TradeAI: Empowering Algorithmic Trading with Deep Learning for Cryptocurrency Data. Explore the potential of deep learning in cryptocurrency trading through our full-stack algorithmic trading system, fueled by state-of-the-art transformer models

Implementation of a modified vision transformer on the crypto market space

Using a Transformer Decoder to predict bitcoin prices.

A transformer based model to predict crypto-currency prices using sentiment analysis and cross-correlation

This repository contains the code for the paper "Hybrid ARDL-MIDAS-Transformer Time-Series Regressions for Multi-Topic Crypto Market Sentiment Driven by Price and Technology Factors", by Ioannis Chalkiadakis, Prof. Gareth W. Peters and Dr. Matthew Ames.

A testing pipeline for automated trading signal generation using Transformer neural networks. Fetches live crypto/forex data, engineers technical indicators, trains a custom PyTorch Transformer model, and outputs rolling "buy/hold/sell" predictions. Modular, extensible, and ready for research or backtesting.

MLP, LSTM, Transformers models are used to predict the crypto market on the historic price data using social media sentiment data. Hugging Face Sentiment analyzer is used to extract the sentiment of social media posts

Transformer-based solution for crypto time-series forecasting Kaggle

LLM_Trading_Agent is a demo of how transformer models can be leveraged to analyze crypto markets and make trading decisions. This advanced algorithmic trading analysis tool uses large language models (LLMs) to analyze market data and provide trading insights. The bot processes technical indicators, market sentiment, and historical data to generate

Custom Transformers based model for Crypto Price Prediction

real-time AI system predicting short-term market volatility and micro-movements in stocks & crypto. LSTM & Transformer powered

A financial dashboard built using Streamlit, fine-tuned Transformers models and Prophet. Includes auto summarisation, sentiment analysis, and trend forecasting of stock and crypto news.

Researching transformer architecture applications in the equity and crypto markets

Comparative Analysis of Transformer Encoder and Attention RNN in Predicting Crypto Market

University of York (UK) / Msc in Computer Science With AI

Transformer-based crypto trading research stack for 15-minute SOL data, evaluation, and live scoring experiments.

Scrape Stock and Crypto news from the web using BeautifulSoup. Summarize using fine tuned Pegasus Transformers model. Find Sentiment using Tranformers Pipeline

Automate stock and crypto research using Python and deep learning. Utilize Hugging Face transformers to summarize financial news. Scrape and analyze news for stocks like Tesla, Bitcoin, and Gamestop.

Scrape stock and crypto news from the web using Python and BeautifulSoup 2. Automatically summarize financial news with Deep Learning using a fine-tuned Hugging Face Pegasus Transformers model 3. Calculate sentiment for Bitcoin, Ethereum, Tesla and Gamstop using a pre-trained Transformers deep learning pipeline

AI-driven crypto sentiment engine that collects market chatter from multiple sources, applies NLP sentiment scoring using a local transformer model (no external billing or API keys), and aggregates symbol-level bullish/bearish signals. Includes a Streamlit dashboard for visualization.

A real-time crypto tick prediction engine built with Rust, Redis, and Postgres, streaming over 160k+ rows/day with sub-10ms latency. Combines DAG-based feature engineering and multimodal ML (XGBoost price models + transformer sentiment) to boost intraday prediction accuracy by ~27%.

Reproducible crypto time-series project. Part 1: Keras baselines (FNN, LSTM, GRU, CNN-LSTM, TCN, Transformer-GRU, XGBoost) with Keras Tuner + rolling walk-forward. Part 2: multi-timeframe "CryptoTFT" (H1/H4/D1), split-based training, evaluation, and trading back-tests with risk management.

End-to-end crypto price direction prediction framework built on the Mamba Selective State Space architecture (Python/PyTorch). Covers the full lifecycle: data preparation, Triple Barrier labeling, training, evaluation & live inference — all from a single config. Linear-time sequence processing as a Transformer alternative.

reated a Python tool using Beautiful Soup to fetch live stock and crypto news. Utilized Hugging Face's Pegasus Transformers model for text summarization and calculated sentiment scores for assets. This system analyzes real-time market data, aiding users in making informed cryptocurrency and stock investment decisions.

Cryptocurrency price prediction with Transformers Network

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Empirical study: Can transformers predict crypto prices? (Spoiler: No)

Cryptocurrency price prediction using a PyTorch Transformer on XRP/USDT OHLCV data

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