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Access real-time and historical stock prices, forex exchange rates, cryptocurrency data, and 50+ technical indicators from Alpha Vantage. Widely used in financial data pipelines for market data ingestion, quant research workflows, and building investment analytics systems using the alpha-vantage Python library.
The `alpha_vantage` Python library wraps all API endpoints with built-in rate limiting. Engineers typically call `TimeSeries.get_daily_adjusted()` to pull adjusted close prices and feed them into pandas for technical analysis or ML feature engineering.
Alpha Vantage's financial time-series data powers AI models for stock price prediction, anomaly detection, and automated trading. RAG pipelines retrieve the latest earnings and analyst estimates to help LLM agents answer questions like 'How did AAPL perform last quarter?' with real data.
# pip install alpha_vantage
from alpha_vantage.timeseries import TimeSeries
ts = TimeSeries(key="YOUR_API_KEY", output_format="pandas")
df, meta = ts.get_daily_adjusted("AAPL", outputsize="compact")
print(df[["4. close"]].head())Official dataset source
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