US Key Ratios & Metrics — Quarterly, Top 3,000, 10y (2016–2026)

DatasetCSV.GZOpenFinanceRatiosMetricsFundamentalsValuationFactorsFree

Ten years of pre-computed quarterly financial ratios and metrics for the top 3,000 US-listed issuers, covering 103,013 ratio-period rows across 2,973 companies and 102,983 key-metric rows across 2,973 companies. Two complementary files in one listing: (1) key_ratios_quarterly — profitability margins, turnover ratios, liquidity, solvency, leverage, valuation multiples (P/E, P/B, P/S, P/FCF, EV/EBITDA), per-share book values, dividend metrics, and tax/interest burdens. (2) key_metrics_quarterly — market cap, enterprise value, EV multiples, return-on-capital family (ROA, ROE, ROIC, ROCE), working-capital cycles (DSO, DPO, DIO, cash conversion cycle), and capex/R&D/SBC intensity. Saves buyers weeks of feature engineering on top of raw IS/BS/CFS statements. Read with pandas.read_csv(path, compression='gzip', parse_dates=['date']). Useful for: fundamental factor models, multi-factor backtests, screening by financial health, training ML models on pre-computed financial features.

Use Cases

Fundamental factor model construction (value, quality, profitability)
Cross-sectional screening (e.g. P/E < 15 AND ROIC > 15% AND debt/equity < 1)
Margin-expansion / capital-cycle backtests
Pre-computed feature stacks for ML — no need to derive from raw statements
Pairs trading on valuation-multiple convergence

Methodology

Universe: top 3,000 US-listed issuers by market cap (NASDAQ/NYSE/AMEX). Two FMP endpoints per ticker: /stable/ratios and /stable/key-metrics, both period=quarter, limit=40 (returns up to 40 quarters = ~10 years). All rows filtered to date >= 2016-01-01. Each ticker contributes one row per reported fiscal quarter to each file.

Update Schedule

Static snapshot. Re-run quarterly after earnings season for fresh ratios.

Attribution

Source: pre-computed ratios and metrics via Financial Modeling Prep.

Schema

name	type
symbol	string
date	date
fiscalYear	number
period	string
reportedCurrency	string
grossProfitMargin	number
ebitMargin	number
ebitdaMargin	number
operatingProfitMargin	number
pretaxProfitMargin	number
continuousOperationsProfitMargin	number
netProfitMargin	number
bottomLineProfitMargin	number
receivablesTurnover	number
payablesTurnover	number
inventoryTurnover	number
fixedAssetTurnover	number
assetTurnover	number
currentRatio	number
quickRatio	number

Showing 20 of 63 rows

Sample Data

date	period	symbol	cashRatio	ebitMargin	ebtPerEbit	fiscalYear	quickRatio	cashPerShare	currentRatio	ebitdaMargin	assetTurnover	capexPerShare	dividendYield	solvencyRatio	debtToMarketCap	netIncomePerEBT	netProfitMargin	revenuePerShare	effectiveTaxRate	payablesTurnover	priceToBookRatio	priceToFairValue	reportedCurrency	bookValuePerShare	debtToAssetsRatio	debtToEquityRatio	grossProfitMargin	inventoryTurnover	netIncomePerShare	priceToSalesRatio	debtToCapitalRatio	fixedAssetTurnover	pretaxProfitMargin	dividendPayoutRatio	receivablesTurnover	freeCashFlowPerShare	interestDebtPerShare	priceToEarningsRatio	interestCoverageRatio	operatingProfitMargin	bottomLineProfitMargin	financialLeverageRatio	operatingCashFlowRatio	dividendYieldPercentage	enterpriseValueMultiple	debtServiceCoverageRatio	priceToFreeCashFlowRatio	operatingCashFlowPerShare	tangibleBookValuePerShare	longTermDebtToCapitalRatio	priceToEarningsGrowthRatio	shareholdersEquityPerShare	operatingCashFlowSalesRatio	workingCapitalTurnoverRatio	priceToOperatingCashFlowRatio	operatingCashFlowCoverageRatio	capitalExpenditureCoverageRatio	continuousOperationsProfitMargin	dividendPaidAndCapexCoverageRatio	forwardPriceToEarningsGrowthRatio	freeCashFlowOperatingCashFlowRatio	shortTermOperatingCashFlowCoverageRatio
2016-04-30	Q2	A	1.887908208296558	0.1324828263002944	0.8931297709923665	2016	2.5887025595763458	6.561349693251533	3.078552515445719	0.19528949950932287	0.13337696335078533	0.07668711656441718	0.0027736298268655284	0.04460431654676259	0.14160504710672928	0.7777777777777778	0.08930323846908735	3.125766871165644	0.2222222222222222	2.2227272727272727	3.2051705910619894	3.2051705910619894	USD	12.776073619631902	0.24725130890052355	0.4538683325324363	0.5201177625122669	0.8810810810810811	0.2791411042944785	13.091187438665358	0.3121798049909106	1.6704918032786886	0.11481844946025516	0.4065934065934066	1.6926910299003322	0.7024539877300614	5.8496932515337425	36.64813186813187	7.277777777777778	0.12855740922473013	0.08930323846908735	1.8356559346468044	0.22418358340688438	0.2773629826865528	65.77849246231156	0.6837944664031621	58.25292576419214	0.7791411042944786	3.432515337423313	0.2843878954607978	-1.5066454212454217	12.766871165644172	0.2492639842983317	0.4239650509673393	52.51937007874015	0.13446267866596082	10.16	0.08930323846908735	4.096774193548387	-1.5066454212454217	0.9015748031496063	1.0808510638297872
2016-07-31	Q3	A	1.9105125977410946	0.1446360153256705	0.9178082191780822	2016	2.5951346655082537	6.766153846153846	3.066898349261512	0.2021072796934866	0.13498836307214895	0.07384615384615385	0.002378235927431666	0.05430932703659976	0.12129003229901496	0.9253731343283582	0.11877394636015326	3.212307692307692	0.07462686567164178	1.9233716475095786	3.582258807276076	3.582258807276076	USD	13.372307692307693	0.24398758727695888	0.43449228643794613	0.5191570881226054	0.9244935543278084	0.38153846153846155	14.90205938697318	0.3028892455858748	1.6757624398073836	0.12835249042145594	0.29838709677419356	1.769491525423729	0.5230769230769231	5.858461538461539	31.3664314516129	8.588235294117647	0.13984674329501914	0.11877394636015326	1.7807966843195948	0.1685490877497828	0.2378235927431666	72.25473933649289	0.7976190476190477	91.51617647058823	0.5969230769230769	4.243076923076923	0.2755629691409508	0.8782600806451616	13.363076923076923	0.18582375478927204	0.44106463878326996	80.19458762886597	0.10280869104398516	8.083333333333334	0.11877394636015326	3.180327868852459	0.8782600806451616	0.8762886597938144	0.825531914893617
2016-10-31	Q4	A	2.422222222222222	0.15301530153015303	0.825136612021858	2016	3.2825396825396824	7.064814814814815	3.8465608465608465	0.20342034203420342	0.14254554785732615	0.16049382716049382	0.0026918510584641713	0.05073280721533258	0.1348759056662048	0.8211920529801324	0.1116111611161116	3.4290123456790123	0.16556291390728478	2.035019455252918	3.3286206083470877	3.3286206083470877	USD	13.104938271604938	0.24429047985629973	0.44895071917000706	0.5292529252925292	0.9812382739212008	0.38271604938271603	12.706282628262827	0.3098454027664768	1.7386541471048513	0.13591359135913592	0.3064516129032258	1.7606973058637083	0.5617283950617284	5.935185185185185	28.461048387096778	9.631578947368421	0.16471647164716471	0.1116111611161116	1.8377741098797453	0.24761904761904763	0.26918510584641714	60.75964601769911	10.578947368421053	77.56417582417582	0.7222222222222222	4.077160493827161	0.3098454027664768	0.0	13.089506172839506	0.21062106210621062	0.4383507595186427	60.32769230769231	0.12289915966386554	4.5	0.11341134113411341	2.6	0.0	0.7777777777777778	0.0

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# 1. Add dAgentBase once, in any MCP client. No install, no vendor keys.
#    Claude.ai / Claude Desktop: Settings -> Connectors -> Add custom connector
#    Cursor / Claude Code / others: mcp.json
{
  "mcpServers": {
    "dagentbase": {
      "url": "https://dagentbase.com/api/mcp",
      "headers": { "Authorization": "Bearer dm_live_YOUR_KEY" }
    }
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}

# 2. Then ask your agent, in plain language:
#    "Preview 'US Key Ratios & Metrics — Quarterly, Top 3,000, 10y (2016–2026)' and, if it fits, claim it and download the files."
#    Tools it will use: search_listings -> preview_listing -> purchase_listing -> get_download_urls

# 1. Add dAgentBase once, in any MCP client. No install, no vendor keys.
#    Claude.ai / Claude Desktop: Settings -> Connectors -> Add custom connector
#    Cursor / Claude Code / others: mcp.json
{
  "mcpServers": {
    "dagentbase": {
      "url": "https://dagentbase.com/api/mcp",
      "headers": { "Authorization": "Bearer dm_live_YOUR_KEY" }
    }
  }
}

# 2. Then ask your agent, in plain language:
#    "Preview 'US Key Ratios & Metrics — Quarterly, Top 3,000, 10y (2016–2026)' and, if it fits, claim it and download the files."
#    Tools it will use: search_listings -> preview_listing -> purchase_listing -> get_download_urls

Free

one time · open license

Details

Date Range2016-01-01 — 2026-09-05

Rows205,996

Size73.9 MB

Files2

FormatCSV.GZ

Available formats

CSV.GZ2 files · 73.9 MB

Freeopen