ZhuZhouFan / CQVAELinks
This resposity is a pre-released verison of Python code used in the paper "Asset pricing via the conditional quantile variational autoencoder".
☆16Updated last year
Alternatives and similar repositories for CQVAE
Users that are interested in CQVAE are comparing it to the libraries listed below
Sorting:
- Reproduce AAAI22-FactorVAE☆67Updated 2 years ago
- Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019)☆133Updated 2 months ago
- The official implementation of the paper "MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Fo…☆33Updated 8 months ago
- ☆20Updated 10 months ago
- Reimplementation of Paper: (Re-)Imag(in)ing Price Trends☆65Updated last month
- PyTorch implementation of FactorVAE☆84Updated 11 months ago
- ☆15Updated last month
- PyTorch autoencoder implementation of asset pricing model using monthly returns/metrics☆50Updated 5 years ago
- Implementation of (Re-)Imag(in)ing Price Trends☆80Updated 3 years ago
- 复现华泰证 券《强化学习初探与DQN择时》研报中的DQN模型与效果☆36Updated 3 years ago
- Data Science Project: Replication of "Forest Through the Trees: Building Cross-Sections of Stock Returns" - creation of assets to test va…☆19Updated 2 years ago
- Accepted at AAAI 25 Workshop Long Research Paper☆22Updated 5 months ago
- DCC GARCH modeling in Python☆98Updated 5 years ago
- 通过遗传算法、强化学习来自动选择高频因子☆23Updated 2 years ago
- ☆54Updated 3 years ago
- Official Implementation of SimStock : Representation Model for Stock Similarities☆86Updated last year
- The official API of DoubleAdapt (KDD'23), an incremental learning framework for online stock trend forecasting, WITHOUT dependencies on t…☆108Updated 10 months ago
- A study on volume-price factor stock selection model based on wavelet transform and multitask self-attention network☆89Updated 6 months ago
- Contrastive Multi-granularity Learning for Stock Trend Prediction☆24Updated 4 years ago
- An end-to-end stock factors mining neural network framework.☆45Updated 2 years ago
- ☆30Updated last year
- Backtest Framework designed by YuminQuant&Yumin.☆19Updated last year
- ☆77Updated 2 years ago
- Code for paper "Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions"☆17Updated 3 years ago
- Blaze☆16Updated 4 years ago
- ☆72Updated 3 years ago
- ☆53Updated 9 months ago
- Equity return and characteristics of China A-Share market☆22Updated last year
- ☆34Updated 10 months ago
- Empirical asset pricing via Machine Learning in the Korean market☆45Updated last year