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Baridhi Malakar

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Download and Installation

To begin using this template, choose one of the following options to get started:

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Usage

Basic Usage

After downloading, simply edit the HTML and CSS files included with the template in your favorite text editor to make changes. These are the only files you need to worry about, you can ignore everything else! To preview the changes you make to the code, you can open the index.html file in your web browser.

Advanced Usage

After installation, run npm install and then run gulp dev which will open up a preview of the template in your default browser, watch for changes to core template files, and live reload the browser when changes are saved. You can view the gulpfile.js to see which tasks are included with the dev environment.

Gulp Tasks

gulp the default task that builds everything
gulp dev browserSync opens the project in your default browser and live reloads when changes are made
gulp sass compiles SCSS files into CSS
gulp minify-css minifies the compiled CSS file
gulp minify-js minifies the themes JS file
gulp copy copies dependencies from node_modules to the vendor directory

Bugs and Issues

Have a bug or an issue with this template? Open a new issue here on GitHub or leave a comment on the template overview page at Start Bootstrap.

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About

Start Bootstrap is an open source library of free Bootstrap templates and themes. All of the free templates and themes on Start Bootstrap are released under the MIT license, which means you can use them for any purpose, even for commercial projects.

Start Bootstrap was created by and is maintained by David Miller, Owner of Blackrock Digital.

Start Bootstrap is based on the Bootstrap framework created by Mark Otto and Jacob Thorton.

Copyright and License

baridhi's Projects

adv_appliedmetrics

(Advanced) Applied Econometrics

advanced_causalinf

Advanced causal inference and research design course

agency-loan-level

Loan-level analysis of Fannie Mae and Freddie Mac data

algorithmic-trading-python

The repository for freeCodeCamp's YouTube course, Algorithmic Trading in Python

algorithmic_trading_machine_learning

aoai-angular-app

Azure OpenAI with LLM to read PDF

automated-machine-learning-with-microsoft-azure

Automated Machine Learning with Microsoft Azure, published by Packt

baridhi.github.io

Personal website

bartik-weight

beamer-tips

block_codes

This depository uses SEC EDGAR data in Schedule 13D and Schedule 13G data to find all positions above 5% in all US stocks between 1994 and 2018.

cafr-parser

GASB CAFR text extraction and parsing tools

call-options-pnl-heatmap

A simple tool for visualising profitability for a given European call option. The user inputs a stock symbol, expiry date and number of contracts, and the output is the corresponding call's profit & loss displayed as a heatmap across various strike and underlying prices.

ch13learner

Predicting Chapter 13 Case Results using a Neural Network and Random Forest Decision Tree

chen_jfe-2024_ml-portfolios

Imputing missing stock anomalies data with EM implementation

christiansilva

A. Meucci code from bootcamp and book

codechella

Data, Code and other material for CodeChella concert

county-elect

covid_mobility

To study the impact on mobility after the pandemic

crosssection

Code to accompany our paper Chen and Zimmermann (2020), "Open source cross-sectional asset pricing"

crosssectiondemos

OpenSourceAssetPricing. Example code of simple things one can do with our open-source asset pricing data

data-school-shootings

The Washington Post is compiling a database of school shootings in the United States since Columbine.

deep-hedging2

Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

deephedging

Implementation of the vanilla Deep Hedging engine

deeplearningcreditriskmodeling

Replication codes for Deep Learning Credit Risk Modeling by Manzo, Qiao

edgar-parsing

This repo contains all the code necessary to download, extract, and parse 13F filings on EDGAR.

education-data-package-stata

entrydatar

Public data on firm entry from the Census and the BLS in a R package

faads-nccs-crosswalk

This directory contains scripts in the R language that provide a crosswalk between the Federal Assistance Award Data System (FAADS) that contains federal grants information, and the National Center for Charitable Statistics (NCCS) Core Data files, which provide IRS financial data on nonprofits.

factordata

Calculates 103 firm characteristics from CRSP + Compustat directly in Python – no WRDS SAS cloud