Rajandran R Founder of Marketcalls and Co-Founder Algomojo. Full-Time Derivative Trader. Expert in Designing Trading Systems (Amibroker, Ninjatrader, Metatrader, Python, Pinescript). Trading the markets since 2006. Mentoring Traders on Trading System Designing, Market Profile, Orderflow and Trade Automation.

Implementation of Kalman Filter Estimation of Mean in Python using PyKalman, Bokeh and NSEPy

1 min read

Kalman Filter is an optimal estimation algorithm to estimate the variable which can be measured indirectly and to find the best estimate of states by combining measurement from various sensors in the presence of noise.

Kalman filter is named after Rudolf E. Kálmán, one of the primary developers of its theory. Kalman filter has wide range of applications from aerospace Navigation, Robotics, Trajectory Optimization, Control Systems , Signal Processing, time series analysis and econometrics. Infact the very first application of kalman filter was made at NASA AMES center in the early 1960s during the feasibility study of circumlinear navigation control of the apollo space capsule.

Kalman Filters is ideal for systems which are continuously changing and well suited for building real time systems as kalman filter is a dynamic linear model, that is able to adapt to an ever changing environment. The major advantage of Kalman Filter is that it is predictive , adaptive as well and and it is really fast as it doesn’t tracks the historical data but rather the previous state.

Kalman Filter Explained in Simple Terms

Kalman Filters State Estimation

Kalman Filter – Optimal State Estimator

When comes to implementation of Kalman filter python comes very handy as the librry PyKalman makes life easier rather than digging with complex math stuff
to calculate kalman estimation.

Implementation of Kalman Filter Mean Estimation in IPython Notebook using PyKalman, Bokeh, NSEPy and pandas to plot Interactive Intraday Candlestick Charts with Kalman Filter

In the next tutorial we will be discussing more interesting statistical model and how to implement the same in python.

Rajandran R Founder of Marketcalls and Co-Founder Algomojo. Full-Time Derivative Trader. Expert in Designing Trading Systems (Amibroker, Ninjatrader, Metatrader, Python, Pinescript). Trading the markets since 2006. Mentoring Traders on Trading System Designing, Market Profile, Orderflow and Trade Automation.

How to Retrieve Opening Balance, Total Turnover, Realized and…

This tutorial explores how to access the limits API functionality in Algomojo using python to retrieve the Account Balance, Total Turnover, Realized, UnRealized PNL,...
Rajandran R
1 min read

Introduction to Backtrader – Creating your First Trading Strategy…

Backtrader is an open-source python framework for backtesting, optimizing, and deploying live algorithmic trading strategies.
Rajandran R
54 sec read

Getting Started with Google Colab

Google Colab also known as Google Colaboratory is a product from Google Research which allows user to run their python code from their browser...
Rajandran R
1 min read

2 Replies to “Implementation of Kalman Filter Estimation of Mean in Python…”

  1. Hey Raj,

    This is awesome!

    Just one question & one request.

    Can we use this as a Buy or Sell Signal?

    If yes, then can you guide me how to backtest this in python?

    I have a very basic knowledge, if you can point some articles or tutorials, I will be good to go.

    Mihir

  2. sir nsepy is support only idle python and it is not support to jupytor notebook .please give me some advice for what to do

Leave a Reply

Get Notifications, Alerts on Market Updates, Trading Tools, Automation & More