FinanceToolkit

FinanceToolkit is an open-source Python library for transparent financial analysis. It fetches historical market data and financial statements (primarily via Financial Modeling Prep, with fallback to Yahoo Finance unless enforced), and computes a wide set of financial ratios, models, technical indicators, options analytics/Greeks, performance metrics, and risk metrics (e.g., Sharpe ratio, Value at Risk).

Evaluated Mar 29, 2026 (23d ago)
Homepage ↗ Repo ↗ Ai Ml finance financial-analysis python ratios risk-management technical-analysis options data-retrieval pandas
⚙ Agent Friendliness
54
/ 100
Can an agent use this?
🔒 Security
49
/ 100
Is it safe for agents?
⚡ Reliability
29
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
0
Documentation
70
Error Messages
0
Auth Simplicity
70
Rate Limits
55

🔒 Security

TLS Enforcement
50
Auth Strength
60
Scope Granularity
20
Dep. Hygiene
60
Secret Handling
55

The toolkit uses an external API key for the primary provider. The README indicates the key is passed in code, but does not document secure handling practices (e.g., environment variables, avoiding logging). No explicit claims are provided about transport security, scope granularity, or dependency scanning; HTTPS is assumed for upstream APIs but not confirmed here.

⚡ Reliability

Uptime/SLA
0
Version Stability
60
Breaking Changes
0
Error Recovery
55
AF Security Reliability

Best When

You want reproducible, transparent implementations of financial formulas and consistent metric definitions across many tickers in a Python environment.

Avoid When

You need a hosted API/SDK, strong guarantees of data-provider accuracy, or standardized error/contract semantics at the HTTP layer.

Use Cases

  • Compute standardized financial ratios and profitability metrics from consistent, explicit formulas
  • Run factor/model analyses (e.g., Dupont/extended analyses, WACC, Altman Z-Score) over equities or other supported instruments
  • Generate technical indicators and options analytics (e.g., Black-Scholes and Greeks) for research workflows
  • Backtest/performance and risk measurement using consistent methodology across tickers
  • Build custom analysis pipelines in Python using pandas DataFrame outputs (multi-indexed by ticker)

Not For

  • Turnkey web/API service usage (it is a client-side Python toolkit)
  • Production-grade, audited financial reporting where broker/data-provider audit trails are required
  • Use without an external data-provider key if relying on Financial Modeling Prep

Interface

REST API
No
GraphQL
No
gRPC
No
MCP Server
No
SDK
No
Webhooks
No

Authentication

Methods: API key for Financial Modeling Prep (provided to Toolkit via api_key) Secondary data source via Yahoo Finance (appears to require no explicit auth in the library interface, based on README description)
OAuth: No Scopes: No

Authentication is at the library-to-data-provider level: the README requires an FMP API key for primary data access; Yahoo Finance fallback is described without explicit auth.

Pricing

Free tier: Yes
Requires CC: No

Pricing depends on Financial Modeling Prep plans. The toolkit itself is open-source; costs arise from upstream data-provider usage.

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • No MCP/HTTP interface: an agent must run Python code locally/in its environment to use the toolkit.
  • Data retrieval may switch between Financial Modeling Prep and Yahoo Finance depending on access/config; results may differ if enforced-source settings change.
  • Upstream rate limits and plan restrictions (e.g., FMP free plan) can cause partial failures or fallback behavior; automated pipelines should handle provider-specific limitations.

Alternatives

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Scores are editorial opinions as of 2026-03-29.

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