Surpriver

Find big moving stocks before they move using machine learning and anomaly detection

AI & Machine LearningPythonGPL-3.0

Abstract

Surpriver is an open-source AI & Machine Learning project. Find big moving stocks before they move using machine learning and anomaly detection. Find high moving stocks before they move using anomaly detection and machine learning. Surpriver uses machine learning to look at volume + price action and infer unusual patterns which can result in big moves in stocks. It is built using Python, Machine Learning. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Find high moving stocks before they move using anomaly detection and machine learning. Surpriver uses machine learning to look at volume + price action and infer unusual patterns which can result in big moves in stocks.

2. Objective

Find big moving stocks before they move using machine learning and anomaly detection

This project demonstrates how Python, Machine Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

PythonMachine Learning

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/tradytics/surpriver.git
cd surpriver
python detection_engine.py --top_n 25 --min_volume 5000 --data_granularity_minutes 60 --history_to_use 14 --is_load_from_dictionary 1 --data_dictionary_path 'dictionaries/data_dict.npy' --is_save_dictionary 0 --is_test 0 --future_bars 0 --output_format 'CLI'
Last Bar Time: 2020-08-25 11:30:00-04:00
Symbol: SPI
Anomaly Score: -0.029
Today Volume (Today = Date Above): 313.94K
Average Volume 5d: 206.53K
Average Volume 20d: 334.14K
Volatility 5bars: 0.013
Volatility 20bars: 0.038
Future Absolute Sum Price Changes: 72.87

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by tradytics and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

Want to build this as your internship project?

Work on an AI & Machine Learning project like this with mentor guidance, weekly reviews and an internship certificate from Training Trains, Erode — online or offline.

Apply for AI & Machine Learning Internship