lsy617004926

10-k-Filing--Sentiment-analysis-NLP-ML

NLP-10 k report Sentiment Analysis

448Project

High Frequency Trading

500lines

500 Lines or Less

Accounting-Factors

This repo contains works related to fundamental factors

AdvancedML

Reading list for the Advanced Machine Learning Course

ai-economist

Foundation is a flexible, modular, and composable framework to model socio-economic behaviors and dynamics with both agents and governments. This framework can be used in conjunction with reinforcement learning to learn optimal economic policies, as done by the AI Economist (https://www.einstein.ai/the-ai-economist).

Amazing-Feature-Engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

github-cheat-sheet

A list of cool features of Git and GitHub.

lsy617004926.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

QuantResearch

Quantitative analysis, strategies and backtests

lsy617004926/alphasickle

多因子指数增强策略/多因子全流程实现

lsy617004926/applied-methods-phd

Repo for Yale Applied Empirical Methods PHD Course

lsy617004926/coding-for-economists

This repository hosts the code behind the online book, Coding for Economists.

lsy617004926/CommonOwnerReplication

Replication for Common Owner 1980-2017 (https://www.aeaweb.org/articles?id=10.1257/mic.20190389)

lsy617004926/CrossSection

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

lsy617004926/CrossSectionDemos

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

lsy617004926/DScourseS21

ECON 5253: Data Science for Economists, University of Oklahoma (Spring 2021)

lsy617004926/empirical-finance-literature

This is a literature review on the topic of empirical finance. I thank Dr. Svetlana Bryzgalova for her valuable insights.

lsy617004926/empirical-methods

Materials for Empirical Methods for Applied Microeconomics PhD course.

lsy617004926/Empirical_Macroeconomics

This is a 10 classes course in Empirical Macroeconomics methods to identify shocks

lsy617004926/EmpiricalFinancePhD

Empirical Finance Course (PhD, Julia code)

lsy617004926/example-hftish

Example Order Book Imbalance Algorithm

lsy617004926/FR

Financial Ratios for Accounting Research

lsy617004926/GaryChamberlainLectureNotes

lsy617004926/guidelines

Guidelines and onboarding for research assistants and research managers working with professors

lsy617004926/heiss-causal-inference

Chapter on causal inference for *R for Political Data Science: A Practical Guide*

lsy617004926/limperg_python

Repository with material for the Limperg Python course by Ties de Kok.

lsy617004926/lstm

Minimal, clean example of lstm neural network training in python, for learning purposes.

lsy617004926/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.

lsy617004926/pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

lsy617004926/Quantitative-Finance

Q-quant和因子投资实证汇总

lsy617004926/R_guide

R instructions for our research projects

lsy617004926/RDD

lsy617004926/sumhdfe

Summary and diagnostic information for evaluating within-fixed-effect variation.

lsy617004926/Swin-Transformer-Object-Detection

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

lsy617004926/talib-document

talib学习 talib中文翻译 talib中文文档

lsy617004926/text-as-data-class-spring2021

Lectures and "flipped session" materials from my NYU DS "Text as Data" course, spring 2021

lsy617004926/Time-Series-Analysis-Tutorial

时间序列分析教程

lsy617004926/Trading

Quantitative Trading

lsy617004926/treat

A template for reproducible empirical accounting research - fork me!