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aifinhub

Generator

Synthetic Market Data Generator

Generate synthetic price series — GBM, GARCH(1,1), regime-switching, or copula-linked pairs. Download CSV/JSON. Scaffold backtests safely. Free.

AI Fin Hub Research Methodology Corrections
Inputs
Configuration
Runtime
Instant
Privacy
Client-side · no upload
API key
Not required
Methodology
Open →

Education · Not investment advice. BaFin/EU framework. Past performance does not indicate future results. Editorial standards Sponsor disclosure Corrections

Synthetic data cannot replicate real-market microstructure, regime transitions, or news-driven jumps. Use these paths to scaffold pipeline code and sanity-check risk estimators — never as the sole validation of a strategy.

Generated path

19.9%

GBM · 252 days · realized annualized vol

drift +8% · target vol 20%

1 · Pick a model

2 · Parameters

3 · Preview

98108119129139

Ann. return

+25.15%

Ann. vol

+19.89%

Max DD

-9.98%

Sharpe (ret/vol)

1.26

When this is useful

  • · Scaffold backtest code without touching proprietary data. Generate a CSV, wire up your pipeline, then swap in real data once the plumbing works.
  • · Unit-test risk estimators. GARCH with a known σ lets you verify your vol-targeting layer clips correctly.
  • · Sanity-check drawdown distributions. Run 1,000 seeds of GBM at your target drift/vol, see what Max DD looks like — calibrate your stops against that distribution, not a single historical path.
  • · Share a reproducible bug report. Seed + parameters → exact same series on any machine.

See methodology for each model's assumptions, parameter heuristics, and known limitations.

How to use

Step-by-step

Full calculator guide →
  1. 1

    Upload empirical return series for the assets you want to model.

  2. 2

    Pick the model: GARCH(1,1) for vol clustering, Skewed-t for fat tails, regime-switching for stress scenarios.

  3. 3

    Set generation parameters: number of paths, length of each path.

  4. 4

    Read calibration diagnostics — does the synthetic data match your real data on the metrics that matter (skew, kurtosis, vol clustering)?

  5. 5

    Use the synthetic series for stress-testing. Do NOT use it as a live trading signal — by construction it has no real-world predictive content.

For agents

Use in an agent

Same math, same result shape as the UI above — as a static ES module. No HTTP request, no auth, no rate limit. 

import { compute } from "https://aifinhub.io/engines/synthetic-market-data-generator.js";

Contract: /contracts/synthetic-market-data-generator.json Full agent guide →

Glossary references

Terms used by this tool

All glossary →

Questions people ask next

FAQ

Why generate synthetic data instead of using real?

Three reasons: (1) backtest more market regimes than the historical sample contains (stress-testing), (2) preserve privacy when sharing strategy results without exposing real positions, (3) generate edge-case scenarios for execution-system testing. The methodology page documents each use case.

How realistic is the synthetic data?

Calibrated to match key statistical properties of the underlying real series: returns distribution (skew, kurtosis), volatility clustering (GARCH-like persistence), autocorrelation structure, and price-volume relationship. The tool reports a battery of similarity metrics so you can see the fit. Synthetic data is 'realistic enough' for most strategy stress-testing; not for HFT/microstructure work.

What model is under the hood?

Combination of GARCH(1,1) for volatility dynamics + a Skewed-t distribution for shock injection + a copula for cross-asset dependence. The methodology page documents the full specification. The tool also supports regime-switching variants for stress scenarios.

Can I generate correlated multi-asset data?

Yes — input the empirical correlation matrix from your real data, the generator will produce synthetic series that preserve those correlations. For more sophisticated dependence (asymmetric tail dependence), the tool supports t-copulas. Methodology page documents both.

Is synthetic data appropriate for production trading?

No. Synthetic data is for stress-testing strategies, not for live signal generation. Don't trade signals computed on synthetic series — by construction, they have no real-world predictive content. The methodology page is explicit about this.

Related deep dive

All articles →

Read further

Long-form context behind the tool output.

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