金融时间序列（预测分析 / 相似度 / 数据处理）

Our startup, Mela, aims to simplify cryptocurrency trading for everyone and provide reliable investment sources while mitigating risks. We aim to design and build a reliable, large-scale trading data pipeline that can run various backtests and store useful artifacts in a robust data warehouse.

This repository implements a WaveNet model for predicting financial instrument prices, such as currencies, stocks, and cryptocurrencies, using advanced AI techniques like gradient boosting to capture intricate patterns in price movements.

Predictive analysis of Hermes and BlackRock financial returns using AR and ARMA models for dynamic portfolio management.

This repository implements an XGBoost model for predicting the prices of financial instruments, such as stocks and cryptocurrencies. Using gradient boosting techniques, it aims to capture patterns in price movements, enhancing prediction accuracy across various datasets.

This repository implements the KNeighbors Regressor (KNN) model for predicting financial instrument prices such as stocks, currencies, and cryptocurrencies. It leverages gradient boosting techniques to improve accuracy by capturing complex patterns in price movements.