This project leverages the Binance API to acquire real-time data for Bitcoin (BTC) and Ethereum (ETH). The data is then used to train a Long Short-Term Memory (LSTM) neural network model for predicting cryptocurrency prices.

The goal of this study is to predict prices for Cryptocurrencies using Time series analysis and machine learning techniques. The purpose of this project is to take a sneak peek into the future by forecasting the next 30 days' average daily Realized Volatility (RV) of ETH-BTC using 2 different approaches - the traditional econometric approach to volatility prediction of financial time series GARCH and state-of-the-art LSTM Neural Networks. Quantitative research methodology was used in this study and the The dataset Consist the historical data values of any any crypto-pair such as Open/Close/High/Low prices of any interval such as 15-minutes, Hourly, 1-day interval weekly, monthly. Dataset were obtained using the Binance API .

This project leverages the Binance API to acquire real-time data for Bitcoin (BTC) and Ethereum (ETH). The data is then used to train a Long Short-Term Memory (LSTM) neural network model for predicting cryptocurrency prices.

Crypto Market Prediction using LSTM & Sentiment Analysis . 🚀 An AI-powered cryptocurrency market prediction system using Binance data, LSTM models, and Twitter sentiment analysis.

A time series forecasting project using LSTM to predict Binance crypto prices. It processes historical data, trains a deep learning model, and evaluates performance. Predictions are mapped back to real-world price scales for clarity. Ideal for exploring deep learning in financial trend analysis.

Developed a production-ready Binance trading bot integrating technical indicators, grid strategy, ML-based price prediction, and real-time Twitter sentiment analysis. Features include persistent state management, live trading, robust error handling, and secure API key management—delivering an adaptive, intelligent crypto trading solution.