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symbol
string
time
int64
open
float64
high
float64
low
float64
close
float64
volume
int64
0001.HK
946,944,000
23.996487
24.236453
23.516558
23.516558
3,194,413
0001.HK
947,030,400
22.43672
22.79667
21.776819
21.896799
6,058,531
0001.HK
947,116,800
22.076767
22.19675
20.397014
20.816952
10,440,479
0001.HK
947,203,200
21.116911
21.356876
20.756963
21.236893
6,049,796
0001.HK
947,462,400
21.956784
22.316733
21.416864
21.416864
5,195,404
0001.HK
947,548,800
21.956789
22.196755
21.476859
21.956789
6,175,861
0001.HK
947,635,200
21.596842
22.136763
21.296885
21.596842
5,453,898
0001.HK
947,721,600
21.716822
21.776813
21.1769
21.296883
3,499,841
0001.HK
947,808,000
21.356873
21.536846
20.696969
20.996925
3,903,579
0001.HK
948,067,200
21.356874
21.356874
20.936934
21.236891
3,106,959
0001.HK
948,153,600
21.056921
22.436719
21.056921
22.316738
4,871,528
0001.HK
948,240,000
21.836805
22.076771
21.596841
22.076771
7,926,159
0001.HK
948,326,400
22.07677
22.376726
21.956787
22.016779
4,246,079
0001.HK
948,412,800
22.076767
22.67668
21.776812
22.136759
5,885,942
0001.HK
948,672,000
22.556703
23.036633
22.136765
22.43672
5,841,425
0001.HK
948,758,400
22.316735
22.976637
22.196752
22.736673
4,556,341
0001.HK
948,844,800
22.916645
23.636541
22.916645
23.516558
5,974,693
0001.HK
948,931,200
23.636538
24.476414
23.576546
24.476414
6,492,447
0001.HK
949,017,600
24.716388
24.836371
23.936501
24.596405
8,468,821
0001.HK
949,276,800
23.996489
23.996489
23.396576
23.456568
5,745,866
0001.HK
949,363,200
23.936489
23.996481
23.276588
23.756517
5,250,377
0001.HK
949,449,600
23.876509
24.596404
23.876509
24.476421
4,732,215
0001.HK
949,536,000
24.476422
24.716388
23.81652
24.596405
4,005,775
0001.HK
949,622,400
24.596405
24.596405
24.596405
24.596405
0
0001.HK
949,881,600
24.596405
24.596405
24.596405
24.596405
0
0001.HK
949,968,000
24.596396
25.436272
23.996483
25.196308
4,574,670
0001.HK
950,054,400
25.196318
26.396143
24.956351
26.036194
8,507,928
0001.HK
950,140,800
25.676239
28.075887
25.556256
26.876064
10,401,002
0001.HK
950,227,200
27.47597
29.635654
27.35599
29.035742
8,254,244
0001.HK
950,486,400
29.275715
29.275715
27.236013
27.475977
5,595,736
0001.HK
950,572,800
27.475969
27.595952
24.956339
26.03618
10,268,257
0001.HK
950,659,200
26.15617
26.396136
25.67624
26.15617
7,014,653
0001.HK
950,745,600
26.156169
26.276152
25.436273
25.676239
6,044,340
0001.HK
950,832,000
25.436278
25.676244
24.836368
24.956348
5,456,556
0001.HK
951,091,200
23.996489
24.716384
23.996489
24.356438
5,182,742
0001.HK
951,177,600
24.356443
24.71639
23.576556
23.876511
5,682,279
0001.HK
951,264,000
23.996489
25.076331
23.996489
24.956348
3,839,415
0001.HK
951,350,400
25.076326
25.796222
25.076326
25.676239
3,266,443
0001.HK
951,436,800
25.436284
25.916214
24.716391
25.076338
3,915,606
0001.HK
951,696,000
25.076323
25.196306
23.516551
23.936489
4,949,363
0001.HK
951,782,400
24.476425
25.076338
23.876512
25.076338
4,170,539
0001.HK
951,868,800
24.956348
25.196314
23.996489
24.356438
4,317,226
0001.HK
951,955,200
24.716384
24.716384
23.816517
24.356438
3,979,918
0001.HK
952,041,600
24.236458
25.196317
23.81652
24.476421
4,259,301
0001.HK
952,300,800
25.196308
26.036184
24.956342
25.796221
4,226,141
0001.HK
952,387,200
25.436281
26.636106
25.076334
26.636106
4,821,854
0001.HK
952,473,600
25.196311
27.11603
25.196311
26.276154
4,487,083
0001.HK
952,560,000
26.636105
27.116035
25.43628
25.556263
4,943,417
0001.HK
952,646,400
26.036189
27.116032
25.916206
26.276155
7,663,776
0001.HK
952,905,600
26.396142
27.236018
25.43628
25.556263
7,398,287
0001.HK
952,992,000
25.676242
25.916206
24.476417
24.716383
6,224,831
0001.HK
953,078,400
24.476419
26.036191
24.476419
24.836369
6,820,171
0001.HK
953,164,800
25.196307
25.676237
23.996482
24.236448
6,505,879
0001.HK
953,251,200
25.436272
25.916202
24.716379
25.316292
5,664,358
0001.HK
953,510,400
24.716385
25.676244
24.716385
24.956348
2,578,638
0001.HK
953,596,800
25.436271
26.156167
25.196308
25.916201
4,152,686
0001.HK
953,683,200
26.396137
26.996047
26.396137
26.876066
4,275,111
0001.HK
953,769,600
28.075888
28.315854
27.116029
27.835924
7,220,063
0001.HK
953,856,000
27.835926
28.315856
27.11603
27.475977
4,603,703
0001.HK
954,115,200
27.835919
28.195866
27.116024
27.955902
5,985,856
0001.HK
954,201,600
28.075881
28.675794
27.595952
28.555811
5,242,066
0001.HK
954,288,000
28.075897
28.795793
28.075897
28.67581
5,798,276
0001.HK
954,374,400
28.075898
28.915777
27.955917
27.955917
5,973,728
0001.HK
954,460,800
27.955902
28.315849
26.87606
27.955902
3,863,061
0001.HK
954,720,000
27.955915
27.955915
26.396143
26.396143
4,460,413
0001.HK
954,806,400
26.396141
26.396141
26.396141
26.396141
0
0001.HK
954,892,800
25.196306
26.39613
24.236447
25.076323
8,359,064
0001.HK
954,979,200
24.956344
25.436274
24.476414
25.436274
7,864,403
0001.HK
955,065,600
26.156176
26.156176
25.43628
25.556263
3,451,455
0001.HK
955,324,800
25.676251
26.156181
25.556268
25.796234
2,792,160
0001.HK
955,411,200
25.676238
25.676238
24.956342
25.196308
2,843,679
0001.HK
955,497,600
25.076326
25.556256
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0001.HK
955,584,000
24.356431
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11,217,440
0001.HK
955,670,400
23.996492
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23.756529
23.756529
4,563,017
0001.HK
955,929,600
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14,346,279
0001.HK
956,016,000
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0001.HK
956,102,400
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0001.HK
956,188,800
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0001.HK
956,275,200
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0001.HK
956,534,400
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0001.HK
956,620,800
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0001.HK
956,707,200
21.116917
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0001.HK
956,793,600
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0001.HK
956,880,000
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0001.HK
957,225,600
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0001.HK
957,398,400
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0001.HK
957,484,800
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0001.HK
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0001.HK
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0001.HK
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0001.HK
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0001.HK
958,608,000
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0001.HK
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0001.HK
958,953,600
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18.264605
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18.081348
23,956,801
End of preview. Expand in Data Studio

๐Ÿ—ƒ๏ธ TroveLedger โ€” Financial Time Series Dataset

TroveLedger Banner

A growing ledger of accumulated market history.

โš ๏ธ Temporary Notice: Intraday Data Adjustments (January 2026)

What happened:
A discrepancy has been identified in the minute- and hourly-resolution data: these series are currently not fully adjusted for stock splits and dividends. Daily-resolution data remains correctly adjusted (as provided by the source).

Why this matters:
For accurate backtesting and model training โ€“ especially in intraday strategies โ€“ proper adjustments are essential to avoid distorted price histories.

What I'm doing:
I am actively working on a comprehensive fix to bring all resolutions to full adjustment consistency. This will ensure the highest possible data quality moving forward.

Short-term plan:
Until the fix is complete, no new indices or symbols will be added to avoid introducing further unadjusted data.

If the full correction takes longer than expected, an intermediate step may be applied: removing affected (unadjusted) intraday segments to keep the published dataset accurate, even if temporarily reduced in scope.

Current Status: (last updated: January 10, 2026)
Active development on full adjustment fix. No new additions in progress.
Do not use the dataset in its current state for intraday strategies requiring split/dividend adjustments.

Outlook:
This pause prioritizes reliability over rapid expansion โ€“ a necessary step for a dataset meant to support serious quantitative research and autonomous trading systems.

Updates will be shared here and on X (@TroveLedger) as progress is made.

Thank you for your understanding and patience. Your interest helps keep the treasury growing stronger!

Error


๐Ÿ”” Latest Dataset Update

Date: 2026-01-06
New addition: ๐Ÿ‡ง๐Ÿ‡ช BEL 20 (Belgium)

Today, the dataset expands with the BEL 20 โ€” Belgiumโ€™s primary equity index and the central barometer of its domestic capital market.
Composed of the 20 most liquid and heavily traded companies listed in Brussels, the BEL 20 reflects an economy shaped by finance, consumer goods, industry, and cross-border European integration.

๐Ÿ“Š Market context:
Region: Europe โ€” Belgium
Scope: Large-cap benchmark (BEL 20 constituents)
Sector exposure: Financials, Consumer Staples, Industrials, Utilities
Data coverage: Minute, hourly, and daily OHLC data

๐Ÿ“ˆ What this means:
A compact, liquid market segment that adds depth to Western European coverage with consistent, high-resolution time series.

๐Ÿ”œ Whatโ€™s next:
Continued expansion across international markets, preserving uniform structure and historical depth.

Click to expand

Recent Index Additions

Date Index Region Symbols
2026-01-06 BEL 20 Belgium ๐Ÿ‡ง๐Ÿ‡ช 20
2026-01-05 DAX Germany ๐Ÿ‡ฉ๐Ÿ‡ช 40
2026-01-02 ASX 200 Australia ๐Ÿ‡ฆ๐Ÿ‡บ 200
2025-12-30 OMX Stockholm 30 Sweden ๐Ÿ‡ธ๐Ÿ‡ช 30
2025-12-29 TSX (S&P/TSX Composite) Canada ๐Ÿ‡จ๐Ÿ‡ฆ 222
2025-12-24 SMI ๐Ÿ‡จ๐Ÿ‡ญ Switzerland 20
2025-12-23 NIFTY 50 ๐Ÿ‡ฎ๐Ÿ‡ณ India 50
2025-12-22 FTSE 100 ๐Ÿ‡ฌ๐Ÿ‡ง United Kingdom 100
2025-12-19 S&P 500 ๐Ÿ‡บ๐Ÿ‡ธ US 503
2025-12-18 Hang Seng Index ๐Ÿ‡ญ๐Ÿ‡ฐ Asia 82
2025-12-17 EURO STOXX 50 ๐Ÿ‡ช๐Ÿ‡บ Europe 50

๐Ÿ“Œ Overview

TroveLedger is a public financial time series dataset focused on long-term accumulation of high-quality intraday data.

The dataset provides OHLC and volume data at multiple time resolutions and is designed primarily for machine learning, quantitative research, and systematic trading experiments.

Unlike many freely available data sources, TroveLedger emphasizes continuity over time, especially for minute-level data.

Scale & Granularity

  • Total: Over 40 million rows across all symbols and resolutions (growing rapidly)
  • Per symbol: Varies significantly โ€“ from <1,000 rows (young stocks, daily) to >500,000 rows (established stocks, minute-resolution)
  • Ideal for both focused single-symbol training and large-scale multi-market models

๐Ÿ”‘ What makes TroveLedger different

High-resolution intraday data is difficult to obtain from free sources over extended periods.

Typical public data access (e.g. via yfinance) provides:

  • Daily candles: often spanning decades
  • Hourly candles: roughly one year into the past
  • Minute candles: usually limited to the most recent 7 days

Repeatedly downloading rolling 7-day windows results in short, fragmented histories that are poorly suited for training models on intraday behavior.

TroveLedger takes a different approach:

  • Minute-level data is accumulated continuously
  • Time series are extended, not replaced
  • Over time, this results in months of gap-free minute data per instrument

This accumulated depth forms a substantially more reliable foundation for intraday research and model training.

๐Ÿงฑ Data Integrity Philosophy

TroveLedger prioritizes continuity over frequency.
The primary goal is not to fetch data as often as possible, but to ensure that once a time series starts, it remains gap-free.

Minute-level data is accumulated incrementally over time, creating long, uninterrupted histories that are not obtainable from fresh API queries alone.

This makes the dataset particularly suitable for model training, backtesting, and regime analysis.

๐Ÿ“ฆ Dataset Structure

The dataset is organized as follows:

  • /data/{category}/{symbol}/{symbol}.{interval}.valid.parquet

Where:

  • {category}: e.g., equities/us, indices/sp500, indices/eurostoxx50 (growing with new indices)
  • {symbol}: Stock ticker (e.g., AAPL, BMW.DE)
  • {interval}: One of days (daily), hours (hourly), or minutes (1-minute)

The .valid suffix indicates that these files have passed quality checks and are ready for use. Only these cleaned, validated files are included in the dataset โ€“ temporary or intermediate files from the pipeline are excluded.

Tip for users: The .valid part is intentionally kept as a flexible "state" marker. You can easily rename or copy files to add your own states (e.g., .train.parquet or .test.parquet) for train/validation/test splits in your ML workflows. This pattern makes it simple to organize experiments without changing the core data.

Data Instances

Here's an example row from a typical daily Parquet file (e.g., for AAPL.days.valid.parquet):

symbol time open high low close volume
AAPL 1704067200 192.28 192.69 191.73 192.53 42672100
  • time is a Unix timestamp (e.g., 1704067200 = January 1, 2024, 00:00 UTC).
  • All prices are in the symbol's native currency (e.g., USD for US equities).

Dataset Creation

Curation Rationale

TroveLedger was created to provide a reliable, expanding source of historical OHLCV data for AI-driven trading research, addressing gaps in continuity and international coverage.

Source Data

All data is sourced from Yahoo Finance via the yfinance Python library. Index components are automatically extracted from Wikipedia pages using a custom API-based pipeline for sustainability.

Data Collection and Processing

  • Symbols are selected from major indices (e.g., S&P 500, EURO STOXX 50) and equities.
  • Data is fetched at daily, hourly, and 1-minute resolutions, validated for completeness, and stored in Parquet format for efficiency.
  • Quality checks remove gaps or anomalies; only ".valid" files are included.
  • Updates occur periodically to extend histories and add new indices based on community input.

Who are the source data producers?

Yahoo Finance (public market data). No personal data is included.

๐Ÿ”„ Update Philosophy

The primary objective is data continuity, not guaranteed daily updates.

In particular:

  • Daily updates are not guaranteed
  • Preventing gaps in accumulated minute data has priority
  • Updates are performed on trading days whenever possible

Minute data is updated most frequently to ensure continuity.

Hourly and daily data are updated on a rotation basis to reduce unnecessary repeated downloads and to remain considerate of public data sources. These datasets are guaranteed to be no older than one week.

For most training scenarios, this is fully sufficient. When models are deployed in real-world environments, current market data is typically provided directly by the target trading platform.

๐Ÿ“ˆ Scope & Growth

TroveLedger started with a curated universe of approximately 500 equities inherited from earlier Preliminary datasets.

Going forward:

  • Entire indices are added step by step
  • The covered universe will grow continuously
  • Expansion is performed incrementally to ensure data integrity and operational stability

This gradual approach allows issues to be detected early and handled without disrupting existing data.

๐ŸŽฏ Intended Uses

  • Primary Use: Training and evaluating machine learning models for trading strategies and autonomous AI bots.
  • Other Uses: Time series analysis, financial research, educational projects, and community-driven extensions.

TroveLedger is suitable for:

  • machine learning on financial time series
  • intraday and swing trading research
  • feature engineering on OHLC data
  • backtesting strategies requiring dense intraday history
  • exploratory quantitative analysis

โš ๏ธ Limitations & Notes on Data Sources

  • Data Freshness: Data is typically a few days old, not real-time.
  • Coverage: Not all symbols may have complete historical data, especially for minute-resolution or newly added indices.
  • Growth Phase: The dataset is actively expanding; check for updates on new indices and symbols.
  • Not financial advice: This dataset is for research and educational purposes only. Past performance is no guarantee of future results.

Data is derived from publicly accessible market data sources (e.g. via yfinance).

While care is taken to ensure consistency and continuity, this dataset is provided as-is and without guarantees regarding completeness or correctness.

Users are responsible for verifying suitability for their specific use cases and for complying with the terms of the original data providers.

๐Ÿ“œ License & Usage

This dataset is provided solely for non-commercial research and educational purposes.

The data is retrieved from public sources via the yfinance library (Yahoo Finance). All rights remain with the original data providers.

Redistribution of this dataset is not permitted without explicit permission from the original sources.

See the LICENSE file for full details.

NO WARRANTY IS PROVIDED. Use at your own risk.

๐Ÿ’ฌ Feedback, Suggestions & Community Support

TroveLedger is a growing, community-driven project providing high-quality OHLCV data for training AI models on financial markets and trading strategies. Your input makes it better!

  • What are you building? I'd love to hear how you're using TroveLedger! Share your projects, trading bot ideas, ML models, or research directions โ€“ it motivates me to keep expanding and might inspire others.
  • Desired indices: Which major indices are you waiting for most? I'll prioritize based on demand and feasibility.
  • Helping expand indices: The pipeline uses the Wikipedia API to automatically extract components. It works best with a structured table containing both company names and clean, yfinance-compatible ticker symbols.
    • Simply share the Wikipedia page URL (any language) for your desired index.
    • If the table needs tweaks (e.g., missing or unclear ticker column, prefixes in symbols), improving it on Wikipedia is the most sustainable way โ€“ the global community then keeps it updated long-term!
    • Once ready, post the link here, and I'll integrate it quickly.

Interested in a deeper dive into the exact table format and config options my pipeline supports (with examples like zero-padding, suffixes, or language overrides)? Let me know โ€“ if there's demand, I'll create a dedicated guide soon!

Join the discussion in Hugging Face Discussions.


๐Ÿ›๏ธ The Growing Treasury

Watch TroveLedger expand across global markets โ€“ a visual chronicle of added indices:  
๐Ÿ‡ง๐Ÿ‡ช BEL 20 (January 6, 2026) โ€“ Measured Wealth in a Small Vault

Behind heavy stone walls and careful accounting, the BEL 20 represents a market where scale is limited but liquidity is deliberate. Belgiumโ€™s leading financial, industrial, and consumer firms form a tightly curated index, reflecting a capital market defined by restraint, stability, and European connectivity.

This addition strengthens TroveLedgerโ€™s continental European archive, capturing a compact exchange whose value lies not in breadth, but in precision and consistency across timeframes.

๐Ÿ‡ฉ๐Ÿ‡ช DAX (January 5, 2026) โ€“ Engineered Capital at Industrial Scale

At the intersection of precision engineering and global trade, the DAX captures Germanyโ€™s most influential publicly listed corporations. Industrial manufacturers, automotive leaders, chemical groups, and financial institutions dominate the index, forming a market shaped by export depth and operational discipline.

This entry anchors TroveLedgerโ€™s core European coverage, adding a market where long-cycle industry, efficiency, and global exposure define capital behavior across timeframes.

๐Ÿ‡ฆ๐Ÿ‡บ ASX 200 (January 2, 2026) โ€“ Weighed by Earth and Capital

Across vast distances and resource-rich ground, the ASX 200 captures an equity market anchored in tangible assets and institutional capital. Mining conglomerates, major banks, energy firms, and healthcare leaders dominate the index, forming a market profile distinct from technology-heavy regions.

This addition extends TroveLedgerโ€™s reach into Oceania, preserving a market where commodities, yield, and global demand cycles leave clear historical traces across intraday and long-term data.

๐Ÿ‡ธ๐Ÿ‡ช OMX (December 30, 2025) โ€“ Order in the Nordic Ledger

In the measured calm of Northern Europe, the Stockholm Stock Exchange (OMX) records value through discipline, transparency, and long-term orientation. Industrial groups, financial institutions, and globally oriented consumer firms dominate the OMX Stockholm 30, forming a compact yet internationally relevant market profile.

This entry adds a distinctly Nordic balance to TroveLedger โ€” one shaped by export strength, institutional stability, and methodical capital allocation across intraday and long-horizon views.

๐Ÿ‡จ๐Ÿ‡ฆ TSX (December 29, 2025) โ€“ Beneath the Surface of Canadian Capital

Deep underground, where resources are extracted and value is carefully recorded, the Toronto Stock Exchange (TSX) reflects the structural foundations of the Canadian economy. Banks, miners, energy producers, and industrial firms form the backbone of the S&P/TSX Composite, making it a distinctive counterweight to more tech-heavy global indices.

This entry extends TroveLedgerโ€™s North American coverage beyond the United States, adding a market shaped by commodities, capital discipline, and long-cycle industries โ€” all captured across consistent intraday and long-horizon timeframes.

๐Ÿ‡จ๐Ÿ‡ญ SMI (December 24, 2025) โ€“ Alpine quality meets market stability

The Swiss Market Index (SMI) has been added to TroveLedger, bringing the premier blue-chip index of Switzerland into our global dataset.
Representing 20 of the largest and most liquid companies listed on the SIX Swiss Exchange โ€” including giants like Nestlรฉ, Roche, and Novartis โ€” the SMI offers a unique exposure to one of the worldโ€™s most stable and innovation-driven economies.

The SMI reflects Switzerlandโ€™s enduring role as a benchmark for quality, resilience, and long-term value.

TroveLedger as Santa Claus riding a golden sleigh filled with gold coins and gifts through snowy Swiss Alps, with a Swiss flag flying, next to a treasure chest labeled 'SMI'
๐Ÿ‡ฎ๐Ÿ‡ณ NIFTY 50 (December 23, 2025) โ€“ India takes center stage

The NIFTY 50 Index from India has been incorporated into TroveLedger, enriching the dataset with one of South Asiaโ€™s most referenced equity benchmarks. It represents 50 of the largest and most liquid Indian stocks listed on the National Stock Exchange.

TroveLedger riding a golden bull through a festive scene, next to a dancer in traditional Indian clothing
๐Ÿ‡ฌ๐Ÿ‡ง FTSE 100 (December 22, 2025) โ€“ Britain weathers the storm

The FTSE 100 represents 100 of the most capitalized and liquid firms on the London Stock Exchange, spanning finance, energy, consumer goods, healthcare, and industrial sectors.
As the UK is no longer part of the European Union, this addition extends TroveLedgerโ€™s European coverage beyond the Eurozone without overlap with previously added indices.

TroveLedger safeguarding British market wealth along the Thames during a storm
๐Ÿ‡บ๐Ÿ‡ธ S&P 500 (December 19, 2025) โ€“ America answers the call

The complete S&P 500 Index (503 constituents) has been fully integrated, adding 173 new symbols.
This provides the premier US large-cap benchmark with extended intraday histories โ€“ ideal for multi-sector trading bot training.

TroveLedger as Uncle Sam proudly presenting the S&P 500 treasure chest
๐Ÿ‡ญ๐Ÿ‡ฐ Hang Seng Index (December 18, 2025) โ€“ Asia opens its doors

The Hang Seng Index (HSI) adds 82 entirely new symbols โ€“ major Hong Kong-listed companies with strong China exposure across finance, tech, energy, and consumer sectors.

TroveLedger welcoming representatives to the HSI vault
๐Ÿ‡ช๐Ÿ‡บ EURO STOXX 50 (December 17, 2025) โ€“ Europe uncovers its treasures

The EURO STOXX 50 introduces 50 blue-chip companies from the Eurozone, spanning multiple countries and sectors โ€“ a cornerstone for European market exposure.

TroveLedger unveiling the EU flag from a treasure chest labeled STOXX50

๐Ÿ”– Citation

If you use TroveLedger in your work, please cite it as:

@dataset{Traders-Lab_TroveLedger_2025,
  author = {Traders-Lab},
  title = {TroveLedger Financial Time Series Dataset},
  year = {2025},
  url = {https://huggingface.co/datasets/Traders-Lab/TroveLedger}
}

๐Ÿ’ฐ Support the Treasury Expansion

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Thank you for helping grow the trove!

๐Ÿ”š Final note

Markets are not measured by size alone โ€”
but by how faithfully their records endure.

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