Evaluación del universo ETF, los costes de negociación y la viabilidad del backtest

# 01_feasibility_analysis.py


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# %% [markdown]
# # ETFs: Feasibility Analysis
#
# Before building a trading strategy it is worth asking whether the data can support one at all.
# This notebook does that and nothing else: it fits no model and makes no forecast.
#
# The strategy being checked is described in `config/setup.yaml`. It trades exchange-traded funds,
# ranks them once a month, and holds the ones that rank highest. That file says which funds it
# trades, which of them count as liquid enough to hold, how often it changes positions, what it
# assumes a trade costs, and how the history is divided between fitting a model and testing it.
# This notebook checks each of those assumptions against the data and reports what it finds.
#
# ## Learning objectives
#
# By the end of this notebook you will be able to:
#
# - Decide which funds a strategy was allowed to hold on a given date using only information that
#   existed before that date, and count how many that leaves on each date it changes positions
# - Turn a commission quoted in cents per share into a cost that can be compared across funds
#   trading at very different prices
# - Read off one chart what fraction of price moves are larger than the cost of trading them
# - Measure how much of one month's return carries into the next month, computing the correlation
#   inside each fund rather than across a hundred funds stacked into one series
# - Check that a walk-forward split of the history fits the sample available and leaves the test
#   period unread
#
# ## Book reference
#
# Chapter 6, Sections 6.2-6.6. This notebook reads daily fund prices and `config/setup.yaml`, and
# writes the eligibility table that the next two notebooks filter on.
#
# ## Prerequisites
#
# None beyond what the sections below define. A reader who has not traded funds or split a sample
# for walk-forward evaluation will find both explained where they are first used.

# %%
"""ETF Case Study - Feasibility Analysis."""

import re

import matplotlib.pyplot as plt
import numpy as np
import polars as pl
import yaml
from IPython.display import display

from case_studies.utils.feasibility import exceedance_curve, fold_timeline, panel_acf
from case_studies.utils.warning_policy import apply_notebook_warning_policy
from data import load_etfs, load_etfs_unadjusted
from utils.cv_splits import generate_cv_splits
from utils.paths import get_case_study_dir
from utils.style import COLORS, FIGSIZE, add_message_title, show_with_alt

apply_notebook_warning_policy()

# %% tags=["parameters"]
CASE_STUDY_ID = "etfs"
START_DATE = "2006-01-01"
END_DATE = "2025-12-31"
ADV_THRESHOLD = 10e6
MIN_SESSIONS_PER_YEAR = 200

# %% [markdown]
# ## Configuration
#
# Everything the strategy assumes is declared in `config/setup.yaml`, and this notebook reads those
# values rather than repeating them, so the two can never disagree. Four groups of settings matter
# here, and each one decides something the sections below test.
#
# **How the history is divided.** The sample runs from 2006 to the end of 2025. The last two years
# are the *holdout*: a stretch of history that is not looked at while the strategy is being
# designed, so that when it is finally evaluated there, the result is not a rehearsal of choices
# already tuned on the same data. Everything computed in this notebook uses the earlier part,
# called the development period. `holdout_start` is where the line falls.
#
# **What the strategy trades.** `setup.yaml` names 100 funds. Not all of them are tradable at all
# times: a fund is admitted to a year only if it traded enough in the year before, which Section
# B.2 explains and applies. The strategy holds the leaders only, up to 20 of them, so at least 20
# funds have to be admitted on any date it rebalances. That floor comes from the grid of portfolio
# sizes the strategy will later search over, not from a separate assumption.
#
# **What a trade is assumed to cost.** Two charges, both quoted in dollars per share: a broker
# commission, and half the gap between the price a buyer will pay and the price a seller will
# accept. Section B.3 turns them into a cost that is comparable across funds.
#
# **What is being predicted.** The strategy forecasts returns 21 sessions ahead, roughly one month,
# and a second variant looks 5 sessions ahead. The 21-session horizon is the primary one, and it
# sets both the rebalancing frequency and the gap that has to separate training from validation
# data.
#
# One value the strategy depends on is not machine-readable in `setup.yaml`: the dollar-volume
# floor a fund has to clear appears only inside `universe.eligibility_note`, as prose. It is
# therefore set in the parameters cell above, and Section B.2 says what it does.

# %%
CASE_DIR = get_case_study_dir(CASE_STUDY_ID)
SETUP = yaml.safe_load((CASE_DIR / "config" / "setup.yaml").read_text())

HOLDOUT_START = str(SETUP["evaluation"]["holdout_start"])
HOLDOUT_END = str(SETUP["evaluation"]["holdout_end"])
PRIMARY_LABEL = SETUP["labels"]["primary"]
LABEL_BUFFER = SETUP["labels"]["buffer"]
DECLARED_ASSETS = set(SETUP["universe"]["assets"])
BREADTH_FLOOR = max(SETUP["backtest"]["sweep"]["top_k_grid"][PRIMARY_LABEL])
PER_SHARE = SETUP["costs"]["per_share"]
HALF_SPREADS = SETUP["costs"]["asset_spreads"]
DEFAULT_HALF_SPREAD = SETUP["costs"]["default_half_spread_usd"]
LABELS = [PRIMARY_LABEL, *SETUP["labels"]["variants"]]
HORIZONS = sorted(int(re.search(r"(\d+)d$", n).group(1)) for n in LABELS)
PRIMARY_HORIZON = int(re.search(r"(\d+)d$", PRIMARY_LABEL).group(1))

print(f"Sample: {START_DATE} to {END_DATE}")
print(f"  Development period, used everywhere below:  {START_DATE} to {HOLDOUT_START}")
print(f"  Holdout, not read by this notebook:         {HOLDOUT_START} to {HOLDOUT_END}")
print(f"Universe: {len(DECLARED_ASSETS)} funds declared")
print(
    f"  Up to {BREADTH_FLOOR} held at once, so at least {BREADTH_FLOOR} must be admitted on a "
    f"rebalancing date to fill the book"
)
print(
    f"  Admitted to a year after averaging ${ADV_THRESHOLD / 1e6:.0f}M of daily turnover in the "
    f"year before"
)
print(
    f"Assumed cost: ${PER_SHARE} per share in commission, plus a half-spread of "
    f"${min(HALF_SPREADS.values())} to ${DEFAULT_HALF_SPREAD} per share by liquidity tier"
)
print(
    f"Forecast horizons: {' and '.join(f'{h} sessions' for h in HORIZONS)} ahead; "
    f"{PRIMARY_HORIZON} sessions is the primary horizon and sets the rebalancing frequency"
)

# %% [markdown]
# ## A. Orientation
#
# ### What an exchange-traded fund is
#
# An exchange-traded fund holds a portfolio - the shares in an index, a basket of bonds, a stock of
# gold in a vault - and issues shares in itself that trade on an exchange like any other stock. A
# buyer of one share owns a slice of whatever the fund holds, and can buy or sell it during the
# trading day at whatever price the exchange is quoting, rather than subscribing or redeeming with
# the fund manager. That is what makes a portfolio like this one practical: exposure to Japanese
# equities, to investment-grade credit and to gold are the same kind of instrument, bought the same
# way, in the same account, with the same order type.
#
# The hundred funds this strategy trades cover most of what an investor can hold that way: broad
# equity indices, the sectors within them, individual countries, government and corporate bonds,
# currencies, commodities, and rules-based selections such as high-dividend or low-volatility
# baskets.
#
# ### Why ranking funds is a strategy at all
#
# The strategy does not take a view on any one market. Once a month it sorts the funds it is
# allowed to hold by some measure of their recent behaviour, buys the ones at the top, and sells
# whatever has dropped out. What it is betting on is that the ordering carries: that a fund near the
# top this month is more likely than not to be above average next month. Whether that is true is a
# question for Chapter 7 onwards. What this notebook asks is whether the data could support the
# attempt.
#
# A strategy of that shape needs breadth more than it needs depth. Choosing 20 funds out of 5 is not
# a choice, so it matters more that many funds are quoting at once than that any one of them is
# quoting well.
#
# ### The three questions this notebook asks
#
# 1. **Does the universe exist when the strategy trades?** Positions change once a month, so enough
#    funds have to be tradable on each of those dates to fill the book.
# 2. **Is a typical price move worth more than it costs to capture?** Every round trip pays a
#    commission and crosses the gap between the buying and the selling price, twice, and those
#    charges are a different fraction of the price for a $20 fund than for a $500 one.
# 3. **Is there enough history to evaluate this honestly?** Enough to split into training and
#    validation periods several times over, with the holdout left untouched.

# %% [markdown]
# ## B. Universe and cost feasibility
#
# ### B.1 Load the data and look at the universe
#
# The loader returns one row per fund and session. Its `close` is *adjusted*: when a fund splits its
# shares or pays a distribution, the whole earlier history is rescaled so that the change does not
# appear as a price move. Differences of an adjusted series are therefore returns rather than
# corporate-action artefacts, which is why it is the series used for measuring moves.
#
# A dollar amount cannot use it. The rescaling divides out of a ratio and does not divide out of a
# price, so an early adjusted close is not the price anyone paid. A second series is therefore
# loaded alongside, carrying the traded price and the shares that traded. **Returns come from the
# adjusted series and dollars from the traded one**, and every quantity below says which it used.
#
# The two agree at the end of the sample and diverge going back, for two separate reasons that the
# first session below shows together. Every distribution a fund has paid since lowers its adjusted
# price, which is why the adjusted close is the lower of the two for most funds. Every split
# rescales it as well, and that one runs either way: a fund that later split four for one has an
# adjusted 2006 close near a quarter of what it traded at, and a fund that later did a reverse
# split has one several times above it.
#
# Three properties are checked before anything is computed: nothing outside the declared list of
# funds, no close at or below zero, since every ratio below divides by one, and a traded price for
# every adjusted one, since a missing one would silently drop a fund from the screen.

# %%
traded = load_etfs_unadjusted(start_date=START_DATE, end_date=END_DATE).select(
    [
        "symbol",
        "timestamp",
        pl.col("close").alias("traded_close"),
        pl.col("volume").alias("traded_volume"),
    ]
)
prices = (
    load_etfs(start_date=START_DATE, end_date=END_DATE)
    .select(["symbol", "timestamp", "close", "volume"])
    .join(traded, ["symbol", "timestamp"], how="left")
    .sort(["symbol", "timestamp"])
)
research = prices.filter(pl.col("timestamp") < pl.lit(HOLDOUT_START).str.to_date())

undeclared = sorted(set(prices["symbol"].unique().to_list()) - DECLARED_ASSETS)
assert not undeclared, f"loaded but absent from setup.yaml::universe.assets: {undeclared}"
assert prices["close"].min() > 0, "a non-positive close is not a denominator"
untraded = prices.filter(pl.col("traded_close").is_null())
assert untraded.is_empty(), (
    f"{len(untraded):,} adjusted closes have no traded price, so any dollar screen would "
    f"silently skip them: {sorted(untraded['symbol'].unique().to_list())[:5]}"
)
print(
    f"{research['symbol'].n_unique()} funds, {len(research):,} daily closes, "
    f"{research['timestamp'].min()} to {research['timestamp'].max()}"
)

_first = research.filter(pl.col("timestamp") == pl.col("timestamp").min())
_gaps = _first.with_columns(traded_over_adjusted=pl.col("traded_close") / pl.col("close")).sort(
    "traded_over_adjusted", descending=True
)
print(
    f"On the first session, {_first['timestamp'][0]}, {len(_gaps)} funds were quoting. The traded "
    f"close is above the adjusted one for "
    f"{(_gaps['traded_over_adjusted'] > 1).sum()} of them, median "
    f"{_gaps['traded_over_adjusted'].median():.2f}x, and the two extremes are the funds that later "
    f"split:"
)
display(
    pl.concat([_gaps.head(2), _gaps.tail(2)]).select(
        "symbol",
        adjusted=pl.col("close").round(2),
        traded=pl.col("traded_close").round(2),
        traded_over_adjusted=pl.col("traded_over_adjusted").round(2),
    )
)

# %% [markdown]
# A hundred tickers is a list, not a description. The way the cost model already groups them is by
# how much they trade, because that is what decides how wide the gap between the buying and the
# selling price is likely to be. `setup.yaml::costs.asset_spreads` names two tiers and leaves the
# rest to a default:
#
# - **Half a cent** for the seven funds tracking the largest indices: the S&P 500, the Nasdaq 100,
#   the Dow, the Russell 2000, the total US market, developed markets outside the US, and emerging
#   markets.
# - **One cent** for the eleven funds holding the sectors of the S&P 500, one each.
# - **Two cents** for the remaining funds: countries, bonds, currencies, commodities, industries and
#   rules-based selections.
#
# That assignment comes from industry knowledge rather than from the data, because daily bars carry
# no bid and no ask. The table below is the check on it. *Turnover* is the number of shares that
# changed hands multiplied by the price, so it measures money traded rather than shares traded, and
# it is the quantity Section B.2's eligibility rule reads. If the tiers are sensible, turnover should
# fall as the assumed spread widens, and it does, by roughly a factor of five at each step.

# %%
half_spread = pl.col("symbol").replace_strict(
    HALF_SPREADS, default=DEFAULT_HALF_SPREAD, return_dtype=pl.Float64
)
arrivals = research.group_by("symbol").agg(pl.col("timestamp").min().alias("arrived"))
tiers = (
    research.with_columns(
        half_spread_usd=half_spread,
        turnover=pl.col("traded_close") * pl.col("traded_volume"),
    )
    .join(arrivals, "symbol")
    .group_by("half_spread_usd")
    .agg(
        pl.col("symbol").n_unique().alias("funds"),
        pl.col("close").median().round(2).alias("median_close_usd"),
        (pl.col("turnover").median() / 1e6).round().cast(pl.Int64).alias("median_turnover_musd"),
        pl.col("arrived").max().alias("last_arrival"),
    )
    .sort("half_spread_usd")
)
with pl.Config(tbl_rows=tiers.height, tbl_cols=tiers.width):
    display(tiers)

# %% [markdown]
# ### B.2 How many funds the strategy is allowed to hold when it rebalances
#
# Not every fund can be held on every date. A fund that barely traded in 2009 could not have been
# bought in size in 2009, whatever it does today, so the strategy admits a fund to a year only if it
# averaged more than the declared floor in daily turnover over the *previous* year. Deciding
# membership from the year before is what makes the rule usable in real time: on the first trading
# day of any year, everything it reads has already happened.
#
# The alternative is the mistake this guards against. Selecting funds on turnover measured over the
# whole sample would admit exactly the funds that turned out to stay liquid, and the strategy would
# be tested on a universe assembled with knowledge it could not have had. A rule read on prior
# information only is called *point-in-time*, and it is the difference between a backtest and a
# rehearsal.
#
# Two details of the rule are worth stating. A fund quoting for only part of a year has no full year
# of turnover to be admitted on, so a minimum number of trading days is required before the average
# means anything. And the turnover is the traded price times the shares that changed hands, not the
# adjusted price: a distribution rescales the earlier history of an adjusted series, so reading a
# dollar floor off it would apply the rule to a number no one ever traded at, and apply it hardest
# in the earliest years where the accumulated distributions are largest.

# %%
eligibility = (
    prices.with_columns(
        turnover=pl.col("traded_close") * pl.col("traded_volume"),
        year=pl.col("timestamp").dt.year(),
    )
    .group_by(["symbol", "year"])
    .agg(pl.col("turnover").mean().alias("avg_turnover"), pl.len().alias("n_days"))
    .filter((pl.col("n_days") >= MIN_SESSIONS_PER_YEAR) & (pl.col("avg_turnover") >= ADV_THRESHOLD))
    .select("symbol", (pl.col("year") + 1).alias("eligible_year"))
    .unique()
    .sort(["symbol", "eligible_year"])
)

# %% [markdown]
# A single count over the whole sample would hide the question a strategy of this shape has to
# answer, which is whether enough funds are admitted *at the moment it has to choose between them*.
# The strategy rebalances at the last session of each month, so that is where the count is taken,
# and it is compared against the 20 positions the largest book has to fill.

# %%
month_end = research.filter(
    pl.col("timestamp") == pl.col("timestamp").max().over(pl.col("timestamp").dt.truncate("1mo"))
)
eligible = eligibility.rename({"eligible_year": "year"}).with_columns(eligible=pl.lit(True))
breadth = (
    month_end.with_columns(year=pl.col("timestamp").dt.year())
    # a left join keeps the dates on which nothing is eligible, which an inner join drops
    .join(eligible, ["symbol", "year"], how="left")
    .group_by("timestamp")
    .agg(pl.col("eligible").fill_null(False).sum().alias("n_eligible"))
    .sort("timestamp")
)

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
ax.plot(breadth["timestamp"], breadth["n_eligible"], color=COLORS["blue"], linewidth=1.2)
ax.axhline(
    BREADTH_FLOOR, color=COLORS["copper"], ls="--", lw=1.5, label="most positions ever held at once"
)
ax.set_ylim(0, len(DECLARED_ASSETS) + 5)
ax.set_ylabel("Funds eligible on the decision date")
ax.legend(frameon=False, fontsize=8, loc="lower right")
add_message_title(
    ax,
    "From the second year on, more funds are eligible than the strategy can hold",
    subtitle="Funds clearing the prior year's dollar-volume floor, counted at each month-end",
)
show_with_alt(
    fig,
    "A step line rising from zero at the start of 2006 to about 96 funds by 2018 and flat "
    "afterwards, against a dashed horizontal line at 20 marking the largest book the strategy "
    "ever holds. The line crosses 20 during 2007 and stays well above it for the rest of the "
    "sample.",
)

# %% [markdown]
# ### B.3 What a round trip costs, and what a move is worth
#
# Buying and later selling the same fund pays two charges, and pays each of them twice.
#
# The first is the broker's **commission**, quoted in cents per share. The second is the **spread**:
# at any moment there is a price at which someone will sell and a slightly higher price at which
# someone will buy, and a trade that has to happen now crosses that gap. By convention the cost is
# charged as half the gap on each side of the round trip, which is why `setup.yaml` states it as a
# *half-spread*. Both charges are dollars per share, and neither has anything to do with how
# expensive the share is - so the same two cents is a heavier charge on a fund trading at $20 than
# on one trading at $500.
#
# That is why the cost has to be converted before it can be compared to a return. Expressed as a
# fraction of the price, it becomes a number that means the same thing for every fund. The unit
# below is the **basis point**, one hundredth of one percent, which is the conventional unit for
# quantities this small.
#
# One caveat on the spread. Daily bars record what traded, not what was quoted, so there is no bid
# and no ask in this data and the half-spread cannot be measured from it. It is assigned by tier, as
# B.1 described, and `17_costs` re-runs the strategy under harsher assumptions to see how much the
# answer depends on it.

# %%
cost = (
    research.group_by("symbol")
    .agg(pl.col("traded_close").median().alias("price"))
    .with_columns((2 * (half_spread + PER_SHARE) / pl.col("price") * 1e4).alias("cost_bps"))
    .sort("cost_bps")
)
COST_BPS = float(cost["cost_bps"].median())

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
ax.bar(cost["symbol"], cost["cost_bps"], color=COLORS["blue"], width=0.7)
ax.axhline(COST_BPS, color=COLORS["copper"], ls="--", lw=1.5, label="universe median")
ax.set_ylabel("Round-trip cost (bps)")
ax.set_xlabel("Funds, sorted by round-trip cost")
ax.set_xticks([])  # a hundred tickers are unreadable, and the spread of the curve is the point
ax.legend(frameon=False, fontsize=8)
add_message_title(
    ax,
    "Round-trip cost spans an order of magnitude across the same universe",
    subtitle="Each fund's two half-spreads and two commissions over its median traded close, sorted",
)
show_with_alt(
    fig,
    "One bar per fund, sorted left to right, rising from under 1 basis point to about 35. A "
    "dashed line marks the universe median near 6 basis points; most funds sit between 2 and "
    "12, and a short tail at the right runs above 20.",
)

# %% [markdown]
# Because those costs differ by an order of magnitude, a single cost line drawn across raw returns
# would answer the question for no fund in particular: a move that clears the charge on a
# large-index fund need not clear it on a thinly traded one. Each move is therefore divided by what
# its own fund would have charged at the price the position opened at. On that scale the break-even
# point is 1 for every fund, whatever its own cost happens to be.
#
# The chart below is an **exceedance curve**, and it reads from the right: for each multiple on the
# horizontal axis, the curve gives the fraction of moves at least that large. Where it crosses the
# line at 1 is the fraction of moves bigger than the cost of trading them.
#
# It is drawn over the fund-years the eligibility rule admits, not over every row of the panel. A
# move in a fund the strategy was not allowed to hold that year is not an opportunity it missed, and
# counting it would overstate how often a move clears its cost.
#
# One thing this chart is not. It is the distribution of how far prices move, measured over every
# admitted fund and every session in the development period, ignoring direction. It is not the
# return a strategy would earn: nothing here is signed, nothing waits a day to enter, and nothing is
# restricted to the rebalancing dates. Whether the strategy can pick which moves to be on the right
# side of is the question Chapter 7 onwards asks. This is only whether the moves are large enough to
# be worth trying.

# %%
tradable = research.with_columns(year=pl.col("timestamp").dt.year()).join(
    eligibility.rename({"eligible_year": "year"}), ["symbol", "year"], how="semi"
)
returns = tradable.with_columns(
    cost_bps=2 * (half_spread + PER_SHARE) / pl.col("traded_close") * 1e4
).with_columns(
    (pl.col("close").pct_change(h).abs() * 1e4 / pl.col("cost_bps").shift(h))
    .over("symbol")
    .alias(f"h{h}")
    for h in HORIZONS
)

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
for h, color in zip(HORIZONS, (COLORS["amber"], COLORS["blue"]), strict=True):
    multiple, fraction = exceedance_curve(returns[f"h{h}"].drop_nulls().to_numpy())
    ax.plot(multiple, fraction, color=color, lw=1.6, label=f"{h}-session move")
ax.axvline(1, color=COLORS["copper"], ls="--", lw=1.5, label="break-even on the round trip")
ax.set_xscale("log")
ax.set_xlim(0.02, 2_000)
ax.set_xlabel("Absolute move as a multiple of the fund's own round trip (log scale)")
ax.set_ylabel("Fraction of moves at least this large")
ax.legend(frameon=False, fontsize=8, loc="lower left")
add_message_title(ax, "Almost every move at either horizon exceeds the cost of taking it")
show_with_alt(
    fig,
    "Two falling curves on a logarithmic horizontal axis giving the fraction of moves at least "
    "a given multiple of the fund's own round-trip cost. Both start near 1.0 and are still "
    "close to it at the dashed break-even line at one times cost, the 21-session curve sitting "
    "above the 5-session one throughout, and both reach zero beyond a hundred times cost.",
)

# %% [markdown]
# ### B.4 How much of one month's return carries into the next
#
# A position opened at one month-end and closed at the next earns that fund's return over the
# interval. Before building anything that forecasts that return, it is worth asking how much of it
# the fund's own recent history already accounts for. If funds that rose last month tend to rise
# again, the simplest imaginable ranking - buy last month's leaders - is already a strategy, and the
# rebalancing schedule has to be fast enough to act on that tendency before it fades.
#
# The measurement is an **autocorrelation**: the correlation between a fund's return in one month
# and its return some number of months later. Plotted against that number of months, it shows how
# much of the series its own past accounts for, and how quickly that fades.
#
# It is computed inside each fund and then averaged across funds. Stacking a hundred funds into one
# long series and correlating that returns a number too, and the number is wrong: at every point
# where one fund's history ends and the next begins, it correlates gold with Brazilian equities. The
# shaded region shows how much the result varies from fund to fund, and the band around zero shows
# how large a correlation could plausibly be if a fund's returns carried no information about their
# own past at all.

# %%
monthly = month_end.with_columns(monthly_return=pl.col("close").pct_change().over("symbol"))
# a series correlated with itself is 1 by construction, and that bar would flatten every other one
acf = panel_acf(monthly, entity_col="symbol", value_col="monthly_return", max_lags=12).filter(
    pl.col("lag") > 0
)

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
ax.axhspan(
    -acf["band"][0],
    acf["band"][0],
    color=COLORS["copper"],
    alpha=0.18,
    zorder=0,
    label="range expected from no information",
)
ax.fill_between(
    acf["lag"],
    acf["acf_p10"],
    acf["acf_p90"],
    color=COLORS["blue"],
    alpha=0.15,
    label="10th to 90th percentile across funds",
)
ax.bar(acf["lag"], acf["acf"], color=COLORS["blue"], width=0.6)
ax.set_xlabel("Months between the two returns")
ax.set_ylabel("Correlation of a fund's return with its own past")
ax.legend(frameon=False, fontsize=8, ncol=2, loc="upper center", bbox_to_anchor=(0.5, -0.18))
add_message_title(
    ax,
    "A fund's own past return accounts for almost none of its next one",
    subtitle="Averaged within each fund",
)
show_with_alt(
    fig,
    "Bars for lags of one to twelve months, alternating in sign and all between -0.05 and "
    "+0.05, drawn against a shaded band marking what no information would produce. Every bar "
    "is small enough to sit inside that band, and the 10th-to-90th-percentile spread across "
    "funds is several times wider than the average bar at every lag.",
)

# %% [markdown]
# ### B.5 Move size against cost
#
# Two numbers summarise what B.3 drew. The first is the median absolute move over one rebalancing
# interval divided by the median round trip, which says how much larger a typical move is than a
# typical cost. The second is the share of moves larger than what their own fund charges, which is
# where the exceedance curve crosses its break-even line.
#
# Neither says the strategy earns anything. Both count a move down exactly as they count a move up,
# and nothing here decides which side of it a position would have been on. What they rule out is the
# case where the design fails immediately, because a typical move is smaller than the cost of
# capturing it.

# %%
moves = returns.select(
    move_bps=1e4 * pl.col("close").pct_change(PRIMARY_HORIZON).abs().over("symbol"),
    clears=pl.col(f"h{PRIMARY_HORIZON}") > 1,
)
print(
    f"Round trip {cost['cost_bps'].min():.2f} to {cost['cost_bps'].max():.2f} bps across funds, "
    f"median {COST_BPS:.2f} bps\n"
    f"Median absolute {PRIMARY_HORIZON}-session move {moves['move_bps'].median():.1f} bps, "
    f"{moves['move_bps'].median() / COST_BPS:.0f}x the median round trip\n"
    f"Share of moves larger than their own fund's round trip {moves['clears'].mean():.3f}"
)

# %% [markdown] tags=["results"]
# The round trip costs between 0.86 and 35.23 bps at each fund's median traded close, a universe
# median of 6.29 bps. The median absolute 21-session move is 288.3 bps, forty-six times that, and
# 0.973 of moves are larger than the round trip charged by the fund on which they occurred.

# %% [markdown]
# ## C. Design decisions
#
# The sections above are evidence. This section is where that evidence meets the choices recorded in
# `setup.yaml`, and says what each one rests on.
#
# ### C.1 How often to rebalance
#
# `setup.yaml` ranks the funds at the month-end close and trades at the next open. Section B.3
# supports trading that often: moves over one rebalancing interval are far larger than the round
# trip their own fund charges, so cost is not what would force a slower schedule. Section B.4
# supports it from the other side, by ruling out the reason to trade faster. A fund's own past
# return accounts for almost none of its next one, so there is no quickly fading tendency that a
# monthly schedule would arrive too late for. Whatever the ranking ends up reading has to come from
# somewhere other than the fund's last return, and Chapter 7 onwards builds it.
#
# The interval has one further consequence. The return being predicted is the one 21 sessions
# ahead, which is about the number of trading sessions in a month, so each position is held for
# roughly the period its own label measures and one holding is largely finished before the next
# begins. The 5-session variant stays in `labels.variants` so a shorter holding period is measured
# as well.
#
# ### C.2 What would send this design back
#
# A feasibility study is only useful if some result would have stopped it. Three would, and each is
# measured where its evidence exists rather than here.
#
# The one this notebook could have produced is a cost failure: if a typical move were smaller than
# the round trip needed to capture it, the ranking would pay more to trade than the move it is
# trying to catch, and no model would repair that. Section B.5 is that measurement, and Chapter 18
# repeats it against the trades a backtest actually places rather than against raw moves.
#
# The other two are outcomes of the strategy rather than properties of the data. Chapter 7 asks
# whether the ranking has any relationship at all to the returns that follow it, at any lookback
# window. Chapter 16 asks whether simply holding every eligible fund in equal weight earns more per
# unit of risk, and loses less at its worst, than the ranking does - if it does, the ranking is not
# paying for the trading it causes.
#
# ### C.3 What the strategy does with the ranking
#
# `setup.yaml` buys the funds at the top of the ranking and takes no position in the ones at the
# bottom. Betting against a fund means borrowing its shares in order to sell them, and for many of
# these funds those shares are expensive or impossible to borrow, so a short leg would be measuring
# the cost of the borrow as much as the quality of the ranking.
#
# Each fund held gets an equal share of the money. A weighting optimised for risk would fold an
# estimate of how the funds move together into the result, and the ranking's own contribution could
# no longer be separated from that estimate's. Chapter 17 compares the alternatives with the ranking
# held fixed.

# %% [markdown]
# ## D. Walk-forward structure
#
# ### D.1 How much an evaluation has to spend
#
# A panel of daily prices looks large, but a strategy that changes its positions once a month does
# not get to treat every row as an independent opportunity. What it spends is decision dates. Three
# numbers describe the sample from that point of view: how many sessions it contains, how many of
# them are dates the strategy acts on, and how many funds it can choose between on a typical one.

# %%
print(
    f"Sessions {research['timestamp'].n_unique():,} | decision dates {len(breadth):,} "
    f"| eligible funds per decision date {breadth['n_eligible'].mean():.0f}"
)

# %% [markdown]
# ### D.2 The folds
#
# A model is fitted on one stretch of history and evaluated on the stretch that follows it, then the
# pair moves forward and the process repeats. Each fit-then-evaluate pair is a **fold**, and
# evaluating this way is called **walk-forward**, because the split always runs in the direction
# time does.
#
# One detail decides whether the evaluation is honest. The return being predicted lands 21 sessions
# ahead, so a training row dated near the end of its block is labelled with a price from after the
# block ends. Validating on the session immediately after training would score the model on data it
# had partly seen already. The fix is to leave a gap between the two, at least as wide as the
# horizon, and that gap is called **purging**. Its width comes from `labels.buffer` in `setup.yaml`;
# the 5-session variant declares a shorter gap of its own, which the primary label's gap covers.
#
# The three assertions below establish what the figure cannot. The gap is 21 sessions against
# training blocks measured in years, too narrow to see, so only counting it off the session timeline
# can confirm it is as wide as the horizon. The other two check that the number of folds is the
# number `setup.yaml` declares, and that no validation window reaches into the holdout. The figure
# then draws the boundaries the splitter returned rather than recomputing them, so the picture and
# the folds cannot disagree.
#
# `generate_cv_splits` numbers folds chronologically, so fold 0 is the earliest and the highest
# number is the last one before the holdout. The figure draws them earliest-first and labels each
# with that number, so the labels count up alongside the dates; every later stage prints the same
# ones.
#
# The splitter is given the whole sample, holdout included, and applies the holdout boundary itself
# from `evaluation.holdout_start`, which is what every later stage does too. Trimming the data first
# would shift the first training date of most folds by a few sessions, and the figure would then
# show a training window the pipeline never trains on.

# %%
splits = generate_cv_splits(
    prices.select("timestamp"),
    case_study_id=CASE_STUDY_ID,
    label_buffer=LABEL_BUFFER,
    date_col="timestamp",
)
sessions = prices.select("timestamp").unique().sort("timestamp")["timestamp"].to_numpy()
purge_gaps = {
    int(
        (
            (sessions > np.datetime64(s["train_end"])) & (sessions < np.datetime64(s["val_start"]))
        ).sum()
    )
    for s in splits
}
last_val = max(s["val_end"] for s in splits)
assert len(splits) == SETUP["evaluation"]["n_splits"], "fold count differs from setup.yaml"
assert last_val < np.datetime64(HOLDOUT_START), "a fold reaches into the holdout"
assert purge_gaps == {PRIMARY_HORIZON}, "a purge gap is not the primary label horizon"

fig, ax = plt.subplots(figsize=FIGSIZE["single"])
fold_timeline(ax, splits, holdout=(HOLDOUT_START, HOLDOUT_END))
purge_note = f"Training, the {PRIMARY_HORIZON}-session purge gap, validation, and the holdout"
add_message_title(
    ax, "Each fold trains, pauses, then validates, and none reaches the holdout", purge_note
)
show_with_alt(
    fig,
    "Eight horizontal bars, one per fold, each made of a long dark training block, a thin pale "
    "purge gap and a short amber validation block. Each fold sits one year earlier than the "
    "one below it, and the shaded holdout column on the right is reached by none of them.",
)

# %% [markdown]
# ## E. What this notebook hands on
#
# One table: the fund-year pairs that cleared the liquidity rule. `02_labels` and
# `03_financial_features` join on it, so a fund contributes rows only in the years it was admitted
# to. It covers the holdout years as well as the development period, because the strategy has to
# know which funds it was allowed to hold when it is finally evaluated there. That costs nothing in
# terms of what the design has seen: membership in a year is decided entirely by the year before it,
# and none of the diagnostics above read a holdout year.

# %%
eligibility.write_csv(CASE_DIR / "eligibility.csv")
print(f"Written: eligibility.csv, {len(eligibility):,} fund-year pairs")

# %% [markdown]
# ## F. What the evidence says about each setting
#
# One row per setting: the evidence behind it, and the condition under which a reader working on
# their own data would choose differently.
#
# | Setting | Evidence | Choose differently when |
# |---|---|---|
# | `universe.eligibility_rule` | B.2, funds admitted on each decision date | fewer funds are admitted than the strategy has positions to fill |
# | `decision.cadence` | B.3 move sizes against cost, B.4 how much of a month's return carries | moves stop covering the round trip, or the return being predicted resolves after the next decision date |
# | `costs.asset_spreads` | B.3, the cost per fund from the declared commission and half-spread | quoted spreads become available and disagree with the assigned tier |
# | `evaluation.n_splits` | D.1 decision dates, D.2 fold boundaries | the folds no longer fit the development period |
#
# The breadth chart raises a question it cannot answer on its own. A count below the number of
# positions to fill matters if it happens in the middle of the sample, where the strategy would find
# itself unable to fill the book on an ordinary date. It matters much less if it is confined to the
# beginning, before any fund has a prior year to be admitted on, since the evaluation can simply
# start after it. So the dates below the floor are counted per year, and the first date after which
# breadth never falls below it again is read off the series rather than off the chart.

# %%
thin = breadth.filter(pl.col("n_eligible") < BREADTH_FLOOR).sort("timestamp")
by_year = thin.group_by(pl.col("timestamp").dt.year().alias("year")).len().sort("year")
cleared = breadth.filter(pl.col("timestamp") > thin["timestamp"].max())["timestamp"].min()

print(
    f"universe.n_assets {SETUP['universe']['n_assets']}, eligible per decision date "
    f"{breadth['n_eligible'].min()} to {breadth['n_eligible'].max()}\n"
    f"below the floor of {BREADTH_FLOOR} on {thin.height} of {len(breadth)} decision dates ("
    + ", ".join(f"{n} in {y}" for y, n in zip(by_year["year"], by_year["len"], strict=True))
    + f"), and never again from {cleared}\n"
    f"decision.cadence {SETUP['decision']['cadence']} | labels.primary {PRIMARY_LABEL}\n"
    f"evaluation.n_splits {SETUP['evaluation']['n_splits']}, generated {len(splits)}, "
    f"last validation ends {last_val.date()}, holdout untouched\n"
    f"labels.buffer {LABEL_BUFFER}, purge gap {min(purge_gaps)} sessions against the "
    f"{PRIMARY_HORIZON}-session primary horizon"
)

# %% [markdown] tags=["results"]
# The number of funds eligible on a decision date runs from 0 to 96 of the declared 100. It sits
# below the twenty positions the largest book has to fill on 12 of 216 decision dates, all of them
# in 2006, the first year of the sample, when no fund yet has a prior year to be admitted on; from
# 2007-01-31 it never falls below twenty again. Eight folds are generated, the last validation
# window ending 2023-11-29, and the gap between each training block and the validation window that
# follows it is 21 sessions, exactly the horizon of the primary label.

# %% [markdown]
# ## Key takeaways
#
# 1. **Decide what a strategy was allowed to hold from information that existed before the date the
#    decision applies to.** A liquidity filter applied to the whole sample at once admits exactly
#    the funds that turned out to stay liquid, and a backtest run on that universe is measuring a
#    choice nobody could have made at the time.
# 2. **Count the universe on the dates the strategy acts, not over the sample.** An average taken
#    over every session hides whether the book could have been filled on the dates that decide the
#    result.
# 3. **Turn a per-share cost into a fraction of the price before comparing it to a return**, and
#    divide each move by what its own instrument charges. A cent of spread is a heavy cost on a $20
#    fund and a negligible one on a $500 one, so a single cost line drawn across raw returns answers
#    the question for no fund in particular.
# 4. **Measure opportunity over the population the strategy was allowed to trade.** A move in a fund
#    that failed the liquidity rule that year was never available, and counting it overstates how
#    often a move covers its own cost.
# 5. **Compute a panel autocorrelation inside each entity, then average.** Stacking entities into
#    one series measures the joins between them.
#
# ### Known limitations
#
# - The hundred funds were chosen knowing which of them still trade today. The point-in-time rule
#   removes a bias within that list; it cannot remove the bias in the list itself.
# - The traded price is read from Yahoo, which restates it for splits but not for distributions, so
#   the splits are multiplied back out when the series is built. A fund's split history is therefore
#   part of the input, and a split Yahoo has not recorded would misstate that fund's cost per share.
# - The half-spread is assigned by liquidity tier rather than measured, because daily bars carry no
#   bid and no ask. The dollar-volume floor is a fixed amount that is not adjusted for inflation.
# - Eligibility is decided once a year while positions change once a month, so a fund that becomes
#   illiquid in March keeps its place until January.
#
# **Next**: labels at the declared horizons, built on this development period.

```