AxionQuant Python SDK

The AxionQuant Python SDK is a Python client for the Axion Financial Data API. Access market data, company fundamentals, financial statements, SEC filings, economic data, ETF data, cryptocurrency, forex, futures, news, and alternative financial data directly from Python.

Built for quantitative research, financial analysis, financial modeling, and machine learning, the SDK integrates naturally with pandas, Jupyter notebooks, Plotly, scikit-learn, and TensorFlow.

• Market data API for stocks, crypto, forex, futures, and indices
• Fundamental data API for financial statements, ratios, earnings, and valuation
• SEC filings API for 10-K, 10-Q, and other regulatory filings
• Economic data API for macroeconomic and FRED data
• ETF data API for holdings, exposure, weights, and fund information
• Technical analysis with common indicators including RSI, MACD, SMA, EMA, ATR, Bollinger Bands, and VWAP
• Interactive financial charts powered by Plotly
• Machine learning tools for regression, beta analysis, and forecasting
• Pandas integration for quantitative analysis and research workflows

Get a free Axion API key · Read the API documentation · Learn about the Python SDK

Installation

Install the AxionQuant Python SDK from PyPI:

pip install axionquant-sdk

Quick Start

Get your free Axion API key and start retrieving financial market data in Python:

from axion import Axion, ta, visualize, utils as axion_utils

client = Axion(api_key="your_api_key_here")

# Fetch historical stock prices and convert to a DataFrame
prices = client.stocks.prices("AAPL", from_date="2024-01-01")
df = axion_utils.df(prices)

# Calculate a technical indicator
roc = ta.roc(df, "close")

# Create an interactive candlestick chart
visualize.candles(df)

The SDK is designed for workflows ranging from simple stock market data analysis in Python to larger quantitative research and machine learning pipelines.

Financial Market Data

The AxionQuant API provides programmatic access to financial market data across multiple asset classes.

Stocks and Equities

Retrieve stock quotes, historical prices, ticker information, market gainers and losers, and other equity market data.

client.stocks.tickers(country="america")
client.stocks.ticker("AAPL")
client.stocks.quote("AAPL")
client.stocks.prices(
    "AAPL",
    from_date="2024-01-01",
    to_date="2024-12-31",
    frame="daily"
)
client.stocks.gainers(days=5, limit=10)
client.stocks.losers(days=5, limit=10)

Cryptocurrency Market Data

Retrieve cryptocurrency tickers, quotes, historical prices, gainers, and losers.

client.crypto.tickers(type="coin")
client.crypto.ticker("BTC")
client.crypto.quote("BTC")
client.crypto.prices("BTC", from_date="2024-01-01", frame="weekly")
client.crypto.gainers(days=5, limit=10)
client.crypto.losers(days=5, limit=10)

Forex Market Data

Access foreign exchange tickers, quotes, historical prices, and market performance data.

client.forex.tickers()
client.forex.ticker("EURUSD")
client.forex.quote("EURUSD")
client.forex.prices("EURUSD", from_date="2024-01-01")
client.forex.gainers(limit=5)
client.forex.losers(limit=5)

Futures Market Data

Access futures contracts, quotes, historical prices, and market performance.

client.futures.tickers(exchange="CME")
client.futures.ticker("ES")
client.futures.quote("ES")
client.futures.prices("ES", from_date="2024-01-01")
client.futures.gainers(limit=5)
client.futures.losers(limit=5)

Index Data

Retrieve index prices, constituents, exposure, quotes, gainers, and losers.

client.indices.tickers()
client.indices.ticker("SPX")
client.indices.quote("SPX")
client.indices.prices("SPX", from_date="2024-01-01")
client.indices.components("SPX")
client.indices.exposure("AAPL")
client.indices.gainers(limit=5)
client.indices.losers(limit=5)

Fundamental Data and Financial Analysis

Use the AxionQuant Python SDK to retrieve company fundamentals, financial statements, earnings data, valuation metrics, ownership data, and analyst information.

Company Profiles

client.profiles.profile("AAPL")
client.profiles.info("AAPL")
client.profiles.statistics("AAPL")
client.profiles.summary("AAPL")
client.profiles.recommendation("AAPL")
client.profiles.calendar("AAPL")

Earnings Data

Access historical earnings, earnings trends, reports, earnings call transcripts, and transcript sentiment.

client.earnings.history("AAPL")
client.earnings.trend("AAPL")
client.earnings.index("AAPL")
client.earnings.report("AAPL", year="2024", quarter="Q1")
client.earnings.transcript("AAPL", year="2024", quarter="Q1")
client.earnings.transcript_sentiment("base64_encoded_id")

Financial Statements and Metrics

Retrieve balance sheets, income statements, cash flow statements, historical financial metrics, valuation ratios, and calculated financial ratios.

# Financial statements
client.financials.balance_sheet("AAPL")
client.financials.income_statement("AAPL")
client.financials.cash_flow_statement("AAPL")

# Historical financial metrics
client.financials.revenue("AAPL", periods=8)
client.financials.net_income("AAPL")
client.financials.free_cash_flow("AAPL")
client.financials.total_assets("AAPL")
client.financials.total_liabilities("AAPL")
client.financials.current_assets("AAPL")
client.financials.current_liabilities("AAPL")
client.financials.stockholders_equity("AAPL")
client.financials.operating_cash_flow("AAPL")
client.financials.capital_expenditures("AAPL")
client.financials.shares_outstanding_basic("AAPL")
client.financials.shares_outstanding_diluted("AAPL")

# Financial ratios and valuation
client.financials.metrics("AAPL")
client.financials.eps("AAPL", from_date="2024-01-01", to_date="2024-12-31")
client.financials.pe("AAPL")
client.financials.market_cap("AAPL")
client.financials.roe("AAPL")
client.financials.enterprise_value("AAPL")
client.financials.ebitda("AAPL")
client.financials.debt_to_equity("AAPL")

# DCF valuation
client.financials.dcf_value("AAPL")
client.financials.dcf_rate("AAPL")

SEC Filings and Regulatory Data

Access SEC filings and corporate regulatory data programmatically, including 10-K and 10-Q filings.

client.filings.recent("AAPL", form="10-K", limit=10)

client.filings.history(
    "AAPL",
    form_type="10-Q",
    start_date="2024-01-01",
    end_date="2024-03-31"
)

client.filings.search(
    ticker="AAPL",
    form="10-K",
    year="2024",
    quarter="Q1"
)

client.filings.list_forms()
client.filings.document_text("document_id")
client.filings.document_sentiment("document_id")

Insider Trading and Institutional Ownership

Analyze insider transactions, institutional ownership, fund ownership, and major shareholders.

client.insiders.individuals("AAPL")
client.insiders.institutions("AAPL")
client.insiders.funds("AAPL")
client.insiders.ownership("AAPL")
client.insiders.transactions("AAPL")
client.insiders.activity("AAPL")

Economic and Macroeconomic Data

Access economic indicators, macroeconomic datasets, economic calendars, and FRED data for quantitative research.

client.econ.find("semiconductor spending")
client.econ.search("unemployment rate")
client.econ.dataset("UNRATE")

client.econ.calendar(
    from_date="2024-01-01",
    to_date="2024-12-31",
    country="US",
    min_importance=3,
    currency="USD",
    category="employment"
)

ETF Data

Retrieve ETF information, fund holdings, portfolio exposure, sector weights, regional weights, quotes, and historical performance.

client.etfs.tickers()
client.etfs.ticker("SPY")
client.etfs.fund("SPY")
client.etfs.holdings("SPY")
client.etfs.holdings_all("SPY")
client.etfs.exposure("SPY")
client.etfs.weights("SPY")
client.etfs.quote("SPY")
client.etfs.gainers(limit=5)
client.etfs.losers(limit=5)

Financial News and Sentiment

Retrieve general financial news, company news, country-specific news, category-based news, and market sentiment.

client.news.general()
client.news.company("AAPL")
client.news.country("US")
client.news.category("technology")

client.sentiment.all("AAPL")
client.sentiment.social("AAPL")
client.sentiment.news("AAPL")
client.sentiment.analyst("AAPL")

Alternative Financial Data

The SDK also provides access to additional datasets useful for fundamental and quantitative research.

ESG Data

client.esg.data("AAPL")

Credit Ratings

client.credit.search("Apple Inc")
client.credit.ratings("entity_id")

Supply Chain Data

client.supply_chain.customers("AAPL")
client.supply_chain.suppliers("AAPL")
client.supply_chain.peers("AAPL")

Web Traffic Data

client.web_traffic.traffic("AAPL")

Technical Analysis

The ta module provides common technical analysis indicators for Python and pandas, including trend, momentum, volatility, volume, and market structure indicators.

import axion.ta as ta

Trend Indicators

ta.sma(df, column="close", period=14)
ta.ema(df, column="close", period=14)
ta.dema(df, column="close", period=14)
ta.ssma(df, column="close", period=14)
ta.trima(df, column="close", period=14)
ta.kama(df, column="close", period=14)

Momentum and Oscillators

ta.rsi(df, column="close", period=14)
ta.macd(df)
ta.roc(df, column="close", period=10)
ta.mom(df, column="close", period=10)
ta.cmo(df, column="close", period=20)
ta.stochastic_oscillator(df)
ta.williams_r(df, period=14)
ta.adx(df, period=14)

Volatility and Channels

ta.atr(df, period=14)
ta.bbands(df)
ta.kc(df)

Volume Indicators

ta.obv(df)
ta.vpt(df)
ta.vwap(df)

Trend Direction

ta.vi(df, period=14)
ta.ichi(df)
ta.sar(df)
ta.fib(df)

Financial Data Visualization

The visualize module provides interactive Plotly charts for financial and quantitative analysis.

import axion.visualize as visualize

visualize.candles(df)
visualize.line(df, x="time", y="close")
visualize.bar(df, x="time", y="volume")
visualize.barh(df, x="value", y="label")
visualize.scatter(df, x="time", y="close")
visualize.fit(df, x="revenue", y="price")
visualize.area(df, x="time", y="value", group="sector")
visualize.pie(df, values="marketCap", labels="ticker")
visualize.radar(df, values="score", labels="category")
visualize.heatmap(df, x="col1", y="col2")
visualize.cov(df)
visualize.polls(df)
visualize.spread(dfs, x="time", y="close")
visualize.tree(df)
visualize.graph(
    df,
    x="time",
    bars=["volume"],
    lines=["close", "sma"]
)

Python Data Utilities

The utils module provides date handling, DataFrame transformation, comparison, resampling, caching, and concurrency utilities for financial data workflows.

import axion.utils as axion_utils

# Date helpers
axion_utils.d("1 month ago")
axion_utils.to_timestamp("2024-01-01")
axion_utils.nearest_day("2024-01-06")

# Date shorthand
axion_utils.today
axion_utils.yesterday
axion_utils.weekago
axion_utils.monthago
axion_utils.yearago
axion_utils.yearfrom

# DataFrame helpers
axion_utils.df(items)
axion_utils.pds(list_of_lists)
axion_utils.stack(dfs)
axion_utils.stitch(dfs, col="time")
axion_utils.snap(dfs, names, overwrite)
axion_utils.filter(df, col, items)
axion_utils.dedup(lst)
axion_utils.simmer(arr)
axion_utils.resample(df, "2024-01-01 2024-12-31")

Machine Learning and Financial Modeling

The models module provides machine learning utilities for financial forecasting, regression analysis, benchmark analysis, and quantitative modeling.

import axion.models as models

Linear Regression

preds = models.linearRegression(
    df,
    x="time",
    target="close",
    n_preds=10
)

Multi-Feature Regression

preds = models.multiLinearRegression(
    df,
    x="time",
    target="close",
    features=["volume", "rsi"],
    n_preds=10
)

Beta Analysis

b = models.beta(
    df,
    x="stock_return",
    y="market_return"
)

LSTM Forecasting

preds = models.lstm(
    df,
    x="time",
    target="close",
    features=["volume", "rsi"],
    n_preds=10
)

Supported Date Formats and Time Frames

API date parameters use the YYYY-MM-DD format.

Supported historical price time frames:

• daily
• weekly
• monthly
• quarterly
• yearly

Error Handling

try:
    data = client.stocks.prices("INVALID")
except Exception as e:
    print(f"Error: {e}")

Common errors include:

• HTTP errors
• Connection errors
• Timeout errors
• Authentication errors

Documentation and Resources

• AxionQuant Financial Data API
• API Documentation
• Python SDK Documentation
• Get a Free API Key
• AxionQuant GitHub

License

MIT