Generate, store, analyze, and visualize synthetic market scenarios — calibrated to real assets, powered by AI. Via browser, Python SDK, or REST API.
Free tier at launch. No credit card required.
Historical data gives you one path through every crisis. One speed. One depth. One recovery shape. If your strategy survives that sequence, you don't know if it's genuinely robust — or just lucky on that particular accident.
The alternative is a quant spending days manually fitting stochastic models, calibrating parameters, and writing simulation code from scratch. We do it in one click or one API call.
Historical data is one sequence of events. Generate thousands of statistically realistic variations — different crash speeds, depths, recovery shapes — and know if your strategy survives the regime, not just the accident.
→ Strategy backtesting · ML training data · Factor stress testsSimulate flash crashes, rate shocks, and bear markets on demand via the browser. No code required. Built-in Sharpe, VaR, and drawdown analysis — computed natively, no export needed.
→ VaR modeling · Scenario planning · Stress testingReal data is sparse for tail events by definition. Build robo-advisors, risk engines, and portfolio tools on top of synthetic data that passes rigorous statistical validity tests — not just random noise.
→ Model training · Regime exposure · Embedded simulationGBM for baselines. Heston for stochastic volatility and fat tails. Bates adds jump diffusion for flash crash behavior.
Pre-built: low_vol_bull, flash_crash, bear_market,
rate_shock, high_vol_choppy. Ready to use without parameter tuning.
Describe any market condition in plain text — "a 2008-style crisis with faster recovery" — and the AI translates it to calibrated parameters automatically.
Pass any ticker and get paths that mirror its real statistical behavior — volatility clustering, tail risk, mean reversion. No manual tuning.
Every generated dataset is stored with a unique ID. Named, tagged, searchable. Retrieve, don't regenerate. Your work accumulates across sessions.
Sharpe ratio, VaR, max drawdown, realised volatility, and a statistical quality score — all computed natively. No export, no pandas, no manual work.
Fan chart with percentile bands, return distribution histogram, single path explorer, and side-by-side scenario comparison with metric diffs.
Browser GUI for non-technical users. Python SDK for developers. Raw REST API for full control. Same platform, same data, same capabilities.
From signup to first dataset in under 5 minutes. Free tier included, no credit card required.
Pick a named scenario, describe one in plain English, or pass a ticker for auto-calibration. The platform generates, runs analysis, and stores the dataset in one step.
Datasets persist with a unique ID. Pull built-in metrics — Sharpe, VaR, drawdown — or retrieve and compare two scenarios side by side. No regeneration needed.
Browser GUI for no-code exploration. Python SDK for programmatic access. Raw REST API for full control. Same platform, same data, all three ways.
from qpaths import Client client = Client(api_key="...") # Generate and store a dataset ds = client.generate( scenario="flash_crash", asset="AAPL", # auto-calibrate paths=1000 ) # Retrieve later — no regeneration ds = client.datasets.get("ds_abc123") # Native analysis ds.sharpe() ds.var(confidence=0.95) ds.max_drawdown() ds.summary() # all metrics at once # Compare two scenarios client.datasets.compare( "ds_abc123", "ds_xyz456" ) # Drop into pandas df = ds.to_dataframe()
We're building. Join the waitlist and help shape what we ship.