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.
Plotly provides a web-service for hosting graphs and currently Public hosting is free. Just create the account and get the API key for authentication to host your data visualization stuff.
Create your account with plotly and Get the Plotly API Key here in order to use plotly service.
Installation
In the CLI terminal, copy and paste the following to install the Plotly library and set your user credentials.
You’ll need to replace ‘DemoAccount’ and ‘lr1c37zw81’ with your Plotly username and API key.
It is recommended to upgrade to the latest version of Pandas and Plotly libraries.
Sample IPython Notebook using Plotly to plot Interactive Candlestick Charts :
Related Readings and Observations
- 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 […]
- 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 […]
- 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 […]
- 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
- Python For Trading 2-Day Bootcamp Python has taken the data analytics space by storm – more so in the financial services space. With the rapid growth of data and ever-increasing need to process data faster and getting […]
- 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 […]
how to catch sudden spike / spurts in puts/calls in nse data using python