alfred's Introduction

Alfred

🏆 Code.Jam(2018): Patter Recognition 🏆

The implementation of efficient Sentiment Analysis models used for Stock Prediction earned me the Second Position in the Natural Language Processing category at a Machine Learning Hackathon at McGill University.

Introduction

Alfred is a web app designed with Streamlit. This appealing web-interface for will be your personal virtual assistant for your Stock Market Investments. From the visualization of recent stock price variations for any of the current Top 100 NASDAQ companies, to recent articles about these companies, you can explore sources of information to make accurate decisions for your next trades.

Installation

Firstly, ensure that you have pip install. In which case follow these steps using the command line:

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
python get-pip.py

Then install the required libraries listed in the requirements.txt

pip install -r requirements.txt

Requirements

Create an account on newsapi to get an API key. Then add it to the file api_key.json.

Usage Example

streamlit run app.py

Release history

3.0
- Streamlit POC - web-interface, visualizations of stock prices and articles about a company.
2.0
- Dash Proof of Concept: web-interface using Dash, visualizations of stock prices and articles about a company.
1.0
- Codejam Submission: web-interface using Flask, language Recognition to record stock trade requests, logistic regression Sentiment Analysis on headlines.

Built With

Streamlit The fastest way to build and share data apps
newsapi Search worldwide news with code

Contribute

To build on this tool, please fork it and make pull requests. Or simply send me some suggestions !

Authors

Paul Fournier - Initial work - Fournierp

License

This project is licensed under the Apache License - see the LICENSE.md file for details

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My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates.

I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators).
All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others.

The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction).
For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation.
And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM.

With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike).

I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box.

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fournierp / alfred Goto Github PK