Kalman Filters are used in signal processing to estimate the underlying state of a process. They are incredibly useful for finance, as we are constantly taking noisy estimates of key quantities and trading indicators. This notebook introduces Kalman Filters and shows some examples of application to quantitative finance.
The lecture will be presented at this meetup. We will be releasing a video lecture as well, watch this thread for a link.
Also in this lecture:
This is part of Quantopian’s Summer Lecture Series. We are currently developing a quant finance curriculum and will be releasing clone-able notebooks and algorithms to teach key concepts. Stay tuned for more. We are also working on a permanent home for all of our notebooks.
Credit for the notebooks goes to Evgenia 'Jenny' Nitishinskaya, and credit for the algorithms goes to David Edwards.