Update README.md

README.md

@@ -39,8 +39,8 @@ market conditions map to price.
 ## TL;DR
 
 - **Task:** tabular regression — predict `PV` (fair value of the note) from product terms + market state.
-- **Underlying models:** Bachelier, Black–Scholes, CEV, and Heston (stochastic volatility).
-- **Pricing engine:** Monte Carlo, up to 1,000,000 paths per scenario, with a reported MC standard error (`PV_std`).
+- **Underlying models:** Heston (stochastic volatility).
+- **Pricing engine:** Monte Carlo, with a reported MC standard error (`PV_std`).
 - **Scope:** single- and multi-asset baskets (1–4 assets), worst-of / min-basket payoffs, optional memory coupons.
 
 ## Supported tasks
@@ -170,7 +170,7 @@ X = df.drop(columns=["PV", "PV_std", "model_idx", "observation_idx"])
 - **Synthetic, not observed.** Prices and volatility surfaces come from model assumptions, not
   traded quotes; a model trained here learns *the simulated pricer*, not real-market mispricings.
 - **Label noise.** `PV` carries Monte Carlo error; use `PV_std` to weight or filter rows.
-- **Model coverage.** Limited to Bachelier, Black–Scholes, CEV, and Heston with the parameter
+- **Model coverage.** Limited to Heston with the parameter
   ranges above; out-of-range terms are out of distribution.
 - **Wide and sparse.** The volatility-surface block dominates the column count; for `num_assets < 4`
   many columns are zero-filled.