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. This tutorial talks about implementation of Kalman Filter Estimation of Mean in IPython Notebook using PyKalman, Bokeh, NSEPy and pandas to plot Interactive Intraday Candlestick Charts with Kalman Filter
Here is a quick tutorial in python to compute Correlation Matrix between multiple stock instruments using python packages like NSEpy & Pandas. Generally Correlation Coefficient is a statistical measure that reflects the correlation between two stocks/financial instruments. Determining the relationship between two securities is useful for analyzing intermarket relationships, sector/stock relationships and sector/market relationships.
First of all thanks for your impressive and motivational reponse for the Nifty Returns Heatmap Generation post. Here is yet another simple visualization stuff using python to compute bunch of NSE Sectoral Indices returns (52 Weeks, YTD , MTD, Last month returns) and visualize the same in a barchart.
In the last tutorial we had seen how to import data from NSEindia using NSEpy library and how to compute co-integration. In this tutorial we will be using plotly – a library to visualize your data interactively and pandas – library to manage time series data to build interactive candle stick charts.
Here is a simple example to compute Cointegration between two stock pairs using python libraries like NSEpy, Pandas, statmodels, matplotlib