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aarogyasetu_android

Aarogya Setu Android app native code

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

course-v3

The 3rd edition of course.fast.ai

course-v4

Pre-release of v4 of course.fast.ai

covid19india-react

Tracking the impact of COVID-19 in India

dex2jar

Tools to work with android .dex and java .class files

fastai

The fastai deep learning library, plus lessons and tutorials

fastai1

v1 of the fastai library. v2 is the current version. v1 is still supported for bug fixes, but will not receive new features.

freqtrade

Free, open source crypto trading bot

mibianlib

Python Options Pricing Library

mlfinlab

MlFinlab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.

monte-carlo-stoch-vol

Collection of resources used on QuantPy YouTube channel.

nsepy

Python Library to get publicly available data on NSE website ie. stock quotes, historical data, live indices

project_corona_tracker

This is a code repository for the corresponding YouTube video. In this tutorial we are going to build and deploy a corona tracker application. Covered topics: React.js, Chart.js, Material UI and much more.

saleor

A modular, high performance, headless e-commerce storefront built with Python, GraphQL, Django, and ReactJS.

sdoosa-algo-trade-python

Algo trade project in python

sec-doc-info-extraction

Using NLP to find and extract specific information from long, unstructured documents

sigma_coding_youtube

This is a collection of all the code that can be found on my YouTube channel Sigma Coding.

simple-proxy

stochastic-volatility

three stochastic volatility model: Heston, SABR, SVI

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

svi

Surface SVI parameterisation and corresponding local volatility

svi-volatility-surface-calibration

SVI volatility surface model and an example of China 50ETF option

test

Coursera test

vol_prediction

Realized Volatility Forecasting modeling

z0fcourse_reverseengineering

Reverse engineering focusing on x64 Windows.