README.md

metadata

license: other
language:
  - en
pretty_name: skfolio Masterclass ETF Teaching Data
tags:
  - finance
  - etf
  - portfolio-optimization
  - skfolio
  - time-series
  - parquet
  - education
  - google-colab
size_categories:
  - 100K<n<1M
configs:
  - config_name: prices
    data_files: etf_daily_prices.parquet
    default: true
  - config_name: metadata
    data_files: instrument_metadata.parquet
  - config_name: liquidity
    data_files: etf_liquidity_365d.parquet
  - config_name: indexes
    data_files: index_daily_prices.parquet

skfolio Masterclass ETF Teaching Data

This dataset provides cleaned ETF price, metadata, liquidity, and market-index context tables for educational portfolio-research notebooks, especially the skfolio Masterclass by The Chart Truth.

The goal is to give learners a public, no-login data source that can be loaded in Google Colab and transformed into the return matrices used by skfolio portfolio workflows. It is designed for teaching diversification, liquidity screening, price-to-return transformation, risk, correlation, drawdowns, and allocation research.

What Is Included

File	Rows	What it contains	Main teaching use
etf_daily_prices.parquet	117,959	Daily ETF OHLCV-style price and volume records for 95 ETF symbols	Raw market history used to teach price checks and return conversion
instrument_metadata.parquet	98	Symbol-level metadata and trading-status fields	Universe inspection, labeling, and practical data-quality checks
etf_liquidity_365d.parquet	95	Average and median volume and dollar-volume rankings	Liquidity screening and realistic universe design
index_daily_prices.parquet	3,784	Daily index history for SPX, VIX, and NDX	Market-regime context and benchmark/risk discussion
manifest.json	1	Public snapshot metadata and row counts	Lightweight audit trail for notebooks and examples

The dataset intentionally does not include raw API responses, account data, balances, holdings, transactions, orders, credentials, or trading instructions.

Quick Start

import pandas as pd
from huggingface_hub import hf_hub_download
from skfolio.preprocessing import prices_to_returns

repo_id = "thecharttruth/etf-data"

prices_path = hf_hub_download(
    repo_id=repo_id,
    repo_type="dataset",
    filename="etf_daily_prices.parquet",
)

prices_long = pd.read_parquet(prices_path)
prices_wide = (
    prices_long
    .pivot(index="date", columns="symbol", values="close")
    .sort_index()
)

returns = prices_to_returns(prices_wide)

No Hugging Face token is required for the public dataset.

You can also load the tables through the datasets library. The dataset card defines three named configs so the tables stay separate in the Hugging Face Dataset Viewer:

from datasets import load_dataset

prices = load_dataset("thecharttruth/etf-data", "prices", split="train")
metadata = load_dataset("thecharttruth/etf-data", "metadata", split="train")
liquidity = load_dataset("thecharttruth/etf-data", "liquidity", split="train")
indexes = load_dataset("thecharttruth/etf-data", "indexes", split="train")

Current Snapshot

Item	Value
ETF symbols with price history	95
Price rows	117,959
Metadata rows	98
Liquidity rows	95
Index rows	3,784
Index symbols	SPX, VIX, NDX
Date range	2021-05-12 to 2026-05-13
Default file format	Parquet

The metadata table can contain symbols that are not present in the current price table. That is useful for teaching real-world data checks: not every instrument discovered by a data source is automatically suitable for a clean portfolio universe.

Table Schemas

etf_daily_prices.parquet

Column	Meaning
date	Trading date used for analysis and pivoting
timestamp	Source timestamp value
symbol	ETF ticker symbol
asset_type	Asset label, currently ETF for this file
period	Source period used by the collector
source_run	Public run label for traceability
open, high, low, close	Daily price fields
value	Source value field when provided
volume	Daily volume
gain_amount, gain_percentage	Source-provided daily change fields

instrument_metadata.parquet

Column	Meaning
symbol	Instrument ticker symbol
asset_type	Asset label
name, type, exchange, exchange_name	Descriptive fields from the source
trading	Trading-status field from the source
fractional_trading	Fractional-trading status field from the source, when available
option_trading, option_spread_trading, shorting_availability	Instrument capability fields from the source
source_run	Public run label for traceability

Some descriptive metadata fields may be blank. Treat metadata as a practical teaching aid, not as a complete security master.

etf_liquidity_365d.parquet

Column	Meaning
rank_avg_daily_volume	Rank by average daily share volume
rank_avg_daily_dollar_volume	Rank by average daily dollar volume
symbol	ETF ticker symbol
avg_daily_volume, median_daily_volume	Share-volume liquidity estimates
avg_daily_dollar_volume, median_daily_dollar_volume	Dollar-volume liquidity estimates
latest_volume, latest_close, latest_date	Most recent values in the lookback window
lookback_bars, lookback_days	Liquidity lookback settings
history_bars, history_start, history_end	Available history summary

index_daily_prices.parquet

Column	Meaning
date	Trading date used for analysis and pivoting
timestamp	Source timestamp value
symbol	Index ticker symbol, currently SPX, VIX, and NDX
asset_type	Asset label, currently INDEX for this file
period	Source period used by the collector
source_run	Public run label for traceability
open, high, low, close	Daily index level fields
value	Source value field when provided
volume	Daily volume field when provided by the source
gain_amount, gain_percentage	Source-provided daily change fields

Intended Use

This dataset is meant for:

• educational notebooks
• reproducible portfolio-research examples
• skfolio tutorials and demonstrations
• learning how to inspect, reshape, and validate market data before optimization

The recommended teaching flow is:

1. Load the long price table.
2. Validate symbols, dates, duplicates, and missing values.
3. Pivot prices into a date-by-symbol matrix.
4. Convert prices to linear returns.
5. Use returns for risk, correlation, optimization, and validation.

Limitations

This dataset is intentionally compact and teaching-oriented. It may contain errors, omissions, stale values, adjusted values, vendor quirks, transformation mistakes, missing metadata, survivorship bias, or incomplete histories. It is not a complete market database and should not be treated as a live trading feed.

The dataset is suitable for learning a research process. It is not suitable as the only source for real-money trading, tax reporting, regulatory reporting, or production investment operations.

Independence And Responsible Use

This dataset is independent educational material. It is not endorsed by, sponsored by, or affiliated with any broker, ETF issuer, index provider, exchange, data provider, Hugging Face, Google Colab, or the skfolio project maintainers.

Nothing in this dataset is financial, investment, tax, or legal advice. Nothing here is a recommendation to buy, sell, short, hold, or allocate to any security. Historical data cannot guarantee future results.

Users are responsible for validating data quality, confirming rights and licenses, and deciding whether any use is appropriate for their own situation.

Access, Updates, And License

The public files are provided for educational and research use under the custom terms in LICENSE.

Updates may be published periodically from a maintained data pipeline, but no update cadence is guaranteed. Existing files may remain available even if future refreshes pause or change.

Redistribution, resale, and commercial use are not permitted without written permission from the maintainer and any applicable upstream rights holders.

Suggestions

Learners and researchers may suggest additional ETF fields, documentation improvements, or derived teaching tables. Suggestions are evaluated based on educational value, licensing and redistribution limits, privacy, data quality, maintainability, and fit with the cleaned teaching-data scope.