| zvtvz/zvt |
2,729 |
|
0 |
0 |
over 2 years ago |
68 |
January 17, 2023 |
23 |
mit |
Python |
| modular quant framework. |
| anfederico/clairvoyant |
2,389 |
|
0 |
0 |
almost 5 years ago |
4 |
July 30, 2017 |
5 |
mit |
Python |
| Software designed to identify and monitor social/historical cues for short term stock movement |
| 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. |
| shashankvemuri/Finance |
1,317 |
|
0 |
0 |
about 2 years ago |
0 |
|
3 |
mit |
Python |
| 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data |
| achillesrasquinha/bulbea |
1,203 |
|
0 |
0 |
about 8 years ago |
0 |
|
28 |
other |
Python |
| :boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling |
| 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: |
| TreborNamor/TradingView-Machine-Learning-GUI |
608 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
mit |
HTML |
| Embark on a trading journey with this project's cutting-edge stop loss/take profit generator, fine-tuning your TradingView strategy to perfection. Harness the power of sklearn's machine learning algorithms to unlock unparalleled strategy optimization and unleash your trading potential. |
| kaushikjadhav01/Stock-Market-Prediction-Web-App-using-Machine-Learning-And-Sentiment-Analysis |
460 |
|
0 |
0 |
about 2 years ago |
0 |
|
21 |
mit |
Python |
| Stock Market Prediction Web App based on Machine Learning and Sentiment Analysis of Tweets (API keys included in code). The front end of the Web App is based on Flask and Wordpress. The App forecasts stock prices of the next seven days for any given stock under NASDAQ or NSE as input by the user. Predictions are made using three algorithms: ARIMA, LSTM, Linear Regression. The Web App combines the predicted prices of the next seven days with the sentiment analysis of tweets to give recommendation whether the price is going to rise or fall |
| 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 |
| golsun/deep-RL-trading |
248 |
|
0 |
0 |
almost 5 years ago |
0 |
|
3 |
mit |
Python |
| playing idealized trading games with deep reinforcement learning |
