DEV Community: Jalvart Studio

ADA OHLCV Pack — 1-Week Free Sample

Jalvart Studio — Thu, 13 Aug 2026 04:37:02 +0000

ADA OHLCV Pack - FREE Sample

1-week sample with 6 timeframes (1m, 5m, 15m, 1h, 4h, 1d).

Full dataset (6+ years, $19): https://jalvart.gumroad.com/l/tstark

More assets and datasets: theglitchlist.com?utm_source=devto&utm_medium=referral&utm_campaign=free_sample

LTC OHLCV Pack — 1-Week Free Sample

Jalvart Studio — Thu, 13 Aug 2026 04:36:15 +0000

LTC OHLCV Pack - FREE Sample

1-week sample with 6 timeframes (1m, 5m, 15m, 1h, 4h, 1d).

Full dataset (6+ years, $19): https://jalvart.gumroad.com/l/vymfd

More assets and datasets: theglitchlist.com?utm_source=devto&utm_medium=referral&utm_campaign=free_sample

LINK OHLCV Pack — 1-Week Free Sample

Jalvart Studio — Thu, 13 Aug 2026 04:34:53 +0000

LINK OHLCV Pack - FREE Sample

1-week sample with 6 timeframes (1m, 5m, 15m, 1h, 4h, 1d).

Full dataset (6+ years, $19): https://jalvart.gumroad.com/l/bblha

More assets and datasets: theglitchlist.com?utm_source=devto&utm_medium=referral&utm_campaign=free_sample

SOL OHLCV Pack — 1-Week Free Sample

Jalvart Studio — Thu, 13 Aug 2026 04:33:37 +0000

SOL OHLCV Pack

1-week sample. Full: jalvart.gumroad.com/l/hhcaktn ($19)

Download: Kaggle | HuggingFace | Zenodo | theglitchlist.com?utm_source=devto&utm_medium=referral&utm_campaign=free_sample

XRP OHLCV Pack — 1-Week Free Sample

Jalvart Studio — Thu, 13 Aug 2026 04:31:29 +0000

XRP OHLCV Pack — Free 1-Week Sample

Ready-to-backtest OHLCV data for XRP/USDT across 6 timeframes.

Download

Kaggle: https://kaggle.com/datasets/jalvartstudio/xrp-ohlcv-pack-free-sample
HuggingFace: https://huggingface.co/datasets/jalvart/xrp-ohlcv-pack-free-sample
Zenodo: https://zenodo.org/records/21912074 (DOI: 10.5281/zenodo.21912074)
B2: https://f003.backblazeb2.com/file/the-glitch-list-prod/OHLCV/XRP/FREE_SAMPLE/XRP_OHLCV_Sample_1week.zip

Full Dataset

6.5 years at $19: jalvart.gumroad.com/l/vglzf

Code GLITCH20 for 20% off.

More at theglitchlist.com?utm_source=devto&utm_medium=referral&utm_campaign=free_sample

Ethereum OHLCV Dataset: 6 Timeframes from 4.1 Billion Ticks

Jalvart Studio — Wed, 12 Aug 2026 05:47:14 +0000

Ethereum OHLCV Dataset: 6 Timeframes from 4.1 Billion Ticks

A practical guide to aggregating tick-level crypto data into OHLCV candles — with a free sample to download

The Problem

If you're building a trading bot or backtesting a strategy, you need OHLCV data (Open, High, Low, Close, Volume) at multiple timeframes. But raw tick-level data from exchanges is messy: millions of rows per day, timestamps in different units, bid-ask spreads, and no standard format.

You could download it yourself from Binance Vision (~85GB per asset) and spend weeks engineering the pipeline. Or you could skip the engineering and get production-ready data in minutes.

The Solution: Pre-Aggregated OHLCV Packs

We've done the heavy lifting. Here's what we published:

4.1 billion Ethereum trades (6.8 years: 2020–2026)
6 timeframes: 1m, 5m, 15m, 1h, 4h, 1d
Apache Parquet format (ZSTD compression, ~120 MB)
Zero look-ahead bias (verified by ratio scaling)
Ready to backtest (no preprocessing required)

Free Sample

Try the 1-week sample first: 2024-08-05 to 2024-08-12. Same 6 timeframes, same quality, ~600 KB.

Available on:

Technical Specs

Candle Counts

Timeframe	Count	Ratio vs 1m
1m	3,457,824	—
5m	691,571	5.00x ✓
15m	230,529	15.00x ✓
1h	57,642	60.00x ✓
4h	14,417	240.00x ✓
1d	2,403	1440.00x ✓

Perfect ratio scaling confirms zero bucketing errors.

Data Quality

✅ Chronological order verified

✅ Zero look-ahead bias (each candle's close uses data ≤ that candle's time)

✅ No negative volumes

✅ OHLC logical constraints (high ≥ close ≥ low, high ≥ open ≥ low)

✅ Complete coverage — zero undeclared gaps from 2020-01-01 to 2026-07-30

How It's Made

Aggregated from Binance Vision tick data using DuckDB:

Load ~4.1B trades from Parquet
Bucket by timestamp (FLOOR to nearest minute)
Aggregate: min(low), max(high), first(open), last(close), sum(volume), sum(buy_volume)
Verify ratio scaling across timeframes
Compress with ZSTD and deliver

No magic. Pure engineering.

Use Cases

Backtesting: Load all 6 timeframes and run your strategy on 6.8 years of real data
ML training: 3.4M one-minute candles = millions of labeled examples
Technical analysis: Study Ethereum's intraday patterns without the download grind

Price & License

Full dataset (6.8 years): $19 USD (one-time)

Use code GLITCH20 for 20% off

License: Personal and research use only. Commercial use prohibited.

Get Started

Download the free sample (1 week, ~600 KB)
Load it in Pandas/Polars/DuckDB (see code example below)
Backtest or analyze
Buy the full dataset when you're ready

Example Code

import pandas as pd

# Load all 6 timeframes
timeframes = ["1m", "5m", "15m", "1h", "4h", "1d"]
data = {tf: pd.read_parquet(f"ETH_OHLCV_{tf}.parquet") for tf in timeframes}

# Plot 1h candles
data["1h"].set_index("candle_time")["close"].plot()

# Backtest: resample to 4h and calculate RSI
candles_4h = data["1h"].set_index("candle_time").resample("4H").agg({"open": "first", "high": "max", "low": "min", "close": "last", "volume": "sum"})

print(f"Ready to backtest on {len(candles_4h):,} 4-hour candles")

Questions?

Visit theglitchlist.com?utm_source=devto&utm_medium=referral&utm_campaign=free_sample or reply in the comments.

Free sample: Download 1-week sample on Kaggle

Full dataset: Buy on Gumroad for $19

Use code GLITCH20 for 20% off all products at The Glitch List.

Free Bitcoin OHLCV Dataset — 6 Timeframes, Ready for Backtesting

Jalvart Studio — Tue, 11 Aug 2026 09:47:58 +0000

Free Bitcoin OHLCV Dataset — 6 Timeframes, Ready for Backtesting

If you're building a trading bot or backtesting a strategy, raw tick data is usually more than you need. What most developers actually want is clean OHLCV candles across multiple timeframes — ready to load and go.

We just published a free 1-week sample of our Bitcoin OHLCV Pack: six timeframes (1m, 5m, 15m, 1h, 4h, 1d), aggregated from tick-level trade data, in Apache Parquet format.

What's in the sample

Period: August 5-12, 2024 (one week, includes real volatility)
Base resolution: 11,520 one-minute candles
Derived timeframes: 5m, 15m, 1h, 4h, 1d — all aggregated from the same 1-minute base, with verified scaling ratios (5m ~ 1m/5, 15m ~ 1m/15, etc.)
Format: Apache Parquet, ZSTD compressed
Zero look-ahead bias: every candle's open/close is computed with explicit chronological ordering, verified before packaging

Quick start

import pandas as pd

df = pd.read_parquet("BTC_OHLCV_1m_SAMPLE.parquet")
print(df.head())
print(f"{len(df):,} candles from {df['candle_time'].min()} to {df['candle_time'].max()}")

Why OHLCV instead of raw ticks

Most bot developers don't need billions of individual trades — they need candles. Aggregating tick data into multiple timeframes correctly is more subtle than it looks: naive bucketing approaches can silently produce identical candle counts across different timeframes (a bug that's easy to miss if you don't cross-check the ratios). We validate every timeframe against its neighbors before anything ships.

Get the full dataset

The sample covers one week. The full Bitcoin OHLCV Pack covers 6.8 years (2020-2026) across the same six timeframes - $19, one-time purchase, no subscription.

Full dataset: https://jalvart.gumroad.com/l/ttrtkx (code GLITCH20 for 20% off)

More free samples and datasets: theglitchlist.com?utm_source=devto&utm_medium=referral&utm_campaign=free_sample

Have questions about the aggregation methodology or want a different timeframe combination? Drop a comment below.

Free tick-level crypto trade data — audited samples on Kaggle, HuggingFace, and Zenodo

Jalvart Studio — Sun, 09 Aug 2026 08:05:56 +0000

Free tick-level crypto trade data samples are now available across Kaggle, HuggingFace, and Zenodo — audited, no gaps hidden, Apache Parquet format.

What's in the samples

Each sample covers a 1-week window of raw trade data for one asset (BTC, ETH, XRP, SOL, LINK, LTC, ADA, BNB, DOGE, AVAX). Every row is a single executed trade — price, quantity, timestamp, and aggressor side. No OHLCV aggregation, no smoothing, no gap-filling.

The samples come from a fully audited multi-year historical dataset (2020-2026) built and maintained by The Glitch List, a data engineering lab based in Barcelona.

Quick start with pandas

import pandas as pd

df = pd.read_parquet("sample_BTC_2025-02-19_to_2025-02-26.parquet")
print(df.shape)
print(df.head())

# Basic trade imbalance by minute
df['minute'] = pd.to_datetime(df['exchange_ts'], unit='ms').dt.floor('min')
imbalance = df.groupby('minute')['quantity'].sum()

Where to find the samples

Kaggle: search "jalvartstudio" — 8 public datasets, one per asset (LINK/LTC/ADA bundled as a pack)
HuggingFace: huggingface.co/jalvart — native Parquet, interactive Dataset Viewer built in, no download needed to preview
Zenodo: DOI-backed permanent records, citable in academic work

Data quality notes

Every sample ships with a Data Quality Disclosure documenting known gaps and any timestamp normalization applied. Source raw data switched timestamp units from milliseconds to microseconds starting 2025-01-01 — this is documented and corrected in the pipeline, not silently patched.

Full historical datasets

The free samples are 1-week excerpts. Full datasets span multiple years (up to 6.5 years for BTC, 6.2 billion rows), fully audited row by row.

🎁 Launch offer — 20% off, limited to 50 uses: GLITCH20 at theglitchlist.com?utm_source=devto&utm_medium=referral&utm_campaign=free_sample

Feedback and questions welcome — this is an ongoing project and disclosure of any data quality issues found is part of how we keep the catalog honest.