wehub-resource-sync
5334 lines
565 KiB
Plaintext
5334 lines
565 KiB
Plaintext
{
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"cells": [
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}
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},
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"source": [
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"# Cross-Validation for Financial Machine Learning\n",
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"\n",
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"**ML4T Third Edition — Chapter 6: Strategy Definition**\n",
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"\n",
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"**Docker image**: `ml4t`\n",
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"\n",
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"This notebook builds cross-validation from first principles for time series:\n",
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"\n",
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"1. **Decision-time admissibility** — the central design constraint\n",
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"2. **Three dataset roles** — train, validation, and holdout test\n",
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"3. **K-fold CV** — and why random shuffling fails for time series\n",
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"4. **Walk-forward CV** — expanding and rolling windows\n",
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"5. **Label buffer (purging)** — preventing forward-looking label leakage\n",
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"6. **Feature buffer (embargo)** — preventing backward-looking feature leakage\n",
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"7. **Calendar-aware CV** — trading days vs calendar days\n",
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"8. **Nested walk-forward** — retuning across multiple test years\n",
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"9. **Combinatorial purged CV (CPCV)** — multiple backtest paths\n",
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"10. **Putting it together** — from config to protocol\n",
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"\n",
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"**Book Reference**: Section 6.5 (Evaluation Protocol for Time Series)\n",
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"\n",
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"**References**:\n",
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"- López de Prado (2018). *Advances in Financial Machine Learning*, Ch. 7\n",
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"- Bailey, Borwein, López de Prado, and Zhu (2014). \"The Probability of Backtest Overfitting\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e8d6f415",
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"execution": {
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"duration": 3.751501,
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"end_time": "2026-06-13T03:12:04.792182+00:00",
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"exception": false,
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"start_time": "2026-06-13T03:12:01.040681+00:00",
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"status": "completed"
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}
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},
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"outputs": [],
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"source": [
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"\"\"\"Cross-validation foundations for Chapter 6.\"\"\"\n",
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"\n",
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"import warnings\n",
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"from math import comb\n",
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"\n",
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"import exchange_calendars as xcals\n",
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"import matplotlib.dates as mdates\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import plotly.graph_objects as go\n",
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"from matplotlib.patches import Patch, Rectangle\n",
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"from ml4t.diagnostic.splitters import CombinatorialCV, WalkForwardCV\n",
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"from ml4t.diagnostic.splitters.config import WalkForwardConfig\n",
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"from plotly.subplots import make_subplots\n",
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"from sklearn.model_selection import KFold\n",
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"\n",
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"from utils.modeling import get_cv_config\n",
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"from utils.reproducibility import set_global_seeds\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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||
"execution_count": 2,
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"id": "f8e0993b",
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"metadata": {
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"execution": {
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},
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"duration": 0.005431,
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"end_time": "2026-06-13T03:12:04.800028+00:00",
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"start_time": "2026-06-13T03:12:04.794597+00:00",
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"status": "completed"
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},
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"N_VIZ = 504\n",
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"SEED = 42"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "19987d6b",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T03:12:04.808908Z",
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"status": "completed"
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}
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},
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"outputs": [],
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"source": [
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"set_global_seeds(SEED)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "80071e4b",
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"metadata": {
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"papermill": {
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"duration": 0.003795,
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"exception": false,
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"start_time": "2026-06-13T03:12:04.843128+00:00",
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"status": "completed"
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}
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},
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"source": [
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"---\n",
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"\n",
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"## Data Setup\n",
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"\n",
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"We use 12 years of NYSE trading sessions (2014–2025) throughout."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "802a029c",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T03:12:04.853651Z",
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"iopub.status.busy": "2026-06-13T03:12:04.853488Z",
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"shell.execute_reply": "2026-06-13T03:12:05.021361Z"
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},
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"lines_to_next_cell": 2,
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"papermill": {
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"duration": 0.172913,
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"end_time": "2026-06-13T03:12:05.022567+00:00",
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"exception": false,
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"status": "completed"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"NYSE sessions 2014–2025: 3,018\n"
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]
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}
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],
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"source": [
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"nyse = xcals.get_calendar(\"XNYS\")\n",
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"sessions = nyse.sessions_in_range(\"2014-01-01\", \"2025-12-31\")\n",
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"dates = pd.DatetimeIndex(sessions, tz=\"UTC\")\n",
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"df_dates = pd.DataFrame({\"idx\": np.arange(len(dates))}, index=dates)\n",
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"\n",
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"print(f\"NYSE sessions 2014–2025: {len(dates):,}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "117977bb",
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"status": "completed"
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}
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},
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"source": [
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"### Visualization Helper"
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||
]
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||
},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "99695b08",
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"metadata": {
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"execution": {
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"status": "completed"
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}
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},
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"outputs": [],
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"source": [
|
||
"def plot_splits(splits, dates, *, title=\"\", figsize=(12, 3.5)):\n",
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" \"\"\"Plot walk-forward splits as horizontal bars with real dates.\"\"\"\n",
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" n_folds = len(splits)\n",
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" fig, ax = plt.subplots(figsize=figsize)\n",
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"\n",
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" for i, (tr, va) in enumerate(splits):\n",
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" y = n_folds - i\n",
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" tr_start, tr_end = dates[tr[0]], dates[tr[-1]]\n",
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" va_start, va_end = dates[va[0]], dates[va[-1]]\n",
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"\n",
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" # Detect purge gap (more than 1 index between train end and val start)\n",
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" gap = va[0] - tr[-1]\n",
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"\n",
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" ax.barh(y, tr_end - tr_start, left=tr_start, height=0.6, color=\"0.75\", edgecolor=\"0.5\")\n",
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" ax.barh(y, va_end - va_start, left=va_start, height=0.6, color=\"0.35\", edgecolor=\"0.2\")\n",
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"\n",
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" if gap > 2:\n",
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" ax.barh(\n",
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" y,\n",
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" va_start - tr_end,\n",
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" left=tr_end,\n",
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" height=0.6,\n",
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" color=\"0.9\",\n",
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" edgecolor=\"0.7\",\n",
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" hatch=\"//\",\n",
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" linewidth=0.5,\n",
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" )\n",
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"\n",
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" ax.set_yticks(range(1, n_folds + 1))\n",
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" ax.set_yticklabels([f\"Fold {n_folds - i}\" for i in range(n_folds)])\n",
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||
" ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y\"))\n",
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" ax.xaxis.set_major_locator(mdates.YearLocator(2))\n",
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" ax.set_title(title)\n",
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"\n",
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" handles = [\n",
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" Patch(facecolor=\"0.75\", label=\"Training\"),\n",
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" Patch(facecolor=\"0.35\", label=\"Validation\"),\n",
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" ]\n",
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" if any(va[0] - tr[-1] > 2 for tr, va in splits):\n",
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" handles.insert(1, Patch(facecolor=\"0.9\", edgecolor=\"0.7\", hatch=\"//\", label=\"Label buffer\"))\n",
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" ax.legend(handles=handles, loc=\"upper right\")\n",
|
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" fig.tight_layout()\n",
|
||
" return fig"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0fcc6aa6",
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"metadata": {
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"papermill": {
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"duration": 0.003971,
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"exception": false,
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"start_time": "2026-06-13T03:12:05.049506+00:00",
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"status": "completed"
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}
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},
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"source": [
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||
"---\n",
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"\n",
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||
"## 1. Decision-Time Admissibility\n",
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"\n",
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"The central question behind every CV design choice:\n",
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"\n",
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"> **At decision time $t$, which labeled samples were actually available?**\n",
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"\n",
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"Any sample whose label, feature, or selection criterion depends on\n",
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"information unavailable at $t$ must be excluded from training.\n",
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"Section 6.5 identifies five channels through which future information can\n",
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"leak into past decisions:\n",
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"\n",
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"| Leakage Channel | Example | Prevention |\n",
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"|---|---|---|\n",
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"| **Label leakage** | 21-day forward return overlaps validation | Label buffer (purging) |\n",
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"| **Standardization leakage** | Z-scoring with full-sample mean/std | Expanding-window transforms |\n",
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"| **Threshold leakage** | Percentile labels from full sample | Rolling percentile thresholds |\n",
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"| **Survivorship leakage** | Training only on surviving firms | Rebalance universe per period |\n",
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"| **Point-in-time leakage** | Fundamentals reported after decision | Conservative publication lag |\n",
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"\n",
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"The CV schemes in this notebook operationalize the first channel (label\n",
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"leakage via the **label buffer**) and flag where the others apply."
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]
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},
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{
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"cell_type": "markdown",
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"id": "4ff332b2",
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"exception": false,
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"status": "completed"
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}
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},
|
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"source": [
|
||
"---\n",
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"\n",
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"## 2. Three Dataset Roles\n",
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"\n",
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"Before any experiment, partition data into three disjoint sets:\n",
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"\n",
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"| Dataset | Role | When Accessed |\n",
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"|---|---|---|\n",
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"| **Training** | Fit model parameters | During training |\n",
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"| **Validation** | Select hyperparameters, compare models | During development |\n",
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"| **Holdout Test** | Final unbiased performance estimate | Once, at the end |\n",
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"\n",
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"The **holdout test set is sealed** from the start. Using it for any\n",
|
||
"development decision contaminates the final estimate."
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||
]
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||
},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "a0008676",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T03:12:05.064237Z",
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"iopub.status.busy": "2026-06-13T03:12:05.064110Z",
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"iopub.status.idle": "2026-06-13T03:12:05.083619Z",
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"shell.execute_reply": "2026-06-13T03:12:05.083115Z"
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"papermill": {
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"duration": 0.023176,
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"exception": false,
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"status": "completed"
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}
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},
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"outputs": [
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{
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"data": {
|
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"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Period</th>\n",
|
||
" <th>Start</th>\n",
|
||
" <th>End</th>\n",
|
||
" <th>Trading Days</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>Training</td>\n",
|
||
" <td>2014-01-02</td>\n",
|
||
" <td>2018-12-31</td>\n",
|
||
" <td>1258</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>Validation</td>\n",
|
||
" <td>2019-01-02</td>\n",
|
||
" <td>2023-12-31</td>\n",
|
||
" <td>1258</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>Holdout Test</td>\n",
|
||
" <td>2024-01-02</td>\n",
|
||
" <td>2025-12-31</td>\n",
|
||
" <td>502</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Period Start End Trading Days\n",
|
||
"0 Training 2014-01-02 2018-12-31 1258\n",
|
||
"1 Validation 2019-01-02 2023-12-31 1258\n",
|
||
"2 Holdout Test 2024-01-02 2025-12-31 502"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Partition: Train 5Y | Validation 5Y | Holdout 2Y\n",
|
||
"train_end = pd.Timestamp(\"2018-12-31\", tz=\"UTC\")\n",
|
||
"val_end = pd.Timestamp(\"2023-12-31\", tz=\"UTC\")\n",
|
||
"\n",
|
||
"train_mask = dates <= train_end\n",
|
||
"val_mask = (dates > train_end) & (dates <= val_end)\n",
|
||
"test_mask = dates > val_end\n",
|
||
"\n",
|
||
"partition_df = pd.DataFrame(\n",
|
||
" {\n",
|
||
" \"Period\": [\"Training\", \"Validation\", \"Holdout Test\"],\n",
|
||
" \"Start\": [\n",
|
||
" dates[0].date(),\n",
|
||
" dates[train_mask.sum()].date(),\n",
|
||
" dates[train_mask.sum() + val_mask.sum()].date(),\n",
|
||
" ],\n",
|
||
" \"End\": [train_end.date(), val_end.date(), dates[-1].date()],\n",
|
||
" \"Trading Days\": [train_mask.sum(), val_mask.sum(), test_mask.sum()],\n",
|
||
" }\n",
|
||
")\n",
|
||
"partition_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8d49b831",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002403,
|
||
"end_time": "2026-06-13T03:12:05.089360+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.086957+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Rule**: The holdout test set is NEVER used for model selection,\n",
|
||
"hyperparameter tuning, or any development decision."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b8c324d4",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002449,
|
||
"end_time": "2026-06-13T03:12:05.094461+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.092012+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 3. K-Fold Cross-Validation\n",
|
||
"\n",
|
||
"Standard k-fold CV shuffles samples randomly and rotates the held-out fold.\n",
|
||
"This works for i.i.d. data but **destroys temporal structure** in time series:\n",
|
||
"the model trains on future data to predict the past."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "634a5d81",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:05.100817Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:05.100688Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:05.503969Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:05.503209Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.407234,
|
||
"end_time": "2026-06-13T03:12:05.504385+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.097151+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x500 with 5 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"n_samples = 100\n",
|
||
"sample_indices = np.arange(n_samples)\n",
|
||
"\n",
|
||
"kfold = KFold(n_splits=5, shuffle=True, random_state=SEED)\n",
|
||
"\n",
|
||
"fig, axes = plt.subplots(5, 1, figsize=(12, 5), sharex=True)\n",
|
||
"\n",
|
||
"for fold_idx, (train_idx, val_idx) in enumerate(kfold.split(sample_indices)):\n",
|
||
" ax = axes[fold_idx]\n",
|
||
" colors = np.zeros(n_samples)\n",
|
||
" colors[val_idx] = 1\n",
|
||
"\n",
|
||
" for i in range(n_samples):\n",
|
||
" color = \"0.3\" if colors[i] == 1 else \"0.8\"\n",
|
||
" ax.bar(i, 1, width=1, color=color, edgecolor=\"none\")\n",
|
||
"\n",
|
||
" ax.set_ylabel(f\"Fold {fold_idx + 1}\", rotation=0, labelpad=30, va=\"center\")\n",
|
||
" ax.set_ylim(0, 1)\n",
|
||
" ax.set_yticks([])\n",
|
||
" ax.set_xlim(-0.5, n_samples - 0.5)\n",
|
||
"\n",
|
||
"axes[-1].set_xlabel(\"Sample Index (Time)\")\n",
|
||
"axes[0].set_title(\"K-Fold CV: Random Train/Validation Distribution\")\n",
|
||
"axes[0].legend(\n",
|
||
" handles=[Patch(facecolor=\"0.8\", label=\"Training\"), Patch(facecolor=\"0.3\", label=\"Validation\")],\n",
|
||
" loc=\"upper right\",\n",
|
||
" fontsize=8,\n",
|
||
")\n",
|
||
"fig.tight_layout()\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "324d56fd",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002903,
|
||
"end_time": "2026-06-13T03:12:05.510499+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.507596+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"In Fold 1, the model trains on samples from the future (indices 80–100) to\n",
|
||
"predict the past. This violates decision-time admissibility."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b29ef47b",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002999,
|
||
"end_time": "2026-06-13T03:12:05.516659+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.513660+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 4. Walk-Forward Cross-Validation\n",
|
||
"\n",
|
||
"**Walk-forward CV** respects temporal ordering: training always **precedes**\n",
|
||
"validation. Two variants — **expanding** and **rolling** windows.\n",
|
||
"\n",
|
||
"We use `WalkForwardCV` from `ml4t.diagnostic.splitters` to generate the\n",
|
||
"actual folds on NYSE trading sessions."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "570aa905",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003059,
|
||
"end_time": "2026-06-13T03:12:05.522732+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.519673+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Expanding Window\n",
|
||
"\n",
|
||
"Training grows with each fold — uses all available history."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "b03221c9",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:05.529662Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:05.529552Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:05.772731Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:05.772237Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.247592,
|
||
"end_time": "2026-06-13T03:12:05.773379+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.525787+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1200x350 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"cv_exp = WalkForwardCV(n_splits=5, test_size=252, expanding=True)\n",
|
||
"splits_exp = list(cv_exp.split(df_dates))\n",
|
||
"\n",
|
||
"fig = plot_splits(splits_exp, dates, title=\"Expanding Window Walk-Forward CV\")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "548dd254",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00325,
|
||
"end_time": "2026-06-13T03:12:05.779464+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.776214+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Advantage**: Uses all available data. **Disadvantage**: Training set size\n",
|
||
"varies, which can affect model behavior."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "05019c72",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003809,
|
||
"end_time": "2026-06-13T03:12:05.787169+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.783360+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Rolling Window\n",
|
||
"\n",
|
||
"Fixed training window — old data drops off as we move forward."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "20efe455",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:05.795500Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:05.795347Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:05.968105Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:05.967664Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.177357,
|
||
"end_time": "2026-06-13T03:12:05.968484+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.791127+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1200x350 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"cv_roll = WalkForwardCV(n_splits=5, test_size=252, train_size=1260, expanding=False)\n",
|
||
"splits_roll = list(cv_roll.split(df_dates))\n",
|
||
"\n",
|
||
"fig = plot_splits(splits_roll, dates, title=\"Rolling Window Walk-Forward CV\")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e6899cf5",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002706,
|
||
"end_time": "2026-06-13T03:12:05.974762+00:00",
|
||
"exception": false,
|
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"start_time": "2026-06-13T03:12:05.972056+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Advantage**: Consistent training size; stale data doesn't influence the model.\n",
|
||
"**Disadvantage**: Discards data. Choose expanding if older data is still\n",
|
||
"relevant, rolling if you believe regimes change."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "eeb40319",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003167,
|
||
"end_time": "2026-06-13T03:12:05.980474+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.977307+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 5. Label Buffer (Purging)\n",
|
||
"\n",
|
||
"Walk-forward CV respects temporal order, but there's a subtler problem:\n",
|
||
"**labels take time to materialize**. A 21-day forward return label computed\n",
|
||
"at time $t$ uses prices from $t$ to $t+21$. If validation starts at $t+10$,\n",
|
||
"the training label has \"seen\" 11 validation-period prices.\n",
|
||
"\n",
|
||
"This is **label leakage** — the most common form of look-ahead bias."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "75bb59eb",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003316,
|
||
"end_time": "2026-06-13T03:12:05.986435+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.983119+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Visualizing the Overlap Problem"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "1a0d3bcc",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:05.994171Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:05.994021Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:06.976775Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:06.976084Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.987728,
|
||
"end_time": "2026-06-13T03:12:06.977381+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:05.989653+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
|
||
},
|
||
"data": [
|
||
{
|
||
"marker": {
|
||
"color": "steelblue",
|
||
"size": 20
|
||
},
|
||
"mode": "markers",
|
||
"name": "Training sample",
|
||
"type": "scatter",
|
||
"x": [
|
||
52
|
||
],
|
||
"y": [
|
||
0.5
|
||
]
|
||
},
|
||
{
|
||
"line": {
|
||
"color": "steelblue",
|
||
"width": 3
|
||
},
|
||
"marker": {
|
||
"size": 10
|
||
},
|
||
"mode": "lines+markers",
|
||
"name": "Label horizon (5 days)",
|
||
"type": "scatter",
|
||
"x": [
|
||
52,
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||
57
|
||
],
|
||
"y": [
|
||
0.4,
|
||
0.4
|
||
]
|
||
}
|
||
],
|
||
"layout": {
|
||
"annotations": [
|
||
{
|
||
"font": {
|
||
"size": 12
|
||
},
|
||
"showarrow": false,
|
||
"text": "Validation Set",
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||
"x": 56.5,
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||
"y": 0.7
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},
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{
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"font": {
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"color": "red",
|
||
"family": "Arial Black",
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||
"size": 16
|
||
},
|
||
"showarrow": false,
|
||
"text": "LEAKAGE!",
|
||
"x": 54,
|
||
"y": 0.25
|
||
}
|
||
],
|
||
"height": 300,
|
||
"shapes": [
|
||
{
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||
"fillcolor": "coral",
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"line": {
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"width": 0
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},
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"opacity": 0.2,
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"type": "rect",
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"x0": 53,
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"x1": 60,
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|
||
"#FF6692",
|
||
"#B6E880",
|
||
"#FF97FF",
|
||
"#FECB52"
|
||
],
|
||
"font": {
|
||
"color": "#2a3f5f"
|
||
},
|
||
"geo": {
|
||
"bgcolor": "white",
|
||
"lakecolor": "white",
|
||
"landcolor": "white",
|
||
"showlakes": true,
|
||
"showland": true,
|
||
"subunitcolor": "#C8D4E3"
|
||
},
|
||
"hoverlabel": {
|
||
"align": "left"
|
||
},
|
||
"hovermode": "closest",
|
||
"mapbox": {
|
||
"style": "light"
|
||
},
|
||
"paper_bgcolor": "white",
|
||
"plot_bgcolor": "white",
|
||
"polar": {
|
||
"angularaxis": {
|
||
"gridcolor": "#EBF0F8",
|
||
"linecolor": "#EBF0F8",
|
||
"ticks": ""
|
||
},
|
||
"bgcolor": "white",
|
||
"radialaxis": {
|
||
"gridcolor": "#EBF0F8",
|
||
"linecolor": "#EBF0F8",
|
||
"ticks": ""
|
||
}
|
||
},
|
||
"scene": {
|
||
"xaxis": {
|
||
"backgroundcolor": "white",
|
||
"gridcolor": "#DFE8F3",
|
||
"gridwidth": 2,
|
||
"linecolor": "#EBF0F8",
|
||
"showbackground": true,
|
||
"ticks": "",
|
||
"zerolinecolor": "#EBF0F8"
|
||
},
|
||
"yaxis": {
|
||
"backgroundcolor": "white",
|
||
"gridcolor": "#DFE8F3",
|
||
"gridwidth": 2,
|
||
"linecolor": "#EBF0F8",
|
||
"showbackground": true,
|
||
"ticks": "",
|
||
"zerolinecolor": "#EBF0F8"
|
||
},
|
||
"zaxis": {
|
||
"backgroundcolor": "white",
|
||
"gridcolor": "#DFE8F3",
|
||
"gridwidth": 2,
|
||
"linecolor": "#EBF0F8",
|
||
"showbackground": true,
|
||
"ticks": "",
|
||
"zerolinecolor": "#EBF0F8"
|
||
}
|
||
},
|
||
"shapedefaults": {
|
||
"line": {
|
||
"color": "#2a3f5f"
|
||
}
|
||
},
|
||
"ternary": {
|
||
"aaxis": {
|
||
"gridcolor": "#DFE8F3",
|
||
"linecolor": "#A2B1C6",
|
||
"ticks": ""
|
||
},
|
||
"baxis": {
|
||
"gridcolor": "#DFE8F3",
|
||
"linecolor": "#A2B1C6",
|
||
"ticks": ""
|
||
},
|
||
"bgcolor": "white",
|
||
"caxis": {
|
||
"gridcolor": "#DFE8F3",
|
||
"linecolor": "#A2B1C6",
|
||
"ticks": ""
|
||
}
|
||
},
|
||
"title": {
|
||
"x": 0.05
|
||
},
|
||
"xaxis": {
|
||
"automargin": true,
|
||
"gridcolor": "#EBF0F8",
|
||
"linecolor": "#EBF0F8",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "#EBF0F8",
|
||
"zerolinewidth": 2
|
||
},
|
||
"yaxis": {
|
||
"automargin": true,
|
||
"gridcolor": "#EBF0F8",
|
||
"linecolor": "#EBF0F8",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "#EBF0F8",
|
||
"zerolinewidth": 2
|
||
}
|
||
}
|
||
},
|
||
"title": {
|
||
"text": "Label Overlap Creates Information Leakage"
|
||
},
|
||
"xaxis": {
|
||
"title": {
|
||
"text": "Day"
|
||
}
|
||
},
|
||
"yaxis": {
|
||
"visible": false
|
||
}
|
||
}
|
||
},
|
||
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = go.Figure()\n",
|
||
"\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=[52],\n",
|
||
" y=[0.5],\n",
|
||
" mode=\"markers\",\n",
|
||
" marker=dict(size=20, color=\"steelblue\"),\n",
|
||
" name=\"Training sample\",\n",
|
||
" )\n",
|
||
")\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=[52, 57],\n",
|
||
" y=[0.4, 0.4],\n",
|
||
" mode=\"lines+markers\",\n",
|
||
" line=dict(color=\"steelblue\", width=3),\n",
|
||
" marker=dict(size=10),\n",
|
||
" name=\"Label horizon (5 days)\",\n",
|
||
" )\n",
|
||
")\n",
|
||
"fig.add_vrect(x0=53, x1=60, fillcolor=\"coral\", opacity=0.2, line_width=0)\n",
|
||
"fig.add_annotation(x=56.5, y=0.7, text=\"Validation Set\", showarrow=False, font=dict(size=12))\n",
|
||
"\n",
|
||
"fig.add_vrect(x0=53, x1=55, fillcolor=\"yellow\", opacity=0.5, line_width=0)\n",
|
||
"fig.add_annotation(\n",
|
||
" x=54,\n",
|
||
" y=0.25,\n",
|
||
" text=\"LEAKAGE!\",\n",
|
||
" showarrow=False,\n",
|
||
" font=dict(color=\"red\", size=16, family=\"Arial Black\"),\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" title=\"Label Overlap Creates Information Leakage\",\n",
|
||
" xaxis_title=\"Day\",\n",
|
||
" yaxis_visible=False,\n",
|
||
" template=\"plotly_white\",\n",
|
||
" height=300,\n",
|
||
" showlegend=True,\n",
|
||
")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5c4ec1c4",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.003269,
|
||
"end_time": "2026-06-13T03:12:06.984049+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:06.980780+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### The Solution: Label Buffer (Purge Gap)\n",
|
||
"\n",
|
||
"End training at least $h$ samples before validation starts, where $h$ is the\n",
|
||
"label horizon. López de Prado (2018) calls this **purging**.\n",
|
||
"\n",
|
||
"$$\\text{Training End} \\leq \\text{Validation Start} - h$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "5556cbc0",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:06.991434Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:06.991238Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:07.066908Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:07.066072Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.080436,
|
||
"end_time": "2026-06-13T03:12:07.067556+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:06.987120+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
|
||
},
|
||
"data": [
|
||
{
|
||
"marker": {
|
||
"color": "steelblue",
|
||
"size": 15
|
||
},
|
||
"mode": "markers+text",
|
||
"name": "Training",
|
||
"showlegend": false,
|
||
"text": [
|
||
"Last train sample"
|
||
],
|
||
"textposition": "middle left",
|
||
"type": "scatter",
|
||
"x": [
|
||
47
|
||
],
|
||
"y": [
|
||
0.3
|
||
]
|
||
},
|
||
{
|
||
"line": {
|
||
"color": "steelblue",
|
||
"dash": "dot",
|
||
"width": 2
|
||
},
|
||
"marker": {
|
||
"size": 8
|
||
},
|
||
"mode": "lines+markers",
|
||
"name": "Label (safe)",
|
||
"type": "scatter",
|
||
"x": [
|
||
47,
|
||
52
|
||
],
|
||
"y": [
|
||
0.3,
|
||
0.3
|
||
]
|
||
}
|
||
],
|
||
"layout": {
|
||
"annotations": [
|
||
{
|
||
"font": {
|
||
"color": "steelblue",
|
||
"size": 12
|
||
},
|
||
"showarrow": false,
|
||
"text": "Training",
|
||
"x": 38.5,
|
||
"y": 0.8
|
||
},
|
||
{
|
||
"font": {
|
||
"color": "gray",
|
||
"size": 10
|
||
},
|
||
"showarrow": false,
|
||
"text": "Label Buffer\n(5 days)",
|
||
"x": 50,
|
||
"y": 0.5
|
||
},
|
||
{
|
||
"font": {
|
||
"color": "coral",
|
||
"size": 12
|
||
},
|
||
"showarrow": false,
|
||
"text": "Validation",
|
||
"x": 61.5,
|
||
"y": 0.8
|
||
}
|
||
],
|
||
"height": 300,
|
||
"shapes": [
|
||
{
|
||
"fillcolor": "steelblue",
|
||
"line": {
|
||
"width": 0
|
||
},
|
||
"opacity": 0.3,
|
||
"type": "rect",
|
||
"x0": 30,
|
||
"x1": 47,
|
||
"xref": "x",
|
||
"y0": 0,
|
||
"y1": 1,
|
||
"yref": "y domain"
|
||
},
|
||
{
|
||
"fillcolor": "gray",
|
||
"line": {
|
||
"width": 0
|
||
},
|
||
"opacity": 0.2,
|
||
"type": "rect",
|
||
"x0": 48,
|
||
"x1": 52,
|
||
"xref": "x",
|
||
"y0": 0,
|
||
"y1": 1,
|
||
"yref": "y domain"
|
||
},
|
||
{
|
||
"fillcolor": "coral",
|
||
"line": {
|
||
"width": 0
|
||
},
|
||
"opacity": 0.3,
|
||
"type": "rect",
|
||
"x0": 53,
|
||
"x1": 70,
|
||
"xref": "x",
|
||
"y0": 0,
|
||
"y1": 1,
|
||
"yref": "y domain"
|
||
}
|
||
],
|
||
"showlegend": true,
|
||
"template": {
|
||
"data": {
|
||
"bar": [
|
||
{
|
||
"error_x": {
|
||
"color": "#2a3f5f"
|
||
},
|
||
"error_y": {
|
||
"color": "#2a3f5f"
|
||
},
|
||
"marker": {
|
||
"line": {
|
||
"color": "white",
|
||
"width": 0.5
|
||
},
|
||
"pattern": {
|
||
"fillmode": "overlay",
|
||
"size": 10,
|
||
"solidity": 0.2
|
||
}
|
||
},
|
||
"type": "bar"
|
||
}
|
||
],
|
||
"barpolar": [
|
||
{
|
||
"marker": {
|
||
"line": {
|
||
"color": "white",
|
||
"width": 0.5
|
||
},
|
||
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"zerolinecolor": "#EBF0F8"
|
||
},
|
||
"yaxis": {
|
||
"backgroundcolor": "white",
|
||
"gridcolor": "#DFE8F3",
|
||
"gridwidth": 2,
|
||
"linecolor": "#EBF0F8",
|
||
"showbackground": true,
|
||
"ticks": "",
|
||
"zerolinecolor": "#EBF0F8"
|
||
},
|
||
"zaxis": {
|
||
"backgroundcolor": "white",
|
||
"gridcolor": "#DFE8F3",
|
||
"gridwidth": 2,
|
||
"linecolor": "#EBF0F8",
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"showbackground": true,
|
||
"ticks": "",
|
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"zerolinecolor": "#EBF0F8"
|
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}
|
||
},
|
||
"shapedefaults": {
|
||
"line": {
|
||
"color": "#2a3f5f"
|
||
}
|
||
},
|
||
"ternary": {
|
||
"aaxis": {
|
||
"gridcolor": "#DFE8F3",
|
||
"linecolor": "#A2B1C6",
|
||
"ticks": ""
|
||
},
|
||
"baxis": {
|
||
"gridcolor": "#DFE8F3",
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"linecolor": "#A2B1C6",
|
||
"ticks": ""
|
||
},
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||
"bgcolor": "white",
|
||
"caxis": {
|
||
"gridcolor": "#DFE8F3",
|
||
"linecolor": "#A2B1C6",
|
||
"ticks": ""
|
||
}
|
||
},
|
||
"title": {
|
||
"x": 0.05
|
||
},
|
||
"xaxis": {
|
||
"automargin": true,
|
||
"gridcolor": "#EBF0F8",
|
||
"linecolor": "#EBF0F8",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "#EBF0F8",
|
||
"zerolinewidth": 2
|
||
},
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"yaxis": {
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"automargin": true,
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"gridcolor": "#EBF0F8",
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"linecolor": "#EBF0F8",
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"ticks": "",
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"title": {
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"standoff": 15
|
||
},
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||
"zerolinecolor": "#EBF0F8",
|
||
"zerolinewidth": 2
|
||
}
|
||
}
|
||
},
|
||
"title": {
|
||
"text": "Label Buffer Prevents Leakage"
|
||
},
|
||
"xaxis": {
|
||
"title": {
|
||
"text": "Day"
|
||
}
|
||
},
|
||
"yaxis": {
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||
"visible": false
|
||
}
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||
}
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},
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig = go.Figure()\n",
|
||
"\n",
|
||
"fig.add_vrect(x0=30, x1=47, fillcolor=\"steelblue\", opacity=0.3, line_width=0)\n",
|
||
"fig.add_annotation(\n",
|
||
" x=38.5, y=0.8, text=\"Training\", showarrow=False, font=dict(size=12, color=\"steelblue\")\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.add_vrect(x0=48, x1=52, fillcolor=\"gray\", opacity=0.2, line_width=0)\n",
|
||
"fig.add_annotation(\n",
|
||
" x=50, y=0.5, text=\"Label Buffer\\n(5 days)\", showarrow=False, font=dict(size=10, color=\"gray\")\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.add_vrect(x0=53, x1=70, fillcolor=\"coral\", opacity=0.3, line_width=0)\n",
|
||
"fig.add_annotation(\n",
|
||
" x=61.5, y=0.8, text=\"Validation\", showarrow=False, font=dict(size=12, color=\"coral\")\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=[47],\n",
|
||
" y=[0.3],\n",
|
||
" mode=\"markers+text\",\n",
|
||
" marker=dict(size=15, color=\"steelblue\"),\n",
|
||
" text=[\"Last train sample\"],\n",
|
||
" textposition=\"middle left\",\n",
|
||
" name=\"Training\",\n",
|
||
" showlegend=False,\n",
|
||
" )\n",
|
||
")\n",
|
||
"fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=[47, 52],\n",
|
||
" y=[0.3, 0.3],\n",
|
||
" mode=\"lines+markers\",\n",
|
||
" line=dict(color=\"steelblue\", width=2, dash=\"dot\"),\n",
|
||
" marker=dict(size=8),\n",
|
||
" name=\"Label (safe)\",\n",
|
||
" )\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" title=\"Label Buffer Prevents Leakage\",\n",
|
||
" xaxis_title=\"Day\",\n",
|
||
" yaxis_visible=False,\n",
|
||
" template=\"plotly_white\",\n",
|
||
" height=300,\n",
|
||
" showlegend=True,\n",
|
||
")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "db974830",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.007362,
|
||
"end_time": "2026-06-13T03:12:07.082658+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.075296+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Walk-Forward CV with Label Buffer\n",
|
||
"\n",
|
||
"`WalkForwardCV` implements label buffer via the `label_horizon` parameter.\n",
|
||
"With `calendar='XNYS'`, the gap is counted in **NYSE trading days**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "1968e9e3",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:07.096256Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:07.096058Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:07.336743Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:07.336226Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.247989,
|
||
"end_time": "2026-06-13T03:12:07.337136+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.089147+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1200x350 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Expanding with 21-day label buffer\n",
|
||
"cv_purge_exp = WalkForwardCV(\n",
|
||
" n_splits=5,\n",
|
||
" test_size=252,\n",
|
||
" expanding=True,\n",
|
||
" label_horizon=21,\n",
|
||
" calendar=\"XNYS\",\n",
|
||
")\n",
|
||
"splits_purge_exp = list(cv_purge_exp.split(df_dates))\n",
|
||
"\n",
|
||
"fig = plot_splits(\n",
|
||
" splits_purge_exp, dates, title=\"Expanding Window with Label Buffer (21 trading days)\"\n",
|
||
")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"id": "5ad29d17",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:07.349205Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:07.349082Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:07.595144Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:07.594643Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.253179,
|
||
"end_time": "2026-06-13T03:12:07.595871+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.342692+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1200x350 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Rolling with 21-day label buffer\n",
|
||
"cv_purge_roll = WalkForwardCV(\n",
|
||
" n_splits=5,\n",
|
||
" test_size=252,\n",
|
||
" train_size=1260,\n",
|
||
" expanding=False,\n",
|
||
" label_horizon=21,\n",
|
||
" calendar=\"XNYS\",\n",
|
||
")\n",
|
||
"splits_purge_roll = list(cv_purge_roll.split(df_dates))\n",
|
||
"\n",
|
||
"fig = plot_splits(\n",
|
||
" splits_purge_roll, dates, title=\"Rolling Window with Label Buffer (21 trading days)\"\n",
|
||
")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "90b24c43",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.005589,
|
||
"end_time": "2026-06-13T03:12:07.607049+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.601460+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"The hatched regions are **label buffer gaps**: 21 NYSE trading days where\n",
|
||
"training labels would overlap with validation. These samples are excluded\n",
|
||
"from training but not used for validation — they are simply discarded."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c87d190",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.005867,
|
||
"end_time": "2026-06-13T03:12:07.619013+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.613146+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 6. Feature Buffer (Embargo)\n",
|
||
"\n",
|
||
"The label buffer prevents **forward-looking** leakage: training labels that\n",
|
||
"peek into the validation period. But there is a second leakage channel:\n",
|
||
"**backward-looking features** computed from validation data that bleed into\n",
|
||
"training.\n",
|
||
"\n",
|
||
"This matters in **CPCV** and **k-fold** schemes where a training block can\n",
|
||
"appear *after* a validation block in calendar time. A 60-day rolling feature\n",
|
||
"computed for the first training sample after the validation block uses 60 days\n",
|
||
"of validation-period prices.\n",
|
||
"\n",
|
||
"López de Prado (2018) calls this the **embargo**.\n",
|
||
"\n",
|
||
"| Buffer | Direction | Protects Against | When It Matters |\n",
|
||
"|---|---|---|---|\n",
|
||
"| **Label buffer** (purge) | Forward-looking | Training labels peeking into validation | Always |\n",
|
||
"| **Feature buffer** (embargo) | Backward-looking | Training features using validation data | CPCV, k-fold |\n",
|
||
"\n",
|
||
"In pure walk-forward CV (training always precedes validation), the feature\n",
|
||
"buffer is **zero** because no training sample follows a validation block."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "ef293af4",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:07.631366Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:07.631234Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:07.749972Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:07.749439Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.125725,
|
||
"end_time": "2026-06-13T03:12:07.750432+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.624707+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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|
||
"text/plain": [
|
||
"<Figure size 1300x350 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, axes = plt.subplots(1, 2, figsize=(13, 3.5))\n",
|
||
"\n",
|
||
"# Panel (a): Label buffer — forward-looking\n",
|
||
"ax = axes[0]\n",
|
||
"ax.barh(1, 5, left=0, height=0.6, color=\"0.75\", edgecolor=\"0.5\")\n",
|
||
"ax.barh(1, 1.5, left=5, height=0.6, color=\"0.9\", edgecolor=\"0.7\", hatch=\"//\", linewidth=0.5)\n",
|
||
"ax.barh(1, 3, left=6.5, height=0.6, color=\"0.35\", edgecolor=\"0.2\")\n",
|
||
"ax.annotate(\n",
|
||
" \"\", xy=(6.3, 0.55), xytext=(5.2, 0.55), arrowprops=dict(arrowstyle=\"->\", color=\"0.4\", lw=1.5)\n",
|
||
")\n",
|
||
"ax.text(5.75, 0.45, \"label horizon\", ha=\"center\", va=\"center\", fontsize=8, color=\"0.4\")\n",
|
||
"ax.set_xlim(-0.5, 10)\n",
|
||
"ax.set_ylim(0.3, 1.7)\n",
|
||
"ax.set_yticks([])\n",
|
||
"ax.set_title(\"(a) Label Buffer (Purge)\")\n",
|
||
"ax.legend(\n",
|
||
" handles=[\n",
|
||
" Patch(facecolor=\"0.75\", label=\"Train\"),\n",
|
||
" Patch(facecolor=\"0.9\", hatch=\"//\", label=\"Buffer\"),\n",
|
||
" Patch(facecolor=\"0.35\", label=\"Validation\"),\n",
|
||
" ],\n",
|
||
" fontsize=7,\n",
|
||
" loc=\"upper right\",\n",
|
||
")\n",
|
||
"\n",
|
||
"# Panel (b): Feature buffer — backward-looking (CPCV scenario)\n",
|
||
"ax = axes[1]\n",
|
||
"ax.barh(1, 3, left=0, height=0.6, color=\"0.35\", edgecolor=\"0.2\")\n",
|
||
"ax.barh(1, 1.5, left=3, height=0.6, color=\"0.9\", edgecolor=\"0.7\", hatch=\"\\\\\\\\\", linewidth=0.5)\n",
|
||
"ax.barh(1, 5, left=4.5, height=0.6, color=\"0.75\", edgecolor=\"0.5\")\n",
|
||
"ax.annotate(\n",
|
||
" \"\", xy=(3.2, 0.55), xytext=(4.3, 0.55), arrowprops=dict(arrowstyle=\"->\", color=\"0.4\", lw=1.5)\n",
|
||
")\n",
|
||
"ax.text(3.75, 0.45, \"feature lookback\", ha=\"center\", va=\"center\", fontsize=8, color=\"0.4\")\n",
|
||
"ax.set_xlim(-0.5, 10)\n",
|
||
"ax.set_ylim(0.3, 1.7)\n",
|
||
"ax.set_yticks([])\n",
|
||
"ax.set_title(\"(b) Feature Buffer (Embargo)\")\n",
|
||
"ax.legend(\n",
|
||
" handles=[\n",
|
||
" Patch(facecolor=\"0.35\", label=\"Validation\"),\n",
|
||
" Patch(facecolor=\"0.9\", hatch=\"\\\\\\\\\", label=\"Buffer\"),\n",
|
||
" Patch(facecolor=\"0.75\", label=\"Train\"),\n",
|
||
" ],\n",
|
||
" fontsize=7,\n",
|
||
" loc=\"upper right\",\n",
|
||
")\n",
|
||
"\n",
|
||
"fig.tight_layout()\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cbf0c10f",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.009119,
|
||
"end_time": "2026-06-13T03:12:07.765819+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.756700+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**(a)** In walk-forward, training precedes validation. The label buffer\n",
|
||
"removes training samples whose labels extend into the validation period.\n",
|
||
"\n",
|
||
"**(b)** In CPCV, a training block can follow a validation block. The feature\n",
|
||
"buffer removes training samples whose backward-looking features use\n",
|
||
"validation data."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "108e6b3e",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.007468,
|
||
"end_time": "2026-06-13T03:12:07.783074+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.775606+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 7. Calendar-Aware Cross-Validation\n",
|
||
"\n",
|
||
"A **critical subtlety**: financial markets don't trade every calendar day.\n",
|
||
"The NYSE has ~252 trading days per year, not 365. When we specify a label\n",
|
||
"horizon of 21 days for monthly forward returns, we mean **21 trading days**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "875015e0",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.008069,
|
||
"end_time": "2026-06-13T03:12:07.796355+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.788286+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### The Problem: Naive Calendar-Day Purging\n",
|
||
"\n",
|
||
"January 2024 has 31 calendar days but only 21 NYSE trading days.\n",
|
||
"A naive 21-calendar-day purge before February 1 only removes 15 trading\n",
|
||
"days — allowing 6 trading days of leakage."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "eb2fa810",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:07.811365Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:07.811229Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:07.982243Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:07.981710Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.177949,
|
||
"end_time": "2026-06-13T03:12:07.982700+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.804751+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1200x400 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, ax = plt.subplots(figsize=(12, 4))\n",
|
||
"\n",
|
||
"jan_dates = pd.date_range(\"2024-01-01\", \"2024-01-31\", freq=\"D\")\n",
|
||
"holidays = pd.to_datetime([\"2024-01-01\", \"2024-01-15\"]) # New Year's, MLK Day\n",
|
||
"\n",
|
||
"for i, d in enumerate(jan_dates):\n",
|
||
" is_weekend = d.dayofweek >= 5\n",
|
||
" is_holiday = d in holidays\n",
|
||
" is_non_trading = is_weekend or is_holiday\n",
|
||
"\n",
|
||
" color = \"0.9\" if is_non_trading else \"white\"\n",
|
||
" edge = \"0.7\" if is_non_trading else \"0.4\"\n",
|
||
" rect = Rectangle((i, 0), 0.9, 0.9, facecolor=color, edgecolor=edge, linewidth=0.5)\n",
|
||
" ax.add_patch(rect)\n",
|
||
" ax.text(i + 0.45, 0.45, str(d.day), ha=\"center\", va=\"center\", fontsize=7)\n",
|
||
"\n",
|
||
"# Naive purge region (Jan 11-31 = 21 calendar days before Feb 1)\n",
|
||
"for i in range(10, 31):\n",
|
||
" rect = Rectangle(\n",
|
||
" (i, 0),\n",
|
||
" 0.9,\n",
|
||
" 0.9,\n",
|
||
" facecolor=\"0.55\",\n",
|
||
" edgecolor=\"0.3\",\n",
|
||
" linewidth=1.2,\n",
|
||
" alpha=0.6,\n",
|
||
" zorder=2,\n",
|
||
" )\n",
|
||
" ax.add_patch(rect)\n",
|
||
"\n",
|
||
"# Correct additional purge (Jan 2-10)\n",
|
||
"for i in range(1, 10):\n",
|
||
" rect = Rectangle(\n",
|
||
" (i, 0),\n",
|
||
" 0.9,\n",
|
||
" 0.9,\n",
|
||
" facecolor=\"0.8\",\n",
|
||
" edgecolor=\"0.5\",\n",
|
||
" linewidth=1.2,\n",
|
||
" alpha=0.6,\n",
|
||
" zorder=2,\n",
|
||
" )\n",
|
||
" ax.add_patch(rect)\n",
|
||
"\n",
|
||
"ax.text(\n",
|
||
" 15.5,\n",
|
||
" 1.4,\n",
|
||
" \"Naive: 21 calendar days (only 15 trading days!)\",\n",
|
||
" ha=\"center\",\n",
|
||
" fontsize=9,\n",
|
||
" fontweight=\"bold\",\n",
|
||
")\n",
|
||
"ax.text(5, -1.2, \"Correct: extend purge to get\\n21 TRADING days\", ha=\"center\", fontsize=8)\n",
|
||
"\n",
|
||
"ax.set_xlim(-0.5, 31.5)\n",
|
||
"ax.set_ylim(-1.6, 1.9)\n",
|
||
"ax.set_aspect(\"equal\")\n",
|
||
"ax.axis(\"off\")\n",
|
||
"ax.set_title(\"January 2024: Naive vs Trading-Day-Aware Purging\", pad=14)\n",
|
||
"ax.legend(\n",
|
||
" handles=[\n",
|
||
" Rectangle((0, 0), 1, 1, facecolor=\"0.55\", edgecolor=\"0.3\", label=\"Naive purge only\"),\n",
|
||
" Rectangle((0, 0), 1, 1, facecolor=\"0.8\", edgecolor=\"0.5\", label=\"Additional correct purge\"),\n",
|
||
" Rectangle((0, 0), 1, 1, facecolor=\"0.9\", edgecolor=\"0.7\", label=\"Non-trading day\"),\n",
|
||
" ],\n",
|
||
" loc=\"lower right\",\n",
|
||
" bbox_to_anchor=(1.0, -0.05),\n",
|
||
" fontsize=7,\n",
|
||
" ncol=3,\n",
|
||
")\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "68fb40ca",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.004376,
|
||
"end_time": "2026-06-13T03:12:07.991564+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.987188+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"**Rule**: Always count purge gaps in **trading days**, not calendar days.\n",
|
||
"`WalkForwardCV` handles this automatically when given a `calendar` parameter."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a19a1bc6",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.004234,
|
||
"end_time": "2026-06-13T03:12:08.000038+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:07.995804+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Calendar-Aware Splits in Practice\n",
|
||
"\n",
|
||
"Passing `calendar='XNYS'` ensures `WalkForwardCV` counts the label buffer\n",
|
||
"in NYSE trading days, skipping weekends and holidays."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"id": "bbfb7482",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:08.009572Z",
|
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"iopub.status.busy": "2026-06-13T03:12:08.009461Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:08.154677Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:08.154138Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.151037,
|
||
"end_time": "2026-06-13T03:12:08.155265+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.004228+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Walk-forward with 21 trading-day label buffer (NYSE calendar):\n",
|
||
"\n",
|
||
" Fold 1: train ends 2014-12-01, val starts 2015-01-02\n",
|
||
" gap = 22 trading days (32 calendar days)\n",
|
||
" Fold 2: train ends 2018-07-31, val starts 2018-08-30\n",
|
||
" gap = 22 trading days (30 calendar days)\n",
|
||
" Fold 3: train ends 2022-03-29, val starts 2022-04-29\n",
|
||
" gap = 22 trading days (31 calendar days)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Calendar-aware splits: 21 trading-day label buffer\n",
|
||
"cv_nyse = WalkForwardCV(\n",
|
||
" n_splits=3, test_size=252, expanding=True, label_horizon=21, calendar=\"XNYS\"\n",
|
||
")\n",
|
||
"splits_nyse = list(cv_nyse.split(df_dates))\n",
|
||
"\n",
|
||
"print(\"Walk-forward with 21 trading-day label buffer (NYSE calendar):\\n\")\n",
|
||
"for i, (tr, va) in enumerate(splits_nyse):\n",
|
||
" gap_sessions = va[0] - tr[-1]\n",
|
||
" train_end_date = dates[tr[-1]]\n",
|
||
" val_start_date = dates[va[0]]\n",
|
||
" gap_calendar = (val_start_date - train_end_date).days\n",
|
||
" print(f\" Fold {i + 1}: train ends {train_end_date.date()}, val starts {val_start_date.date()}\")\n",
|
||
" print(f\" gap = {gap_sessions} trading days ({gap_calendar} calendar days)\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c84875d",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.004933,
|
||
"end_time": "2026-06-13T03:12:08.167752+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.162819+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"Each fold's label buffer spans 22 trading sessions, which covers ~30 calendar\n",
|
||
"days due to weekends and holidays. A naive implementation that counts 21\n",
|
||
"*calendar* days would only purge ~15 trading days — leaving 6 days of label\n",
|
||
"leakage. The `calendar='XNYS'` parameter ensures the gap is measured in\n",
|
||
"actual trading days."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "62b977b0",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.004239,
|
||
"end_time": "2026-06-13T03:12:08.176337+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.172098+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 8. Nested Walk-Forward\n",
|
||
"\n",
|
||
"Standard walk-forward CV tunes hyperparameters on one validation period\n",
|
||
"(e.g., 2019–2023), then tests once on the holdout (2024–2025). By the time\n",
|
||
"we reach 2025, those hyperparameters may be stale.\n",
|
||
"\n",
|
||
"**Nested walk-forward** adds an **outer loop** that rolls the test window\n",
|
||
"forward, retuning hyperparameters at each step.\n",
|
||
"\n",
|
||
"- **Inner loop**: Walk-forward CV selects hyperparameters $\\lambda^*$\n",
|
||
"- **Outer loop**: Advances the test window and reruns the inner loop\n",
|
||
"\n",
|
||
"This produces **multiple test points**, each with freshly tuned\n",
|
||
"hyperparameters — capturing tuning instability over time."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"id": "88d4d328",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:08.187267Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:08.187108Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:08.193586Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:08.192992Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.01351,
|
||
"end_time": "2026-06-13T03:12:08.194135+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.180625+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Outer</th>\n",
|
||
" <th>Inner Fold</th>\n",
|
||
" <th>Train</th>\n",
|
||
" <th>Validation</th>\n",
|
||
" <th>Test</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2014–2018</td>\n",
|
||
" <td>2019</td>\n",
|
||
" <td>2024</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2014–2019</td>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>2024</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2014–2020</td>\n",
|
||
" <td>2021</td>\n",
|
||
" <td>2024</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>2014–2021</td>\n",
|
||
" <td>2022</td>\n",
|
||
" <td>2024</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>1</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2014–2022</td>\n",
|
||
" <td>2023</td>\n",
|
||
" <td>2024</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2014–2019</td>\n",
|
||
" <td>2020</td>\n",
|
||
" <td>2025</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2014–2020</td>\n",
|
||
" <td>2021</td>\n",
|
||
" <td>2025</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2014–2021</td>\n",
|
||
" <td>2022</td>\n",
|
||
" <td>2025</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>2014–2022</td>\n",
|
||
" <td>2023</td>\n",
|
||
" <td>2025</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2014–2023</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>2025</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Outer Inner Fold Train Validation Test\n",
|
||
"0 1 1 2014–2018 2019 2024\n",
|
||
"1 1 2 2014–2019 2020 2024\n",
|
||
"2 1 3 2014–2020 2021 2024\n",
|
||
"3 1 4 2014–2021 2022 2024\n",
|
||
"4 1 5 2014–2022 2023 2024\n",
|
||
"5 2 1 2014–2019 2020 2025\n",
|
||
"6 2 2 2014–2020 2021 2025\n",
|
||
"7 2 3 2014–2021 2022 2025\n",
|
||
"8 2 4 2014–2022 2023 2025\n",
|
||
"9 2 5 2014–2023 2024 2025"
|
||
]
|
||
},
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Two outer folds: test on 2024 (tune on 2019-2023), test on 2025 (tune on 2020-2024)\n",
|
||
"nested_folds = []\n",
|
||
"\n",
|
||
"for fold in range(5):\n",
|
||
" val_year = 2019 + fold\n",
|
||
" nested_folds.append(\n",
|
||
" {\n",
|
||
" \"Outer\": 1,\n",
|
||
" \"Inner Fold\": fold + 1,\n",
|
||
" \"Train\": f\"2014–{val_year - 1}\",\n",
|
||
" \"Validation\": str(val_year),\n",
|
||
" \"Test\": 2024,\n",
|
||
" }\n",
|
||
" )\n",
|
||
"\n",
|
||
"for fold in range(5):\n",
|
||
" val_year = 2020 + fold\n",
|
||
" nested_folds.append(\n",
|
||
" {\n",
|
||
" \"Outer\": 2,\n",
|
||
" \"Inner Fold\": fold + 1,\n",
|
||
" \"Train\": f\"2014–{val_year - 1}\",\n",
|
||
" \"Validation\": str(val_year),\n",
|
||
" \"Test\": 2025,\n",
|
||
" }\n",
|
||
" )\n",
|
||
"\n",
|
||
"pd.DataFrame(nested_folds)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3e7d6ad2",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.009135,
|
||
"end_time": "2026-06-13T03:12:08.210875+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.201740+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Nested Walk-Forward Timeline\n",
|
||
"\n",
|
||
"Each outer fold runs a full inner walk-forward CV to select\n",
|
||
"$\\lambda^*$, then evaluates on its test year."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "1fbd868f",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:08.264110Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:08.263912Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:08.355122Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:08.354646Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.131378,
|
||
"end_time": "2026-06-13T03:12:08.355449+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.224071+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
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|
||
"text/plain": [
|
||
"<Figure size 1300x450 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"fig, ax = plt.subplots(figsize=(13, 4.5))\n",
|
||
"\n",
|
||
"outer_folds = [\n",
|
||
" {\"test_year\": 2024, \"val_years\": list(range(2019, 2024)), \"color_test\": \"0.2\"},\n",
|
||
" {\"test_year\": 2025, \"val_years\": list(range(2020, 2025)), \"color_test\": \"0.2\"},\n",
|
||
"]\n",
|
||
"\n",
|
||
"y = 0\n",
|
||
"outer_boundaries = []\n",
|
||
"for oi, outer in enumerate(outer_folds):\n",
|
||
" # Inner folds\n",
|
||
" for fi, val_yr in enumerate(outer[\"val_years\"]):\n",
|
||
" y += 1\n",
|
||
" train_s = pd.Timestamp(\"2014-01-02\", tz=\"UTC\")\n",
|
||
" train_e = pd.Timestamp(f\"{val_yr - 1}-12-31\", tz=\"UTC\")\n",
|
||
" val_s = pd.Timestamp(f\"{val_yr}-01-02\", tz=\"UTC\")\n",
|
||
" val_e = pd.Timestamp(f\"{val_yr}-12-31\", tz=\"UTC\")\n",
|
||
"\n",
|
||
" ax.barh(y, train_e - train_s, left=train_s, height=0.5, color=\"0.8\", edgecolor=\"0.6\")\n",
|
||
" ax.barh(y, val_e - val_s, left=val_s, height=0.5, color=\"0.5\", edgecolor=\"0.3\")\n",
|
||
"\n",
|
||
" # Test bar (spans all inner folds visually)\n",
|
||
" test_s = pd.Timestamp(f\"{outer['test_year']}-01-02\", tz=\"UTC\")\n",
|
||
" test_e = pd.Timestamp(f\"{outer['test_year']}-12-31\", tz=\"UTC\")\n",
|
||
" y_mid = y - len(outer[\"val_years\"]) / 2 + 0.5\n",
|
||
" ax.barh(\n",
|
||
" y_mid,\n",
|
||
" test_e - test_s,\n",
|
||
" left=test_s,\n",
|
||
" height=len(outer[\"val_years\"]) * 0.55,\n",
|
||
" color=\"0.2\",\n",
|
||
" edgecolor=\"0.1\",\n",
|
||
" alpha=0.3,\n",
|
||
" )\n",
|
||
" ax.text(\n",
|
||
" test_s + (test_e - test_s) / 2,\n",
|
||
" y_mid,\n",
|
||
" f\"Test {outer['test_year']}\",\n",
|
||
" ha=\"center\",\n",
|
||
" va=\"center\",\n",
|
||
" fontsize=9,\n",
|
||
" fontweight=\"bold\",\n",
|
||
" color=\"white\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Outer-fold group label (left of training bars)\n",
|
||
" ax.text(\n",
|
||
" pd.Timestamp(\"2013-04-01\", tz=\"UTC\"),\n",
|
||
" y_mid,\n",
|
||
" f\"Outer\\nfold {oi + 1}\",\n",
|
||
" ha=\"right\",\n",
|
||
" va=\"center\",\n",
|
||
" fontsize=8,\n",
|
||
" fontweight=\"bold\",\n",
|
||
" color=\"0.3\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" outer_boundaries.append(y + 0.5)\n",
|
||
" # Separator between outer folds\n",
|
||
" if oi < len(outer_folds) - 1:\n",
|
||
" y += 1.0\n",
|
||
"\n",
|
||
"# Draw horizontal dividers between outer folds\n",
|
||
"for boundary in outer_boundaries[:-1]:\n",
|
||
" ax.axhline(boundary + 0.5, color=\"0.5\", linestyle=\"--\", linewidth=0.8, alpha=0.7)\n",
|
||
"\n",
|
||
"ax.set_yticks([])\n",
|
||
"ax.xaxis.set_major_formatter(mdates.DateFormatter(\"%Y\"))\n",
|
||
"ax.xaxis.set_major_locator(mdates.YearLocator(2))\n",
|
||
"ax.set_title(\"Nested Walk-Forward: 2 Outer Folds × 5 Inner Folds\")\n",
|
||
"ax.legend(\n",
|
||
" handles=[\n",
|
||
" Patch(facecolor=\"0.8\", label=\"Train (inner)\"),\n",
|
||
" Patch(facecolor=\"0.5\", label=\"Validation (inner)\"),\n",
|
||
" Patch(facecolor=\"0.2\", alpha=0.3, label=\"Test (outer)\"),\n",
|
||
" ],\n",
|
||
" loc=\"upper left\",\n",
|
||
")\n",
|
||
"fig.tight_layout()\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7e1f1081",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00479,
|
||
"end_time": "2026-06-13T03:12:08.365176+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.360386+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"Each outer fold produces test predictions with freshly selected $\\lambda^*$.\n",
|
||
"If $\\lambda^*_1 \\neq \\lambda^*_2$, that signals hyperparameter instability —\n",
|
||
"a red flag for production deployment."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "86873911",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.009193,
|
||
"end_time": "2026-06-13T03:12:08.381859+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.372666+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 9. Combinatorial Purged CV (CPCV)\n",
|
||
"\n",
|
||
"Walk-forward CV produces **one backtest path** — a single sequence of\n",
|
||
"out-of-sample predictions. That path might reflect luck.\n",
|
||
"\n",
|
||
"**CPCV** divides time into $N$ contiguous blocks, holds out $k$ blocks for\n",
|
||
"validation, trains on the remaining $N - k$ (with purging at boundaries),\n",
|
||
"and repeats for all $\\binom{N}{k}$ combinations. The validation predictions\n",
|
||
"are then assembled into **multiple complete backtest paths**, each covering\n",
|
||
"every time block exactly once.\n",
|
||
"\n",
|
||
"Each path is a complete out-of-sample backtest — one prediction per block,\n",
|
||
"assembled from different splits so no block's prediction depends on its own\n",
|
||
"training data."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "4a698591",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:08.397046Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:08.396918Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:08.399697Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:08.399122Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.009333,
|
||
"end_time": "2026-06-13T03:12:08.400170+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.390837+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"N=6 blocks, k=2 held out → 15 splits, 5 backtest paths\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"N, K = 6, 2\n",
|
||
"n_splits = comb(N, K) # C(6,2) = 15\n",
|
||
"n_paths = (K * n_splits) // N # = 5\n",
|
||
"\n",
|
||
"print(f\"N={N} blocks, k={K} held out → {n_splits} splits, {n_paths} backtest paths\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "9003d6d5",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:08.414099Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:08.413978Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:08.703187Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:08.702434Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.297094,
|
||
"end_time": "2026-06-13T03:12:08.703708+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.406614+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.plotly.v1+json": {
|
||
"config": {
|
||
"plotlyServerURL": "https://plot.ly"
|
||
},
|
||
"data": [
|
||
{
|
||
"marker": {
|
||
"color": "gray",
|
||
"size": 6
|
||
},
|
||
"mode": "markers",
|
||
"name": "Purged",
|
||
"showlegend": true,
|
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"type": "scatter",
|
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"x": {
|
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"bdata": "qACpAA==",
|
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"dtype": "i2"
|
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},
|
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"xaxis": "x",
|
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"y": {
|
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"bdata": "+93Q5Jw36D/JfAMoWJnjPw==",
|
||
"dtype": "f8"
|
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},
|
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"yaxis": "y"
|
||
},
|
||
{
|
||
"marker": {
|
||
"color": "steelblue",
|
||
"size": 6
|
||
},
|
||
"mode": "markers",
|
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"name": "Training",
|
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"showlegend": true,
|
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"type": "scatter",
|
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|
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},
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{
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"marker": {
|
||
"color": "coral",
|
||
"size": 6
|
||
},
|
||
"mode": "markers",
|
||
"name": "Validation",
|
||
"showlegend": true,
|
||
"type": "scatter",
|
||
"x": {
|
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"bdata": "AAABAAIAAwAEAAUABgAHAAgACQAKAAsADAANAA4ADwAQABEAEgATABQAFQAWABcAGAAZABoAGwAcAB0AHgAfACAAIQAiACMAJAAlACYAJwAoACkAKgArACwALQAuAC8AMAAxADIAMwA0ADUANgA3ADgAOQA6ADsAPAA9AD4APwBAAEEAQgBDAEQARQBGAEcASABJAEoASwBMAE0ATgBPAFAAUQBSAFMAVABVAFYAVwBYAFkAWgBbAFwAXQBeAF8AYABhAGIAYwBkAGUAZgBnAGgAaQBqAGsAbABtAG4AbwBwAHEAcgBzAHQAdQB2AHcAeAB5AHoAewB8AH0AfgB/AIAAgQCCAIMAhACFAIYAhwCIAIkAigCLAIwAjQCOAI8AkACRAJIAkwCUAJUAlgCXAJgAmQCaAJsAnACdAJ4AnwCgAKEAogCjAKQApQCmAKcA",
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"dtype": "i2"
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"xaxis": "x",
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"y": {
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|
||
"dtype": "f8"
|
||
},
|
||
"yaxis": "y"
|
||
},
|
||
{
|
||
"marker": {
|
||
"color": "gray",
|
||
"size": 6
|
||
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Visualize first 4 splits\n",
|
||
"set_global_seeds(SEED)\n",
|
||
"X_viz = np.random.randn(N_VIZ, 5)\n",
|
||
"y_viz = np.random.randn(N_VIZ)\n",
|
||
"\n",
|
||
"cv_cpcv = CombinatorialCV(n_groups=6, n_test_groups=2, label_horizon=5, embargo_size=2)\n",
|
||
"splits_cpcv = list(cv_cpcv.split(X_viz))\n",
|
||
"\n",
|
||
"fig = make_subplots(rows=4, cols=1, shared_xaxes=True, vertical_spacing=0.08)\n",
|
||
"\n",
|
||
"for split_idx in range(min(4, len(splits_cpcv))):\n",
|
||
" train_idx, val_idx = splits_cpcv[split_idx]\n",
|
||
" split_type = np.zeros(N_VIZ)\n",
|
||
" split_type[train_idx] = 1\n",
|
||
" split_type[val_idx] = 2\n",
|
||
"\n",
|
||
" for stype, (color, label) in enumerate(\n",
|
||
" [(\"gray\", \"Purged\"), (\"steelblue\", \"Training\"), (\"coral\", \"Validation\")]\n",
|
||
" ):\n",
|
||
" mask = split_type == stype\n",
|
||
" if np.any(mask):\n",
|
||
" fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=np.arange(N_VIZ)[mask],\n",
|
||
" y=y_viz[mask],\n",
|
||
" mode=\"markers\",\n",
|
||
" marker=dict(color=color, size=6),\n",
|
||
" name=label if split_idx == 0 else None,\n",
|
||
" showlegend=(split_idx == 0),\n",
|
||
" ),\n",
|
||
" row=split_idx + 1,\n",
|
||
" col=1,\n",
|
||
" )\n",
|
||
" fig.update_yaxes(title_text=f\"Split {split_idx + 1}\", row=split_idx + 1, col=1)\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" title=f\"CPCV: First 4 of {n_splits} Splits (N={N}, k={K})\",\n",
|
||
" height=500,\n",
|
||
" width=900,\n",
|
||
" template=\"plotly_white\",\n",
|
||
" showlegend=True,\n",
|
||
" margin=dict(l=80),\n",
|
||
")\n",
|
||
"fig.update_xaxes(title_text=\"Sample Index\", row=4, col=1)\n",
|
||
"fig.update_yaxes(title_standoff=8)\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "08eded50",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.011803,
|
||
"end_time": "2026-06-13T03:12:08.723386+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.711583+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"Gray regions are samples removed by the label buffer (purge) and feature\n",
|
||
"buffer (embargo) at block boundaries. With $N=6$, $k=2$: 15 splits produce\n",
|
||
"5 independent backtest paths.\n",
|
||
"\n",
|
||
"If all paths show similar Sharpe ratios → robust. If they vary wildly →\n",
|
||
"path-dependent (possibly overfit). Bailey et al. (2014) formalize this into\n",
|
||
"the **Probability of Backtest Overfitting (PBO)** — the fraction of paths\n",
|
||
"whose in-sample rank doesn't hold out-of-sample. We return to PBO in\n",
|
||
"Chapter 17 when assembling full backtest results."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "cdfd0e0f",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.011347,
|
||
"end_time": "2026-06-13T03:12:08.746437+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.735090+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## 10. Putting It Together\n",
|
||
"\n",
|
||
"### CV Method Comparison\n",
|
||
"\n",
|
||
"| Method | Paths | Label Buffer | Feature Buffer | When to Use |\n",
|
||
"|---|---|---|---|---|\n",
|
||
"| Walk-Forward | 1 | Yes | No (train < val) | Standard evaluation |\n",
|
||
"| Nested Walk-Forward | 1 per test year | Yes | No | Multi-year test with retuning |\n",
|
||
"| CPCV | Multiple | Yes | Yes | Robustness testing (Ch17) |"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "8d0fc931",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.006002,
|
||
"end_time": "2026-06-13T03:12:08.758201+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:12:08.752199+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### From Config to Protocol\n",
|
||
"\n",
|
||
"`WalkForwardConfig` encodes all CV design decisions in a single object.\n",
|
||
"Each case study's `config/setup.yaml` stores these commitments; downstream\n",
|
||
"notebooks load them via `get_cv_config()`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "f5ba284a",
|
||
"metadata": {
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"iopub.status.idle": "2026-06-13T03:12:08.776105Z",
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"shell.execute_reply": "2026-06-13T03:12:08.775366Z"
|
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|
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|
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|
||
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|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Parameter</th>\n",
|
||
" <th>Value</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>n_splits</td>\n",
|
||
" <td>5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>label_horizon</td>\n",
|
||
" <td>21</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>embargo_td</td>\n",
|
||
" <td>None</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>align_to_sessions</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>session_col</td>\n",
|
||
" <td>session_date</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>timestamp_col</td>\n",
|
||
" <td>None</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>filter_non_trading</td>\n",
|
||
" <td>True</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>isolate_groups</td>\n",
|
||
" <td>False</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>test_size</td>\n",
|
||
" <td>252</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>train_size</td>\n",
|
||
" <td>1260</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>step_size</td>\n",
|
||
" <td>None</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>test_period</td>\n",
|
||
" <td>None</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>test_start</td>\n",
|
||
" <td>None</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>test_end</td>\n",
|
||
" <td>None</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>fold_direction</td>\n",
|
||
" <td>forward</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>calendar_id</td>\n",
|
||
" <td>NYSE</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Parameter Value\n",
|
||
"0 n_splits 5\n",
|
||
"1 label_horizon 21\n",
|
||
"2 embargo_td None\n",
|
||
"3 align_to_sessions False\n",
|
||
"4 session_col session_date\n",
|
||
"5 timestamp_col None\n",
|
||
"6 filter_non_trading True\n",
|
||
"7 isolate_groups False\n",
|
||
"8 test_size 252\n",
|
||
"9 train_size 1260\n",
|
||
"10 step_size None\n",
|
||
"11 test_period None\n",
|
||
"12 test_start None\n",
|
||
"13 test_end None\n",
|
||
"14 fold_direction forward\n",
|
||
"15 calendar_id NYSE"
|
||
]
|
||
},
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"config = WalkForwardConfig(\n",
|
||
" n_splits=5,\n",
|
||
" test_size=252,\n",
|
||
" train_size=1260,\n",
|
||
" label_horizon=21,\n",
|
||
" fold_direction=\"forward\",\n",
|
||
" calendar_id=\"NYSE\",\n",
|
||
")\n",
|
||
"\n",
|
||
"config_table = pd.DataFrame(\n",
|
||
" {\n",
|
||
" \"Parameter\": list(config.model_dump().keys()),\n",
|
||
" \"Value\": [str(v) for v in config.model_dump().values()],\n",
|
||
" }\n",
|
||
")\n",
|
||
"config_table"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
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|
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|
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|
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|
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|
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|
||
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|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### Loading a Case Study Protocol\n",
|
||
"\n",
|
||
"Each case study stores its CV protocol in `case_studies/{id}/config/setup.yaml`.\n",
|
||
"The `get_cv_config()` function loads it into a `WalkForwardConfig`."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "13cb905f",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:12:08.815262Z",
|
||
"iopub.status.busy": "2026-06-13T03:12:08.815046Z",
|
||
"iopub.status.idle": "2026-06-13T03:12:08.828129Z",
|
||
"shell.execute_reply": "2026-06-13T03:12:08.827580Z"
|
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},
|
||
"papermill": {
|
||
"duration": 0.022495,
|
||
"end_time": "2026-06-13T03:12:08.828594+00:00",
|
||
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|
||
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|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"ETF case study CV protocol:\n",
|
||
" n_splits: 8\n",
|
||
" train_size: 10Y\n",
|
||
" test_size: 1Y\n",
|
||
" embargo_td: P21D\n",
|
||
" label_horizon: P21D\n",
|
||
" timestamp_col: timestamp\n",
|
||
" calendar_id: NYSE\n",
|
||
" test_start: 2024-01-01\n",
|
||
" test_end: 2025-12-31\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"etf_config = get_cv_config(\"etfs\")\n",
|
||
"\n",
|
||
"print(\"ETF case study CV protocol:\")\n",
|
||
"for k, v in etf_config.model_dump().items():\n",
|
||
" print(f\" {k}: {v}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7ab4ecf6",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.009216,
|
||
"end_time": "2026-06-13T03:12:08.845711+00:00",
|
||
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|
||
"start_time": "2026-06-13T03:12:08.836495+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"This protocol specifies 8 walk-forward splits with 10-year rolling\n",
|
||
"training windows, 1-year test windows, and a 21-trading-day label buffer\n",
|
||
"(matching the 1-month forward return labels). The holdout period\n",
|
||
"(2024–2025) is sealed for final confirmation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c81a87a",
|
||
"metadata": {
|
||
"papermill": {
|
||
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|
||
"end_time": "2026-06-13T03:12:08.874640+00:00",
|
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|
||
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|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"---\n",
|
||
"\n",
|
||
"## Key Takeaways\n",
|
||
"\n",
|
||
"### Decision-Time Admissibility\n",
|
||
"- The central constraint: only information available at decision time $t$\n",
|
||
" can enter training for predictions at $t$\n",
|
||
"- Five leakage channels: label, standardization, threshold, survivorship,\n",
|
||
" point-in-time\n",
|
||
"\n",
|
||
"### Walk-Forward CV\n",
|
||
"- Training always precedes validation (respects temporal order)\n",
|
||
"- Expanding (all history) vs rolling (fixed window) — depends on regime beliefs\n",
|
||
"\n",
|
||
"### Label Buffer (Purging)\n",
|
||
"- Forward-looking labels can leak validation data into training\n",
|
||
"- Remove training samples within one label horizon of validation start\n",
|
||
"- Count in **trading days** using a proper market calendar\n",
|
||
"\n",
|
||
"### Feature Buffer (Embargo)\n",
|
||
"- Backward-looking features can leak validation data into training\n",
|
||
"- Matters in CPCV / k-fold where training follows validation in time\n",
|
||
"- Zero in pure walk-forward (training always precedes validation)\n",
|
||
"\n",
|
||
"### Nested Walk-Forward\n",
|
||
"- Outer loop advances test window; inner loop retunes hyperparameters\n",
|
||
"- Captures tuning instability — $\\lambda^*$ should be stable across periods\n",
|
||
"\n",
|
||
"### Combinatorial Purged CV\n",
|
||
"- Multiple backtest paths from the same data reveal robustness\n",
|
||
"- Connects to Probability of Backtest Overfitting (Bailey et al. 2014)\n",
|
||
"- Full treatment in Chapter 17\n",
|
||
"\n",
|
||
"**Next**: See `case_studies/*/01_feasibility_analysis.py` for applying these concepts\n",
|
||
"to real trading strategies."
|
||
]
|
||
}
|
||
],
|
||
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|
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|
||
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|
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|
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|
||
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|
||
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|
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|
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"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
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"nbconvert_exporter": "python",
|
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|
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|
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|
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|
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|
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|
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|
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"version": "2.7.0"
|
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|
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"ml4t_provenance": {
|
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|
||
"executed_at": "2026-06-13T03:12:11.712960+00:00",
|
||
"executor": "cpu-local",
|
||
"production": true,
|
||
"parameters": {},
|
||
"notes": "executed-state finalization batch (2026-06-13 run)"
|
||
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|
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|
||
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|
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|
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|