| AI4Finance-Foundation/FinGPT |
18,673 |
|
0 |
0 |
2 months ago |
2 |
October 20, 2023 |
57 |
mit |
Jupyter Notebook |
| FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace. |
| TradeMaster-NTU/TradeMaster |
912 |
|
0 |
0 |
over 2 years ago |
9 |
April 02, 2023 |
12 |
apache-2.0 |
Jupyter Notebook |
| TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning :fire: :zap: :rainbow: |
| jankrepl/deepdow |
790 |
|
0 |
0 |
about 2 years ago |
5 |
February 16, 2021 |
26 |
apache-2.0 |
Python |
| Portfolio optimization with deep learning. |
| fracdiff/fracdiff |
238 |
|
0 |
2 |
over 2 years ago |
32 |
March 14, 2022 |
8 |
bsd-3-clause |
Python |
| Compute fractional differentiation super-fast. Processes time-series to be stationary while preserving memory. cf. "Advances in Financial Machine Learning" by M. Prado. |
| psnonis/FinBERT |
101 |
|
0 |
0 |
almost 6 years ago |
0 |
|
2 |
|
C++ |
| BERT for Finance : UC Berkeley MIDS w266 Final Project |
| chancefocus/trials |
49 |
|
0 |
0 |
over 2 years ago |
0 |
|
1 |
mit |
Python |
| Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarchical reinforcement learning. It includes the code for the proposed method and experimental results on real-world stock data to demonstrate its effectiveness. |
| proceduralia/pytorch-GAN-timeseries |
47 |
|
0 |
0 |
over 6 years ago |
0 |
|
2 |
|
Python |
| GANs for time series generation in pytorch |
| radoslawkrolikowski/financial-market-data-analysis |
39 |
|
0 |
0 |
about 4 years ago |
0 |
|
3 |
|
Python |
| Real-Time Financial Market Data Processing and Prediction application |
| daviddwlee84/SentenceSimilarity |
36 |
|
0 |
0 |
almost 5 years ago |
0 |
|
1 |
|
Python |
| The enhanced RCNN model used for sentence similarity classification |
| rbosh/ml-adp |
17 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
Python |
| Approximate dynamic programming for stochastic optimal control in Pytorch |
