DOI: 10.5281/zenodo.18529679
title: Instrumetriq — Crypto Market Activity & Sentiment Context Dataset (Weekly Samples)

description: This dataset provides time-aligned observational snapshots of crypto market activity and social sentiment across 270+ crypto assets, designed to contextualize market structure, liquidity, and attention dynamics rather than produce forecasts or signals.
The archive contains weekly Sunday samples drawn from Instrumetriq's continuous monitoring pipeline.

Data Collection
Spot market data sourced from Binance

Mid prices, bid–ask spreads, liquidity percentiles
Aggregated per observation window
Social sentiment data sourced from X (Twitter)

Posts are continuously collected and classified using a hybrid transformer-based sentiment system
Sentiment is exposed only in aggregated form (counts and averages)

Each asset is monitored in ~2-hour observation cycles, producing one row per asset per session
Approximately ~2,500 observations per day, regardless of tier

Archive Contents
This archive contains:

8 weekly Sunday samples (2025-12-21 through 2026-02-08)
Three dataset tiers per week, sharing the same observations but differing in schema depth
Apache Parquet format with Snappy compression
Schema documentation for all tiers
Methodology overview

Dataset Tiers
All tiers contain the same number of observations. They differ only in column structure and depth.
Tier 1 — Explorer

19 flat columns
Aggregated sentiment counts and averages
Spot prices, spreads, liquidity, and quality scores
Designed for lightweight inspection, dashboards, and general analysis
Tier 2 — Analyst

Extends Tier 1 with nested columns
Detailed sentiment aggregates, author statistics, and engagement metrics
Designed for deeper behavioral and cross-sectional analysis
Tier 3 — Researcher

Extends Tier 2 with nested futures and microstructure data
Includes 700+ spot price samples per observation window (10-second resolution)
Multi-window sentiment, diagnostics, and futures positioning data
Designed for research, validation, and archival analysis
Note: High-frequency (10-second) spot price samples are available only in Tier 3.

Intended Use
This dataset is intended for:

Market structure research
Behavioral and sentiment analysis
Liquidity and execution context studies
Exploratory and descriptive analytics

Limitations & Ethics

Observational data only
No trading advice, predictions, or signal generation
No individual social media posts or personal data are included
All sentiment data is aggregated and anonymized

Access

Free weekly samples: github.com/SiCkGFX/instrumetriq-public
Methodology: instrumetriq.com/research
Full access via subscription at instrumetriq.com/access. Interactive demo: Open in Colab.
Observational data only. No trading advice, predictions, or signal generation.
description: Market data is sourced from the Binance spot market via the public REST API. Spot prices, bid–ask spreads, and liquidity-related metrics are sampled internally at high frequency and aggregated into fixed observation windows.

Social sentiment data is sourced from publicly available X (Twitter) posts. Posts are collected continuously and classified using a hybrid transformer-based sentiment system. Sentiment outputs are aggregated into per-window counts and summary statistics.

Each tracked asset is monitored in rolling observation cycles of approximately ~2 hours, producing one observation per asset per cycle. All tiers share the same observation timing and coverage.

High-frequency spot price samples (10-second resolution) are retained only in the highest dataset tier. Lower tiers expose aggregated spot and sentiment statistics only.

No raw social media content, user identifiers, or personally identifiable information are included. The dataset is strictly observational and descriptive in nature.
description: Market data: Binance spot market REST API, sampled at 10-second intervals. Sentiment: X (Twitter) public posts scraped via twscrape, classified using a hybrid two-model DistilBERT system — primary model (88.5% accuracy) for classification, referee model for confidence calibration and edge-case arbitration. Each asset is monitored in ~2-hour observation cycles (~120-130 minutes). Aggregation produces per-cycle summaries with sentiment counts, mean scores, and silence detection.