Free Crypto Daily Price and Volume APIs in Python

Overview

This guide shows practical, free ways to fetch daily crypto price and volume data in Python for Algorithmic Trading with Python. You’ll get minimal code, trade-offs, rate-limit notes, and pitfalls to avoid.

Quickstart

1. Choose a source

• CoinGecko: simple, no key, daily prices and total volume (not strict exchange OHLCV volume).
• Binance: exchange-grade OHLCV for pairs like BTCUSDT, no key, strict rate weights.
• CryptoCompare: unified OHLCV across many pairs, free key required.
• Alpha Vantage: free key, slower limits, daily OHLCV in USD.
• yfinance: convenient wrapper for daily bars like BTC-USD; unofficial.

1. Install packages

pip install requests pandas yfinance

1. Run the minimal working example (CoinGecko)

• Great for quick daily close + total volume in fiat.

Minimal working example: CoinGecko daily close + volume

import requests
import pandas as pd

def coingecko_daily(coin_id="bitcoin", vs="usd", days=180):
    url = f"https://api.coingecko.com/api/v3/coins/{coin_id}/market_chart"
    params = {"vs_currency": vs, "days": days, "interval": "daily"}
    r = requests.get(url, params=params, timeout=30)
    r.raise_for_status()
    data = r.json()

    prices = pd.DataFrame(data["prices"], columns=["ts", "close"])  # close-like spot
    volumes = pd.DataFrame(data["total_volumes"], columns=["ts", "volume"])

    df = prices.merge(volumes, on="ts")
    df["date"] = pd.to_datetime(df["ts"], unit="ms", utc=True).dt.tz_localize(None).dt.date
    df = df[["date", "close", "volume"]].sort_values("date").reset_index(drop=True)
    return df

if __name__ == "__main__":
    df = coingecko_daily("bitcoin", "usd", days=365)
    print(df.tail())
    # Example: simple daily returns
    df["ret"] = df["close"].pct_change()
    print(df[["date", "close", "volume", "ret"]].tail())

Notes

• volume is total traded volume in the quote currency (here USD-equivalent), aggregated across many venues.
• For strict OHLCV per exchange/pair, use Binance (below).

More free sources (with short Python snippets)

1) Binance (exchange OHLCV for pairs; no key)

import requests, pandas as pd

def binance_klines(symbol="BTCUSDT", limit=1000):
    url = "https://api.binance.com/api/v3/klines"
    params = {"symbol": symbol, "interval": "1d", "limit": limit}
    r = requests.get(url, params=params, timeout=30)
    r.raise_for_status()
    raw = r.json()
    cols = [
        "open_time","open","high","low","close","volume",
        "close_time","qav","trades","tbbav","tbqav","ignore"
    ]
    df = pd.DataFrame(raw, columns=cols)
    for c in ["open","high","low","close","volume"]:
        df[c] = df[c].astype(float)
    df["date"] = pd.to_datetime(df["open_time"], unit="ms").dt.date
    return df[["date","open","high","low","close","volume"]]

# Example
# df = binance_klines("ETHUSDT", limit=1000)

Notes

• volume is base-asset volume (e.g., BTC for BTCUSDT).
• Symbol format is BASEQUOTE (e.g., BTCUSDT, ETHUSDT).

2) CryptoCompare (free key; unified OHLCV)

import os, requests, pandas as pd

def cryptocompare_histoday(fsym="BTC", tsym="USD", limit=2000, api_key=None):
    url = "https://min-api.cryptocompare.com/data/v2/histoday"
    params = {"fsym": fsym, "tsym": tsym, "limit": limit}
    headers = {"authorization": f"Apikey {api_key}"} if api_key else {}
    r = requests.get(url, params=params, headers=headers, timeout=30)
    r.raise_for_status()
    j = r.json()
    if j.get("Response") != "Success":
        raise RuntimeError(j.get("Message", "API error"))
    d = j["Data"]["Data"]
    df = pd.DataFrame(d)
    df["date"] = pd.to_datetime(df["time"], unit="s").dt.date
    df = df.rename(columns={"volumefrom": "volume_base", "volumeto": "volume_quote"})
    return df[["date","open","high","low","close","volume_base","volume_quote"]]

# Example
# api_key = os.getenv("CRYPTOCOMPARE_KEY")
# df = cryptocompare_histoday("BTC", "USD", 2000, api_key)

Notes

• volume_base is in fsym (BTC); volume_quote is in tsym (USD).
• Requires free API key via header authorization.

3) Alpha Vantage (free key; daily OHLCV in USD)

import requests, pandas as pd

def alphavantage_daily(symbol="BTC", market="USD", api_key="demo"):
    url = "https://www.alphavantage.co/query"
    params = {
        "function": "DIGITAL_CURRENCY_DAILY",
        "symbol": symbol,
        "market": market,
        "apikey": api_key,
    }
    r = requests.get(url, params=params, timeout=30)
    r.raise_for_status()
    j = r.json()
    ts = j.get("Time Series (Digital Currency Daily)")
    if not ts:
        raise RuntimeError(j.get("Note") or j.get("Error Message") or "No data")
    df = pd.DataFrame.from_dict(ts, orient="index")
    df.index = pd.to_datetime(df.index)
    cols = {
        "1a. open (USD)": "open",
        "2a. high (USD)": "high",
        "3a. low (USD)": "low",
        "4a. close (USD)": "close",
        "5. volume": "volume",
    }
    out = df[list(cols.keys())].rename(columns=cols).astype(float)
    out["date"] = out.index.date
    return out.reset_index(drop=True)[["date","open","high","low","close","volume"]]

# Example
# df = alphavantage_daily("ETH", "USD", api_key="YOUR_KEY")

Notes

• Free tier is rate-limited; batch smartly.

4) yfinance (convenient daily bars; unofficial)

import pandas as pd
import yfinance as yf

def yf_daily(ticker="BTC-USD", period="max"):
    df = yf.download(ticker, interval="1d", period=period, auto_adjust=False)
    df = df.rename(columns=str.lower).reset_index()
    df["date"] = pd.to_datetime(df["date"]).dt.date
    return df[["date","open","high","low","close","volume"]]

# Example
# df = yf_daily("BTC-USD", period="5y")

Notes

• Subject to upstream changes; validate against a reference.

Quick comparison

Common pitfalls

• Symbol mismatches
  • BTCUSDT (Binance) vs BTC-USD (yfinance) vs BTC/USD (generic). Map explicitly per source.
• Volume definitions vary
  • Exchange OHLCV often uses base-asset volume; aggregate feeds may present quote volume. Do not mix without conversion.
• Timezones and daily cutoffs
  • Daily bars are typically UTC-based. Align to UTC when merging across sources.
• Missing/partial last bar
  • The current day’s bar may be incomplete; filter out today if you need closed bars only.
• Rate limits
  • Expect HTTP 429. Add retries with exponential backoff and caching.

Performance notes

• Batch and cache
  • Cache responses on disk (e.g., parquet) keyed by source/symbol/date to avoid re-downloading.
• Use vectorized pandas ops
  • Convert types once and keep DataFrames tidy to avoid repeated parsing.
• Retry with backoff

import time, requests

def get_with_backoff(url, params=None, headers=None, attempts=5):
    delay = 1.0
    for i in range(attempts):
        try:
            r = requests.get(url, params=params, headers=headers, timeout=30)
            if r.status_code == 429 and i < attempts - 1:
                time.sleep(delay); delay *= 2; continue
            r.raise_for_status()
            return r
        except requests.RequestException:
            if i == attempts - 1:
                raise
            time.sleep(delay); delay *= 2

• Minimize JSON-to-DataFrame overhead
  • Select and cast columns once; avoid holding unused fields.

Example workflow (end-to-end)

1. Pull OHLCV from Binance for backtesting a USDT pair.
2. Pull CoinGecko daily close for broad market sanity checks.
3. Align on date, inner-join by date, drop today’s partial bar.
4. Compute features (returns, rolling vol) and export to parquet.

Tiny FAQ

• Q: I need OHLCV for BTCUSD without an account. What should I use?
  • A: Binance BTCUSDT daily klines are free and require no key.
• Q: Why don’t CoinGecko OHLC match exchange candles?
  • A: CoinGecko aggregates across venues; definitions and timing differ.
• Q: How can I get more than 1000 days from Binance?
  • A: Page backward using the startTime/endTime parameters or store incrementally.
• Q: My last bar is zero volume; is that a bug?
  • A: Often it’s an incomplete current-day bar. Filter to completed days for signals.