avantage

Modern, fully-typed Python wrapper for the Alpha Vantage API.

• Async-first -- built on httpx for native async/await support
• Fully typed -- every parameter and return value has type annotations; passes mypy --strict
• 110+ endpoints -- stocks, forex, crypto, commodities, fundamentals, economic indicators, 50+ technical indicators, options, news sentiment, and more
• Pydantic v2 models -- all responses parsed into typed dataclasses, not raw dicts
• Built-in resilience -- token-bucket rate limiting, retry with exponential backoff + jitter
• Minimal dependencies -- just httpx and pydantic

Installation

pip install avantage

With optional pandas support:

pip install avantage[pandas]

Quick Start

import asyncio
from avantage import AlphaVantageClient

async def main():
    async with AlphaVantageClient("YOUR_API_KEY") as client:
        # Get a stock quote
        quote = await client.equity.quote("AAPL")
        print(f"{quote.symbol}: ${quote.price} ({quote.change_percent})")

        # Get daily time series
        series = await client.equity.daily("MSFT")
        for entry in series.data[:3]:
            print(f"  {entry.timestamp}: O={entry.open} H={entry.high} L={entry.low} C={entry.close}")

        # Technical indicators
        rsi = await client.indicators.rsi("AAPL", "daily", time_period=14, series_type="close")
        print(f"RSI: {rsi.data[0].values}")

asyncio.run(main())

You can also set your API key via the environment variable ALPHAVANTAGE_API_KEY:

import os
os.environ["ALPHAVANTAGE_API_KEY"] = "YOUR_KEY"

client = AlphaVantageClient(os.environ["ALPHAVANTAGE_API_KEY"])

Plotting

Responses are Pydantic models, so you'll need to convert them to pandas DataFrames for plotting. Install the extras:

pip install avantage[pandas] matplotlib

Intraday Time Series

import asyncio
import pandas as pd
import matplotlib.pyplot as plt
from avantage import AlphaVantageClient

async def main():
    async with AlphaVantageClient("YOUR_API_KEY") as client:
        series = await client.equity.intraday("IBM", "5min")

    df = pd.DataFrame([
        {"time": e.timestamp, "close": e.close} for e in series.data
    ])
    df["time"] = pd.to_datetime(df["time"])
    df = df.sort_values("time")

    df.plot(x="time", y="close", title="IBM Intraday Close Price (5-min)", legend=False)
    plt.ylabel("Price (USD)")
    plt.tight_layout()
    plt.show()

asyncio.run(main())

Bollinger Bands

async def main():
    async with AlphaVantageClient("YOUR_API_KEY") as client:
        bbands = await client.indicators.bbands("IBM", "daily", time_period=20, series_type="close")

    df = pd.DataFrame([
        {
            "date": e.timestamp,
            "upper": e.values["Real Upper Band"],
            "middle": e.values["Real Middle Band"],
            "lower": e.values["Real Lower Band"],
        }
        for e in bbands.data
    ])
    df["date"] = pd.to_datetime(df["date"])
    df = df.sort_values("date").tail(90)

    fig, ax = plt.subplots()
    ax.plot(df["date"], df["upper"], label="Upper Band")
    ax.plot(df["date"], df["middle"], label="Middle Band (SMA 20)")
    ax.plot(df["date"], df["lower"], label="Lower Band")
    ax.fill_between(df["date"], df["upper"], df["lower"], alpha=0.1)
    ax.set_title("IBM Bollinger Bands (Daily)")
    ax.legend()
    plt.tight_layout()
    plt.show()

asyncio.run(main())

Crypto Daily

async def main():
    async with AlphaVantageClient("YOUR_API_KEY") as client:
        crypto = await client.crypto.daily("BTC", "USD")

    df = pd.DataFrame([
        {"date": e.timestamp, "close": e.close} for e in crypto.data
    ])
    df["date"] = pd.to_datetime(df["date"])
    df = df.sort_values("date").tail(90)

    df.plot(x="date", y="close", title="Bitcoin Daily Close Price (BTC/USD)", legend=False)
    plt.ylabel("Price (USD)")
    plt.tight_layout()
    plt.show()

asyncio.run(main())

API Reference

The client exposes domain-specific API groups as attributes:

async with AlphaVantageClient(api_key) as client:
    client.equity          # Stocks: quotes, time series, search, market status
    client.forex           # Foreign exchange rates and time series
    client.crypto          # Cryptocurrency rates and time series
    client.commodities     # Oil, gas, metals, agriculture prices
    client.fundamentals    # Company financials, earnings, dividends, splits
    client.economic        # GDP, CPI, unemployment, treasury yields
    client.indicators      # 50+ technical indicators (SMA, RSI, MACD, ...)
    client.intelligence    # News sentiment, top movers, insider transactions
    client.options         # Realtime and historical options chains
    client.analytics       # Fixed and sliding window analytics
    client.calendar        # Earnings and IPO calendars

Equity

# Realtime quote
quote = await client.equity.quote("AAPL")
# quote.symbol, quote.price, quote.change, quote.change_percent, quote.volume

# Time series (intraday, daily, weekly, monthly -- with adjusted variants)
series = await client.equity.daily("AAPL", outputsize="full")
series = await client.equity.intraday("AAPL", "5min")
series = await client.equity.weekly_adjusted("AAPL")

# Search
matches = await client.equity.search("Apple")
# [SymbolMatch(symbol="AAPL", name="Apple Inc", ...)]

# Bulk quotes (up to 100 symbols)
quotes = await client.equity.bulk_quotes("AAPL,MSFT,GOOGL")

# Market status
markets = await client.equity.market_status()

Forex

rate = await client.forex.exchange_rate("USD", "EUR")
# rate.exchange_rate, rate.bid_price, rate.ask_price

series = await client.forex.daily("USD", "EUR")
# [FXDataPoint(timestamp="2024-01-02", open=0.85, high=0.86, low=0.84, close=0.855)]

Crypto

rate = await client.crypto.exchange_rate("BTC", "USD")

daily = await client.crypto.daily("BTC", "USD")
# [CryptoDataPoint(timestamp=..., open=..., volume=..., market_cap=...)]

Commodities

oil = await client.commodities.wti(interval="daily")
# CommodityResponse(name="WTI", interval="daily", unit="dollars per barrel", data=[...])

gas = await client.commodities.natural_gas()
copper = await client.commodities.copper()

# Precious metals
gold_spot = await client.commodities.gold_silver_spot("GOLD")
gold_history = await client.commodities.gold_silver_history("GOLD", interval="daily")

Fundamentals

overview = await client.fundamentals.overview("AAPL")
# overview.market_capitalization, overview.pe_ratio, overview.eps, overview.beta, ...

income = await client.fundamentals.income_statement("AAPL")
# income.annual_reports, income.quarterly_reports

earnings = await client.fundamentals.earnings("AAPL")
estimates = await client.fundamentals.earnings_estimates("AAPL")
dividends = await client.fundamentals.dividends("AAPL")
splits = await client.fundamentals.splits("AAPL")

Economic Indicators

gdp = await client.economic.real_gdp(interval="quarterly")
cpi = await client.economic.cpi()
unemployment = await client.economic.unemployment()
yields = await client.economic.treasury_yield(maturity="10year")

Technical Indicators

All 50+ Alpha Vantage indicators are supported:

sma = await client.indicators.sma("AAPL", "daily", time_period=20, series_type="close")
rsi = await client.indicators.rsi("AAPL", "daily", time_period=14, series_type="close")
macd = await client.indicators.macd("AAPL", "daily", series_type="close")
bbands = await client.indicators.bbands("AAPL", "daily", time_period=20, series_type="close")
stoch = await client.indicators.stoch("AAPL", "daily")
adx = await client.indicators.adx("AAPL", "daily", time_period=14)
obv = await client.indicators.obv("AAPL", "daily")
# ... and 40+ more

All indicators return IndicatorResponse with metadata and data:

result = await client.indicators.macd("AAPL", "daily", series_type="close")
for point in result.data[:3]:
    print(point.timestamp, point.values)
    # 2024-01-02 {"MACD": 1.23, "MACD_Signal": 0.98, "MACD_Hist": 0.25}

Options

chain = await client.options.chain("AAPL", require_greeks=True)
for contract in chain.contracts[:3]:
    print(f"{contract.type} {contract.strike} exp:{contract.expiration} delta:{contract.delta}")

historical = await client.options.historical("AAPL", date="2024-01-19")

Intelligence

news = await client.intelligence.news_sentiment(tickers="AAPL", limit=10)
for article in news:
    print(f"{article.title} ({article.overall_sentiment_label})")

movers = await client.intelligence.top_movers()
# movers.top_gainers, movers.top_losers, movers.most_actively_traded

Configuration

client = AlphaVantageClient(
    api_key="YOUR_KEY",
    timeout=30.0,            # HTTP timeout in seconds (default: 30)
    max_retries=3,           # Retry attempts on transient failures (default: 3)
    retry_base_delay=1.0,    # Base delay for exponential backoff (default: 1.0)
    retry_max_delay=30.0,    # Maximum backoff delay (default: 30.0)
    rate_limit=75,           # Requests per minute (default: 75, safe for free tier)
)

Rate Limiting

The built-in token-bucket rate limiter prevents exceeding your API quota. The default of 75 requests/minute is safe for the free tier. Premium users can increase this:

client = AlphaVantageClient("YOUR_KEY", rate_limit=600)  # premium: 600/min

Retry Behavior

Transient failures (HTTP 5xx, 429, connection errors, timeouts) are automatically retried with exponential backoff and full jitter. Client errors (4xx) and business logic errors (invalid symbol, bad API key) are never retried.

Error Handling

from avantage import (
    AlphaVantageError,    # Base exception
    AuthenticationError,  # Invalid API key
    RateLimitError,       # API rate limit exceeded
    SymbolNotFoundError,  # Unknown symbol
    InvalidParameterError,# Bad request parameters
    APIResponseError,     # Malformed response
    UpstreamError,        # Server/network failures
)

try:
    quote = await client.equity.quote("INVALID")
except SymbolNotFoundError:
    print("Symbol not found")
except RateLimitError as e:
    print(f"Rate limited. Retry after: {e.retry_after}")
except AlphaVantageError as e:
    print(f"API error: {e.message}")

Contributing

Contributions are welcome! Please read CONTRIBUTING.md before opening a pull request.

Requirements

• Python 3.11+
• httpx >= 0.28
• pydantic >= 2.10

License

MIT