AaravMehta-07/LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna
A hybrid AI-based stock market prediction system using LSTM, Random Forest, and XGBoost, built for real-world deployment with Optuna-powered tuning, feature-rich engineering, and ensemble prediction logic. Designed to optimize F1 score and accuracy, this system aims to generate reliable buy/sell signals on stocks.
This system helps individual traders or quantitative analysts predict daily stock movements to generate buy/sell signals. It takes historical stock price data, including indicators like RSI and moving averages, and outputs a clear 'BUY' or 'SELL' recommendation for a given stock. The primary users are those looking to automate or enhance their stock trading decisions.
No commits in the last 6 months.
Use this if you are an individual investor or quantitative trader seeking to generate automated, data-driven buy/sell signals for stocks.
Not ideal if you need a visual interface, real-time streaming data integration, or sophisticated risk management features beyond basic signal generation.
Stars
7
Forks
1
Language
Python
License
MIT
Category
Last pushed
Jul 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/AaravMehta-07/LSTM-Random-Forest-XGBoost-Stock-Predictor-with-Optuna"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
TimRivoli/Stock-Price-Trade-Analyzer
This is a Python 3 project for analyzing stock prices and methods of stock trading. It uses...
rafa-rod/pytrendseries
Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum...
LeonardoBerti00/TLOB
This is the official repository for the paper TLOB: A Novel Transformer Model with Dual...
JordiCorbilla/stock-prediction-deep-neural-learning
Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for...
jcamiloangarita/stocker
Stock Price Prediction