bguzzo2k/ohlc_1d_mixture

Macroeconomic & S&P 500 Yahoo Finance Dataset

This repository contains a comprehensive historical dataset for 936 financial instruments, including S&P 500 components, broad market indices, commodities, currencies, and macroeconomic indicators. The data is programmatically extracted from the Yahoo Finance API, cleaned, and normalized for use in quantitative modeling and machine learning.

Dataset Hub: bguzzo2k/ohlc_1d_mixture

Repository Structure

• 1d/: Raw daily OHLCV data in Parquet format ({TICKER}_max_1d.parquet).
• log_ret_norm/: Normalized log-return data optimized for stationarity ({TICKER}_max_1d_norm.parquet).
• bulk/: Multi-ticker raw download aggregates before individual splitting.
• utils/: Python scripts for data acquisition, normalization, and configuration.
• tickers_list.toml: Central configuration file defining all 936 tracked assets across 9 distinct asset classes.

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Dataset Construction Pipeline

The dataset is built using a modular pipeline that ensures reproducibility and quality:

1. Asset Configuration (tickers_list.toml)

The scope of the dataset is defined in tickers_list.toml. It organizes tickers into logical groups:

• Broad Market Indices (S&P 500, NASDAQ, Global Benchmarks)
• Fixed Income & Yields (US Treasuries, Corporate/International Bonds)
• Commodities & Futures (Energy, Metals, Agriculture)
• FX Matrices (Major/Emerging Currency Pairs)
• Sectors & Thematics (GICS Sectors, AI, Robotics)
• Digital Assets (Cryptocurrencies)
• S&P 500 Constituents (Individual equities)

2. Data Acquisition (utils/yfinace_dowloader.py)

Utilizes the yfinance library to fetch maximum historical data (period="max") at a 1-day interval.

• Filtering: Discards non-trading days (NaN Open values).
• Standardization: Retains core OHLCV columns and formats indices to YYYY-MM-DD.
• Persistence: Individual ticker files are stored in 1d/ with Snappy compression.

3. Log-Return Normalization (utils/dataset_normalizartion.py)

Transforms raw price levels into stationary log-returns using the previous day's close ($Close_{t-1}$) as the reference:

• Calculation: $Metric_{norm} = \ln(Metric_t / Close_{t-1})$
• Numerical Stability: Implements clipping (threshold: $1 \times 10^{-9}$) to prevent numerical instability.
• Parallelization: Employs a ThreadPoolExecutor for high-throughput processing across the 936-file dataset.

4. S&P 500 Maintenance (utils/sp500_toml_generator.py)

A utility script that scrapes the current S&P 500 composition from Wikipedia to update the tickers_list.toml configuration dynamically.

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Data Schema

Raw Data (1d/)

Column	Type	Description
Index (Date)	String	ISO 8601 Date (YYYY-MM-DD)
Open/High/Low/Close	Float64	Price metrics (USD or Local Currency)
Volume	Int64	Trading volume

Normalized Data (log_ret_norm/)

Column	Type	Description
Index (Date)	String	ISO 8601 Date (YYYY-MM-DD)
Open_Norm	Float64	$\ln(Open_t / Close_{t-1})$
High_Norm	Float64	$\ln(High_t / Close_{t-1})$
Low_Norm	Float64	$\ln(Low_t / Close_{t-1})$
Close_Norm	Float64	$\ln(Close_t / Close_{t-1})$