alpaxa-quant 1.0.1

# AlpaxaQuant

AlpaxaQuant

AlpaxaQuant is a comprehensive quantitative finance and data acquisition library that unifies financial, macroeconomic, insider, and market-structure data from multiple APIs — including EODHD, FINRA, and FRED — into one modular Python package.

It is designed for quantitative analysts, researchers, and developers who want clean access to financial datasets for model training, signal generation, and econometric analysis.

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Table of Contents

• AlpaxaQuant
  • Table of Contents
  • Installation
  • Package Overview
    • utils
    • eodhd
    • insider_trades
    • finra
    • fred
  • Example Usage
  • License
  • Contributing
  • Connect

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Installation

pip install alpaxa_quant

or install directly from source:

git clone https://github.com/Joanmascastella/AlpaxaQuant.git
cd AlpaxaQuant
pip install .

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Package Overview

utils

Utility functions and request handlers used internally across the package.

• make_safe_request() — robust, retry-aware HTTP request wrapper with timeout and structured DataFrame return.
• request_util() — generic API request helper for GET/POST calls.
• make_yf_request() — lightweight Yahoo Finance helper for quick ticker data retrieval.

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eodhd

Wrapper around the EODHD API, offering market-wide coverage of financial and sentiment data for all global tickers.

Key functions include:

• get_historical_ticker_price() – OHLCV data retrieval for any ticker
• get_financials(), get_balance_sheet(), get_cash_flow() – full fundamental statements
• get_earnings() – historical and forward EPS data
• get_technicals() – moving averages, oscillators, and technical indicators
• fetch_news_sentiment() – sentiment analysis scores and metadata from recent news
• get_analyst_ratings() – aggregated analyst buy/hold/sell recommendations

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insider_trades

High-performance scraper and API interface for U.S. insider trading data.

• get_insider_trades() – retrieves Form 4 insider transactions for all U.S.-listed tickers.

• Supports filtering by:

  • Transaction type (Buy, Sell, Option Exercise)
  • Date range
  • Position (Officer, Director, Major Shareholder)
  • Minimum transaction value or share size

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finra

Comprehensive interface for FINRA’s fixed-income and equity market datasets — covering everything from short interest to block trading and debt market breadth.

Highlights:

• Market Structure Data

  • get_blocks_summary() – aggregated ATS trades
  • get_otc_block_summary() – OTC-block transaction data
  • get_weekly_summary() / get_monthly_summary() – market-wide summary data

• Short Interest

  • get_consolidated_short_interest() – historical short positions per ticker

• Debt Markets

  • get_corporate_debt_market_sentiment()
  • get_agency_debt_market_breadth()
  • get_corporate_and_agency_capped_volume()

• Treasury & Fixed Income

  • get_treasury_daily_aggregates()
  • get_treasury_monthly_aggregates()

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fred

Extensive collection of macroeconomic, monetary, and commodity data via the Federal Reserve (FRED) API.

Organized by category for clarity:

• Housing: get_homeownership_rate(), get_median_house_sale_price(), etc.
• Commodities: get_daily_crude_oil_prices(), get_daily_gold_prices(), get_avg_beef_prices(), etc.
• Inflation & Recession: get_monthly_cpi(), get_financial_stress(), get_sahm_rule_recession_indicator()
• Labour Market: get_job_openings(), get_unemployed_rate(), get_labour_participation_rate()
• Fiscal & Monetary Policy: get_federal_surplus_deficit(), get_m1_supply(), get_m2_velocity()
• Foreign Exchange: get_daily_US_vs_EURO_rate(), get_JPY_vs_US(), get_yuan_vs_US()
• Yields & Credit Spreads: get_ICE_BofA_H_Y_effective_yield(), get_daily_moodys_seasoned_BAA_corp_yield()
• Treasury Yields: get_ten_yield_us(), get_twenty_yield_us(), get_thirty_yield_us()
• GDP & Growth: get_real_gdp_growth()
• Volatility & Sentiment (VIX): get_VIX(), get_equity_market_VIX_sentiment(), get_oil_VIX()

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Example Usage

import alpaxa_quant as aq
import pandas as pd
from dotenv import load_dotenv
import os

load_dotenv(".env")

# --- EODHD Example ---
base_endpoint = os.getenv('EXAMPLE_BASE_ENDPOINT')
api_key = os.getenv('EXAMPLE_API_KEY')

df_prices = aq.eodhd.get_historical_ticker_price(
    base_endpoint=base_endpoint,
    api_token=api_key,
    ticker='AAPL',
    fmt='json',
    period='d',
    order='a',
    from_date='2020-01-01',
    to_date='2025-01-01',
    verbose=False
)
print(df_prices.head())

# --- Insider Trades Example ---
config = {
    "scraping": {
        "start_year": 2024,
        "start_month": 6,
        "max_workers": 5,
        "retry_attempts": 3,
        "timeout": 30
    },
    "filters": {
        "min_transaction_value": 10000,
        "transaction_types": ["P", "S"],
        "exclude_companies": [],
        "include_companies": ["TSLA", "NVDA"],
        "min_shares_traded": 500
    },
    "cache": {
        "enabled": True,
        "directory": ".cache",
        "max_age": 12
    }
}
insiders = aq.insider_trades.get_insider_trades(config)
print(insiders.head())

# --- FINRA Example ---
finra_client_id = os.getenv('FINRA_CLIENT_ID')
finra_secret = os.getenv('FINRA_CLIENT_SECRET')

jwt, _ = aq.finra.get_bearer_token(client_id=finra_client_id, client_secret=finra_secret)
short_interest = aq.finra.get_consolidated_short_interest(jwt_token=jwt, ticker="TSLA")
print(short_interest.head())

# --- FRED Example ---
fred_endpoint = os.getenv('FRED_ENDPOINT')
fred_api = os.getenv('FRED_API_KEY')

cpi = aq.fred.get_monthly_cpi(api_key=fred_api, base_url=fred_endpoint, start_date='2020-01-01', end_date='2021-01-01', verbose=False)
print(cpi.tail())

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License

Apache License 2.0 © 2025 Joan Mas Castella

You may freely use, modify, and distribute AlpaxaQuant under the terms of the Apache 2.0 license.

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Contributing

Contributions are welcome. If you’d like to add new endpoints, fix bugs, or improve documentation:

git clone https://github.com/Joanmascastella/AlpaxaQuant.git
git checkout -b feature/new-endpoint

Then submit a Pull Request describing your change.

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Connect

• Author: Joan Mas Castella
• Organization: JMasSolutions
• Email: [e-mail]

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AlpaxaQuant — bridging financial data, macroeconomic insight, and quantitative modeling into one unified Python toolkit.