Financial Time-Series Forecasting
Leakage-safe deep-learning pipelines that judge model families on real trading metrics.
The challenge
Short-horizon index forecasting is hard. Price-only models miss the market regime, and sloppy backtests leak future data, so results look great until they meet reality.
What we built
Deep-learning pipelines for S&P 500 prices that blend OHLCV data with about 20 technical indicators, macro signals (VIX, 10Y Treasury, CPI) and daily FinBERT news sentiment. Models run from classical regressors through LSTM/GRU to a TimesNet architecture in PyTorch, all judged with leakage-safe time-series cross-validation and tracked in Weights & Biases.
The result
Reproducible, experiment-tracked pipelines that compare model families honestly on both statistical and trading metrics like Sharpe, Sortino and max drawdown, plus reusable learned features for later work.