adarsh-jha Goto Github PK

amazonml

Code for the 2023 Amazon ML Challenge

beep-morp-zorp

bilip

An automated web scrapper that retrieves data from HTML tables on websites and concatenates them with a pre-existent CSV file.

grid-4.0

Blockchain for File integrity check

leetpracmahboiiiiiii

Collection of LeetCode questions i've done

meity

objdetectionyolov7

Training and realtime object detection using YoloV7

oss

For Flipkart Grid 4.0

portfoliooptimisationmatlab

This code can be use to find an optimal weighting allocation across S&P500 stocks over a historical lag period. It is setup to download most recent s&p500 data (this step can be commented out) and optimize historical day to yesterday's date (yesterday would be an out-of-sample date) The Algorithm takes each stock, within the s&p 500, and submits the following limit orders: 1) Bid Limit order: mean-alpha std deviations 2) Off Limit order: mean+alpha std deviations The historical performance of each bidding strategy over the past 'hist_lag' period is treated as individual strategy within a portfolio basket of strategies. The algorithm assigns a weighting, between 0 and 1, to each individual strategy, so that the Mean-Variance criteria over the entire portfolio basket of strategies is optimized. This code applies a unique approach to this optimization (see optimization section), using ideas from dynamic programming, to quickly compute the optimization of a large portfolio matrix The optimal allocation, determine over the previous hist_lag period, is then applied the next day 'out of sample'. This procedure is iteratively backtested from the 'begin_date' to the 'end_date'; the daily % return performance is computed and stored.

prodrecbag

A smart cart that, based on past order history, can predict future orders and create a smart order basket for the customer - selecting the items required in advance and enabling a faster buying experience.

stock-comparision

During my Virtual Training with JP Morgan I worked on an interface with a stock price data feed of two stocks that can be used to compare the stocks.

userengagementprediction

Machine learning approach to predict the engagement score of the video on the user level without any data points regarding the video/content itself