In this tutorial we discussed how to bring Cointegration statistics into Amibroker using Amipy and how to interpret the values returned by Augmented Dickey Fuller test.
Kalman Filter and Unscented Kalman Filter AFL in Amibroker using Python ComServer
In the last tutorial we explored Kalman filter and how to build kalman filter using pykalman python library. In this section we will be dealing with python com server to integrate Amibroker + Python to compute Kalman Filter and Unscented Kalman Filter Mean Estimation and plot the same in Amibroker.
Implementation of Kalman Filter Estimation of Mean in Python using PyKalman, Bokeh and NSEPy
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
A Quick Start Guide to Compute Correlation Matrix in Python using NSEpy & Pandas
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.
Fetch Intraday Data from Google and Plot using Python
Here is an yet another interesting python tutorial to fetch intraday data using Google Finance API , , store the data in csv format and also plot the intraday data as candlestick format. We are using plotly library for plotting candlestick charts and pandas to manage time-series data.
Compute Cointegration using NsePy, Pandas Library
Here is a simple example to compute Cointegration between two stock pairs using python libraries like NSEpy, Pandas, statmodels, matplotlib