NeuroFi: An Intelligent Multi-Model AI Platform for Crypto Price Forecasting and Smart Trading

Abstract

The cryptocurrency market exhibits extreme volatility and non-linear price dynamics, presenting significant challenges for accurate price prediction and trading decision support. Existing approaches often rely on single-model architectures that fail to capture the multifaceted nature of market movements influenced by technical patterns, market sentiment, and temporal dependencies. This paper presents NeuroFi, a comprehensive AI-powered trading decision support framework that integrates multiple prediction models, real-time sentiment analysis, and technical indicators through a novel ensemble architecture. The proposed system combines Long Short-Term Memory (LSTM) deep learning networks with statistical models including linear regression, moving average convergence-divergence (MACD), and Relative Strength Index (RSI) analysis. Additionally, NeuroFi incorporates a dual-engine sentiment analysis pipeline utilizing VADER and TextBlob algorithms to process news and social media data from multiple sources. The framework introduces a risk-aware recommendation engine that generates personalized trading signals based on configurable risk profiles. Implemented using a three-tier architecture with React, Express.js, and FastAPI, NeuroFi demonstrates the feasibility of real-time multi-model integration for cryptocurrency trading applications. The proposed methodology addresses critical gaps in existing literature by providing adaptive ensemble weighting, multi-source data fusion, and explainable trading recommendations

Introduction

The rapid growth of cryptocurrency markets, characterized by high volatility and continuous trading, has created challenges for accurate price prediction. Traditional systems often rely on single models, delayed sentiment analysis, and risk-agnostic strategies, making them less effective in real-world trading scenarios.

This research proposes NeuroFi, a multi-model, real-time cryptocurrency prediction and recommendation system. It integrates deep learning (LSTM), statistical models, technical indicators, and sentiment analysis into a unified ensemble framework. The system processes real-time data from market prices, news, and social media, combining predictions using weighted aggregation with confidence scoring.

A key innovation is the risk-aware recommendation engine, which generates trading signals (buy/sell/hold) with position sizing, stop-loss, and take-profit levels tailored to different risk profiles (low, medium, high). Additionally, the system includes a resilient architecture with fallback mechanisms to ensure continuous operation even when data sources fail.

The methodology uses multiple components: LSTM for capturing time-series patterns, regression for trend estimation, technical indicators (RSI, MACD, moving averages), and volume analysis. Sentiment analysis is performed using a dual-engine approach (VADER and TextBlob) with relevance filtering. All outputs are combined through an ensemble model to improve accuracy and robustness.

The system is implemented using modern technologies such as React, Node.js, FastAPI, TensorFlow, and MongoDB, with real-time data from Binance and social platforms like Twitter and Reddit.

Conclusion

This paper presented NeuroFi, a comprehensive AI-powered cryptocurrency trading decision support system designed to handle the high volatility and non-linear behavior of crypto markets. NeuroFi addresses key weaknesses of many existing approaches by combining multi-model ensemble prediction, real-time sentiment integration, and risk-aware recommendation generation in one unified pipeline. The proposed three-tier architecture (React frontend, Express.js backend, and FastAPI-based ML service) demonstrates that deep learning, statistical models, and NLP-based sentiment analysis can be integrated in a practical, production-oriented manner to support near real-time decision making. The main contributions of NeuroFi are: 1) A weighted ensemble framework that fuses five different prediction/indicator models, where aggregation is guided by model agreement and confidence to reduce reliance on any single method. 2) A dual-engine sentiment analysis pipeline that combines VADER and TextBlob scoring on multi-source text (news and social media), with crypto-specific keyword relevance filtering to reduce noise and improve signal quality. 3) A configurable recommendation layer that translates predictions into user-facing actions (BUY/SELL/HOLD style signals) while adapting thresholds and weighting according to three risk profiles (low/medium/high). 4) A resilient system design with graceful degradation, enabling core functionality to continue even when specific APIs or services become unavailable, which is important for 24/7 crypto markets.

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Copyright

Copyright © 2026 Adarsh Paswan, Harsha Pandey, Mohd Fardeen Khan, Mohd Waqas Jamal Siddiqui, Mr. Ranjeet Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.