> ## Documentation Index
> Fetch the complete documentation index at: https://docs.davidhf.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Synthetic data & provenance

> What David generates, what is real, and how determinism and consistency are guaranteed.

David is a **synthetic** data API. Understanding exactly what is generated versus what is real is essential to using it correctly.

## What is synthetic

Effectively everything that carries financial meaning is generated:

* Prices and OHLCV bars
* Income statements, balance sheets, cash-flow statements, and metrics
* Earnings results, surprises, guidance, and analyst estimates
* News articles, analyst notes, and SEC-style filings
* Insider trades, institutional holdings, and corporate actions
* The macro tape and central-bank policy path
* Events and their effects

None of these are real issuer histories. They are deterministic functions of a scenario's seed and configuration.

## What is real

Two things are grounded in real-world reference data:

1. **Ticker symbols.** David uses real exchange-style symbol aliases (e.g. `AAPL`, `NVDA`, `SPY`) so your tooling and ticker lists work unchanged. The bundled universe contains **16,143 unique public US symbols** from SEC/Nasdaq snapshots.
2. **Company reference identity.** Each company's display metadata (name, sector, industry, business description, market-cap tier, instrument type) reflects the real-world company, so `AAPL` reads as *Apple, Consumer Electronics* and `SPY` reads as *an S\&P 500 ETF*.

<Warning>
  Real symbols and real names do **not** mean real data. The prices, fundamentals, news, filings, ownership, and events behind every symbol are synthetic scenario data.
</Warning>

## Determinism

David's defining property is determinism. A scenario is built from a seed plus configuration, and the same inputs always produce the same world, down to individual price bars and headline text.

Generation also follows a fixed dependency order, so the world is *internally* consistent rather than a pile of independent random series:

<Steps>
  <Step title="Macro regime + trading calendar">
    The economic backdrop and the scenario clock are set first.
  </Step>

  <Step title="Company identities + sector archetypes">
    Each issuer's sector behavior and business profile are established.
  </Step>

  <Step title="Financial statements + metrics">
    Income statement, balance sheet, cash flow, and ratios are generated.
  </Step>

  <Step title="Consensus estimates + guidance">
    Sell-side expectations and management guidance are set against those fundamentals.
  </Step>

  <Step title="Event timeline">
    Earnings, macro catalysts, and idiosyncratic events are scheduled.
  </Step>

  <Step title="OHLCV prices">
    Daily bars are built from market, sector, and event return components.
  </Step>

  <Step title="Grounded artifacts">
    News, filings, and analyst notes are written from the structured records.
  </Step>

  <Step title="Validation report">
    The finished world is checked for consistency and leakage.
  </Step>
</Steps>

David first generates the **hidden truth** of the world, then derives fundamentals, expectations, events, prices, and finally the public artifacts from that truth. The hidden state is never exposed through the API; only its downstream, point-in-time observable effects are.

## Internal consistency

Because the world is derived from a single hidden truth, the data ties together:

* Accounting identities hold (balance sheet balances, cash reconciles, EPS ties out).
* OHLC and volume invariants hold (`low ≤ open, close ≤ high`; event days spike volume).
* Prices react to earnings: significant beats/misses move the stock in the right direction.
* News and filings **repeat** the numbers in the structured records instead of drifting from them.
* Filings carry the metadata (accession, CIK, item codes) and link back to the events that triggered them via `source_event_id`.

Every scenario ships a [validation report](/api-reference/scenarios#validation-report) that checks these properties.

## Realism

David's generator is tuned so the synthetic data reproduces the **stylized facts** of real markets: return distributions, volatility clustering, drawdown frequency, volume/reaction coupling, tail behavior, and cross-sector correlation.

<Info>
  This means the synthetic data *resembles* real markets statistically. It does not mean David copies real time series. David is a research and evaluation tool, not a licensed feed of real market data.
</Info>

## Next steps

<CardGroup cols={2}>
  <Card title="Ticker universe" icon="hashtag" href="/concepts/ticker-universe">
    Real symbols, instrument types, and ETF suppression rules.
  </Card>

  <Card title="Market coverage" icon="globe" href="/concepts/market-coverage">
    Universe size, horizons, and endpoint coverage.
  </Card>
</CardGroup>