This directory contains pre-generated .dat files with historical OHLCV data
(Open, High, Low, Close, Volume) for over 155 financial instruments.
data/
├── manifest.json – index of all available .dat files
├── indices/ – 30 files: major stock market indices & ETFs
├── fortune500/ – 92 files: Fortune 500 companies (top by market cap)
└── crypto/ – 34 files: top cryptocurrencies & crypto ETFs
Each .dat file is UTF-8 JSON with the following structure:
{
"ticker": "SPY",
"name": "SPDR S&P 500 ETF Trust",
"category": "index",
"currency": "USD",
"last_updated": "2026-04-08T22:00:00Z",
"record_count": 528,
"meta": {},
"ohlcv": [
{ "date": "2024-04-01", "open": 521.0, "high": 523.5, "low": 520.1, "close": 522.8, "volume": 80000000 },
...
]
}
SPY, QQQ, DIA, IWM, GLD, SLV, TLT, AGG, XLE, XLF, XLK, XLV, SMH, ARKK, EEM, EFA, VNQ, ^FTSE, ^N225, ^GDAXI, ^FCHI, ^BVSP, ^NSEI, ^GSPTSE, ^AXJO, ^KS11, ^HSI, ^STOXX50E, ^RUT, ^VIX
Technology: AAPL, MSFT, NVDA, AMZN, GOOGL, META, TSLA, AVGO, ORCL, CRM, AMD, INTC, CSCO, QCOM, TXN, IBM, ADBE, NOW, INTU, PLTR, SNOW, CRWD, NET, PANW
Financials: JPM, BAC, WFC, GS, MS, BLK, V, MA, AXP, SCHW, C, SPGI, CME, ICE
Healthcare: JNJ, LLY, UNH, ABBV, MRK, PFE, TMO, ABT, AMGN, GILD, VRTX, REGN, ISRG
Consumer: WMT, PG, KO, PEP, MCD, SBUX, NKE, HD, LOW, COST, TGT, DIS, NFLX, CMCSA
Energy: XOM, CVX, COP, SLB, OXY
Industrials: BA, CAT, GE, HON, UPS, FDX, RTX, LMT, NOC, DE, MMM
Telecom: T, VZ, TMUS · Real Estate: AMT, PLD · Utilities: NEE, DUK Materials: LIN, SHW, NEM, FCX
BTC-USD, ETH-USD, BNB-USD, SOL-USD, XRP-USD, DOGE-USD, ADA-USD, AVAX-USD, DOT-USD, LINK-USD, LTC-USD, BCH-USD, ALGO-USD, XLM-USD, HBAR-USD, FIL-USD, VET-USD, NEAR-USD, ATOM-USD, APT-USD, ARB-USD, OP-USD, INJ-USD, SUI-USD, ICP-USD, TON-USD, SHIB-USD, MATIC-USD, UNI-USD
Crypto ETFs/Trusts: GBTC, ETHE, BITO, IBIT, FBTC
Data is automatically refreshed daily (weekdays after US market close) via GitHub Actions:
.github/workflows/financial_data_update.yml
To manually trigger an update, run:
python scripts/generate_financial_data.py --period 2y
Requires: pip install yfinance pandas
const res = await fetch('/data/manifest.json');
const manifest = await res.json();
// Load a specific ticker
const spy = await fetch('/data/indices/spy.dat').then(r => r.json());
const prices = spy.ohlcv; // [{date, open, high, low, close, volume}, ...]
import json, pathlib
data = json.loads(pathlib.Path('data/crypto/btc_usd.dat').read_text())
df = pd.DataFrame(data['ohlcv'])