---
license: cc-by-4.0
task_categories:
  - time-series-forecasting
  - tabular-regression
  - text-generation
  - question-answering
language:
  - en
size_categories:
  - 1M<n<10M
tags:
  - finance
  - macroeconomic
  - multimodal
  - benchmark
  - sec-edgar
  - xbrl
  - rentcast
  - small-cap
  - russell-2000
  - private-valuation
  - scenario-conditioned-forecasting
pretty_name: MacroLens
configs:
  - config_name: panel_daily
    data_files:
      - split: train
        path: data/daily/panel_train.parquet
      - split: test
        path: data/daily/panel_test.parquet
  - config_name: panel_weekly
    data_files:
      - split: train
        path: data/weekly/panel_train.parquet
      - split: test
        path: data/weekly/panel_test.parquet
  - config_name: panel_monthly
    data_files:
      - split: train
        path: data/monthly/panel_train.parquet
      - split: test
        path: data/monthly/panel_test.parquet
  - config_name: scenarios_daily
    data_files: data/daily/scenarios.parquet
  - config_name: valuation_inputs_daily
    data_files: data/daily/valuation_inputs.parquet
  - config_name: private_valuation_inputs_daily
    data_files: data/daily/private_valuation_inputs.parquet
  - config_name: generation_inputs_daily
    data_files: data/daily/generation_inputs.parquet
  - config_name: generation_ground_truth_daily
    data_files: data/daily/generation_ground_truth.parquet
  - config_name: generator_eval_inputs_daily
    data_files: data/daily/generator_eval_inputs.parquet
  - config_name: generator_eval_ground_truth_daily
    data_files: data/daily/generator_eval_ground_truth.parquet
  - config_name: scenario_forecast_ground_truth_daily
    data_files: data/daily/scenario_forecast_ground_truth.parquet
  - config_name: real_estate_train
    data_files: data/real_estate/re_train_properties.parquet
  - config_name: real_estate_eval
    data_files: data/real_estate/re_eval_inputs.parquet
---

# MacroLens

A benchmarking corpus for **contextual financial reasoning under macroeconomic scenarios** across **4,416 U.S. small- and micro-cap equities (2021-01-04 — 2026-03-31)**. MacroLens unifies seven tasks over a single point-in-time panel: contextual time-series forecasting, public valuation, financial-statement generation, scenario-conditioned return forecasting, private-company valuation, generator evaluation from natural-language descriptions, and real-estate valuation.

![visual_summary](https://cdn-uploads.huggingface.co/production/uploads/65dff6bdd546250b182edb86/KXknSZ-_zv23FYNLDkZnu.png)

| Task | Type | Output |
|---|---|---|
| **T1** Contextual Forecasting | Time-series | Horizon-length close trajectory |
| **T2** Public Valuation | Tabular regression | Equity market cap |
| **T3** Financial Statement Generation | Structured generation | 11 canonical XBRL fields per (ticker, fiscal year) |
| **T4** Scenario-Conditioned Return | Event forecasting | 63-day post-event return percentage |
| **T5** Private-Company Valuation | Tabular regression (price-stripped) | Equity value w/o market data |
| **T6** Generator Evaluation | NL→ structured | Same 11 fields from a natural-language company description |
| **T7** Real-Estate Valuation | Cross-domain regression | Rent + price per RentCast address |

Every instance carries a 131-numeric / 141-column point-in-time panel (prices, 46.8M XBRL accounting facts, 53 macroeconomic series, filing recency, derived ratios), an optional macroeconomic scenario object (1,130 events across 49 types), and optional SEC filings + financial-news context. Temporal alignment is strictly point-in-time: every observation visible at prediction timestamp $t$ was publicly available by $t$.

## Quickstart

```python
import macrolens as ml

# 1. Load (X, y, meta) — identical schema across train/test
X_train, y_train, meta_train = ml.load("T1", "train", granularity="daily")
X_test,  y_test,  meta_test  = ml.load("T1", "test")

# 2. Fit + Predict
model = ml.methods.LightGBMRegressor(task="T1")
model.fit(X_train, y_train, seed=42)
y_pred = model.predict(X_test)

# 3. Score (cluster-bootstrap CIs by ticker for T1; adaptive n_boot)
metrics = ml.score("T1", y_test, y_pred)
print(metrics["mse"]["value"], metrics["mse"]["ci_lo"], metrics["mse"]["ci_hi"])
```

10 lines per task; swap the model class to compare methods.
## Dataset structure

```
data/
├── daily/                     # primary granularity (4.84M panel rows)
│   ├── panel_train.parquet                       # T1, T4 train side
│   ├── panel_test.parquet                        # T1, T4 eval side
│   ├── scenarios.parquet                         # 1,130 macroeconomic events
│   ├── valuation_inputs.parquet                  # T2 features
│   ├── valuation_ground_truth.parquet            # T2 ground truth (market cap)
│   ├── private_valuation_inputs.parquet          # T5 features (price-stripped)
│   ├── private_valuation_ground_truth.parquet    # T5 ground truth
│   ├── generation_inputs.parquet                 # T3 fundamentals snapshot
│   ├── generation_ground_truth.parquet           # T3 long-form (ticker, FY, field, value)
│   ├── generator_eval_inputs.parquet             # T6 NL company descriptions
│   ├── generator_eval_ground_truth.parquet       # T6 long-form
│   └── scenario_forecast_ground_truth.parquet    # T4 ground truth
├── weekly/                    # Friday-close resampled (1.01M rows) — same file set as daily/
├── monthly/                   # Last-trading-day resampled (232k rows) — same file set as daily/
├── real_estate/
│   ├── re_train_properties.parquet   # T7 train (53,804 unique addresses)
│   ├── re_eval_inputs.parquet        # T7 eval (23,190 unique addresses)
│   └── re_eval_ground_truth.parquet  # T7 ground truth (rent + price)
├── xbrl/                      # 46.8M standardized XBRL facts, 92.6% ticker coverage
├── filings/                   # 295,860 SEC filings (10-K, 10-Q, 8-K, 20-F, 6-K, N-CSR, N-CSRS) — markdown + PDF
├── prices/                    # OHLCV + adjusted close (yfinance)
├── fundamentals/              # README placeholder only; raw CSVs reproducible via code/collect_fundamentals.py (see note below)
└── macro/                     # 46 FRED + 6 EIA series

manifest.json                  # SHA-256 over every parquet (provenance)
```

> **Note on `data/fundamentals/`**: Raw per-ticker fundamentals CSVs (~12,000 files: `{TICKER}_balance.csv`, `{TICKER}_income.csv`, `{TICKER}_cashflow.csv`) are **not shipped directly** here because HF enforces a hard cap of 10,000 files per directory. They are reproducible end-to-end via the released pipeline:
>
> ```bash
> python code/collect_fundamentals.py
> ```
>
> The aggregated/processed versions used by the benchmark API (e.g. `valuation_inputs.parquet`, `private_valuation_inputs.parquet`, `generation_inputs.parquet`) are shipped directly under `data/daily/`, `data/weekly/`, `data/monthly/`, and `data/real_estate/`.

## Data sources & access requirements

**What's bundled in this HF release** (no user credentials required):

| Source | Bundled artifact | License |
|---|---|---|
| SEC EDGAR | `filings/` (295k docs), `xbrl/` (46.8M facts) | Public domain (US gov) |
| FRED | 46 macroeconomic series | Public domain |
| EIA | 7 commodity series | Public domain |
| yfinance | `prices/` (OHLCV), `fundamentals/` (quarterly) — derived features | Non-commercial (yfinance ToU) |
| RentCast | `real_estate/` (address-level derived features only — rent + price targets, property attributes) | RentCast ToU — derived only |
| Macroeconomic events | `scenarios.parquet` (1,130 events × 49 types) | Curated by us, CC-BY-4.0 |

**What's NOT bundled** (gated — user credentials required for raw re-fetch via `collect_*.py`):

| Source | Status | User-side requirement |
|---|---|---|
| **Financial-news provider** | **Excluded** — provider ToU prohibits redistribution. The release ships derived counts (`filing_8k_count_30d`, `news_count_7d`, `has_press_release_7d`) only. | **User's own news-API key required** for `collect_news.py` |
| **RentCast raw listings** | **Excluded raw** — proprietary. Derived features bundled. | **User's own RentCast subscription** required for `collect_real_estate.py` raw mode |

## Universe

The 4,416-ticker universe combines: full Russell 2000 (1,923 IWM holdings), full S&P SmallCap 600 (72 IJR-only additions), iShares Micro-Cap (225 IWC additions), and 2,196 small-cap NASDAQ/NYSE tickers outside all three indices. The split is **3,857 operating companies + 333 funds + 226 SPACs**, with `security_type` recorded for applicability-aware stratification.

## Splits

- **Forecasting (T1, T4)**: chronological 70/30 split at **2024-09-03**.
- **Valuation + generation (T2, T3, T5, T6)**: **30% company-level holdout = 1,324 tickers** (seed = 42), each contributing its latest valid snapshot.
- **Real-estate (T7)**: 30% address-level holdout (random, seeded), with per-property time-axis features.

Cluster-bootstrap 95% CIs are computed per task: by `ticker` (T1/T2/T3/T5/T6), `scenario_id` (T4), or `address` (T7). Number of bootstrap resamples is adaptive in [1k, 10k] until `(ci_hi - ci_lo) / |mean| < 0.05`.

## Methods (panel)

The release ships a 18-method baseline panel across 7 families: 4 naive, 2 classical, 3 deep sequence, 3 zero-shot TSFM, 2 LLM-adapted multi-task systems, 3 zero-shot frontier LLMs (gpt-oss-120b, gpt-5.1, gemini-3-flash + qwen35). Every method registers via `@register(name=…, family=…, tasks=…)` and exposes the sklearn-style `(fit, predict, save, load)` contract.

```python
ml.list_methods()                 # all registered methods
ml.list_methods(task="T1")        # methods that support T1
ml.list_methods(family="naive")   # naive baselines
```

## License

- **Data**: CC-BY-4.0 (derived features + curated panel)
- **Code**: MIT (`code/macrolens/`, `code/dataloader/`, `code/methods/`, `code/eval.py`, `code/experiments/`)
- **Vendored libraries** (under `code/methods/_vendored/`):
  - `tslib/` — MIT (DLinear, iTransformer source)
  - `moderntcn/` — Apache 2.0 (ModernTCN source)
- **Reconstruction scripts** (`collect_*.py`) provided for sources with redistribution restrictions: SEC filings (re-fetch from EDGAR), financial news (re-fetch from provider), real-estate (re-fetch from RentCast).

## Citation

```bibtex
@inproceedings{macrolens2026,
  title = {{MacroLens}: A Multi-Task Benchmark for Contextual Financial Reasoning under Macroeconomic Scenarios},
  author = {<authors>},
  booktitle = {<arxiv>},
  year = {2026}
}
```

## Reproducibility

When `code/experiments/run_all.py` is executed, each method run produces a `RunRecord` JSON under `code/experiments/results/` recording: `git_sha`, `lib_versions`, `hardware`, `artifact_sha256` (SHA-256 of every parquet read), `timestamp`, and `deterministic_mode`. Predictions are also persisted at `code/experiments/predictions/<method>_<task>_seed<seed>.pkl` so eval logic can be re-applied via `code/experiments/re_evaluate.py` without re-running the models. These output directories are not shipped on Hugging Face — reviewers reproduce them by running the released code.

## Reconstruction (raw filings + news)

The release ships derived features and reconstruction scripts; raw artifacts subject to redistribution restrictions remain re-fetchable:

```bash
python collect_universe.py        # iShares ETF holdings + NASDAQ Trader directory
python collect_filings.py         # SEC EDGAR (10-K, 10-Q, 8-K, 20-F, 6-K, N-CSR, N-CSRS)
python collect_fundamentals.py    # XBRL company facts via SEC EDGAR
python collect_prices.py          # yfinance OHLCV + adjusted close
python collect_news.py            # provider-specific (~215k articles)
python collect_real_estate.py     # RentCast (100 metros, 139,855 properties)
python collect_macro.py           # FRED + EIA series
python preprocess.py
python assemble_benchmark.py
python generate_scenarios.py
python enrich_benchmark.py
python build_valuation_tasks.py
python validate_all.py
```

## Authors / Contact

Anonymous. Contact at `<email>` after author notification.