Uses MLFinLab's trend scanning package to detect price trends (up/down/no-trend) for timing Bitcoin trades.
Applies different preprocessing techniques and PCA on market factors to predict SPY's weekly returns using a multiclass random forest model.
Uses neural networks to predict whether the next trading day will favor momentum or reversion risk exposure by analyzing volatility indicators.
Employs Hidden Markov Models to predict market volatility regimes and allocate funds between different ETFs and options accordingly.
Utilizes a multiple linear regression model to estimate future returns when stock splits are imminent and trades accordingly.
Uses a decision tree regression model to predict future dividend yields based on financial ratios to build a high-yield portfolio.
Applies PCA and linear regression for statistical arbitrage to exploit price differences between related securities.
Uses regression models to dynamically adjust stop-loss levels based on market conditions.
Employs a one-dimensional CNN to detect head-and-shoulders trading patterns in forex markets.
Uses PCA and learning-to-rank algorithms to predict relative performance of stocks based on fundamental data.
Applies ridge regression to predict volatility and allocate futures contracts inversely proportional to their expected volatility.
Uses a DecisionTreeRegressor to predict trading costs and optimize trade execution timing.
Implements PCA and linear regression for statistical arbitrage to identify trading opportunities in related securities.
Uses a temporal CNN to predict the direction of future stock prices based on OHLCV data.
Employs Gaussian Naive Bayes classifiers to predict daily returns of technology stocks.
Uses OpenAI's GPT-4 to analyze sentiment from news articles for trading decisions.
Uses a one-dimensional CNN to detect head-and-shoulders patterns and trade forex accordingly.
Utilizes Amazon's Chronos model to forecast future price paths and optimize portfolio weights.
Applies the FinBERT language model to assess news sentiment and make trading decisions based on aggregate sentiment scores.