Back to case studies
AI · Quantitative Finance

DeepS&P

Institutional-grade S&P 500 forecasting platform powered by a 3-layer LSTM trained on 90+ years of historical data, with Monte Carlo path simulation up to 2000 stochastic paths.

DeepS&P 1DeepS&P 2

Problem

Retail forecasting tools tend to be either point estimates with no uncertainty or full-blown black boxes. The goal was a forecasting harness that exposes both the central path and the dispersion of plausible paths.

Approach

  1. Pulled 90+ years of S&P 500 daily closes; engineered 22 features including lagged returns, volatility, and macro overlays.
  2. Trained a 3-layer stacked LSTM with attention; tuned via Optuna with 60 trials.
  3. Layered Monte Carlo simulation on top of LSTM-derived drift / volatility estimates to render up to 2000 paths.
  4. Shipped as a Streamlit app with a caching layer so first-paint stays under 2s.

Results

Direction Acc
61.4%
Sharpe (paper)
1.42
MC Paths
up to 2000