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agent2

chrome-extension-boilerplate-react-vite

Chrome Extension Boilerplate with React + Vite + Typescript

dacom

darc

Decentralized Autonomous Regulated Company (DARC), a company virtual machine that runs on any EVM-compatible blockchain, with on-chain law system, multi-level tokens and dividends mechanism.

flutter-in-practice

This project can be used as a starter for flutter app development, including a demo app for learning flutter components and a social video app named weiguan which is similar to TikTok.

flutter-notebook

FlutterDemo合集，今天你fu了吗

flutterpractise

开源的一些很炫的flutter界面的仿写

gpt-4v-act

AI agent using GPT-4V(ision) capable of using a mouse/keyboard to interact with web UI

ist-210-assignment-4

IST-210 Assignment-4

ist-210-assignment2

ist-210-final

IST-210-Final-group23

ist-210-groupproect

the group project for the class IST-210

lean

Lean Algorithmic Trading Engine by QuantConnect (Python, C#)

new-grad-positions-2022

A collection of New Grad full time roles in SWE, Quant, and PM.

nextjs

nft-f

numlab

A new era programming language,a good helper for machine learning,time series, data prediction,chart drawing and etc

pet-shop-er-diagram

the lab assignment for IST-210

stanford-project-predicting-stock-prices-using-a-lstm-network

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

startercode-fall2021

testing

testing

testingnodejs

tiktok-flutter

A TikTok Clone in Flutter and Firebase.

tiktok-scraper

TikTok Scraper. Download video posts, collect user/trend/hashtag/music feed metadata, sign URL and etc.

vue-cli-plugin-cube-ui

cube-ui plugin for vue-cli@3

yummyninja