Quant for Prediction Trading Model Design

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Project Overview We're seeking an experienced Quantitative Developer to design and implement the core algorithms for a sophisticated cryptocurrency price prediction system. This project involves creating a hierarchical Bayesian model that combines multiple predictive approaches, including market microstructure analysis (Hawkes processes), deep learning (LSTM+Attention), and on-chain metrics integration. Scope of Work Design the mathematical framework for our prediction models Develop multiple model components (Hawkes process, LSTM+Attention) Create a Bayesian model integration framework Build evaluation and comparison methodology Create efficient code implementations of the models Provide mathematical and statistical consultation Deliverables Complete source code for prediction models with documentation Implementation of Hawkes process for market microstructure analysis LSTM+Attention model for time series prediction Bayesian framework for model integration Evaluation metrics and comparison methodology Knowledge transfer sessions on model architecture and statistical approach Technical Requirements Strong background in quantitative finance and machine learning Experience with Bayesian modeling (PyMC, Pyro, or similar) Expertise in deep learning frameworks (TensorFlow or PyTorch) Familiarity with cryptocurrency markets and price dynamics Coding proficiency in Python Understanding of market microstructure concepts Nice to Have Previous work on cryptocurrency prediction models Experience with Hawkes processes or point process models Published research in quantitative finance or ML Knowledge of on-chain analytics and blockchain metrics Project Timeline & Budget Duration: 6-8 weeks Part-time (10-15 hours per week) Budget range: $2,000-$3,000 based on experience and qualifications

Keyword: Python

Price: $40.0

Python Statistics TensorFlow Machine Learning Data Science Quantitative Analysis