Free Alpha Vantage Alternative for Fundamental Analysis
Alpha Vantage provides raw financial statements that you have to compute scores from yourself. SecuritiesDB gives you pre-computed quant scores — Piotroski F-Score, Altman Z-Score, Beneish M-Score, DCF fair values — in a single API call. No API key, no 5-calls/minute limit.
Key Differences
| Feature | Alpha Vantage | SecuritiesDB |
|---|---|---|
| Piotroski F-Score | ❌ Compute yourself | ✅ Pre-computed |
| Altman Z-Score | ❌ Compute yourself | ✅ Pre-computed |
| Beneish M-Score | ❌ No | ✅ Pre-computed |
| DCF Fair Value | ❌ No | ✅ With sensitivity matrix |
| Fama-French Factors | ❌ No | ✅ 5-factor exposures |
| ROIC-WACC Spread | ❌ Compute yourself | ✅ Pre-computed |
| Rate Limit (free tier) | 5 calls/min, 500/day | No hard limit |
| API Key Required | Yes | No |
| Earnings Evasiveness AI | ❌ No | ✅ NLP-scored |
The Problem with Raw Data
Alpha Vantage gives you income statements, balance sheets, and cash flow statements. That's the starting point, not the finish line. To compute a Piotroski F-Score you need to:
- Fetch 2+ years of income statements, balance sheets, and cash flows
- Handle missing/null fields and inconsistent reporting periods
- Implement all 9 Piotroski criteria with proper year-over-year comparisons
- Deal with Alpha Vantage's 5-calls/minute rate limit (15+ calls needed)
With SecuritiesDB, one call gets everything pre-computed and validated:
GET /api/v1/stocks/AAPL/quant-health → piotroski_f: 7, altman_z: 8.2, beneish_m: -2.8, roic_wacc_spread: 0.32
Migration Example — Alpha Vantage → SecuritiesDB
# Before: Alpha Vantage (multiple calls, manual computation)
# import alpha_vantage
# fd = FundamentalData(key='YOUR_KEY')
# income = fd.get_income_statement_annual('AAPL') # Call 1
# balance = fd.get_balance_sheet_annual('AAPL') # Call 2
# cash = fd.get_cash_flow_annual('AAPL') # Call 3
# ... 50 lines of Piotroski computation ...
# After: SecuritiesDB (one call, pre-computed)
import requests
data = requests.get(
"https://securitiesdb.com/api/v1/stocks/AAPL/quant-health"
).json()["data"]
scores = data["scores"]
print(f"Piotroski F-Score: {scores['piotroski_f']}/9")
print(f"Altman Z-Score: {scores['altman_z']:.2f}")
print(f"Beneish M-Score: {scores['beneish_m']:.2f}")
vc = data["value_creation"]
print(f"ROIC: {vc['roic']*100:.1f}%")
print(f"WACC: {vc['wacc']*100:.1f}%")
print(f"Spread: {vc['roic_wacc_spread']*100:.1f}%")
# Bonus: DCF fair value (Alpha Vantage doesn't have this at all)
dcf = requests.get(
"https://securitiesdb.com/api/v1/stocks/AAPL/dcf"
).json()["data"]
print(f"\nDCF Fair Value: ${dcf['fair_value']:.2f}")
print(f"Current Price: ${dcf['current_price']:.2f}")
print(f"Upside: {dcf['upside_pct']:.1f}%")