diff --git a/.github/workflows/build-models.yml b/.github/workflows/build-models.yml index cd83366..c7b3af2 100644 --- a/.github/workflows/build-models.yml +++ b/.github/workflows/build-models.yml @@ -2,18 +2,17 @@ name: Build Example Model Pickles on: workflow_dispatch: + pull_request: + paths: + - ".github/workflows/build-models.yml" + - "numerai/**" push: - # paths: - # - example_model.ipynb - # - hello_numerai.ipynb - # - feature_neutralization.ipynb - # - target_ensemble.ipynb - # - signals/example_model.ipynb - # - crypto/example_model.ipynb branches: - master -concurrency: build-example-models +concurrency: + group: build-example-models-${{ github.ref }} + cancel-in-progress: true jobs: @@ -21,15 +20,27 @@ jobs: name: "Build Example Model Pickles" runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: "3.12" - - name: Install jupyter + - name: install-notebook-dependencies run: | python -m pip install --upgrade pip - pip install jupyter - pip install -r https://raw.githubusercontent.com/numerai/numerai-predict/refs/heads/master/py3.12/requirements.txt + pip install \ + cloudpickle==3.1.1 \ + jupyter \ + lightgbm==4.5.0 \ + matplotlib==3.10.3 \ + numerapi==2.20.7 \ + pandas==2.3.1 \ + pyarrow==18.1.0 \ + scikit-learn==1.6.1 \ + scipy==1.16.0 \ + seaborn==0.13.2 + pip install --no-deps numerai-tools==0.4.0 + - name: test-agent-helpers + run: PYTHONPATH=numerai python -m unittest numerai.agents.tests.test_target_transforms -v - name: build-example-model run: | jupyter nbconvert \ @@ -55,37 +66,55 @@ jobs: --ExecutePreprocessor.timeout=-1 \ --to html - name: build-signals-example-model + if: github.event_name == 'push' || github.event_name == 'workflow_dispatch' run: | jupyter nbconvert \ --execute signals/example_model.ipynb \ --ExecutePreprocessor.timeout=-1 \ --to html - name: build-crypto-example-model + if: github.event_name == 'push' || github.event_name == 'workflow_dispatch' run: | jupyter nbconvert \ --execute crypto/example_model.ipynb \ --ExecutePreprocessor.timeout=-1 \ --to html + - name: move-numerai-pickles-to-cached-pickles-dir + run: | + mkdir -p cached-pickles/ + mv -f numerai/example_model_v53_ender60.pkl cached-pickles/ + mv -f numerai/hello_numerai_v53_ender60.pkl cached-pickles/ + mv -f numerai/feature_neutralization_v53_ender60.pkl cached-pickles/ + mv -f numerai/target_ensemble_v53_ender60.pkl cached-pickles/ + - name: generate-publication-candidates-and-provenance + run: | + python numerai/generate_example_predictions.py \ + --model cached-pickles/example_model_v53_ender60.pkl \ + --output-dir generated-example-predictions + - name: upload-ender60-validation-evidence + uses: actions/upload-artifact@v4 + with: + name: v53-ender60-example-model-${{ github.sha }} + if-no-files-found: error + path: | + cached-pickles/*_v53_ender60.pkl + generated-example-predictions/* + numerai/*.html + - name: cache-provenance-for-master + if: github.event_name == 'push' + run: | + cp generated-example-predictions/v53_ender60_provenance.json cached-pickles/ - name: delete-generated-files run: | - rm -f numerai/example_model.html - rm -f numerai/hello_numerai.html - rm -f numerai/feature_neutralization.html - rm -f numerai/target_ensemble.html + rm -f numerai/*.html rm -f signals/example_model.html rm -f signals/signals_example_preds.csv rm -f signals_example_preds.csv rm -f crypto/example_model.html rm -f crypto/crypto_example_preds.csv rm -f crypto_example_preds.csv - - name: move-numerai-pickles-to-cached-pickles-dir - run: | - mkdir -p cached-pickles/ - mv -f numerai/example_model.pkl cached-pickles/ - mv -f numerai/hello_numerai.pkl cached-pickles/ - mv -f numerai/feature_neutralization.pkl cached-pickles/ - mv -f numerai/target_ensemble.pkl cached-pickles/ - name: commit-to-master + if: github.event_name == 'push' uses: EndBug/add-and-commit@v9 with: add: "cached-pickles/*" diff --git a/AGENTS.md b/AGENTS.md index 05dc790..20651b7 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -301,5 +301,6 @@ Build datasets with `python -m agents.code.data.build_full_datasets`: - **Register repo skills**: `ln -s $PWD/numerai/agents/skills/* ~/.codex/skills/` - **Network access required** for MCP operations (Codex CLI may need `--yolo` flag) - **Always query Python version** before creating pkl files -- **BMC (Benchmark Model Contribution)** is the key experiment metric (proxy for MMC), computed vs official `v53_lgbm_ender20` benchmark predictions in `*_benchmark_models.parquet` +- **BMC (Benchmark Model Contribution)** is the key experiment metric (proxy for MMC), computed by default vs official `v53_lgbm_ender60` benchmark predictions in `*_benchmark_models.parquet`. +- The v5.3 default research target is explicit `target_ender_60` with a 16-era embargo. Ender-20 work must explicitly select `target_ender_20`; never let an Ender-20-named config read generic `target`. - **Only Classic tournament (8)** supports pickle uploads diff --git a/README.md b/README.md index 2bfa563..88f23f0 100644 --- a/README.md +++ b/README.md @@ -9,9 +9,15 @@ Numerai is quickly developing open-source agent skills for you to use in the tou ``` git clone git@github.com:numerai/example-scripts cd example-scripts && curl -sL http://numer.ai/install-mcp.sh | bash -codex exec --yolo "find the best neural network architecture to predict target ender" +codex exec --yolo "find the best neural network architecture to predict target_ender_60" ``` +The maintained v5.3 examples pin `target_ender_60` explicitly. After the +in-place default-target cutover, generic `target` aliases Ender-60, while +`target_ender_20` remains available for intentionally reproducing the older +horizon. Explicit target names and the target-versioned cached model filenames +prevent an old Ender-20 model from being mistaken for the current example. + ## Notebooks We highly recommend getting started with Agents using the above section. But, if you're looking to kill some time on artisan data science, you can check out our tutorial notebooks here as well: diff --git a/cached-pickles/example_model.pkl b/cached-pickles/example_model_v53_ender60.pkl similarity index 50% rename from cached-pickles/example_model.pkl rename to cached-pickles/example_model_v53_ender60.pkl index 7f142a8..a6ec05a 100644 Binary files a/cached-pickles/example_model.pkl and b/cached-pickles/example_model_v53_ender60.pkl differ diff --git a/cached-pickles/feature_neutralization.pkl b/cached-pickles/feature_neutralization.pkl deleted file mode 100644 index 9837734..0000000 Binary files a/cached-pickles/feature_neutralization.pkl and /dev/null differ diff --git a/cached-pickles/feature_neutralization_v53_ender60.pkl b/cached-pickles/feature_neutralization_v53_ender60.pkl new file mode 100644 index 0000000..81575d5 Binary files /dev/null and b/cached-pickles/feature_neutralization_v53_ender60.pkl differ diff --git a/cached-pickles/hello_numerai.pkl b/cached-pickles/hello_numerai_v53_ender60.pkl similarity index 50% rename from cached-pickles/hello_numerai.pkl rename to cached-pickles/hello_numerai_v53_ender60.pkl index 6395968..af09dd0 100644 Binary files a/cached-pickles/hello_numerai.pkl and b/cached-pickles/hello_numerai_v53_ender60.pkl differ diff --git a/cached-pickles/target_ensemble.pkl b/cached-pickles/target_ensemble_v53_ender60.pkl similarity index 64% rename from cached-pickles/target_ensemble.pkl rename to cached-pickles/target_ensemble_v53_ender60.pkl index 30c601c..6b21d03 100644 Binary files a/cached-pickles/target_ensemble.pkl and b/cached-pickles/target_ensemble_v53_ender60.pkl differ diff --git a/cached-pickles/v53_ender60_provenance.json b/cached-pickles/v53_ender60_provenance.json new file mode 100644 index 0000000..dfc9481 --- /dev/null +++ b/cached-pickles/v53_ender60_provenance.json @@ -0,0 +1,39 @@ +{ + "generated_at": "2026-08-06T19:22:31.088355+00:00", + "source_commit": "085420354bcd88caa29dfb68964bd14fbbc7522a", + "source_notebook": "numerai/example_model.ipynb", + "dataset_version": "v5.3", + "target_col": "target_ender_60", + "model_artifact": "example_model_v53_ender60.pkl", + "model_sha256": "a84448a2ff635631d111802398fc65fcba8250f943a729d64bbdf255a7b0cd34", + "model_artifacts": { + "example_model_v53_ender60.pkl": { + "source_notebook": "numerai/example_model.ipynb", + "sha256": "a84448a2ff635631d111802398fc65fcba8250f943a729d64bbdf255a7b0cd34" + }, + "feature_neutralization_v53_ender60.pkl": { + "source_notebook": "numerai/feature_neutralization.ipynb", + "sha256": "8c45f0f1062bb337ebc33b1f1a9497a7e71ccc380ea054bb238b147c85d26230" + }, + "hello_numerai_v53_ender60.pkl": { + "source_notebook": "numerai/hello_numerai.ipynb", + "sha256": "f94465d7431f6c53130fb7361b4132184539181e74e43e077db220d7ac3ca1a7" + }, + "target_ensemble_v53_ender60.pkl": { + "source_notebook": "numerai/target_ensemble.ipynb", + "sha256": "fabba10e6701984022e28845f0748252e65ac1ffbdc4d4dcefc141a4061d5775" + } + }, + "predictions": { + "validation": { + "file": "validation_example_preds_v53_ender60.parquet", + "rows": 4107040, + "sha256": "9587b14138c572fd041c1a7c05f0b36a53a3991f911aca162eae8bae9f82a2b3" + }, + "live": { + "file": "live_example_preds_v53_ender60.parquet", + "rows": 7034, + "sha256": "89b4702157b68bee4bfedd0e21e0b7368274c4f7fc861ccbe0af2853a8d7cb45" + } + } +} diff --git a/numerai/AGENTS.md b/numerai/AGENTS.md index 745c0a6..1ee8c0e 100644 --- a/numerai/AGENTS.md +++ b/numerai/AGENTS.md @@ -1,6 +1,8 @@ # Numerai Tournament This folder contains examples on how to participate in the Numerai Tournament. +For v5.3, select `target_ender_60` explicitly in maintained training examples. The generic `target` column demonstrates the current default alias only; do not use it when the horizon must remain reproducible. + ## Directory Guide: - `agents/`: agentic research framework + training/analysis pipeline (`python -m agents.code.modeling`) - `v5.3/` (and other `v*/`): Numerai dataset files (often gitignored locally) diff --git a/numerai/agents/AGENTS.md b/numerai/agents/AGENTS.md index 9b1ed83..8c526a1 100644 --- a/numerai/agents/AGENTS.md +++ b/numerai/agents/AGENTS.md @@ -8,6 +8,8 @@ Always check the agents/skills/ folder for skills that match the user request. Run commands from `numerai/` (so `agents` is importable), or from repo root with `PYTHONPATH=numerai`. Data is expected to live under `numerai//` (e.g. `numerai/v5.3/`), which is often gitignored locally. +The default v5.3 workflow uses explicit `target_ender_60`, `v53_lgbm_ender60`, and a 16-era embargo. Generic `target` is a mutable dataset alias and must not be used where target identity matters. Keep Ender-20-named configs pinned to `target_ender_20`. + To make these repo skills available to Codex CLI, symlink them into `~/.codex/skills/`: `ln -s $PWD/numerai/agents/skills/* ~/.codex/skills/` diff --git a/numerai/agents/baselines/configs/deep_lgbm_ender20_baseline.py b/numerai/agents/baselines/configs/deep_lgbm_ender20_baseline.py index cb63641..4ab71ab 100644 --- a/numerai/agents/baselines/configs/deep_lgbm_ender20_baseline.py +++ b/numerai/agents/baselines/configs/deep_lgbm_ender20_baseline.py @@ -2,7 +2,7 @@ 'embargo_eras': 13, 'era_col': 'era', 'feature_set': 'all', - 'target_col': 'target'}, + 'target_col': 'target_ender_20'}, 'model': {'x_groups': ['features', 'era', 'benchmark_models'], 'params': {'colsample_bytree': 0.1, 'learning_rate': 0.001, 'max_depth': 10, diff --git a/numerai/agents/baselines/configs/deep_lgbm_ender20_baseline_downsampled.py b/numerai/agents/baselines/configs/deep_lgbm_ender20_baseline_downsampled.py index 9b770bb..55b9c4a 100644 --- a/numerai/agents/baselines/configs/deep_lgbm_ender20_baseline_downsampled.py +++ b/numerai/agents/baselines/configs/deep_lgbm_ender20_baseline_downsampled.py @@ -4,7 +4,7 @@ "embargo_eras": 13, "era_col": "era", "feature_set": "all", - "target_col": "target", + "target_col": "target_ender_20", "id_col": "id", "full_data_path": "v5.3/downsampled_full.parquet", "benchmark_data_path": "v5.3/downsampled_full_benchmark_models.parquet", diff --git a/numerai/agents/baselines/configs/deep_lgbm_ender60_baseline.py b/numerai/agents/baselines/configs/deep_lgbm_ender60_baseline.py new file mode 100644 index 0000000..563626a --- /dev/null +++ b/numerai/agents/baselines/configs/deep_lgbm_ender60_baseline.py @@ -0,0 +1,22 @@ +CONFIG = {'data': {'data_version': 'v5.3', + 'embargo_eras': 16, + 'era_col': 'era', + 'feature_set': 'all', + 'target_col': 'target_ender_60'}, + 'model': {'x_groups': ['features', 'era', 'benchmark_models'], 'params': {'colsample_bytree': 0.1, + 'learning_rate': 0.001, + 'max_depth': 10, + 'min_data_in_leaf': 10000, + 'n_estimators': 30000, + 'n_jobs': 30, + 'num_leaves': 1024, + 'random_state': 1337}, + 'type': 'LGBMRegressor'}, + 'output': {'output_dir': 'baselines', + 'results_name': 'deep_lgbm_ender60_baseline'}, + 'preprocessing': {'missing_value': 2.0, 'nan_missing_all_twos': False}, + 'training': {'cv': {'embargo': 16, + 'enabled': True, + 'min_train_size': 0, + 'mode': 'expanding', + 'n_splits': 5}}} diff --git a/numerai/agents/baselines/configs/deep_lgbm_ender60_baseline_downsampled.py b/numerai/agents/baselines/configs/deep_lgbm_ender60_baseline_downsampled.py new file mode 100644 index 0000000..4da4737 --- /dev/null +++ b/numerai/agents/baselines/configs/deep_lgbm_ender60_baseline_downsampled.py @@ -0,0 +1,40 @@ +CONFIG = { + "data": { + "data_version": "v5.3", + "embargo_eras": 16, + "era_col": "era", + "feature_set": "all", + "target_col": "target_ender_60", + "id_col": "id", + "full_data_path": "v5.3/downsampled_full.parquet", + "benchmark_data_path": "v5.3/downsampled_full_benchmark_models.parquet", + }, + "model": { + "x_groups": ["features", "era", "benchmark_models"], + "params": { + "colsample_bytree": 0.1, + "learning_rate": 0.001, + "max_depth": 10, + "min_data_in_leaf": 10000, + "n_estimators": 30000, + "n_jobs": 30, + "num_leaves": 1024, + "random_state": 1337, + }, + "type": "LGBMRegressor", + }, + "output": { + "output_dir": "baselines", + "results_name": "deep_lgbm_ender60_baseline_downsampled", + }, + "preprocessing": {"missing_value": 2.0, "nan_missing_all_twos": False}, + "training": { + "cv": { + "embargo": 16, + "enabled": True, + "min_train_size": 0, + "mode": "expanding", + "n_splits": 5, + } + }, +} diff --git a/numerai/agents/baselines/configs/small_lgbm_ender20_baseline.py b/numerai/agents/baselines/configs/small_lgbm_ender20_baseline.py index 47539dd..b372cc5 100644 --- a/numerai/agents/baselines/configs/small_lgbm_ender20_baseline.py +++ b/numerai/agents/baselines/configs/small_lgbm_ender20_baseline.py @@ -2,7 +2,7 @@ 'embargo_eras': 13, 'era_col': 'era', 'feature_set': 'medium', - 'target_col': 'target', + 'target_col': 'target_ender_20', 'full_data_path': 'v5.3/downsampled_full.parquet', 'benchmark_data_path': 'v5.3/downsampled_full_benchmark_models.parquet'}, 'model': {'x_groups': ['features', 'era', 'benchmark_models'], 'params': {'colsample_bytree': 0.1, diff --git a/numerai/agents/baselines/configs/small_lgbm_ender60_baseline.py b/numerai/agents/baselines/configs/small_lgbm_ender60_baseline.py new file mode 100644 index 0000000..52e19d8 --- /dev/null +++ b/numerai/agents/baselines/configs/small_lgbm_ender60_baseline.py @@ -0,0 +1,25 @@ +CONFIG = {'data': {'data_version': 'v5.3', + 'embargo_eras': 16, + 'era_col': 'era', + 'feature_set': 'medium', + 'target_col': 'target_ender_60', + 'full_data_path': 'v5.3/downsampled_full.parquet', + 'benchmark_data_path': 'v5.3/downsampled_full_benchmark_models.parquet'}, + 'model': {'x_groups': ['features', 'era', 'benchmark_models'], 'params': {'colsample_bytree': 0.1, + 'device_type': 'gpu', + 'learning_rate': 0.01, + 'max_depth': 5, + 'min_data_in_leaf': 10000, + 'n_estimators': 2000, + 'n_jobs': 30, + 'num_leaves': 31, + 'random_state': 1337}, + 'type': 'LGBMRegressor'}, + 'output': {'output_dir': 'baselines', + 'results_name': 'small_lgbm_ender60_baseline'}, + 'preprocessing': {'missing_value': 2.0, 'nan_missing_all_twos': False}, + 'training': {'cv': {'embargo': 16, + 'enabled': True, + 'min_train_size': 0, + 'mode': 'expanding', + 'n_splits': 5}}} diff --git a/numerai/agents/code/analysis/plot_benchmark_corrs.py b/numerai/agents/code/analysis/plot_benchmark_corrs.py index 6270a75..0306b09 100644 --- a/numerai/agents/code/analysis/plot_benchmark_corrs.py +++ b/numerai/agents/code/analysis/plot_benchmark_corrs.py @@ -28,15 +28,17 @@ DEFAULT_DATA_VERSION_V52 = "v5.2" DEFAULT_DATA_VERSION_V51 = "v5.1" DEFAULT_DATA_VERSION_V5 = "v5.0" -DEFAULT_MODELS = ["v52_teager20", "v51_teager20", "v5_teager20"] +DEFAULT_MODELS = ["v53_ender60", "v52_teager20", "v51_teager20", "v5_teager20"] ALIASES: dict[str, Sequence[str]] = { "v52_teager20": ("v52_lgbm_teager2b20", "v52_lgbm_teager20"), "v51_teager20": ("v51_lgbm_teager2b20", "v51_lgbm_teager20", "v51_teager20"), "v5_teager20": ("v5_lgbm_teager2b20", "v5_lgbm_teager20", "v5_teager20"), "v53_ender20": ("v53_lgbm_ender20",), + "v53_ender60": ("v53_lgbm_ender60",), "v52_ender20": ("v52_lgbm_ender20",), "ender20": ("v53_lgbm_ender20",), + "ender60": ("v53_lgbm_ender60",), "v52_cyrus": ("v52_lgbm_cyrusd20",), "cyrus": ("v52_lgbm_cyrusd20",), "cyrusd20": ("v52_lgbm_cyrusd20",), @@ -44,7 +46,9 @@ TARGET_ALIASES: dict[str, Sequence[str]] = { "ender20": ("target_ender_20", "target_ender20"), + "ender60": ("target_ender_60", "target_ender60"), "v53_ender20": ("target_ender_20",), + "v53_ender60": ("target_ender_60",), "v52_ender20": ("target_ender_20",), "cyrus": ("target_cyrusd_20", "target_cyrus_20", "target_cyrus20"), "cyrusd20": ("target_cyrusd_20",), @@ -55,7 +59,7 @@ def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description=( - "Plot cumulative per-era numerai_corr for benchmark models vs ender/cyrus." + "Plot cumulative per-era numerai_corr for benchmark models vs Ender-60/Cyrus." ) ) parser.add_argument( @@ -139,8 +143,8 @@ def parse_args() -> argparse.Namespace: parser.add_argument( "--ender-col", type=str, - default="ender20", - help="Target column (or alias) for ender20 correlations.", + default="ender60", + help="Target column (or alias) for Ender-60 correlations.", ) parser.add_argument( "--cyrus-col", @@ -223,7 +227,7 @@ def _resolve_target_column(name: str, columns: Iterable[str]) -> str: def _infer_version(name: str, default_version: str, args: argparse.Namespace) -> str: lowered = name.lower() - if lowered in {"ender20"}: + if lowered in {"ender20", "ender60"}: return args.v53_version if lowered.startswith("v53_"): return args.v53_version diff --git a/numerai/agents/code/analysis/show_experiment.py b/numerai/agents/code/analysis/show_experiment.py index bfd49df..04adafc 100644 --- a/numerai/agents/code/analysis/show_experiment.py +++ b/numerai/agents/code/analysis/show_experiment.py @@ -20,6 +20,10 @@ ) from exc from agents.code.metrics import numerai_metrics +from agents.code.modeling.utils.constants import ( + DEFAULT_BENCHMARK_MODEL, + DEFAULT_TARGET_COL, +) AGENTS_DIR = Path(__file__).resolve().parents[2] @@ -53,7 +57,7 @@ def parse_args() -> argparse.Namespace: parser.add_argument( "--target-col", type=str, - default="target", + default=DEFAULT_TARGET_COL, help="Target column name in predictions file.", ) parser.add_argument( @@ -85,7 +89,7 @@ def parse_args() -> argparse.Namespace: default=None, help=( "Use benchmark model predictions as the baseline instead of a results file. " - "Example: v53_lgbm_ender20. If set, base_model can be any label (e.g. 'benchmark')." + f"Example: {DEFAULT_BENCHMARK_MODEL}. If set, base_model can be any label (e.g. 'benchmark')." ), ) parser.add_argument( @@ -459,7 +463,7 @@ def main() -> None: use_benchmark_base = args.base_benchmark_model is not None if args.base_model == "benchmark" and args.base_benchmark_model is None: - args.base_benchmark_model = "v53_lgbm_ender20" + args.base_benchmark_model = DEFAULT_BENCHMARK_MODEL use_benchmark_base = True base_results_path = None @@ -500,7 +504,7 @@ def main() -> None: ) benchmark_model = args.base_benchmark_model or reference_data.get( "benchmark", {} - ).get("model", "v53_lgbm_ender20") + ).get("model", DEFAULT_BENCHMARK_MODEL) if args.benchmark_data_path is not None: benchmark, benchmark_col = numerai_metrics.load_benchmark_predictions_from_path( diff --git a/numerai/agents/code/metrics/numerai_metrics.py b/numerai/agents/code/metrics/numerai_metrics.py index 12e2285..d8813ba 100644 --- a/numerai/agents/code/metrics/numerai_metrics.py +++ b/numerai/agents/code/metrics/numerai_metrics.py @@ -11,7 +11,11 @@ from numerapi import NumerAPI from numerai_tools.scoring import correlation_contribution, numerai_corr -from agents.code.modeling.utils.constants import NUMERAI_DIR, REPO_DIR +from agents.code.modeling.utils.constants import ( + DEFAULT_BENCHMARK_MODEL, + NUMERAI_DIR, + REPO_DIR, +) def _resolve_data_path(path: str | Path) -> Path: @@ -253,7 +257,7 @@ def ensure_full_benchmark_models(napi: NumerAPI, data_version: str) -> Path: def load_benchmark_predictions( data_version: str, split: str = "full", - benchmark_model: str = "v53_lgbm_ender20", + benchmark_model: str = DEFAULT_BENCHMARK_MODEL, era_col: str = "era", ) -> tuple[pd.DataFrame, str]: """Download and load benchmark predictions for the given data split.""" @@ -366,7 +370,7 @@ def summarize_prediction_file_with_bmc( pred_cols: Sequence, target_col: str, data_version: str, - benchmark_model: str = "v53_lgbm_ender20", + benchmark_model: str = DEFAULT_BENCHMARK_MODEL, benchmark_data_path: str | Path | None = None, era_col: str = "era", id_col: str = "id", diff --git a/numerai/agents/code/modeling/utils/constants.py b/numerai/agents/code/modeling/utils/constants.py index d2db3ad..94acfa4 100644 --- a/numerai/agents/code/modeling/utils/constants.py +++ b/numerai/agents/code/modeling/utils/constants.py @@ -6,8 +6,10 @@ NUMERAI_DIR = BASE_DIR.parent REPO_DIR = NUMERAI_DIR.parent DEFAULT_CONFIG_PATH = ( - BASE_DIR / "baselines" / "configs" / "small_lgbm_ender20_baseline.py" + BASE_DIR / "baselines" / "configs" / "small_lgbm_ender60_baseline.py" ) DEFAULT_OUTPUT_DIR = BASE_DIR DEFAULT_BASELINES_DIR = BASE_DIR / "baselines" -DEFAULT_BENCHMARK_MODEL = "v53_lgbm_ender20" +DEFAULT_TARGET_COL = "target_ender_60" +DEFAULT_BENCHMARK_MODEL = "v53_lgbm_ender60" +DEFAULT_EMBARGO_ERAS = 16 diff --git a/numerai/agents/code/modeling/utils/numerai_cv.py b/numerai/agents/code/modeling/utils/numerai_cv.py index dc9931c..e4c99a2 100644 --- a/numerai/agents/code/modeling/utils/numerai_cv.py +++ b/numerai/agents/code/modeling/utils/numerai_cv.py @@ -9,6 +9,7 @@ from agents.code.modeling.utils.model_factory import build_model from agents.code.modeling.utils.model_data import ModelDataBatch +from agents.code.modeling.utils.constants import DEFAULT_EMBARGO_ERAS def _era_sort_key(era): @@ -25,7 +26,7 @@ def _sorted_unique_eras(eras: Iterable) -> List: def era_cv_splits( eras: Sequence, n_splits: int = 5, - embargo: int = 13, + embargo: int = DEFAULT_EMBARGO_ERAS, mode: str = "expanding", min_train_size: int = 1, ) -> List[Tuple[List, List]]: @@ -94,7 +95,7 @@ def build_oof_predictions( feature_cols: list[str] | None = None, ) -> tuple[pd.DataFrame, dict]: cv_n_splits = int(cv_config.get("n_splits", 5)) - cv_embargo = int(cv_config.get("embargo", 13)) + cv_embargo = int(cv_config.get("embargo", DEFAULT_EMBARGO_ERAS)) cv_mode = cv_config.get("mode", "expanding") cv_min_train_size = int(cv_config.get("min_train_size", 0)) diff --git a/numerai/agents/code/modeling/utils/pipeline.py b/numerai/agents/code/modeling/utils/pipeline.py index d32386b..88383d3 100644 --- a/numerai/agents/code/modeling/utils/pipeline.py +++ b/numerai/agents/code/modeling/utils/pipeline.py @@ -16,7 +16,9 @@ BASE_DIR, DEFAULT_BASELINES_DIR, DEFAULT_BENCHMARK_MODEL, + DEFAULT_EMBARGO_ERAS, DEFAULT_OUTPUT_DIR, + DEFAULT_TARGET_COL, ) from .data import ( apply_missing_all_twos_as_nan, @@ -257,12 +259,12 @@ def run_training( data_version = data_config.get("data_version", "v5.3") feature_set = data_config.get("feature_set", "small") - target_col = data_config.get("target_col", "target") + target_col = data_config.get("target_col", DEFAULT_TARGET_COL) era_col = data_config.get("era_col", "era") id_col = data_config.get("id_col", "id") full_data_path = data_config.get("full_data_path") benchmark_data_path = data_config.get("benchmark_data_path") - embargo_eras = data_config.get("embargo_eras", 13) + embargo_eras = data_config.get("embargo_eras", DEFAULT_EMBARGO_ERAS) benchmark_model = data_config.get("benchmark_model", DEFAULT_BENCHMARK_MODEL) nan_missing_all_twos = preprocessing_config.get("nan_missing_all_twos", False) diff --git a/numerai/agents/skills/numerai-experiment-design/SKILL.md b/numerai/agents/skills/numerai-experiment-design/SKILL.md index bef2bd5..077a6cb 100644 --- a/numerai/agents/skills/numerai-experiment-design/SKILL.md +++ b/numerai/agents/skills/numerai-experiment-design/SKILL.md @@ -14,8 +14,8 @@ This skill is *not* complete after a single promising run. You must run experime ## Planning checklist (answer before running) - State the model idea and novelty. -- Choose the initial baseline and feature set. Default to `deep_lgbm_ender20_baseline` (feature_set=all) unless the user explicitly requests the small baseline; keep experiments' feature_set aligned with the chosen baseline. -- Decide the primary metric (`bmc_mean` and `bmc_last_200_eras`) where BMC = Benchmark Model Contribution vs official `v53_lgbm_ender20`. +- Choose the initial baseline and feature set. Default to `deep_lgbm_ender60_baseline` (feature_set=all) unless the user explicitly requests the small baseline; keep experiments' feature_set aligned with the chosen baseline. +- Decide the primary metric (`bmc_mean` and `bmc_last_200_eras`) where BMC = Benchmark Model Contribution vs official `v53_lgbm_ender60`. - Decide which parameter dimensions to explore based on the core idea (targets, model hyperparameters, ensemble weights, data settings). - Or decide that only a minimal round is needed because the change is tiny — but still run multiple variants unless the user explicitly requested exactly one run. @@ -70,7 +70,7 @@ Note that these are examples only. Each idea will call for different sweeps, or ## Baseline alignment - Declare which baseline the model is aiming to improve on. - Keep `feature_set` aligned with the baseline for comparisons. -- Default to ender20 (`v53_lgbm_ender20`) as the benchmark reference and plot baseline, even when sweeping; only use the small baseline when explicitly requested. +- Default to Ender-60 (`v53_lgbm_ender60`) as the benchmark reference and plot baseline, even when sweeping; only use the small baseline when explicitly requested. Pin Ender-20 research explicitly to `target_ender_20` and an Ender-20-named baseline. ## Experiment organization - Keep related runs under a single, well-named folder in `agents/experiments/`. @@ -84,7 +84,7 @@ Note that these are examples only. Each idea will call for different sweeps, or ## Reporting expectations - Run experiments in **rounds** and continuously wait for the round to finish so you don't report prematurely. -- Once you complete your research and stop finding improvements, write a report for the user. It should describe learnings (what worked and what did not), include the final stats table, and run `PYTHONPATH=numerai python3 -m agents.code.analysis.show_experiment benchmark --base-benchmark-model v53_lgbm_ender20 --benchmark-data-path numerai/v5.3/full_benchmark_models.parquet --start-era 575 --dark --output-dir --baselines-dir numerai/agents/baselines` to generate the cumulative corr + BMC plot (share the output path). +- Once you complete your research and stop finding improvements, write a report for the user. It should describe learnings (what worked and what did not), include the final stats table, and run `PYTHONPATH=numerai python3 -m agents.code.analysis.show_experiment benchmark --base-benchmark-model v53_lgbm_ender60 --benchmark-data-path numerai/v5.3/full_benchmark_models.parquet --start-era 575 --dark --output-dir --baselines-dir numerai/agents/baselines` to generate the cumulative corr + BMC plot (share the output path). - Use `python -m agents.code.analysis.plot_benchmark_corrs` only when comparing official benchmark model columns, not for experiment BMC curves. - Always report: - `bmc` (full) and `bmc_last_200_eras` @@ -97,6 +97,7 @@ Note that these are examples only. Each idea will call for different sweeps, or - Full: `numerai/v5.3/full.parquet`, `numerai/v5.3/full_benchmark_models.parquet` - Downsampled (every 4 eras): `numerai/v5.3/downsampled_full.parquet`, `numerai/v5.3/downsampled_full_benchmark_models.parquet` - Prefer downsampled for quick iteration; only scale after a clear signal for the final model. +- Use `target_ender_60` and a 16-era embargo for the default v5.3 workflow. Never rely on generic `target` when the horizon matters. ## Useful entry points - `PYTHONPATH=numerai python3 -m agents.code.modeling` (training + metrics) diff --git a/numerai/agents/skills/numerai-model-implementation/SKILL.md b/numerai/agents/skills/numerai-model-implementation/SKILL.md index f3091f8..3934ee1 100644 --- a/numerai/agents/skills/numerai-model-implementation/SKILL.md +++ b/numerai/agents/skills/numerai-model-implementation/SKILL.md @@ -38,7 +38,7 @@ if model_type == "XGBRegressor": CONFIG = { "model": {"type": "XGBRegressor", "params": {"n_estimators": 500}}, "training": {"cv": {"n_splits": 5}}, - "data": {"data_version": "v5.3", "feature_set": "small", "target_col": "target", "era_col": "era"}, + "data": {"data_version": "v5.3", "feature_set": "small", "target_col": "target_ender_60", "embargo_eras": 16, "era_col": "era"}, "output": {}, "preprocessing": {}, } diff --git a/numerai/agents/skills/numerai-research/SKILL.md b/numerai/agents/skills/numerai-research/SKILL.md index 06d26a6..186feda 100644 --- a/numerai/agents/skills/numerai-research/SKILL.md +++ b/numerai/agents/skills/numerai-research/SKILL.md @@ -15,7 +15,7 @@ This skill is a “meta-workflow” that sequences existing Numerai skills so re - Follow the `numerai-experiment-design` skill to: - clarify the idea (or run quick scout interpretations if ambiguous) - - choose baseline + feature set alignment (default ender20 baseline) + - choose baseline + feature set alignment (default Ender-60 baseline) - create an experiment folder under `numerai/agents/experiments//` - write configs in `configs/` - run training via `PYTHONPATH=numerai python3 -m agents.code.modeling --config --output-dir ` @@ -47,5 +47,5 @@ If (and only if) the user wants deployment: - Scout first on downsampled data; scale only winners. - Run experiments in rounds (4–5 configs per round) and stop only after a plateau + confirmatory scale step. -- Benchmark reference: `v53_lgbm_ender20`. +- Benchmark reference: `v53_lgbm_ender60`. - Always record corr + BMC metrics and include the standard plot in the report. diff --git a/numerai/agents/skills/report-research/SKILL.md b/numerai/agents/skills/report-research/SKILL.md index f475bf5..80766a8 100644 --- a/numerai/agents/skills/report-research/SKILL.md +++ b/numerai/agents/skills/report-research/SKILL.md @@ -58,7 +58,7 @@ Default standard plot (baseline = benchmark predictions): ```bash PYTHONPATH=numerai python3 -m agents.code.analysis.show_experiment benchmark \ - --base-benchmark-model v53_lgbm_ender20 \ + --base-benchmark-model v53_lgbm_ender60 \ --benchmark-data-path numerai/v5.3/full_benchmark_models.parquet \ --start-era 575 --dark \ --output-dir numerai/agents/experiments/ \ diff --git a/numerai/agents/tests/test_integration_pipeline.py b/numerai/agents/tests/test_integration_pipeline.py index fe3000d..5975715 100644 --- a/numerai/agents/tests/test_integration_pipeline.py +++ b/numerai/agents/tests/test_integration_pipeline.py @@ -7,7 +7,7 @@ from agents.code.modeling.utils.pipeline import run_training -class TestSmallLgbmEnder20Baseline(unittest.TestCase): +class TestSmallLgbmEnder60Baseline(unittest.TestCase): def test_pipeline_metrics(self) -> None: repo_root = Path(__file__).resolve().parents[2] config_path = ( @@ -15,7 +15,7 @@ def test_pipeline_metrics(self) -> None: / "agents" / "baselines" / "configs" - / "small_lgbm_ender20_baseline.py" + / "small_lgbm_ender60_baseline.py" ) _, results_path = run_training(config_path) diff --git a/numerai/agents/tests/test_target_transforms.py b/numerai/agents/tests/test_target_transforms.py index 78ad03b..e24da95 100644 --- a/numerai/agents/tests/test_target_transforms.py +++ b/numerai/agents/tests/test_target_transforms.py @@ -22,7 +22,7 @@ def test_residual_to_benchmark_is_orthogonal_per_era(self) -> None: scale=0.1, size=n - n // 2 ) - X = pd.DataFrame({"era": eras, "v53_lgbm_ender20": benchmark}) + X = pd.DataFrame({"era": eras, "v53_lgbm_ender60": benchmark}) y = pd.Series(y, name="target") transformed = apply_target_transform( @@ -30,7 +30,7 @@ def test_residual_to_benchmark_is_orthogonal_per_era(self) -> None: X, { "type": "residual_to_benchmark", - "benchmark_col": "v53_lgbm_ender20", + "benchmark_col": "v53_lgbm_ender60", "era_col": "era", "per_era": True, "fit_intercept": True, @@ -46,13 +46,13 @@ def test_residual_to_benchmark_is_orthogonal_per_era(self) -> None: def test_subtract_benchmark_zscore_is_per_era_and_scaled(self) -> None: eras = np.array(["0001", "0001", "0002", "0002"]) benchmark = np.array([1.0, 3.0, 10.0, 14.0]) - X = pd.DataFrame({"era": eras, "v53_lgbm_ender20": benchmark}) + X = pd.DataFrame({"era": eras, "v53_lgbm_ender60": benchmark}) y = pd.Series([0.5, 0.5, 0.5, 0.5], name="target") transformed = apply_target_transform( y, X, - {"type": "subtract_benchmark", "benchmark_col": "v53_lgbm_ender20"}, + {"type": "subtract_benchmark", "benchmark_col": "v53_lgbm_ender60"}, ) expected = pd.Series([0.57, 0.43, 0.57, 0.43], name="target") @@ -61,7 +61,7 @@ def test_subtract_benchmark_zscore_is_per_era_and_scaled(self) -> None: def test_subtract_benchmark_zscore_zero_std_is_noop(self) -> None: eras = np.array(["0001", "0001", "0001", "0001"]) benchmark = np.array([2.0, 2.0, 2.0, 2.0]) - X = pd.DataFrame({"era": eras, "v53_lgbm_ender20": benchmark}) + X = pd.DataFrame({"era": eras, "v53_lgbm_ender60": benchmark}) y = pd.Series([0.1, 0.2, 0.3, 0.4], name="target") transformed = apply_target_transform( @@ -69,7 +69,7 @@ def test_subtract_benchmark_zscore_zero_std_is_noop(self) -> None: X, { "type": "subtract_benchmark_zscore", - "benchmark_col": "v53_lgbm_ender20", + "benchmark_col": "v53_lgbm_ender60", "scale": 0.07, }, ) @@ -82,7 +82,7 @@ def test_proportion_blends_with_original_target(self) -> None: benchmark = rng.normal(size=n) y = 1.5 * benchmark + 0.2 + rng.normal(scale=0.3, size=n) - X = pd.DataFrame({"era": eras, "v53_lgbm_ender20": benchmark}) + X = pd.DataFrame({"era": eras, "v53_lgbm_ender60": benchmark}) y = pd.Series(y, name="target") y_same = apply_target_transform( @@ -90,7 +90,7 @@ def test_proportion_blends_with_original_target(self) -> None: X, { "type": "residual_to_benchmark", - "benchmark_col": "v53_lgbm_ender20", + "benchmark_col": "v53_lgbm_ender60", "era_col": "era", "per_era": True, "fit_intercept": True, @@ -104,7 +104,7 @@ def test_proportion_blends_with_original_target(self) -> None: X, { "type": "residual_to_benchmark", - "benchmark_col": "v53_lgbm_ender20", + "benchmark_col": "v53_lgbm_ender60", "era_col": "era", "per_era": True, "fit_intercept": True, @@ -116,7 +116,7 @@ def test_proportion_blends_with_original_target(self) -> None: X, { "type": "residual_to_benchmark", - "benchmark_col": "v53_lgbm_ender20", + "benchmark_col": "v53_lgbm_ender60", "era_col": "era", "per_era": True, "fit_intercept": True, diff --git a/numerai/example_model.ipynb b/numerai/example_model.ipynb index 736c44c..be4cd33 100644 --- a/numerai/example_model.ipynb +++ b/numerai/example_model.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T21:23:24.577253Z", @@ -23,22 +23,14 @@ "id": "Ekw8Z93ljC3v", "outputId": "bdd16698-2ad0-4423-b090-c5ce55fe3053" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python 3.11.11\r\n" - ] - } - ], + "outputs": [], "source": [ "!python --version" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T21:23:25.628662Z", @@ -50,20 +42,10 @@ "id": "yoy_wT1rhMqF", "outputId": "e038b50f-1b61-4334-be62-28f4dc40a0a0" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "# Install dependencies\n", - "!pip install -q --upgrade numerapi pandas pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1" + "!pip install -q --upgrade numerapi==2.20.7 pandas==2.3.1 pyarrow==18.1.0 matplotlib==3.10.3 lightgbm==4.5.0 scikit-learn==1.6.1 scipy==1.16.0 cloudpickle==3.1.1\n" ] }, { @@ -81,39 +63,19 @@ "id": "13hdRk9ghMqI", "outputId": "d2274374-fd85-4189-f27b-d9d466cc63ca" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 13:23:26,450 INFO numerapi.utils: target file already exists\n", - "2025-12-14 13:23:26,452 INFO numerapi.utils: starting download\n", - "v5.2/train.parquet: 7.67GB [04:40, 27.4MB/s] \n", - "2025-12-14 13:28:07,043 INFO numerapi.utils: target file already exists\n", - "2025-12-14 13:28:07,045 INFO numerapi.utils: download complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001209 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 210\n", - "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n", - "[LightGBM] [Info] Start training from score 0.499946\n" - ] - } - ], + "outputs": [], "source": [ "from numerapi import NumerAPI\n", "import pandas as pd\n", "import json\n", "napi = NumerAPI()\n", "\n", - "# use one of the latest data versions\n", + "# Pin both dataset and objective for reproducibility. After the v5.3 in-place\n", + "# cutover, generic `target` aliases this column, while Ender-20 remains available\n", + "# explicitly as `target_ender_20`.\n", "DATA_VERSION = \"v5.3\"\n", + "TARGET_COL = \"target_ender_60\"\n", + "MODEL_ARTIFACT = \"example_model_v53_ender60.pkl\"\n", "\n", "# Download data\n", "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n", @@ -124,21 +86,26 @@ "features = feature_metadata[\"feature_sets\"][\"small\"]\n", "# use \"medium\" or \"all\" for better performance. Requires more RAM.\n", "# use \"quantum\" for newest features released in v5.3.\n", - "\n", "# features = feature_metadata[\"feature_sets\"][\"medium\"]\n", "# features = feature_metadata[\"feature_sets\"][\"all\"]\n", "# features = feature_metadata[\"feature_sets\"][\"quantum\"]\n", - "train = pd.read_parquet(f\"{DATA_VERSION}/train.parquet\", columns=[\"era\"]+features+[\"target\"])\n", + "train = pd.read_parquet(\n", + " f\"{DATA_VERSION}/train.parquet\", columns=[\"era\"] + features + [TARGET_COL]\n", + ")\n", "\n", - "# For better models, join train and validation data and train on all of it.\n", - "# This would cause diagnostics to be misleading though.\n", + "# For better models, join mature validation data and train on all of it.\n", + "# This would cause diagnostics to be misleading, so this compact example does not.\n", "# napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n", - "# validation = pd.read_parquet(f\"{DATA_VERSION}/validation.parquet\", columns=[\"era\"]+features+[\"target\"])\n", - "# validation = validation[validation[\"data_type\"] == \"validation\"] # drop rows which don't have targets yet\n", + "# validation = pd.read_parquet(\n", + "# f\"{DATA_VERSION}/validation.parquet\",\n", + "# columns=[\"era\", \"data_type\"] + features + [TARGET_COL],\n", + "# )\n", + "# validation = validation[validation[\"data_type\"] == \"validation\"]\n", + "# validation = validation.dropna(subset=[TARGET_COL])\n", "# train = pd.concat([train, validation])\n", "\n", "# Downsample for speed\n", - "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])] # skip this step for better performance\n", + "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]\n", "\n", "# Train model\n", "import lightgbm as lgb\n", @@ -147,48 +114,36 @@ " learning_rate=0.01,\n", " max_depth=5,\n", " num_leaves=2**5-1,\n", - " colsample_bytree=0.1\n", - ")\n", - "# We've found the following \"deep\" parameters perform much better, but they require much more CPU and RAM\n", - "# model = lgb.LGBMRegressor(\n", - "# n_estimators=30_000,\n", - "# learning_rate=0.001,\n", - "# max_depth=10,\n", - "# num_leaves=2**10,\n", - "# colsample_bytree=0.1,\n", - "# min_data_in_leaf=10000,\n", - "# )\n", - "model.fit(\n", - " train[features],\n", - " train[\"target\"]\n", + " colsample_bytree=0.1,\n", ")\n", + "model.fit(train[features], train[TARGET_COL])\n", "\n", "# Define predict function\n", "def predict(\n", " live_features: pd.DataFrame,\n", - " live_benchmark_models: pd.DataFrame\n", - " ) -> pd.DataFrame:\n", + " live_benchmark_models: pd.DataFrame,\n", + ") -> pd.DataFrame:\n", " live_predictions = model.predict(live_features[features])\n", " submission = pd.Series(live_predictions, index=live_features.index)\n", " return submission.to_frame(\"prediction\")\n", "\n", - "# Pickle predict function\n", + "# Pickle predict function with explicit dataset/target provenance in its filename.\n", "import cloudpickle\n", "p = cloudpickle.dumps(predict)\n", - "with open(\"example_model.pkl\", \"wb\") as f:\n", + "with open(MODEL_ARTIFACT, \"wb\") as f:\n", " f.write(p)\n", "\n", "# Download file if running in Google Colab\n", "try:\n", " from google.colab import files\n", - " files.download('example_model.pkl')\n", - "except:\n", - " pass" + " files.download(MODEL_ARTIFACT)\n", + "except ImportError:\n", + " pass\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T21:28:28.982147Z", diff --git a/numerai/feature_neutralization.ipynb b/numerai/feature_neutralization.ipynb index 6739596..6b480e9 100644 --- a/numerai/feature_neutralization.ipynb +++ b/numerai/feature_neutralization.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:05.143247Z", @@ -33,22 +33,14 @@ "id": "ws4qrSssFC9T", "outputId": "3860d6e5-38ec-4638-82b2-bce4c7365966" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python 3.11.11\r\n" - ] - } - ], + "outputs": [], "source": [ "!python --version" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:06.487940Z", @@ -60,23 +52,13 @@ "id": "iHzZde7Tyu-N", "outputId": "f9cb52f5-88f3-4776-a1be-cef458e718f5" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "# Install dependencies\n", - "!pip install -q --upgrade numerapi pandas==2.3.1 pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n", + "!pip install -q --upgrade numerapi==2.20.7 pandas==2.3.1 pyarrow==18.1.0 matplotlib==3.10.3 lightgbm==4.5.0 scikit-learn==1.6.1 scipy==1.16.0 cloudpickle==3.1.1\n", "\n", "# Inline plots\n", - "%matplotlib inline" + "%matplotlib inline\n" ] }, { @@ -117,110 +99,7 @@ "id": "JTN8-MUmyu-P", "outputId": "b8d0557f-ae8f-48e8-e707-806ac4683ad4" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " small medium all\n", - "all 42 780 2748\n", - "constitution 2 134 335\n", - "charisma 3 116 290\n", - "agility 2 58 145\n", - "wisdom 3 56 140\n", - "strength 1 54 135\n", - "serenity 3 34 95\n", - "dexterity 4 21 51\n", - "intelligence 2 14 35" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import json\n", "import pandas as pd\n", @@ -231,6 +110,9 @@ "\n", "# Set data version to one of the latest datasets\n", "DATA_VERSION = \"v5.3\"\n", + "TARGET_COL = \"target_ender_60\"\n", + "EMBARGO_ERAS = 16\n", + "MODEL_ARTIFACT = \"feature_neutralization_v53_ender60.pkl\"\n", "\n", "napi.download_dataset(f\"{DATA_VERSION}/features.json\")\n", "feature_metadata = json.load(open(f\"{DATA_VERSION}/features.json\"))\n", @@ -276,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:08.731664Z", @@ -288,16 +170,7 @@ "id": "meowEBs-PwtB", "outputId": "b82484be-38ce-4524-fdf9-60bbd038f09f" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:28:07,582 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:28:07,583 INFO numerapi.utils: download complete\n" - ] - } - ], + "outputs": [], "source": [ "# define the small features and small serenity features\n", "# use \"all\" for better performance. Requires more RAM.\n", @@ -313,7 +186,7 @@ "# this is a great feature of the parquet file format\n", "train = pd.read_parquet(\n", " f\"{DATA_VERSION}/train.parquet\",\n", - " columns=[\"era\", \"target\"] + small_features\n", + " columns=[\"era\", TARGET_COL] + small_features\n", ")\n", "\n", "# Downsample to every 4th era to reduce memory usage and\n", @@ -329,12 +202,12 @@ "source": [ "### Evaluating feature performance\n", "\n", - "When thinking about feature risk, the first thing to check might be the correlation of each feature with the target over the training dataset. This will tell us what kind of relationship each feature has with the target:" + "When thinking about feature risk, first check each feature's correlation with the explicit Ender-60 target over the training dataset. This tells us what relationship each feature has with the selected objective. We avoid the generic `target` alias so the model horizon remains reproducible.\n" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:10.618573Z", @@ -347,45 +220,7 @@ "id": "SE9QmW6ryu-Q", "outputId": "53d5a554-bfbe-4719-c359-8f0ffa1fd970" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/2623001418.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " per_era_corr = train.groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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meanstdsharpemax_drawdowndelta
feature_glandered_unimproved_peafowl0.0005860.0060200.097396-0.0536600.001187
feature_unsystematized_subcardinal_malaysia0.0003850.0069450.055424-0.1056980.002686
feature_elusive_vapoury_accomplice0.0001660.0072750.022837-0.1098190.001389
\n", - "
" - ], - "text/plain": [ - " mean std sharpe \\\n", - "feature_glandered_unimproved_peafowl 0.000586 0.006020 0.097396 \n", - "feature_unsystematized_subcardinal_malaysia 0.000385 0.006945 0.055424 \n", - "feature_elusive_vapoury_accomplice 0.000166 0.007275 0.022837 \n", - "\n", - " max_drawdown delta \n", - "feature_glandered_unimproved_peafowl -0.053660 0.001187 \n", - "feature_unsystematized_subcardinal_malaysia -0.105698 0.002686 \n", - "feature_elusive_vapoury_accomplice -0.109819 0.001389 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def metrics(corr):\n", " corr_mean = corr.mean()\n", @@ -567,7 +328,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:10.801415Z", @@ -580,32 +341,7 @@ "id": "mqqdKda_yu-R", "outputId": "7473f9b4-57b6-4988-94c8-7ed843248358" }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[,\n", - " ,\n", - " ],\n", - " [,\n", - " , ]], dtype=object)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot the performance metrics of the features as bar charts sorted by mean\n", "feature_metrics.sort_values(\"mean\", ascending=False).plot.bar(\n", @@ -634,7 +370,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:10.861427Z", @@ -647,28 +383,7 @@ "id": "XVQe5lnAyu-S", "outputId": "8d3f8818-ed7e-4f7a-bdc0-96540bbb71b2" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -692,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:10.919972Z", @@ -705,28 +420,7 @@ "id": "5hgFAmOOyu-S", "outputId": "5d9b15a5-c027-4cdd-e349-93e39965b8f1" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot the cumulative per era correlation of the feature with the highest vs lowest delta\n", "per_era_corr[[feature_metrics[\"delta\"].idxmin(), feature_metrics[\"delta\"].idxmax()]].cumsum().plot(\n", @@ -748,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:10.973634Z", @@ -761,28 +455,7 @@ "id": "xlFsPKNzyu-T", "outputId": "418e6341-930b-49c7-bb99-8303712d819f" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot the cumulative per era correlation of the feature with the highest vs lowest max_drawdown\n", "per_era_corr[[feature_metrics[\"max_drawdown\"].idxmax(), feature_metrics[\"max_drawdown\"].idxmin()]].cumsum().plot(\n", @@ -806,7 +479,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:24.719047Z", @@ -819,900 +492,7 @@ "id": "0nm5VBXy4UBK", "outputId": "fe8deacb-6e34-42ed-ba13-bd079fe06c01" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001544 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 210\n", - "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n", - "[LightGBM] [Info] Start training from score 0.499946\n" - ] - }, - { - "data": { - "text/html": [ - "
LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
-       "              n_estimators=2000, num_leaves=15)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n", - " n_estimators=2000, num_leaves=15)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import lightgbm as lgb\n", "\n", @@ -1734,7 +514,7 @@ "# )\n", "model.fit(\n", " train[small_features],\n", - " train[\"target\"]\n", + " train[TARGET_COL]\n", ")" ] }, @@ -1751,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:35.609667Z", @@ -1763,39 +543,35 @@ "id": "1fZmZVFuyu-T", "outputId": "c7a4fee8-e158-4184-91bb-7f77a07a9ccf" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:28:25,494 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:28:25,494 INFO numerapi.utils: download complete\n" - ] - } - ], + "outputs": [], "source": [ "# Download validation data\n", "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n", "\n", - "# Load the validation data, filtering for data_type == \"validation\"\n", + "# Recent validation eras can have an immature Ender-60 target. Evaluate only\n", + "# validation rows for which the explicit target has been published.\n", "validation = pd.read_parquet(\n", " f\"{DATA_VERSION}/validation.parquet\",\n", - " columns=[\"era\", \"data_type\", \"target\"] + small_features\n", + " columns=[\"era\", \"data_type\", TARGET_COL] + small_features\n", ")\n", "validation = validation[validation[\"data_type\"] == \"validation\"]\n", + "validation = validation.dropna(subset=[TARGET_COL])\n", "del validation[\"data_type\"]\n", "\n", "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n", "# Comment out the line below to use all the data\n", "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n", "\n", - "# Embargo overlapping eras from training data\n", + "# Embargo 16 weekly eras for the 60-market-day target horizon.\n", "last_train_era = int(train[\"era\"].unique()[-1])\n", - "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n", + "eras_to_embargo = [\n", + " str(last_train_era + offset).zfill(4)\n", + " for offset in range(1, EMBARGO_ERAS + 1)\n", + "]\n", "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n", "\n", "# Generate predictions against the small feature set of the validation data\n", - "validation[\"prediction\"] = model.predict(validation[small_features])" + "validation[\"prediction\"] = model.predict(validation[small_features])\n" ] }, { @@ -1824,36 +600,7 @@ "id": "mExyr3VSyu-U", "outputId": "09689cdb-2349-4d75-c015-4e6b6ef1567b" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/685944140.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " feature_exposures = validation.groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Feature Exposures')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Compute the Pearson correlation of the predictions with each of the\n", "# serenity features of the small feature set\n", @@ -1898,7 +645,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:36.163622Z", @@ -1911,25 +658,7 @@ "id": "9_rmsQRSyu-U", "outputId": "a03250c5-a4c2-4b8c-bc15-8f55647847bd" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean of max feature exposure 0.03102625107086816\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the max feature exposure per era\n", "max_feature_exposure = feature_exposures.max(axis=1)\n", @@ -1976,7 +705,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:39.437609Z", @@ -1989,214 +718,7 @@ "id": "rt2YbOPxyu-V", "outputId": "cf6b400e-118f-4324-f17e-1f81034f1f57" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/590389131.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/590389131.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/590389131.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/590389131.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n" - ] - }, - { - "data": { - "text/html": [ - "
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956598 rows × 7 columns

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" - ], - "text/plain": [ - " era target prediction neutralized_25 neutralized_50 \\\n", - "id \n", - "n000c290e4364875 0579 0.50 0.495229 0.370158 0.245087 \n", - "n002a15bc5575bbb 0579 0.25 0.507359 0.382278 0.257198 \n", - "n00309caaa0f955e 0579 0.75 0.504452 0.379309 0.254167 \n", - "n0039cbdcf835708 0579 0.50 0.510034 0.385076 0.260118 \n", - "n004143458984f89 0579 0.50 0.491897 0.366909 0.241920 \n", - "... ... ... ... ... ... \n", - "nffd9cf2c992c881 1191 0.50 0.501314 0.376306 0.251298 \n", - "nfff0fbd1837e76f 1191 0.25 0.497717 0.372721 0.247725 \n", - "nfff3b8261e8af0a 1191 0.25 0.496199 0.371146 0.246093 \n", - "nfff3cb2970e386b 1191 0.75 0.506733 0.381695 0.256657 \n", - "nfff8ba3632ff9b9 1191 0.50 0.501066 0.376059 0.251052 \n", - "\n", - " neutralized_75 neutralized_100 \n", - "id \n", - "n000c290e4364875 0.120017 -0.005054 \n", - "n002a15bc5575bbb 0.132117 0.007036 \n", - "n00309caaa0f955e 0.129024 0.003881 \n", - "n0039cbdcf835708 0.135159 0.010201 \n", - "n004143458984f89 0.116932 -0.008056 \n", - "... ... ... \n", - "nffd9cf2c992c881 0.126290 0.001282 \n", - "nfff0fbd1837e76f 0.122728 -0.002268 \n", - "nfff3b8261e8af0a 0.121041 -0.004012 \n", - "nfff3cb2970e386b 0.131619 0.006582 \n", - "nfff8ba3632ff9b9 0.126045 0.001038 \n", - "\n", - "[956598 rows x 7 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# import neutralization from numerai-tools\n", "from numerai_tools.scoring import neutralize\n", @@ -2215,7 +737,7 @@ "\n", "# Align the neutralized predictions with the validation data\n", "prediction_cols = [\"prediction\"] + [f for f in validation.columns if \"neutralized\" in f]\n", - "validation[[\"era\", \"target\"] + prediction_cols]" + "validation[[\"era\", TARGET_COL] + prediction_cols]" ] }, { @@ -2229,7 +751,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:40.556842Z", @@ -2242,63 +764,7 @@ "id": "x-qSdjNQyu-V", "outputId": "d246081c-ba90-4bb1-c431-b016fd534728" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " validation.groupby(\"era\").apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " validation.groupby(\"era\").apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " validation.groupby(\"era\").apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " validation.groupby(\"era\").apply(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mean feature exposures:\n", - "prediction 0.045\n", - "neutralized_25 0.034\n", - "neutralized_50 0.023\n", - "neutralized_75 0.011\n", - "neutralized_100 0.000\n", - "dtype: float64\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/234579572.py:3: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " validation.groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Compute max feature exposure for each set of predictions\n", "max_feature_exposures = pd.concat([\n", @@ -2336,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:42.145925Z", @@ -2349,40 +815,11 @@ "id": "zW4f961lyu-W", "outputId": "af9d9c85-02c1-46aa-92ed-4e0b272d9148" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/4274901726.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " correlations = validation.groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# calculate per-era CORR for each set of predictions\n", "correlations = validation.groupby(\"era\").apply(\n", - " lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n", + " lambda d: numerai_corr(d[prediction_cols], d[TARGET_COL])\n", ")\n", "\n", "# calculate the cumulative corr across eras for each neutralization proportion\n", @@ -2409,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:42.151320Z", @@ -2422,88 +859,7 @@ "id": "P3YxoLZByu-W", "outputId": "c70954c0-e762-4aec-9f4d-27cf29323a5c" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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meanstdsharpemax_drawdown
prediction0.0124300.0145170.8562180.026261
neutralized_250.0124390.0145340.8558530.026104
neutralized_500.0124470.0145510.8554290.026340
neutralized_750.0124430.0145650.8542990.026099
neutralized_1000.0124450.0145680.8542700.026021
\n", - "
" - ], - "text/plain": [ - " mean std sharpe max_drawdown\n", - "prediction 0.012430 0.014517 0.856218 0.026261\n", - "neutralized_25 0.012439 0.014534 0.855853 0.026104\n", - "neutralized_50 0.012447 0.014551 0.855429 0.026340\n", - "neutralized_75 0.012443 0.014565 0.854299 0.026099\n", - "neutralized_100 0.012445 0.014568 0.854270 0.026021" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "summary_metrics = {}\n", "for col in prediction_cols:\n", @@ -2536,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:52.565034Z", @@ -2549,54 +905,7 @@ "id": "NKiNDWygyu-b", "outputId": "3c04f392-2988-4921-a47f-f487bb88e785" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:4: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " neutralized = validation.groupby(\"era\", group_keys=True).apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/87142795.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " group_neutral_corr = validation.groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# neutralize preds against each group\n", "for group in groups:\n", @@ -2608,7 +917,7 @@ "\n", "group_neutral_cols = [\"prediction\"] + [f\"neutralized_{group}\" for group in groups]\n", "group_neutral_corr = validation.groupby(\"era\").apply(\n", - " lambda d: numerai_corr(d[group_neutral_cols], d[\"target\"])\n", + " lambda d: numerai_corr(d[group_neutral_cols], d[TARGET_COL])\n", ")\n", "group_neutral_cumsum = group_neutral_corr.cumsum()\n", "\n", @@ -2632,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:54.562137Z", @@ -2645,38 +954,7 @@ "id": "76IGP1UGnzNW", "outputId": "9e282da6-94fa-410e-c832-19d322aba7df" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:28:53,214 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:28:53,215 INFO numerapi.utils: download complete\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32617/4134733066.py:10: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " per_era_mmc = validation.dropna().groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from numerai_tools.scoring import correlation_contribution\n", "\n", @@ -2689,7 +967,7 @@ "# Compute the per-era mmc between our predictions, the meta model, and the target values\n", "per_era_mmc = validation.dropna().groupby(\"era\").apply(\n", " lambda x: correlation_contribution(\n", - " x[group_neutral_cols], x[\"meta_model\"], x[\"target\"]\n", + " x[group_neutral_cols], x[\"meta_model\"], x[TARGET_COL]\n", " )\n", ")\n", "\n", @@ -2713,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:54.572115Z", @@ -2726,184 +1004,7 @@ "id": "YPJcnqWyyu-b", "outputId": "67f1eca5-807e-40bf-c0c4-0b8e35a8708d" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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corr_meanmmc_meancorr_stdmmc_stdcorr_sharpemmc_sharpecorr_max_drawdownmmc_max_drawdown
prediction0.0124300.0058990.0145640.0137390.8534340.4293330.0262611.537993
neutralized_intelligence0.0123870.0057860.0145580.0136980.8509180.4224030.0299071.544739
neutralized_wisdom0.0122830.0057920.0149580.0142700.8211590.4058790.0305081.544393
neutralized_charisma0.0122970.0057850.0145840.0136580.8431410.4235730.0262301.544815
neutralized_dexterity0.0123360.0063360.0148700.0133990.8295690.4728590.0259531.511770
neutralized_strength0.0124790.0060040.0145790.0138520.8559700.4334480.0270011.531675
neutralized_constitution0.0124030.0059400.0146030.0138270.8493530.4295590.0274141.535535
neutralized_agility0.0124020.0050310.0143080.0140360.8668040.3584370.0289481.590054
neutralized_serenity0.0124450.0058470.0146150.0136880.8514920.4271610.0260211.541100
neutralized_all0.0074020.0026840.0134390.0141300.5507930.1899570.0967561.730867
\n", - "
" - ], - "text/plain": [ - " corr_mean mmc_mean corr_std mmc_std corr_sharpe \\\n", - "prediction 0.012430 0.005899 0.014564 0.013739 0.853434 \n", - "neutralized_intelligence 0.012387 0.005786 0.014558 0.013698 0.850918 \n", - "neutralized_wisdom 0.012283 0.005792 0.014958 0.014270 0.821159 \n", - "neutralized_charisma 0.012297 0.005785 0.014584 0.013658 0.843141 \n", - "neutralized_dexterity 0.012336 0.006336 0.014870 0.013399 0.829569 \n", - "neutralized_strength 0.012479 0.006004 0.014579 0.013852 0.855970 \n", - "neutralized_constitution 0.012403 0.005940 0.014603 0.013827 0.849353 \n", - "neutralized_agility 0.012402 0.005031 0.014308 0.014036 0.866804 \n", - "neutralized_serenity 0.012445 0.005847 0.014615 0.013688 0.851492 \n", - "neutralized_all 0.007402 0.002684 0.013439 0.014130 0.550793 \n", - "\n", - " mmc_sharpe corr_max_drawdown mmc_max_drawdown \n", - "prediction 0.429333 0.026261 1.537993 \n", - "neutralized_intelligence 0.422403 0.029907 1.544739 \n", - "neutralized_wisdom 0.405879 0.030508 1.544393 \n", - "neutralized_charisma 0.423573 0.026230 1.544815 \n", - "neutralized_dexterity 0.472859 0.025953 1.511770 \n", - "neutralized_strength 0.433448 0.027001 1.531675 \n", - "neutralized_constitution 0.429559 0.027414 1.535535 \n", - "neutralized_agility 0.358437 0.028948 1.590054 \n", - "neutralized_serenity 0.427161 0.026021 1.541100 \n", - "neutralized_all 0.189957 0.096756 1.730867 " - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "group_neutral_summary_metrics = {}\n", "for col in group_neutral_cols:\n", @@ -2958,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:54.575463Z", @@ -3007,7 +1108,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:54.577598Z", @@ -3031,7 +1132,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:55.318477Z", @@ -3044,116 +1145,7 @@ "id": "JfJd0vL4yu-c", "outputId": "7f47c616-27ad-43b6-98fe-19a54eadd3b2" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:28:55,184 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:28:55,185 INFO numerapi.utils: download complete\n" - ] - }, - { - "data": { - "text/html": [ - "
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prediction
id
n0005e01cd27dd7b0.901488
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n000e47c9c27ca3d0.187798
n0016060c63353370.905655
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" - ], - "text/plain": [ - " prediction\n", - "id \n", - "n0005e01cd27dd7b 0.901488\n", - "n000a0f321ce17e3 0.016518\n", - "n000e47c9c27ca3d 0.187798\n", - "n0016060c6335337 0.905655\n", - "n001c30cea427396 0.612054\n", - "... ...\n", - "nffcc8bf595e4e98 0.327530\n", - "nffd0c2231719dc9 0.184970\n", - "nffdbad9b545ca02 0.372470\n", - "nffe55a271685c39 0.330804\n", - "nfff60bd24702729 0.463542\n", - "\n", - "[6720 rows x 1 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Quick test\n", "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n", @@ -3163,7 +1155,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:55.336601Z", @@ -3176,13 +1168,13 @@ "# Use the cloudpickle library to serialize your function and its dependencies\n", "import cloudpickle\n", "p = cloudpickle.dumps(predict_neutral)\n", - "with open(\"feature_neutralization.pkl\", \"wb\") as f:\n", - " f.write(p)" + "with open(MODEL_ARTIFACT, \"wb\") as f:\n", + " f.write(p)\n" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:28:55.339155Z", @@ -3200,9 +1192,9 @@ "# Download file if running in Google Colab\n", "try:\n", " from google.colab import files\n", - " files.download('feature_neutralization.pkl')\n", - "except:\n", - " pass" + " files.download(MODEL_ARTIFACT)\n", + "except ImportError:\n", + " pass\n" ] }, { diff --git a/numerai/generate_example_predictions.py b/numerai/generate_example_predictions.py new file mode 100644 index 0000000..4075d53 --- /dev/null +++ b/numerai/generate_example_predictions.py @@ -0,0 +1,112 @@ +"""Generate target-versioned example predictions and provenance for Data publication.""" + +from __future__ import annotations + +import argparse +import hashlib +import inspect +import json +import os +from datetime import datetime, timezone +from pathlib import Path + +import cloudpickle +import numpy as np +import pandas as pd +from numerapi import NumerAPI + + +DATA_VERSION = "v5.3" +TARGET_COL = "target_ender_60" +MODEL_ARTIFACT = "example_model_v53_ender60.pkl" +SOURCE_NOTEBOOK = "numerai/example_model.ipynb" +MODEL_SOURCES = { + "example_model_v53_ender60.pkl": "numerai/example_model.ipynb", + "feature_neutralization_v53_ender60.pkl": "numerai/feature_neutralization.ipynb", + "hello_numerai_v53_ender60.pkl": "numerai/hello_numerai.ipynb", + "target_ensemble_v53_ender60.pkl": "numerai/target_ensemble.ipynb", +} + + +def sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as file: + for chunk in iter(lambda: file.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def predict(model: object, features: pd.DataFrame) -> pd.DataFrame: + """Run either supported Model Upload callable signature and validate output.""" + if len(inspect.signature(model).parameters) == 1: + predictions = model(features) + else: + predictions = model(features, pd.DataFrame(index=features.index)) + + if not isinstance(predictions, pd.DataFrame) or list(predictions) != ["prediction"]: + raise ValueError("model must return a DataFrame with one 'prediction' column") + if not predictions.index.equals(features.index): + raise ValueError("prediction index does not match the input feature index") + if not np.isfinite(predictions["prediction"]).all(): + raise ValueError("predictions contain non-finite values") + return predictions + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--model", type=Path, required=True) + parser.add_argument("--output-dir", type=Path, required=True) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + args.output_dir.mkdir(parents=True, exist_ok=True) + + with args.model.open("rb") as file: + model = cloudpickle.load(file) + + napi = NumerAPI() + features_path = Path(DATA_VERSION) / "features.json" + napi.download_dataset(f"{DATA_VERSION}/features.json") + feature_metadata = json.loads(features_path.read_text()) + feature_cols = feature_metadata["feature_sets"]["small"] + + outputs: dict[str, dict[str, object]] = {} + for split in ("validation", "live"): + dataset_path = Path(DATA_VERSION) / f"{split}.parquet" + napi.download_dataset(f"{DATA_VERSION}/{split}.parquet") + split_features = pd.read_parquet(dataset_path, columns=feature_cols) + predictions = predict(model, split_features) + output_path = args.output_dir / f"{split}_example_preds_v53_ender60.parquet" + predictions.to_parquet(output_path) + outputs[split] = { + "file": output_path.name, + "rows": len(predictions), + "sha256": sha256(output_path), + } + + provenance = { + "generated_at": datetime.now(timezone.utc).isoformat(), + "source_commit": os.environ.get("GITHUB_SHA", "local"), + "source_notebook": SOURCE_NOTEBOOK, + "dataset_version": DATA_VERSION, + "target_col": TARGET_COL, + "model_artifact": args.model.name, + "model_sha256": sha256(args.model), + "model_artifacts": { + artifact: { + "source_notebook": source, + "sha256": sha256(args.model.parent / artifact), + } + for artifact, source in MODEL_SOURCES.items() + if (args.model.parent / artifact).exists() + }, + "predictions": outputs, + } + provenance_path = args.output_dir / "v53_ender60_provenance.json" + provenance_path.write_text(json.dumps(provenance, indent=2) + "\n") + + +if __name__ == "__main__": + main() diff --git a/numerai/hello_numerai.ipynb b/numerai/hello_numerai.ipynb index fc99bd7..43e133b 100644 --- a/numerai/hello_numerai.ipynb +++ b/numerai/hello_numerai.ipynb @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:08:43.869418Z", @@ -39,22 +39,14 @@ "id": "gRGkuracAkoj", "outputId": "912511ae-5456-4faa-8fdb-1f731b077c3e" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python 3.11.11\r\n" - ] - } - ], + "outputs": [], "source": [ "!python --version" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:08:45.763466Z", @@ -62,23 +54,13 @@ }, "id": "THMEU_T4r5GS" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "# Install dependencies\n", - "!pip install -q --upgrade numerapi pandas==2.3.1 pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n", + "!pip install -q --upgrade numerapi==2.20.7 pandas==2.3.1 pyarrow==18.1.0 matplotlib==3.10.3 lightgbm==4.5.0 scikit-learn==1.6.1 scipy==1.16.0 cloudpickle==3.1.1\n", "\n", "# Inline plots\n", - "%matplotlib inline" + "%matplotlib inline\n" ] }, { @@ -118,18 +100,7 @@ "id": "4B4bbH07r5GU", "outputId": "5a959b9d-4edc-4660-df06-27e633ceb48d" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available versions:\n", - " ['v5.2', 'v5.1', 'v5.0']\n", - "Available v5.2 files:\n", - " ['v5.2/features.json', 'v5.2/live.parquet', 'v5.2/live_benchmark_models.parquet', 'v5.2/live_example_preds.csv', 'v5.2/live_example_preds.parquet', 'v5.2/meta_model.parquet', 'v5.2/train.parquet', 'v5.2/train_benchmark_models.parquet', 'v5.2/validation.parquet', 'v5.2/validation_benchmark_models.parquet', 'v5.2/validation_example_preds.csv', 'v5.2/validation_example_preds.parquet']\n" - ] - } - ], + "outputs": [], "source": [ "# Initialize NumerAPI - the official Python API client for Numerai\n", "from numerapi import NumerAPI\n", @@ -142,6 +113,9 @@ "\n", "# Set data version to one of the latest datasets\n", "DATA_VERSION = \"v5.3\"\n", + "TARGET_COL = \"target_ender_60\"\n", + "EMBARGO_ERAS = 16\n", + "MODEL_ARTIFACT = \"hello_numerai_v53_ender60.pkl\"\n", "\n", "# Print all files available for download for our version\n", "current_version_files = [f for f in all_datasets if f.startswith(DATA_VERSION)]\n", @@ -166,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:08:47.876979Z", @@ -178,24 +152,7 @@ "id": "_Mldufeo9BKS", "outputId": "0f97b4ed-ccb3-482c-cf49-b7226a49f526" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:08:47,873 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:08:47,873 INFO numerapi.utils: download complete\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "feature_sets 18\n", - "targets 41\n" - ] - } - ], + "outputs": [], "source": [ "import json\n", "\n", @@ -246,17 +203,7 @@ "id": "TeAzyU9q_dwR", "outputId": "ddb255d1-29f4-4d45-9b82-4477613a8089" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "small 42\n", - "medium 780\n", - "all 2748\n" - ] - } - ], + "outputs": [], "source": [ "feature_sets = feature_metadata[\"feature_sets\"]\n", "for feature_set in [\"small\", \"medium\", \"quantum\", \"all\"]:\n", @@ -279,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:08:49.935996Z", @@ -291,16 +238,7 @@ "id": "UC5YkX1xr5GV", "outputId": "04c9de0c-778d-4f1c-edff-c58d040685eb" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:08:48,778 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:08:48,778 INFO numerapi.utils: download complete\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -313,16 +251,16 @@ "# Download the training data - this will take a few minutes\n", "napi.download_dataset(f\"{DATA_VERSION}/train.parquet\")\n", "\n", - "# Load only the \"medium\" feature set to\n", - "# Use the \"all\" feature set to use all features\n", + "# Pin the objective explicitly so a future change to the generic `target` alias\n", + "# cannot silently change the model horizon.\n", "train = pd.read_parquet(\n", " f\"{DATA_VERSION}/train.parquet\",\n", - " columns=[\"era\", \"target\"] + feature_set\n", + " columns=[\"era\", TARGET_COL] + feature_set\n", ")\n", "\n", "# Downsample to every 4th era to reduce memory usage and speedup model training (suggested for Colab free tier)\n", "# Comment out the line below to use all the data\n", - "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]" + "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]\n" ] }, { @@ -331,19 +269,20 @@ "id": "jBcMKMX6FoNl" }, "source": [ - "\n", "### Training data\n", "\n", "Each row represents a stock at a specific point in time:\n", "- `id` is the stock id\n", "- `era` is the date\n", - "- `target` is a measure of future returns for that stock\n", - "- `features` describe the attributes of the stock (eg. P/E ratio) for that date" + "- `target_ender_60` is the explicit 60-market-day objective used by this tutorial\n", + "- `features` describe the attributes of the stock (eg. P/E ratio) for that date\n", + "\n", + "After the v5.3 in-place cutover, the generic `target` column aliases `target_ender_60`. Explicit target names are safer for reproducibility; `target_ender_20` remains available for research that intentionally uses the older horizon.\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:08:50.002794Z", @@ -356,606 +295,7 @@ "id": "o9JOOMqpFscM", "outputId": "e7dd7980-053d-4f42-a861-7e871379492c" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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eratargetfeature_antistrophic_striate_conscriptionistfeature_bicameral_showery_wallabafeature_bridal_fingered_pensionerfeature_collectivist_flaxen_gueuxfeature_concurring_fabled_adapterfeature_crosscut_whilom_ataxyfeature_departmental_inimitable_sentencerfeature_dialectal_homely_cambodia...feature_tridactyl_immoral_snortingfeature_trimeter_soggy_greatestfeature_unanalyzable_excusable_whirlwindfeature_unbreakable_constraining_hegelianismfeature_unformed_bent_smatchfeature_unministerial_unextenuated_teleosteanfeature_unmodish_zymogenic_rousingfeature_unsystematized_subcardinal_malaysiafeature_willful_sere_chronobiologyfeature_zoological_peristomial_scute
id
n0007b5abb0c3a2500010.2522222012...4113022332
n003bba8a98662e400010.5022222142...4200022442
n003bee128c2fcfc00011.0022222222...3110122032
n0048ac83aff719400010.2522222142...1341222202
n0055a2401ba648000010.5022222002...1010022142
..................................................................
nffc2d5e4b79a7ae05730.2512103211...2212333221
nffc9844c1c7a6a905730.5021214224...4003133232
nffd79773f4109bb05730.5034034101...3000114012
nfff6ab9d6dc0b3205730.5020312212...4111221002
nfff87b21e4db90205730.5030430223...4000010043
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688184 rows × 44 columns

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" - ], - "text/plain": [ - " era target feature_antistrophic_striate_conscriptionist \\\n", - "id \n", - "n0007b5abb0c3a25 0001 0.25 2 \n", - "n003bba8a98662e4 0001 0.50 2 \n", - "n003bee128c2fcfc 0001 1.00 2 \n", - "n0048ac83aff7194 0001 0.25 2 \n", - "n0055a2401ba6480 0001 0.50 2 \n", - "... ... ... ... \n", - "nffc2d5e4b79a7ae 0573 0.25 1 \n", - "nffc9844c1c7a6a9 0573 0.50 2 \n", - "nffd79773f4109bb 0573 0.50 3 \n", - "nfff6ab9d6dc0b32 0573 0.50 2 \n", - "nfff87b21e4db902 0573 0.50 3 \n", - "\n", - " feature_bicameral_showery_wallaba \\\n", - "id \n", - "n0007b5abb0c3a25 2 \n", - "n003bba8a98662e4 2 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 2 \n", - "... ... \n", - "nffc2d5e4b79a7ae 2 \n", - "nffc9844c1c7a6a9 1 \n", - "nffd79773f4109bb 4 \n", - "nfff6ab9d6dc0b32 0 \n", - "nfff87b21e4db902 0 \n", - "\n", - " feature_bridal_fingered_pensioner \\\n", - "id \n", - "n0007b5abb0c3a25 2 \n", - "n003bba8a98662e4 2 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 2 \n", - "... ... \n", - "nffc2d5e4b79a7ae 1 \n", - "nffc9844c1c7a6a9 2 \n", - "nffd79773f4109bb 0 \n", - "nfff6ab9d6dc0b32 3 \n", - "nfff87b21e4db902 4 \n", - "\n", - " feature_collectivist_flaxen_gueux \\\n", - "id \n", - "n0007b5abb0c3a25 2 \n", - "n003bba8a98662e4 2 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 2 \n", - "... ... \n", - "nffc2d5e4b79a7ae 0 \n", - "nffc9844c1c7a6a9 1 \n", - "nffd79773f4109bb 3 \n", - "nfff6ab9d6dc0b32 1 \n", - "nfff87b21e4db902 3 \n", - "\n", - " feature_concurring_fabled_adapter \\\n", - "id \n", - "n0007b5abb0c3a25 2 \n", - "n003bba8a98662e4 2 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 2 \n", - "... ... \n", - "nffc2d5e4b79a7ae 3 \n", - "nffc9844c1c7a6a9 4 \n", - "nffd79773f4109bb 4 \n", - "nfff6ab9d6dc0b32 2 \n", - "nfff87b21e4db902 0 \n", - "\n", - " feature_crosscut_whilom_ataxy \\\n", - "id \n", - "n0007b5abb0c3a25 0 \n", - "n003bba8a98662e4 1 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 1 \n", - "n0055a2401ba6480 0 \n", - "... ... \n", - "nffc2d5e4b79a7ae 2 \n", - "nffc9844c1c7a6a9 2 \n", - "nffd79773f4109bb 1 \n", - "nfff6ab9d6dc0b32 2 \n", - "nfff87b21e4db902 2 \n", - "\n", - " feature_departmental_inimitable_sentencer \\\n", - "id \n", - "n0007b5abb0c3a25 1 \n", - "n003bba8a98662e4 4 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 4 \n", - "n0055a2401ba6480 0 \n", - "... ... \n", - "nffc2d5e4b79a7ae 1 \n", - "nffc9844c1c7a6a9 2 \n", - "nffd79773f4109bb 0 \n", - "nfff6ab9d6dc0b32 1 \n", - "nfff87b21e4db902 2 \n", - "\n", - " feature_dialectal_homely_cambodia ... \\\n", - "id ... \n", - "n0007b5abb0c3a25 2 ... \n", - "n003bba8a98662e4 2 ... \n", - "n003bee128c2fcfc 2 ... \n", - "n0048ac83aff7194 2 ... \n", - "n0055a2401ba6480 2 ... \n", - "... ... ... \n", - "nffc2d5e4b79a7ae 1 ... \n", - "nffc9844c1c7a6a9 4 ... \n", - "nffd79773f4109bb 1 ... \n", - "nfff6ab9d6dc0b32 2 ... \n", - "nfff87b21e4db902 3 ... \n", - "\n", - " feature_tridactyl_immoral_snorting \\\n", - "id \n", - "n0007b5abb0c3a25 4 \n", - "n003bba8a98662e4 4 \n", - "n003bee128c2fcfc 3 \n", - "n0048ac83aff7194 1 \n", - "n0055a2401ba6480 1 \n", - "... ... \n", - "nffc2d5e4b79a7ae 2 \n", - "nffc9844c1c7a6a9 4 \n", - "nffd79773f4109bb 3 \n", - "nfff6ab9d6dc0b32 4 \n", - "nfff87b21e4db902 4 \n", - "\n", - " feature_trimeter_soggy_greatest \\\n", - "id \n", - "n0007b5abb0c3a25 1 \n", - "n003bba8a98662e4 2 \n", - "n003bee128c2fcfc 1 \n", - "n0048ac83aff7194 3 \n", - "n0055a2401ba6480 0 \n", - "... ... \n", - "nffc2d5e4b79a7ae 2 \n", - "nffc9844c1c7a6a9 0 \n", - "nffd79773f4109bb 0 \n", - "nfff6ab9d6dc0b32 1 \n", - "nfff87b21e4db902 0 \n", - "\n", - " feature_unanalyzable_excusable_whirlwind \\\n", - "id \n", - "n0007b5abb0c3a25 1 \n", - "n003bba8a98662e4 0 \n", - "n003bee128c2fcfc 1 \n", - "n0048ac83aff7194 4 \n", - "n0055a2401ba6480 1 \n", - "... ... \n", - "nffc2d5e4b79a7ae 1 \n", - "nffc9844c1c7a6a9 0 \n", - "nffd79773f4109bb 0 \n", - "nfff6ab9d6dc0b32 1 \n", - "nfff87b21e4db902 0 \n", - "\n", - " feature_unbreakable_constraining_hegelianism \\\n", - "id \n", - "n0007b5abb0c3a25 3 \n", - "n003bba8a98662e4 0 \n", - "n003bee128c2fcfc 0 \n", - "n0048ac83aff7194 1 \n", - "n0055a2401ba6480 0 \n", - "... ... \n", - "nffc2d5e4b79a7ae 2 \n", - "nffc9844c1c7a6a9 3 \n", - "nffd79773f4109bb 0 \n", - "nfff6ab9d6dc0b32 1 \n", - "nfff87b21e4db902 0 \n", - "\n", - " feature_unformed_bent_smatch \\\n", - "id \n", - "n0007b5abb0c3a25 0 \n", - "n003bba8a98662e4 0 \n", - "n003bee128c2fcfc 1 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 0 \n", - "... ... \n", - "nffc2d5e4b79a7ae 3 \n", - "nffc9844c1c7a6a9 1 \n", - "nffd79773f4109bb 1 \n", - "nfff6ab9d6dc0b32 2 \n", - "nfff87b21e4db902 0 \n", - "\n", - " feature_unministerial_unextenuated_teleostean \\\n", - "id \n", - "n0007b5abb0c3a25 2 \n", - "n003bba8a98662e4 2 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 2 \n", - "... ... \n", - "nffc2d5e4b79a7ae 3 \n", - "nffc9844c1c7a6a9 3 \n", - "nffd79773f4109bb 1 \n", - "nfff6ab9d6dc0b32 2 \n", - "nfff87b21e4db902 1 \n", - "\n", - " feature_unmodish_zymogenic_rousing \\\n", - "id \n", - "n0007b5abb0c3a25 2 \n", - "n003bba8a98662e4 2 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 2 \n", - "... ... \n", - "nffc2d5e4b79a7ae 3 \n", - "nffc9844c1c7a6a9 3 \n", - "nffd79773f4109bb 4 \n", - "nfff6ab9d6dc0b32 1 \n", - "nfff87b21e4db902 0 \n", - "\n", - " feature_unsystematized_subcardinal_malaysia \\\n", - "id \n", - "n0007b5abb0c3a25 3 \n", - "n003bba8a98662e4 4 \n", - "n003bee128c2fcfc 0 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 1 \n", - "... ... \n", - "nffc2d5e4b79a7ae 2 \n", - "nffc9844c1c7a6a9 2 \n", - "nffd79773f4109bb 0 \n", - "nfff6ab9d6dc0b32 0 \n", - "nfff87b21e4db902 0 \n", - "\n", - " feature_willful_sere_chronobiology \\\n", - "id \n", - "n0007b5abb0c3a25 3 \n", - "n003bba8a98662e4 4 \n", - "n003bee128c2fcfc 3 \n", - "n0048ac83aff7194 0 \n", - "n0055a2401ba6480 4 \n", - "... ... \n", - "nffc2d5e4b79a7ae 2 \n", - "nffc9844c1c7a6a9 3 \n", - "nffd79773f4109bb 1 \n", - "nfff6ab9d6dc0b32 0 \n", - "nfff87b21e4db902 4 \n", - "\n", - " feature_zoological_peristomial_scute \n", - "id \n", - "n0007b5abb0c3a25 2 \n", - "n003bba8a98662e4 2 \n", - "n003bee128c2fcfc 2 \n", - "n0048ac83aff7194 2 \n", - "n0055a2401ba6480 2 \n", - "... ... \n", - "nffc2d5e4b79a7ae 1 \n", - "nffc9844c1c7a6a9 2 \n", - "nffd79773f4109bb 2 \n", - "nfff6ab9d6dc0b32 2 \n", - "nfff87b21e4db902 3 \n", - "\n", - "[688184 rows x 44 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "train" ] @@ -974,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:08:50.077560Z", @@ -987,28 +327,7 @@ "id": "7JX0Bs95r5GX", "outputId": "475e99c8-577d-401f-c0cf-bd4c46b80016" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot the number of rows per era\n", "train.groupby(\"era\").size().plot(\n", @@ -1025,14 +344,15 @@ }, "source": [ "### Target\n", - "The `target` is a measure of stock market returns over the next 20 (business) days. Specifically, it is a measure of \"stock-specific\" returns that are not explained by well-known \"factors\" or broader trends in the market, country, or sector. For example, if Apple went up and the tech sector also went up, we only want to know if Apple went up more or less than the tech sector.\n", "\n", - "Target values are binned into 5 unequal bins: `0`, `0.25`, `0.5`, `0.75`, `1.0`. Again, this heavy regularization of target values is to avoid overfitting as the underlying values are extremely noisy." + "This tutorial explicitly trains on `target_ender_60`, a measure of stock market returns over the next 60 market days. Specifically, it measures \"stock-specific\" returns that are not explained by well-known factors or broader market, country, or sector trends.\n", + "\n", + "Target values are binned into 5 unequal bins: `0`, `0.25`, `0.5`, `0.75`, `1.0`. This heavy regularization helps avoid overfitting because the underlying values are extremely noisy.\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:08:50.198670Z", @@ -1045,38 +365,17 @@ "id": "8ALp0YQ6r5GZ", "outputId": "0afccf3e-13a6-4d30-bc7d-a3e14cdb6253" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "# Plot density histogram of the target\n", - "train[\"target\"].plot(\n", + "# Plot density histogram of the explicit Ender-60 target\n", + "train[TARGET_COL].plot(\n", " kind=\"hist\",\n", - " title=\"Target\",\n", + " title=\"Ender-60 Target\",\n", " figsize=(5, 3),\n", " xlabel=\"Value\",\n", " density=True,\n", " bins=50\n", - ")" + ")\n" ] }, { @@ -1088,7 +387,7 @@ "### Features\n", "The `features` are quantitative attributes of each stock: fundamentals like P/E ratio, technical signals like RSI, market data like short interest, secondary data like analyst ratings, and much more.\n", "\n", - "The underlying definition of each feature is not important, just know that Numerai has included these features in the dataset because we believe they are predictive of the `target` either by themselves or in combination with other features.\n", + "The underlying definition of each feature is not important, just know that Numerai has included these features in the dataset because we believe they are predictive of the selected target either by themselves or in combination with other features.\n", "\n", "Feature values are binned into 5 equal bins: `0`, `1`, `2`, `3`, `4`. This heavy regularization of feature values is to avoid overfitting as the underlying values are extremely noisy. Unlike the target, these are integers instead of floats to reduce the storage needs of the overall dataset.\n", "\n", @@ -1097,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:08:50.362681Z", @@ -1110,28 +409,7 @@ "id": "CHlSJccVr5GY", "outputId": "e59bb818-a976-47af-bc71-5bdbd0fa4ea1" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 3))\n", @@ -1160,16 +438,16 @@ }, "source": [ "## 2. Modeling\n", - "At a high level, our task is to model and predict the `target` using the `features`.\n", + "At a high level, our task is to model the explicit `target_ender_60` objective using the `features`.\n", "\n", "### Model training\n", "\n", - "You are free to use any tool or framework, but here we will be using LGBMRegressor, a popular choice amongst tournament participants. While you wait for the model to train, watch this [video](https://www.youtube.com/watch?v=w8Y7hY05z7k) to learn why tree-based models work so well on tabular datasets from our Chief Scientist MDO." + "You are free to use any tool or framework, but here we will be using LGBMRegressor, a popular choice amongst tournament participants. While you wait for the model to train, watch this [video](https://www.youtube.com/watch?v=w8Y7hY05z7k) to learn why tree-based models work so well on tabular datasets from our Chief Scientist MDO.\n" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:09:11.237650Z", @@ -1182,900 +460,7 @@ "id": "prHdeg5Nr5GZ", "outputId": "02a58e7b-b32e-424c-818f-100bbc5b95f5" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001287 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 210\n", - "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n", - "[LightGBM] [Info] Start training from score 0.499946\n" - ] - }, - { - "data": { - "text/html": [ - "
LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n",
-       "              n_estimators=2000)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" - ], - "text/plain": [ - "LGBMRegressor(colsample_bytree=0.1, learning_rate=0.01, max_depth=5,\n", - " n_estimators=2000)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html\n", "import lightgbm as lgb\n", @@ -2101,7 +486,7 @@ "# This will take a few minutes 🍵\n", "model.fit(\n", " train[feature_set],\n", - " train[\"target\"]\n", + " train[TARGET_COL]\n", ")" ] }, @@ -2118,7 +503,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:15:33.751270Z", @@ -2131,168 +516,38 @@ "id": "ImonnvQYr5Ga", "outputId": "e4fa1d3e-003a-4824-ad6f-a73798830533" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:09:11,758 INFO numerapi.utils: starting download\n", - "v5.2/validation.parquet: 4.06GB [06:12, 10.9MB/s] \n" - ] - }, - { - "data": { - "text/html": [ - "
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erapredictiontarget
id
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............
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\n", - "
" - ], - "text/plain": [ - " era prediction target\n", - "id \n", - "n000c290e4364875 0579 0.495463 0.50\n", - "n002a15bc5575bbb 0579 0.508138 0.25\n", - "n00309caaa0f955e 0579 0.504767 0.75\n", - "n0039cbdcf835708 0579 0.511343 0.50\n", - "n004143458984f89 0579 0.492206 0.50\n", - "... ... ... ...\n", - "nffd9cf2c992c881 1191 0.500466 0.50\n", - "nfff0fbd1837e76f 1191 0.496647 0.25\n", - "nfff3b8261e8af0a 1191 0.497235 0.25\n", - "nfff3cb2970e386b 1191 0.506804 0.75\n", - "nfff8ba3632ff9b9 1191 0.501417 0.50\n", - "\n", - "[956598 rows x 3 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Download validation data - this will take a few minutes\n", "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n", "\n", - "# Load the validation data and filter for data_type == \"validation\"\n", + "# Recent validation eras may not have a mature 60-day target yet, so keep only\n", + "# eligible rows with a published Ender-60 value before evaluating.\n", "validation = pd.read_parquet(\n", " f\"{DATA_VERSION}/validation.parquet\",\n", - " columns=[\"era\", \"data_type\", \"target\"] + feature_set\n", + " columns=[\"era\", \"data_type\", TARGET_COL] + feature_set\n", ")\n", "validation = validation[validation[\"data_type\"] == \"validation\"]\n", + "validation = validation.dropna(subset=[TARGET_COL])\n", "del validation[\"data_type\"]\n", "\n", "# Downsample to every 4th era to reduce memory usage and speedup evaluation (suggested for Colab free tier)\n", "# Comment out the line below to use all the data (slower and higher memory usage, but more accurate evaluation)\n", "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n", "\n", - "# Eras are 1 week apart, but targets look 20 days (o 4 weeks/eras) into the future,\n", - "# so we need to \"embargo\" the first 4 eras following our last train era to avoid \"data leakage\"\n", + "# The 60-market-day objective overlaps substantially across weekly eras. Use a\n", + "# conservative 16-era embargo after the final training era to prevent leakage.\n", "last_train_era = int(train[\"era\"].unique()[-1])\n", - "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n", + "eras_to_embargo = [\n", + " str(last_train_era + offset).zfill(4)\n", + " for offset in range(1, EMBARGO_ERAS + 1)\n", + "]\n", "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n", "\n", "# Generate predictions against the out-of-sample validation features\n", "# This will take a few minutes 🍵\n", "validation[\"prediction\"] = model.predict(validation[feature_set])\n", - "validation[[\"era\", \"prediction\", \"target\"]]" + "validation[[\"era\", \"prediction\", TARGET_COL]]\n" ] }, { @@ -2303,18 +558,17 @@ "source": [ "### Performance evaluation\n", "\n", - "Numerai calculates scores designed to \"align incentives\" between your model and the hedge fund - a model with good scores should help the hedge fund make good returns. The primary scoring metrics in Numerai are:\n", - "\n", - "- `CORR` (or \"Correlation\") which is calculated by the function `numerai_corr` - a Numerai specific variant of the Pearson Correlation between your model and the target.\n", + "This notebook evaluates its model against explicit `target_ender_60` values. Numerai's round scoring and payout target is configured independently and can differ across historical and post-cutover rounds; changing the dataset alias does not rewrite historical scores.\n", "\n", - "- `MMC` (or \"Meta Model Contribution\") which is a calculated by the function `correlation_contribution` - a measure of how uniquely additive your model is to the Numerai Meta Model.\n", + "The primary scoring metrics are:\n", "\n", - "On the Numerai website you will see `CORR` referred to as `CORR20V2`, where the \"20\" refers to the 20-day return target and \"v2\" specifies that we are using the 2nd version of the scoring function." + "- `CORR` (or \"Correlation\"), calculated by `numerai_corr`, is a Numerai-specific variant of Pearson correlation between a model and the evaluation target.\n", + "- `MMC` (or \"Meta Model Contribution\"), calculated by `correlation_contribution`, measures how uniquely additive a model is to the Numerai Meta Model.\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:15:35.109707Z", @@ -2326,25 +580,7 @@ "id": "lTdo3r_Kr5Ga", "outputId": "85d7e416-dc88-4062-9782-4c9d82ff652a" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:15:34,656 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:15:34,658 INFO numerapi.utils: download complete\n" - ] - } - ], + "outputs": [], "source": [ "# install Numerai's open-source scoring tools\n", "!pip install -q --no-deps numerai-tools\n", @@ -2372,7 +608,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:15:36.535207Z", @@ -2385,57 +621,16 @@ "id": "u_qnP9QVr5Gb", "outputId": "9e168f8b-4865-40b8-ad30-e84c7f7e39cf" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32041/1642615225.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " per_era_corr = validation.groupby(\"era\").apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_32041/1642615225.py:7: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " per_era_mmc = validation.dropna().groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Compute the per-era corr between our predictions and the target values\n", "per_era_corr = validation.groupby(\"era\").apply(\n", - " lambda x: numerai_corr(x[[\"prediction\"]].dropna(), x[\"target\"].dropna())\n", + " lambda x: numerai_corr(x[[\"prediction\"]].dropna(), x[TARGET_COL])\n", ")\n", "\n", "# Compute the per-era mmc between our predictions, the meta model, and the target values\n", "per_era_mmc = validation.dropna().groupby(\"era\").apply(\n", - " lambda x: correlation_contribution(x[[\"prediction\"]], x[\"meta_model\"], x[\"target\"])\n", + " lambda x: correlation_contribution(x[[\"prediction\"]], x[\"meta_model\"], x[TARGET_COL])\n", ")\n", "\n", "\n", @@ -2481,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:15:36.621370Z", @@ -2494,38 +689,7 @@ "id": "T62k0nGpr5Gb", "outputId": "1c0db1d3-f518-4c7c-93b2-2e352c13e53d" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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sharpeprediction 0.855762\n", - "dtype: float64prediction 0.385088\n", - "dtype: float64
max_drawdownprediction 0.032412\n", - "dtype: float64prediction 0.073799\n", - "dtype: float64
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" - ], - "text/plain": [ - " prediction\n", - "id \n", - "n0005e01cd27dd7b 0.507644\n", - "n000a0f321ce17e3 0.487444\n", - "n000e47c9c27ca3d 0.494772\n", - "n0016060c6335337 0.506582\n", - "n001c30cea427396 0.502284\n", - "... ...\n", - "nffcc8bf595e4e98 0.496650\n", - "nffd0c2231719dc9 0.493927\n", - "nffdbad9b545ca02 0.497923\n", - "nffe55a271685c39 0.494947\n", - "nfff60bd24702729 0.499332\n", - "\n", - "[6720 rows x 1 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Download latest live features\n", "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n", @@ -2866,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:15:38.030144Z", @@ -2885,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:15:38.060565Z", @@ -2898,13 +866,13 @@ "# Use the cloudpickle library to serialize your function\n", "import cloudpickle\n", "p = cloudpickle.dumps(predict)\n", - "with open(\"hello_numerai.pkl\", \"wb\") as f:\n", - " f.write(p)" + "with open(MODEL_ARTIFACT, \"wb\") as f:\n", + " f.write(p)\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:15:38.063369Z", @@ -2922,9 +890,9 @@ "# Download file if running in Google Colab\n", "try:\n", " from google.colab import files\n", - " files.download('hello_numerai.pkl')\n", - "except:\n", - " pass" + " files.download(MODEL_ARTIFACT)\n", + "except ImportError:\n", + " pass\n" ] }, { @@ -2933,9 +901,9 @@ "id": "iplRaPPLr5Gd" }, "source": [ - "That's it! You now have a pickle file that is ready for upload.\n", + "That's it! You now have a target-versioned pickle file that is ready for upload. Its filename records that it was trained on v5.3 Ender-60, preventing an older Ender-20 cache from being mistaken for this model.\n", "\n", - "Head back to the [Hello Numerai Tutorial](https://numer.ai/tutorial/hello-numerai) to upload your model!" + "Head back to the [Hello Numerai Tutorial](https://numer.ai/tutorial/hello-numerai) to upload your model!\n" ] } ], diff --git a/numerai/target_ensemble.ipynb b/numerai/target_ensemble.ipynb index ed50b6d..cb094e9 100644 --- a/numerai/target_ensemble.ipynb +++ b/numerai/target_ensemble.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "NOTE: this example is out of date and was made when cyrus was the main target rather than ender. DYOR" + "This example uses the post-cutover v5.3 default objective, Ender-60. It pins every horizon-sensitive operation to an explicit target column; the generic `target` column appears only once to verify the documented alias.\n" ] }, { @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:11.651667Z", @@ -40,22 +40,14 @@ "id": "Ej4poji3G1Df", "outputId": "171d8edf-bd43-406a-e782-64ed09624904" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python 3.11.11\r\n" - ] - } - ], + "outputs": [], "source": [ "!python --version" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:12.623941Z", @@ -67,60 +59,25 @@ "id": "KD826S8uxnNY", "outputId": "2d93c23b-7937-4dba-8dde-1f51df9884c0" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "# Install dependencies\n", - "!pip install -q --upgrade numerapi pandas==2.3.1 pyarrow matplotlib lightgbm scikit-learn scipy cloudpickle==3.1.1\n", + "!pip install -q --upgrade numerapi==2.20.7 pandas==2.3.1 pyarrow==18.1.0 matplotlib==3.10.3 lightgbm==4.5.0 scikit-learn==1.6.1 scipy==1.16.0 cloudpickle==3.1.1\n", "\n", "# Inline plots\n", - "%matplotlib inline" + "%matplotlib inline\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:13.156169Z", "start_time": "2025-12-14T22:42:12.625011Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: seaborn in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (0.13.2)\r\n", - "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from seaborn) (2.3.4)\r\n", - "Requirement already satisfied: pandas>=1.2 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from seaborn) (2.3.3)\r\n", - "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from seaborn) (3.10.8)\r\n", - "Requirement already satisfied: contourpy>=1.0.1 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.3)\r\n", - "Requirement already satisfied: cycler>=0.10 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\r\n", - "Requirement already satisfied: fonttools>=4.22.0 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.60.1)\r\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.9)\r\n", - "Requirement already satisfied: packaging>=20.0 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (25.0)\r\n", - "Requirement already satisfied: pillow>=8 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (12.0.0)\r\n", - "Requirement already satisfied: pyparsing>=3 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.2.5)\r\n", - "Requirement already satisfied: python-dateutil>=2.7 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\r\n", - "Requirement already satisfied: pytz>=2020.1 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.2)\r\n", - "Requirement already satisfied: tzdata>=2022.7 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.2)\r\n", - "Requirement already satisfied: six>=1.5 in /Users/numerai/Desktop/work_space/example-scripts/python311_venv/example-scripts/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\r\n", - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "!pip install seaborn" ] @@ -151,463 +108,23 @@ "id": "R1I_xkY4xnNa", "outputId": "62fdbb5e-df86-4e4e-f648-fe52d726c949" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:42:13,763 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:42:13,765 INFO numerapi.utils: download complete\n", - "2025-12-14 14:42:14,090 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:42:14,090 INFO numerapi.utils: download complete\n" - ] - }, - { - "data": { - "text/html": [ - "
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eratarget_agnes_20target_agnes_60target_alpha_20target_alpha_60target_bravo_20target_bravo_60target_caroline_20target_caroline_60target_charlie_20...target_teager2b_60target_tyler_20target_tyler_60target_victor_20target_victor_60target_waldo_20target_waldo_60target_xerxes_20target_xerxes_60target
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" - ], - "text/plain": [ - " era target_agnes_20 target_agnes_60 target_alpha_20 \\\n", - "id \n", - "n0007b5abb0c3a25 0001 0.25 0.00 0.25 \n", - "n003bba8a98662e4 0001 0.25 0.25 0.25 \n", - "n003bee128c2fcfc 0001 1.00 1.00 1.00 \n", - "n0048ac83aff7194 0001 0.25 0.25 0.25 \n", - "n0055a2401ba6480 0001 0.25 0.50 0.25 \n", - "... ... ... ... ... \n", - "nffc2d5e4b79a7ae 0573 0.00 0.25 0.00 \n", - "nffc9844c1c7a6a9 0573 0.50 0.50 0.25 \n", - "nffd79773f4109bb 0573 0.50 0.50 0.75 \n", - "nfff6ab9d6dc0b32 0573 0.50 0.50 0.25 \n", - "nfff87b21e4db902 0573 0.75 0.75 0.50 \n", - "\n", - " target_alpha_60 target_bravo_20 target_bravo_60 \\\n", - "id \n", - "n0007b5abb0c3a25 0.25 0.00 0.00 \n", - "n003bba8a98662e4 0.00 0.25 0.00 \n", - "n003bee128c2fcfc 1.00 0.75 1.00 \n", - "n0048ac83aff7194 0.25 0.50 0.25 \n", - "n0055a2401ba6480 0.50 0.25 0.50 \n", - "... ... ... ... \n", - "nffc2d5e4b79a7ae 0.25 0.00 0.25 \n", - "nffc9844c1c7a6a9 0.50 0.50 0.50 \n", - "nffd79773f4109bb 0.50 0.75 0.50 \n", - "nfff6ab9d6dc0b32 0.50 0.50 0.50 \n", - "nfff87b21e4db902 0.75 0.50 0.75 \n", - "\n", - " target_caroline_20 target_caroline_60 target_charlie_20 \\\n", - "id \n", - "n0007b5abb0c3a25 0.25 0.00 0.25 \n", - "n003bba8a98662e4 0.25 0.25 0.25 \n", - "n003bee128c2fcfc 0.75 0.75 0.75 \n", - "n0048ac83aff7194 0.50 0.25 0.50 \n", - "n0055a2401ba6480 0.25 0.50 0.25 \n", - "... ... ... ... \n", - "nffc2d5e4b79a7ae 0.25 0.50 0.00 \n", - "nffc9844c1c7a6a9 0.50 0.50 0.50 \n", - "nffd79773f4109bb 0.50 0.50 0.75 \n", - "nfff6ab9d6dc0b32 0.25 0.50 0.25 \n", - "nfff87b21e4db902 0.50 0.50 0.50 \n", - "\n", - " ... target_teager2b_60 target_tyler_20 target_tyler_60 \\\n", - "id ... \n", - "n0007b5abb0c3a25 ... 0.50 0.25 0.25 \n", - "n003bba8a98662e4 ... 0.50 0.25 0.25 \n", - "n003bee128c2fcfc ... 1.00 1.00 0.75 \n", - "n0048ac83aff7194 ... 0.25 0.25 0.25 \n", - "n0055a2401ba6480 ... 0.50 0.25 0.50 \n", - "... ... ... ... ... \n", - "nffc2d5e4b79a7ae ... 0.50 0.25 0.50 \n", - "nffc9844c1c7a6a9 ... 0.75 0.50 0.50 \n", - "nffd79773f4109bb ... 0.75 0.50 0.50 \n", - "nfff6ab9d6dc0b32 ... 0.50 0.50 0.25 \n", - "nfff87b21e4db902 ... 0.75 0.75 0.75 \n", - "\n", - " target_victor_20 target_victor_60 target_waldo_20 \\\n", - "id \n", - "n0007b5abb0c3a25 0.25 0.25 0.25 \n", - "n003bba8a98662e4 0.25 0.00 0.25 \n", - "n003bee128c2fcfc 0.75 0.75 0.75 \n", - "n0048ac83aff7194 0.50 0.25 0.25 \n", - "n0055a2401ba6480 0.25 0.50 0.25 \n", - "... ... ... ... \n", - "nffc2d5e4b79a7ae 0.25 0.50 0.00 \n", - "nffc9844c1c7a6a9 0.50 0.50 0.50 \n", - "nffd79773f4109bb 0.50 0.50 0.50 \n", - "nfff6ab9d6dc0b32 0.25 0.50 0.50 \n", - "nfff87b21e4db902 0.50 0.75 0.50 \n", - "\n", - " target_waldo_60 target_xerxes_20 target_xerxes_60 target \n", - "id \n", - "n0007b5abb0c3a25 0.00 0.25 0.00 0.25 \n", - "n003bba8a98662e4 0.25 0.25 0.25 0.50 \n", - "n003bee128c2fcfc 1.00 0.75 0.75 1.00 \n", - "n0048ac83aff7194 0.25 0.25 0.25 0.25 \n", - "n0055a2401ba6480 0.50 0.25 0.50 0.50 \n", - "... ... ... ... ... \n", - "nffc2d5e4b79a7ae 0.50 0.00 0.25 0.25 \n", - "nffc9844c1c7a6a9 0.50 0.50 0.50 0.50 \n", - "nffd79773f4109bb 0.75 0.50 0.50 0.50 \n", - "nfff6ab9d6dc0b32 0.50 0.25 0.50 0.50 \n", - "nfff87b21e4db902 0.50 0.50 0.50 0.50 \n", - "\n", - "[688184 rows x 42 columns]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import json\n", "from numerapi import NumerAPI\n", "\n", - "# Set the data version to one of the most recent versions\n", "DATA_VERSION = \"v5.3\"\n", - "MAIN_TARGET = \"target_ender_20\"\n", + "MAIN_TARGET = \"target_ender_60\"\n", "TARGET_CANDIDATES = [\n", " MAIN_TARGET,\n", - " \"target_victor_20\",\n", - " \"target_xerxes_20\",\n", - " \"target_teager2b_20\"\n", + " \"target_victor_60\",\n", + " \"target_xerxes_60\",\n", + " \"target_teager2b_60\"\n", "]\n", - "FAVORITE_MODEL = \"v53_lgbm_ender20\"\n", + "FAVORITE_MODEL = \"v53_lgbm_ender60\"\n", + "EMBARGO_ERAS = 16\n", + "MODEL_ARTIFACT = \"target_ensemble_v53_ender60.pkl\"\n", "\n", "# Download data\n", "napi = NumerAPI()\n", @@ -631,7 +148,7 @@ "train = train[train[\"era\"].isin(train[\"era\"].unique()[::4])]\n", "\n", "# Print target columns\n", - "train[[\"era\"] + target_cols]" + "train[[\"era\"] + target_cols]\n" ] }, { @@ -649,12 +166,12 @@ "id": "R1o6PJcbxnNa" }, "source": [ - "First thing to note is that `target` is just an alias for the `cyrus` target, so we can drop this column for the rest of the notebook." + "After the v5.3 in-place cutover, generic `target` aliases `target_ender_60`. We check that contract once for demonstration, then use `MAIN_TARGET` everywhere horizon identity matters. A pre-cutover or stale cached dataset is reported explicitly but does not change this notebook's Ender-60 training objective. Use `target_ender_20` explicitly when reproducing an older 20-day experiment.\n" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:15.369910Z", @@ -664,9 +181,19 @@ }, "outputs": [], "source": [ - "# Drop `target` column\n", - "assert train[\"target\"].equals(train[MAIN_TARGET])\n", - "targets_df = train[[\"era\"] + target_cols]" + "# Verify the documented post-cutover alias when using a current dataset. A stale\n", + "# pre-cutover cache is called out, but training and evaluation remain explicitly\n", + "# pinned to MAIN_TARGET below.\n", + "alias_is_ender60 = train[\"target\"].equals(train[MAIN_TARGET])\n", + "if alias_is_ender60:\n", + " assert train[\"target\"].equals(train[MAIN_TARGET])\n", + "else:\n", + " print(\n", + " \"Generic target is not Ender-60 in this dataset snapshot. \"\n", + " \"Refresh v5.3 after the announced cutover; this notebook still trains \"\n", + " f\"explicitly on {MAIN_TARGET}.\"\n", + " )\n", + "targets_df = train[[\"era\"] + target_cols]\n" ] }, { @@ -677,16 +204,16 @@ "source": [ "### Target names\n", "\n", - "At a high level, each target represents a different kind of stock market return\n", - "- the `name` represents the type of stock market return (eg. residual to market/country/sector vs market/country/style)\n", - "- the `_20` or `_60` suffix denotes the time horizon of the target (ie. 20 vs 60 market days)\n", + "At a high level, each target represents a different kind of stock market return:\n", + "- the `name` represents the type of return (for example, residual to market/country/sector versus market/country/style)\n", + "- the `_20` or `_60` suffix denotes the target horizon in market days\n", "\n", - "The reason why `cyrus` as our main target is because it most closely matches the type of returns we want for our hedge fund. Just like how we are always in search for better features to include in the dataset, we are also always in search for better targets to make our main target. During our research, we often come up with targets we like but not as much as the main target, and these are instead released as auxiliary targets." + "Ender-60 is the post-cutover v5.3 default objective, while explicit Ender-20 and other auxiliary columns remain available. Dataset aliases and Tournament round scoring are separate: changing the v5.3 alias does not rewrite historical scoring or payouts.\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:15.374907Z", @@ -699,172 +226,7 @@ "id": "P7uAdarxxnNb", "outputId": "ece63dd6-310d-4b40-c863-c22be9170fad" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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2060
name
agnestarget_agnes_20target_agnes_60
alphatarget_alpha_20target_alpha_60
bravotarget_bravo_20target_bravo_60
carolinetarget_caroline_20target_caroline_60
charlietarget_charlie_20target_charlie_60
claudiatarget_claudia_20target_claudia_60
cyrusdtarget_cyrusd_20target_cyrusd_60
deltatarget_delta_20target_delta_60
echotarget_echo_20target_echo_60
endertarget_ender_20target_ender_60
jaspertarget_jasper_20target_jasper_60
jeremytarget_jeremy_20target_jeremy_60
ralphtarget_ralph_20target_ralph_60
rowantarget_rowan_20target_rowan_60
samtarget_sam_20target_sam_60
teager2btarget_teager2b_20target_teager2b_60
tylertarget_tyler_20target_tyler_60
victortarget_victor_20target_victor_60
waldotarget_waldo_20target_waldo_60
xerxestarget_xerxes_20target_xerxes_60
\n", - "
" - ], - "text/plain": [ - " 20 60\n", - "name \n", - "agnes target_agnes_20 target_agnes_60\n", - "alpha target_alpha_20 target_alpha_60\n", - "bravo target_bravo_20 target_bravo_60\n", - "caroline target_caroline_20 target_caroline_60\n", - "charlie target_charlie_20 target_charlie_60\n", - "claudia target_claudia_20 target_claudia_60\n", - "cyrusd target_cyrusd_20 target_cyrusd_60\n", - "delta target_delta_20 target_delta_60\n", - "echo target_echo_20 target_echo_60\n", - "ender target_ender_20 target_ender_60\n", - "jasper target_jasper_20 target_jasper_60\n", - "jeremy target_jeremy_20 target_jeremy_60\n", - "ralph target_ralph_20 target_ralph_60\n", - "rowan target_rowan_20 target_rowan_60\n", - "sam target_sam_20 target_sam_60\n", - "teager2b target_teager2b_20 target_teager2b_60\n", - "tyler target_tyler_20 target_tyler_60\n", - "victor target_victor_20 target_victor_60\n", - "waldo target_waldo_20 target_waldo_60\n", - "xerxes target_xerxes_20 target_xerxes_60" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Print target names grouped by name and time horizon\n", "pd.set_option('display.max_rows', 100)\n", @@ -889,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:15.604222Z", @@ -902,29 +264,7 @@ "id": "Uw_4oswnxnNb", "outputId": "a208b358-fc87-4423-bf2a-9e8d67007274" }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[, ],\n", - " [, ]], dtype=object)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot target distributions\n", "targets_df[TARGET_CANDIDATES].plot(\n", @@ -946,12 +286,12 @@ "id": "2DM-mHG_xnNc" }, "source": [ - "It is also important to note that the auxilary targets can be `NaN`, but the primary target will never be `NaN`. Since we are using tree-based models here we won't need to do any special pre-processing." + "Auxiliary targets can be `NaN`. The explicit Ender-60 target can also be null in recent validation eras while its 60-market-day outcome is still maturing. Training and evaluation must select eligible rows rather than assuming the main target is always present.\n" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:15.747922Z", @@ -964,36 +304,7 @@ "id": "FT3YCXrYxnNc", "outputId": "1dbaa0ec-86b7-43ed-dde8-6be3c37c02e6" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/1621777384.py:2: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# print number of NaNs per era\n", "nans_per_era = targets_df.groupby(\"era\").apply(lambda x: x.isna().sum())\n", @@ -1013,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:17.160897Z", @@ -1026,28 +337,7 @@ "id": "jAvpw-XvxnNc", "outputId": "1c0e3f11-e25a-4df6-a0aa-7c040ddfbd33" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Plot correlation matrix of targets\n", "import seaborn as sns\n", @@ -1070,7 +360,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:42:17.322859Z", @@ -1083,257 +373,14 @@ "id": "sFN8_azMxnNd", "outputId": "4516093b-af13-4ea8-e8c6-efb301a842b6" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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corr_with_ender_20
target1.000000
target_ender_201.000000
target_jasper_200.795446
target_teager2b_200.793102
target_ralph_200.762704
target_claudia_200.761308
target_cyrusd_200.756719
target_waldo_200.755872
target_xerxes_200.755835
target_rowan_200.748943
target_caroline_200.745993
target_sam_200.743136
target_victor_200.740955
target_jeremy_200.733176
target_echo_200.728146
target_delta_200.708655
target_bravo_200.701593
target_charlie_200.699084
target_alpha_200.697213
target_agnes_200.677060
target_tyler_200.660450
target_ender_600.464128
target_teager2b_600.428481
target_ralph_600.418770
target_jasper_600.418463
target_claudia_600.416140
target_cyrusd_600.415800
target_xerxes_600.415144
target_waldo_600.414520
target_caroline_600.410135
target_rowan_600.409341
target_sam_600.408719
target_victor_600.408640
target_jeremy_600.403939
target_echo_600.401272
target_delta_600.391699
target_bravo_600.388731
target_charlie_600.385879
target_alpha_600.385499
target_agnes_600.373223
target_tyler_600.364531
\n", - "
" - ], - "text/plain": [ - " corr_with_ender_20\n", - "target 1.000000\n", - "target_ender_20 1.000000\n", - "target_jasper_20 0.795446\n", - "target_teager2b_20 0.793102\n", - "target_ralph_20 0.762704\n", - "target_claudia_20 0.761308\n", - "target_cyrusd_20 0.756719\n", - "target_waldo_20 0.755872\n", - "target_xerxes_20 0.755835\n", - "target_rowan_20 0.748943\n", - "target_caroline_20 0.745993\n", - "target_sam_20 0.743136\n", - "target_victor_20 0.740955\n", - "target_jeremy_20 0.733176\n", - "target_echo_20 0.728146\n", - "target_delta_20 0.708655\n", - "target_bravo_20 0.701593\n", - "target_charlie_20 0.699084\n", - "target_alpha_20 0.697213\n", - "target_agnes_20 0.677060\n", - "target_tyler_20 0.660450\n", - "target_ender_60 0.464128\n", - "target_teager2b_60 0.428481\n", - "target_ralph_60 0.418770\n", - "target_jasper_60 0.418463\n", - "target_claudia_60 0.416140\n", - "target_cyrusd_60 0.415800\n", - "target_xerxes_60 0.415144\n", - "target_waldo_60 0.414520\n", - "target_caroline_60 0.410135\n", - "target_rowan_60 0.409341\n", - "target_sam_60 0.408719\n", - "target_victor_60 0.408640\n", - "target_jeremy_60 0.403939\n", - "target_echo_60 0.401272\n", - "target_delta_60 0.391699\n", - "target_bravo_60 0.388731\n", - "target_charlie_60 0.385879\n", - "target_alpha_60 0.385499\n", - "target_agnes_60 0.373223\n", - "target_tyler_60 0.364531" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "(\n", " targets_df[target_cols]\n", " .corrwith(targets_df[MAIN_TARGET])\n", " .sort_values(ascending=False)\n", - " .to_frame(\"corr_with_ender_20\")\n", - ")" + " .to_frame(\"corr_with_ender_60\")\n", + ")\n" ] }, { @@ -1346,13 +393,12 @@ "\n", "Our goal is to create an ensemble of models trained on different targets. But which targets should we use?\n", "\n", - "When deciding which model to ensemble, we should consider a few things:\n", - "\n", - "- The performance of the predictions of the model trained on the target vs the main target\n", + "When deciding which models to ensemble, consider:\n", "\n", - "- The correlation between the target and the main target\n", + "- performance of predictions from each auxiliary-target model against explicit Ender-60\n", + "- correlation between each auxiliary target and Ender-60\n", "\n", - "To keep things simple and fast, let's just arbitrarily pick a few 20-day targets to evaluate." + "To keep the tutorial simple and horizon-consistent, we evaluate a few 60-market-day targets.\n" ] }, { @@ -1371,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:43:34.048415Z", @@ -1383,38 +429,7 @@ "id": "VqBaYwmqxnNd", "outputId": "6716cbd7-827c-4593-b65d-25d120de0af5" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001261 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 210\n", - "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n", - "[LightGBM] [Info] Start training from score 0.499946\n", - "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001446 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 210\n", - "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n", - "[LightGBM] [Info] Start training from score 0.500003\n", - "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001500 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 210\n", - "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n", - "[LightGBM] [Info] Start training from score 0.500031\n", - "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001485 seconds.\n", - "You can set `force_row_wise=true` to remove the overhead.\n", - "And if memory is not enough, you can set `force_col_wise=true`.\n", - "[LightGBM] [Info] Total Bins 210\n", - "[LightGBM] [Info] Number of data points in the train set: 688184, number of used features: 42\n", - "[LightGBM] [Info] Start training from score 0.499948\n" - ] - } - ], + "outputs": [], "source": [ "import lightgbm as lgb\n", "\n", @@ -1433,14 +448,12 @@ " # learning_rate=0.001,\n", " # max_depth=10,\n", " # num_leaves=2**10,\n", - " # colsample_bytree=0.1\n", + " # colsample_bytree=0.1,\n", " # min_data_in_leaf=10000,\n", " # )\n", - " model.fit(\n", - " train[feature_cols],\n", - " train[target]\n", - " )\n", - " models[target] = model" + " target_train = train.dropna(subset=[target])\n", + " model.fit(target_train[feature_cols], target_train[target])\n", + " models[target] = model\n" ] }, { @@ -1454,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:06.225217Z", @@ -1467,188 +480,30 @@ "id": "Ic9eGKxSxnNe", "outputId": "e3522459-1f43-4d45-d20c-a6edfcf6f28a" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:43:34,745 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:43:34,746 INFO numerapi.utils: download complete\n" - ] - }, - { - "data": { - "text/html": [ - "
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prediction_target_ender_20prediction_target_victor_20prediction_target_xerxes_20prediction_target_teager2b_20
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" - ], - "text/plain": [ - " prediction_target_ender_20 prediction_target_victor_20 \\\n", - "id \n", - "n000c290e4364875 0.495229 0.491972 \n", - "n002a15bc5575bbb 0.507359 0.512950 \n", - "n00309caaa0f955e 0.504452 0.512101 \n", - "n0039cbdcf835708 0.510034 0.505156 \n", - "n004143458984f89 0.491897 0.485125 \n", - "... ... ... \n", - "nffd9cf2c992c881 0.501314 0.501444 \n", - "nfff0fbd1837e76f 0.497717 0.499122 \n", - "nfff3b8261e8af0a 0.496199 0.496723 \n", - "nfff3cb2970e386b 0.506733 0.499831 \n", - "nfff8ba3632ff9b9 0.501066 0.509136 \n", - "\n", - " prediction_target_xerxes_20 prediction_target_teager2b_20 \n", - "id \n", - "n000c290e4364875 0.495561 0.496844 \n", - "n002a15bc5575bbb 0.515098 0.508923 \n", - "n00309caaa0f955e 0.513682 0.505769 \n", - "n0039cbdcf835708 0.506836 0.504405 \n", - "n004143458984f89 0.486912 0.490126 \n", - "... ... ... \n", - "nffd9cf2c992c881 0.501463 0.499682 \n", - "nfff0fbd1837e76f 0.494147 0.496017 \n", - "nfff3b8261e8af0a 0.498597 0.495877 \n", - "nfff3cb2970e386b 0.503588 0.507004 \n", - "nfff8ba3632ff9b9 0.509395 0.500131 \n", - "\n", - "[956598 rows x 4 columns]" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Download validation data\n", "napi.download_dataset(f\"{DATA_VERSION}/validation.parquet\")\n", "\n", - "# Load the validation data, filtering for data_type == \"validation\"\n", + "# Load validation rows and retain only eras with a mature Ender-60 target.\n", "validation = pd.read_parquet(\n", " f\"{DATA_VERSION}/validation.parquet\",\n", " columns=[\"era\", \"data_type\"] + feature_cols + target_cols\n", ")\n", "validation = validation[validation[\"data_type\"] == \"validation\"]\n", + "validation = validation.dropna(subset=[MAIN_TARGET])\n", "del validation[\"data_type\"]\n", "\n", "# Downsample every 4th era to reduce memory usage and speedup validation (suggested for Colab free tier)\n", "# Comment out the line below to use all the data\n", "validation = validation[validation[\"era\"].isin(validation[\"era\"].unique()[::4])]\n", "\n", - "# Embargo overlapping eras from training data\n", + "# A conservative 16-era embargo reflects the 60-market-day target horizon.\n", "last_train_era = int(train[\"era\"].unique()[-1])\n", - "eras_to_embargo = [str(era).zfill(4) for era in [last_train_era + i for i in range(4)]]\n", + "eras_to_embargo = [\n", + " str(last_train_era + offset).zfill(4)\n", + " for offset in range(1, EMBARGO_ERAS + 1)\n", + "]\n", "validation = validation[~validation[\"era\"].isin(eras_to_embargo)]\n", "\n", "# Generate validation predictions for each model\n", @@ -1656,7 +511,7 @@ " validation[f\"prediction_{target}\"] = models[target].predict(validation[feature_cols])\n", "\n", "pred_cols = [f\"prediction_{target}\" for target in TARGET_CANDIDATES]\n", - "validation[pred_cols]" + "validation[pred_cols]\n" ] }, { @@ -1672,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:06.745081Z", @@ -1680,17 +535,7 @@ }, "id": "NjuAERHhxnNe" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m25.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "# install Numerai's open-source scoring tools\n", "!pip install -q --no-deps numerai-tools\n", @@ -1710,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:08.581577Z", @@ -1723,50 +568,21 @@ "id": "WUvsFi-VxnNf", "outputId": "39a65698-cf42-4a58-fe75-eaca8ec2d224" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/2595143125.py:5: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " correlations = validation.groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "prediction_cols = [\n", " f\"prediction_{target}\"\n", " for target in TARGET_CANDIDATES\n", "]\n", "correlations = validation.groupby(\"era\").apply(\n", - " lambda d: numerai_corr(d[prediction_cols], d[\"target\"])\n", + " lambda d: numerai_corr(d[prediction_cols], d[MAIN_TARGET])\n", ")\n", "cumsum_corrs = correlations.cumsum()\n", "cumsum_corrs.plot(\n", - " title=\"Cumulative Correlation of validation Predictions\",\n", + " title=\"Cumulative Correlation of Validation Predictions to Ender-60\",\n", " figsize=(10, 6),\n", " xticks=[]\n", - ")" + ")\n" ] }, { @@ -1775,14 +591,12 @@ "id": "EDUjJ3guxnNf" }, "source": [ - "Looking at the summary metrics below:\n", - "- the models trained on `victor` and `xerxes` have the highest means, but `victor` is less correlated with `cyrus` than `xerxes` is, which means `victor` could be better in ensembling\n", - "- the model trained on `teager` has the lowest mean, but `teager` is significantly less correlated with `cyrus` than any other target shown" + "The summary below compares every candidate against Ender-60. Auxiliary-target performance and diversity can both matter: a somewhat weaker but less correlated model may still improve an ensemble.\n" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:09.028572Z", @@ -1795,109 +609,11 @@ "id": "smz_GLLAxnNf", "outputId": "75d86754-9e57-492a-c776-748dd04ca32f" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/2523563052.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " mean_corr_with_ender = validation.groupby(\"era\").apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/2523563052.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " mean_corr_with_ender = validation.groupby(\"era\").apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/2523563052.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " mean_corr_with_ender = validation.groupby(\"era\").apply(\n", - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/2523563052.py:22: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " mean_corr_with_ender = validation.groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " mean std sharpe max_drawdown \\\n", - "prediction_target_ender_20 0.012430 0.014564 0.853434 0.026261 \n", - "prediction_target_victor_20 0.012177 0.013758 0.885128 0.027565 \n", - "prediction_target_xerxes_20 0.013083 0.013658 0.957902 0.019911 \n", - "prediction_target_teager2b_20 0.012613 0.014549 0.866929 0.030551 \n", - "\n", - " mean_corr_with_ender \n", - "prediction_target_ender_20 0.013074 \n", - "prediction_target_victor_20 0.012347 \n", - "prediction_target_xerxes_20 0.013278 \n", - "prediction_target_teager2b_20 0.013081 " - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def get_summary_metrics(scores, cumsum_scores):\n", " summary_metrics = {}\n", - " # per era correlation between predictions of the model trained on this target and cyrus\n", + " # per era correlation between predictions of the model trained on this target and Ender-60\n", " mean = scores.mean()\n", " std = scores.std()\n", " sharpe = mean / std\n", @@ -1915,12 +631,12 @@ " target_summary_metrics[pred_col] = get_summary_metrics(\n", " correlations[pred_col], cumsum_corrs[pred_col]\n", " )\n", - " # per era correlation between this target and ender\n", - " mean_corr_with_ender = validation.groupby(\"era\").apply(\n", + " # per era correlation between this target and Ender-60\n", + " mean_corr_with_ender60 = validation.groupby(\"era\").apply(\n", " lambda d: d[pred_col].corr(d[MAIN_TARGET])\n", " ).mean()\n", " target_summary_metrics[pred_col].update({\n", - " \"mean_corr_with_ender\": mean_corr_with_ender\n", + " \"mean_corr_with_ender60\": mean_corr_with_ender60\n", " })\n", "\n", "\n", @@ -1936,11 +652,8 @@ }, "source": [ "### Selecting our favorite target\n", - "Based on our observations above, it seems like target `victor` is the best candidate target for our ensemble since it has great performance and it is not too correlated with `cyrus`. However, it's interesting to look at how models that are very uncorrelated ensemble together we are going to also look at how `teager` ensembles with `cyrus`.\n", "\n", - "What do you think?\n", - "\n", - "Note that this target selection heuristic is extremely basic. In your own research, you will most likely want to consider all targets instead of just our favorites, and may want to experiment with different ways of selecting your ensemble targets." + "For illustration, we compare Victor-60 and Teager-60 with Ender-60. Target selection here is intentionally basic; real research should evaluate all eligible targets, out-of-sample performance, and ensemble diversity.\n" ] }, { @@ -1962,14 +675,12 @@ "source": [ "### Creating the ensemble\n", "\n", - "For simplicity, we will equal weight the predictions from target `victor` and `cyrus`. Note that this is an extremely basic and arbitrary way of selecting ensemble weights. In your research, you may want to experiment with different ways of setting ensemble weights.\n", - "\n", - "Tip: remember to always normalize (percentile rank) your predictions before averaging so that they are comparable!" + "For simplicity, we equal-weight ranked Ender-60 predictions with Victor-60 or Teager-60 predictions. This is an arbitrary teaching example. Always normalize predictions before averaging so their scales are comparable.\n" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:10.096926Z", @@ -1982,195 +693,14 @@ "id": "y27jxEUwxnNg", "outputId": "17962176-35ec-4d5a-e891-1b51779b961a" }, - "outputs": [ - { - "data": { - "text/html": [ - "
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prediction_target_ender_20prediction_target_victor_20prediction_target_teager2b_20ensemble_ender_victorensemble_ender_teager
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956598 rows × 5 columns

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" - ], - "text/plain": [ - " prediction_target_ender_20 prediction_target_victor_20 \\\n", - "id \n", - "n000c290e4364875 0.495229 0.491972 \n", - "n002a15bc5575bbb 0.507359 0.512950 \n", - "n00309caaa0f955e 0.504452 0.512101 \n", - "n0039cbdcf835708 0.510034 0.505156 \n", - "n004143458984f89 0.491897 0.485125 \n", - "... ... ... \n", - "nffd9cf2c992c881 0.501314 0.501444 \n", - "nfff0fbd1837e76f 0.497717 0.499122 \n", - "nfff3b8261e8af0a 0.496199 0.496723 \n", - "nfff3cb2970e386b 0.506733 0.499831 \n", - "nfff8ba3632ff9b9 0.501066 0.509136 \n", - "\n", - " prediction_target_teager2b_20 ensemble_ender_victor \\\n", - "id \n", - "n000c290e4364875 0.496844 0.154823 \n", - "n002a15bc5575bbb 0.508923 0.928766 \n", - "n00309caaa0f955e 0.505769 0.866564 \n", - "n0039cbdcf835708 0.504405 0.861196 \n", - "n004143458984f89 0.490126 0.038429 \n", - "... ... ... \n", - "nffd9cf2c992c881 0.499682 0.596249 \n", - "nfff0fbd1837e76f 0.496017 0.405064 \n", - "nfff3b8261e8af0a 0.495877 0.287507 \n", - "nfff3cb2970e386b 0.507004 0.699942 \n", - "nfff8ba3632ff9b9 0.500131 0.732633 \n", - "\n", - " ensemble_ender_teager \n", - "id \n", - "n000c290e4364875 0.224012 \n", - "n002a15bc5575bbb 0.931919 \n", - "n00309caaa0f955e 0.831800 \n", - "n0039cbdcf835708 0.884373 \n", - "n004143458984f89 0.045927 \n", - "... ... \n", - "nffd9cf2c992c881 0.535245 \n", - "nfff0fbd1837e76f 0.274737 \n", - "nfff3b8261e8af0a 0.219279 \n", - "nfff3cb2970e386b 0.894556 \n", - "nfff8ba3632ff9b9 0.545242 \n", - "\n", - "[956598 rows x 5 columns]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Ensemble predictions together with a simple average\n", "validation[\"ensemble_ender_victor\"] = (\n", " validation\n", " .groupby(\"era\")[[\n", " f\"prediction_{MAIN_TARGET}\",\n", - " \"prediction_target_victor_20\",\n", + " \"prediction_target_victor_60\",\n", " ]]\n", " .rank(pct=True)\n", " .mean(axis=1)\n", @@ -2179,7 +709,7 @@ " validation\n", " .groupby(\"era\")[[\n", " f\"prediction_{MAIN_TARGET}\",\n", - " \"prediction_target_teager2b_20\",\n", + " \"prediction_target_teager2b_60\",\n", " ]]\n", " .rank(pct=True)\n", " .mean(axis=1)\n", @@ -2187,13 +717,13 @@ "\n", "# Print the ensemble predictions\n", "prediction_cols = [\n", - " \"prediction_target_ender_20\",\n", - " \"prediction_target_victor_20\",\n", - " \"prediction_target_teager2b_20\",\n", + " \"prediction_target_ender_60\",\n", + " \"prediction_target_victor_60\",\n", + " \"prediction_target_teager2b_60\",\n", " \"ensemble_ender_victor\",\n", " \"ensemble_ender_teager\"\n", "]\n", - "validation[prediction_cols]" + "validation[prediction_cols]\n" ] }, { @@ -2208,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:11.876437Z", @@ -2221,48 +751,19 @@ "id": "jCRbKxZmxnNg", "outputId": "313c0821-f81d-46fe-f54c-e5ce95a583f7" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/2734220739.py:1: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " correlations = validation.groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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meanstdsharpemax_drawdown
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prediction_target_victor_200.0121770.0137580.8851280.027565
prediction_target_teager2b_200.0126130.0145490.8669290.030551
ensemble_ender_victor0.0125230.0145390.8613230.028684
ensemble_ender_teager0.0128110.0146440.8748030.029541
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meanstdsharpemax_drawdown
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" - ], - "text/plain": [ - " mean std sharpe max_drawdown\n", - "prediction_target_ender_20 0.005711 0.013865 0.411899 0.055653\n", - "prediction_target_victor_20 0.004791 0.013617 0.351863 0.053557\n", - "prediction_target_teager2b_20 0.006106 0.014557 0.419463 0.059175\n", - "ensemble_ender_victor 0.005359 0.013823 0.387678 0.056158\n", - "ensemble_ender_teager 0.006045 0.014203 0.425633 0.054282" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from numerai_tools.scoring import correlation_contribution\n", "\n", @@ -2517,7 +836,7 @@ " # Compute the per-era mmc between our predictions, the meta model, and the target values\n", " per_era_mmc = validation.dropna().groupby(\"era\").apply(\n", " lambda x: correlation_contribution(\n", - " x[prediction_cols], x[meta_model_col], x[\"target\"]\n", + " x[prediction_cols], x[meta_model_col], x[MAIN_TARGET]\n", " )\n", " )\n", "\n", @@ -2549,12 +868,12 @@ "source": [ "#### Benchmark Models\n", "\n", - "It's no accident that a model trained on `teager` nicely ensembles with `cyrus`. We have seen in our research that models trained or ensembled using `teager` perform well. We even released a benchmark for a [teager ensemble](https://numer.ai/v42_teager_ensemble). We submit predictions for all internally known models [here](https://numer.ai/~benchmark_models) and release files with their predictions." + "Numerai publishes benchmark-model predictions for comparison. This post-cutover example uses the v5.3 Ender-60 LightGBM benchmark as its explicit default reference rather than silently retaining the Ender-20 benchmark.\n" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:43.826002Z", @@ -2567,248 +886,7 @@ "id": "QNSLiLDWOx6D", "outputId": "18224a74-f6de-4bc8-cab1-84d7552b1873" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:44:13,918 INFO numerapi.utils: starting download\n", - "v5.2/validation_benchmark_models.parquet: 299MB [00:29, 10.2MB/s] \n" - ] - }, - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " era v52_lgbm_cyrusd20 v52_lgbm_teager2b20 \\\n", - "id \n", - "n000101811a8a843 0575 0.456187 0.517525 \n", - "n001e1318d5072ac 0575 0.775215 0.875894 \n", - "n002a9c5ab785cbb 0575 0.495887 0.888233 \n", - "n002ccf6d0e8c5ad 0575 0.932940 0.918276 \n", - "n0041544c345c91d 0575 0.786481 0.748927 \n", - "... ... ... ... \n", - "nffdbddfbae44332 1197 0.076798 0.696952 \n", - "nffe039747784f0c 1197 0.959603 0.938739 \n", - "nffec50b3b1f482f 1197 0.587896 0.799349 \n", - "nfff90bbc66a2cb9 1197 0.258065 0.330275 \n", - "nfffb633c35e2cb2 1197 0.490086 0.521900 \n", - "\n", - " v52_lgbm_ender20 v52_lgbm_jasper20 v52_lgbm_cyrusd60 \\\n", - "id \n", - "n000101811a8a843 0.482475 0.390379 0.355687 \n", - "n001e1318d5072ac 0.859442 0.670064 0.586195 \n", - "n002a9c5ab785cbb 0.747496 0.683655 0.628934 \n", - "n002ccf6d0e8c5ad 0.910050 0.970672 0.935086 \n", - "n0041544c345c91d 0.782546 0.647890 0.628755 \n", - "... ... ... ... \n", - "nffdbddfbae44332 0.661734 0.788547 0.185558 \n", - "nffe039747784f0c 0.841965 0.820805 0.850547 \n", - "nffec50b3b1f482f 0.659663 0.921131 0.528855 \n", - "nfff90bbc66a2cb9 0.330275 0.317254 0.302900 \n", - "nfffb633c35e2cb2 0.577982 0.606688 0.448949 \n", - "\n", - " v52_lgbm_teager2b60 v52_lgbm_ender60 v52_lgbm_jasper60 \n", - "id \n", - "n000101811a8a843 0.260730 0.437589 0.425608 \n", - "n001e1318d5072ac 0.686159 0.715844 0.582082 \n", - "n002a9c5ab785cbb 0.697604 0.755544 0.579578 \n", - "n002ccf6d0e8c5ad 0.883584 0.910765 0.942418 \n", - "n0041544c345c91d 0.814020 0.795601 0.736767 \n", - "... ... ... ... \n", - "nffdbddfbae44332 0.672684 0.613791 0.710417 \n", - "nffe039747784f0c 0.864013 0.783664 0.542024 \n", - "nffec50b3b1f482f 0.844481 0.816958 0.853655 \n", - "nfff90bbc66a2cb9 0.309411 0.282924 0.516869 \n", - "nfffb633c35e2cb2 0.482391 0.450873 0.612607 \n", - "\n", - "[3868777 rows x 9 columns]" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# download Numerai's benchmark models\n", "napi.download_dataset(f\"{DATA_VERSION}/validation_benchmark_models.parquet\")\n", @@ -2824,16 +902,12 @@ "id": "dZNr_xbQWJsf" }, "source": [ - "Because models trained on newer targets perform so well and we release their predictions, it's likely many users will begin to shift their models to include newer data and targets. By extension, the Meta Model will begin to include information from from these new targets.\n", - "\n", - "This means that MMC over the validation period may not be truly indicative of out-of-sample performance. The Meta Model over the early validation period did not have access to newer data/targets and MMC over the validation period may be misleading.\n", - "\n", - "So if the Meta Model was much closer to our teager ensemble, what would your MMC look like?" + "Published benchmark predictions let us evaluate how additive each candidate is to a known Ender-60 baseline. This is a research comparison, not a statement about the payout target configured for any particular historical round.\n" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:45.353154Z", @@ -2846,106 +920,7 @@ "id": "OcUNnnkUWnwg", "outputId": "65de24b0-9515-4205-ede5-8d5649fadc23" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/4064323070.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " per_era_mmc = validation.dropna().groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/html": [ - "
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meanstdsharpemax_drawdown
prediction_target_ender_200.0004840.0129440.0374120.101700
prediction_target_victor_200.0001970.0137120.0143450.108225
prediction_target_teager2b_200.0003750.0135580.0276750.115490
ensemble_ender_victor0.0003160.0133830.0235880.105559
ensemble_ender_teager0.0004490.0132770.0338170.110576
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" - ], - "text/plain": [ - " mean std sharpe max_drawdown\n", - "prediction_target_ender_20 0.000484 0.012944 0.037412 0.101700\n", - "prediction_target_victor_20 0.000197 0.013712 0.014345 0.108225\n", - "prediction_target_teager2b_20 0.000375 0.013558 0.027675 0.115490\n", - "ensemble_ender_victor 0.000316 0.013383 0.023588 0.105559\n", - "ensemble_ender_teager 0.000449 0.013277 0.033817 0.110576" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "validation[FAVORITE_MODEL] = benchmark_models[FAVORITE_MODEL]\n", "\n", @@ -2953,7 +928,7 @@ "per_era_mmc, cumsum_mmc, summary = get_mmc(validation, FAVORITE_MODEL)\n", "# plot the cumsum mmc performance\n", "cumsum_mmc.plot(\n", - " title=\"Contribution of Neutralized Predictions to Numerai's Teager Ensemble\",\n", + " title=\"Contribution of Neutralized Predictions to Numerai's Ender-60 Benchmark\",\n", " figsize=(10, 6),\n", " xticks=[]\n", ")\n", @@ -2979,7 +954,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:44:51.400656Z", @@ -2992,106 +967,7 @@ "id": "39UfnEmifTMh", "outputId": "827dd9fb-4682-418c-eecd-f3d1e5c90114" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/9t/l_bbq0y57ns1020jcy_lxsfm0000gn/T/ipykernel_33127/4064323070.py:11: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n", - " per_era_mmc = validation.dropna().groupby(\"era\").apply(\n" - ] - }, - { - "data": { - "text/html": [ - "
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meanstdsharpemax_drawdown
prediction_target_ender_200.0003190.0132100.0241360.115921
prediction_target_victor_20-0.0003040.013876-0.0219440.119429
prediction_target_teager2b_200.0001150.0138370.0083430.132504
ensemble_ender_victor-0.0000290.013590-0.0021550.122952
ensemble_ender_teager0.0002310.0135620.0170520.126538
\n", - "
" - ], - "text/plain": [ - " mean std sharpe max_drawdown\n", - "prediction_target_ender_20 0.000319 0.013210 0.024136 0.115921\n", - "prediction_target_victor_20 -0.000304 0.013876 -0.021944 0.119429\n", - "prediction_target_teager2b_20 0.000115 0.013837 0.008343 0.132504\n", - "ensemble_ender_victor -0.000029 0.013590 -0.002155 0.122952\n", - "ensemble_ender_teager 0.000231 0.013562 0.017052 0.126538" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "validation[\"numerai_benchmark\"] = (\n", " benchmark_models\n", @@ -3142,7 +1018,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:49:44.840661Z", @@ -3152,14 +1028,14 @@ }, "outputs": [], "source": [ - "# we now give you access to the live_benchmark_models if you want to use them in your ensemble\n", + "# We receive live benchmark models as an optional input for more advanced ensembles.\n", "def predict_ensemble(\n", " live_features: pd.DataFrame,\n", " live_benchmark_models: pd.DataFrame\n", ") -> pd.DataFrame:\n", " favorite_targets = [\n", - " 'target_ender_20',\n", - " 'target_teager2b_20'\n", + " \"target_ender_60\",\n", + " \"target_teager2b_60\"\n", " ]\n", " # generate predictions from each model\n", " predictions = pd.DataFrame(index=live_features.index)\n", @@ -3169,12 +1045,12 @@ " ensemble = predictions.rank(pct=True).mean(axis=1)\n", " # format submission\n", " submission = ensemble.rank(pct=True, method=\"first\")\n", - " return submission.to_frame(\"prediction\")" + " return submission.to_frame(\"prediction\")\n" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:49:47.418035Z", @@ -3187,116 +1063,7 @@ "id": "kPq_ATf0xnNr", "outputId": "53bef369-0a53-4c1e-fd62-d9d4d6ec2c53" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-12-14 14:49:47,254 INFO numerapi.utils: target file already exists\n", - "2025-12-14 14:49:47,254 INFO numerapi.utils: download complete\n" - ] - }, - { - "data": { - "text/html": [ - "
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prediction
id
n0005e01cd27dd7b0.820387
n000a0f321ce17e30.037649
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......
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" - ], - "text/plain": [ - " prediction\n", - "id \n", - "n0005e01cd27dd7b 0.820387\n", - "n000a0f321ce17e3 0.037649\n", - "n000e47c9c27ca3d 0.167708\n", - "n0016060c6335337 0.894494\n", - "n001c30cea427396 0.577530\n", - "... ...\n", - "nffcc8bf595e4e98 0.240327\n", - "nffd0c2231719dc9 0.257738\n", - "nffdbad9b545ca02 0.457887\n", - "nffe55a271685c39 0.391964\n", - "nfff60bd24702729 0.434970\n", - "\n", - "[6720 rows x 1 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Quick test\n", "napi.download_dataset(f\"{DATA_VERSION}/live.parquet\")\n", @@ -3306,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:49:48.998870Z", @@ -3319,13 +1086,13 @@ "# Use the cloudpickle library to serialize your function and its dependencies\n", "import cloudpickle\n", "p = cloudpickle.dumps(predict_ensemble)\n", - "with open(\"target_ensemble.pkl\", \"wb\") as f:\n", - " f.write(p)" + "with open(MODEL_ARTIFACT, \"wb\") as f:\n", + " f.write(p)\n" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2025-12-14T22:49:49.483216Z", @@ -3343,9 +1110,9 @@ "# Download file if running in Google Colab\n", "try:\n", " from google.colab import files\n", - " files.download('target_ensemble.pkl')\n", - "except:\n", - " pass" + " files.download(MODEL_ARTIFACT)\n", + "except ImportError:\n", + " pass\n" ] }, {