All US Stock Daily OHLCV — Top 3,000 By Market Cap (2016–2026, 10y)
Ten years of daily open/high/low/close/volume bars for the top 3,000 US-listed stocks by market cap (NVIDIA, GOOGL/GOOG, AAPL, MSFT, … through ~$1B mid-caps). 6,148,886 rows packaged as 11 per-year gzipped CSVs. Universe spans NASDAQ, NYSE, and AMEX listings only (filtered out foreign exchanges and ADRs with dot-suffixes). Read with pandas.read_csv(path, compression='gzip', parse_dates=['date']) or duckdb.read_csv('us_stocks_ohlcv_*.csv.gz') to load all years at once. Pair this with the Index Constituent Membership History listing for survivorship-bias-free Russell-3000-style backtesting. Bedrock dataset for full-universe quant research, factor model construction, and ML training across small/mid/large-cap regimes.
Use Cases
- Full-universe US equity backtesting (small / mid / large cap regimes)
- Factor model construction across the broad US market
- Cross-sectional momentum / mean reversion research
- Survivorship-bias-free studies (combine with Index Constituent History dataset)
- Training sequence models on a comprehensive 10-year window of US prices
Methodology
Universe: top 3,000 US-listed stocks by market cap as of build date, drawn from FMP's company-screener filtered to marketCap > $100M, isFund=false, isEtf=false, exchangeShortName ∈ {NASDAQ, NYSE, AMEX}, and excluding tickers with foreign exchange suffixes (dot in symbol). For each, fetched daily OHLCV from historical-price-eod/full for date range 2016-01-01 through build date. Output split by calendar year for downstream tractability.
Update Schedule
Static snapshot. Reflects what FMP had indexed as of the build date.
Attribution
Source: NASDAQ / NYSE / AMEX market data via Financial Modeling Prep.
Schema
| name | type |
|---|---|
| symbol | string |
| date | date |
| open | number |
| high | number |
| low | number |
| close | number |
| volume | number |
Sample Data
| low | date | high | open | close | symbol | volume |
|---|---|---|---|---|---|---|
| 188.26 | 2026-01-02 | 192.93 | 189.84 | 188.85 | NVDA | 148240500 |
| 186.15 | 2026-01-05 | 193.63 | 191.76 | 188.12 | NVDA | 183529733 |
| 186.82 | 2026-01-06 | 192.17 | 190.52 | 187.24 | NVDA | 176862604 |
Get this via API
# 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 'All US Stock Daily OHLCV — Top 3,000 by Market Cap (2016–2026, 10y)' 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 'All US Stock Daily OHLCV — Top 3,000 by Market Cap (2016–2026, 10y)' and, if it fits, claim it and download the files."
# Tools it will use: search_listings -> preview_listing -> purchase_listing -> get_download_urlsone time · open license
Details
Available formats