In this Jupyter Notebook, I've used LSTM RNN with Technical Indicators namely Simple Moving Average (SMA), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), and Bollinger Bands to predict the price of Bank Nifty.

Stock Price prediction using LSTM neural network and Technical Indicators

Stock-Price-Prediction-using-LSTM-and-Technical-Indicators

SPY Stock Price Prediction Using LSTM And Technical Indicators

A machine learning system that forecasts stock prices using LSTM deep learning models. Features technical indicator analysis (RSI, MACD), Flask web interface, and performance metrics tracking. Achieves 78% prediction accuracy in backtesting.

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LSTM-based stock price prediction system using real-time Yahoo Finance data with technical indicators and an interactive Streamlit interface.

Machine learning project for stock price prediction with uncertainty quantification using LSTM, MC Dropout, Bayesian Neural Networks, and Transformers. Features 21 technical indicators, hyperparameter optimization, backtesting, and interactive Streamlit dashboard.

"A machine learning project for predicting stock market prices using historical data from Yahoo Finance. The model utilizes technical indicators (RSI, MACD, Bollinger Bands) and implements deep learning techniques (LSTM) for price prediction."

Stock price predictor using an ensemble of LSTM, GRU, and Hybrid CNN-LSTM models. Features real-time data, technical indicators, and dynamic weight adjustment. Interactive Streamlit UI provides easy visualization of predictions and model performance metrics.

This project utilizes advanced machine learning techniques such as Bagging, Boosting, and Stacking to improve stock price forecasts. This combines models like Random Forest, XGBoost, and LSTM for more accurate predictions using historical stock data and technical indicators.

This Stock Price Prediction App uses Streamlit to forecast stock prices for the next five days, combining XGBoost and LSTM models. It fetches historical stock data, applies technical indicators, and visualizes past and predicted prices. Users can select stocks, view percentage changes, and explore interactive price charts, making it a useful tool.

A complete Stock Market Price Prediction project using LSTM in Python. Trains on Nifty 50 data with technical indicators (SMA, RSI, MACD), visualizes performance, and deploys as a real-time web app using Flask. Includes model training, predictions, and user-friendly web interface.

This project uses Long Short-Term Memory (LSTM) networks to predict stock prices by analyzing historical data and technical indicators. LSTM captures long-term dependencies in time series, improving prediction accuracy. The tool helps traders and investors make informed, data-driven decisions with real-time analysis and robust modeling.

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This project is a Streamlit-based web application that predicts stock prices using LSTM (Long Short-Term Memory) neural networks. The app also incorporates sentiment analysis of financial news and technical indicators like moving averages (MA) and RSI (Relative Strength Index) for enhanced stock price prediction.

A research-grade stock price prediction system using LSTM neural networks and advanced technical indicators.

A deep learning-based stock price prediction system using LSTM neural networks and technical indicators.

Stock price prediction for Maybank (1155.KL) using LSTM with attention mechanism and technical indicators (RSI, MACD).

Implementation of an RNN and LSTM for Stock Price Prediction using Technical Indicators to train the model

Stock price prediction using LSTM deep learning with Flask dashboard, technical indicators (RSI, SMA, EMA), and multi-market support.

Machine Learning based stock price prediction system using technical indicators and multiple regression models (Linear Regression, Random Forest, LSTM).

AAPL stock price prediction comparing LSTM+Attention, XGBoost and Temporal Fusion Transformer using 19 technical indicators from Yahoo Finance 2015-2024

Developed LSTM-based forecasting model for stock price prediction. Achieved 85% directional accuracy using multi-variate time series analysis and technical indicators.

"A stock market prediction project using deep learning (Keras + LSTM) and Streamlit, featuring historical data analysis, technical indicators, and interactive visualizations for forecasting stock price trends."

Explainable Stock Prediction using LSTM & SHAP. Complete end-to-end workflow for time-series forecasting. Predicts stock prices with an LSTM model and uses SHAP (Explainable AI) to attribute predictions to specific technical and financial indicators.

An advanced AI-powered stock price prediction web app using LSTM, technical indicators, NLP sentiment analysis, and real-time financial data — with Streamlit interface.

An AI-powered Stock Price Predictor using LSTM, technical indicators, and Streamlit web app interface. Built for real-time prediction and deployed on the cloud.

Developed an AI-powered stock market prediction system using LSTM to forecast price trends based on historical data and technical indicators, improving prediction accuracy over traditional models.