Update README.md

README.md

@@ -1,12 +1,204 @@
 ---
 license: cc-by-4.0
+language:
+  - en
+pretty_name: Autocallable Notes Pricing (Synthetic)
 task_categories:
-- tabular-regression
+  - tabular-regression
 tags:
-- finance
-- quantitative-finance
-- derivatives
-- autocallable
-- option-pricing
-- synthetic
----
+  - finance
+  - quantitative-finance
+  - derivatives
+  - structured-products
+  - autocallable
+  - option-pricing
+  - monte-carlo
+  - heston
+  - synthetic
+size_categories:
+  - n<1K
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/*/dataset.csv
+---
+
+# Autocallable Notes Pricing (Synthetic)
+
+A fully **synthetic** dataset of autocallable structured-note scenarios paired with their
+**fair value (`PV`)**, computed by Monte Carlo simulation under several stochastic models for
+the underlying asset dynamics. It is designed for **derivative pricing with machine learning**:
+training surrogate pricers, benchmarking tabular regressors, and studying how product terms and
+market conditions map to price.
+
+> All data is generated from first-principles simulation. It contains **no real market data, no
+> proprietary quotes, and no scraped content** — every row is produced by the open-source pipeline
+> at [VegaInstitute/RG-ML-Autocall-Dataset](https://github.com/VegaInstitute/RG-ML-Autocall-Dataset).
+
+## 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`).
+- **Scope:** single- and multi-asset baskets (1–4 assets), worst-of / min-basket payoffs, optional memory coupons.
+
+## Supported tasks
+
+- **Tabular regression (primary):** learn a fast surrogate that maps `(product terms, spots, correlations, implied-vol surfaces)` → `PV`.
+- **Pricing acceleration / model distillation:** approximate the Monte Carlo pricer with a neural or gradient-boosted model.
+- **Sensitivity & calibration studies:** analyze how `PV` responds to coupons, barriers, correlations, and the volatility surface.
+
+## Underlying models
+
+| Model | Dynamics | Notes |
+|-------|----------|-------|
+| **Heston** | Stochastic volatility | Produces a volatility smile; parameters sampled from ranges |
+
+## Generation parameters
+
+Defaults used by the reference pipeline (see `configs/` in the source repo):
+
+| Parameter | Value / range |
+|-----------|---------------|
+| Trading days per year | 252 |
+| Tenors (years) | {1, 2, 3} |
+| Fixings per year | {1, 2, 4} |
+| Assets per basket | 1–4 |
+| Coupon | [0.01, 0.50] |
+| Coupon barrier | [0.80, 1.00] |
+| Autocall barrier | [1.00, 1.30] |
+| Put strike | [0.70, 1.30] |
+| Inter-asset correlation | [-0.80, 0.80] |
+| Risk-free rate | 0.0 |
+| Spot / notional | 1.0 |
+| Monte Carlo paths | up to 1,000,000 |
+| Basket convention | worst-of / min-basket |
+| MC quality filter | scenarios kept when `PV_std` ≤ 0.01 |
+| Heston: mean-reversion κ | [1.0, 10.0] |
+| Heston: vol-of-vol ν | [0.01, 1.0] |
+| Heston: price/var corr ρ | [-0.95, -0.10] |
+| Heston: long-run var θ | [0.01, 0.20] |
+| Heston: initial var V₀ | [0.0001, 0.04] |
+
+Each model family is sampled over multiple parameter draws (`n_models`) and several product
+draws per model (`n_observations`), so the total row count depends on the generation settings
+you run. The published files are produced by re-running the pipeline; regenerate or extend them
+with the CLI documented in the source repository.
+
+## Data structure
+
+The data is **wide tabular CSV**, one row per priced scenario, with **~1,531 columns** in the
+full multi-asset configuration. Columns fall into the following groups.
+
+### Identifiers & metadata
+
+| Column | Description |
+|--------|-------------|
+| `value_date` | Valuation (pricing) date as a year-fraction |
+| `value_date_memory` | Valuation date for the memory-coupon feature |
+| `model_idx` | Index of the sampled model parameter set |
+| `observation_idx` | Index of the product draw within a model |
+| `frequency` | Observation/fixing frequency |
+| `use_min_basket` | Whether the worst-of / min-basket convention applies |
+
+### Product terms
+
+| Column | Description |
+|--------|-------------|
+| `tenor` | Maturity in years |
+| `coupon` | Coupon rate |
+| `coupon_barrier` | Coupon barrier (fraction of spot) |
+| `autocall_barrier` | Autocall (early-redemption) barrier |
+| `is_put` | Whether a down-and-in put applies at maturity |
+| `has_memory` | Whether unpaid coupons accumulate (memory effect) |
+| `strike_put` | Put strike (fraction of spot) |
+
+### Market state
+
+| Column | Description |
+|--------|-------------|
+| `rate` | Risk-free rate |
+| `num_assets` | Number of underlying assets (1–4) |
+| `spot_asset_{1..4}` | Initial spot of each asset |
+| `value_date_spot_asset_{1..4}` | Spot at valuation date for each asset |
+| `corr_asset_i_j` | Pairwise correlations between assets |
+
+### Fixing schedule & implied-volatility surfaces
+
+| Column | Description |
+|--------|-------------|
+| `Date{1..12}` | Fixing/observation dates (year-fractions) |
+| `Asset{a}_Date{d}_vol_{k}` | Implied volatility for asset `a`, fixing date `d`, strike index `k` (1–31) |
+
+The `Asset{a}_Date{d}_vol_{1..31}` block encodes a **discretized implied-volatility surface**:
+31 strikes per asset, per fixing date, for up to 4 assets and 12 dates.
+
+### Target
+
+| Column | Description |
+|--------|-------------|
+| **`PV`** | **Fair value of the note (regression target)** |
+| `PV_std` | Monte Carlo standard error of `PV` (uncertainty of the label) |
+
+> **Tip:** treat `PV` as the label and `PV_std` as a per-row noise estimate. Drop `model_idx` /
+> `observation_idx` before training — they are bookkeeping indices, not features. Unused asset
+> slots (when `num_assets < 4`) are zero-filled.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("VegaInstitute/autocallable-notes-pricing", split="train")
+print(ds.features)        # columns
+print(ds[0]["PV"])        # target for the first scenario
+```
+
+With pandas:
+
+```python
+import pandas as pd
+
+df = pd.read_csv("hf://datasets/VegaInstitute/autocallable-notes-pricing/data/heston/dataset.csv")
+y = df["PV"]
+X = df.drop(columns=["PV", "PV_std", "model_idx", "observation_idx"])
+```
+
+## Limitations & biases
+
+- **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
+  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.
+- **Rate = 0.** Generated with a zero risk-free rate by default.
+
+## Source code & reproduction
+
+Generation pipeline, configs, and CLI:
+**https://github.com/VegaInstitute/RG-ML-Autocall-Dataset**
+
+```bash
+python gen.py --model heston
+```
+
+## License
+
+Released under **Creative Commons Attribution 4.0 International (CC-BY-4.0)**.
+You may share and adapt the data, including commercially, with appropriate attribution.
+
+## Citation
+
+```bibtex
+@misc{vegainstitute_autocall_synthetic,
+  title        = {Autocallable Notes Pricing (Synthetic)},
+  author       = {Vega Institute},
+  year         = {2026},
+  howpublished = {Hugging Face Datasets},
+  url          = {https://huggingface.co/datasets/VegaInstitute/autocallable-notes-pricing},
+  note         = {Synthetic Monte Carlo dataset for pricing autocallable structured notes}
+}
+```