def get_context_data(self, **kwargs):
context = super(PairStatsDetailView, self).get_context_data(**kwargs)
if hasattr(self, 'form'):
pvalue = self.form.cleaned_data['pvalue']
zscore = self.form.cleaned_data['zscore']
periodo = self.form.cleaned_data['periodo']
beta_plot = cointegration.get_beta_plot(
context['object'].beta_rotation)
context['pvalue'] = float(pvalue)
context['zscore'] = float(zscore)
context['beta_plot'] = beta_plot.decode("utf-8")
context['period'] = periodo
try:
periodo = int(periodo)
ativo_x = self.kwargs['x']
ativo_y = self.kwargs['y']
_x = Quotes.objects.get(ticker=ativo_x).get_series()[-periodo:]
_y = Quotes.objects.get(ticker=ativo_y).get_series()[-periodo:]
series_x, series_y = cointegration.clean_timeseries(_x, _y)
plot_context = cointegration.get_plot_context(
series_x, series_y, ativo_x, ativo_y)
except Exception as e:
print(e)
plot_context = {'resultados': False}
context.update(plot_context)
return context
def producer(idx, pair):
_x = market_data[('Close', pair[0])]
_y = market_data[('Close', pair[1])]
series_x, series_y = clean_timeseries(_x, _y)
obj_pair = create_pairstats(pair,
'BOVESPA',
series_x=series_x,
series_y=series_y)
print(idx, pair)
try:
beta_list = beta_rotation(series_x=series_x, series_y=series_y)
obj_pair.beta_rotation = beta_list
except MissingDataError:
print('FAIL - MissingDataError - Beta')
#raise
for periodo in PERIODOS_CALCULO:
slice_x = series_x[-periodo:]
slice_y = series_y[-periodo:]
try:
test_params = coint_model(slice_x, slice_y)
obj_data = create_cointparams(True, test_params)
obj_pair.success = True
except MissingDataError:
obj_data = create_cointparams(False, test_params)
print('FAIL - MissingDataError - OLS ADF', periodo)
#raise
obj_pair.model_params[periodo] = model_to_dict(obj_data)
return copy.deepcopy(obj_pair)
def calcula_modelo(refdate, pair, market='BINANCE'):
def create_pairstats(pair,
market,
series_x=pd.Series([]),
series_y=pd.Series([])):
obj = PairStats(
pair=" ".join(pair),
market=market,
refdate=refdate,
ticker_x=pair[0],
ticker_y=pair[1],
)
if not series_x.empty:
obj.x_quote = series_x.iloc[-1]
if not series_y.empty:
obj.y_quote = series_y.iloc[-1]
return obj
df = qsquotes.get(ticker=pair[0]).get_series()
df2 = df[df.index < refdate]
_x = df2[df2.index > refdate - timedelta(days=365)]
df = qsquotes.get(ticker=pair[1]).get_series()
df2 = df[df.index < refdate]
_y = df2[df2.index > refdate - timedelta(days=365)]
series_x, series_y = clean_timeseries(_x, _y)
model_params = {}
obj_pair = create_pairstats(pair,
market,
series_x=series_x,
series_y=series_y)
# Valor esperado: 690
#print(refdate, pair, 'len_X', len(series_x), 'len_Y', len(series_y))
#print(refdate, pair)
for periodo in PERIODOS_CALCULO:
obj_data = {}
slice_x = series_x[-periodo:]
slice_y = series_y[-periodo:]
try:
test_params = coint_model(slice_x, slice_y)
obj_data = create_cointparams(True, test_params)
obj_pair.success = True
except MissingDataError:
obj_data = create_cointparams(False, test_params)
print('FAIL - MissingDataError - OLS ADF', periodo)
#raise
model_params[periodo] = copy.deepcopy(model_to_dict(obj_data))
obj_pair.model_params = model_params
return copy.deepcopy(obj_pair)
def get_plot(x_ticker, y_ticker):
from dashboard.models import PairStats, CointParams, Quotes
from coint.cointegration import fp_savefig, _get_residuals_plot
from coint.cointegration import coint_model, clean_timeseries
_x = Quotes.objects.get(ticker=x_ticker).get_series()
_y = Quotes.objects.get(ticker=y_ticker).get_series()
series_x, series_y = clean_timeseries(_x, _y)
r = coint_model(series_x[-120:], series_y[-120:])
return fp_savefig(_get_residuals_plot(r['OLS']))
def init(self):
Close = self.data.Close
self.ma1 = self.I(SMA, Close, 10)
self.ma2 = self.I(SMA, Close, 20)
def next(self):
if crossover(self.ma1, self.ma2):
self.buy()
elif crossover(self.ma2, self.ma1):
self.sell()
if __name__ == '__main__':
Quotes.objects.filter(market='BINANCE').delete()
download_hquotes_binance()
_x = Quotes.objects.get(ticker='BTCUSDT').get_series()
_y = Quotes.objects.get(ticker='LTCUSDT').get_series()
series_x, series_y = clean_timeseries(_x, _y)
df = pd.DataFrame()
df['BTCUSDT'] = series_x
df['LTCUSDT'] = series_y
from IPython import embed
embed()
bt = Backtest(df, PairTradingCointegration, cash=10000, commission=.002)
bt.run()
bt.plot()