Risk Runs
Overnight.
Until Now.
I built QuantRisk to stop waiting overnight for VaR numbers. GPU-parallelized Monte Carlo runs 1M simulated price paths in under 8 seconds on a laptop GPU. Full portfolio VaR, Greeks, and stress tests on demand, not the next morning.
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Your Risk Tools Are Holding You Back
Portfolios containing options, swaps, and structured derivatives require VaR recomputation across millions of stochastic price paths. Most tools make this an overnight batch job. QuantRisk makes it a real-time query.
Risk Runs Overnight
A risk manager submits a VaR batch at market close. The engine churns through the night on CPU clusters. By the time results land, European markets have already opened, and the exposure has changed.
Blind to Intraday Risk
A volatility spike hits at 1:30 PM. The derivatives desk has no real-time view of their gamma exposure. Without intraday VaR recomputation, hedging decisions are gut calls, not data-driven responses.
Capital Frozen
Capital allocation tables depend on stress-test outputs. With CPU engines taking hours per scenario, treasury teams operate on stale numbers, over-reserving capital that could be deployed elsewhere.
“Capital decisions made on yesterday's risk data are nothing more than guesses. Real-time desks need real-time risk intelligence.”
Up to 16x Faster Than CPU
Benchmarked on an RTX 3060 Laptop GPU vs an i9-12900H running NumPy/SciPy. At 500K paths the GPU is 16.3x faster. At 1M paths it pulls slightly ahead of the memory bus and settles at 15.7x. Still the difference between waiting and working.
Peak GPU Speedup
500K paths
Paths Per Run
FP16/FP32 mixed
1M Path Runtime
RTX 3060 Laptop
Runtime Comparison
Monte Carlo VaRGPU Speedup at 1M paths
CPU: 112.8s → GPU: 7.18s
faster
“At 1M paths, the i9-12900H (20 threads, NumPy vectorized) takes 1 minute 52 seconds. The RTX 3060 Laptop finishes in 7.18 seconds. Same result, 15.7x less waiting.”
All Workloads
From Market Data to Risk Output
Eight pipeline stages take raw market data and return real-time portfolio risk metrics, all GPU-accelerated.
Market Data
Historical & Synthetic
Equity prices, implied vol surfaces, interest rate curves, and FX rates. Synthetic data generated via GBM or sourced from Parquet stores.
Path Generator
GBM · Heston · Cholesky
GPU-parallelized stochastic path simulation. Cholesky decomposition enforces asset correlations. Output: [paths × timesteps × assets].
Monte Carlo Engine
CUDA · PyTorch · FP16/FP32
Batched random number generation, drift+diffusion update, and payoff computation. Async CUDA streams pipeline simulation and pricing.
Derivatives Pricing
5 Instrument Types
GPU kernels for European, Asian, Barrier, Basket, and American options. Black-Scholes analytical baseline for validation.
Portfolio Aggregator
PnL · Greeks · Scenarios
Aggregates instrument exposures into portfolio PnL distribution. Computes Delta and Gamma via bump-and-reprice on GPU.
VaR Engine
95% · 99% · 99.9%
Historical VaR, Monte Carlo VaR, and Expected Shortfall (CVaR) at three confidence levels. Sub-second recomputation.
Stress Testing
Macro Shock Scenarios
Parametric shocks: +300bps rate spike, volatility doubling, equity crash. Outputs drawdown curves and exposure heatmaps.
Dashboard & Reports
Streamlit · Plotly · DuckDB
Interactive real-time dashboard for risk managers. Simulation results persisted in DuckDB, visualized with Plotly.
Each stage is independently benchmarkable. NVTX annotations mark every module boundary for Nsight Systems profiling, from raw data ingestion to final VaR output.
Five Derivative Instrument Types
Price European, Asian, Barrier, Basket, and American options out of the box. Every payoff kernel runs on GPU across millions of paths simultaneously.
European Option
Priced via Black-Scholes closed form and validated against GPU Monte Carlo. Baseline benchmark for all pricing kernels.
Asian Option
Payoff depends on arithmetic average price over the path. Requires full path simulation, ideal for GPU parallelism.
Barrier Option
Up-and-out and down-and-in variants. Barrier monitoring at every timestep across all paths is parallelized on GPU.
Basket Option
Payoff on a weighted basket of correlated assets. Uses Cholesky-decomposed covariance on GPU for correlated path generation.
American Option
Longstaff-Schwartz regression on GPU. Backward induction over simulated paths determines optimal exercise boundary.
Why GPU Matters Here
Each option type requires a full pass through all simulated paths. GPU parallelism means 1M payoff evaluations execute simultaneously, not sequentially.
Built on Solid Foundations
GPU compute for raw throughput, rigorous quantitative models for accuracy, and a full risk metrics stack. Open-source. Runs locally.
GPU Compute
Quant Models
Risk Metrics
Nsight Systems Profiling
NVTX · SM utilization · kernel traces
Every hot-path function is annotated with NVTX range markers. Nsight Systems captures kernel launch timelines, memory transfer overlaps, and warp stall reasons, enabling data-driven kernel optimization.
GPU Optimization Targets
Memory · Warp · Precision
Aligned 128-byte global memory access patterns
Overlap path generation with pricing kernels
Half precision paths, full precision accumulation
Drift + diffusion + payoff in single pass
Real Numbers, Real Speed
See exactly what QuantRisk produces: benchmark runtimes, VaR distributions, and stress scenario outputs, ready whenever you need them.
Benchmark Results
RTX 3060 vs i9-12900HRisk Output
Portfolio PnLPnL Distribution
Monte Carlo · 1M paths
Stress Testing Scenarios
Macro shock analysis · Portfolio drawdown
Scenario 01
+300bps Rate Spike
Portfolio Drawdown
Scenario 02
2× Volatility
Portfolio Drawdown
Scenario 03
Equity Crash −30%
Portfolio Drawdown
Scenario 04
Combined Shock
Portfolio Drawdown
Your Risk Dashboard, On Demand
QuantRisk runs entirely on your hardware. No cloud subscription, no data leaving your machine. Trigger a full VaR recomputation at any time and get results in seconds.