tensorquantlib 0.3.0

Comprehensive quantitative finance library with tensor-train compression, autodiff, and stochastic models

TensorQuantLib

A comprehensive quantitative finance library with tensor-train compression, automatic differentiation, and stochastic models — built from scratch with NumPy and SciPy.

TensorQuantLib provides a complete toolkit for derivatives pricing, risk management, and portfolio analysis. It compresses high-dimensional pricing surfaces using Tensor-Train (TT) decomposition, prices options with Black-Scholes, Heston, American, Exotic, and Monte Carlo engines, and computes Greeks via a custom reverse-mode autodiff engine — including second-order Greeks (Gamma, Vanna, Volga).

No PyTorch. No TensorFlow. No JAX. Custom autograd, custom TT-SVD, custom stochastic models — built entirely with NumPy and SciPy.

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Documentation

Complete documentation with API reference, tutorials, and examples: https://raswanthmalai19.github.io/TensorQuantLib./

Quick Links

• API Reference — Full module documentation
• Quick Start — Getting started guide
• Theory — Mathematical foundations
• Changelog — Version history

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Features

Module	Capability	Description
Core	Autograd Engine	Reverse-mode AD — 23+ differentiable ops (sin, cos, tanh, softmax, clip, where, abs)
Core	Second-Order AD	Hessians, HVPs, mixed partials, Gamma/Vanna/Volga via autodiff
Black-Scholes	Analytic Pricing	BS call/put + full Greeks (Delta, Gamma, Vega, Theta, Rho)
Implied Vol	IV Solvers	Brent, Newton-Raphson, batch IV, volatility surface builder
Heston	Stochastic Vol	Semi-analytic (Gil-Pelaez CF), QE Monte Carlo, calibration
American	Early Exercise	Longstaff-Schwartz LSM, exercise boundary, Greeks
Exotics	Exotic Options	Asian (arithmetic/geometric), Digital (cash/asset), Barrier (8 types), Lookback, Cliquet, Rainbow
Volatility	Vol Surface Models	SABR (Hagan 2002), SVI (Gatheral 2004), calibration to market data
Rates	Interest Rate Models	Vasicek, CIR, Nelson-Siegel, yield curve bootstrapping
FX	FX Derivatives	Garman-Kohlhagen, FX Greeks, FX forwards, quanto options
Credit	Credit Risk	Merton structural model, CDS pricing, hazard rates, survival probabilities
Jump-Diffusion	Jump Models	Merton jump-diffusion, Kou double-exponential
Local Vol	Local Volatility	Dupire local vol, local vol Monte Carlo
IR Derivatives	Rate Products	Black-76 caps/floors, swaptions, swap rates, swaption parity
Variance Reduction	MC Efficiency	Antithetic, control variate, QMC (Sobol), importance sampling, stratified
Risk	Risk Metrics	VaR (parametric/historical/MC), CVaR, scenario analysis, portfolio Greeks
Backtesting	Strategy Testing	Backtesting engine, strategy framework, performance metrics (Sharpe, Sortino, max drawdown)
TT Compression	Tensor Trains	TT-SVD, TT-cross, rounding, arithmetic, surrogate pricing
Basket Options	Multi-Asset	Correlated GBM Monte Carlo, analytic moment-matching (Gentle 1993)
Visualization	Plots	Pricing surfaces, Greek surfaces, TT-rank charts
Data	Market Data	Yahoo Finance integration, historical prices, options chains
CLI	Command Line	python -m tensorquantlib — info, price, greeks, demo

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Installation

pip install (recommended)

pip install git+https://github.com/raswanthmalai19/TensorQuantLib.git

With optional dependencies

# Visualization support
pip install "git+https://github.com/raswanthmalai19/TensorQuantLib.git#egg=tensorquantlib[viz]"

# Market data support
pip install "git+https://github.com/raswanthmalai19/TensorQuantLib.git#egg=tensorquantlib[data]"

# Everything
pip install "git+https://github.com/raswanthmalai19/TensorQuantLib.git#egg=tensorquantlib[all]"

Clone & install (development)

git clone https://github.com/raswanthmalai19/TensorQuantLib.git
cd TensorQuantLib
pip install -e ".[dev]"
python -m pytest tests/ -q   # 588 passed

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Performance at a Glance

Measured on Apple M1. Numbers scale linearly on multi-core servers.

Workflow	Latency	Notes
black_scholes / bs_greeks	< 5 µs	Analytic; production vanilla book default
barrier_price	< 5 µs	Rubinstein-Reiner closed form
garman_kohlhagen	< 5 µs	FX vanilla
vasicek_bond_price	< 1 µs	Closed-form A(T)/B(T)
implied_vol	< 1 ms	Brent solver
heston_price (CF)	~1 ms	100-pt Gaussian quadrature
TTSurrogate.evaluate 3D	1.5 µs	After one-time 2 ms build
TTSurrogate.evaluate 5D	~5 µs	42× memory compression vs full grid
heston_price_mc	200–500 ms	Validation only; use CF for live pricing
HestonCalibrator.fit	5–15 s default → < 0.5 s optimised	See Performance Guide

Rule of thumb: for anything that needs a price in a hot loop, use the analytic or CF pricer. Build a TTSurrogate once at startup to replace any MC pricer with µs-latency evaluation. See the full Performance & Production Guide for tuning knobs, parallel calibration, memory profiling, and the production checklist.

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Quick Start

Black-Scholes with Autodiff Greeks

from tensorquantlib import Tensor, bs_price_tensor
import numpy as np

S = Tensor(np.array([100.0]), requires_grad=True)
price = bs_price_tensor(S, K=100, T=1.0, r=0.05, sigma=0.2)
price.backward()

print(f"Price: {price.item():.4f}")        # 10.4506
print(f"Delta: {S.grad[0]:.4f}")           # 0.6368

Heston Stochastic Volatility

from tensorquantlib import HestonParams, heston_price, heston_price_mc

params = HestonParams(kappa=2.0, theta=0.04, xi=0.3, rho=-0.7, v0=0.04)

# Semi-analytic (characteristic function)
price = heston_price(S=100, K=100, T=1.0, r=0.05, params=params)

# Monte Carlo with QE scheme
mc_price, mc_se = heston_price_mc(
    S=100, K=100, T=1.0, r=0.05, params=params,
    n_paths=100_000, scheme='qe', return_stderr=True,
)

American Options (Longstaff-Schwartz)

from tensorquantlib import american_option_lsm

price, se = american_option_lsm(
    S=100, K=100, T=1.0, r=0.05, sigma=0.2,
    option_type='put', n_paths=100_000, n_steps=100,
)

Implied Volatility

from tensorquantlib import implied_vol, iv_surface
import numpy as np

iv = implied_vol(market_price=10.45, S=100, K=100, T=1.0, r=0.05)
# iv ≈ 0.20

# Build a full IV surface
strikes = np.linspace(80, 120, 9)
expiries = np.array([0.25, 0.5, 1.0])
surface = iv_surface(S=100, r=0.05, sigma=0.2, strikes=strikes, expiries=expiries)

Exotic Options

from tensorquantlib import asian_price_mc, barrier_price, digital_price

# Arithmetic Asian call
asian = asian_price_mc(S=100, K=100, T=1.0, r=0.05, sigma=0.2)

# Cash-or-nothing digital
digital = digital_price(S=100, K=100, T=1.0, r=0.05, sigma=0.2)

# Down-and-out barrier call
barrier = barrier_price(
    S=100, K=100, T=1.0, r=0.05, sigma=0.2,
    barrier=90, barrier_type='down-and-out',
)

Volatility Surface Models (SABR & SVI)

from tensorquantlib import sabr_implied_vol, svi_implied_vol
import numpy as np

# SABR implied volatility
vol = sabr_implied_vol(F=100, K=100, T=1.0, alpha=0.3, beta=0.5, rho=-0.3, nu=0.4)

# SVI parameterization (k = log-moneyness ln(K/F))
k = np.linspace(-0.2, 0.2, 50)  # log-moneyness grid
vols = svi_implied_vol(k, T=1.0, a=0.04, b=0.1, rho=-0.3, m=0.0, sigma=0.1)

Interest Rate Models

from tensorquantlib import vasicek_bond_price, cir_bond_price, nelson_siegel
import numpy as np

# Vasicek zero-coupon bond
bond = vasicek_bond_price(r0=0.05, kappa=0.3, theta=0.05, sigma=0.02, T=5.0)

# CIR bond price
cir = cir_bond_price(r0=0.05, kappa=0.5, theta=0.05, sigma=0.1, T=5.0)

# Nelson-Siegel yield curve
maturities = np.array([0.25, 0.5, 1, 2, 5, 10, 30])
yields = nelson_siegel(maturities, beta0=0.05, beta1=-0.02, beta2=0.03, tau=1.5)

FX Options

from tensorquantlib import garman_kohlhagen, fx_forward

# Garman-Kohlhagen European FX option
price = garman_kohlhagen(S=1.25, K=1.30, T=0.5, r_d=0.05, r_f=0.02, sigma=0.1)

# FX forward rate
fwd = fx_forward(S=1.25, r_d=0.05, r_f=0.02, T=1.0)

Credit Risk

from tensorquantlib import merton_default_prob, cds_spread

# Merton structural model — probability of default
pd = merton_default_prob(V=100, D=80, T=1.0, r=0.05, sigma_V=0.25)

# CDS spread (from constant hazard rate)
spread = cds_spread(hazard_rate=0.02, T=5.0, recovery=0.4, r=0.03)

Second-Order Greeks (Autodiff)

from tensorquantlib import second_order_greeks, bs_price_tensor

result = second_order_greeks(
    price_fn=bs_price_tensor,
    S=100, K=100, T=1.0, r=0.05, sigma=0.2,
)
# result = {'gamma': ..., 'vanna': ..., 'volga': ...}

Risk Metrics

from tensorquantlib import var_parametric, cvar
import numpy as np

returns = np.random.normal(0.0005, 0.02, 252)
var_95 = var_parametric(returns, confidence=0.95)
es_95 = cvar(returns, confidence=0.95)

TT Compression Surrogate

from tensorquantlib import TTSurrogate

surr = TTSurrogate.from_basket_analytic(
    S0_ranges=[(80, 120)] * 3,
    K=100, T=1.0, r=0.05,
    sigma=[0.20, 0.25, 0.30],
    weights=[1/3, 1/3, 1/3],
    n_points=30, eps=1e-4,
)

surr.print_summary()
price = surr.evaluate([100.0, 105.0, 95.0])
greeks = surr.greeks([100.0, 105.0, 95.0])

CLI

python -m tensorquantlib info           # Library info
python -m tensorquantlib price          # Price an option
python -m tensorquantlib greeks         # Compute Greeks
python -m tensorquantlib demo           # Run quick demo

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Benchmark Results

TT Compression — Memory Scaling

Assets	Full Grid	TT Size	Compression Ratio
2	0.002 MB	3.3 KB	1x
3	0.026 MB	28 KB	1x
4	0.39 MB	91 KB	4x
5	5.79 MB	142 KB	42x

Full numbers: benchmarks/RESULTS.md

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Architecture

tensorquantlib/
├── core/
│   ├── tensor.py            # Tensor class — reverse-mode autograd, 23+ ops
│   ├── second_order.py      # Hessians, HVPs, Gamma/Vanna/Volga autodiff
│   └── ops.py               # Public re-exports
├── finance/
│   ├── black_scholes.py     # Analytic + Tensor BS pricing, full Greeks
│   ├── implied_vol.py       # Brent/Newton IV, batch, surface builder
│   ├── heston.py            # Semi-analytic CF, QE MC, calibration, Greeks
│   ├── american.py          # Longstaff-Schwartz LSM, grid, Greeks
│   ├── exotics.py           # Asian, Digital, Barrier, Lookback, Cliquet, Rainbow
│   ├── volatility.py        # SABR & SVI vol surface models + calibration
│   ├── rates.py             # Vasicek, CIR, Nelson-Siegel, yield bootstrapping
│   ├── fx.py                # Garman-Kohlhagen FX options, forwards, quanto
│   ├── credit.py            # Merton structural model, CDS pricing
│   ├── jump_diffusion.py    # Merton & Kou jump-diffusion
│   ├── local_vol.py         # Dupire local vol, local vol MC
│   ├── ir_derivatives.py    # Black-76 caps/floors, swaptions
│   ├── variance_reduction.py  # Antithetic, CV, QMC, IS, stratified
│   ├── risk.py              # VaR, CVaR, scenario analysis, portfolio risk
│   ├── greeks.py            # Autograd-based Greeks
│   └── basket.py            # MC basket pricing, analytic grid construction
├── tt/
│   ├── decompose.py         # TT-SVD, TT-cross, TT-rounding
│   ├── ops.py               # tt_eval, tt_to_full, arithmetic (12 ops)
│   ├── surrogate.py         # TTSurrogate end-to-end pipeline
│   └── pricing.py           # Pre-built TT surrogates (Heston, American, exotic)
├── backtest/
│   ├── engine.py            # Backtesting engine with portfolio tracking
│   ├── strategy.py          # Strategy base class + built-in strategies
│   └── metrics.py           # Sharpe, Sortino, max drawdown, Calmar, etc.
├── data/
│   └── market.py            # Yahoo Finance integration, options chains
├── viz/
│   └── plots.py             # Pricing surfaces, Greek surfaces, rank plots
├── utils/
│   └── validation.py        # Numerical gradient checking
└── __main__.py              # CLI entry point

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Test Coverage

Module	Tests	Description
Core autograd engine	73	Forward/backward pass, all 23+ ops, broadcasting
Second-order autodiff	12	Hessian, HVP, mixed partials, second-order Greeks
Black-Scholes + Greeks	32	Pricing, put-call parity, all 5 Greeks
Implied volatility	18	Brent, Newton, batch, surface, edge cases
Heston model	22	Semi-analytic, MC (QE/Euler), calibration, Greeks
American options	15	LSM, grid, Greeks, put-call comparison
Exotic options	28	Asian, digital, barrier, lookback, cliquet, rainbow
Volatility models	14	SABR, SVI, calibration, surface construction
Interest rates	20	Vasicek, CIR, Nelson-Siegel, bootstrapping
FX options	12	Garman-Kohlhagen, Greeks, forwards, quanto
Credit risk	14	Merton, CDS, hazard rates, survival probabilities
Jump-diffusion	10	Merton, Kou, analytic vs MC
Local volatility	8	Dupire, local vol MC
IR derivatives	14	Caps, floors, swaptions, swap rate
Variance reduction	20	Antithetic, CV, QMC, IS, stratified
Risk metrics	18	VaR (3 methods), CVaR, scenarios, portfolio
Backtesting	12	Engine, strategies, metrics
TT decomposition & ops	34	SVD, cross, rounding, arithmetic (12 ops)
TT surrogate & pricing	20	Surrogate construction, evaluation, Greeks
Visualization	21	Pricing surfaces, Greek surfaces, rank plots
Integration & edge cases	57	Cross-module, boundary conditions, numerics
Total	588	98% line coverage

python -m pytest tests/ -v
python -m pytest tests/ --cov=tensorquantlib --cov-report=term-missing

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API Reference

150+ public functions and classes exported from the top-level package:

from tensorquantlib import (
    # Core autograd
    Tensor,
    tensor_sin, tensor_cos, tensor_tanh, tensor_abs,
    tensor_clip, tensor_where, tensor_softmax,

    # Second-order autodiff
    hvp, hessian, hessian_diag, vhp, mixed_partial,
    gamma_autograd, vanna_autograd, volga_autograd, second_order_greeks,

    # Black-Scholes
    bs_price_numpy, bs_price_tensor,
    bs_delta, bs_gamma, bs_vega, bs_theta, bs_rho,
    compute_greeks, compute_greeks_vectorized,

    # Implied volatility
    implied_vol, implied_vol_batch, implied_vol_nr, iv_surface,

    # Heston
    HestonParams, HestonCalibrator,
    heston_price, heston_price_mc, heston_greeks,

    # American options
    american_option_lsm, american_option_grid, american_greeks,

    # Exotics
    asian_price_mc, asian_geometric_price,
    digital_price, digital_price_mc, digital_greeks,
    barrier_price, barrier_price_mc,
    lookback_fixed_analytic, lookback_floating_analytic, lookback_price_mc,
    cliquet_price_mc, rainbow_price_mc,

    # Volatility surface
    sabr_implied_vol, sabr_calibrate,
    svi_raw, svi_implied_vol, svi_calibrate, svi_surface,

    # Interest rates
    vasicek_bond_price, vasicek_yield, vasicek_option_price, vasicek_simulate,
    cir_bond_price, cir_yield, cir_simulate, feller_condition,
    nelson_siegel, nelson_siegel_calibrate, bootstrap_yield_curve,

    # FX options
    garman_kohlhagen, gk_greeks, fx_forward, quanto_option,

    # Credit risk
    merton_default_prob, merton_credit_spread,
    survival_probability, hazard_rate_from_spread,
    cds_spread, cds_price,

    # Jump-diffusion
    merton_jump_price, merton_jump_price_mc, kou_jump_price_mc,

    # Local volatility
    dupire_local_vol, local_vol_mc,

    # IR derivatives (Black-76)
    black76_caplet, black76_floorlet, cap_price, floor_price,
    swap_rate, swaption_price, swaption_parity,

    # Variance reduction
    bs_price_antithetic, asian_price_cv, bs_price_qmc,
    bs_price_importance, bs_price_stratified, compare_variance_reduction,

    # Risk
    PortfolioRisk, OptionPosition,
    var_parametric, var_historical, var_mc, cvar,
    scenario_analysis, greeks_portfolio,

    # Basket
    simulate_basket, build_pricing_grid, build_pricing_grid_analytic,

    # TT compression
    TTSurrogate,
    tt_svd, tt_round, tt_cross,
    tt_eval, tt_eval_batch, tt_to_full,
    tt_ranks, tt_memory, tt_error, tt_compression_ratio,
    tt_add, tt_scale, tt_hadamard, tt_dot, tt_frobenius_norm,

    # TT-accelerated pricers
    heston_surrogate, american_surrogate,
    exotic_surrogate, jump_diffusion_surrogate,

    # Visualization
    plot_pricing_surface, plot_greeks_surface, plot_tt_ranks,
)

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Dependencies

Package	Version	Purpose
numpy	>= 1.24	Core numerics
scipy	>= 1.10	Special functions, optimization, integration
matplotlib	>= 3.7	Visualization (optional [viz])
yfinance	>= 0.2	Market data (optional [data])

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References

• Oseledets, I.V. (2011). Tensor-Train Decomposition. SIAM J. Sci. Comput. 33(5).
• Heston, S.L. (1993). A Closed-Form Solution for Options with Stochastic Volatility. RFS 6(2).
• Andersen, L.B.G. (2008). Efficient Simulation of the Heston Stochastic Volatility Model. J. Comp. Fin.
• Longstaff, F.A. & Schwartz, E.S. (2001). Valuing American Options by Simulation. RFS.
• Hagan, P.S. et al. (2002). Managing Smile Risk. Wilmott Magazine. (SABR model)
• Gatheral, J. (2004). A Parsimonious Arbitrage-Free Implied Volatility Parameterization. (SVI)
• Vasicek, O.A. (1977). An Equilibrium Characterization of the Term Structure. J. Fin. Econ.
• Cox, J.C., Ingersoll, J.E. & Ross, S.A. (1985). A Theory of the Term Structure of Interest Rates. Econometrica.
• Merton, R.C. (1974). On the Pricing of Corporate Debt. J. Finance.
• Garman, M.B. & Kohlhagen, S.W. (1983). Foreign Currency Option Values. J. Int. Money & Finance.
• Black, F. (1976). The Pricing of Commodity Contracts. J. Fin. Econ.
• Black, F. & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. J. Pol. Econ.
• Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer.

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Contributing

See CONTRIBUTING.md for development setup, coding standards, and guidelines.

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License

MIT — see LICENSE.

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Roadmap

• Custom autograd engine (23+ differentiable ops)
• Second-order autodiff (Hessian, HVP, Gamma/Vanna/Volga)
• Black-Scholes pricing + full Greeks
• TT-SVD compression + surrogate pricing
• Heston stochastic volatility (analytic + MC + calibration)
• American options (Longstaff-Schwartz LSM)
• Exotic options (Asian, Digital, Barrier, Lookback, Cliquet, Rainbow)
• Variance reduction techniques (5 methods + comparison)
• Risk metrics (VaR, CVaR, scenario analysis)
• Volatility surface models (SABR, SVI + calibration)
• Interest rate models (Vasicek, CIR, Nelson-Siegel)
• FX options (Garman-Kohlhagen, forwards, quanto)
• Credit risk (Merton structural, CDS pricing)
• Jump-diffusion models (Merton, Kou)
• Local volatility (Dupire, MC)
• IR derivatives (Black-76 caps/floors, swaptions)
• Market data integration (Yahoo Finance)
• Backtesting framework (engine, strategies, metrics)
• CLI interface
• PyPI release
• GPU acceleration via CuPy

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Built from scratch — no ML framework dependencies. Custom autograd + custom TT-SVD + custom stochastic models.