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Alex Spinov
Alex Spinov

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Alpha Vantage API: Build a Free Stock Market Dashboard in Python (Real-Time Prices, Charts, Alerts)

#python #finance #api #tutorial

Last month, my friend paid $49/month for a stock tracking tool. I built the same thing for free in 45 minutes using Alpha Vantage API.

Here's exactly how.

Why Alpha Vantage?

Most stock APIs either cost money (Bloomberg Terminal: $24,000/year) or require complex authentication. Alpha Vantage gives you:

  • Real-time & historical stock prices (every US stock)
  • Technical indicators (RSI, MACD, Bollinger Bands — 50+ indicators)
  • Forex & crypto prices
  • Fundamental data (earnings, balance sheets, income statements)
  • Free tier: 25 requests/day (enough for a personal dashboard)

Step 1: Get Your Free API Key 

# Sign up at alphavantage.co (takes 10 seconds)
# Your key looks like: ABCD1234EFGH5678
export ALPHA_VANTAGE_KEY="your_key_here"
pip install requests pandas matplotlib
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Step 2: Fetch Real-Time Stock Prices 

import requests
import pandas as pd

API_KEY = "your_key_here"

def get_stock_price(symbol):
    """Get current price for any stock ticker."""
    url = f"https://www.alphavantage.co/query"
    params = {
        "function": "GLOBAL_QUOTE",
        "symbol": symbol,
        "apikey": API_KEY
    }
    data = requests.get(url, params=params).json()
    quote = data["Global Quote"]

    return {
        "symbol": quote["01. symbol"],
        "price": float(quote["05. price"]),
        "change": float(quote["09. change"]),
        "change_percent": quote["10. change percent"],
        "volume": int(quote["06. volume"]),
    }

# Check Apple stock right now
apple = get_stock_price("AAPL")
print(f"AAPL: ${apple['price']} ({apple['change_percent']})")
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Step 3: Get Historical Data & Create Charts 

import matplotlib.pyplot as plt

def get_daily_history(symbol, days=90):
    """Fetch daily prices for the last N days."""
    url = "https://www.alphavantage.co/query"
    params = {
        "function": "TIME_SERIES_DAILY",
        "symbol": symbol,
        "outputsize": "compact",  # Last 100 days
        "apikey": API_KEY
    }
    data = requests.get(url, params=params).json()
    ts = data["Time Series (Daily)"]

    df = pd.DataFrame.from_dict(ts, orient="index")
    df.index = pd.to_datetime(df.index)
    df = df.sort_index()
    df.columns = ["open", "high", "low", "close", "volume"]
    df = df.astype(float)
    return df.tail(days)

# Plot AAPL vs MSFT
fig, ax = plt.subplots(figsize=(12, 6))
for symbol in ["AAPL", "MSFT", "GOOGL"]:
    df = get_daily_history(symbol)
    # Normalize to percentage change
    normalized = (df["close"] / df["close"].iloc[0] - 1) * 100
    ax.plot(normalized.index, normalized.values, label=symbol)

ax.set_title("Stock Performance Comparison (90 days)")
ax.set_ylabel("% Change")
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig("stock_comparison.png", dpi=150)
print("Chart saved!")
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Step 4: Add Technical Indicators

This is where Alpha Vantage shines. Bloomberg charges thousands for this data:

def get_rsi(symbol, period=14):
    """Relative Strength Index — is the stock overbought/oversold?"""
    params = {
        "function": "RSI",
        "symbol": symbol,
        "interval": "daily",
        "time_period": period,
        "series_type": "close",
        "apikey": API_KEY
    }
    data = requests.get(
        "https://www.alphavantage.co/query", params=params
    ).json()

    latest = list(data["Technical Analysis: RSI"].items())[0]
    return float(latest[1]["RSI"])

rsi = get_rsi("AAPL")
if rsi > 70:
    print(f"⚠️ AAPL RSI={rsi:.1f} — Overbought! Consider selling.")
elif rsi < 30:
    print(f"📈 AAPL RSI={rsi:.1f} — Oversold! Potential buying opportunity.")
else:
    print(f"AAPL RSI={rsi:.1f} — Neutral zone.")
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Step 5: Build a Simple Alert System 

import time

WATCHLIST = {
    "AAPL": {"buy_below": 170, "sell_above": 200},
    "MSFT": {"buy_below": 380, "sell_above": 450},
    "NVDA": {"buy_below": 800, "sell_above": 1000},
}

def check_alerts():
    """Check all stocks against your price targets."""
    alerts = []
    for symbol, targets in WATCHLIST.items():
        stock = get_stock_price(symbol)
        price = stock["price"]

        if price < targets["buy_below"]:
            alerts.append(
                f"🟢 BUY {symbol}: ${price} (target: <${targets['buy_below']})"
            )
        elif price > targets["sell_above"]:
            alerts.append(
                f"🔴 SELL {symbol}: ${price} (target: >${targets['sell_above']})"
            )
    return alerts

# Run once (or schedule with cron)
for alert in check_alerts():
    print(alert)
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Step 6: Fundamental Analysis (Earnings, Revenue) 

def get_earnings(symbol):
    """Get quarterly earnings data."""
    params = {
        "function": "EARNINGS",
        "symbol": symbol,
        "apikey": API_KEY
    }
    data = requests.get(
        "https://www.alphavantage.co/query", params=params
    ).json()

    for q in data["quarterlyEarnings"][:4]:
        surprise = float(q["surprisePercentage"]) if q["surprisePercentage"] != "None" else 0
        emoji = "✅" if surprise > 0 else "❌"
        print(f"{emoji} {q['fiscalDateEnding']}: "
              f"EPS ${q['reportedEPS']} "
              f"(expected ${q['estimatedEPS']}) "
              f"Surprise: {surprise:+.1f}%")

get_earnings("AAPL")
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The Complete Dashboard (Put It All Together) 

def stock_dashboard(symbol):
    """One function to rule them all."""
    price = get_stock_price(symbol)
    rsi = get_rsi(symbol)
    history = get_daily_history(symbol, 30)

    avg_30d = history["close"].mean()
    trend = "📈 Uptrend" if price["price"] > avg_30d else "📉 Downtrend"

    print(f"\n{=*50}")
    print(f"  {symbol} Dashboard")
    print(f"{=*50}")
    print(f"  Price:    ${price['price']:.2f} ({price['change_percent']})")
    print(f"  Volume:   {price['volume']:,}")
    print(f"  RSI(14):  {rsi:.1f}")
    print(f"  30d Avg:  ${avg_30d:.2f}")
    print(f"  Trend:    {trend}")
    print(f"{=*50}\n")

# Your free Bloomberg Terminal:
for ticker in ["AAPL", "MSFT", "GOOGL"]:
    stock_dashboard(ticker)
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Alpha Vantage vs Paid Alternatives

Feature Alpha Vantage (Free) Yahoo Finance API ($100/mo) Bloomberg ($24K/yr)
Real-time prices ✅ ✅ ✅
Technical indicators ✅ 50+ ❌ ✅
Fundamental data ✅ Limited ✅
Rate limit 25/day free 500/day Unlimited
Price $0 $100/mo $2,000/mo

What I Built With This

I used these exact functions to build:

  • A daily stock screener that runs via cron job
  • A portfolio tracker that emails me weekly performance
  • An earnings alert bot that notifies me before earnings dates

The entire codebase is on my GitHub.

Want me to build something like this for your data source? I build custom API integrations, scrapers, and data pipelines. Check my portfolio or find me on GitHub.

Previously in this series: SEC EDGAR API | FRED API | US Treasury API

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