Haoxuan Zou's repositories
pyqstrat_example_strategies
For users of pyqstrat to submit example strategies for feedback and discussion in the pyqstrat groups.io group
awesome-research
:seedling: a curated list of tools to help you with research/life
binance-public-data
Details on how to get Binance public data
coursera-machine-learning-engineering-for-prod-mlops-specialization
Programming assignments and quizzes from all courses within the Machine Learning Engineering for Production (MLOps) specialization offered by deeplearning.ai
Deep-Learning-Specialization-Coursera
This repo contains the updated version of all the assignments/labs (done by me) of Deep Learning Specialization on Coursera by Andrew Ng. It includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc.
DL-NLP-Readings
My Reading Lists of Deep Learning and Natural Language Processing
EquityCharacteristics
Calculate U.S. equity (portfolio) characteristics
ExamplePackage.jl
Testing Julia and Github
lecture-source-py
Source files for "Lectures in Quantitative Economics" -- Python version
Market-Report-Generator
通过Wind数据库数据自动生成券商研报常规部分。
mostly-harmless-replication
Replication of tables and figures from "Mostly Harmless Econometrics" in Stata, R, Python and Julia.
python-geospatial
A collection of Python packages for geospatial analysis with binder-ready notebook examples
quantecon-notebooks-python
A Repository of Notebooks for the Python Lecture Site
QuantEcon.jl
Julia implementation of QuantEcon routines
QuantLibPython
Example Python scripts for interest rate modelling and QuantLib usage
REMARK
Replications and Explorations Made using the ARK
StateSpaceRoutines.jl
Package implementing common state-space routines.
Stock-Price-Predictions
We compiled the analyst reports from Morningstar for 15 largest companies in retail and technology sector and extracted the specific text. Then extracteed sentiments using VADER general sentiment lexicon and through Loughran and MCdonald financial sentiment lexicon. S&P Capital IQ and Yahoo Finance was also our data source. We applied statistical modeling, both linear and logisitc regressions to predict the percentage change in the stock price from day of publication of report to 3 time periods and our model showed some sigificant results with over 95% accuracy and validated our hypothesis.
vnpy-zhx
基于Python的开源量化交易平台开发框架
vnpy_deribit
vn.py框架的Deribit交易接口
vnpy_webtrader
VeighNa框架的Web端管理服务器