| 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. |
| zvtvz/zvt |
2,729 |
|
0 |
0 |
over 2 years ago |
68 |
January 17, 2023 |
23 |
mit |
Python |
| modular quant framework. |
| LastAncientOne/Deep_Learning_Machine_Learning_Stock |
1,721 |
|
0 |
0 |
about 2 years ago |
0 |
|
4 |
mit |
Jupyter Notebook |
| Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders. |
| shobrook/BitVision |
981 |
|
0 |
2 |
almost 5 years ago |
11 |
February 10, 2019 |
28 |
mit |
JavaScript |
| Terminal dashboard for trading Bitcoin, predicting price movements, and losing all your money |
| jmrichardson/tuneta |
326 |
|
0 |
0 |
over 2 years ago |
31 |
July 15, 2022 |
5 |
mit |
Python |
| Intelligently optimizes technical indicators and optionally selects the least intercorrelated for use in machine learning models |
| bshaw2019/Crypto_Trader |
186 |
|
0 |
0 |
about 5 years ago |
0 |
|
|
mit |
Python |
| Q-Learning Based Cryptocurrency Trader and Portfolio Optimizer for the Poloniex Exchange |
| devfinwiz/Fin-Maestro-Web |
168 |
|
0 |
0 |
over 2 years ago |
2 |
October 06, 2023 |
0 |
mit |
Python |
| Find your trading, investing edge using the most advanced web app for technical and fundamental research combined with real time sentiment analysis. |
| amicks/Speculator |
90 |
|
1 |
0 |
over 7 years ago |
11 |
December 19, 2017 |
0 |
mit |
Python |
| API for predicting the next Bitcoin and Ethereum with machine learning and technical analysis |
| philiparvidsson/Sequence-to-Sequence-Learning-of-Financial-Time-Series-in-Algorithmic-Trading |
61 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
cc-by-4.0 |
TeX |
| My bachelor's thesis—analyzing the application of LSTM-based RNNs on financial markets. 🤓 |
| gandalf1819/Stock-Market-Sentiment-Analysis |
38 |
|
0 |
0 |
over 5 years ago |
0 |
|
6 |
gpl-2.0 |
R |
| Identification of trends in the stock prices of a company by performing fundamental analysis of the company. News articles were provided as training data-sets to the model which classified the articles as positive or neutral. Sentiment score was computed by calculating the difference between positive and negative words present in the news article. Comparisons were made between the actual stock prices and the sentiment scores. Naive Bayes, OneR and Random Forest algorithms were used to observe the results of the model using Weka |
