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David is a synthetic financial data API. It generates deterministic, internally consistent public-company “worlds” and serves them through clean, REST endpoints that mirror the shape of real market-data APIs: prices, financial statements, earnings, filings, news, ownership, and macro data. Every response is synthetic and scenario-scoped. Instead of querying one fixed history of the real market, you pick a scenario (a self-contained market world with its own companies, price paths, fundamentals, and news flow) and then query that world exactly like you would a real data vendor. David builds and curates the scenarios; you pull data from them.

Jump to the Quickstart

Pick a scenario and pull your first prices in under five minutes.

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Why synthetic data

Real market history is a single, finite, and heavily-studied path. That makes it a poor foundation for training and evaluating financial AI agents:
  • Leakage. Models memorize the one real future instead of reasoning about it.
  • Scarcity. There is only one 2008, one COVID crash, one dot-com bust.
  • No ground truth. You can never see the hidden state that caused a price move.
David solves this by generating an unlimited supply of realistic-but-counterfactual markets. Because David authors the world, it knows the hidden truth behind every price, earnings surprise, and headline, so you can build clean train/validation/test/holdout splits, run point-in-time backtests without lookahead, and stress agents against crises that never happened.
David is built for financial agents: backtesting harnesses, research copilots, RL training loops, and evaluation suites. It is not a feed of real, licensed market data.

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What you get

David ships a complete market-data surface across a single, consistent scenario:

Stock prices

Daily OHLCV with adjusted close, plus point-in-time and market-wide snapshots.

Financial statements

Income statements, balance sheets, and cash-flow statements, normalized, as-reported, and segmented.

Financial metrics

Valuation, margin, return, and leverage ratios, plus a market-wide metrics snapshot and screener.

Earnings & guidance

Consensus, actuals, surprises, earnings calendars, KPI guidance, and non-GAAP reconciliations.

SEC-style filings

10-Q / 8-K filings and item-level text, linked back to the events that generated them.

News & analyst notes

Company and market news with sentiment, entities, and event lineage.

Insider & institutional

Form 4–style insider trades, 13F-style institutional holdings, and index-fund constituents.

Macro & rates

Macro tape, central-bank policy rates, corporate actions, and an event timeline.

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How it works

1

Choose a scenario

David generates and curates the scenarios. Browse the library and pick a scenario_id that fits your task.
2

Query the world

Pass that scenario_id to any data endpoint, /prices, /financials, /news, /earnings, and so on.
3

Reason point-in-time

Use as_of, start_date, and end_date to enforce visibility. David never reveals data before its release date, so agents can’t peek at the future.
The library already contains 720 ready-made scenarios spanning 80+ market themes (war energy shocks, contested elections, AI IPO manias, regional-bank credit crunches, Fed pivots, and more), so you can start querying immediately. Scenario generation is handled by David, not by API consumers.

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Design principles

  • Deterministic. The same seed and configuration always produce the same world, bit for bit. Scenarios are reproducible and replayable.
  • Internally consistent. Accounting identities hold, OHLC invariants hold, prices react to earnings, and news repeats the numbers in the structured records. Every scenario ships a validation report.
  • Point-in-time first. Data carries release dates and visibility windows so you can build leakage-free backtests.
  • Familiar. Endpoints, parameters, and response shapes follow real market-data conventions, so existing tooling ports over with minimal changes.

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Next steps

Quickstart

Authenticate, pick a scenario, and pull your first prices.

Understand scenarios

The core abstraction behind every David response.

Authentication

API keys, accounts, and rate limits.

API reference

Every endpoint, parameter, and response field.

Python SDK

Install david-data and pull data in a few lines.