xhua0336
MEOR @ Columbia University & MCIT @ University of Pennsylvania| Quantitative Researcher | Protein Fold AI Researcher
@Columbia UniversityNew York
Pinned Repositories
2019APMCM-Evaluation-of-regional-economic-vitality
ag_Factor_Evaluation
Airbnb-house-pricing-prediction
Airbnb Price Prediction
CTA-strategy
cta_Factor_Evaluation
Digital-Love-Letter
How would a programmer propose?
high_frequency_limit_order_book_dynamic_prediction
Lasso-and-linear-regression-prediction-for-percentage-body-fat
MCMC-FFVB-estimation-of-poisson-regression-model
Risk-parity-model-for-Chinese-stock-market
Risk parity for Chinese stock market
xhua0336's Repositories
xhua0336/high_frequency_limit_order_book_dynamic_prediction
xhua0336/Risk-parity-model-for-Chinese-stock-market
Risk parity for Chinese stock market
xhua0336/CTA-strategy
xhua0336/2019APMCM-Evaluation-of-regional-economic-vitality
xhua0336/Airbnb-house-pricing-prediction
Airbnb Price Prediction
xhua0336/Lasso-and-linear-regression-prediction-for-percentage-body-fat
xhua0336/MCMC-FFVB-estimation-of-poisson-regression-model
xhua0336/ag_Factor_Evaluation
xhua0336/cta_Factor_Evaluation
xhua0336/Digital-Love-Letter
How would a programmer propose?
xhua0336/Factor_Evaluation
xhua0336/Kaggle-Walmart-unit-sales-prediction
xhua0336/MCMC-Gaussian-VB-estimation-of-GARCH-1-1-
Using MCMC and VB to estimate GARCH(1,1) volatility model
xhua0336/models
Trained caffe models
xhua0336/OnionNet-2
OnionNet-2 is constructed based on convolutional neural network (CNN) to predict the protein-ligand binding affinity.
xhua0336/pandas-ta
Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators
xhua0336/prospr
ProSPr: Protein Structure Prediction
xhua0336/SGX-Full-OrderBook-Tick-Data-Trading-Strategy
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
xhua0336/Stock-price-prediction-using-GAN-Capstone-Group1
In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.
xhua0336/Taxi_booking_analysis