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Laguerre implementation
#485819
04/25/22 04:35
04/25/22 04:35
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Joined: Feb 2022
Posts: 21
TrumpLost
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Joined: Feb 2022
Posts: 21
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I want to replicate the Laguerre filter in Python for my notebook research, but I don't fully understand how it works. I've read through Ehler's paper but I don't completely understand that either. The main thing I'm confused about in the lite-C code is this line:
vars L = series(Data[0],8);
Here's the full code:
var Laguerre(var *Data, var alpha)
{
vars L = series(Data[0],8);
SETSERIES(Data,0);
if(alpha > 1.) alpha = smoothF(alpha);
var alpha1 = 1.-alpha;
L[0] = alpha*Data[0] + alpha1*L[1];
L[2] = -alpha1*L[0] + L[1] + alpha1*L[2+1];
L[4] = -alpha1*L[2] + L[2+1] + alpha1*L[4+1];
L[6] = -alpha1*L[4] + L[4+1] + alpha1*L[6+1];
return (L[0]+2.*L[2]+2.*L[4]+L[6])/6.;
}
And here's a rewrite in Python, which I think is not right.
class Laguerre():
def __init__(self, data: pd.Series, period):
self.L = np.zeros(8)
self.data0 = data[0]
self.L[0] = self.data0
if(period > 1):
period = smoothF(period)
self.period1 = 1 - period
def Update(self, price: float):
self.L[0] = price * self.data0 + self.period1 * self.L[1]
self.L[2] = -self.period1 * self.L[0] + self.L[1] + self.period1 * self.L[2+1]
self.L[4] = -self.period1 * self.L[2] + self.L[2+1] + self.period1 * self.L[4+1]
self.L[6] = -self.period1 * self.L[4] + self.L[4+1] + self.period1 * self.L[6+1]
return (self.L[0] + 2 * self.L[2] + 2 * self.L[4] + self.L[6]) / 6
Would really appreciate some insights as to how L in the original functions. I don't see where L[1] is being assigned a value, are the values inside the L list rolling forward each time this function runs? What does SETSERIES() do? I couldn't find it in the manual. And what is the return value from series(Data[0],8)? Thanks.
Last edited by TrumpLost; 04/25/22 04:37.
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Re: Laguerre implementation
[Re: TrumpLost]
#485835
04/27/22 14:27
04/27/22 14:27
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Joined: Feb 2022
Posts: 21
TrumpLost
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Here is the working Python code. It's really slow, especially if applied to lower timeframes, so perhaps what would be useful is an implementation in the form of a numpy extension.
class Laguerre():
def __init__(self, alpha=0.5):
if alpha > 1.0:
self.alpha = smoothF(alpha)
else:
self.alpha = alpha
self.L = None
self.initialized = False
def Update(self, price):
if not self.initialized:
self.L = np.array([price for _ in range(1, 8)])
self.initialized = True
self.L = np.insert(self.L, 0, price, axis=0)[:8]
alpha1 = 1.0-self.alpha
self.L[0] = self.alpha * price + alpha1 * self.L[1]
self.L[2] = -alpha1 * self.L[0] + self.L[1] + alpha1 * self.L[3]
self.L[4] = -alpha1 * self.L[2] + self.L[3] + alpha1 * self.L[5]
self.L[6] = -alpha1 * self.L[4] + self.L[5] + alpha1 * self.L[7]
return (self.L[0] + 2.0 * self.L[2] + 2.0 * self.L[4] + self.L[6]) / 6.0
Here's the notebook snippet where I'm generating the smoothed curve:
L = Laguerre(0.5)
for i, r in df.iterrows():
df.loc[i,'L'] = L.Update(r.Close)
I'm wondering, is the intent of Zorro to be able to do the sort of data research that people are normally using jupyter notebooks for? Because Python is painfully slow, but tools like pandas and numpy are not, and work really well with matplotlib. Any advice is much appreciated.
Last edited by TrumpLost; 04/27/22 16:24.
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Re: Laguerre implementation
[Re: Petra]
#485915
05/09/22 16:37
05/09/22 16:37
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Joined: Feb 2022
Posts: 21
TrumpLost
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In case anyone finds it useful, here's the laguerre function as a numpy extension. https://github.com/rm-rf-etc/ehlersIt's much faster. I'll try to get this published as a module so others can get it using pip.
Last edited by TrumpLost; 05/09/22 16:38.
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