Topics created by jirathh

I am sorry, I got it now.

It is in:

https://github.com/mementum/backtrader/blob/master/backtrader/indicators/basicops.py

Thank you @Paska-Houso for your actively help.

Thank you so much, @Paska-Houso .
I managed to create a new indicator of OLS to be used in my strategy! :)

Here is the code:

class TORO_OLS_Slope_InterceptN(bt.Indicator):
packages = (
('pandas', 'pd'),
('sklearn.linear_model', 'lm'),
)
lines = ('slope', 'intercept',)
params = (
('period', 10),
)
def init(self):
self.addminperiod(self.params.period)

def next(self): p0 = pd.Series(self.data.close.get(size=self.p.period)) p1 = np.arange(self.p.period).reshape(-1, 1) p1 = sm.add_constant(p1, prepend=True) slope, intercept = sm.OLS(p0, p1).fit().params self.lines.slope[0] = slope self.lines.intercept[0] = intercept

I tried to create a new indicator myself and try to get the last N days of data to do OLS and return the value to line, unfortunately my strategy get 0 in value.

class toroOLS(bt.Indicator):
#lines = ('slope','intercept',)
lines=('slope',)
params = (('period', 20),)

def __init__(self): self.addminperiod(self.params.period) def next(self): closes = pd.Series(self.data.close.get(size=self.p.period)) x = np.arange(self.p.period).reshape(-1,1) x = sm.add_constant(x, prepend=True) #toro, intercept = sm.OLS(x, closes).fit().params #self.lines.slope[0] = toro #self.lines.intercept[0] = intercept toro = linear_model.LinearRegression().fit(x, closes) annualize_return = (((1+toro.coef_[0])**250) - 1)*100 self.lines.slope[0] = annualize_return

==========================================
This is where the indicator is used. Can you please suggest?

class TestStrategy(bt.Strategy):
params = (
('exitbars', 5),
)

def log(self, txt, dt=None): ''' Logging function fot this strategy''' dt = dt or self.datas[0].datetime.date(0) print('%s, %s' % (dt.isoformat(), txt)) def __init__(self): # Keep a reference to the "close" line in the data[0] dataseries self.dataclose = self.datas[0].close self.slope = toroOLS(period=20)

.....
def next(self):
# Simply log the closing price of the series from the reference
self.log('Close, %.2f' % self.dataclose[0])
self.log('Slope, %.10f' % self.slope[0])