---
license: unknown
task_categories:
- tabular-classification
tags:
- finance
- stocks
- qrt
pretty_name: QRT Data Challenge — stock return sign prediction
---

# QRT Data Challenge — data & artifacts

Data for the QRT challenge: predict the sign of a stock's next-day return from its
last 20 daily returns and relative volumes plus GICS classification. Code that
consumes and produces these files: **https://github.com/edereynaldesaintmichel/qrt_stock_prediction**

## `data/` — raw challenge files

- `x_train.csv` / `x_test.csv` — one row per (ID, DATE, STOCK): `RET_1..RET_20`
  (daily returns, RET_h is calendar day t−h), `VOLUME_1..VOLUME_20` (relative
  volumes), and GICS columns `SECTOR`, `INDUSTRY_GROUP`, `INDUSTRY`, `SUB_INDUSTRY`.
- `y_train.csv` — target `RET`: 1 if the next-day return is positive.
- `submission_random_benchmark.csv` — random-benchmark submission example.
- `benchmark/` — the organizers' benchmark notebook and its submission.

DATEs are shuffled and non-adjacent, but at a fixed DATE every stock's `RET_h`
refers to the same calendar day — each (DATE, h) is an aligned cross-sectional
observation ("pseudo-day").

## `artifacts/` — generated by the repo's pipeline

- `features_{train,test}.parquet` — ~200 cross-sectional features (`src/features.py`).
- `panel_{ret,vol}_mktadj.npy` (stocks × pseudo-days, market-adjusted), `panel_mkt.npy`,
  `graph_stocks.npy` — pseudo-day panels (`src/build_graph.py`).
- `nbr_{pos,neg}.npy`, `w_{pos,neg}.npy` — top-100 positively/negatively correlated
  neighbors per stock with empirical-Bayes-shrunk correlation weights.
- `peer_{train,test}.parquet` — correlation-weighted peer momentum scalars.
- `oof_*.npy` / `test_*.npy` — out-of-fold and test predictions per model
  (LightGBM, CatBoost, MLP v1/v2, LGB+peer), aligned with the csv row order.
- `feature_importance.csv`, `submission.csv`.

All artifacts are reproducible from `data/` with the repo's scripts.