How to get stock prices in Python: real-time and historical

import os

import requests

resp = requests.get(
    "https://api.tickerlayer.com/stocks/snapshot/US:KO",
    headers={"x-api-key": os.environ["TICKERLAYER_API_KEY"]},
    timeout=10,
)
resp.raise_for_status()
print(resp.json())

python3 -m venv .venv && source .venv/bin/activate
pip install requests pandas
export TICKERLAYER_API_KEY="YOUR_API_KEY"   # from the dashboard; never commit it

import os
import time
from datetime import datetime
from zoneinfo import ZoneInfo

import pandas as pd
import requests

BASE_URL = "https://api.tickerlayer.com"
NEW_YORK = ZoneInfo("America/New_York")

session = requests.Session()
session.headers.update({"x-api-key": os.environ["TICKERLAYER_API_KEY"]})


class TickerLayerError(RuntimeError):
    """A 4xx or 5xx answer, with the status and the API's own message."""


def get(path, params=None, retries=3):
    """GET a REST path and return decoded JSON, retrying per-second 429s."""
    for attempt in range(retries + 1):
        resp = session.get(BASE_URL + path, params=params, timeout=10)
        # A per-second 429 carries Retry-After; a monthly-quota 429 does not.
        if resp.status_code == 429 and "Retry-After" in resp.headers and attempt < retries:
            time.sleep(float(resp.headers["Retry-After"]))
            continue
        if resp.ok:
            return resp.json()
        try:
            message = resp.json().get("message", resp.text)
        except ValueError:
            message = resp.text
        raise TickerLayerError(f"{resp.status_code} {path}: {message}")


def ny_time(ms):
    """Unix milliseconds to a New York wall-clock datetime."""
    return datetime.fromtimestamp(ms / 1000, tz=NEW_YORK)


def bars(symbol, multiplier, timespan, start, end):
    """Every OHLCV bar in an inclusive UTC date range, as a DataFrame."""
    path = f"/stocks/agg/{symbol}/{multiplier}/{timespan}/{start}/{end}"
    params = {"sort": "asc", "limit": 5000, "offset": 0}
    rows = []
    while True:
        body = get(path, params)
        rows.extend(body["results"])
        if body["next_offset"] is None:
            break
        params["offset"] = body["next_offset"]
    if not rows:  # a holiday, or a range before the listing
        return pd.DataFrame(columns=["open", "high", "low", "close", "volume"])
    df = pd.DataFrame(rows).rename(columns={"o": "open", "h": "high", "l": "low", "c": "close", "v": "volume"})
    df.index = pd.to_datetime(df.pop("t"), unit="ms", utc=True).rename("time")
    return df


# 1. One stock, right now
snap = get("/stocks/snapshot/US:KO")
print(f'{snap["symbol"]} last {snap["last_price"]} at {ny_time(snap["last_timestamp"]):%Y-%m-%d %H:%M %Z}')
print(f'bid {snap["bid"]} / ask {snap["ask"]}, previous close {snap["prev_close"]}')

# 2. A small watchlist as a DataFrame
rows = []
for symbol in ["US:KO", "US:JPM", "US:DIS"]:
    s = get(f"/stocks/snapshot/{symbol}")
    rows.append({"symbol": s["symbol"], "last": s["last_price"], "prev_close": s["prev_close"], "change_%": s.get("change_percent")})
print(pd.DataFrame(rows).to_string(index=False))

# 3. Daily history, every page
daily = bars("US:KO", 1, "day", "2026-09-01", "2026-09-25")
daily.index = daily.index.date  # a daily bar's t labels a session; keep the date
print(daily.tail(3))
print(f"{len(daily)} sessions, close {daily['close'].iloc[0]} -> {daily['close'].iloc[-1]}")

# 4. Intraday bars in New York time, saved to CSV
five = bars("US:KO", 5, "minute", "2026-09-25", "2026-09-25")
five.index = five.index.tz_convert(NEW_YORK)
print(five.head(3))
five.to_csv("KO_5min_2026-09-25.csv")

# 5. What an error looks like
try:
    get("/stocks/quote/KO")
except TickerLayerError as err:
    print("error:", err)

US:KO last 88.16 at 2026-09-28 06:50 EDT
bid 88.15 / ask 88.2, previous close 87.81
symbol   last  prev_close  change_%
 US:KO  88.16       87.81    0.3986
US:JPM 341.00      343.06   -0.6005
US:DIS 105.97      106.15   -0.1696
             open     high     low  close    volume
2026-09-23  88.95  88.9950  87.605  88.09  13105701
2026-09-24  88.82  89.3600  88.100  88.10  13252762
2026-09-25  88.16  88.3183  87.555  87.81  12261067
18 sessions, close 88.0 -> 87.81
                            open     high    low  close  volume
time
2026-09-25 09:30:00-04:00  88.16  88.3183  87.73  87.75  400268
2026-09-25 09:35:00-04:00  87.72  87.8250  87.62  87.77  182136
2026-09-25 09:40:00-04:00  87.77  87.7700  87.61  87.71  182892
error: 400 /stocks/quote/KO: invalid symbol