Regressão regularizada para previsão transversal de retornos

# 02_regularization_paths.py


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# %% [markdown] tags=[]
# # Regularized Regression for Return Prediction
#
# **Chapter 11 | Section 11.2**
#
# **Docker image**: `ml4t`
#
# This notebook compares OLS, Ridge (L2), LASSO (L1), and Elastic Net regression
# for predicting 21-day forward returns on the ETF universe. Every model is fitted
# on the same walk-forward folds, declared once in `setup.yaml` and loaded by
# `generate_cv_splits`, so that any difference between them comes from the
# penalty and not from the split.
#
# **Learning objectives**
#
# - Understand the bias-variance tradeoff in regularized regression
# - Compare Ridge, LASSO, and Elastic Net on real financial features
# - Analyze regularization paths and LASSO feature selection
# - Evaluate predictive performance via Information Coefficient (IC)
#
# **Book reference**
#
# Section 11.2 ("Regularized Regression"). The next notebook,
# `03_logistic_classification`, applies the same regularization toolkit to the
# direction-prediction task; `04_nested_cv_hpo` extends the alpha sweep into
# nested cross-validation.
#
# **Prerequisites**
#
# - Working knowledge of OLS and the bias-variance tradeoff.
# - Conceptual exposure to L1, L2, and Elastic-Net penalties.
# - ETF case-study features at `case_studies/etfs/features/financial.parquet`
#   (Ch8) and labels at `case_studies/etfs/labels/fwd_ret_21d.parquet` (Ch7).
#
# **Downstream**: Ch12 (gradient boosting), Ch16-17 (backtesting + portfolio).

# %% [markdown] tags=[]
# ## Setup

# %% tags=[]
"""Regularized Regression for Return Prediction - compare Ridge, LASSO, and Elastic Net via walk-forward CV."""

import joblib
import matplotlib.pyplot as plt
import numpy as np
import polars as pl
from matplotlib.colors import ListedColormap
from scipy.stats import spearmanr
from sklearn.linear_model import (
    ElasticNet,
    Lasso,
    LinearRegression,
    Ridge,
    SGDRegressor,
    lasso_path,
)
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import StandardScaler

from utils.cv_splits import generate_cv_splits
from utils.modeling import cross_sectional_ic_mean
from utils.paths import get_case_study_dir, get_chapter_dir
from utils.reproducibility import set_global_seeds
from utils.style import COLORS, show_with_alt

# %% tags=["parameters"]
SEED = 42
MAX_SYMBOLS = 0
RETRAIN = False

# %% tags=[]
RANDOM_SEED = SEED
set_global_seeds(SEED)

# %% [markdown] tags=[]
# ## Load Features and Labels
#
# We load pre-computed ETF features from Ch8 and 21-day forward return labels
# from Ch7. No synthetic fallback - if files are missing, run upstream notebooks.

# %% tags=[]
CASE_DIR = get_case_study_dir("etfs")
FEATURES_PATH = CASE_DIR / "features" / "financial.parquet"
LABELS_PATH = CASE_DIR / "labels" / "fwd_ret_21d.parquet"

assert FEATURES_PATH.exists(), (
    f"Features not found: {FEATURES_PATH}\nRun the Ch8 ETF features notebook first."
)
assert LABELS_PATH.exists(), (
    f"Labels not found: {LABELS_PATH}\nRun the Ch7 ETF labels notebook first."
)

features_df = pl.read_parquet(FEATURES_PATH).with_columns(pl.col("timestamp").cast(pl.Date))
labels_df = pl.read_parquet(LABELS_PATH).with_columns(pl.col("timestamp").cast(pl.Date))

# %% tags=[]
TARGET_COL = "fwd_ret_21d"
ASSET_COL = "symbol"

df = features_df.join(labels_df, on=["timestamp", ASSET_COL], how="inner")

META_COLS = {"timestamp", ASSET_COL, TARGET_COL}
FEATURE_COLS = sorted(c for c in df.columns if c not in META_COLS)

# Drop features that are entirely null (can happen with reduced test universes)
all_null = [c for c in FEATURE_COLS if df[c].null_count() == df.height]
if all_null:
    print(f"Dropping {len(all_null)} all-null features: {all_null}")
    df = df.drop(all_null)
    FEATURE_COLS = [c for c in FEATURE_COLS if c not in all_null]

# Replace inf/NaN with null, then drop all nulls
df = df.with_columns(
    [
        pl.when(pl.col(c).is_nan() | pl.col(c).is_infinite())
        .then(None)
        .otherwise(pl.col(c))
        .alias(c)
        for c in FEATURE_COLS
    ]
)
df = df.drop_nulls(subset=FEATURE_COLS + [TARGET_COL]).sort(["timestamp", ASSET_COL])

if MAX_SYMBOLS > 0:
    assets = df[ASSET_COL].unique().sort().head(MAX_SYMBOLS).to_list()
    df = df.filter(pl.col(ASSET_COL).is_in(assets))

print(f"Shape: {df.height:,} rows x {len(FEATURE_COLS)} features")
print(f"Assets: {df[ASSET_COL].n_unique()}")
print(f"Date range: {df['timestamp'].min()} to {df['timestamp'].max()}")

# %% [markdown] tags=[]
# ## Walk-Forward Cross-Validation Setup
#
# We load the canonical walk-forward splits from `setup.yaml` via
# `generate_cv_splits`: rolling train/validation windows with purge gap.
# This ensures consistency across all Ch11 notebooks and case studies.

# %% tags=[]
splits = generate_cv_splits(df, case_study_id="etfs", label_buffer="21D", date_col="timestamp")

features_array = df.select(FEATURE_COLS).to_numpy()
target_array = df[TARGET_COL].to_numpy()
dates_np = df["timestamp"].to_numpy()
symbols_np = df[ASSET_COL].to_numpy()

cv_splits = []
for s in splits:
    tr_start, tr_end = np.datetime64(s["train_start"]), np.datetime64(s["train_end"])
    te_start, te_end = np.datetime64(s["val_start"]), np.datetime64(s["val_end"])
    train_idx = np.where((dates_np >= tr_start) & (dates_np <= tr_end))[0]
    test_idx = np.where((dates_np >= te_start) & (dates_np <= te_end))[0]
    if len(train_idx) > 0 and len(test_idx) > 0:
        cv_splits.append((train_idx, test_idx))

train_sizes = [len(tr) for tr, _ in cv_splits]
test_sizes = [len(te) for _, te in cv_splits]
if cv_splits:
    print(
        f"{len(cv_splits)} walk-forward folds - train size "
        f"{min(train_sizes):,}–{max(train_sizes):,}, test size "
        f"{min(test_sizes):,}–{max(test_sizes):,}"
    )
else:
    print("0 walk-forward folds - every candidate split failed the train/test size gate")

# %% [markdown] tags=[]
# ## Helper Functions
#
# Every model below is scored the same way: rank the cross-section on each
# validation date, correlate that ranking with the realized forward return, and
# average over the dates where that correlation exists. Two kinds of date have
# none. A date with fewer symbols quoting than the minimum cross-section is not
# scored at all. A date where the model gives every symbol the same prediction
# has no ranking to correlate, so its Spearman coefficient is undefined. Strong
# regularization produces exactly that: at a high enough alpha every coefficient
# is zero and the model predicts one constant for everything.
#
# `utils.modeling.cross_sectional_ic_mean` is the shared implementation, and
# dropping those dates is the part worth knowing about - a single undefined date
# left in the series turns the average of the whole series into NaN.


# %% tags=[]
def evaluate_predictions(
    y_true: np.ndarray,
    y_pred: np.ndarray,
    dates: np.ndarray,
    symbols: np.ndarray,
) -> dict:
    """Compute mean cross-sectional IC, RMSE, and R-squared on a test fold."""
    ic = cross_sectional_ic_mean(y_true, y_pred, dates, symbols)
    rmse = np.sqrt(mean_squared_error(y_true, y_pred))
    r2 = r2_score(y_true, y_pred)
    return {"ic": ic, "rmse": rmse, "r2": r2}


# %% [markdown] tags=[]
# ### Winsorization
#
# Clipping features at the 1st and 99th percentiles (computed on training data)
# limits the influence of extreme observations on standardization. Without
# winsorization, a single outlier can inflate $\sigma_j$ and compress all other
# observations toward zero. The percentile bounds are fitted on training data
# only to prevent look-ahead bias.


# %% tags=[]
def winsorize_train_test(
    X_train: np.ndarray, X_test: np.ndarray, lower: float = 1.0, upper: float = 99.0
) -> tuple[np.ndarray, np.ndarray]:
    """Clip features at training-set percentiles to limit outlier influence."""
    lo = np.percentile(X_train, lower, axis=0)
    hi = np.percentile(X_train, upper, axis=0)
    return np.clip(X_train, lo, hi), np.clip(X_test, lo, hi)


# %% [markdown] tags=[]
# ### Walk-Forward Cross-Validation
# Run walk-forward CV with per-fold winsorization and standardization.


# %% tags=[]
def cross_validate(
    model_class, model_params: dict
) -> tuple[list[dict], list[np.ndarray], list[dict]]:
    """Run walk-forward CV on pre-computed splits.

    Preprocessing per fold: winsorize (1st/99th on train) → standardize.
    Returns per-fold metrics, coefficient arrays, and (model, scaler) pairs.
    """
    results, coefficients, fold_models = [], [], []

    for i, (train_idx, test_idx) in enumerate(cv_splits):
        X_tr, X_te = features_array[train_idx], features_array[test_idx]
        y_tr, y_te = target_array[train_idx], target_array[test_idx]

        # Winsorize before standardizing (percentiles fitted on training data)
        X_tr, X_te = winsorize_train_test(X_tr, X_te)

        scaler = StandardScaler()
        X_tr_s = scaler.fit_transform(X_tr)
        X_te_s = scaler.transform(X_te)

        model = model_class(**model_params)
        model.fit(X_tr_s, y_tr)

        y_pred = model.predict(X_te_s)
        metrics = evaluate_predictions(y_te, y_pred, dates_np[test_idx], symbols_np[test_idx])
        metrics["fold"] = i + 1
        metrics["train_size"] = len(X_tr)
        metrics["test_size"] = len(X_te)
        results.append(metrics)

        if hasattr(model, "coef_"):
            coefficients.append(model.coef_.ravel().copy())
        fold_models.append({"model": model, "scaler": scaler})

    return results, coefficients, fold_models


# %% [markdown] tags=[]
# ## Model Cache
#
# Training results (including fitted models and scalers) are cached to disk.
# Set `RETRAIN = True` to force retraining.

# %% tags=[]
MODELS_DIR = get_chapter_dir(11) / "models" / "02_regularization_paths"
RESULTS_PATH = MODELS_DIR / "cv_results.joblib"
NEED_TRAINING = RETRAIN or not RESULTS_PATH.exists()

if not NEED_TRAINING:
    _cached = joblib.load(RESULTS_PATH)
    ols_df = _cached["ols_df"]
    ols_coeffs = _cached["ols_coeffs"]
    ols_models = _cached["ols_models"]
    ridge_all = _cached["ridge_all"]
    ridge_summary = _cached["ridge_summary"]
    best_ridge_alpha = _cached["best_ridge_alpha"]
    RIDGE_ALPHAS = _cached["RIDGE_ALPHAS"]
    lasso_all = _cached["lasso_all"]
    lasso_summary = _cached["lasso_summary"]
    best_lasso_alpha = _cached["best_lasso_alpha"]
    alpha_max = _cached["alpha_max"]
    LASSO_ALPHAS = _cached["LASSO_ALPHAS"]
    en_all = _cached["en_all"]
    en_summary = _cached["en_summary"]
    best_en_key = _cached.get("best_en_key", en_summary.row(0, named=True)["params"])
    path_alphas = _cached["path_alphas"]
    path_coefs = _cached["path_coefs"]
    loss_comparison = _cached.get("loss_comparison")
    del _cached
    print(
        f"  Ridge: {len(ridge_all)} alphas | LASSO: {len(lasso_all)} alphas | EN: {len(en_all)} ratios"
    )
else:
    print("Training models (RETRAIN=True or no cache found)...")

# %% [markdown] tags=[]
# ## OLS Baseline
#
# Ordinary Least Squares provides the unregularized reference point every
# penalty below is measured against. It minimizes squared error on the training
# fold with nothing holding its coefficients back, which on this many correlated
# features and this little signal is the condition overfitting needs.

# %% tags=[]
if NEED_TRAINING:
    res, coeffs, models = cross_validate(LinearRegression, {})
    ols_df = pl.DataFrame(res)
    ols_coeffs = coeffs
    ols_models = models

# %% tags=[]
print(f"OLS - Mean IC: {ols_df['ic'].mean():.4f} (std: {ols_df['ic'].std():.4f})")
ols_df.select("fold", "ic", "rmse", "r2", "train_size", "test_size")

# %% [markdown] tags=[]
# ## Ridge Regression (L2)
#
# Ridge shrinks all coefficients towards zero without setting any exactly to zero.
# We sweep 23 log-spaced $\alpha$ values from $10^{-2}$ to $10^{9}$, covering the
# full transition from under-regularized (indistinguishable from OLS) through the
# optimal range to over-regularized (coefficients crushed to near zero).

# %% tags=[]
if NEED_TRAINING:
    RIDGE_ALPHAS = np.logspace(-2, 9, 23)
    ridge_all = {}

    for alpha in RIDGE_ALPHAS:
        res, coeffs, models = cross_validate(Ridge, {"alpha": alpha, "random_state": RANDOM_SEED})
        ridge_all[alpha] = {
            "results": pl.DataFrame(res),
            "coeffs": np.array(coeffs),
            "models": models,
        }

    ridge_summary = pl.DataFrame(
        [
            {"alpha": a, "mean_ic": d["results"]["ic"].mean(), "std_ic": d["results"]["ic"].std()}
            for a, d in ridge_all.items()
        ]
    ).sort("mean_ic", descending=True)

    best_ridge_alpha = ridge_summary.row(0, named=True)["alpha"]

# %% tags=[]
print(
    f"Best Ridge alpha: {best_ridge_alpha:.2f}  IC: {ridge_summary.filter(pl.col('alpha') == best_ridge_alpha)['mean_ic'].item():.4f}"
)
ridge_summary

# %% [markdown] tags=[]
# Read the sweep for its shape rather than its maximum. A flat stretch means the
# penalty is doing little; a rise means shrinkage is buying more in variance than
# it costs in bias; a fall at the far end means the coefficients have been
# crushed to the point where the model has stopped ranking. Where the turn comes
# is a property of how much signal the features carry, which is why a sweep this
# wide is worth running on a new dataset rather than reusing an alpha from an
# old one.

# %% [markdown] tags=[]
# ### Sample Weighting: Recency via Exponential Decay
#
# Section 11.2 discusses sample weighting as a way to emphasize recent data
# without discarding older observations. The weight decays exponentially with
# age, $w_t = e^{-\lambda(T - t)}$, so the newest observation counts fully and
# older ones fade smoothly rather than falling off a cliff at a cutoff date.
#
# Age has to be measured in trading sessions, not in rows. This is a panel: every
# session contributes one row per symbol, so about a hundred rows share each
# date. Counting age down the rows would decay the weight roughly a hundred times
# faster than intended, and would hand two symbols quoting on the same day
# different weights for no reason. Each row is therefore aged by the session it
# belongs to.
#
# The decay rate is set below as a half-life: the number of sessions after which
# an observation counts half as much as today's. The effective sample size turns
# that rate into the quantity that decides whether the trade is worth making,
# which is how many rows the fit is effectively left with. It is Kish's,
#
# $$N_{\text{eff}} = \frac{\bigl(\sum w_t\bigr)^2}{\sum w_t^2}$$
#
# rather than $\sum w_t$, because only this form is unchanged when every weight
# is multiplied by a constant. That matters here: the weights are rescaled to
# mean one before fitting, so $\sum w_t$ would answer for a vector the fit does
# not use and would report the full sample every time. We compare a single Ridge fit with and without weighting
# on the last fold.

# %% tags=[]
if NEED_TRAINING:
    # Last fold for demonstration
    tr_last, te_last = cv_splits[-1]
    X_tr_raw, X_te_raw = features_array[tr_last], features_array[te_last]
    X_tr_raw, X_te_raw = winsorize_train_test(X_tr_raw, X_te_raw)
    scaler_sw = StandardScaler()
    X_tr_sw = scaler_sw.fit_transform(X_tr_raw)
    X_te_sw = scaler_sw.transform(X_te_raw)
    y_tr_sw = target_array[tr_last]
    y_te_sw = target_array[te_last]

    # Exponential recency weights aged in sessions: w_t = exp(-lambda * (T - t)),
    # so every row quoting on a session shares that session's weight.
    HALF_LIFE_SESSIONS = 252  # one trading year: a row that old counts half
    n_train_sw = len(y_tr_sw)
    train_dates_sw = dates_np[tr_last]
    train_sessions_sw = np.unique(train_dates_sw)
    age_in_sessions = (len(train_sessions_sw) - 1) - np.searchsorted(
        train_sessions_sw, train_dates_sw
    )
    lam = np.log(2.0) / HALF_LIFE_SESSIONS
    weights = np.exp(-lam * age_in_sessions)
    # Kish, not weights.sum(): the rescaling below multiplies every weight by a
    # constant, which leaves this unchanged and would send a plain sum to n_train.
    n_eff_sw = float(weights.sum() ** 2 / (weights**2).sum())
    # Normalized to mean one before fitting. sklearn scales rows by sqrt(w) without
    # renormalizing, so weights averaging well below one would raise alpha's weight against
    # the data term and the IC gap below would measure that rather than the recency bet.
    weights = weights * (n_train_sw / weights.sum())

    dates_te_sw = dates_np[te_last]
    symbols_te_sw = symbols_np[te_last]

    # Unweighted Ridge
    ridge_uw = Ridge(alpha=best_ridge_alpha, random_state=RANDOM_SEED)
    ridge_uw.fit(X_tr_sw, y_tr_sw)
    ic_uw = cross_sectional_ic_mean(y_te_sw, ridge_uw.predict(X_te_sw), dates_te_sw, symbols_te_sw)

    # Weighted Ridge
    ridge_w = Ridge(alpha=best_ridge_alpha, random_state=RANDOM_SEED)
    ridge_w.fit(X_tr_sw, y_tr_sw, sample_weight=weights)
    ic_w = cross_sectional_ic_mean(y_te_sw, ridge_w.predict(X_te_sw), dates_te_sw, symbols_te_sw)

    # Store for cache
    sample_weight_results = {
        "n_train": n_train_sw,
        "n_sessions": len(train_sessions_sw),
        "half_life": HALF_LIFE_SESSIONS,
        "n_eff": n_eff_sw,
        "ic_uw": ic_uw,
        "ic_w": ic_w,
        # Stamps which formula produced n_eff. A cache written before the Kish change
        # carries every other key with a plain sum-of-weights value, so key presence alone
        # cannot tell the two apart and a stale cache would be read as this notebook's.
        "n_eff_kind": "kish",
    }

# %% tags=[]
if not NEED_TRAINING:
    sample_weight_results = joblib.load(RESULTS_PATH).get("sample_weight_results", {})
    # A cache written before n_sessions and half_life were recorded would KeyError
    # below. Treat it as absent so the reader gets the RETRAIN message instead.
    if (
        not sample_weight_results.keys()
        >= {"n_train", "n_sessions", "half_life", "n_eff", "ic_uw", "ic_w"}
        or sample_weight_results.get("n_eff_kind") != "kish"
    ):
        sample_weight_results = {}

if sample_weight_results:
    print(f"Training rows: {sample_weight_results['n_train']:,}")
    print(f"Training sessions: {sample_weight_results['n_sessions']:,}")
    print(f"Weight half-life: {sample_weight_results['half_life']} sessions")
    print(
        f"Effective sample size: {sample_weight_results['n_eff']:,.0f} rows "
        f"({sample_weight_results['n_eff'] / sample_weight_results['n_train']:.0%} of the fold)"
    )
    print(f"IC unweighted: {sample_weight_results['ic_uw']:.4f}")
    print(f"IC weighted:   {sample_weight_results['ic_w']:.4f}")
else:
    print("Sample weighting results not available - set RETRAIN=True to generate them.")

# %% [markdown] tags=[]
# Compare the two ICs printed above. Recency weighting is a bet that the recent
# past resembles the near future more than the distant past does, and the
# effective sample size says what the bet costs: the decay leaves the fit with
# the equivalent of a fraction of its training rows. Where the bet does not pay,
# the model has traded data for nothing. One fold decides none of this - the
# effect varies across folds and market regimes, and after a structural break
# recency weighting is what lets a model respond at all. What to take from the
# cell is the mechanics and the size of the sample-size cost, not the sign of
# the difference on one window. The `sample_weight` parameter is available on
# all sklearn estimators, including the logistic models in `03_logistic_classification`
# and gradient boosting in Ch12.
#
# > **Note on uniqueness weighting**: The text also discusses $\bar{H}$-bar
# > uniqueness weighting to correct for label overlap. Computing uniqueness weights
# > requires the concurrency structure from Chapter 7's triple-barrier labels, which
# > the ETF case study does not use (it uses simple forward returns). The per-case-study
# > runner notebooks (`06_linear.py`) apply uniqueness weighting where applicable.

# %% [markdown] tags=[]
# ## LASSO Regression (L1)
#
# LASSO drives some coefficients to exactly zero - automatic feature selection.
# To find a meaningful alpha grid, we first compute `alpha_max`, the smallest
# alpha that sets every coefficient to zero, from the first fold's training data.
# Above it there is nothing to see, because every model is the same empty model.
# The grid then runs log-spaced from there down to a small fraction of it, which
# is where the model is close to unpenalized.

# %% tags=[]
if NEED_TRAINING:
    # Compute alpha_max from first fold
    tr0 = cv_splits[0][0]
    scaler_tmp = StandardScaler()
    X0 = scaler_tmp.fit_transform(features_array[tr0])
    y0 = target_array[tr0]
    alpha_max = float(np.max(np.abs(X0.T @ y0)) / len(y0))
    LASSO_ALPHAS = np.logspace(np.log10(alpha_max), np.log10(0.01 * alpha_max), 10)

    lasso_all = {}
    for alpha in LASSO_ALPHAS:
        res, coeffs, models = cross_validate(
            Lasso, {"alpha": alpha, "random_state": RANDOM_SEED, "max_iter": 5000}
        )
        lasso_all[alpha] = {
            "results": pl.DataFrame(res),
            "coeffs": np.array(coeffs),
            "models": models,
        }

    lasso_summary = pl.DataFrame(
        [
            {
                "alpha": a,
                "mean_ic": d["results"]["ic"].mean(),
                "std_ic": d["results"]["ic"].std(),
                "n_nonzero": int((np.abs(d["coeffs"]).mean(axis=0) > 1e-8).sum())
                if d["coeffs"].size > 0
                else 0,
            }
            for a, d in lasso_all.items()
        ]
    ).sort("mean_ic", descending=True)

    # An alpha that zeroes every coefficient scores NaN: it has no ranking to select on.
    best_lasso_alpha = lasso_summary.filter(pl.col("mean_ic").is_not_nan()).row(0, named=True)[
        "alpha"
    ]

# %% tags=[]
print(
    f"Best LASSO alpha: {best_lasso_alpha:.6f}  IC: {lasso_summary.filter(pl.col('alpha') == best_lasso_alpha)['mean_ic'].item():.4f}"
)
lasso_summary

# %% [markdown] tags=[]
# LASSO achieves comparable IC to Ridge while zeroing out a substantial fraction of
# features. The `n_nonzero` column shows the sparsity-performance tradeoff: too few
# features (high $\alpha$) loses signal, while too many (low $\alpha$) approaches
# OLS overfitting. Which features survive varies across folds - LASSO's instability
# with correlated inputs, as discussed in the text.

# %% [markdown] tags=[]
# ### LASSO Feature Selection Stability
#
# Which features survive varies across folds - a binary heatmap reveals the
# instability. Columns that flicker on and off confirm LASSO's sensitivity
# to the training window when features are correlated.

# %% tags=[]
best_lasso_coeffs = lasso_all[best_lasso_alpha]["coeffs"]  # (n_folds, n_features)
nonzero_mask = (np.abs(best_lasso_coeffs) > 1e-8).astype(int)

# Show features that are nonzero in at least 2 folds (reduces clutter for print)
fold_count = nonzero_mask.sum(axis=0)
show_mask = fold_count >= 2
mask_subset = nonzero_mask[:, show_mask]
names_subset = [FEATURE_COLS[i] for i in range(len(FEATURE_COLS)) if show_mask[i]]

cmap_binary = ListedColormap(["white", COLORS["blue"]])  # white / dark navy

fig, ax = plt.subplots(figsize=(12, max(4, len(names_subset) * 0.35)))
ax.imshow(mask_subset.T, aspect="auto", cmap=cmap_binary, interpolation="nearest")
ax.set_xticks(range(mask_subset.shape[0]))
ax.set_xticklabels([f"Fold {i + 1}" for i in range(mask_subset.shape[0])])
ax.set_yticks(range(len(names_subset)))
ax.set_yticklabels(names_subset, fontsize=9)
ax.set_title("Features LASSO retains, by fold")

# Add grid lines between cells
for i in range(mask_subset.shape[1] + 1):
    ax.axhline(i - 0.5, color="lightgray", lw=0.5)
for j in range(mask_subset.shape[0] + 1):
    ax.axvline(j - 0.5, color="lightgray", lw=0.5)
show_with_alt(
    fig,
    "Grid with one row per feature and one column per fold, filled where LASSO "
    "gave that feature a non-zero coefficient on that fold.",
)

# %% [markdown] tags=[]
# Features present in all folds represent robust LASSO selections. Features that
# flicker on and off are unstable - their inclusion depends on which training
# window the model sees. This motivates Elastic Net, which retains correlated
# feature groups rather than picking one arbitrarily.

# %% [markdown] tags=[]
# ## Elastic Net (L1 + L2)
#
# Elastic Net blends the two penalties, and `l1_ratio` sets the mix: at one it is
# LASSO, at zero it is Ridge. Holding alpha at the value the LASSO sweep selected
# and varying only the ratio isolates what the mix decides, which is how much
# sparsity you get for a given total penalty.

# %% tags=[]
if NEED_TRAINING:
    EN_RATIOS = [0.25, 0.50, 0.75]
    en_all = {}

    for ratio in EN_RATIOS:
        key = f"l1={ratio:.2f}"
        res, coeffs, models = cross_validate(
            ElasticNet,
            {
                "alpha": best_lasso_alpha,
                "l1_ratio": ratio,
                "random_state": RANDOM_SEED,
                "max_iter": 5000,
            },
        )
        en_all[key] = {
            "results": pl.DataFrame(res),
            "coeffs": np.array(coeffs),
            "models": models,
            "alpha": best_lasso_alpha,
            "l1_ratio": ratio,
        }

# %% tags=[]
if NEED_TRAINING:
    en_summary = pl.DataFrame(
        [
            {
                "params": k,
                "mean_ic": d["results"]["ic"].mean(),
                "std_ic": d["results"]["ic"].std(),
                "n_nonzero": int((np.abs(d["coeffs"]).mean(axis=0) > 1e-8).sum())
                if d["coeffs"].size > 0
                else 0,
            }
            for k, d in en_all.items()
        ]
    ).sort("mean_ic", descending=True)

    # Filter NaN before selecting best EN key
    _en_valid = en_summary.filter(pl.col("mean_ic").is_not_nan())
    best_en_key = (
        _en_valid.row(0, named=True)["params"]
        if _en_valid.height > 0
        else en_summary.row(0, named=True)["params"]
    )

# %% tags=[]
print("Elastic Net Summary:")
en_summary

# %% [markdown] tags=[]
# Higher `l1_ratio` produces more sparsity, closer to LASSO; lower values retain
# more features, closer to Ridge. Holding alpha fixed is what makes the row-to-row
# difference readable, and it also means the mix has not been tuned against the
# alpha. Joint optimization of both $\alpha$ and
# `l1_ratio` is demonstrated in `04_nested_cv_hpo`.

# %% [markdown] tags=[]
# ## Loss Function Comparison
#
# The section text discusses MSE, MAE, and Huber loss. `SGDRegressor` provides a
# unified interface: all three accept an L2 penalty and `sample_weight`. Fitting
# them at one alpha, on one set of folds, isolates the loss function as the only
# thing that differs.

# %% tags=[]
if NEED_TRAINING:
    # SGDRegressor alpha maps differently: divide by n_samples for comparable regularization
    n_sgd = len(cv_splits[0][0])
    sgd_alpha = best_ridge_alpha / n_sgd

    loss_configs = {
        "MSE (squared)": {"loss": "squared_error", "penalty": "l2", "alpha": sgd_alpha},
        "Huber": {"loss": "huber", "penalty": "l2", "alpha": sgd_alpha},
        "MAE (ε-insensitive)": {"loss": "epsilon_insensitive", "penalty": "l2", "alpha": sgd_alpha},
    }
    loss_results = {}
    for name, params in loss_configs.items():
        res, _, _ = cross_validate(
            SGDRegressor, {**params, "max_iter": 5000, "random_state": RANDOM_SEED}
        )
        loss_results[name] = pl.DataFrame(res)

    loss_comparison = pl.DataFrame(
        [
            {"Loss": name, "Mean IC": df["ic"].mean(), "Std IC": df["ic"].std()}
            for name, df in loss_results.items()
        ]
    ).sort("Mean IC", descending=True)

# %% tags=[]
print("Loss Function Comparison (L2 penalty, same α):")
loss_comparison

# %% [markdown] tags=[]
# The three losses differ in how much weight they give a large residual. Squared
# error grows with the square of it, so one extreme return can move the fit more
# than a hundred ordinary ones. Huber is quadratic for small residuals and linear
# beyond a threshold, so an outlier still counts but stops dominating.
# The epsilon-insensitive loss ignores residuals smaller than epsilon entirely
# and is linear outside that band. Return distributions are heavy-tailed, which
# is the condition under which that difference shows up in a ranking metric, and
# it is why the case-study pipelines in Ch12 reach for LightGBM's Huber
# objective.
#
# Read the table for the ordering, not for the level. `SGDRegressor` optimizes
# by stochastic gradient descent under a learning-rate schedule, so its ICs are
# not comparable with the closed-form Ridge solution above even at the same
# alpha; only the comparison between the three losses, fitted the same way on
# the same folds, is meaningful here.

# %% [markdown] tags=[]
# ### LASSO Coefficient Path
#
# We use `sklearn.linear_model.lasso_path` on the first fold for an efficient
# warm-started coefficient path (much faster than refitting per alpha).

# %% tags=[]
if NEED_TRAINING:
    tr0 = cv_splits[0][0]
    scaler_path = StandardScaler()
    X_path = scaler_path.fit_transform(features_array[tr0])
    y_path = target_array[tr0]
    path_alphas, path_coefs, _ = lasso_path(X_path, y_path, n_alphas=80, random_state=RANDOM_SEED)

# %% [markdown] tags=[]
# ### Save Cache

# %% tags=[]
if NEED_TRAINING:
    MODELS_DIR.mkdir(parents=True, exist_ok=True)
    joblib.dump(
        {
            "ols_df": ols_df,
            "ols_coeffs": ols_coeffs,
            "ols_models": ols_models,
            "ridge_all": ridge_all,
            "ridge_summary": ridge_summary,
            "best_ridge_alpha": best_ridge_alpha,
            "RIDGE_ALPHAS": RIDGE_ALPHAS,
            "lasso_all": lasso_all,
            "lasso_summary": lasso_summary,
            "best_lasso_alpha": best_lasso_alpha,
            "alpha_max": alpha_max,
            "LASSO_ALPHAS": LASSO_ALPHAS,
            "en_all": en_all,
            "en_summary": en_summary,
            "best_en_key": best_en_key,
            "path_alphas": path_alphas,
            "path_coefs": path_coefs,
            "sample_weight_results": sample_weight_results,
            "loss_comparison": loss_comparison,
        },
        RESULTS_PATH,
    )
    print(f"Saved results to {RESULTS_PATH}")

# %% [markdown] tags=[]
# ## Model Comparison
#
# Each method is summarized by its mean IC across the walk-forward folds and by
# the standard deviation of that IC, which says how much the number depends on
# which fold you look at.
#
# One caveat governs the whole comparison, and it is the reason `04_nested_cv_hpo`
# exists. The alpha for each penalized model was chosen by taking the highest mean
# IC over these same validation folds, and the score reported here is that same
# highest mean IC. A number picked as the maximum of a sweep is biased upward by
# the picking: some of it is the penalty helping, and some of it is the luck of
# whichever alpha happened to suit these folds. Nothing here is held back to
# measure the difference, so read the chart as a comparison of methods under a
# tuning budget, not as an estimate of what any of them would score on data
# nobody selected on. Nested cross-validation, in `04_nested_cv_hpo`, is what
# separates the two.

# %% tags=[]
selected = {
    "OLS": ols_df,
    "Ridge": ridge_all[best_ridge_alpha]["results"],
    "LASSO": lasso_all[best_lasso_alpha]["results"],
    "Elastic Net": en_all[best_en_key]["results"],
}
model_ics = [frame["ic"].mean() for frame in selected.values()]
model_stds = [frame["ic"].std() for frame in selected.values()]

fig, ax = plt.subplots(figsize=(8, 4.5))
ax.bar(
    np.arange(len(selected)),
    model_ics,
    yerr=model_stds,
    capsize=4,
    color=[COLORS["slate"], COLORS["blue"], COLORS["amber"], COLORS["copper"]],
)
ax.axhline(0, color="gray", lw=0.8)
ax.set_xticks(np.arange(len(selected)))
ax.set_xticklabels(list(selected))
ax.set_ylabel("Mean IC across folds, ±1 standard deviation")
ax.set_title("Mean IC across folds by method, with one standard deviation")
show_with_alt(
    fig,
    "Bar chart of mean information coefficient for OLS, Ridge, LASSO and Elastic "
    "Net, each with an error bar one standard deviation wide.",
)
# %% [markdown] tags=[]
# The error bars are wider than the gaps between the bars, which is worth seeing and is less
# than it looks. Every method is scored on the same folds, so their scores rise and fall
# together with whatever each fold happens to contain - and a method can be consistently
# ahead of another while both have wide marginal spreads. Overlapping error bars across
# methods do not settle whether one is ahead of another.
#
# What settles it is the paired difference: subtract the two methods' IC fold by fold and
# look at the spread of that series, which cancels the fold effect these bars are dominated
# by. This chart shows the spread; it does not compare the methods.
# %% [markdown] tags=[]
# ### Prediction Rank Stability
#
# How much do the model's cross-sectional rankings change between consecutive
# folds? We apply both models (fold $t$ and fold $t+1$) to the same test set
# and compute their Spearman rank correlation. High correlation means stable
# rankings; low means the model learned fold-specific patterns.

# %% tags=[]
best_ridge_data = ridge_all[best_ridge_alpha]
rank_corrs = []

for i in range(len(cv_splits) - 1):
    _, te_next = cv_splits[i + 1]
    X_te = features_array[te_next]
    m_curr = best_ridge_data["models"][i]
    m_next = best_ridge_data["models"][i + 1]
    pred_curr = m_curr["model"].predict(m_curr["scaler"].transform(X_te))
    pred_next = m_next["model"].predict(m_next["scaler"].transform(X_te))
    rc, _ = spearmanr(pred_curr, pred_next)
    rank_corrs.append(rc)

rank_corr_df = pl.DataFrame(
    {
        "Fold pair": [f"{i + 1}→{i + 2}" for i in range(len(rank_corrs))],
        "Spearman ρ": [round(rc, 4) for rc in rank_corrs],
    }
)
print(f"Mean rank correlation: {np.mean(rank_corrs):.4f}")
rank_corr_df

# %% [markdown] tags=[]
# High rank correlation confirms that Ridge produces stable cross-sectional
# rankings across folds - the same features rank near the top across folds
# regardless of the training window. Lower values would indicate instability
# and high implied turnover.
#
# > **Turnover**: Rank stability is a proxy for signal turnover - the fraction
# > of the portfolio that changes between rebalancing dates. The per-case-study
# > runner notebooks (`06_linear.py`) compute explicit turnover alongside IC;
# > Chapter 17 develops the full turnover-adjusted evaluation framework.
# > `ml4t.diagnostic.signal.compute_turnover()` provides the production implementation.

# %% [markdown] tags=[]
# ## Regularization Paths
#
# ### What LASSO gives up as the penalty rises
#
# Two things move together as $\alpha$ increases: the model keeps fewer
# features, and its ranking accuracy changes. Plotting them on one axis shows
# where dropping features stops being free. The two vertical scales are
# different quantities, so each is labelled in the colour of its own series.

# %% tags=[]
alphas_sorted = sorted(ridge_all.keys())
alphas_l = sorted(lasso_all.keys(), reverse=True)
ics_lasso = [lasso_all[a]["results"]["ic"].mean() for a in alphas_l]
nz_lasso = [int((np.abs(lasso_all[a]["coeffs"]).mean(axis=0) > 1e-8).sum()) for a in alphas_l]

fig, ax = plt.subplots(figsize=(9, 5))
ax.semilogx(alphas_l, ics_lasso, "s-", color=COLORS["blue"], lw=2, label="Mean IC")
ax.set_xlabel("α (regularization strength)")
ax.set_ylabel("Mean IC across folds", color=COLORS["blue"])
ax.tick_params(axis="y", labelcolor=COLORS["blue"])

ax_nz = ax.twinx()
ax_nz.semilogx(alphas_l, nz_lasso, "^--", color=COLORS["amber"], lw=1.5, label="Non-zero features")
ax_nz.set_ylabel("Features with a non-zero coefficient", color=COLORS["amber"])
ax_nz.tick_params(axis="y", labelcolor=COLORS["amber"])

lines, labels = ax.get_legend_handles_labels()
lines_nz, labels_nz = ax_nz.get_legend_handles_labels()
ax.legend(lines + lines_nz, labels + labels_nz, loc="lower left", frameon=False)
ax.set_title("Mean IC and surviving feature count against LASSO penalty")
show_with_alt(
    fig,
    "Two series against regularization strength on a log axis: mean information "
    "coefficient on the left scale and the count of non-zero coefficients on the right.",
)
# %% [markdown] tags=[]
# The two series come apart, and where they do is the point. The feature count falls away
# over a range where mean validation IC does not, so across that range LASSO produces a
# sparser model at no observed cost in ranking accuracy.
#
# That is not the same as saying the dropped features carried nothing. Their coefficients
# were non-zero and did contribute; what happens on refitting is that correlated features
# take up the contribution. The chart shows the trade is available, not that it is free.
# %% [markdown] tags=[]
# ### LASSO Coefficient Path (Top 10 Features)
#
# The coefficient path from `lasso_path` shows how features enter the model
# as $\alpha$ decreases. We highlight only the 10 features with largest
# peak magnitude to keep the plot readable.

# %% tags=[]
peak_coef = np.max(np.abs(path_coefs), axis=1)
top10_idx = np.argsort(peak_coef)[-10:]
top10_names = [FEATURE_COLS[i] for i in top10_idx]

fig, ax = plt.subplots(figsize=(12, 6))

for i in top10_idx:
    ax.semilogx(path_alphas, path_coefs[i], lw=2, label=FEATURE_COLS[i])

for i in range(path_coefs.shape[0]):
    if i not in top10_idx:
        ax.semilogx(path_alphas, path_coefs[i], lw=0.5, color="lightgray", alpha=0.5)

ax.axhline(0, color="gray", lw=0.5)
ax.axvline(
    best_lasso_alpha, ls="--", color="red", alpha=0.5, label=f"Best α={best_lasso_alpha:.4f}"
)
ax.set_xlabel("α (LASSO regularization)")
ax.set_ylabel("Coefficient value (standardized)")
ax.set_title("LASSO coefficient paths against penalty")
ax.legend(bbox_to_anchor=(1.02, 1), loc="upper left", fontsize=9)
show_with_alt(
    fig,
    "Coefficient values against the LASSO penalty on a log axis. The ten largest paths "
    "are drawn in colour and the rest in grey, with a vertical marker at the selected "
    "alpha. The coloured paths reach zero at different penalties rather than together.",
)

# %% [markdown] tags=[]
# ### Ridge IC and ICIR vs Alpha
#
# The extended alpha grid reveals the full regularization landscape: a flat zone
# where Ridge matches OLS, an improving zone where shrinkage helps, and an
# over-regularized zone where coefficients are crushed too aggressively.

# %% tags=[]
alphas_sorted_full = sorted(ridge_all.keys())
ics_full = np.array([ridge_all[a]["results"]["ic"].mean() for a in alphas_sorted_full])
stds_full = np.array([ridge_all[a]["results"]["ic"].std() for a in alphas_sorted_full])
icirs_full = ics_full / np.where(stds_full > 0, stds_full, np.nan)

ols_ic = ols_df["ic"].mean()
ols_std = ols_df["ic"].std()
ols_icir = ols_ic / ols_std if ols_std > 0 else 0

# %% tags=[]
fig, ax1 = plt.subplots(figsize=(10, 5))

# IC with ±1σ band
ax1.semilogx(alphas_sorted_full, ics_full, "o-", color=COLORS["blue"], lw=2, ms=4, label="Mean IC")
ax1.fill_between(
    alphas_sorted_full,
    ics_full - stds_full,
    ics_full + stds_full,
    alpha=0.2,
    color=COLORS["blue"],
    label="±1σ",
)
ax1.axhline(ols_ic, ls="--", color="gray", lw=1, label=f"OLS IC ({ols_ic:.4f})")
ax1.set_xlabel("α (Ridge regularization strength)")
ax1.set_ylabel("Mean IC (8 folds)", color=COLORS["blue"])
ax1.tick_params(axis="y", labelcolor=COLORS["blue"])

# ICIR on secondary axis
ax2 = ax1.twinx()
ax2.semilogx(
    alphas_sorted_full, icirs_full, "s--", color=COLORS["amber"], lw=1.5, ms=4, label="ICIR"
)
ax2.axhline(
    ols_icir, ls=":", color=COLORS["amber"], lw=1, alpha=0.7, label=f"OLS ICIR ({ols_icir:.2f})"
)
ax2.set_ylabel("ICIR (IC / σ_IC)", color=COLORS["amber"])
ax2.tick_params(axis="y", labelcolor=COLORS["amber"])

# Combined legend
lines1, labels1 = ax1.get_legend_handles_labels()
lines2, labels2 = ax2.get_legend_handles_labels()
ax1.legend(lines1 + lines2, labels1 + labels2, loc="lower left", fontsize=9)

ax1.set_title("Mean IC and ICIR against Ridge penalty")
show_with_alt(
    fig,
    "Mean information coefficient with a one-standard-deviation band and the "
    "IC information ratio, both against Ridge regularization strength on a log axis.",
)

# %% [markdown] tags=[]
# ### Ridge IC/ICIR Statistics
#
# Key statistics from the regularization sweep.

# %% tags=[]
best_idx = np.argmax(icirs_full)
best_alpha = alphas_sorted_full[best_idx]
best_icir = icirs_full[best_idx]
best_ic = ics_full[best_idx]
best_std = stds_full[best_idx]
n_samples = sum(len(tr) + len(te) for tr, te in cv_splits)

print("=== Ridge IC/ICIR Statistics ===")
print(
    f"Alpha range: {alphas_sorted_full[0]:.0e} to {alphas_sorted_full[-1]:.0e} ({len(alphas_sorted_full)} values)"
)
print(f"Total sample size: {df.height:,}")
print(f"Features: {len(FEATURE_COLS)}")
print(f"OLS baseline: IC={ols_ic:.4f}, std={ols_std:.4f}, ICIR={ols_icir:.2f}")
print(f"Best ICIR: {best_icir:.2f} at α={best_alpha:.0e}")
print(f"  Mean IC: {best_ic:.4f}, Std IC: {best_std:.4f}")
print(f"  IC improvement: {(best_ic - ols_ic) / abs(ols_ic) * 100:.0f}%")
print(f"  Std reduction: {(1 - best_std / ols_std) * 100:.0f}%")
print(f"  ICIR improvement: {best_icir / ols_icir:.1f}x over OLS ({ols_icir:.2f})")

# %% [markdown] tags=[]
# ### Ridge Coefficient Path
#
# The same sweep read through the coefficients rather than the score. Every
# feature keeps a non-zero weight at every alpha, and the weights shrink towards
# zero together as the penalty rises. That is the difference from the LASSO path
# above, where features leave the model one at a time and do not come back.

# %% tags=[]
fig, ax = plt.subplots(figsize=(10, 6))
for j, feat in enumerate(FEATURE_COLS):
    coefs_by_alpha = [ridge_all[a]["coeffs"].mean(axis=0)[j] for a in alphas_sorted]
    if feat in top10_names:
        ax.semilogx(alphas_sorted, coefs_by_alpha, lw=2, label=feat)
    else:
        ax.semilogx(alphas_sorted, coefs_by_alpha, lw=0.4, color="lightgray", alpha=0.5)
ax.axhline(0, color="gray", lw=0.5)
ax.set_xlabel("α (Ridge regularization strength)")
ax.set_ylabel("Coefficient value (standardized feature)")
ax.set_title("Ridge coefficient paths against penalty")
ax.legend(fontsize=7, loc="lower left", ncol=2, frameon=False)
show_with_alt(
    fig,
    "Coefficient values against regularization strength on a log axis, one line "
    "per feature, all converging towards zero as the penalty rises.",
)
# %% [markdown] tags=[]
# Compare this against the LASSO paths above. Ridge pulls every coefficient toward zero and
# leaves all of them non-zero; LASSO sets them to exactly zero one at a time. That difference
# is the reason to reach for one or the other: LASSO answers which features to keep, Ridge
# answers how much to believe all of them.
# %% [markdown] tags=[]
# The two coefficient paths are the whole difference between the penalties in
# one picture. Ridge attenuates every weight and keeps every feature; LASSO
# removes features one at a time, and which ones it removes depends on the
# training window. Elastic Net sits between them: it can zero a coefficient, but
# its L2 component keeps correlated features together rather than picking one of
# the group arbitrarily.

# %% [markdown] tags=[]
# ## Key Takeaways
#
# 1. **Shrinkage buys variance reduction with bias.** Ridge pulls every
#    coefficient towards zero, which costs accuracy on the training fold and can
#    repay it on the next one. Where features are many, correlated, and weakly
#    related to the target, that trade is usually worth making, and the alpha
#    sweep is how you find out where it stops paying.
#
# 2. **LASSO performs feature selection**: at the selected alpha some
#    coefficients are exactly zero and those features leave the model. The
#    coefficient path shows the order in which the rest enter as the penalty
#    weakens, and the fold heatmap shows how much of that selection is repeated
#    when the training window changes.
#
# 3. **Elastic Net** blends both penalties. Higher L1 ratio produces more
#    sparsity; the choice depends on whether you value interpretability or
#    want to retain correlated features.
#
# 4. **Walk-forward CV with purging** is what makes any of these numbers
#    meaningful. Standard K-fold trains on data that follows its test window and
#    scores a model against outcomes it has already seen, and with a 21-day
#    forward label the fold boundary has to be widened by the label horizon
#    before the two sides are genuinely separate.
#
# **Next**: See `03_logistic_classification` for direction prediction, or
# `04_nested_cv_hpo` for hyperparameter optimization with Optuna.

```