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https://github.com/koriavinash1/stock-price-forecasting-using-artificial-intelligence
Stock price prediction using Bidirectional LSTM and sentiment analysis
https://github.com/koriavinash1/stock-price-forecasting-using-artificial-intelligence
ai-in-trading angular-django-app artificial-intelligence bidirectional-lstm finance fintech machine-learning news-analysis quantitative-finance regression sentiment-analysis trading-statergies tweet-analysis
Last synced: 16 days ago
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Stock price prediction using Bidirectional LSTM and sentiment analysis
- Host: GitHub
- URL: https://github.com/koriavinash1/stock-price-forecasting-using-artificial-intelligence
- Owner: koriavinash1
- License: mit
- Created: 2017-09-03T04:55:45.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-09-09T07:47:19.000Z (about 6 years ago)
- Last Synced: 2024-10-04T12:47:00.916Z (about 1 month ago)
- Topics: ai-in-trading, angular-django-app, artificial-intelligence, bidirectional-lstm, finance, fintech, machine-learning, news-analysis, quantitative-finance, regression, sentiment-analysis, trading-statergies, tweet-analysis
- Language: JavaScript
- Homepage:
- Size: 20.7 MB
- Stars: 33
- Watchers: 8
- Forks: 12
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Stock-Price-Forecasting-Using-Artificial-Intelligence
New approach for stock market prediction using Artificial Intelligence
## Techniques used
> Bidirectional LSTM on historical data
> Sentiment analysis with news and twitter feeds
> Django + Angular JS combination for web app
## Requirements
### Python packages
> LAMP installed
> Django
> celery
> keras
> tensorflow
> pandas
> numpy
### JS packages
> Jquery
> Angular JS
> Chart JS
## How to run
first terminal
``` bash
git clone [email protected]:koriavinash1/FIN_ishers.git
cd Stock-Price-Forecasting-Using-Artificial-Intelligence/StockNest
python manage.py makemigrations
python manage.py migrate
python manage.py runserver
```
second ternimal
```bash
cd Stock-Price-Forecasting-Using-Artificial-Intelligence/StockNest
celery -A StockNest worker -l info
```
## First steps
+ data collection: Once server is on, navigate to localhost:8000/stocksadmin download all the required data
+ train model from same stocksadmin page
+ once model is trained, you can use product from localhost:8000/index paage
## DL model information
+ DL model information can be found in FIN_ishers/StockNest/stock_backend
+ Django restapis can be found from all apps/apis.py script
## Predictions
### Observed RMSE < 0.05 on test data
## Contact
* Avinash Kori ([email protected])
If any comments or information required, pull requests/issues are Welcomed....
Thankyou