jb3 0.1.4

JB3 package – unified access to LJSE, FRED and NASDAQ financial data

JB3

JB3 is a Python package providing unified access to LJSE, NASDAQ, and FRED (Federal Reserve Economic Data). It enables unified downloading, cleaning, transforming, and pivoting of financial and macroeconomic time-series data for analysis, trading strategies, or machine-learning pipelines.

Features

NASDAQ & LJSE Stock Market Data

• Download historical NASDAQ and LJSE stock data
• Clean and normalize numeric columns (open, high, low, close, volume)
• Pivot data by ticker for analysis-ready DataFrames
• Screener with flexible filtering:
  • Exchange
  • Market cap
  • Analyst rating
  • Sector
  • Region
  • Country

FRED Macroeconomic Data

• Search FRED series by keyword (e.g., GDP, CPI, M2, UNRATE)
• Download multiple macroeconomic series at once
• Automatic detection of the lowest valid FRED frequency (Daily → Weekly → Monthly → Quarterly → Annual)
• Optional server-side unit transformations:
  • pch = percent change
  • pc1 = percent change from year ago (YoY)
  • chg = change from previous period
  • log = natural log
• Clean, aligned DataFrames with datetime index
• Built-in retry logic for stable API access

Installation

pip install jb3

Usage Examples

1. Download NASDAQ Historical Data

from jb3 import nasdaq

df = nasdaq.download_ticker_data("AAPL", clean=True, pivot_col="close") print(df.head())

2. NASDAQ Screener Example

from jb3 import nasdaq

screener = nasdaq.get_nasdaq_screener( exchange="NASDAQ", marketcap="Large", sector="Technology", limit=50 )

print(screener.head())

3. FRED Example — GDP & CPI (auto frequency detection)

from jb3 import fred

df = fred.get_fred_series( ["GDP", "CPIAUCSL"], api_key="YOUR_FRED_API_KEY" ) print(df.tail())

4. FRED Example — YoY Inflation (pc1 transformation)

df = fred.get_fred_series( "CPIAUCSL", api_key="YOUR_FRED_API_KEY", units="pc1" ) print(df.head())

5. Search FRED Tickers (GDP-related)

tickers = fred.get_fred_tickers("GDP", api_key="YOUR_FRED_API_KEY") print(tickers[["id", "title", "frequency"]].head())

LJSE Documentation

https://pypi.org/project/LJSE/