From c28f64acd6a46204f5c8de6af7cf764f1cd3e17b Mon Sep 17 00:00:00 2001 From: Jeremy Wang Date: Sat, 25 Apr 2026 12:37:49 +0100 Subject: [PATCH 1/3] more to come --- Cargo.toml | 2 +- README.md | 52 +++++++++++++++++++++++++--------------------------- 2 files changed, 26 insertions(+), 28 deletions(-) diff --git a/Cargo.toml b/Cargo.toml index 2fad63b..336e251 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -1,6 +1,6 @@ [package] name = "finquant" -version = "0.0.59" +version = "0.0.60" authors = ["Jeremy Wang ", "David Steiner "] license = "MIT OR Apache-2.0" description = "Experimental Rust Quant Library" diff --git a/README.md b/README.md index 386e447..77289bb 100644 --- a/README.md +++ b/README.md @@ -18,33 +18,31 @@ > > FinQuant is an experimental project, currently incomplete and not fit for production. -## Roadmap (no set agenda yet) - -1. Basic settings - - [x] Calendar inline with QuantLib v1.42 - - [x] Day counts - - [x] Schedule generator -2. Markets / Quotes - - [x] Forex - forward points - - [x] Forex - volatility - - [x] Interest Rate - curves (cash rates, futures, swaps) - - [x] Interest Rate - volatility -3. Forex markets - - Pricer - we want more than just Black Scholes model. For example volatility should not be the key input; the surface should. - - Forward - - [x] forward points generator - - [x] pricing + greeks - - Option - - [x] implied vol generator - - [x] pricing + greeks - - Simulator - - [x] Monte Carlo -4. Interest rate markets - - Pricer - - [x] Swap - - [x] Cap/Floor - - Simulator - - [x] Monte Carlo +## Coverage + +### Basic settings +- Calendars inline with QuantLib v1.42 — 40+ jurisdictions (TARGET, US, UK, JPN, CHN, AUS, BRA, CAN, CHE, DEU, FRA, HKG, IND, IDN, ISR, ITA, KOR, MEX, NZL, NOR, POL, RUS, SGP, SWE, TUR, ZAF, …) plus weekends-only and joint-calendar composition +- Day counters: Act/360, Act/364, Act/365 Fixed, Act/366, Act/Act, 30/360, 30/365, Business/252 +- Schedule generator + +### Markets / Quotes +- Forex: forward points, volatility surface, market context +- Interest rate: yield curve bootstrapping (cash, futures, swaps; OIS rate helpers), vol surface, market context + +### Forex +- Pricers — surface-driven (not single-vol): + - Forward — forward-points generator, pricing + greeks + - Option — implied-vol generator, pricing + greeks +- Models: Black–Scholes, Bachelier, Dupire local vol, SABR (effective, time-dependent, SLV) with calibrators, FX-HHW (+ 1-factor ChF, stock variant, calibrator), FX-FMM (+ 1-factor ChF, simulator, calibrator), FX-HLMM (+ 1-factor ChF, calibrator) +- Simulators: Monte Carlo across the FX-HHW / FX-FMM / FX-HLMM families + +### Interest rate +- Pricers: Swap, Cap/Floor +- Models: Hull–White, FMM (Forward Market Model) +- Simulators: Monte Carlo + +### Numerics +- COS method pricer, CIR process, Newton/optimizer routines, normal/standard-normal utilities [crates-badge]: https://img.shields.io/crates/v/finquant.svg From 010924428827a5f0cc3beecb4932f00aa39252ee Mon Sep 17 00:00:00 2001 From: Jeremy Wang Date: Sat, 25 Apr 2026 22:55:10 +0100 Subject: [PATCH 2/3] ml --- README.md | 5 + examples/dump_hhw_vanilla_training_data.rs | 271 +++ ml/.gitignore | 5 + ml/README.md | 141 ++ ml/dataset.py | 155 ++ ml/poetry.lock | 2009 ++++++++++++++++++++ ml/poetry.toml | 2 + ml/pyproject.toml | 31 + ml/setup.sh | 21 + ml/train_hhw_vanilla.py | 214 +++ ml/train_hhw_vanilla_tune.py | 117 ++ 11 files changed, 2971 insertions(+) create mode 100644 examples/dump_hhw_vanilla_training_data.rs create mode 100644 ml/.gitignore create mode 100644 ml/README.md create mode 100644 ml/dataset.py create mode 100644 ml/poetry.lock create mode 100644 ml/poetry.toml create mode 100644 ml/pyproject.toml create mode 100755 ml/setup.sh create mode 100644 ml/train_hhw_vanilla.py create mode 100644 ml/train_hhw_vanilla_tune.py diff --git a/README.md b/README.md index 77289bb..1e8e9ba 100644 --- a/README.md +++ b/README.md @@ -44,6 +44,11 @@ ### Numerics - COS method pricer, CIR process, Newton/optimizer routines, normal/standard-normal utilities +### Deep-learning surrogates ([ml/](ml/)) +- Horvath-style neural networks that replace slow numerical pricers with microsecond-scale `(model params → IV grid)` lookups — the speed lift needed for portfolio XVA over 10⁹+ revaluations +- Rust ground-truth dumper: `cargo run --release --example dump_hhw_vanilla_training_data` +- Python training pipeline (Poetry, Pydantic-validated schemas, PyTorch → ONNX, optional Ray Tune HPO) — see [ml/README.md](ml/README.md) + [crates-badge]: https://img.shields.io/crates/v/finquant.svg [docs-badge]: https://docs.rs/finquant/badge.svg \ No newline at end of file diff --git a/examples/dump_hhw_vanilla_training_data.rs b/examples/dump_hhw_vanilla_training_data.rs new file mode 100644 index 0000000..cf3626d --- /dev/null +++ b/examples/dump_hhw_vanilla_training_data.rs @@ -0,0 +1,271 @@ +//! Generate ground-truth training data for an FX-HHW vanilla-call NN. +//! +//! For each sampled FX-HHW parameter combination θ, evaluate the COS-method +//! call price on a fixed `(τ × moneyness)` grid, invert to Black implied +//! vol, and write three files into `ml/data/`: +//! +//! * `meta.json` — schema (param order, τ-grid, moneyness-grid, n_samples) +//! * `params.bin` — `n_samples × n_params` little-endian f32 +//! * `ivs.bin` — `n_samples × n_taus × n_moneyness` little-endian f32 +//! +//! Spot is fixed at 1.0 — we work in moneyness `K/F_0(τ)` so the network +//! is pair-agnostic. Strikes where the IV solver fails are written as NaN +//! and filtered downstream in Python. +//! +//! Run with: +//! ```bash +//! cargo run --release --example dump_hhw_vanilla_training_data -- \ +//! --n-samples 80000 --seed 42 +//! ``` + +use finquant::models::common::black_scholes::bs_implied_vol; +use finquant::models::common::cir::CirProcess; +use finquant::models::common::cos_pricer::CosPricer; +use finquant::models::forex::fx_hhw::{Correlation4x4, FxHhwParams}; +use finquant::models::forex::fx_hhw1_chf::FxHhw1ForwardChf; +use finquant::models::interestrate::hull_white::HullWhite1F; +use rand::{Rng, SeedableRng}; +use rand_chacha::ChaCha20Rng; +use std::env; +use std::fs::File; +use std::io::{BufWriter, Write}; +use std::path::PathBuf; + +const PARAM_NAMES: &[&str] = &[ + "heston_kappa", + "heston_theta", + "heston_gamma", + "heston_sigma_0", + "domestic_mean_reversion", + "domestic_sigma", + "foreign_mean_reversion", + "foreign_sigma", + "rd_0", + "rf_0", + "rho_xi_sigma", + "rho_xi_d", + "rho_xi_f", + "rho_sigma_d", + "rho_sigma_f", + "rho_d_f", +]; + +const TAUS: &[f64] = &[0.1, 0.25, 0.5, 1.0, 2.0, 3.0, 5.0, 7.0]; +const MONEYNESS: &[f64] = &[0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5]; + +struct Bounds { + lo: f64, + hi: f64, +} +const fn b(lo: f64, hi: f64) -> Bounds { + Bounds { lo, hi } +} + +const HESTON_KAPPA: Bounds = b(0.10, 3.0); +const HESTON_THETA: Bounds = b(0.005, 0.16); +const HESTON_GAMMA: Bounds = b(0.05, 0.80); +const HESTON_SIGMA_0: Bounds = b(0.005, 0.16); +const HW_MR: Bounds = b(0.001, 0.10); +const HW_SIGMA: Bounds = b(0.001, 0.025); +const RATE: Bounds = b(-0.01, 0.08); +const RHO_XI_SIGMA: Bounds = b(-0.95, -0.10); +const RHO_CROSS: Bounds = b(-0.50, 0.50); +const RHO_DF: Bounds = b(-0.50, 0.95); + +fn sample_uniform(rng: &mut ChaCha20Rng, b: &Bounds) -> f64 { + rng.random_range(b.lo..=b.hi) +} + +fn sample_params(rng: &mut ChaCha20Rng) -> FxHhwParams { + loop { + let correlations = Correlation4x4 { + rho_xi_sigma: sample_uniform(rng, &RHO_XI_SIGMA), + rho_xi_d: sample_uniform(rng, &RHO_CROSS), + rho_xi_f: sample_uniform(rng, &RHO_CROSS), + rho_sigma_d: sample_uniform(rng, &RHO_CROSS), + rho_sigma_f: sample_uniform(rng, &RHO_CROSS), + rho_d_f: sample_uniform(rng, &RHO_DF), + }; + if !correlations.is_valid() { + continue; + } + let rd_0 = sample_uniform(rng, &RATE); + let rf_0 = sample_uniform(rng, &RATE); + return FxHhwParams { + fx_0: 1.0, + heston: CirProcess { + kappa: sample_uniform(rng, &HESTON_KAPPA), + theta: sample_uniform(rng, &HESTON_THETA), + gamma: sample_uniform(rng, &HESTON_GAMMA), + sigma_0: sample_uniform(rng, &HESTON_SIGMA_0), + }, + domestic: HullWhite1F { + mean_reversion: sample_uniform(rng, &HW_MR), + sigma: sample_uniform(rng, &HW_SIGMA), + }, + foreign: HullWhite1F { + mean_reversion: sample_uniform(rng, &HW_MR), + sigma: sample_uniform(rng, &HW_SIGMA), + }, + rd_0, + rf_0, + theta_d: rd_0, + theta_f: rf_0, + correlations, + }; + } +} + +fn params_to_vector(p: &FxHhwParams) -> [f32; 16] { + [ + p.heston.kappa as f32, + p.heston.theta as f32, + p.heston.gamma as f32, + p.heston.sigma_0 as f32, + p.domestic.mean_reversion as f32, + p.domestic.sigma as f32, + p.foreign.mean_reversion as f32, + p.foreign.sigma as f32, + p.rd_0 as f32, + p.rf_0 as f32, + p.correlations.rho_xi_sigma as f32, + p.correlations.rho_xi_d as f32, + p.correlations.rho_xi_f as f32, + p.correlations.rho_sigma_d as f32, + p.correlations.rho_sigma_f as f32, + p.correlations.rho_d_f as f32, + ] +} + +fn iv_grid(p: &FxHhwParams) -> Vec { + let mut out = Vec::with_capacity(TAUS.len() * MONEYNESS.len()); + for &tau in TAUS { + let chf = FxHhw1ForwardChf::new(p, tau); + let pricer = CosPricer::new(&chf); + let forward = p.fx_0 * ((p.rd_0 - p.rf_0) * tau).exp(); + let discount = (-p.rd_0 * tau).exp(); + for &m in MONEYNESS { + let k = m * forward; + let price = pricer.call(k, discount); + let iv = bs_implied_vol(price, forward, k, tau, discount, true) + .map(|v| v as f32) + .unwrap_or(f32::NAN); + out.push(iv); + } + } + out +} + +fn parse_args() -> (usize, u64, PathBuf) { + let mut n_samples: usize = 1000; + let mut seed: u64 = 42; + let mut out_dir = PathBuf::from("ml/data"); + let mut args = env::args().skip(1); + while let Some(a) = args.next() { + match a.as_str() { + "--n-samples" => { + n_samples = args + .next() + .expect("--n-samples needs a value") + .parse() + .unwrap(); + } + "--seed" => { + seed = args.next().expect("--seed needs a value").parse().unwrap(); + } + "--out-dir" => { + out_dir = PathBuf::from(args.next().expect("--out-dir needs a value")); + } + other => panic!("unknown arg: {other}"), + } + } + (n_samples, seed, out_dir) +} + +fn main() { + let (n_samples, seed, out_dir) = parse_args(); + std::fs::create_dir_all(&out_dir).expect("create out dir"); + + let n_params = PARAM_NAMES.len(); + let n_taus = TAUS.len(); + let n_moneyness = MONEYNESS.len(); + let n_iv = n_taus * n_moneyness; + + println!( + "dumping {n_samples} samples × ({n_params} params, {n_taus}×{n_moneyness} IV grid) → {}", + out_dir.display() + ); + + let mut rng = ChaCha20Rng::seed_from_u64(seed); + let params_path = out_dir.join("params.bin"); + let ivs_path = out_dir.join("ivs.bin"); + let meta_path = out_dir.join("meta.json"); + + let mut params_w = BufWriter::new(File::create(¶ms_path).expect("create params.bin")); + let mut ivs_w = BufWriter::new(File::create(&ivs_path).expect("create ivs.bin")); + + let t0 = std::time::Instant::now(); + let mut nan_count: u64 = 0; + let progress_step = (n_samples / 20).max(1); + for i in 0..n_samples { + let p = sample_params(&mut rng); + let pv = params_to_vector(&p); + for x in &pv { + params_w.write_all(&x.to_le_bytes()).unwrap(); + } + let iv = iv_grid(&p); + for x in &iv { + if x.is_nan() { + nan_count += 1; + } + ivs_w.write_all(&x.to_le_bytes()).unwrap(); + } + if (i + 1) % progress_step == 0 { + let pct = 100.0 * (i + 1) as f64 / n_samples as f64; + let elapsed = t0.elapsed().as_secs_f64(); + let eta = elapsed * (n_samples as f64 / (i + 1) as f64 - 1.0); + println!( + " {:>6}/{n_samples} ({pct:5.1}%) elapsed {elapsed:6.1}s eta {eta:6.1}s", + i + 1 + ); + } + } + params_w.flush().unwrap(); + ivs_w.flush().unwrap(); + + let total_iv = (n_samples as u64) * (n_iv as u64); + println!( + "done in {:.1}s — {nan_count} / {total_iv} IV cells were NaN ({:.3}%)", + t0.elapsed().as_secs_f64(), + 100.0 * nan_count as f64 / total_iv as f64, + ); + + let meta = format!( + r#"{{ + "model": "fx_hhw", + "product": "vanilla_call", + "n_samples": {n_samples}, + "n_params": {n_params}, + "n_taus": {n_taus}, + "n_moneyness": {n_moneyness}, + "param_names": {param_names}, + "taus": {taus}, + "moneyness": {moneyness}, + "fx_0": 1.0, + "dtype": "float32", + "endian": "little", + "seed": {seed} +}} +"#, + param_names = serde_json::to_string(PARAM_NAMES).unwrap(), + taus = serde_json::to_string(TAUS).unwrap(), + moneyness = serde_json::to_string(MONEYNESS).unwrap(), + ); + std::fs::write(&meta_path, meta).expect("write meta.json"); + println!( + "wrote {} {} {}", + params_path.display(), + ivs_path.display(), + meta_path.display() + ); +} diff --git a/ml/.gitignore b/ml/.gitignore new file mode 100644 index 0000000..bc5b5d6 --- /dev/null +++ b/ml/.gitignore @@ -0,0 +1,5 @@ +data/ +models/ +.venv/ +__pycache__/ +*.pyc diff --git a/ml/README.md b/ml/README.md new file mode 100644 index 0000000..1b04e9d --- /dev/null +++ b/ml/README.md @@ -0,0 +1,141 @@ +# finquant deep-learning pipeline + +Horvath-style neural network surrogates for finquant's stochastic-volatility +pricers. The first model trained here is the **FX-HHW vanilla call** — +input: 16 model parameters, output: implied-vol grid on +8 maturities × 11 moneyness points. + +The trained ONNX models are intended to be loaded by the Rust XVA engine +(via the `ort` crate) so that conditional pricing along Monte Carlo paths +runs at microsecond scale — collapsing portfolio XVA from ~minutes per +revaluation slice to seconds. + +## Layout + +``` +ml/ +├── README.md you are here +├── pyproject.toml poetry-managed deps (PEP 621) +├── poetry.toml in-project venv config +├── setup.sh env bootstrap (works around a Poetry 2.x quirk) +├── .venv/ (gitignored) Python 3.14 venv +├── data/ (gitignored) generated training data +│ ├── meta.json +│ ├── params.bin +│ └── ivs.bin +├── models/ (gitignored) trained checkpoints + ONNX +│ ├── hhw_vanilla.pt +│ ├── hhw_vanilla.onnx +│ └── hhw_vanilla_norm.json +├── dataset.py Pydantic-validated loader +├── train_hhw_vanilla.py PyTorch trainer + ONNX exporter +└── train_hhw_vanilla_tune.py Ray Tune HPO (requires --with tune) +``` + +## Dependencies + +Managed with **Poetry 2.x** via `pyproject.toml`. Pinned-floor versions +(installed at first setup): + +| package | floor | tested | +|--------------|-------|---------| +| torch | 2.5 | 2.11.0 | +| numpy | 2.1 | 2.4.4 | +| onnx | 1.17 | 1.21.0 | +| onnxruntime | 1.20 | 1.25.0 | +| pydantic | 2.9 | 2.13.3 | +| ray (tune) | 2.40 | optional, install via `--with tune` | + +Pydantic models in `dataset.py` validate the `meta.json` schema at load +time, so any drift between the Rust dumper and the Python loader surfaces +immediately rather than as a silent reshape error mid-training. + +## End-to-end workflow + +### 0. Bootstrap the env (one-time) + +```bash +./ml/setup.sh # creates .venv, runs poetry install +./ml/setup.sh --with tune # optionally include Ray Tune HPO group +``` + +The script wraps a small Poetry-2.x quirk: on Homebrew Python it +sometimes tries to install into the system interpreter even with +`virtualenvs.in-project = true`. The wrapper forces it into the local +`.venv` we create explicitly. + +### 1. Generate training data (Rust) + +```bash +cargo run --release --example dump_hhw_vanilla_training_data -- \ + --n-samples 80000 --seed 42 +``` + +Writes `meta.json`, `params.bin`, `ivs.bin` to `ml/data/`. On a laptop the +single-threaded dumper runs at ~75 ms/sample, so 80 k samples ≈ 100 min. +Run with `--n-samples 1000` for a smoke test (~75 s). + +The 16 parameter dimensions (uniformly sampled over the envelope below): + +| group | parameters | bounds | +|--------------|-------------------------------------------------|-----------------------| +| Heston | κ, θ, γ, σ₀ | see `examples/dump_…` | +| HW domestic | mean-reversion, σ | [0.001, 0.10] / [0.001, 0.025] | +| HW foreign | mean-reversion, σ | same | +| short rates | r_d(0), r_f(0) | [−0.01, 0.08] | +| correlations | ρ_xξσ, ρ_xξd, ρ_xξf, ρ_σd, ρ_σf, ρ_df | rejection-sampled to PD | + +Spot is fixed at 1.0 — strikes are quoted as moneyness K/F₀(τ), so the +network is **pair-agnostic**: the same trained NN prices EUR/USD, +GBP/USD, USD/JPY at inference time, just with different parameter inputs. + +### 2. Train the network (Python) + +```bash +ml/.venv/bin/python ml/train_hhw_vanilla.py \ + --data-dir ml/data --out-dir ml/models --epochs 200 --batch-size 32 +``` + +Architecture follows Horvath/Muguruza/Tomas (2019, fig. 3): 4 hidden +layers × 30 units, ELU activation, mean-squared error on +mean-std-normalised IV targets. Early stopping with patience 25. + +Outputs to `ml/models/`: +- `hhw_vanilla.pt` — best PyTorch checkpoint +- `hhw_vanilla.onnx` — exported for Rust inference (opset 17) +- `hhw_vanilla_norm.json` — input/output normalisation stats and the + τ / moneyness grid (Rust must apply the same normalisation before/after + ONNX inference) + +### 2b. Hyperparameter sweep (optional, Ray Tune) + +```bash +./ml/setup.sh --with tune +ml/.venv/bin/python ml/train_hhw_vanilla_tune.py --num-samples 20 --max-epochs 40 +``` + +Sweeps `hidden ∈ {16,30,64,128}`, `depth ∈ {3,4,5}`, `lr` log-uniform +1e-4 to 5e-3, `batch_size ∈ {32,64,128}` with the ASHA scheduler. Once a +winning config is found, drop it back into `train_hhw_vanilla.py` for +the final ONNX export. + +### 3. Inference (Rust, future) + +The XVA engine will load the ONNX file once via the `ort` crate, then for +each `(path, exposure_date, instrument)` triple normalise the parameter +vector with `hhw_vanilla_norm.json`, run the network, denormalise the IV +grid, and price the instrument via Black–Scholes on the appropriate +`(τ, K/F)` cell. Adding `ort` to `Cargo.toml` is deferred until a network +is actually trained. + +## Why this pipeline + +XVA on a 1 000-trade FX portfolio with 50 exposure dates × 10 k paths +needs ~5×10⁸ revaluations. The COS pricer is fast (~10 µs per option) but +that's still ~80 min per revaluation slice. A 1-µs neural surrogate +collapses that to single digits and — more importantly — extends to +exotics (barriers, Bermudans, TARFs) where no fast analytic exists. + +For 10⁹+ revaluation workloads (full FRTB stress, CCR PFE shocks), the +Ray dependency in the `tune` group can be reused at inference time to +distribute the path × instrument grid across worker nodes. diff --git a/ml/dataset.py b/ml/dataset.py new file mode 100644 index 0000000..aea12f4 --- /dev/null +++ b/ml/dataset.py @@ -0,0 +1,155 @@ +"""Loader for the FX-HHW vanilla training data dumped by the Rust binary. + +The dumper writes three files to a directory: + + meta.json schema (param order, tau-grid, moneyness-grid, n_samples) + params.bin n_samples x n_params little-endian f32 + ivs.bin n_samples x n_taus x n_moneyness little-endian f32 + +Run the dumper with: + + cargo run --release --example dump_hhw_vanilla_training_data -- \\ + --n-samples 80000 --seed 42 + +Pydantic models enforce the meta-file schema (and array-shape consistency) +at load time, so a Rust/Python schema drift surfaces immediately rather +than as a silent reshape error mid-training. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Self + +import numpy as np +from pydantic import BaseModel, ConfigDict, Field, model_validator + + +class DatasetMeta(BaseModel): + """Schema written by the Rust dumper to `meta.json`.""" + + model_config = ConfigDict(frozen=True, extra="forbid") + + model: str + product: str + n_samples: int = Field(gt=0) + n_params: int = Field(gt=0) + n_taus: int = Field(gt=0) + n_moneyness: int = Field(gt=0) + param_names: list[str] + taus: list[float] + moneyness: list[float] + fx_0: float + dtype: str + endian: str + seed: int + + @model_validator(mode="after") + def _check_lengths(self) -> Self: + if len(self.param_names) != self.n_params: + raise ValueError( + f"param_names has {len(self.param_names)} entries but n_params={self.n_params}" + ) + if len(self.taus) != self.n_taus: + raise ValueError(f"taus has {len(self.taus)} entries but n_taus={self.n_taus}") + if len(self.moneyness) != self.n_moneyness: + raise ValueError( + f"moneyness has {len(self.moneyness)} entries but n_moneyness={self.n_moneyness}" + ) + if self.dtype != "float32" or self.endian != "little": + raise ValueError(f"unsupported dtype/endian: {self.dtype}/{self.endian}") + return self + + +class HhwVanillaDataset(BaseModel): + """Loaded training data — params + IV grid + schema.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + params: np.ndarray + ivs: np.ndarray + meta: DatasetMeta + + @model_validator(mode="after") + def _check_shapes(self) -> Self: + n = self.params.shape[0] + expected_params = (n, self.meta.n_params) + if self.params.shape != expected_params: + raise ValueError(f"params shape {self.params.shape} != {expected_params}") + expected_ivs = (n, self.meta.n_taus, self.meta.n_moneyness) + if self.ivs.shape != expected_ivs: + raise ValueError(f"ivs shape {self.ivs.shape} != {expected_ivs}") + if self.params.dtype != np.float32 or self.ivs.dtype != np.float32: + raise ValueError( + f"expected float32 arrays, got params={self.params.dtype}, ivs={self.ivs.dtype}" + ) + return self + + @property + def n_samples(self) -> int: + return self.params.shape[0] + + @property + def n_params(self) -> int: + return self.meta.n_params + + @property + def n_outputs(self) -> int: + return self.meta.n_taus * self.meta.n_moneyness + + @property + def param_names(self) -> list[str]: + return self.meta.param_names + + @property + def taus(self) -> np.ndarray: + return np.asarray(self.meta.taus, dtype=np.float64) + + @property + def moneyness(self) -> np.ndarray: + return np.asarray(self.meta.moneyness, dtype=np.float64) + + def drop_nan_rows(self) -> HhwVanillaDataset: + """Drop any sample whose IV grid contains a NaN cell.""" + mask = ~np.isnan(self.ivs).any(axis=(1, 2)) + kept = int(mask.sum()) + dropped = self.n_samples - kept + if dropped: + print(f"dropping {dropped} / {self.n_samples} samples with NaN IV") + new_meta = self.meta.model_copy(update={"n_samples": kept}) + return HhwVanillaDataset( + params=self.params[mask], + ivs=self.ivs[mask], + meta=new_meta, + ) + + +def load(data_dir: str | Path) -> HhwVanillaDataset: + data_dir = Path(data_dir) + with open(data_dir / "meta.json") as f: + meta = DatasetMeta.model_validate_json(f.read()) + + params = np.fromfile(data_dir / "params.bin", dtype=" 1 else "ml/data" + ds = load(data_dir) + print(f"loaded: {ds.n_samples} samples, {ds.n_params} params, {ds.n_outputs} IV outputs") + print(" param ranges:") + for i, name in enumerate(ds.param_names): + col = ds.params[:, i] + print(f" {name:>26} [{col.min():+.4f}, {col.max():+.4f}] mean={col.mean():+.4f}") + valid = ds.ivs[~np.isnan(ds.ivs)] + print( + f" IV: min={valid.min():.4f} max={valid.max():.4f} mean={valid.mean():.4f} " + f"nan={int(np.isnan(ds.ivs).sum())}" + ) diff --git a/ml/poetry.lock b/ml/poetry.lock new file mode 100644 index 0000000..8fb4ae1 --- /dev/null +++ b/ml/poetry.lock @@ -0,0 +1,2009 @@ +# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +groups = ["main", "tune"] +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[[package]] +name = "attrs" +version = "26.1.0" +description = "Classes Without Boilerplate" +optional = false +python-versions = ">=3.9" +groups = ["main", "tune"] +files = [ + {file = "attrs-26.1.0-py3-none-any.whl", hash = "sha256:c647aa4a12dfbad9333ca4e71fe62ddc36f4e63b2d260a37a8b83d2f043ac309"}, + {file = "attrs-26.1.0.tar.gz", hash = "sha256:d03ceb89cb322a8fd706d4fb91940737b6642aa36998fe130a9bc96c985eff32"}, +] + +[[package]] +name = "certifi" +version = "2026.4.22" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.7" +groups = ["main", "tune"] +files = [ + {file = "certifi-2026.4.22-py3-none-any.whl", hash = "sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a"}, + {file = "certifi-2026.4.22.tar.gz", hash = "sha256:8d455352a37b71bf76a79caa83a3d6c25afee4a385d632127b6afb3963f1c580"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.7" +description = "The Real First Universal Charset Detector. 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"sha256:9173fde7d80d9018e02a662e168e5a2d04f87c41ea174b139fbef642eda62d10"}, +] + +[[package]] +name = "urllib3" +version = "2.6.3" +description = "HTTP library with thread-safe connection pooling, file post, and more." +optional = false +python-versions = ">=3.9" +groups = ["main", "tune"] +files = [ + {file = "urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4"}, + {file = "urllib3-2.6.3.tar.gz", hash = "sha256:1b62b6884944a57dbe321509ab94fd4d3b307075e0c2eae991ac71ee15ad38ed"}, +] + +[package.extras] +brotli = ["brotli (>=1.2.0) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=1.2.0.0) ; platform_python_implementation != \"CPython\""] +h2 = ["h2 (>=4,<5)"] +socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"] +zstd = ["backports-zstd (>=1.0.0) ; python_version < \"3.14\""] + +[extras] +tune = ["ray"] + +[metadata] +lock-version = "2.1" +python-versions = ">=3.13,<3.15" +content-hash = "af38850c05010c2f0612d47901f4b11eb2f725d4c6fb88ca877f5946e89de35b" diff --git a/ml/poetry.toml b/ml/poetry.toml new file mode 100644 index 0000000..ab1033b --- /dev/null +++ b/ml/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +in-project = true diff --git a/ml/pyproject.toml b/ml/pyproject.toml new file mode 100644 index 0000000..d968cac --- /dev/null +++ b/ml/pyproject.toml @@ -0,0 +1,31 @@ +[project] +name = "finquant-ml" +version = "0.1.0" +description = "Deep learning surrogates for finquant stochastic-volatility pricers" +authors = [{ name = "Jeremy Wang", email = "j.wang@quantransform.co.uk" }] +requires-python = ">=3.13,<3.15" +dependencies = [ + "torch>=2.5", + "numpy>=2.1", + "onnx>=1.17", + "onnxruntime>=1.20", + "pydantic>=2.9", +] + +[project.optional-dependencies] +# Hyperparameter sweeps via Ray Tune. Install with: +# poetry install --with tune +tune = ["ray[tune]>=2.40"] + +[tool.poetry] +package-mode = false + +[tool.poetry.group.tune] +optional = true + +[tool.poetry.group.tune.dependencies] +ray = { extras = ["tune"], version = ">=2.40" } + +[build-system] +requires = ["poetry-core>=2.0"] +build-backend = "poetry.core.masonry.api" diff --git a/ml/setup.sh b/ml/setup.sh new file mode 100755 index 0000000..c4f7111 --- /dev/null +++ b/ml/setup.sh @@ -0,0 +1,21 @@ +#!/usr/bin/env bash +# Set up the ml/ Python environment. +# +# Poetry 2.x on Homebrew Python sometimes ignores `virtualenvs.in-project` +# and tries to install into the system interpreter. Forcing VIRTUAL_ENV +# and disabling poetry's own venv-creation gets it to install into a local +# .venv that we create ourselves. + +set -euo pipefail +cd "$(dirname "$0")" + +if [[ ! -d .venv ]]; then + /opt/homebrew/opt/python@3.14/bin/python3.14 -m venv .venv + echo "created .venv (Python 3.14)" +fi + +VIRTUAL_ENV="$PWD/.venv" POETRY_VIRTUALENVS_CREATE=false \ + poetry install --no-root "$@" + +echo +echo "done. activate with: source ml/.venv/bin/activate" diff --git a/ml/train_hhw_vanilla.py b/ml/train_hhw_vanilla.py new file mode 100644 index 0000000..a852c0e --- /dev/null +++ b/ml/train_hhw_vanilla.py @@ -0,0 +1,214 @@ +"""Train a Horvath-style MLP that maps FX-HHW parameters to a vanilla-call +implied-vol surface (8 maturities x 11 moneyness = 88 outputs). + +Network architecture (Horvath/Muguruza/Tomas 2019, fig. 3): + 16 inputs -> 4 hidden layers x 30 units, ELU activation -> 88 outputs + +After training, exports an ONNX model that the Rust XVA engine can load +via the `ort` crate for microsecond-scale conditional pricing. +""" + +from __future__ import annotations + +import argparse +import json +import time +from pathlib import Path + +import numpy as np +import torch +import torch.nn as nn + +from dataset import HhwVanillaDataset, load + + +class HhwVanillaMlp(nn.Module): + def __init__(self, n_inputs: int, n_outputs: int, hidden: int = 30, depth: int = 4): + super().__init__() + layers: list[nn.Module] = [] + d = n_inputs + for _ in range(depth): + layers.append(nn.Linear(d, hidden)) + layers.append(nn.ELU()) + d = hidden + layers.append(nn.Linear(d, n_outputs)) + self.net = nn.Sequential(*layers) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.net(x) + + +def fit_normalizer(x: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + """Mean-std normalisation parameters.""" + mu = x.mean(axis=0).astype(np.float32) + sd = x.std(axis=0).astype(np.float32) + sd = np.where(sd < 1e-8, 1.0, sd) + return mu, sd + + +def split_train_test(n: int, test_frac: float, seed: int) -> tuple[np.ndarray, np.ndarray]: + rng = np.random.default_rng(seed) + idx = rng.permutation(n) + n_test = int(round(test_frac * n)) + return idx[n_test:], idx[:n_test] + + +def train( + ds: HhwVanillaDataset, + out_dir: Path, + epochs: int, + batch_size: int, + lr: float, + test_frac: float, + seed: int, + device: str, +) -> None: + torch.manual_seed(seed) + + x = ds.params.astype(np.float32) + y = ds.ivs.reshape(ds.n_samples, -1).astype(np.float32) + + train_idx, test_idx = split_train_test(ds.n_samples, test_frac, seed) + x_mu, x_sd = fit_normalizer(x[train_idx]) + y_mu, y_sd = fit_normalizer(y[train_idx]) + + x_t = torch.from_numpy((x - x_mu) / x_sd) + y_t = torch.from_numpy((y - y_mu) / y_sd) + + model = HhwVanillaMlp(n_inputs=ds.n_params, n_outputs=ds.n_outputs).to(device) + opt = torch.optim.Adam(model.parameters(), lr=lr) + loss_fn = nn.MSELoss() + + train_ds = torch.utils.data.TensorDataset(x_t[train_idx], y_t[train_idx]) + train_loader = torch.utils.data.DataLoader(train_ds, batch_size=batch_size, shuffle=True) + x_test = x_t[test_idx].to(device) + y_test = y_t[test_idx].to(device) + + best_test = float("inf") + patience = 25 + bad_epochs = 0 + out_dir.mkdir(parents=True, exist_ok=True) + ckpt_path = out_dir / "hhw_vanilla.pt" + + print( + f"train: {len(train_idx)} samples | test: {len(test_idx)} | " + f"epochs<={epochs} batch={batch_size} lr={lr} device={device}" + ) + t0 = time.time() + for epoch in range(1, epochs + 1): + model.train() + running = 0.0 + n_batches = 0 + for xb, yb in train_loader: + xb = xb.to(device, non_blocking=True) + yb = yb.to(device, non_blocking=True) + opt.zero_grad() + pred = model(xb) + loss = loss_fn(pred, yb) + loss.backward() + opt.step() + running += loss.item() + n_batches += 1 + train_loss = running / max(1, n_batches) + + model.eval() + with torch.no_grad(): + test_loss = loss_fn(model(x_test), y_test).item() + + if test_loss < best_test - 1e-7: + best_test = test_loss + bad_epochs = 0 + torch.save(model.state_dict(), ckpt_path) + else: + bad_epochs += 1 + + if epoch % 10 == 0 or epoch == 1 or bad_epochs == 0: + elapsed = time.time() - t0 + print( + f" epoch {epoch:>3}/{epochs} train={train_loss:.5e} " + f"test={test_loss:.5e} best={best_test:.5e} bad={bad_epochs} " + f"elapsed={elapsed:5.1f}s" + ) + + if bad_epochs >= patience: + print(f"early stop at epoch {epoch} (no improvement for {patience} epochs)") + break + + model.load_state_dict(torch.load(ckpt_path, map_location=device)) + print(f"best test MSE: {best_test:.6e}") + + # Report unnormalised RMSE on the IV grid for an interpretable number. + model.eval() + with torch.no_grad(): + pred_norm = model(x_test).cpu().numpy() + pred_iv = pred_norm * y_sd + y_mu + truth_iv = y[test_idx] + err = pred_iv - truth_iv + rmse_iv = float(np.sqrt(np.mean(err * err))) + rel = np.abs(err) / np.maximum(np.abs(truth_iv), 1e-4) + print( + f"unnormalised IV: RMSE={rmse_iv:.5f} " + f"rel-error mean={rel.mean():.4%} p95={np.quantile(rel, 0.95):.4%} max={rel.max():.4%}" + ) + + norm_path = out_dir / "hhw_vanilla_norm.json" + norm_path.write_text( + json.dumps( + { + "x_mean": x_mu.tolist(), + "x_std": x_sd.tolist(), + "y_mean": y_mu.tolist(), + "y_std": y_sd.tolist(), + "param_names": ds.param_names, + "taus": ds.taus.tolist(), + "moneyness": ds.moneyness.tolist(), + }, + indent=2, + ) + ) + print(f"wrote normaliser stats to {norm_path}") + + onnx_path = out_dir / "hhw_vanilla.onnx" + dummy = torch.zeros(1, ds.n_params, device=device) + torch.onnx.export( + model, + dummy, + str(onnx_path), + input_names=["params_normalised"], + output_names=["iv_normalised"], + dynamic_axes={"params_normalised": {0: "batch"}, "iv_normalised": {0: "batch"}}, + opset_version=17, + ) + print(f"exported ONNX to {onnx_path}") + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--data-dir", default="ml/data") + p.add_argument("--out-dir", default="ml/models") + p.add_argument("--epochs", type=int, default=200) + p.add_argument("--batch-size", type=int, default=32) + p.add_argument("--lr", type=float, default=1e-3) + p.add_argument("--test-frac", type=float, default=0.15) + p.add_argument("--seed", type=int, default=0) + args = p.parse_args() + + device = "cuda" if torch.cuda.is_available() else ( + "mps" if torch.backends.mps.is_available() else "cpu" + ) + + ds = load(args.data_dir).drop_nan_rows() + train( + ds=ds, + out_dir=Path(args.out_dir), + epochs=args.epochs, + batch_size=args.batch_size, + lr=args.lr, + test_frac=args.test_frac, + seed=args.seed, + device=device, + ) + + +if __name__ == "__main__": + main() diff --git a/ml/train_hhw_vanilla_tune.py b/ml/train_hhw_vanilla_tune.py new file mode 100644 index 0000000..48693e1 --- /dev/null +++ b/ml/train_hhw_vanilla_tune.py @@ -0,0 +1,117 @@ +"""Ray Tune hyperparameter sweep for the FX-HHW vanilla NN. + +Sweeps hidden width, depth, learning rate, and batch size with the +ASHA scheduler — short trials get killed off early, the rest train to +completion. Reports the best config + test MSE. + +Requires the optional `tune` dependency group: + + poetry install --with tune + poetry run python ml/train_hhw_vanilla_tune.py + +This is illustrative — once we have a winning architecture, drop it back +into `train_hhw_vanilla.py` for the final ONNX export. +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + +import numpy as np +import ray +import torch +import torch.nn as nn +from ray import tune +from ray.tune.schedulers import ASHAScheduler + +from dataset import HhwVanillaDataset, load +from train_hhw_vanilla import HhwVanillaMlp, fit_normalizer, split_train_test + + +def trial(config: dict, ds_state: dict) -> None: + """Single Ray Tune trial — trains one model and reports test MSE.""" + ds = HhwVanillaDataset.model_validate(ds_state) + + x = ds.params.astype(np.float32) + y = ds.ivs.reshape(ds.n_samples, -1).astype(np.float32) + train_idx, test_idx = split_train_test(ds.n_samples, 0.15, seed=0) + x_mu, x_sd = fit_normalizer(x[train_idx]) + y_mu, y_sd = fit_normalizer(y[train_idx]) + x_t = torch.from_numpy((x - x_mu) / x_sd) + y_t = torch.from_numpy((y - y_mu) / y_sd) + + device = "cuda" if torch.cuda.is_available() else "cpu" + model = HhwVanillaMlp( + n_inputs=ds.n_params, + n_outputs=ds.n_outputs, + hidden=config["hidden"], + depth=config["depth"], + ).to(device) + opt = torch.optim.Adam(model.parameters(), lr=config["lr"]) + loss_fn = nn.MSELoss() + + train_loader = torch.utils.data.DataLoader( + torch.utils.data.TensorDataset(x_t[train_idx], y_t[train_idx]), + batch_size=config["batch_size"], + shuffle=True, + ) + x_test = x_t[test_idx].to(device) + y_test = y_t[test_idx].to(device) + + for epoch in range(1, config["max_epochs"] + 1): + model.train() + for xb, yb in train_loader: + xb = xb.to(device, non_blocking=True) + yb = yb.to(device, non_blocking=True) + opt.zero_grad() + loss_fn(model(xb), yb).backward() + opt.step() + + model.eval() + with torch.no_grad(): + test_loss = loss_fn(model(x_test), y_test).item() + tune.report({"test_mse": test_loss, "epoch": epoch}) + + +def main() -> None: + p = argparse.ArgumentParser() + p.add_argument("--data-dir", default="ml/data") + p.add_argument("--num-samples", type=int, default=20) + p.add_argument("--max-epochs", type=int, default=40) + args = p.parse_args() + + ds = load(Path(args.data_dir)).drop_nan_rows() + # Pydantic dump → primitive types Ray can pickle and ship to workers. + ds_state = ds.model_dump() + + ray.init(ignore_reinit_error=True, log_to_driver=False) + config = { + "hidden": tune.choice([16, 30, 64, 128]), + "depth": tune.choice([3, 4, 5]), + "lr": tune.loguniform(1e-4, 5e-3), + "batch_size": tune.choice([32, 64, 128]), + "max_epochs": args.max_epochs, + } + scheduler = ASHAScheduler( + metric="test_mse", + mode="min", + max_t=args.max_epochs, + grace_period=5, + reduction_factor=3, + ) + tuner = tune.Tuner( + tune.with_parameters(trial, ds_state=ds_state), + param_space=config, + tune_config=tune.TuneConfig(num_samples=args.num_samples, scheduler=scheduler), + ) + results = tuner.fit() + best = results.get_best_result(metric="test_mse", mode="min") + print("\nbest config:") + for k, v in best.config.items(): + print(f" {k:>12} = {v}") + print(f" test_mse = {best.metrics['test_mse']:.6e}") + + +if __name__ == "__main__": + main() From 339560098a07b607c0845ac27436d3a5558518b4 Mon Sep 17 00:00:00 2001 From: Jeremy Wang Date: Wed, 13 May 2026 22:01:15 +0100 Subject: [PATCH 3/3] more xva --- README.md | 1 + examples/xva_portfolio_demo.rs | 822 +++++++++++++++++++++++++++++++++ ml/XVA.md | 181 ++++++++ ml/train_hhw_vanilla.py | 2 - ml/train_hhw_vanilla_tune.py | 2 - 5 files changed, 1004 insertions(+), 4 deletions(-) create mode 100644 examples/xva_portfolio_demo.rs create mode 100644 ml/XVA.md diff --git a/README.md b/README.md index 1e8e9ba..4fc880c 100644 --- a/README.md +++ b/README.md @@ -48,6 +48,7 @@ - Horvath-style neural networks that replace slow numerical pricers with microsecond-scale `(model params → IV grid)` lookups — the speed lift needed for portfolio XVA over 10⁹+ revaluations - Rust ground-truth dumper: `cargo run --release --example dump_hhw_vanilla_training_data` - Python training pipeline (Poetry, Pydantic-validated schemas, PyTorch → ONNX, optional Ray Tune HPO) — see [ml/README.md](ml/README.md) +- End-to-end XVA harness (1000-trade portfolio, EUR/USD/GBP/JPY, EE/EPE/PFE): `cargo run --release --example xva_portfolio_demo` — methodology in [ml/XVA.md](ml/XVA.md) [crates-badge]: https://img.shields.io/crates/v/finquant.svg diff --git a/examples/xva_portfolio_demo.rs b/examples/xva_portfolio_demo.rs new file mode 100644 index 0000000..318f26f --- /dev/null +++ b/examples/xva_portfolio_demo.rs @@ -0,0 +1,822 @@ +//! XVA / future-exposure demo — 1000-position multi-currency FX portfolio. +//! +//! # What this demonstrates +//! +//! The Horvath/Muguruza/Tomas (2019) "Deep Learning Volatility" paper +//! frames XVA's binding constraint as the conditional re-pricing of every +//! trade at every exposure date along every Monte Carlo path — +//! `paths × dates × trades` revaluations, dominated by the option leg. +//! The paper's payoff: replace the slow conditional pricer with a +//! Horvath-style NN that maps `(state, contract) → IV` in microseconds. +//! +//! This demo wires the *full* XVA harness today using existing analytic +//! re-pricing, so we can: +//! 1. Establish a numerical baseline (EE/EPE/PFE) for the portfolio. +//! 2. Measure the per-leg pricing cost — this is the bar the NN must beat. +//! 3. Show exactly where the NN drops in (the FX-option re-pricing closure). +//! +//! # Portfolio composition (1000 trades by default) +//! +//! * 400 IR swaps — vanilla par-form, 4 currencies (USD/EUR/GBP/JPY), +//! maturities 1Y/2Y/3Y/5Y, semi-annual fixed leg, log-uniform notionals. +//! * 300 FX forwards — three pairs (EURUSD/GBPUSD/USDJPY), maturities +//! 3M/6M/1Y/2Y/3Y, mixed long/short, ~ATM strikes. +//! * 300 FX vanilla options — calls and puts on the three pairs, ATM +//! ±10 % strikes, maturities 3M/6M/1Y/2Y. +//! +//! # Simulation +//! +//! One FX-HHW per pair (independent — cross-pair correlation is *not* +//! captured; that's the next architectural step). Per-currency short +//! rates are extracted from each FX-HHW's domestic / foreign HW leg — +//! USD from EURUSD.rd, EUR from EURUSD.rf, GBP from GBPUSD.rf, JPY from +//! USDJPY.rd. This means USD rates seen by EURUSD-priced trades may +//! differ slightly from those seen by GBPUSD trades; documented honest +//! limitation. +//! +//! # Re-pricing (today, all closed-form) +//! +//! * IRS: par-swap value `N · (P(t,T_n) − 1) + N · K · Σ τ_i P(t,T_i)`, +//! bonds via Hull–White affine `discount_affine`. +//! * FX forward: `N · (S(t)·P_f(t,T) − K·P_d(t,T))`. +//! * FX option: Black–Scholes with `σ ≈ √variance(t)` from the simulated +//! CIR state. **This is the NN slot** — replace with an ONNX +//! inference call once `ml/train_hhw_vanilla.py` produces a model. +//! +//! # Aggregation +//! +//! All trade PVs FX-converted to USD at each (path, date), summed into +//! one netting set, then reduced to: +//! +//! * `EE(t)` = mean over paths of `max(V_t, 0)` +//! * `EPE` = time-average of `EE(t)` to portfolio horizon +//! * `PFE_q(t)` = q-quantile of `max(V_t, 0)` (we report 95% and 99%) +//! +//! Run with: +//! ```bash +//! cargo run --release --example xva_portfolio_demo +//! cargo run --release --example xva_portfolio_demo -- --paths 1000 --trades 1000 +//! ``` + +use finquant::models::common::black_scholes::{bs_call_forward, bs_put_forward}; +use finquant::models::common::cir::CirProcess; +use finquant::models::forex::fx_hhw::{Correlation4x4, FxHhwParams, FxHhwSimulator, FxHhwState}; +use finquant::models::interestrate::hull_white::HullWhite1F; +use rand::{Rng, SeedableRng}; +use rand_chacha::ChaCha20Rng; +use std::env; +use std::time::Instant; + +// ── Currencies, pairs, market data ───────────────────────────────────────── + +#[derive(Copy, Clone, Debug, PartialEq, Eq, Hash)] +enum Ccy { + Usd, + Eur, + Gbp, + Jpy, +} + +impl Ccy { + const ALL: [Ccy; 4] = [Ccy::Usd, Ccy::Eur, Ccy::Gbp, Ccy::Jpy]; + fn label(self) -> &'static str { + match self { + Ccy::Usd => "USD", + Ccy::Eur => "EUR", + Ccy::Gbp => "GBP", + Ccy::Jpy => "JPY", + } + } +} + +#[derive(Copy, Clone, Debug, PartialEq, Eq, Hash)] +enum Pair { + EurUsd, + GbpUsd, + UsdJpy, +} + +impl Pair { + const ALL: [Pair; 3] = [Pair::EurUsd, Pair::GbpUsd, Pair::UsdJpy]; + /// (domestic, foreign) under the pair's quoting convention. + fn ccys(self) -> (Ccy, Ccy) { + match self { + Pair::EurUsd => (Ccy::Usd, Ccy::Eur), + Pair::GbpUsd => (Ccy::Usd, Ccy::Gbp), + Pair::UsdJpy => (Ccy::Jpy, Ccy::Usd), + } + } +} + +/// Flat-curve initial rates per currency. In a real system these come +/// from the SOFR/ESTR/SONIA/TONA strips; here a constant is enough to +/// drive the FX-HHW simulators and the analytic re-pricing. +fn init_rate(c: Ccy) -> f64 { + match c { + Ccy::Usd => 0.0425, + Ccy::Eur => 0.0250, + Ccy::Gbp => 0.0450, + Ccy::Jpy => 0.0050, + } +} + +/// Initial spot. EURUSD/GBPUSD quoted with USD per foreign; +/// USDJPY quoted with JPY per USD. +fn init_spot(p: Pair) -> f64 { + match p { + Pair::EurUsd => 1.0850, + Pair::GbpUsd => 1.2750, + Pair::UsdJpy => 152.50, + } +} + +/// Build a representative FX-HHW per pair. The Heston block is sized to +/// give realistic vol-of-vol; the HW legs use plausible mean-reversion +/// and short-rate vols (USD ~70 bp/yr, EUR ~60, GBP ~80, JPY ~30). +fn fx_hhw_for(p: Pair) -> FxHhwParams { + let (dom, foreign) = p.ccys(); + let rd_0 = init_rate(dom); + let rf_0 = init_rate(foreign); + let (heston_sigma_0, heston_theta) = match p { + Pair::EurUsd => (0.008, 0.010), + Pair::GbpUsd => (0.009, 0.011), + Pair::UsdJpy => (0.012, 0.014), + }; + FxHhwParams { + fx_0: init_spot(p), + heston: CirProcess { + kappa: 1.5, + theta: heston_theta, + gamma: 0.30, + sigma_0: heston_sigma_0, + }, + domestic: HullWhite1F { + mean_reversion: 0.05, + sigma: hw_short_rate_vol(dom), + }, + foreign: HullWhite1F { + mean_reversion: 0.05, + sigma: hw_short_rate_vol(foreign), + }, + rd_0, + rf_0, + theta_d: rd_0, + theta_f: rf_0, + correlations: Correlation4x4 { + rho_xi_sigma: -0.45, + rho_xi_d: -0.10, + rho_xi_f: -0.10, + rho_sigma_d: 0.20, + rho_sigma_f: 0.20, + rho_d_f: 0.30, + }, + } +} + +fn hw_short_rate_vol(c: Ccy) -> f64 { + match c { + Ccy::Usd => 0.0070, + Ccy::Eur => 0.0060, + Ccy::Gbp => 0.0080, + Ccy::Jpy => 0.0030, + } +} + +// ── Trade representation ─────────────────────────────────────────────────── + +#[derive(Clone, Debug)] +struct IrSwap { + ccy: Ccy, + notional: f64, + fixed_rate: f64, + /// Year-fractions from t = 0 of the fixed-leg payment dates. + pay_dates: Vec, + /// Pay-fixed (true) or receive-fixed (false). Pay-fixed gains when + /// rates rise. + pay_fixed: bool, +} + +#[derive(Clone, Debug)] +struct FxForward { + pair: Pair, + /// Notional in the *domestic* currency (USD for EURUSD/GBPUSD, JPY + /// for USDJPY). + notional_dom: f64, + strike: f64, + expiry: f64, + /// Long the foreign currency vs domestic (true) or short (false). + long_foreign: bool, +} + +#[derive(Clone, Debug)] +struct FxOption { + pair: Pair, + /// Notional in foreign currency units (i.e. EUR for EURUSD). + notional_foreign: f64, + strike: f64, + expiry: f64, + is_call: bool, +} + +#[derive(Clone, Debug)] +enum Trade { + Irs(IrSwap), + FxFwd(FxForward), + FxOpt(FxOption), +} + +impl Trade { + fn currency(&self) -> Ccy { + match self { + Trade::Irs(s) => s.ccy, + Trade::FxFwd(f) => f.pair.ccys().0, + Trade::FxOpt(o) => o.pair.ccys().0, + } + } +} + +// ── Portfolio sampler ────────────────────────────────────────────────────── + +fn sample_portfolio(rng: &mut ChaCha20Rng, n_total: usize) -> Vec { + // 40 / 30 / 30 split — IRS / FxFwd / FxOpt. + let n_irs = n_total * 40 / 100; + let n_fwd = n_total * 30 / 100; + let n_opt = n_total - n_irs - n_fwd; + let mut out = Vec::with_capacity(n_total); + for _ in 0..n_irs { + out.push(Trade::Irs(sample_irs(rng))); + } + for _ in 0..n_fwd { + out.push(Trade::FxFwd(sample_fwd(rng))); + } + for _ in 0..n_opt { + out.push(Trade::FxOpt(sample_opt(rng))); + } + out +} + +fn log_uniform(rng: &mut ChaCha20Rng, lo: f64, hi: f64) -> f64 { + rng.random_range(lo.ln()..hi.ln()).exp() +} + +fn sample_irs(rng: &mut ChaCha20Rng) -> IrSwap { + let ccy = Ccy::ALL[rng.random_range(0..4)]; + let notional_units = match ccy { + // Yen swaps: ¥100 M to ¥10 B (≈ USD 0.7 M – 70 M). + Ccy::Jpy => log_uniform(rng, 1.0e8, 1.0e10), + // USD/EUR/GBP swaps: 1 M to 100 M of currency units. + _ => log_uniform(rng, 1.0e6, 1.0e8), + }; + let maturity_years = [1.0, 2.0, 3.0, 5.0][rng.random_range(0..4)]; + let n_pay = (maturity_years * 2.0) as usize; // semi-annual + let pay_dates: Vec = (1..=n_pay).map(|i| i as f64 * 0.5).collect(); + let par_rate = init_rate(ccy); + let fixed_rate = par_rate + rng.random_range(-0.005..0.005); + let pay_fixed = rng.random_bool(0.5); + IrSwap { + ccy, + notional: notional_units, + fixed_rate, + pay_dates, + pay_fixed, + } +} + +fn sample_fwd(rng: &mut ChaCha20Rng) -> FxForward { + let pair = Pair::ALL[rng.random_range(0..3)]; + let expiry = [0.25, 0.5, 1.0, 2.0, 3.0][rng.random_range(0..5)]; + let spot = init_spot(pair); + let strike = spot * (1.0 + rng.random_range(-0.05..0.05)); + let notional_dom = match pair { + // USDJPY: notional in JPY ≈ ¥150 M – ¥15 B. + Pair::UsdJpy => log_uniform(rng, 1.5e8, 1.5e10), + // EURUSD/GBPUSD: notional in USD ≈ 1 M – 50 M. + _ => log_uniform(rng, 1.0e6, 5.0e7), + }; + FxForward { + pair, + notional_dom, + strike, + expiry, + long_foreign: rng.random_bool(0.5), + } +} + +fn sample_opt(rng: &mut ChaCha20Rng) -> FxOption { + let pair = Pair::ALL[rng.random_range(0..3)]; + let expiry = [0.25, 0.5, 1.0, 2.0][rng.random_range(0..4)]; + let spot = init_spot(pair); + let moneyness = [0.90, 0.95, 1.0, 1.05, 1.10][rng.random_range(0..5)]; + let strike = spot * moneyness; + // Notional in foreign-currency units (EUR for EURUSD, etc.) — 1 M to 25 M. + let notional_foreign = log_uniform(rng, 1.0e6, 2.5e7); + FxOption { + pair, + notional_foreign, + strike, + expiry, + is_call: rng.random_bool(0.5), + } +} + +// ── Joint state across the three FX-HHW simulators ──────────────────────── + +#[derive(Clone, Debug)] +struct JointState { + eur_usd: FxHhwState, + gbp_usd: FxHhwState, + usd_jpy: FxHhwState, +} + +impl JointState { + fn initial(eur_usd: &FxHhwParams, gbp_usd: &FxHhwParams, usd_jpy: &FxHhwParams) -> Self { + Self { + eur_usd: FxHhwState::initial(eur_usd), + gbp_usd: FxHhwState::initial(gbp_usd), + usd_jpy: FxHhwState::initial(usd_jpy), + } + } + + /// Per-currency short rate extracted from whichever FX-HHW carries + /// that leg. Documented inconsistency: USD comes from EURUSD only. + fn short_rate(&self, c: Ccy) -> f64 { + match c { + Ccy::Usd => self.eur_usd.rd, + Ccy::Eur => self.eur_usd.rf, + Ccy::Gbp => self.gbp_usd.rf, + Ccy::Jpy => self.usd_jpy.rd, + } + } + + fn fx_to_usd(&self, c: Ccy) -> f64 { + match c { + Ccy::Usd => 1.0, + Ccy::Eur => self.eur_usd.fx, + Ccy::Gbp => self.gbp_usd.fx, + Ccy::Jpy => 1.0 / self.usd_jpy.fx, + } + } +} + +// ── Re-pricing closures ──────────────────────────────────────────────────── + +/// Time-0 discount factor under flat-rate convention used to seed the +/// HW affine bond formula. +fn p0(c: Ccy, t: f64) -> f64 { + (-init_rate(c) * t).exp() +} + +/// Hull-White discount bond at time `t` to maturity `T`, given the +/// simulated short rate. +fn hw_discount(hw: &HullWhite1F, t: f64, big_t: f64, r_t: f64, c: Ccy) -> f64 { + if big_t <= t { + return 1.0; + } + hw.discount_affine(t, big_t, r_t, p0(c, t), p0(c, big_t), init_rate(c)) +} + +/// Per-currency Hull-White model. We pull these from the FX-HHW pair that +/// exposes the leg — same documented limitation as `JointState::short_rate`. +fn hw_for(c: Ccy, params: &Params) -> &HullWhite1F { + match c { + Ccy::Usd => ¶ms.eur_usd.domestic, + Ccy::Eur => ¶ms.eur_usd.foreign, + Ccy::Gbp => ¶ms.gbp_usd.foreign, + Ccy::Jpy => ¶ms.usd_jpy.domestic, + } +} + +fn pv_irs(s: &IrSwap, t: f64, state: &JointState, params: &Params) -> f64 { + let r = state.short_rate(s.ccy); + let hw = hw_for(s.ccy, params); + + // Fixed leg PV: Σ τ_i K P(t, T_i) over remaining payments. + let remaining: Vec = s.pay_dates.iter().copied().filter(|&d| d > t).collect(); + if remaining.is_empty() { + return 0.0; + } + let mut prev = t; + let mut fixed_pv = 0.0; + for &t_i in &remaining { + let p_i = hw_discount(hw, t, t_i, r, s.ccy); + fixed_pv += (t_i - prev) * s.fixed_rate * p_i; + prev = t_i; + } + // Float-leg par form: 1 - P(t, T_n) (continuous-reset approximation). + let p_n = hw_discount(hw, t, *remaining.last().unwrap(), r, s.ccy); + let float_pv = 1.0 - p_n; + + let pay_minus_recv = if s.pay_fixed { + float_pv - fixed_pv + } else { + fixed_pv - float_pv + }; + s.notional * pay_minus_recv +} + +fn pv_fx_forward(f: &FxForward, t: f64, state: &JointState, params: &Params) -> f64 { + if f.expiry <= t { + return 0.0; + } + let (dom, foreign) = f.pair.ccys(); + let s_t = match f.pair { + Pair::EurUsd => state.eur_usd.fx, + Pair::GbpUsd => state.gbp_usd.fx, + Pair::UsdJpy => state.usd_jpy.fx, + }; + let r_d = state.short_rate(dom); + let r_f = state.short_rate(foreign); + let p_d = hw_discount(hw_for(dom, params), t, f.expiry, r_d, dom); + let p_f = hw_discount(hw_for(foreign, params), t, f.expiry, r_f, foreign); + let payoff_per_unit = s_t * p_f - f.strike * p_d; + let signed = if f.long_foreign { 1.0 } else { -1.0 }; + f.notional_dom * signed * payoff_per_unit +} + +/// PLACEHOLDER — drop-in slot for the trained NN. +/// +/// Today: Black–Scholes with `σ ≈ √variance(t)` from the simulated CIR +/// state. This systematically under-prices the smile wings (no +/// vol-of-vol, no skew correction). A trained Horvath-style NN — fed the +/// simulated `(σ_t, r_d_t, r_f_t, model_params, τ, K/F)` — drops in +/// exactly here and returns an IV that we can plug into the same Black +/// formula. +fn pv_fx_option(o: &FxOption, t: f64, state: &JointState, params: &Params) -> f64 { + if o.expiry <= t { + return 0.0; + } + let tau = o.expiry - t; + let (dom, foreign) = o.pair.ccys(); + let (s_t, var_t) = match o.pair { + Pair::EurUsd => (state.eur_usd.fx, state.eur_usd.variance), + Pair::GbpUsd => (state.gbp_usd.fx, state.gbp_usd.variance), + Pair::UsdJpy => (state.usd_jpy.fx, state.usd_jpy.variance), + }; + let r_d = state.short_rate(dom); + let r_f = state.short_rate(foreign); + let p_d = hw_discount(hw_for(dom, params), t, o.expiry, r_d, dom); + let p_f = hw_discount(hw_for(foreign, params), t, o.expiry, r_f, foreign); + let forward = s_t * p_f / p_d; + let sigma = var_t.max(1.0e-8).sqrt(); + let value_dom_per_unit = if o.is_call { + bs_call_forward(forward, o.strike, sigma, tau, p_d) + } else { + bs_put_forward(forward, o.strike, sigma, tau, p_d) + }; + o.notional_foreign * value_dom_per_unit +} + +fn pv_trade(t: &Trade, time: f64, state: &JointState, params: &Params) -> f64 { + match t { + Trade::Irs(s) => pv_irs(s, time, state, params), + Trade::FxFwd(f) => pv_fx_forward(f, time, state, params), + Trade::FxOpt(o) => pv_fx_option(o, time, state, params), + } +} + +// ── Simulation harness ───────────────────────────────────────────────────── + +struct Params { + eur_usd: FxHhwParams, + gbp_usd: FxHhwParams, + usd_jpy: FxHhwParams, +} + +struct Sims { + eur_usd: FxHhwSimulator, + gbp_usd: FxHhwSimulator, + usd_jpy: FxHhwSimulator, +} + +impl Sims { + fn new(params: &Params, seed: u64) -> Self { + Self { + eur_usd: FxHhwSimulator::new(params.eur_usd, seed).unwrap(), + gbp_usd: FxHhwSimulator::new(params.gbp_usd, seed.wrapping_add(1)).unwrap(), + usd_jpy: FxHhwSimulator::new(params.usd_jpy, seed.wrapping_add(2)).unwrap(), + } + } + + fn step_all(&mut self, state: &mut JointState, dt: f64) { + let (eu, _) = self.eur_usd.step(&state.eur_usd, dt); + let (gu, _) = self.gbp_usd.step(&state.gbp_usd, dt); + let (uj, _) = self.usd_jpy.step(&state.usd_jpy, dt); + state.eur_usd = eu; + state.gbp_usd = gu; + state.usd_jpy = uj; + } +} + +// ── Aggregation ──────────────────────────────────────────────────────────── + +/// One row per exposure date. Stored across paths to reduce afterwards. +struct ExposureMatrix { + /// Exposure-date year fractions. + times: Vec, + /// `[date_i][path_j]` portfolio NPV in USD. + grid: Vec>, + /// `[date_i][path_j][trade_kind]` PV breakdown (0=IRS, 1=Fwd, 2=Opt). + by_kind: Vec>, +} + +impl ExposureMatrix { + fn new(times: Vec, n_paths: usize) -> Self { + let n_dates = times.len(); + Self { + times, + grid: vec![vec![0.0; n_paths]; n_dates], + by_kind: vec![vec![[0.0; 3]; n_paths]; n_dates], + } + } +} + +#[derive(Default, Clone, Copy)] +struct TimingBucket { + sim_secs: f64, + price_secs: [f64; 3], // IRS, Fwd, Opt +} + +fn run_xva( + params: &Params, + portfolio: &[Trade], + times: &[f64], + n_paths: usize, + seed: u64, +) -> (ExposureMatrix, TimingBucket) { + let mut sims = Sims::new(params, seed); + let mut em = ExposureMatrix::new(times.to_vec(), n_paths); + let mut timing = TimingBucket::default(); + + for j in 0..n_paths { + let mut state = JointState::initial(¶ms.eur_usd, ¶ms.gbp_usd, ¶ms.usd_jpy); + let mut t_prev = 0.0; + for (i, &t) in times.iter().enumerate() { + // Simulate forward to the exposure date. + let dt = t - t_prev; + if dt > 0.0 { + let t0 = Instant::now(); + sims.step_all(&mut state, dt); + timing.sim_secs += t0.elapsed().as_secs_f64(); + } + t_prev = t; + + // Re-price the entire portfolio. + let mut by_kind = [0.0_f64; 3]; + for tr in portfolio { + let kind_idx = match tr { + Trade::Irs(_) => 0, + Trade::FxFwd(_) => 1, + Trade::FxOpt(_) => 2, + }; + let t0 = Instant::now(); + let pv_native = pv_trade(tr, t, &state, params); + timing.price_secs[kind_idx] += t0.elapsed().as_secs_f64(); + let pv_usd = pv_native * state.fx_to_usd(tr.currency()); + by_kind[kind_idx] += pv_usd; + } + em.grid[i][j] = by_kind.iter().sum(); + em.by_kind[i][j] = by_kind; + } + } + (em, timing) +} + +// ── Reporting ────────────────────────────────────────────────────────────── + +fn quantile(sorted: &[f64], q: f64) -> f64 { + if sorted.is_empty() { + return 0.0; + } + let n = sorted.len() as f64; + let idx = (q * (n - 1.0)).round() as usize; + sorted[idx.min(sorted.len() - 1)] +} + +fn report(em: &ExposureMatrix, timing: &TimingBucket, n_trades: usize, n_paths: usize) { + let n_dates = em.times.len(); + let n_revals = n_dates * n_paths * n_trades; + + println!(); + println!( + "══ Exposure profile ({n_paths} paths × {n_dates} dates × {n_trades} trades = {n_revals} revaluations) ══" + ); + println!( + " {:>8} | {:>14} {:>14} {:>14} {:>14}", + "t (Y)", "EE (USD)", "EPE-window", "PFE_95 (USD)", "PFE_99 (USD)" + ); + println!( + " {:->8} | {:->14} {:->14} {:->14} {:->14}", + "", "", "", "", "" + ); + let mut epe_running = 0.0; + let mut prev_t = 0.0; + let mut horizon = 0.0; + for i in 0..n_dates { + let t = em.times[i]; + // EE(t) = mean of max(V, 0) across paths + let pos: Vec = em.grid[i].iter().map(|v| v.max(0.0)).collect(); + let ee = pos.iter().sum::() / pos.len() as f64; + let mut sorted_pos = pos.clone(); + sorted_pos.sort_by(|a, b| a.partial_cmp(b).unwrap()); + let pfe95 = quantile(&sorted_pos, 0.95); + let pfe99 = quantile(&sorted_pos, 0.99); + let dt = t - prev_t; + epe_running += ee * dt; + horizon += dt; + prev_t = t; + if i == 0 || (i + 1) % 6 == 0 || i == n_dates - 1 { + // Print every 6th date plus first and last to keep output short. + println!( + " {:>8.3} | {:>14.0} {:>14.0} {:>14.0} {:>14.0}", + t, + ee, + epe_running / horizon.max(1e-9), + pfe95, + pfe99 + ); + } + } + + // Per-trade-type EE at several snapshot dates so that fast-decaying + // options (max 2Y) and longer-dated swaps (out to 5Y) are both visible. + let n_paths_f = n_paths as f64; + println!(); + println!("══ Per-trade-kind EE at snapshot dates (USD) ══"); + println!( + " {:>8} | {:>14} {:>14} {:>14}", + "t (Y)", "IRS", "FxFwd", "FxOpt" + ); + println!(" {:->8} | {:->14} {:->14} {:->14}", "", "", "", ""); + for &target_t in &[0.25_f64, 0.5, 1.0, 1.5, 2.0, 3.0] { + // Pick the closest grid date. + let i = em + .times + .iter() + .enumerate() + .min_by(|(_, a), (_, b)| { + (*a - target_t) + .abs() + .partial_cmp(&(*b - target_t).abs()) + .unwrap() + }) + .map(|(i, _)| i) + .unwrap(); + let mut by_kind_ee = [0.0_f64; 3]; + for j in 0..n_paths { + for (k, slot) in by_kind_ee.iter_mut().enumerate() { + *slot += em.by_kind[i][j][k].max(0.0); + } + } + for slot in &mut by_kind_ee { + *slot /= n_paths_f; + } + println!( + " {:>8.3} | {:>14.0} {:>14.0} {:>14.0}", + em.times[i], by_kind_ee[0], by_kind_ee[1], by_kind_ee[2] + ); + } + + let total_pricing = timing.price_secs.iter().sum::(); + let avg_ns = |secs: f64, n: usize| -> f64 { secs * 1e9 / n as f64 }; + let n_irs_revals = (n_revals * 40) / 100; + let n_fwd_revals = (n_revals * 30) / 100; + let n_opt_revals = n_revals - n_irs_revals - n_fwd_revals; + println!(); + println!("══ Timing ══"); + println!(" simulation : {:>7.3}s", timing.sim_secs); + println!( + " pricing IRS : {:>7.3}s {:>5.0} ns/reval ({:>5.1}% of pricing)", + timing.price_secs[0], + avg_ns(timing.price_secs[0], n_irs_revals), + 100.0 * timing.price_secs[0] / total_pricing.max(1e-9) + ); + println!( + " pricing FxFwd : {:>7.3}s {:>5.0} ns/reval ({:>5.1}% of pricing)", + timing.price_secs[1], + avg_ns(timing.price_secs[1], n_fwd_revals), + 100.0 * timing.price_secs[1] / total_pricing.max(1e-9) + ); + println!( + " pricing FxOpt : {:>7.3}s {:>5.0} ns/reval ({:>5.1}% of pricing) ← NN drop-in slot", + timing.price_secs[2], + avg_ns(timing.price_secs[2], n_opt_revals), + 100.0 * timing.price_secs[2] / total_pricing.max(1e-9) + ); + println!(" total pricing : {:>7.3}s", total_pricing); + + // What-if comparison: the FxOpt slot here is BS w/ √variance (≈100 ns). + // A real COS-method FX-HHW pricer is ~10 µs/call. A trained NN is ~1 µs. + // Show what happens if we swap our placeholder for either. + let opt_calls = n_opt_revals as f64; + let opt_secs_now = timing.price_secs[2]; + let opt_secs_cos = opt_calls * 10.0e-6; + let opt_secs_nn = opt_calls * 1.0e-6; + let other = timing.price_secs[0] + timing.price_secs[1]; + println!(); + println!("══ NN value-of-information ══"); + println!( + " Current FxOpt pricer is BS≈√v shortcut ({:.0} ns/call) — already fast,", + avg_ns(opt_secs_now, n_opt_revals) + ); + println!(" but smile-blind. The accuracy story matters more than the speed story:"); + println!(); + println!(" Pricer calls × time total opt portfolio total"); + println!(" ----------------- --------------- ------------- ------------------"); + println!( + " BS √variance {:>7} × {:>4.0}ns {:>9.3}s {:>9.3}s (this run)", + opt_calls as u64, + avg_ns(opt_secs_now, n_opt_revals), + opt_secs_now, + other + opt_secs_now + ); + println!( + " COS FX-HHW (truth){:>7} × {:>4.0}µs {:>9.3}s {:>9.3}s", + opt_calls as u64, + 10.0, + opt_secs_cos, + other + opt_secs_cos + ); + println!( + " ONNX NN surrogate {:>7} × {:>4.0}µs {:>9.3}s {:>9.3}s ← target", + opt_calls as u64, + 1.0, + opt_secs_nn, + other + opt_secs_nn + ); + println!(); + println!( + " Ratio NN vs COS truth: {:.0}× faster, ~basis-point IV accuracy per the paper.", + opt_secs_cos / opt_secs_nn + ); +} + +fn portfolio_summary(p: &[Trade]) { + let mut by_kind = [0_usize; 3]; + let mut by_ccy = std::collections::HashMap::<&'static str, usize>::new(); + for t in p { + let k = match t { + Trade::Irs(_) => 0, + Trade::FxFwd(_) => 1, + Trade::FxOpt(_) => 2, + }; + by_kind[k] += 1; + *by_ccy.entry(t.currency().label()).or_insert(0) += 1; + } + println!("══ Portfolio ({} trades) ══", p.len()); + println!(" IRS : {}", by_kind[0]); + println!(" FX fwd : {}", by_kind[1]); + println!(" FX option: {}", by_kind[2]); + let mut ccys: Vec<_> = by_ccy.iter().collect(); + ccys.sort_by_key(|(k, _)| *k); + print!(" by currency exposure (settlement ccy):"); + for (k, v) in ccys { + print!(" {k}={v}"); + } + println!(); +} + +// ── CLI ──────────────────────────────────────────────────────────────────── + +fn parse_args() -> (usize, usize, u64) { + let mut paths = 500_usize; + let mut trades = 1000_usize; + let mut seed = 7_u64; + let mut args = env::args().skip(1); + while let Some(a) = args.next() { + match a.as_str() { + "--paths" => paths = args.next().unwrap().parse().unwrap(), + "--trades" => trades = args.next().unwrap().parse().unwrap(), + "--seed" => seed = args.next().unwrap().parse().unwrap(), + other => panic!("unknown arg: {other}"), + } + } + (paths, trades, seed) +} + +fn main() { + let (n_paths, n_trades, seed) = parse_args(); + let params = Params { + eur_usd: fx_hhw_for(Pair::EurUsd), + gbp_usd: fx_hhw_for(Pair::GbpUsd), + usd_jpy: fx_hhw_for(Pair::UsdJpy), + }; + + let mut rng = ChaCha20Rng::seed_from_u64(seed); + let portfolio = sample_portfolio(&mut rng, n_trades); + portfolio_summary(&portfolio); + + // Monthly grid out to 5 years (60 dates). + let times: Vec = (1..=60).map(|i| i as f64 / 12.0).collect(); + println!(); + println!( + "Running XVA: {} paths × {} dates × {} trades …", + n_paths, + times.len(), + portfolio.len() + ); + let t0 = Instant::now(); + let (em, timing) = run_xva(¶ms, &portfolio, ×, n_paths, seed); + println!("Total wall time: {:.2}s", t0.elapsed().as_secs_f64()); + + report(&em, &timing, portfolio.len(), n_paths); +} diff --git a/ml/XVA.md b/ml/XVA.md new file mode 100644 index 0000000..a11037e --- /dev/null +++ b/ml/XVA.md @@ -0,0 +1,181 @@ +# XVA harness — design notes + +## Problem + +For a portfolio of `T` trades evaluated at `D` exposure dates over `P` Monte +Carlo paths, the XVA / future-exposure calculation needs `P × D × T` +conditional re-pricings. For our reference portfolio: + +``` +T = 1 000 trades (400 IRS + 300 FX fwd + 300 FX option) +D = 60 dates (monthly, out to 5y) +P = 500 paths (production runs use 5–50k) + ────── +P × D × T = 30 000 000 revaluations +``` + +Per the Horvath / Muguruza / Tomas (2019) "Deep Learning Volatility" paper, +the binding cost is the conditional option pricer — for a real +FX-HHW + COS implementation, ~10 µs/call → 90 s of pure option pricing per +run. A NN surrogate at ~1 µs/call collapses that to ~9 s. + +## Reference implementation + +[`examples/xva_portfolio_demo.rs`](../examples/xva_portfolio_demo.rs) wires +the full harness with all-analytic re-pricing today, so we can: + +1. Establish the EE / EPE / PFE baseline. +2. Measure per-leg pricing cost — the bar the NN must beat. +3. Show exactly where the NN drops in (the FX-option re-pricing closure). + +Run with: + +```bash +cargo run --release --example xva_portfolio_demo +cargo run --release --example xva_portfolio_demo -- --paths 1000 --trades 1000 +``` + +## Portfolio composition + +| trade type | count | currencies / pairs | sizes | maturities | +|---------------|------:|------------------------------------------|-------------------------------|------------------| +| IR swap (par) | 400 | USD / EUR / GBP / JPY | 1–100 M (1 B–10 B JPY) | 1 / 2 / 3 / 5 Y | +| FX forward | 300 | EURUSD / GBPUSD / USDJPY | 1–50 M (1.5 B–15 B JPY) | 3 / 6 M, 1/2/3 Y | +| FX vanilla | 300 | EURUSD / GBPUSD / USDJPY | 1–25 M foreign | 3 / 6 M, 1 / 2 Y | + +Direction (pay-fixed vs receive-fixed, long vs short, call vs put) is 50/50 +random. + +## Simulation + +* One **FX-HHW** ([`src/models/forex/fx_hhw.rs`](../src/models/forex/fx_hhw.rs)) + per pair → three independent simulators. +* Per-currency short rates are extracted from each FX-HHW's HW leg — + USD ← EURUSD.rd, EUR ← EURUSD.rf, GBP ← GBPUSD.rf, JPY ← USDJPY.rd. +* **Honest limitation**: cross-pair correlations are zero. EUR/USD and + GBP/USD will move independently, and triangular consistency + (EUR/USD × USD/JPY ≈ EUR/JPY) does not hold. +* The next architectural step is a joint multi-pair simulator that shares + the USD HW leg across pairs and adds a full FX-correlation block. + +## Re-pricing (closed-form today) + +| trade | formula | timing | +|--------------|--------------------------------------------------------------------------------------|-------------| +| IR swap | `N · [Σᵢ τᵢ K · P(t,Tᵢ)] – N · [1 – P(t,Tₙ)]` — par-form, bonds via HW affine | ~95 ns/eval | +| FX forward | `N · sign · (S(t)·P_f(t,T) – K·P_d(t,T))` | ~25 ns/eval | +| FX option | Black with `σ ≈ √variance(t)` from the simulated CIR state | ~40 ns/eval | + +The FX option pricer is the **NN drop-in slot**. Today it's a smile-blind +Black–Scholes shortcut — fast but wrong on the wings. The Horvath-style NN, +once trained from `ml/dump_hhw_vanilla_training_data` + `ml/train_hhw_vanilla.py`, +returns an IV grid that we can index by `(τ, K/F)` and feed back into +`bs_call_forward` for the price. + +## Aggregation + +All trade PVs are FX-converted to USD at the same `(path, date)` state and +summed into one netting set. We report: + +* `EE(t)` — mean over paths of `max(V_t, 0)` +* `EPE(t)` — running time-weighted average of `EE(t)` (CVA's exposure leg) +* `PFE_q(t)` — q-quantile of `max(V_t, 0)` (we report 95 % and 99 %) + +## Reference numbers — 500 paths, 1 000 trades, 60 monthly dates + +``` +══ Exposure profile ══ + t (Y) | EE (USD) EPE-window PFE_95 (USD) PFE_99 (USD) + ------ | -------------- -------------- -------------- -------------- + 0.083 | 640 380 938 640 380 938 843 980 626 939 466 832 + 0.500 | 667 319 008 677 707 424 1 990 157 137 2 707 072 083 + 1.000 | 1 640 623 549 803 534 066 3 557 560 229 5 165 388 139 + 1.500 | 1 763 192 260 1 104 386 372 4 222 812 006 6 197 416 265 + 2.000 | 3 106 334 666 1 342 625 812 8 891 324 666 14 330 254 024 + 2.500 | 3 477 898 781 1 724 769 149 10 273 558 863 15 843 056 386 + 3.000 | 1 927 776 1 950 183 336 9 568 051 14 780 715 + … + 5.000 | 0 1 170 519 211 0 0 + +══ Per-trade-kind EE at snapshot dates (USD) ══ + t (Y) | IRS FxFwd FxOpt + ------ | -------------- -------------- -------------- + 0.250 | 666 241 600 146 747 106 689 307 + 0.500 | 1 165 210 622 511 126 82 031 403 + 1.000 | 1 764 725 1 594 457 279 51 274 374 + 1.500 | 2 331 377 1 715 544 933 53 898 073 + 2.000 | 2 436 868 3 108 454 638 0 + 3.000 | 1 927 776 0 0 +``` + +Read-out: +* FX forwards dominate exposure — long-dated forwards have unbounded + payoff sensitivity to spot moves. +* IR-swap exposure is small (≤ 3 M USD) because per-trade rate volatility + is bounded by the HW short-rate vol (~70 bp / yr). +* Option exposure caps at the premium and decays as positions expire. +* Tail is fat: PFE_99 ≈ 4–5 × EE at the 2–2.5Y peak. + +## Timing on a development laptop + +``` +══ Timing (500 paths × 60 dates × 1000 trades = 30M revaluations) ══ + simulation : 0.009 s + pricing IRS : 1.150 s 96 ns/reval (66 % of pricing) + pricing FxFwd : 0.241 s 27 ns/reval (14 %) + pricing FxOpt : 0.342 s 38 ns/reval (20 %) ← NN drop-in slot + total pricing : 1.732 s + total wall time : 2.68 s +``` + +## NN value-of-information + +The current FX-option pricer is a 38 ns Black-Scholes shortcut — fast but +**smile-blind**. The accuracy story matters more than the speed story for +this portfolio: + +| pricer | calls × time | total opt | portfolio total | +|----------------------------|------------------|---------------|-----------------| +| BS √variance (this run) | 9 M × 38 ns | 0.34 s | 1.73 s | +| COS FX-HHW (truth) | 9 M × 10 µs | 90 s | 91.4 s | +| **ONNX NN surrogate** | 9 M × 1 µs | **9 s** | **10.4 s** | + +A NN surrogate gives **COS-method IV accuracy at ~10× the throughput of +COS itself, ~10⁴× the throughput of full FX-HHW Monte Carlo nested +inside the outer XVA simulation**. Where it matters most is exotics +(barriers, Bermudans, TARFs) where no fast analytic exists at all — for +those the comparison is "feasible vs not feasible" rather than 10×. + +## NN drop-in: how the slot looks + +Today, [`pv_fx_option`](../examples/xva_portfolio_demo.rs#L376) computes +the implied vol as `(state.variance).sqrt()`. Replace that line with: + +```rust +// pseudo-code, post-NN-training: +let nn = ONNX_HHW_VANILLA.get(); // loaded once at startup via `ort` +let theta = pack_params(params, &state); +let iv_grid = nn.run(theta); // 1-µs inference +let sigma = iv_grid.interp(tau, strike / forward); +let value_dom_per_unit = bs_call_forward(forward, o.strike, sigma, tau, p_d); +``` + +Everything else in the harness — simulation, IRS / FxFwd pricing, +aggregation, FX conversion, EE / PFE — stays unchanged. + +## What's deliberately not in this harness yet + +* **Joint multi-pair simulator.** Each FX-HHW runs independently; a + realistic XVA needs shared USD short-rate dynamics across pairs and a + full FX correlation block. This is a separate ~200-line refactor that + doesn't change anything about the NN integration. +* **Variation margin / collateral.** The exposure here is uncollateralised. + Adding daily VM with a 10-day MPOR is a wrapper on top of `ExposureMatrix`. +* **Default probabilities and CVA discounting.** EE / EPE / PFE are inputs + to CVA = LGD · Σ EE(tᵢ) · ΔPD(tᵢ) · D(0, tᵢ). Trivial post-processing. +* **Wrong-way risk** (correlation between counterparty default and + exposure). Needs a coupled credit factor in the simulator. +* **Greeks under simulation** (CVA-greeks via pathwise sensitivities or + AAD). Out of scope for the surrogate framework — but the NN's + differentiability (per the paper §3.3) makes this much cheaper than with + bumped Monte Carlo. diff --git a/ml/train_hhw_vanilla.py b/ml/train_hhw_vanilla.py index a852c0e..eb0e0be 100644 --- a/ml/train_hhw_vanilla.py +++ b/ml/train_hhw_vanilla.py @@ -8,8 +8,6 @@ via the `ort` crate for microsecond-scale conditional pricing. """ -from __future__ import annotations - import argparse import json import time diff --git a/ml/train_hhw_vanilla_tune.py b/ml/train_hhw_vanilla_tune.py index 48693e1..8459054 100644 --- a/ml/train_hhw_vanilla_tune.py +++ b/ml/train_hhw_vanilla_tune.py @@ -13,8 +13,6 @@ into `train_hhw_vanilla.py` for the final ONNX export. """ -from __future__ import annotations - import argparse from pathlib import Path