https://github.com/BUAA-WJR/PriceGraph
공식 코드Price graphs: Utilizing the structural information of financial time series for stock prediction
Papers with Code 아카이브
Stock Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HeathCiff/Multi-modal-Attention-Network-for-Stock-Movements-Prediction
공식 코드Multi-modal Attention Network for Stock Movements Prediction
https://github.com/Hongshen-Yang/pair-trading-envs
공식 코드Reinforcement Learning Pair Trading: A Dynamic Scaling approach
https://github.com/JingyiGu/IndexGAN
공식 코드Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network
https://github.com/Xinyi6/DP-LSTM-Differential-Privacy-inspired-LSTM-for-Stock-Prediction-Using-Financial-News
공식 코드DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
https://github.com/Zhihan1996/TradeTheEvent
공식 코드Trade the Event: Corporate Events Detection for News-Based Event-Driven Trading
https://github.com/adlnlp/finllms
공식 코드https://github.com/chancefocus/pixiu
공식 코드PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
https://github.com/chancefocus/trials
공식 코드Select and Trade: Towards Unified Pair Trading with Hierarchical Reinforcement Learning
https://github.com/dmis-lab/hats
공식 코드HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
https://github.com/hennande/Temporal_Relational_Stock_Ranking
공식 코드https://github.com/jinanzou/astock
공식 코드Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model
https://github.com/koa-fin/dva
공식 코드https://github.com/koa-fin/sep
공식 코드Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models
https://github.com/liorsidi/StockSimilarity
공식 코드Improving S&P stock prediction with time series stock similarity
https://github.com/microsoft/qlib
공식 코드Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
https://github.com/microsoft/qlib/tree/main/examples/benchmarks_dynamic/DDG-DA
공식 코드DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation
https://github.com/namir0806/flag
공식 코드FLAG: Financial Long Document Classification via AMR-based GNN
https://github.com/rian-dolphin/ICCBR2021-Financial-TS-Similarity
공식 코드https://github.com/start2020/Multi-GCGRU
공식 코드Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction
https://github.com/trytodoit227/dansmp
공식 코드https://github.com/yachuan/exante
공식 코드ExAnte: A Benchmark for Ex-Ante Inference in Large Language Models
https://github.com/yonxie/advfintweet
공식 코드A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions
https://github.com/zshicode/attention-clx-stock-prediction
공식 코드Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction
https://github.com/zshicode/latent-variable-gnn
공식 코드Differential equation and probability inspired graph neural networks for latent variable learning
https://github.com/AI4Finance-LLC/DP-LSTM-Differential-Privacy-Stock-Prediction-Financial-News-NIPS-2019
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
https://github.com/AI4Finance-LLC/Financial-News-for-Stock-Prediction-using-DP-LSTM-NIPS-2019
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
https://github.com/HFrost0/TGC_torch
https://github.com/YunanWu2168/Stock_Prediction_Project
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
https://github.com/codelion/openevolve
AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investment
https://github.com/diablordking/TheWolfofWallStreet
https://github.com/fulifeng/Temporal_Relational_Stock_Ranking
https://github.com/morpheu513/DA_project
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
https://github.com/morpheu513/NASDAQ_stock_analysis
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
https://github.com/nicklamonica/stock-sentiment-analysis
https://github.com/statyui/GRU-PFG
GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.