def summary_stats(self, start, end):
selection = da.selection(self.df['Adj_Close'], start, end)
selection_spy = da.selection(self.df_spy['Adj_Close'], start, end)
returns = selection.pct_change()
returns_spy = selection_spy.pct_change()
beta = returns.cov(returns_spy)/returns_spy.var()
return {'mean': returns.mean(), 'stdev': returns.std(), 'beta': beta}
def summary_stats(self, start, end):
selection = da.selection(self.df['Adj_Close'], start, end)
selection_spy = da.selection(self.df_spy['Adj_Close'], start, end)
returns = selection.pct_change()
returns_spy = selection_spy.pct_change()
beta = returns.cov(returns_spy) / returns_spy.var()
return {'mean': returns.mean(), 'stdev': returns.std(), 'beta': beta}
def cointegration_test(self, start, end):
x = da.selection(self.df1["Adj_Close"], start, end)
y = da.selection(self.df2["Adj_Close"], start, end)
ols_result = sm.OLS(x, y).fit()
resid = ols_result.resid
# compute regression residuals
adfuller_results = ts.adfuller(resid)
print "cointegration: " + str(adfuller_results[1])
bound = pd.Series(movvar(resid, 30)) ** 0.5
resid_rolling_average = da.exp_moving_average(resid, 0.1)
resid_with_bounds = np.array([resid_rolling_average, bound, -bound]).T
columns = ["Residuals", "Upper", "Lower"]
resid_with_bounds_df = pd.DataFrame(resid_with_bounds, index=resid.index, columns=columns)
return {"csv": da.csv(resid_with_bounds_df, True)}
def cointegration_test(self, start, end):
x = da.selection(self.df1['Adj_Close'], start, end)
y = da.selection(self.df2['Adj_Close'], start, end)
ols_result = sm.OLS(x, y).fit()
resid = ols_result.resid
# compute regression residuals
adfuller_results = ts.adfuller(resid)
print "cointegration: " + str(adfuller_results[1])
bound = pd.Series(movvar(resid, 30))**0.5
resid_rolling_average = da.exp_moving_average(resid, 0.1)
resid_with_bounds = np.array([resid_rolling_average, bound, -bound]).T
columns = ['Residuals', 'Upper', 'Lower']
resid_with_bounds_df = pd.DataFrame(resid_with_bounds,
index=resid.index,
columns=columns)
return {'csv': da.csv(resid_with_bounds_df, True)}
def __init__(self, strategy, ticker, start, end):
prices = da.dataframe(ticker)['Adj_Close']
prices = da.selection(prices, start, end)
self.signals = strategy(prices)
self.pnl = pd.Series(np.zeros(len(prices)), index=prices.index)
shares = 0
for i in xrange(len(self.pnl)):
self.pnl[i] = (prices[i] - prices[i - 1]) * shares
shares += self.signals[i]
def __init__(self, strategy, ticker, start, end):
prices = da.dataframe(ticker)['Adj_Close']
prices = da.selection(prices, start, end)
self.signals = strategy(prices)
self.pnl = pd.Series(np.zeros(len(prices)), index=prices.index)
shares = 0
for i in xrange(len(self.pnl)):
self.pnl[i] = (prices[i]-prices[i-1])*shares
shares += self.signals[i]