Hurst exponent is originally developed by the famous hydrologist Harold Edwin Hurst to study the Long-Term Storage Capacity of Reservoirs. Hurst is developed to model reservoirs but later found to be used in other natural systems to measure the long term memory of time series.

# Computing Cointegration and Augmented Dickey Fuller test in Amibroker using Python

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.

# Access Python Functions using Amibroker – AmiPy Plugin

# 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.

# To Compute Sectoral NSE Indices Returns using Python

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.

# Nifty Returns Heatmap Generation using NSEpy and Seaborn

Everyone love to visualize this market in their own way. Python comes handy when comes to visualization. One of the Awesome programming language to code any level of complexity. However I start here by simply generating a heatmap which visualizes the Historical Nifty returns since 2000 and there by visualizing the reality of the market.

# Hodrick Prescott Filter Analysis – Python

Hodrick Prescott Filter does Time series decomposition involves separating a time series into several distinct components(Cycle component and Trend Component). And this filter looks like it can be applied to any timeseries data especially with stock prices to understand the underlying trend and the cycle involved in it. Should be a better tool for Stock Cycle lovers!

# 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.

# How to Plot Candlestick Charts using Python

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.

# 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