Most algorithmic traders only focus on the trade signal.
Then they wonder why they lose money.
It's not the signal that's most important.
It's the filter.
Here are 9 of the most popular filters everyone should know (with Python code):
Then they wonder why they lose money.
It's not the signal that's most important.
It's the filter.
Here are 9 of the most popular filters everyone should know (with Python code):
Moving average filter
Uses a moving average of the data points to smooth out short-term fluctuations and highlight long-term trends.
Uses a moving average of the data points to smooth out short-term fluctuations and highlight long-term trends.
Exponential smoothing filter
Gives more weight to recent data points, making it more responsive to changes in the underlying signal.
Gives more weight to recent data points, making it more responsive to changes in the underlying signal.
Kalman filter
Uses a combination of mathematical techniques to estimate the state of a system based on a series of noisy measurements. It is commonly used for filtering data in the presence of random noise and model uncertainty.
Uses a combination of mathematical techniques to estimate the state of a system based on a series of noisy measurements. It is commonly used for filtering data in the presence of random noise and model uncertainty.
Butterworth filter
A type of low-pass filter that removes high-frequency noise while preserving the low-frequency signal.
A type of low-pass filter that removes high-frequency noise while preserving the low-frequency signal.
Gaussian filter
Uses a Gaussian function to smooth the signal by averaging the data points within a certain range.
Uses a Gaussian function to smooth the signal by averaging the data points within a certain range.
Median filter
Replaces each data point with the median value of the data points in a certain range, effectively removing outliers.
Replaces each data point with the median value of the data points in a certain range, effectively removing outliers.
Wavelet filter
Uses wavelet decomposition to separate the signal into different frequency bands, allowing for the removal of specific frequency components.
Uses wavelet decomposition to separate the signal into different frequency bands, allowing for the removal of specific frequency components.
Savitzky-Golay filter
Uses a polynomial fitting method to smooth the signal while preserving the signal's shape and features.
Uses a polynomial fitting method to smooth the signal while preserving the signal's shape and features.
Fourier transform filter
Uses the Fourier transform to convert the signal from the time domain to the frequency domain, allowing for the removal of specific frequency components.
Uses the Fourier transform to convert the signal from the time domain to the frequency domain, allowing for the removal of specific frequency components.
The 9 time series filters every algo trader must know:
• Exponential smoothing
• Fourier transform
• Moving average
• Savitzky-Golay
• Butterworth
• Gaussian
• Wavelet
• Median
• Kalman
• Exponential smoothing
• Fourier transform
• Moving average
• Savitzky-Golay
• Butterworth
• Gaussian
• Wavelet
• Median
• Kalman
The information in this thread could take you weeks to devour.
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If you can't get to it all now, click the link to hop to the top tweet.
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So save it for later!
If you can't get to it all now, click the link to hop to the top tweet.
Then retweet it (with a comment!) so you can come back to it later!
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Go from beginner to up and running with Python for quant finance in 30 days.
• Community
• Frameworks
• Live sessions
• Special guests
• Jupyter Notebooks
January cohort is open - limited spots.
gettingstartedwithpythonforquantfinance.com
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