def generate_bars(pair,START = "2001-01-01"):
"""this function is used to get bars(adjclose) from pair"""
bars = pd.DataFrame([])
bars[pair[0]] = get_price(pair[0], START)
bars[pair[1]] = get_price(pair[1], START)
return bars
def generate_bars(pair, START="2001-01-01"):
"""this function is used to get bars(adjclose) from pair"""
bars = pd.DataFrame([])
bars[pair[0]] = get_price(pair[0], START)
bars[pair[1]] = get_price(pair[1], START)
return bars
def plot(self):
ticker = str(self.lineEdit.text())
data = gy.get_price(ticker, START)
# create an axis
ax = self.figure.add_subplot(111)
# discards the old graph
ax.hold(False)
# plot data
ax.plot(data.index, data)
# refresh canvas
self.canvas.draw()
def plot_2(self):
ticker = self.lineEdit_2.text()
data = get_price(ticker, START)
# create an axis
ax = self.figure_2.add_subplot(111)
# discards the old graph
ax.hold(False)
# plot data
ax.plot(data.index,data)
# refresh canvas
self.canvas_2.draw()
def backtest_portfolio(self):
portfolio = pd.DataFrame(index=self.signals.index)
pos_diff = self.positions.diff().fillna(0.0)
#CaculationMatrix['pair']
portfolio['holdings'] = (self.positions*self.bars).sum(axis=1)
portfolio['cash'] = self.initial_capital - ((pos_diff*self.bars).sum(axis=1)).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
portfolio['returns'] = portfolio['total'].pct_change()
portfolio['SPY'] = get_price('SPY',self.signals.index[0])
portfolio['SPY'] = self.initial_capital/portfolio['SPY'][0]*portfolio['SPY']
return portfolio
def backtest_portfolio(self):
portfolio = pd.DataFrame(index=self.bars.index)
pos_diff = self.positions.diff()
#CaculationMatrix['pair']
portfolio['holdings'] = (self.positions * self.bars).sum(axis=1)
portfolio['cash'] = self.initial_capital - (pos_diff * self.bars).sum(
axis=1).cumsum()
portfolio['total'] = portfolio['cash'] + portfolio['holdings']
portfolio['returns'] = portfolio['total'].pct_change().fillna(0.0)
portfolio['SPY'] = get_price('SPY', self.bars.index[0])
portfolio['SPY'] = self.initial_capital / portfolio['SPY'][
0] * portfolio['SPY']
return portfolio
