मात्रात्मक रणनीतियों के लिए समय-सुरक्षित मूल्य और वॉल्यूम विशेषताएँ बनाना

# 01_price_volume_features.py


```py
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# %% [markdown]
# # Price and Volume Feature Families
#
# **Chapter 8: Feature Engineering**
# **Section Reference**: 8.2 - Price-Derived Features
# **Docker image**: `ml4t`
#
# ## Purpose
#
# This notebook demonstrates the **core feature families** derived from a single
# asset's price and volume history. These are the workhorse features of most
# quantitative strategies, available for every tradeable instrument.
#
# ## Learning Objectives
#
# 1. Understand the core feature families for price/volume data
# 2. Implement features with explicit time-safety (sorting, window alignment)
# 3. Choose appropriate normalization for cross-sectional vs time-series use
# 4. Compare volatility estimators (close-to-close, Parkinson, Garman-Klass, Yang-Zhang)
# 5. Build volatility state features (vol ratio, percentile, decile)
# 6. Avoid common leakage patterns in feature construction
#
# ## Feature Families Covered
#
# | Family | Representative Features | Key Use Case |
# |--------|------------------------|--------------|
# | **Returns & Horizons** | Simple, log, skip-1, cumulative | Base signals |
# | **Trend & Reversal** | MA distance, regression slope, dist-to-MA | Momentum/reversion |
# | **Volatility** | CC, Parkinson, GK, YZ, vol-of-vol | Risk scaling |
# | **Volatility State** | Vol ratio, percentile, decile | Regime conditioning |
# | **Volume & Liquidity** | Dollar volume, relative volume, VWAP | Capacity signals |
# | **Risk** | VaR, CVaR, tail ratio | Position sizing |
# | **Cross-Sectional** | Ranks, z-scores | Universe normalization |
#
# ## Data Policy
#
# All examples use **real ETF data** (no synthetic data).

# %%
"""Price and Volume Feature Families: the core feature families derived from a single asset's price and volume history."""

from __future__ import annotations

from datetime import datetime

import numpy as np
import plotly.graph_objects as go
import polars as pl
from plotly.subplots import make_subplots

from utils.paths import get_chapter_dir
from utils.style import (  # importing utils.style sets the ml4t Plotly template as default
    COLORS,
    show_plotly_with_alt,
    show_with_alt,
)

# %% tags=["parameters"]
SEED = 42
START_DATE = "2015-01-01"
# The cut the volume figure draws and the spike diagnostic applies, in z-score units.
VOLUME_SPIKE_CUT = 2.0

# %% [markdown]
# ## Feature Discovery with ml4t-engineer
#
# Before building features manually, let's see what the `ml4t-engineer` library
# offers. The registry provides 120 pre-built, validated features that the
# case study notebooks use throughout Chapters 8-12.
#
# For a full tour of the library ecosystem (data loaders, feature computation,
# evaluation tools), see `10_ml4t_library_ecosystem` in Chapter 7.

# %%
from ml4t.engineer import compute_features
from ml4t.engineer.core.registry import get_registry

registry = get_registry()
all_features = registry.list_all()

# Features by category
categories = {}
for name in all_features:
    metadata = registry.get(name)
    categories.setdefault(metadata.category, []).append(name)

print(f"Total features available: {len(all_features)}\n")
print("Features by category:")
for cat, feats in sorted(categories.items()):
    examples = ", ".join(feats[:4])
    suffix = ", ..." if len(feats) > 4 else ""
    print(f"  {cat:20s}: {len(feats):3d}  ({examples}{suffix})")

# %% [markdown]
# ### Feature metadata
#
# Each registry entry carries self-documenting metadata: formula, parameters,
# input type, and description. This makes features discoverable without reading
# source code.

# %%
rsi_meta = registry.get("rsi")
print(f"Feature:     {rsi_meta.name}")
print(f"Category:    {rsi_meta.category}")
print(f"Formula:     {rsi_meta.formula}")
print(f"Parameters:  {rsi_meta.parameters}")
print(f"Input type:  {rsi_meta.input_type}")

# %% [markdown]
# ### Quick computation
#
# `compute_features()` accepts a list of feature names (default parameters)
# or dicts (custom parameters). We'll use it throughout the case study
# notebooks; the sections below show the manual implementations for teaching.

# %%
from data import load_etfs

spy_quick = load_etfs().filter(pl.col("symbol") == "SPY").sort("timestamp").tail(500)

result = compute_features(spy_quick, ["rsi", "atr", "sma"])
new_cols = [c for c in result.columns if c not in spy_quick.columns]
print(f"Computed {len(new_cols)} feature columns: {new_cols}")
result.select(["timestamp", "close"] + new_cols).tail(5)

# %% [markdown]
# The notebooks that follow build these features manually to explain the
# economics and implementation details, then use the registry for production
# pipelines.

# %% [markdown]
# ## Data Loading and Sorting
#
# **Critical**: All rolling/window operations require chronological ordering.
# We establish sorting once at data load, not implicitly per operation.

# %%
# Load and sort by symbol, then timestamp (CRITICAL for .over() operations)
etfs = load_etfs().sort(["symbol", "timestamp"])

# Filter date range
etfs = etfs.filter(pl.col("timestamp") >= datetime.strptime(START_DATE, "%Y-%m-%d"))

# For single-asset demos: SPY
spy = etfs.filter(pl.col("symbol") == "SPY").sort("timestamp")

# For cross-sectional demos: subset of liquid ETFs
cs_symbols = ["SPY", "QQQ", "IWM", "TLT", "GLD", "XLF", "XLE", "XLK"]
cs_etfs = etfs.filter(pl.col("symbol").is_in(cs_symbols)).sort(["symbol", "timestamp"])

print(f"SPY: {len(spy):,} rows")
print(f"Cross-sectional universe: {len(cs_etfs):,} rows, {cs_etfs['symbol'].n_unique()} symbols")
print(f"Date range: {spy['timestamp'].min()} to {spy['timestamp'].max()}")

# %% [markdown]
# ## Returns and Horizons
#
# Returns are the foundation of all momentum features. Key variants:
#
# | Variant | Formula | Use Case |
# |---------|---------|----------|
# | Simple return | $(P_t - P_{t-h}) / P_{t-h}$ | Standard momentum |
# | Log return | $\ln(P_t / P_{t-h})$ | Additive across time |
# | Skip-1 momentum | $r_{t-1:t-h}$ | Avoid microstructure noise |
# | Cumulative | $\sum_{i=0}^{h} r_{t-i}$ | Multi-period signals |

# %% [markdown]
# ### Manual Implementation (Teaching)
#
# Understanding the mechanics of return computation.


# %%
def compute_returns_manual(df: pl.DataFrame, horizons: list[int]) -> pl.DataFrame:
    """
    Compute returns for multiple horizons using pure Polars.

    Note: This is for teaching. Use ml4t-engineer in production.
    """
    return_exprs = []

    for h in horizons:
        # Simple returns
        return_exprs.append(pl.col("close").pct_change(h).alias(f"ret_{h}d"))
        # Log returns
        return_exprs.append(
            (pl.col("close").log() - pl.col("close").log().shift(h)).alias(f"logret_{h}d")
        )

    return df.with_columns(return_exprs)


# Single with_columns for efficiency
returns_df = compute_returns_manual(spy, horizons=[1, 5, 21])

print("Return features computed:")
returns_df.select(["timestamp", "close", "ret_1d", "ret_5d", "ret_21d"]).tail(10)

# %% [markdown]
# ### Skip-1 Momentum
#
# Skip the most recent day to avoid microstructure reversals (bid-ask bounce).
#
# $$\text{Skip-1 Momentum}_{21d} = \frac{P_{t-1}}{P_{t-21}} - 1$$

# %%
# Skip-1 momentum: shift(1) before computing the horizon return
skip1_df = spy.with_columns(
    [
        # Standard 21-day momentum
        pl.col("close").pct_change(21).alias("mom_21d"),
        # Skip-1: use yesterday's close as numerator
        ((pl.col("close").shift(1) / pl.col("close").shift(21)) - 1).alias("mom_21d_skip1"),
    ]
)

# Show correlation - should be high but not identical
correlation = skip1_df.drop_nulls().select(
    [pl.corr("mom_21d", "mom_21d_skip1").alias("correlation")]
)
print(f"Correlation between standard and skip-1 momentum: {correlation[0, 0]:.4f}")

# %% [markdown]
# **Interpretation**: the correlation printed above is close enough to one that the two
# series carry very similar information at this horizon. The skip-1 variant removes
# the last day's microstructure noise (bid-ask bounce), so it is preferred for
# daily-rebalanced strategies where the most recent close is noisiest.

# %% [markdown]
# ### Session-Based Returns
#
# Decomposing returns into overnight (gap) and intraday components:
#
# - **Overnight return**: $\frac{Open_t}{Close_{t-1}} - 1$
# - **Intraday return**: $\frac{Close_t}{Open_t} - 1$

# %%
session_df = spy.with_columns(
    [
        # Overnight (gap)
        ((pl.col("open") / pl.col("close").shift(1)) - 1).alias("overnight_ret"),
        # Intraday
        ((pl.col("close") / pl.col("open")) - 1).alias("intraday_ret"),
        # Total for reference
        pl.col("close").pct_change().alias("total_ret"),
    ]
)

# Verify decomposition: (1 + overnight) * (1 + intraday) ≈ (1 + total)
session_df = session_df.with_columns(
    ((1 + pl.col("overnight_ret")) * (1 + pl.col("intraday_ret")) - 1).alias("reconstructed")
)

print("Session return decomposition:")
session_df.select(
    ["timestamp", "overnight_ret", "intraday_ret", "total_ret", "reconstructed"]
).tail(10)

# %% [markdown]
# ## Trend and Reversal Features
#
# These features capture where price is relative to historical patterns.
#
# | Feature | Signal Type | Interpretation |
# |---------|-------------|----------------|
# | MA Distance | Trend | >0 bullish, <0 bearish |
# | Regression Slope | Trend strength | Steeper = stronger trend |
# | Short-term Reversal | Mean reversion | Oversold/overbought |

# %% [markdown]
# ### MA Distance (Volatility-Scaled)
#
# Raw MA distance varies with price level. Scaling by volatility creates
# a standardized signal comparable across assets and time.
#
# $$\text{MA Distance} = \frac{P_t - SMA_{21}}{ATR_{21}}$$

# %%
from ml4t.engineer.features.volatility import atr

# MA distance scaled by ATR
ma_df = spy.with_columns(
    [
        pl.col("close").rolling_mean(21).alias("sma_21"),
        atr("high", "low", "close", period=21).alias("atr_21"),
    ]
).with_columns(
    [
        # Raw distance (not comparable across assets)
        (pl.col("close") - pl.col("sma_21")).alias("ma_dist_raw"),
        # Vol-scaled (standardized)
        ((pl.col("close") - pl.col("sma_21")) / pl.col("atr_21")).alias("ma_dist_scaled"),
    ]
)

# %%
# Visualize MA distance
fig = make_subplots(
    rows=3,
    cols=1,
    shared_xaxes=True,
    subplot_titles=["SPY Close with SMA(21)", "Raw MA Distance", "Vol-Scaled MA Distance"],
    vertical_spacing=0.08,
)

n = 252  # Last year
fig.add_trace(
    go.Scatter(x=ma_df["timestamp"].to_list()[-n:], y=ma_df["close"].to_list()[-n:], name="Close"),
    row=1,
    col=1,
)
fig.add_trace(
    go.Scatter(
        x=ma_df["timestamp"].to_list()[-n:],
        y=ma_df["sma_21"].to_list()[-n:],
        name="SMA(21)",
        line=dict(dash="dash"),
    ),
    row=1,
    col=1,
)
fig.add_trace(
    go.Scatter(
        x=ma_df["timestamp"].to_list()[-n:],
        y=ma_df["ma_dist_raw"].to_list()[-n:],
        name="Raw",
        fill="tozeroy",
    ),
    row=2,
    col=1,
)
fig.add_trace(
    go.Scatter(
        x=ma_df["timestamp"].to_list()[-n:],
        y=ma_df["ma_dist_scaled"].to_list()[-n:],
        name="Vol-Scaled",
        fill="tozeroy",
    ),
    row=3,
    col=1,
)
fig.add_hline(y=0, line_dash="dash", line_color=COLORS["neutral"], row=2, col=1)
fig.add_hline(y=0, line_dash="dash", line_color=COLORS["neutral"], row=3, col=1)

fig.update_yaxes(title_text="Price ($)", row=1, col=1)
fig.update_yaxes(title_text="Distance ($)", row=2, col=1)
fig.update_yaxes(title_text="Distance (ATR units)", row=3, col=1)
fig.update_xaxes(title_text="Date", row=3, col=1)
fig.update_layout(height=600, title="SPY close, raw MA distance, and MA distance in ATR units")
show_plotly_with_alt(
    fig,
    (
        "Three stacked panels sharing a date axis across 2025. The top panel plots the SPY "
        "close against a dashed simple moving average: a dip in March and April, then a "
        "climb to the end of the year. The middle panel fills the raw distance between the "
        "two in dollars around a dashed zero line, at its deepest below zero at the April "
        "low and at its highest in May. The bottom panel fills the same distance divided "
        "by ATR, in a teal that keeps the middle panel's sign and crosses zero on the same "
        "days but is scaled in ATR units rather than dollars, and whose peaks are at "
        "different relative heights: its deepest trough is the April low as in the middle "
        "panel, while its highest point is in September rather than May."
    ),
)

# %%
# What the ATR divisor does to the curve, rather than what it is assumed to do.
_win = ma_df.tail(n)
_atr, _raw, _scaled = _win["atr_21"], _win["ma_dist_raw"], _win["ma_dist_scaled"]
print(
    f"ATR(21) over the window: {_atr.min():.2f} to {_atr.max():.2f}, a {_atr.max() / _atr.min():.1f}x spread"
)
print(f"days the two panels agree on sign: {(_raw.sign() == _scaled.sign()).mean():.3f}")
print(f"largest positive distance in dollars:   {_win['timestamp'][_raw.arg_max()]}")
print(f"largest positive distance in ATR units: {_win['timestamp'][_scaled.arg_max()]}")

# %% [markdown]
# The bottom panel keeps the middle panel's sign on every day of the window and crosses zero
# on the same dates, but it is not the same curve redrawn. ATR itself moves over the window by
# the spread printed above, so each day is divided by a different number and the relative
# heights change: the largest positive distance in dollars and the largest in ATR units fall
# in different months, because a smaller dollar move during a quiet stretch is the larger one
# once measured against that stretch's typical range.
#
# That is what the scaling is for. A dollar distance cannot be compared across assets or
# across regimes - it is larger for a more expensive instrument and larger again for a more
# volatile one - while a distance in ATR units says how far the price has moved relative to
# how far it usually moves, which means the same thing on any instrument.


# %% [markdown]
# ### Rolling Regression Slope
#
# OLS slope over a rolling window measures trend strength more robustly than
# endpoint-to-endpoint returns: it uses all intermediate prices and is less
# sensitive to start/end outliers.
#
# For evenly spaced data with $x = [0, 1, \ldots, n-1]$:
#
# $$\hat\beta = \frac{\text{Cov}(x, P)}{\text{Var}(x)}$$
#
# We normalize by mean price to get a percentage slope comparable across assets.


# %%
def rolling_regression_slope(
    df: pl.DataFrame,
    price_col: str = "close",
    period: int = 21,
) -> pl.DataFrame:
    """Rolling OLS slope, normalized by mean price."""
    return df.with_columns(
        pl.col(price_col)
        .rolling_map(
            lambda s: np.polyfit(np.arange(len(s)), s.to_numpy(), 1)[0] / s.mean() * len(s),
            window_size=period,
            min_samples=period,
        )
        .alias(f"slope_{period}d")
    )


slope_df = rolling_regression_slope(spy, period=21)
print("Rolling regression slope (normalized %-change over 21d):")
slope_df.select(["timestamp", "close", "slope_21d"]).tail(10)

# %% [markdown]
# ### Short-Term Reversal
#
# Over a horizon of a few days, the assets that fell most recently tend to rise, and the
# reverse. This is the complement to momentum.
#
# $$\text{Reversal} = -r_{1d}$$

# %%
reversal_df = spy.with_columns(
    [
        pl.col("close").pct_change(1).alias("ret_1d"),
        (-pl.col("close").pct_change(1)).alias("reversal_1d"),
        # Z-scored reversal for comparability
        (
            (-pl.col("close").pct_change(1) - (-pl.col("close").pct_change(1)).rolling_mean(63))
            / (-pl.col("close").pct_change(1)).rolling_std(63)
        ).alias("reversal_zscore"),
    ]
)

print("Short-term reversal features:")
reversal_df.select(["timestamp", "close", "ret_1d", "reversal_1d"]).tail(10)

# %% [markdown]
# ### Directional Persistence: Distance to MA in ATR Units
#
# This extends the vol-scaled MA distance from **MA Distance (Volatility-Scaled)** above,
# with different parameters and a different reading. There a fast moving average over a
# matching ATR window measures *current* deviation; here a slower anchor over a shorter
# ATR window captures *directional persistence* - how far price has trended away from
# that anchor.
#
# $$\text{dist-to-ma-atr} = \frac{P_t - MA_{50}}{ATR_{14}}$$
#
# ATR is used here for normalization, in price units, rather than as a volatility
# estimator. **Volatility Features** below draws that distinction.

# %%
dist_ma_df = spy.with_columns(
    [
        pl.col("close").rolling_mean(50).alias("ma_50"),
        atr("high", "low", "close", period=14).alias("atr_14"),
    ]
).with_columns(
    ((pl.col("close") - pl.col("ma_50")) / pl.col("atr_14").clip(1e-10, None)).alias(
        "dist_to_ma_atr"
    ),
)

print("Distance to MA in ATR units:")
dist_ma_df.select(["timestamp", "close", "ma_50", "atr_14", "dist_to_ma_atr"]).tail(10)

# %% [markdown]
# Values beyond $\pm 3$ indicate extreme directional extension. This feature
# works well as both a signal (contrarian at extremes) and a state variable
# (conditioning faster signals on trend context).

# %% [markdown]
# ## Volatility Features
#
# Volatility is essential for:
# - **Risk scaling**: Adjust signals by volatility
# - **Position sizing**: Kelly criterion, risk parity
# - **Regime detection**: High vs low vol environments
#
# ### Estimator Efficiency Comparison
#
# | Estimator | Efficiency | Best For |
# |-----------|------------|----------|
# | Close-to-Close | 1× | Baseline |
# | Parkinson (H-L) | ~5× | High-low only |
# | Garman-Klass | ~7× | Full OHLC |
# | Yang-Zhang | ~8–14× | Best overall |

# %% [markdown]
# ### Realized Volatility (Close-to-Close)
#
# $$\sigma_{CC} = \sqrt{252} \times \text{std}(r_t)$$

# %%
from ml4t.engineer.features.volatility import realized_volatility, yang_zhang_volatility

vol_df = spy.with_columns(
    pl.col("close").pct_change().alias("ret"),
).with_columns(
    [
        # Close-to-close (annualized); realized_volatility expects returns, not prices
        realized_volatility("ret", period=21, annualize=True).alias("vol_cc_21"),
        # Yang-Zhang (most efficient)
        yang_zhang_volatility("open", "high", "low", "close", period=21, annualize=True).alias(
            "vol_yz_21"
        ),
    ]
)

print("Volatility comparison:")
vol_df.select(["timestamp", "close", "vol_cc_21", "vol_yz_21"]).tail(10)

# %% [markdown]
# ### Range-Based Estimators
#
# Range-based estimators use OHLC data for much higher efficiency than
# close-to-close. Key formulas:
#
# **Parkinson (1980)** uses the high-low range only:
#
# $$\hat{\sigma}^2_P = \frac{1}{4 \ln 2} (\ln H_t - \ln L_t)^2$$
#
# **Garman-Klass (1980)** adds open-close information:
#
# $$\hat{\sigma}^2_{GK} = \tfrac{1}{2} (\ln H_t - \ln L_t)^2 - (2\ln 2 - 1)(\ln C_t - \ln O_t)^2$$
#
# **Note**: ATR (Average True Range) measures price *range* in dollar terms,
# not *volatility* in return terms. ATR is useful for stop placement and
# normalization but is not a volatility estimator.

# %% [markdown]
# The four estimators and what each buys. The efficiency column holds the published
# asymptotic variance ratio against the close-to-close estimator, derived in each paper
# under driftless geometric Brownian motion with no overnight gap. Nothing in this
# notebook measures these; they are the reason to reach for a range estimator, and the
# figures below are where you see what the assumption costs on real data. The legend of
# the overlaid figure reads these same values, so the table and the chart cannot disagree.

# %%
# col, label, published efficiency vs close-to-close, prices required
ESTIMATORS = [
    ("vol_cc", "Close-to-Close", 1.0, "close"),
    ("vol_parkinson", "Parkinson", 5.2, "high, low"),
    ("vol_gk", "Garman-Klass", 7.4, "open, high, low, close"),
    ("vol_yz", "Yang-Zhang", 8.4, "open, high, low, close"),
]

print(f"{'Estimator':<16}{'Efficiency vs CC':>18}   Prices required")
for _col, _label, _eff, _inputs in ESTIMATORS:
    print(f"{_label:<16}{_eff:>17.1f}x   {_inputs}")


# %%
# Manual implementations for teaching
def parkinson_vol(period: int = 21) -> pl.Expr:
    """Parkinson range-based volatility (annualized)."""
    log_hl_sq = ((pl.col("high").log() - pl.col("low").log()) ** 2) / (4 * np.log(2))
    return (log_hl_sq.rolling_mean(period) * 252).sqrt()


# %%
def garman_klass_vol(period: int = 21) -> pl.Expr:
    """Garman-Klass OHLC volatility (annualized)."""
    log_hl_sq = 0.5 * (pl.col("high").log() - pl.col("low").log()) ** 2
    log_co_sq = (2 * np.log(2) - 1) * (pl.col("close").log() - pl.col("open").log()) ** 2
    return ((log_hl_sq - log_co_sq).rolling_mean(period) * 252).sqrt()


# %%
# Compare all four estimators
vol_compare_df = spy.with_columns(
    pl.col("close").pct_change().alias("ret"),
).with_columns(
    [
        # Close-to-close; realized_volatility expects returns, not prices
        realized_volatility("ret", period=21, annualize=True).alias("vol_cc"),
        # Parkinson (manual, works on OHLC directly)
        parkinson_vol(period=21).alias("vol_parkinson"),
        # Garman-Klass (manual, works on OHLC directly)
        garman_klass_vol(period=21).alias("vol_gk"),
        # Yang-Zhang (library, works on OHLC directly, most efficient)
        yang_zhang_volatility("open", "high", "low", "close", period=21, annualize=True).alias(
            "vol_yz"
        ),
    ]
)

# %% [markdown]
# The four panels below share one vertical range. Plotly's `shared_yaxes` ties panels
# within a row and not across rows, so without an explicit range the top and bottom rows
# rescale independently and the estimator with the smallest spread is drawn as tall as the
# one with the largest - which is the comparison this figure exists to make.

# %%
n = 504  # Last ~2 years for visualization
fig = make_subplots(
    rows=2,
    cols=2,
    shared_xaxes=True,
    shared_yaxes=True,
    subplot_titles=["Close-to-Close", "Parkinson (H-L)", "Garman-Klass (OHLC)", "Yang-Zhang"],
    vertical_spacing=0.10,
    horizontal_spacing=0.06,
)

for idx, (col, name) in enumerate(
    [("vol_cc", "CC"), ("vol_parkinson", "Park"), ("vol_gk", "GK"), ("vol_yz", "YZ")]
):
    row, c = idx // 2 + 1, idx % 2 + 1
    fig.add_trace(
        go.Scatter(
            x=vol_compare_df["timestamp"].to_list()[-n:],
            y=vol_compare_df[col].to_list()[-n:],
            name=name,
        ),
        row=row,
        col=c,
    )

fig.update_yaxes(title_text="Annualized volatility", col=1)
fig.update_xaxes(title_text="Date", row=2)
_vol_max = max(
    float(max(v for v in vol_compare_df[col].to_list()[-n:] if v is not None))
    for col in ("vol_cc", "vol_parkinson", "vol_gk", "vol_yz")
)
fig.update_yaxes(range=[0, _vol_max * 1.05])
fig.update_layout(
    height=500,
    title="Four volatility estimators on SPY, on one scale",
)
show_plotly_with_alt(
    fig,
    (
        "Four small-multiple panels of annualized SPY volatility over the same two years, "
        "one per estimator, drawn on a single shared vertical range from zero. Each panel "
        "shows the same two events, a bump in mid-2024 and a tall spike in the spring of "
        "2025, against a quiet level for the rest of the span; the mid-2024 bump is "
        "clearest in Yang-Zhang and faintest in Parkinson. The close-to-close panel "
        "reaches the highest peak of the four and is visibly the noisiest line. Parkinson "
        "and Garman-Klass peak lower and run smoother. Yang-Zhang peaks between them and "
        "the close-to-close panel, with far less day-to-day jitter than close-to-close."
    ),
)

# %% [markdown]
# **Interpretation**: on one scale the ordering is legible, and it is not a single
# ordering. Close-to-close reaches the highest peak and is the most jagged of the four,
# but it is not the highest line most of the time: the counts printed below say which
# estimator sits on top day by day, and Yang-Zhang holds that position on the majority of
# days in this window. Parkinson and Garman-Klass are never the highest and are usually
# the lowest.
#
# The smoothness has one cause and the level has another, and it is worth keeping them
# apart. Range estimators are smoother because a day's high and low carry more
# information about that day's variation than its two endpoints do, which is what the
# efficiency column claims. They read lower for a different reason, developed against
# the next figure.
#
# This panel covers only the last two years. The overlaid figure below runs the full
# history, where a much larger volatility event separates the estimators far more than
# this window can.

# %%
# Which estimator is on top, and which at the bottom, day by day.
_cols = [c for c, _l, _e, _i in ESTIMATORS]
_w = vol_compare_df.drop_nulls(subset=_cols).tail(n)
_m = _w.select(_cols).to_numpy()
_top, _bot = _m.argmax(axis=1), _m.argmin(axis=1)
print(f"over the {len(_w)} days plotted above:")
for _j, (_col, _label, _eff, _inputs) in enumerate(ESTIMATORS):
    print(
        f"  {_label:<16} highest on {(_top == _j).mean():>6.1%} of days, "
        f"lowest on {(_bot == _j).mean():>6.1%}, peak {_m[:, _j].max():.3f}"
    )

# %% [markdown]
# ### Four OHLC Estimators Overlaid
#
# Overlaying all four estimators on a single panel makes their level and timing
# differences directly comparable. Line styles vary alongside color so the series
# stay distinct in grayscale.

# %%
import matplotlib.pyplot as plt

# Prepare data: the full history, so the window includes a volatility spike
vol_plot = vol_compare_df.drop_nulls(subset=["vol_cc", "vol_parkinson", "vol_gk", "vol_yz"])

fig_mpl, ax = plt.subplots(figsize=(12, 5))

# SPY close price on secondary axis (context for vol spikes)
ax2 = ax.twinx()
dates = vol_plot["timestamp"].to_list()
ax2.fill_between(dates, vol_plot["close"].to_list(), alpha=0.08, color=COLORS["neutral"])
ax2.set_ylabel("SPY Close ($)", color=COLORS["neutral"])
ax2.tick_params(axis="y", labelcolor=COLORS["neutral"])

# Vol estimators on primary axis (convert to percentage for readability).
# Line styles vary alongside color so the four series stay distinct in grayscale print.
_STYLES = {
    "vol_cc": ("-", COLORS["blue"]),
    "vol_parkinson": ("--", COLORS["amber"]),
    "vol_gk": (":", COLORS["copper"]),
    "vol_yz": ("-.", COLORS["slate"]),
}

for col, label, eff, _inputs in ESTIMATORS:
    ls, color = _STYLES[col]
    vals = [v * 100 for v in vol_plot[col].to_list()]
    ax.plot(dates, vals, ls=ls, color=color, label=f"{label} (eff. {eff:.1f}\u00d7)", linewidth=1.2)

ax.set_ylabel("Annualized Volatility (%)")
ax.set_title("Rolling Volatility Estimates: Four OHLC Estimators on SPY")
ax.legend(frameon=False, fontsize=8, loc="upper left")
ax.set_zorder(ax2.get_zorder() + 1)
ax.patch.set_visible(False)

show_with_alt(
    fig_mpl,
    (
        "A decade of annualized rolling volatility for SPY with four estimators overlaid "
        "in different line styles, the legend naming each with its efficiency relative to "
        "close-to-close. The four lines rise and fall on the same events and stay close "
        "together at quiet levels, but they separate at the peaks: the solid "
        "close-to-close line and the dash-dot Yang-Zhang line run together at the top, "
        "swapping which is higher, while the dashed Parkinson and dotted Garman-Klass "
        "lines reach only about two thirds as high. Close-to-close is the most jagged of "
        "the four. One spike in the spring of 2020 towers over the rest of the decade, "
        "reaching the top of the axis; smaller peaks stand out in 2015, 2018, 2022 and "
        "2025, and it is at these peaks that the range estimators fall furthest behind. "
        "Behind the lines the SPY close is shaded in pale grey against a second axis on "
        "the right, climbing steadily across the span."
    ),
)

# Persist source data so the book figure script can re-render at print resolution
# without re-executing this notebook.
_FIG_8_3_ARTIFACT = (
    get_chapter_dir(8)
    / "output"
    / "book_figure_artifacts"
    / "figure_8_3_ohlc_volatility_estimators.parquet"
)
_FIG_8_3_ARTIFACT.parent.mkdir(parents=True, exist_ok=True)
vol_plot.select(
    ["timestamp", "close", "vol_cc", "vol_parkinson", "vol_gk", "vol_yz"]
).write_parquet(_FIG_8_3_ARTIFACT)

# %% [markdown]
# Why the range estimators read low. Parkinson and Garman-Klass read the high, the low,
# the open and the close of one session, so every price they touch is inside trading
# hours: the jump from the previous close to this open is not in their inputs. They are
# therefore estimating the volatility of the within-session return, while close-to-close
# estimates the volatility of the whole daily return. The reference row below is that
# within-session return volatility, computed directly and divided by the same denominator
# as the estimators, so the comparison is against a measured quantity rather than an
# assumption.

# %%
_ref = vol_compare_df.with_columns(
    ((pl.col("close").log() - pl.col("open").log()).rolling_std(21) * np.sqrt(252)).alias(
        "within_session"
    )
).drop_nulls([c for c, _l, _e, _i in ESTIMATORS] + ["within_session"])

print(f"median ratio to the close-to-close estimator, over {len(_ref)} days:")
for _col, _label, _eff, _inputs in ESTIMATORS:
    print(f"  {_label:<32} {(_ref[_col] / _ref['vol_cc']).median():.3f}")
print(
    f"  {'within-session return volatility':<32} "
    f"{(_ref['within_session'] / _ref['vol_cc']).median():.3f}"
)

print()
_peak = vol_plot.filter(pl.col("vol_cc") == pl.col("vol_cc").max())
print(f"the four estimators at the close-to-close peak, {_peak['timestamp'][0]}:")
for _col, _label, _eff, _inputs in ESTIMATORS:
    print(f"  {_label:<16} {_peak[_col][0] * 100:>5.1f}%")

# %% [markdown]
# Parkinson and Garman-Klass land on the within-session reference, not below
# close-to-close by some amount of noise. That is the whole of the level difference: they
# answer a different question, and they answer it accurately. Yang-Zhang sits at
# close-to-close instead, because it carries an explicit overnight term alongside its range
# terms, which is why it stays with close-to-close through the peak while the other two
# fall away.
#
# The separation is therefore not a question of efficiency, and the distinction is worth
# holding on to: efficiency says how much data an estimator needs to reach a given
# precision, and it says nothing about what the estimator is precise about. A more
# efficient estimator of the wrong quantity is still an estimator of the wrong quantity.
#
# Which one is right follows from the return being modelled. Reach for Parkinson or
# Garman-Klass when the question is about the session itself, such as intraday range or
# execution risk inside the day. When the position is held overnight and the daily
# close-to-close return is what is being scaled or sized against, their level is the wrong
# denominator by the margin in the table above, and the choice is close-to-close for
# simplicity or Yang-Zhang for the same quantity with less noise.

# %% [markdown]
# ### Volatility-of-Volatility (Vol-of-Vol)
#
# Second moment of volatility, which detects unstable regimes.

# %%
from ml4t.engineer.features.volatility import volatility_of_volatility

vov_df = spy.with_columns(
    [
        yang_zhang_volatility("open", "high", "low", "close", period=21, annualize=True).alias(
            "vol"
        ),
        volatility_of_volatility("close", vol_period=21, vov_period=21).alias("vol_of_vol"),
    ]
)

print("Vol-of-vol (last 10 rows):")
vov_df.select(["timestamp", "vol", "vol_of_vol"]).tail(10)

# %% [markdown]
# ### Volatility State Features
#
# Volatility state features transform continuous vol into conditioning variables:
#
# - **Vol ratio** (short/long): Detects expansion/contraction
# - **Vol percentile**: Where current vol sits in its 252-day history
# - **Vol decile**: Binned version for evaluation slicing

# %%
SHORT_VOL_WINDOW = 10
LONG_VOL_WINDOW = 63
PERCENTILE_LOOKBACK = 252

vol_state_df = (
    spy.with_columns(
        pl.col("close").log().diff().alias("log_return"),
    )
    .with_columns(
        [
            (pl.col("log_return").rolling_std(SHORT_VOL_WINDOW) * np.sqrt(252)).alias("vol_short"),
            (pl.col("log_return").rolling_std(LONG_VOL_WINDOW) * np.sqrt(252)).alias("vol_long"),
        ]
    )
    .with_columns(
        # Vol ratio: short over long; above one is expansion, below one contraction
        (pl.col("vol_short") / pl.col("vol_long").clip(1e-10, None)).alias("vol_ratio"),
    )
)

# Vol percentile: rolling rank over 252 days
vol_state_df = vol_state_df.with_columns(
    pl.col("vol_short")
    .rolling_map(
        lambda s: 100 * (s.rank().last() - 1) / max(len(s) - 1, 1),
        window_size=PERCENTILE_LOOKBACK,
        min_samples=PERCENTILE_LOOKBACK // 2,
    )
    .alias("vol_percentile")
)

# Vol decile: clipped 0-9
vol_state_df = vol_state_df.with_columns(
    (pl.col("vol_percentile").fill_nan(None).fill_null(-10.0) / 10)
    .floor()
    .clip(0, 9)
    .cast(pl.Int32)
    .alias("vol_decile")
)

print("Volatility state features:")
vol_state_df.select(["timestamp", "vol_ratio", "vol_percentile", "vol_decile"]).tail(10)

# %% [markdown]
# ### Price-Derived Regime Indicators
#
# `ml4t-engineer` provides rolling statistical tests that detect market regime
# changes without relying on parametric models (HMM, GARCH are in Chapter 9):
#
# | Indicator | Test/Signal | Interpretation |
# |-----------|-------------|----------------|
# | **Variance Ratio** | Lo-MacKinlay RW test | >1 trending, <1 mean-reverting |
# | **Fractal Efficiency** | Mandelbrot efficiency | 1=trending, 0=noise |
# | **Trend Intensity** | ADX-like directional | High=strong trend |

# %%
from ml4t.engineer.features.regime import fractal_efficiency, trend_intensity_index, variance_ratio

vr_exprs = variance_ratio("close", periods=[5], window=21)
regime_ind_df = spy.with_columns(
    [
        vr_exprs["vr_5"].alias("variance_ratio_21d"),
        fractal_efficiency("close", period=21).alias("fractal_eff_21d"),
        trend_intensity_index("close", period=21).alias("trend_intensity_21d"),
    ]
)

print("Regime indicators:")
regime_ind_df.select(
    ["timestamp", "variance_ratio_21d", "fractal_eff_21d", "trend_intensity_21d"]
).tail(10)

# %% [markdown]
# **Variance Ratio**: Values above 1 indicate positive autocorrelation (trending);
# below 1 indicates mean-reversion. This is the Lo-MacKinlay (1988) test as a
# rolling feature.
#
# **Fractal Efficiency**: Measures path efficiency. One means price moves in a
# straight line (trend), zero means random wandering.
#
# **Note**: These are *rolling-window* regime indicators derived purely from price.
# Parametric regime models (HMM, Markov-switching GARCH) appear in Chapter 9.

# %% [markdown]
# ## Volume and Liquidity Features
#
# Volume features proxy for:
# - **Attention**: High volume = information event
# - **Liquidity**: Capacity to trade
# - **Conviction**: Volume confirms price moves

# %% [markdown]
# ### Dollar Volume
#
# Raw volume is not comparable across assets. Dollar volume normalizes.
#
# $$\text{Dollar Volume} = \text{Volume} \times \text{Close}$$

# %%
volume_df = spy.with_columns(
    [
        (pl.col("volume") * pl.col("close")).alias("dollar_volume"),
        # Log transform for better distribution
        (pl.col("volume") * pl.col("close")).log().alias("log_dollar_volume"),
    ]
)

print("Dollar volume:")
volume_df.select(["timestamp", "close", "volume", "dollar_volume"]).tail(10)

# %% [markdown]
# ### Relative Volume
#
# Volume relative to its recent average. >1 means above-average activity.
#
# $$\text{Relative Volume} = \frac{V_t}{\text{SMA}_{21}(V)}$$

# %%
rel_vol_df = spy.with_columns(
    [
        (pl.col("volume") / pl.col("volume").rolling_mean(21)).alias("rel_volume"),
        # Volume shock: z-score of log volume
        (
            (pl.col("volume").log() - pl.col("volume").log().rolling_mean(21))
            / pl.col("volume").log().rolling_std(21)
        ).alias("volume_zscore"),
    ]
)

# %%
# Visualize relative volume features
fig = make_subplots(
    rows=3,
    cols=1,
    shared_xaxes=True,
    subplot_titles=["SPY Close", "Relative Volume", "Volume Z-Score"],
    vertical_spacing=0.08,
)

fig.add_trace(
    go.Scatter(
        x=rel_vol_df["timestamp"].to_list()[-n:], y=rel_vol_df["close"].to_list()[-n:], name="Close"
    ),
    row=1,
    col=1,
)
fig.add_trace(
    go.Scatter(
        x=rel_vol_df["timestamp"].to_list()[-n:],
        y=rel_vol_df["rel_volume"].to_list()[-n:],
        name="Rel Vol",
        fill="tozeroy",
    ),
    row=2,
    col=1,
)
fig.add_hline(y=1, line_dash="dash", line_color=COLORS["neutral"], row=2, col=1)
fig.add_trace(
    go.Scatter(
        x=rel_vol_df["timestamp"].to_list()[-n:],
        y=rel_vol_df["volume_zscore"].to_list()[-n:],
        name="Vol Z-Score",
    ),
    row=3,
    col=1,
)
fig.add_hline(y=0, line_dash="dash", line_color=COLORS["neutral"], row=3, col=1)
fig.add_hline(y=VOLUME_SPIKE_CUT, line_dash="dash", line_color=COLORS["negative"], row=3, col=1)
fig.add_hline(y=-VOLUME_SPIKE_CUT, line_dash="dash", line_color=COLORS["negative"], row=3, col=1)

fig.update_yaxes(title_text="Price ($)", row=1, col=1)
fig.update_yaxes(title_text="Rel. volume (×avg)", row=2, col=1)
fig.update_yaxes(title_text="Volume z-score", row=3, col=1)
fig.update_xaxes(title_text="Date", row=3, col=1)
fig.update_layout(height=600, title="SPY close, relative volume, and the volume z-score")
show_plotly_with_alt(
    fig,
    (
        "Three stacked panels sharing a date axis across 2024 and 2025. The top panel is "
        "the SPY close, rising over the span with a sharp dip in the spring of 2025. The "
        "middle panel fills relative volume as a multiple of its own average, oscillating "
        "around a dashed line at one with spikes reaching about three. The bottom panel "
        "plots the volume z-score with dashed red lines at plus and minus two: the series "
        "crosses the upper line perhaps a dozen times across the two years and touches the "
        "lower line rarely, so high-activity days stand out while quiet days do not."
    ),
)

# %% [markdown]
# The two lower panels are different transformations, so the same numeric cut does not select
# the same days. Measure that disagreement rather than assume it away.

# %%
_v = rel_vol_df.tail(n).drop_nulls(["rel_volume", "volume_zscore"])
_z_hit = _v["volume_zscore"] > VOLUME_SPIKE_CUT
_r_hit = _v["rel_volume"] > VOLUME_SPIKE_CUT
print(f"z-score above the cut:        {_z_hit.sum()} days")
print(f"relative volume above the cut: {_r_hit.sum()} days")
print(f"flagged by both:               {(_z_hit & _r_hit).sum()} days")
_near = _v.filter((pl.col("volume_zscore") - VOLUME_SPIKE_CUT).abs() < 0.1)
if len(_near):
    print(
        f"relative volume on days whose z-score sits at the cut: "
        f"{_near['rel_volume'].min():.2f} to {_near['rel_volume'].max():.2f}"
    )
else:
    # A high enough cut leaves no day near it, and an empty column's min and max are
    # None rather than an error, so the format string is where it would have failed.
    print(f"no day in this window has a z-score within 0.1 of {VOLUME_SPIKE_CUT}")

# %% [markdown]
# The two lower panels are not one quantity in two units. Relative volume divides raw volume
# by its own rolling mean; the z-score standardizes the **logarithm** of volume, which pulls
# in the long right tail that volume always has. The counts printed above are what that costs:
# the same numeric cut applied to each selects overlapping but different sets of days, and the
# relative-volume level corresponding to a z-score at the cut is a range rather than a number.
#
# The z-score is the one worth writing a rule against. A cut expressed in standard deviations
# of log volume means the same thing on an instrument whose volume is more dispersed, while a
# ratio threshold does not.

# %% [markdown]
# ### VWAP Distance
#
# How far price has moved from the volume-weighted average price.
# Useful for intraday strategies and execution.
#
# $$\text{VWAP Distance} = \frac{P_t - VWAP_t}{VWAP_t}$$

# %%
# Daily VWAP using typical price as proxy
vwap_df = (
    spy.with_columns(
        [
            # Typical price as VWAP proxy for daily data
            ((pl.col("high") + pl.col("low") + pl.col("close")) / 3).alias("typical_price"),
        ]
    )
    .with_columns(
        [
            # Rolling VWAP (volume-weighted rolling mean)
            (
                (pl.col("typical_price") * pl.col("volume")).rolling_sum(5)
                / pl.col("volume").rolling_sum(5)
            ).alias("vwap_5d"),
        ]
    )
    .with_columns(
        [
            ((pl.col("close") / pl.col("vwap_5d")) - 1).alias("vwap_distance"),
        ]
    )
)

print("VWAP distance:")
vwap_df.select(["timestamp", "close", "vwap_5d", "vwap_distance"]).tail(10)

# %% [markdown]
# **Interpretation**: Positive VWAP distance means the close is above the
# volume-weighted average, so buying pressure exceeded selling pressure over the
# lookback. Negative indicates the opposite. For intraday strategies this is a
# key mean-reversion anchor; for daily data it proxies volume-weighted trend.

# %% [markdown]
# ## Cross-Sectional Normalization
#
# For multi-asset strategies, raw features are not comparable. Normalization
# creates standardized signals across the universe.
#
# **Warning**: Cross-sectional operations can introduce leakage if not careful
# about timing. Always use point-in-time data.

# %% [markdown]
# ### Cross-Sectional Ranks
#
# Rank-based features are robust to outliers.
#
# $$\text{Rank}_{t,i} = \frac{\text{rank}(f_{t,i})}{\text{N}_t}$$

# %%
# Add momentum to cross-sectional data
cs_mom = cs_etfs.with_columns(
    [
        pl.col("close").pct_change(21).over("symbol").alias("mom_21d"),
    ]
)

# Cross-sectional rank within each day
cs_ranked = cs_mom.with_columns(
    [
        # Rank: 0 = lowest momentum, 1 = highest
        (
            pl.col("mom_21d").rank().over("timestamp") / pl.col("symbol").count().over("timestamp")
        ).alias("mom_rank"),
    ]
)

# Show one day
sample_date = cs_ranked["timestamp"].max()
print(f"Cross-sectional ranks for {sample_date}:")
(
    cs_ranked.filter(pl.col("timestamp") == sample_date)
    .select(["symbol", "mom_21d", "mom_rank"])
    .sort("mom_rank", descending=True)
)

# %% [markdown]
# ### Vol-Scaled Cross-Sectional Momentum
#
# The text's spec-table formula: cumulative return divided by realized volatility,
# then cross-sectional percentile rank. Vol-scaling penalizes momentum driven by
# high volatility: two assets that rose by the same amount are not equally interesting if
# one of them did it with several times the dispersion of the other.
#
# $$\text{Vol-Scaled Mom} = \frac{r_{21d}}{\sigma_{21d}}$$

# %%
# Vol-scaled momentum: return / realized vol
cs_vol_scaled = cs_etfs.with_columns(
    [
        pl.col("close").pct_change(21).over("symbol").alias("mom_21d"),
        (
            pl.col("close").pct_change().over("symbol").rolling_std(21).over("symbol")
            * np.sqrt(252)
        ).alias("vol_21d"),
    ]
).with_columns(
    (pl.col("mom_21d") / pl.col("vol_21d").clip(1e-10, None)).alias("vol_scaled_mom"),
)

# Cross-sectional percentile rank
cs_vol_ranked = cs_vol_scaled.with_columns(
    (
        pl.col("vol_scaled_mom").rank().over("timestamp")
        / pl.col("symbol").count().over("timestamp")
    ).alias("vol_scaled_rank"),
)

# Compare raw vs vol-scaled rankings
print(f"Vol-scaled cross-sectional momentum ({sample_date}):")
(
    cs_vol_ranked.filter(pl.col("timestamp") == sample_date)
    .select(["symbol", "mom_21d", "vol_21d", "vol_scaled_mom", "vol_scaled_rank"])
    .sort("vol_scaled_rank", descending=True)
)

# %% [markdown]
# **Interpretation**: Vol-scaling reshuffles the ranking. High-momentum assets
# with elevated volatility drop, while steady trending assets rise. This is the
# same intuition as the Sharpe ratio applied cross-sectionally.

# %% [markdown]
# ### Cross-Sectional Z-Scores
#
# Z-score normalization assumes (roughly) normal distribution.
#
# $$z_{t,i} = \frac{f_{t,i} - \mu_t}{\sigma_t}$$
#
# **Robustness**: Use median/MAD instead of mean/std for robustness to outliers.

# %%
# Z-score within each day
cs_zscored = cs_mom.with_columns(
    [
        # Standard z-score
        (
            (pl.col("mom_21d") - pl.col("mom_21d").mean().over("timestamp"))
            / pl.col("mom_21d").std().over("timestamp")
        ).alias("mom_zscore"),
        # Robust z-score (using median and MAD)
        # MAD × 1.4826 ≈ σ for normal data (1.4826 = 1/Φ⁻¹(3/4))
        (
            (pl.col("mom_21d") - pl.col("mom_21d").median().over("timestamp"))
            / (
                (pl.col("mom_21d") - pl.col("mom_21d").median().over("timestamp"))
                .abs()
                .median()
                .over("timestamp")
                * 1.4826
            )
        ).alias("mom_zscore_robust"),
    ]
)

# Show one day
print(f"Cross-sectional z-scores for {sample_date}:")
(
    cs_zscored.filter(pl.col("timestamp") == sample_date)
    .select(["symbol", "mom_21d", "mom_zscore", "mom_zscore_robust"])
    .sort("mom_zscore", descending=True)
)

# %% [markdown]
# ### Leakage Warning: Point-in-Time Discipline
#
# **Common Leakage Patterns**:
#
# 1. **Using future data**: Z-score computed over future values
# 2. **Survivorship bias**: Only including current constituents
# 3. **Look-ahead in ranks**: Ranking before data was available
#
# **Safe Pattern**: Always use `.over("timestamp")` for cross-sectional
# operations; that guarantees each date's statistics use only
# contemporaneous data:
#
# ```python
# # WRONG: full-sample z-score includes future data
# pl.col("mom").zscore()
#
# # CORRECT: point-in-time cross-sectional z-score
# (pl.col("mom") - pl.col("mom").mean().over("timestamp"))
# / pl.col("mom").std().over("timestamp")
# ```

# %% [markdown]
# ## Risk Features
#
# Risk features capture the *shape* of the return distribution beyond simple volatility.
# Tail risk measures like VaR, CVaR, and tail ratio are essential for:
# - **Position sizing**: Scale down exposure when tail risk is elevated
# - **Feature conditioning**: Momentum works differently in fat-tail vs thin-tail regimes
# - **Risk-adjusted signals**: Sharpe ratios penalize return/vol; CVaR penalizes tail events

# %%
from ml4t.engineer.features.risk import (
    conditional_value_at_risk,
    downside_deviation,
    tail_ratio,
    value_at_risk,
)

risk_df = spy.with_columns(
    [
        pl.col("close").pct_change().alias("ret"),
    ]
).with_columns(
    [
        value_at_risk("ret", confidence_level=0.95, window=63).alias("var_5pct_63d"),
        conditional_value_at_risk("ret", confidence_level=0.95, window=63).alias("cvar_5pct_63d"),
        downside_deviation("ret", window=63).alias("downside_dev_63d"),
        tail_ratio("ret", confidence_level=0.95, window=63).alias("tail_ratio_63d"),
    ]
)

print("Risk features (last 10 rows):")
risk_df.select(
    ["timestamp", "var_5pct_63d", "cvar_5pct_63d", "downside_dev_63d", "tail_ratio_63d"]
).tail(10)

# %% [markdown]
# | Risk Feature | Interpretation | Trading Use |
# |-------------|----------------|-------------|
# | **VaR** | Loss threshold that the worst tail of days exceeds, at the configured level | Position sizing threshold |
# | **CVaR** | Expected loss beyond VaR | Tail risk penalty |
# | **Downside Deviation** | Volatility of negative returns only | Sortino ratio denominator |
# | **Tail Ratio** | Right tail / left tail size | Asymmetry of return distribution |

# %% [markdown]
# ## ML-Specific Transforms
#
# ML-specific transforms prepare features for tree and linear models:
# - **Fractional differencing**: Makes features stationary while preserving memory
# - **Volatility-adjusted returns**: Standardizes by recent volatility

# %%
from ml4t.engineer.features.fdiff import ffdiff

# Fractional differencing preserves memory while achieving stationarity
# d=0.5 is a common starting point; use find_optimal_d() for data-driven choice
ffd_df = spy.with_columns(
    [
        ffdiff("close", d=0.5).alias("close_ffd_05"),
        ffdiff("close", d=1.0).alias("close_ffd_10"),  # Equivalent to first difference
    ]
)

print("Fractional differencing (d=0.5 vs d=1.0):")
ffd_df.select(["timestamp", "close", "close_ffd_05", "close_ffd_10"]).tail(10)

# %% [markdown]
# | Transform | Differencing order | Stationarity | Memory | Use case |
# |-----------|--------------------|--------------|--------|----------|
# | Original | none | Non-stationary | Full | Not for ML |
# | Fractional | between none and full | Near-stationary | Preserved | Tree models, regressions |
# | First difference | full | Stationary | Lost | Benchmark comparison |
#
# A fractional order around the middle of that range is a good default for financial time
# series - the two orders computed above bracket it. It achieves the stationarity most ML
# models require while retaining the long-range dependence a fully differenced series
# throws away.

# %% [markdown]
# **Caveat**: with finite truncation, a full differencing order *approximates* but does
# not exactly equal first differencing. The truncated weight series drops small
# high-lag coefficients that a true first difference implicitly includes. For
# practical purposes the difference is negligible, but be aware when comparing
# FFD output to `pct_change()`.

# %% [markdown]
# **ml4t-engineer Production API**: For production feature computation using
# config-driven batch processing and library RSI/MACD/Bollinger implementations,
# see `10_ml4t_library_ecosystem` (Chapter 7).

# %% [markdown]
# ## Summary
#
# ### Feature Family Decision Guide
#
# | Family | When to Use | Key Considerations |
# |--------|-------------|-------------------|
# | **Returns** | Base signals, momentum | Skip-1 for short horizons |
# | **Trend** | Trend-following | Vol-scale for comparability |
# | **Volatility** | Risk scaling, sizing | Yang-Zhang most efficient |
# | **Vol State** | Regime conditioning | Percentile > decile for granularity |
# | **Regime Indicators** | Trend detection | Rolling-window, model-free |
# | **Volume** | Liquidity, conviction | Use relative, not raw |
# | **Risk** | Position sizing, tail risk | VaR, CVaR, downside dev |
# | **Cross-sectional** | Multi-asset | Point-in-time discipline |
# | **ML transforms** | Stationarity | Fractional differencing |
#
# ### Implementation Rules
#
# 1. **Sort once at load**: `df.sort(["symbol", "timestamp"])`
# 2. **Single with_columns**: All transforms in one call for parallelism
# 3. **Vol-scale for comparability**: Raw features vary with price level
# 4. **Library for production**: ml4t-engineer for validated implementations
# 5. **Point-in-time for cross-sectional**: Use `.over("timestamp")`
#
# ### Next Notebooks
#
# - `02_microstructure_features`: trade-based features (§8.2)
# - `03_structural_cross_instrument_features`: carry, cross-asset, options (§8.3)
# - `04_fundamentals_macro_calendar`: fundamentals, macro, calendar (§8.4)

```