// tickerapi vs financial modeling prep

TickerAPI vs Financial Modeling Prep

FMP offers hundreds of endpoints covering fundamentals, financials, and analyst data. TickerAPI is built for a different job — pre-computed categories and state changes, no endpoint stitching required.

get started read the docs

// api surface

Simple endpoints, not hundreds.

FMP covers everything — income statements, balance sheets, ratios, DCF valuations, analyst ratings, earnings transcripts, and more. That breadth is powerful for analysts. But for an AI agent, you need a simpler surface with pre-interpreted output.

Financial Modeling Prep raw financial data

// /api/v3/ratios/AAPL response
{
  "peRatioTTM": 28.4521,
  "pegRatioTTM": 2.1834,
  "currentRatioTTM": 0.9881,
  "debtEquityRatioTTM": 1.7623,
  "returnOnEquityTTM": 0.1571,
  // ... 50+ raw ratio fields
}

// + income statement (separate call)
// + balance sheet (separate call)
// + analyst ratings (separate call)

TickerAPI pre-computed, categorical

// GET /v1/summary/AAPL
{
  "fundamentals": {
    "valuation_zone": "fair_value",
    "pe_vs_sector_zone": "premium",
    "growth_zone": "moderate_growth",
    "analyst_consensus": "buy"
  },
  "trend": {
    "direction": "uptrend",
    "ma_alignment": "aligned_bullish"
  },
  "momentum": {
    "rsi_zone": "neutral_high",
    "macd_state": "expanding_positive"
  }
}

// where each shines

Different tools, different strengths.

	TickerAPI	Financial Modeling Prep
Best for	AI agents & automated pipelines	Financial modeling & detailed analysis
Output format	✓ Pre-computed categories	Raw financial data across hundreds of endpoints
API surface	✓ Simple, focused endpoints	✓ Comprehensive (hundreds of endpoints)
Data depth	Categorical summaries + state changes	✓ Income, balance, cash flow, DCF, ESG, 13F
State change tracking	✓ Day-over-day and week-over-week diffs	Point-in-time values only
MCP support	✓ Native MCP server	✓ Official MCP server
Screener	✓ 6 built-in screeners	✓ Stock screener (market cap, sector, etc.)
SDKs	✓ Python, Node.js, Go	✓ Python, JS, Go
Token efficiency	✓ Compact categorical data	Verbose raw data payloads
Pricing	✓ Predictable, request-based	Bandwidth caps (trailing 30-day)

// derived data

Categories, not raw ratios.

FMP gives you the raw numbers — P/E ratios, debt-to-equity, return on equity, and dozens more. Useful for detailed financial modeling. But an LLM doesn't need 50 ratio fields — it needs to know if the stock is overvalued or undervalued.

Financial Modeling Prep raw financial ratios

"peRatioTTM": 28.45
"pegRatioTTM": 2.18
"priceToBookRatioTTM": 47.12
"returnOnEquityTTM": 0.157
"revenueGrowthYOY": 0.082

// is PE 28.45 expensive? vs what?
// is 15.7% ROE good for this sector?
// your agent needs context

TickerAPI pre-computed, categorical

"valuation_zone": "fair_value"
"pe_vs_sector_zone": "premium"
"growth_zone": "moderate_growth"
"analyst_consensus": "buy"
"condition_rarity": "common"

// AI-ready: "fair_value" + "premium" PE
// sector context already computed
// no ratio interpretation needed

// state changes

See exactly what changed, and when.

FMP tells you what a company's financials look like right now. TickerAPI tells you what changed — for every ticker on your watchlist, in a single call.

Financial Modeling Prep point-in-time data

// ratios today
"peRatioTTM": 28.45
"returnOnEquityTTM": 0.157

// ratios last week (separate call)
"peRatioTTM": 30.12
"returnOnEquityTTM": 0.157

// did anything meaningful change?
// you build the diff logic

TickerAPI structured state changes

// GET /v1/watchlist/changes
{
  "changes": {
    "AAPL": [
      {
        "field": "valuation_zone",
        "from":  "overvalued",
        "to":    "fair_value"
      },
      {
        "field": "rsi_zone",
        "from":  "neutral",
        "to":    "oversold"
      }
    ]
  },
  "tickers_changed": 1
}

// why it matters for ai

Built for agents, not analysts.

Financial Modeling Prep is an excellent raw data platform — comprehensive fundamentals, deep financial data, and an official MCP server. TickerAPI is built for a different use case: pre-interpreted output, state change tracking, and compact responses designed for LLM reasoning.

Interpretation built in

FMP gives you raw ratios — P/E, PEG, debt-to-equity, and dozens more. Your agent still needs context to interpret them. TickerAPI returns "valuation_zone": "fair_value" and "pe_vs_sector_zone": "premium" — the context is already computed.

Simple API, focused output

FMP has hundreds of endpoints for fundamentals, earnings, DCF, ESG, 13F holdings, and more — great for deep analysis. TickerAPI gives you simple endpoints that return everything your agent needs in a single call. Less surface area, less complexity.

State transitions, not snapshots

FMP tells you what a company's ratios are right now. TickerAPI shows what changed: "from": "undervalued" → "to": "fair_value". Your agent sees transitions across your entire watchlist without building diff logic.

Predictable costs

FMP uses bandwidth caps — trailing 30-day limits that can be hard to predict at scale. TickerAPI uses straightforward request-based pricing. No bandwidth metering, no surprises.

// historical context

What happened last time?

FMP gives you years of historical prices and ratios. But what happened the last time AAPL was deep oversold? You'd need to pull historical data, compute RSI locally, find threshold crossings, and calculate forward returns yourself. TickerAPI does all of that in one call.

Financial Modeling Prep build it yourself

// step 1: pull historical daily prices
// GET /api/v3/historical-price-full/AAPL
// ... 5 years of OHLCV data

// step 2: compute RSI locally
// apply 14-period formula to close prices

// step 3: find "deep_oversold" crossings
// scan for threshold transitions

// step 4: calculate forward returns
// for each crossing: compute 5d, 10d, 20d
// classify into performance bands

// FMP has no events/aftermath endpoint

TickerAPI pre-computed aftermath

// GET /v1/events?ticker=AAPL&field=rsi_zone&band=deep_oversold
{
  "ticker": "AAPL",
  "field": "rsi_zone",
  "events": [{
    "date": "2025-08-12",
    "band": "deep_oversold",
    "prev_band": "oversold",
    "aftermath": {
      "5d":  { "performance": "moderate_gain" },
      "10d": { "performance": "sharp_gain" },
      "20d": { "performance": "moderate_gain" }
    }
  }],
  "total_occurrences": 7,
  "query_range": "5y"
}

// pricing

What you pay.

FMP uses bandwidth caps that can be hard to predict. TickerAPI uses simple request-based pricing.

	TickerAPI	Financial Modeling Prep
Free tier	✓ 250 requests/day, all endpoints	250 requests/day, EOD only
Starting price	$25/mo (Plus)	$22/mo (Starter, US only)
What you get	50,000 req/day, 2yr history, events & aftermath, webhooks	300 req/min, US data, 5yr history, bandwidth caps
Events & aftermath	✓ Built in, all tiers	✗ Not available
Pricing model	✓ Request-based, predictable	Bandwidth caps (trailing 30-day)

Start building.

No credit card required. See derived data and state changes in your first API call.

try TickerAPI read the docs