Financial data
built for scale.

from axion import Axion, graph, utils
client = Axion(api_key="YOUR_API_KEY")

cpi = utils.df(client.econ.dataset("CPIAUCSL"))
gdp = utils.df(client.econ.dataset("GDP"))

econ = cpi.merge(
  gdp,
  on="date",
  suffixes=("_cpi", "_gdp")
)

graph(
    econ,
    x="date",
    lines=["value_cpi", "value_gdp"],
    title="US CPI vs. GDP",
)

 from axion import Axion, utils, models

client = Axion(api_key="YOUR_API_KEY")

tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA"]

def fetch_history(row):
    prices = client.stocks.prices(t, from_date="2021-01-01")
    return (row["ticker"], prices)

history = {}
utils.work(utils.df([{"ticker": t} for t in tickers]), fetch_history, history)

for ticker, prices in history.items():
    models.lstm(utils.df(prices),
      x="time",
      target="close",
      n_preds=30,
      scale="D"
    )