| --- |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - time-series-forecasting |
| language: |
| - en |
| tags: |
| - finance |
| - multivariate |
| - time-series |
| - panel-data |
| configs: |
| - config_name: close |
| data_files: |
| - split: train |
| path: data/close/train-*.parquet |
| - split: test |
| path: data/close/test-*.parquet |
| - config_name: volume |
| data_files: |
| - split: train |
| path: data/volume/train-*.parquet |
| - split: test |
| path: data/volume/test-*.parquet |
| --- |
| |
| # Finance1K |
|
|
| [](https://arxiv.org/abs/2608.14106) |
|
|
| Finance1K is a synchronized hourly panel for next-step forecasting across |
| 1,000 US equities. It contains 28,510 chronological observations from 2015 to |
| early 2026 and provides two aligned targets: |
|
|
| - close: hourly close log return, log(close_t / close_t-1); |
| - volume: hourly log-volume change, log(volume_t / volume_t-1). |
|
|
| The two configurations use the same timestamps, ticker order, validity mask, |
| and chronological split. The first 19,957 rows form the training set and the |
| following 8,553 rows form the test set. |
|
|
| ## Data structure |
|
|
| Each Parquet row is one timestamp and one synchronized cross-section. The |
| values and valid fields are fixed-size arrays of length 1,000. Array position j |
| matches selection_order j in metadata/tickers.parquet. |
| |
| | Field | Type | Meaning | |
| |---|---|---| |
| | row_index | int64 | Global chronological row index | |
| | timestamp | int64 | UTC Unix timestamp in seconds | |
| | values | float32[1000] | Close or volume target cross-section | |
| | valid | bool[1000] | Entry-level validity mask before zero fill | |
| | xs_vol | float32 | Cross-sectional population standard deviation | |
| | regime | int8 | Calm/stress indicator fitted on training rows | |
| | lagged_vol | float32 | Causal 24-row trailing volatility proxy | |
| | ceiling_series | float32 | Training-normalized predictability proxy | |
| |
| The metadata directory records the full schema, exact split boundaries, ticker |
| order and volatility buckets, per-panel scalar values, canonical array |
| identities, and SHA-256 checksums. |
| |
| ## Loading |
| |
| The close and volume configurations can be read independently: |
| |
| from datasets import load_dataset |
|
|
| close = load_dataset("abel-lab/finance1k", "close") |
| volume = load_dataset("abel-lab/finance1k", "volume") |
| |
| The synchronized arrays must remain intact when evaluating cross-sectional |
| metrics. Flattening ticker and timestamp axes changes the empirical task. |
|
|
| ## Known limitations |
|
|
| The stored timeline is a regular calendar-day hourly grid rather than an |
| exchange-session calendar. Weekend and market-holiday rows are filled with zero |
| log changes. |
|
|
| The ticker universe is limited to names with sufficiently long historical |
| coverage to span the common evaluation window. Eligible tickers are divided |
| into ten historical-volatility buckets, with 100 selected from each bucket, to |
| balance the panel across volatility levels. This construction introduces |
| survivorship and selection effects and should not be interpreted as a |
| representative market sample. |
|
|
| ## License |
|
|
| Finance1K is released under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). |
|
|
