576.116041473877)
Since the calculated value of the test statistic is larger than any of the critical values at the 1,
5 or 10 percent levels, we cannot reject the null hypothesis of
= 0 and thus we are unlikely to
have found a mean reverting time series. This is in line with our tuition as most equities behave
akin to Geometric Brownian Motion (GBM), i.e. a random walk.
:param stockcode:
:return:
'''
data = ts.get_k_data(stockcode, start=begdate, end=enddate)
ret = DP.closetoret(data['close'])
resultret = stattools.adfuller(ret)
resultclose = stattools.adfuller(data['close'])
print('return', resultret)
print('close', resultclose)
return resultret, resultclose
def Var(ticker, begdate, enddate, nshares, ndays):
x = ts.get_k_data(ticker, start=begdate, end=enddate)
ret = DP.closetoret(x)
position = nshares * x['close'][-1]
VaR = position * std(ret) * sqrt(ndays)
print("Holding=", position, "VaR=", round(VaR, 4), "in ", ndays, "Days")
return VaR
def AminudIlliq(closes, volume):
p = closes
dollar_vol = np.array(volume * p)
ret = np.array(DP.closetoret(closes))
illiq = np.mean(np.divide(abs(ret), dollar_vol[1:]))
return illiq