| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| - zh |
| pretty_name: FinForesight |
| tags: |
| - finance |
| - benchmark |
| - deep-research |
| - agent-evaluation |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: val |
| data_files: |
| - split: train |
| path: data/01_val/train.jsonl |
| default: true |
| - config_name: rank_f |
| data_files: |
| - split: train |
| path: data/02_rank_f/train.jsonl |
| - config_name: rank_t |
| data_files: |
| - split: train |
| path: data/03_rank_t/train.jsonl |
| - config_name: rank_h |
| data_files: |
| - split: train |
| path: data/04_rank_h/train.jsonl |
| - config_name: evt |
| data_files: |
| - split: train |
| path: data/05_evt/train.jsonl |
| - config_name: fund |
| data_files: |
| - split: train |
| path: data/06_fund/train.jsonl |
| - config_name: cf |
| data_files: |
| - split: train |
| path: data/07_cf/train.jsonl |
| - config_name: calc |
| data_files: |
| - split: train |
| path: data/08_calc/train.jsonl |
| --- |
| |
| # FinForesight |
|
|
| Evaluation dataset for financial Deep Research Agents (**data only**). |
|
|
| Code, builders, prompt templates, and evaluation scripts live in a separate repository. This Hub repo publishes standardized ready seeds only, loadable by subset. |
|
|
| Chinese documentation: [`README_cn.md`](./README_cn.md). |
|
|
| ## Links |
|
|
| - **Code**: https://github.com/OpenFinMind/FinForesight |
| - **Dataset**: https://huggingface.co/datasets/OpenFinMind/FinForesight |
|
|
| Task configs: `val` / `rank_*` / `evt` / `fund` / `cf` / `calc` |
| (Valuation / Return Ranking / Event Direction / Fundamentals / Counterfactual / Calculation). |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load by task subset (recommended) |
| val = load_dataset("OpenFinMind/FinForesight", "val") |
| fund = load_dataset("OpenFinMind/FinForesight", "fund") |
| cf = load_dataset("OpenFinMind/FinForesight", "cf") |
| |
| print(val["train"][0]["task_id"]) |
| print(val["train"][0]["prompt"][:200]) |
| |
| # Filter by time band |
| t2 = val["train"].filter(lambda x: x["time_band"] == "T2") |
| ``` |
|
|
| Available configs: `val` / `rank_f` / `rank_t` / `rank_h` / `evt` / `fund` / `cf` / `calc` |
| Default config: `val`. |
|
|
| ### EVT / FUND perturbations |
|
|
| `evt` and `fund` include perturbation items. Each is a rewrite of a base item in the same category, asking the same question with identical `ground_truth`, used to measure answer stability under rewrite. |
|
|
| - `variant` is `perturb_layout` (layout reshuffle) / `perturb_paraphrase` (instruction paraphrase) / |
| `perturb_numeric` (disclosure numbers scaled by the same factor ±1%); other `variant` values are base items. |
| - `metadata.perturbation.base_task_id` points to the base item; `metadata.perturbation.perturbation_type` |
| gives the perturbation type. |
| - To evaluate base items only, filter on `variant`: |
|
|
| ```python |
| evt = load_dataset("OpenFinMind/FinForesight", "evt") |
| base = evt["train"].filter(lambda x: not x["variant"].startswith("perturb_")) |
| ``` |
|
|
| ## Scale (v0.9.0) |
|
|
| | config | task | ready count | |
| |--------|------|------------:| |
| | `val` | Valuation (VAL) | 244 | |
| | `rank_f` | Return Ranking F (RANK-F) | 112 | |
| | `rank_t` | Return Ranking T (RANK-T) | 161 | |
| | `rank_h` | Return Ranking H (RANK-H) | 110 | |
| | `evt` | Event Direction (EVT) | 228 | |
| | `fund` | Fundamentals (FUND) | 384 | |
| | `cf` | Counterfactual (CF) | 30 | |
| | `calc` | Calculation (CALC) | 30 | |
| | **total** | | **1299** | |
|
|
| ### Experiment identity (paper primary) |
|
|
| - `backbone_model = gpt-4.1` |
| - `backbone_training_cutoff = 2024-06-30` (OpenAI states “June 2024” only; this release uses month-end conservatively) |
| - `guard_days = 30` → T1 outcome ≤ `2024-05-31`, T2 origin ≥ `2024-07-30` |
| - `experiment_as_of = 2026-08-17` |
|
|
| **Incompatibility note:** the legacy Hub pin (339 items / `model_training_cutoff=2024-06-01`) is a legacy identity and must not be mixed with v0.9 `paper_band` / C1 tables. |
|
|
| ## Fields |
|
|
| One evaluation item per row (JSON object): |
|
|
| | field | description | |
| |------|-------------| |
| | `task_id` | Globally unique ID | |
| | `category` | VAL / RANK / EVT / FUND / CF / CALC | |
| | `variant` | Sub-variant (e.g. F/T/H, earnings) | |
| | `cutoff_date` | Cutoff used for temporal banding | |
| | `time_band` | T1 / T2 / T3 (legacy; use `paper_band` for papers) | |
| | `forecast_origin` | Forecast origin time | |
| | `outcome_available_at` | Outcome availability date; use with `outcome_available_at_source` for observed vs estimated | |
| | `paper_band` | GPT-4.1 paper banding: T1 / T2 / T3 / CF / CALC | |
| | `status` | ready | |
| | `seed` | Structured input | |
| | `prompt` | Fully rendered prompt | |
| | `expected_output` | Output schema description | |
| | `ground_truth` | Evaluation label | |
| | `metadata` | Metadata | |
|
|
| ## Temporal banding |
|
|
| This dataset labels legacy `time_band` with a reproducible rule (based on `cutoff_date` only): |
|
|
| - `T1`: `cutoff_date <= model_training_cutoff` (possible pretraining leakage) |
| - `T2`: `model_training_cutoff < cutoff_date < reference_current_date` |
| - `T3`: `cutoff_date >= reference_current_date` (future-forecast stress) |
|
|
| Default legacy parameters (aligned with paper cutoff month, different rule): |
|
|
| - `model_training_cutoff = 2024-06-30` |
| - `reference_current_date = 2026-08-17` |
|
|
| For paper experiments, use sidecar `paper_band` (see code repo `docs/schema.md` / `docs/c1_pairing_contract.md`); do not use legacy `time_band` for C1. |
|
|
| ## Repo boundary |
|
|
| This repository **only** contains: |
|
|
| - `README.md` (English dataset card) |
| - `README_cn.md` (Chinese dataset card) |
| - `data/<config>/train.jsonl` (ready seeds) |
|
|
| It does **not** include code, scripts, prompts, raw build CSVs, or run outputs. |
|
|
| ## License |
|
|
| [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) |
|
|
