Historical Stock Prices API: Python, JavaScript & cURL Quickstart (FinancialData.Net)

README.md

Historical Stock Prices API: Python, JavaScript & cURL Quickstart (FinancialData.Net)

Get 10+ years of daily OHLCV stock data as JSON or CSV with a few lines of code, load it into pandas, and calculate returns and moving averages. The stock-prices endpoint is available on the free plan (300 requests/day), so you can try everything below without paying.

• API docs: https://financialdata.net/documentation
• Stock Prices API page: https://financialdata.net/stock-prices-api
• Pricing / free key: https://financialdata.net/pricing
• Official Python SDK: https://pypi.org/project/fdnpy/

---

1. Get an API key

Sign up at financialdata.net and copy your key. Every request takes it as a query parameter: ?key=YOUR_API_KEY (or &key=... if other parameters are present).

export FDN_API_KEY="your_api_key_here"

2. Endpoint reference

GET https://financialdata.net/api/v1/stock-prices

Parameter	Required	Description	Example
identifier	yes	Ticker symbol	MSFT
offset	no	Records to skip (default 0); page size is 300 records	300
format	no	json (default) or csv	csv
key	yes	Your API key	YOUR_KEY

Each record contains trading_symbol, date, open, high, low, close and volume. Results come back newest first.

Example record (MSFT, 2026-09-28):

{
  "trading_symbol": "MSFT",
  "date": "2026-09-28",
  "open": 505.47,
  "high": 513.33,
  "low": 502.22,
  "close": 509.22,
  "volume": 19626940.0
}

3. cURL

curl "https://financialdata.net/api/v1/stock-prices?identifier=MSFT&key=$FDN_API_KEY"

# Next page of history (older data)
curl "https://financialdata.net/api/v1/stock-prices?identifier=MSFT&offset=300&key=$FDN_API_KEY"

# CSV output
curl "https://financialdata.net/api/v1/stock-prices?identifier=MSFT&format=csv&key=$FDN_API_KEY" -o msft.csv

4. Python: fetch full price history into pandas

import os
import time
import requests
import pandas as pd

API_KEY = os.environ["FDN_API_KEY"]
BASE_URL = "https://financialdata.net/api/v1/stock-prices"
PAGE_SIZE = 300  # records per request


def get_price_history(symbol: str, max_pages: int = 5) -> pd.DataFrame:
    """Download daily OHLCV history for `symbol`, newest first, then sort oldest -> newest."""
    rows = []
    for page in range(max_pages):
        resp = requests.get(
            BASE_URL,
            params={"identifier": symbol, "offset": page * PAGE_SIZE, "key": API_KEY},
            timeout=30,
        )
        resp.raise_for_status()
        batch = resp.json()
        if not batch:
            break
        rows.extend(batch)
        if len(batch) < PAGE_SIZE:
            break
        time.sleep(0.5)  # be polite; free plan is 300 requests/day

    df = pd.DataFrame(rows)
    df["date"] = pd.to_datetime(df["date"])
    return df.sort_values("date").set_index("date")


df = get_price_history("MSFT", max_pages=3)  # roughly 3 years of trading days
print(df[["open", "high", "low", "close", "volume"]].tail())

Daily returns, moving averages and volatility

df["return"] = df["close"].pct_change()
df["sma_20"] = df["close"].rolling(20).mean()
df["sma_50"] = df["close"].rolling(50).mean()
df["volatility_20d"] = df["return"].rolling(20).std() * (252 ** 0.5)  # annualized

print(df[["close", "sma_20", "sma_50", "volatility_20d"]].tail())

Plot price with moving averages

import matplotlib.pyplot as plt

ax = df[["close", "sma_20", "sma_50"]].plot(figsize=(11, 5), title="MSFT close with 20/50-day SMA")
ax.set_ylabel("USD")
plt.tight_layout()
plt.show()

Compare several tickers

symbols = ["MSFT", "AAPL", "NVDA"]
closes = pd.concat(
    {s: get_price_history(s, max_pages=2)["close"] for s in symbols}, axis=1
).dropna()

normalized = closes / closes.iloc[0] * 100  # rebased to 100
normalized.plot(figsize=(11, 5), title="Price performance (rebased to 100)")

5. Using the official Python SDK

If you would rather not write the HTTP and pagination code yourself:

pip install fdnpy

from fdnpy import FinancialDataClient

# Create the client with your API key (see the PyPI page for the current setup options)
client = FinancialDataClient(api_key="YOUR_API_KEY")
prices = client.get_stock_prices(identifier="MSFT")
print(prices[0])

The SDK handles automatic pagination and retries with exponential backoff. See the PyPI page for the current client options.

6. JavaScript (Node 18+ or browser)

const API_KEY = process.env.FDN_API_KEY;

async function getPrices(symbol, offset = 0) {
  const url = new URL("https://financialdata.net/api/v1/stock-prices");
  url.searchParams.set("identifier", symbol);
  url.searchParams.set("offset", offset);
  url.searchParams.set("key", API_KEY);

  const res = await fetch(url);
  if (!res.ok) throw new Error(`HTTP ${res.status}`);
  return res.json();
}

getPrices("MSFT").then((rows) => {
  const latest = rows[0];
  console.log(`${latest.trading_symbol} closed at ${latest.close} on ${latest.date}`);
});

7. Tips

• Rate limits: the free plan allows 300 requests per day.
• Bulk analysis: use format=csv for quick loads into spreadsheets or pandas.read_csv.
• Other datasets: the same API covers symbol lists, ETF and index prices, international stocks, crypto, forex, financial statements, insider trades and more. See the full endpoint list. Some datasets need a paid plan, so check the plan shown next to each endpoint.