USAGE_GUIDE.md

Usage guide

Understand what each series means

• Read DATA_DICTIONARY.md for plain-language definitions grouped by category.
• Read DATA_CATALOG.md for endpoint, unit, quality notes, and materialization status.
• Download from the dataset-latest GitHub Release for the complete generated dataset. A Git clone contains collector code, configuration, and docs, not the generated dataset.

Download the parquet training archive (recommended)

This archive is the recommended way for model training. It includes only parquet series plus documentation files.

curl -L -o resonance-data-train-parquet.zip \
  https://github.com/JrVillabona/resonance-data/releases/download/dataset-latest/resonance-data-train-parquet.zip
unzip -o resonance-data-train-parquet.zip

Optional integrity verification:

curl -L -o resonance-data-train-parquet.zip.sha256 \
  https://github.com/JrVillabona/resonance-data/releases/download/dataset-latest/resonance-data-train-parquet.zip.sha256
shasum -a 256 -c resonance-data-train-parquet.zip.sha256

Optional manifest inspection:

curl -L -o resonance-data-train-parquet.manifest.json \
  https://github.com/JrVillabona/resonance-data/releases/download/dataset-latest/resonance-data-train-parquet.manifest.json
cat resonance-data-train-parquet.manifest.json

Download the full archive (csv + parquet + meta)

Use this only if you need raw csv files or metadata sidecars.

curl -L -o resonance-data-latest.zip \
  https://github.com/JrVillabona/resonance-data/releases/download/dataset-latest/resonance-data-latest.zip
unzip -o resonance-data-latest.zip

The full archive also publishes resonance-data-latest.manifest.json and resonance-data-latest.zip.sha256.

Load from Hugging Face

Researchers can load the full long-format Parquet table directly from Hugging Face:

from datasets import load_dataset

ds = load_dataset("JrVillabona/resonance-data", split="train")

Or with Pandas/Polars through the Hub filesystem:

import pandas as pd

df = pd.read_parquet("hf://datasets/JrVillabona/resonance-data/data/train.parquet")

Direct browser or CLI download:

• https://huggingface.co/datasets/JrVillabona/resonance-data
• https://huggingface.co/datasets/JrVillabona/resonance-data/resolve/main/data/train.parquet

curl -L -o resonance-train.parquet \
  https://huggingface.co/datasets/JrVillabona/resonance-data/resolve/main/data/train.parquet

Long-format columns:

• symbol
• date
• open, high, low, close, volume
• source, ticker, category, data_type, frequency, description

Load one series

import pandas as pd

df = pd.read_parquet("data/BTC_daily.parquet")

Verify CSV and Parquet parity

import pandas as pd

csv_df = pd.read_csv("data/BTC_daily.csv", parse_dates=["date"])
pq_df = pd.read_parquet("data/BTC_daily.parquet")
assert len(csv_df) == len(pq_df)
assert csv_df["date"].min() == pq_df["date"].min()
assert csv_df["date"].max() == pq_df["date"].max()

Align mixed frequencies

• Daily: crypto prices, yields, commodities
• Weekly: selected FRED series
• Monthly: CPI, M2, labor metrics

Recommended merge strategy:

1. Build a daily index from min/max dates.
2. Left-join all series.
3. Forward-fill lower-frequency series after release date.
4. Keep publication lags in feature engineering logic.