$29/month
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The same data and engine, from your code.
Wiring an agent or a notebook to financial data? Use the same filing-dated data and the same valuation engine the app uses, through a Python SDK and a REST API, with an as_of on every call.
# pip install stoxa STOXA_API_KEY=vdk_…
# key from Settings → API keys; works from any machine
from datetime import date
from stoxa import equities, macro
d = date(2026, 10, 30)
# 10y Treasury as published on d, plus where it came from
rf, src = macro.risk_free(as_of=d)
ctx = macro.context(as_of=d) # curve state, Sahm, HY OAS
# only filings with filed <= d
fin = equities.get_fundamentals("AAPL", as_of=d)
# the same engine the workbench uses
val = equities.value_company("AAPL", as_of=d)
val.value_per_share, val.upside
# the reverse solve: growth the price implies
g = equities.implied_growth("AAPL", as_of=d)
# rank the liquid universe with a built-in system
ranks = equities.rank("Core: Combination", as_of=d, universe="liquid")
# run a strategy as a portfolio: sell rules, a stop, costs
sim = equities.simulate({"start": "2015-01-01", "end": "2024-12-31",
"universe": "liquid", "ranking": "Core: Combination",
"sell_rules": [{"kind": "rank_below", "value": 80}],
"stops": {"trailing_pct": 20}})
sim.equity(); sim.trades()
# a house strategy: rules, live record, holdings
s = equities.house_strategy("magic-formula")
s.definition(); s.holdings()
Take the same data into your notebook.
pip install stoxa. The Python SDK calls the same REST API the app uses, with a personal API key, so it works from any machine. A notebook gets exactly the inputs the workbench saw on that date, whether you are reproducing a model, running a Monte Carlo study or giving an agent a dataset that cannot peek ahead. An agent given this API cannot be fooled by restated history: every call takes the same as_of and returns the same provenance the UI shows.
stoxa.equities: fundamentals, prices, metrics, screens with universes and funnel counts, ranking systems and the bucket test, backtests and rolling backtests, the portfolio simulator, saved strategies, house strategies, follow and clone, valuation and stochastic DCF.stoxa.macro: series and frames as of any date, risk-free rate, regime context.- REST API and API keys: the same endpoints the app uses; every call is authenticated and tied to your account.
What the API exposes.
The SDK is a thin client over the same REST endpoints the app uses. Create API keys under Settings → API keys in the app; every request except the public strategies table is authenticated and tied to your account. The reference lists every endpoint.
- Fundamentals: statement line items with period end, filing date and accession number.
- Prices: daily bars for US-listed companies.
- Metrics: margins, returns, growth, multiples and per-share figures.
- Valuation: run, reverse (the growth the price implies), sensitivity grids and Monte Carlo.
- Screener: the same filters and fields as the app, universes, funnel counts and saved screens.
- Rankings: create, validate and run ranking systems, explain a company's rank, and run the bucket test.
- Backtests: screen and rolling backtests with costs, dividends and a benchmark, as synchronous calls or jobs with progress.
- Simulations and strategies: run, validate and estimate portfolio simulations; save, list and delete strategies.
- House strategies and portfolios: rules, live record, holdings and recommendations; follow, clone, rebalance diffs and notifications.
- Public strategies:
GET /api/public/strategies, the since-launch table of the house strategies, is the one call that needs no key. - Macro series: observations as of any date, the risk-free rate and regime context.
- API keys from Account, revocable at any time.
- Every call takes
as_of, and the answer is what was public on that date.
One plan. Everything included.
$29 a month or $290 a year. The full workbench, the API and the Python SDK. No data add-ons. Cancel any time.
$290/year
Sign in and subscribeUS-listed companies filing with the SEC (no non-US coverage yet). Questions: hello@stoxa.io.