ETFバックテスト設計：対象、ランキング、コスト、評価

# setup.yaml


```yaml
strategy_id: etfs
setup_version: v1

universe:
  assets:
    - ACWI
    - ACWX
    - AGG
    - BIL
    - BND
    - BNDX
    - DBA
    - DBC
    - DIA
    - DVY
    - EEM
    - EFA
    - EMB
    - EWA
    - EWC
    - EWG
    - EWH
    - EWI
    - EWJ
    - EWL
    - EWN
    - EWP
    - EWQ
    - EWT
    - EWU
    - EWW
    - EWY
    - EWZ
    - EZA
    - FXB
    - FXE
    - FXI
    - FXY
    - GLD
    - GOVT
    - GSG
    - HYG
    - IAU
    - IBB
    - IEF
    - IEFA
    - IEMG
    - IJR
    - INDA
    - ITA
    - ITB
    - IVE
    - IVW
    - IWM
    - IYR
    - JNK
    - KRE
    - LQD
    - MCHI
    - MDY
    - MTUM
    - MUB
    - OIH
    - PPLT
    - QQQ
    - QUAL
    - RSP
    - SCHD
    - SDY
    - SHY
    - SLV
    - SMH
    - SOXX
    - SPY
    - THD
    - TIP
    - TLT
    - UNG
    - USMV
    - USO
    - UUP
    - VCSH
    - VEA
    - VGK
    - VIG
    - VLUE
    - VNQ
    - VTI
    - VTV
    - VUG
    - VWO
    - XBI
    - XLB
    - XLC
    - XLE
    - XLF
    - XLI
    - XLK
    - XLP
    - XLRE
    - XLU
    - XLV
    - XLY
    - XME
    - XRT
  n_assets: 100
  eligibility_rule: point_in_time_adv_10m_annual
  eligibility_file: eligibility.csv
  eligibility_note: The 100 ETFs were selected backward-looking (survivorship bias). The $10M ADV threshold is not inflation-adjusted. See eligibility.csv for point-in-time (asset, year) membership.

decision:
  cadence: monthly_month_end
  snapshot: close
  execution_delay: next_bar_open
  # Per-label decision cadence. `cadence` above is the default and applies to every label not
  # named here. fwd_ret_5d predicts a five-session return; held to the monthly grid it was
  # carried for about 21 sessions, so the backtest traded a horizon the label does not describe
  # and the two could not be compared on equal terms. Adding a label here changes its backtest
  # spec identity, so it produces new registered rows and leaves existing ones untouched.
  cadence_by_label:
    fwd_ret_5d: weekly_friday_close

# Engine-level execution defaults. Single source of truth — Ch16-19 notebooks
# read these via get_backtest_config(); never declare a local INITIAL_CASH
# or share_type constant. Changing values here invalidates every existing
# backtest_hash for this case study (cash + share_type are spec-hash inputs).
#
# ``allocator_lookback`` is the bars-of-underlying-price window applied
# uniformly to every moment-based allocator (inverse_vol, risk_parity, hrp,
# mvo_ledoit_wolf). One value per CS keeps allocators comparable — MVO is
# not granted a longer covariance window than IV by default. Bars are
# counted on the price DataFrame the allocator consumes, not the rebalance
# cadence: ETFs has daily Yahoo OHLCV → 63 ≈ 3 months. Conformal-prediction
# allocators have separate calibration windows handled in ``conformal.py``.
execution:
  initial_cash: 100_000          # IBKR retail-cohort default; matches per-share fee tier
  share_type: integer            # ETFs trade in whole shares
  allocator_lookback: 63         # 3 months of daily Yahoo OHLCV

mapping:
  class: long_only_rank_and_rebalance
  position_state_space: long_only
  entry_logic: rank_selection_top_n
  sizing: equal_weight

costs:
  class: material
  # Per-share commission plus tier-based half-spread slippage. Yahoo daily
  # OHLCV does not carry bid/ask, so spreads are tier-assigned from industry
  # knowledge: penny half-spread for the most liquid mega-ETFs and 2c
  # default for the rest. Integer share sizing.
  model: per_share_plus_spread
  per_share: 0.0035   # IBKR Pro Tiered top tier (<=300K shares/mo)
  minimum: 0.0
  asset_spreads:
    SPY: 0.005
    QQQ: 0.005
    IWM: 0.005
    EFA: 0.005
    EEM: 0.005
    DIA: 0.005
    VTI: 0.005
    XLK: 0.01
    XLF: 0.01
    XLV: 0.01
    XLE: 0.01
    XLY: 0.01
    XLI: 0.01
    XLP: 0.01
    XLU: 0.01
    XLB: 0.01
    XLRE: 0.01
    XLC: 0.01
  default_half_spread_usd: 0.02  # 2c for sector/regional/thematic ETFs
  spread_convention: half_spread
  source: Industry-knowledge tiers; Yahoo daily OHLCV has no bid/ask.

labels:
  primary: fwd_ret_21d
  buffer: 21D
  variants:
    - fwd_ret_5d
  variant_buffers:
    fwd_ret_5d: 5D
  # Vectorized-backtest thinning step per label: number of schedule slots
  # to advance per trade so holding periods don't overlap. Authored from
  # (schedule cadence, label horizon); add an entry here for any new label.
  rebalance_step:
    fwd_ret_21d: 1  # monthly_month_end schedule, 21d horizon <= 30d gap
    fwd_ret_5d: 1   # monthly_month_end schedule, 5d horizon <= 30d gap

backtest:
  rebalance:
    # A rebalance is skipped when the per-asset weight change is below
    # min_weight_change AND the resulting trade notional is below
    # min_trade_value. The benchmark profile disables thresholds so that
    # full-universe equal-weight (1/N per asset) rebalances at all.
    default:
      min_weight_change: 0.005
      min_trade_value: 100.0
    benchmark:
      min_weight_change: 0.0
      min_trade_value: 0.0
  sweep:
    # Iteration controls per stage. ``signal: 0`` means "all predictions";
    # downstream stages take the top-N from the upstream stage's rank-1.
    # Notebooks read these via get_top_n_predictions(case_study, stage).
    top_n_predictions:
      signal: 0                 # all signal predictions (eq-weight baseline)
      allocation: 10            # top-10 model configs by equal-weight baseline Sharpe
      cost_sensitivity: 1       # top-1 of {signal+allocation} per label
      risk_overlay: 1           # top-1 of {signal+allocation} per label
    # Each advancing config contributes its single best checkpoint by baseline
    # Sharpe, so ten distinct (family, config_name) fill the ten allocation slots
    # and no model occupies two of them. Only the checkpoint is fixed here: the
    # allocation stage re-sweeps the whole top_k_grid for every advancing config
    # (15_portfolio_management.py:122-168), including mappings that lost the
    # baseline stage, so a config advances with its checkpoint chosen and its
    # top_k still open.
    checkpoints_per_config: 1
    # Skip MVO/HRP when allocator runtime is the bottleneck (intraday CSes).
    expensive_allocators_skip: false
    # All other alternative allocators take the cheap path.
    # Ch16 equal-weight baseline selection. Long-only top-k. Percentile
    # and quantile axes are commented out; uncomment to add them for any
    # label and the loader will synthesize the schemes automatically.
    # k is a concentration choice and only means anything against the tradeable
    # cross-section, which here is 99 names at the median decision date (p10 82).
    # 5 / 10 / 20 is 5% / 10% / 20% of that: a genuinely concentrated low end and a
    # diversified high end that still selects. The grid was [10, 20] and had no 5%
    # end, which is where every comparable case study starts.
    top_k_grid:
      fwd_ret_21d: [5, 10, 20]
      fwd_ret_5d:  [5, 10, 20]
    # percentile_grid:
    #   fwd_ret_21d: [80, 90, 95]
    # quantile_grid:
    #   fwd_ret_21d: [5, 10]
    # Ch17 portfolio: reuses top_k_grid above and sweeps over allocators.
    # Moment-based allocators (IV/RP/HRP/MVO_LW) all use the CS-level
    # ``execution.allocator_lookback`` — no per-allocator vol_window / lookback
    # fields. No max-weight cap: each allocator expresses its natural
    # concentration profile so the cross-method comparison is honest. MVO uses
    # Ledoit-Wolf shrinkage as its covariance regularizer; IV/RP/HRP have
    # method-inherent diversification; score_weighted is reported unconstrained.
    # Equal weight is the Ch16 baseline and is not repeated here.
    allocators:
      - {name: score_weighted,  method: score_weighted}
      - {name: inverse_vol,     method: inverse_vol}
      - {name: risk_parity,     method: risk_parity}
      - {name: mvo_ledoit_wolf, method: mvo_ledoit_wolf}
      - {name: hrp,             method: hrp}
      - {name: conformal_weighted, method: conformal_weighted}
    # Ch18 cost sensitivity (bps regime).
    cost_grid_bps: [0, 1, 2, 3, 5, 7, 10, 15, 20, 30, 50]
    # Companion per-share cost regime: dollars per share half-spread. Swept
    # alongside cost_grid_bps for CSes whose declared cost model is
    # per_share_plus_spread. Values: 0¢, 0.5¢, 1¢, 2.5¢, 5¢, 10¢.
    cost_grid_half_spread_usd: [0.0, 0.005, 0.01, 0.025, 0.05, 0.10]
    # Ch19 risk overlays.
    risk_controls:
      position:
        - {name: stop_loss_3pct,  type: stop_loss,     threshold: 0.03}
        - {name: stop_loss_5pct,  type: stop_loss,     threshold: 0.05}
        - {name: stop_loss_10pct, type: stop_loss,     threshold: 0.10}
        - {name: stop_loss_15pct, type: stop_loss,     threshold: 0.15}
        - {name: trailing_1pct,   type: trailing_stop, threshold: 0.01}
        - {name: trailing_2pct,   type: trailing_stop, threshold: 0.02}
        - {name: trailing_3pct,   type: trailing_stop, threshold: 0.03}
        - {name: trailing_5pct,   type: trailing_stop, threshold: 0.05}
        - {name: trailing_10pct,  type: trailing_stop, threshold: 0.10}
        - {name: trailing_15pct,  type: trailing_stop, threshold: 0.15}
        - {name: trailing_20pct,  type: trailing_stop, threshold: 0.20}
        - {name: time_exit_10,    type: time_exit,     bars: 10}
        - {name: time_exit_20,    type: time_exit,     bars: 20}
        - {name: time_exit_40,    type: time_exit,     bars: 40}

evaluation:
  n_splits: 8
  train_size: 10Y
  val_size: 1Y
  holdout_start: '2024-01-01'
  holdout_end: '2025-12-31'
  calendar: NYSE
  periods_per_year: 252  # NYSE 5d/wk

modeling:
  gbm:
    libraries: [lightgbm]
    preset: default
    device: cpu
    # LightGBM's own CPU default. 63 is the GPU default and was carried over with the
    # device when these runs moved off the GPU, so every CPU fit was quantizing the design
    # matrix into a quarter of the bins the library would have used. Coarser bins are
    # faster and lose split points; the reader running this on a CPU gets what the
    # documentation describes.
    max_bin: 255
  latent_factors:
    persistent_entities: true
    macro_context:
      source: alfred_initial_release
      policy: alfred_initial_release_close_lagged
      version: v1
      series:
        - dgs1
        - dgs2
        - dgs3
        - dgs5
        - dgs7
        - dgs10
        - dgs20
        - dgs30
        - vixcls
        - YIELD_CURVE_SLOPE
        - YIELD_CURVE_5_10
      availability_lag_days: 1
      alignment: backward_asof
    model_kwargs:
      ipca:
        # 100 -> 1000. The eight production folds converge at 53, 59, 69, 77, 85,
        # 97, 107 and 110 alternations, so the old cap fell *inside* its own
        # convergence distribution: folds 2 and 3 reported exactly 100 and were
        # refused by the convergence guard, while fold 1 cleared by three. Which
        # folds published was decided by where the cap happened to land. The
        # legacy runner never checked convergence at this scale, so the
        # truncation registered results and said nothing. 1000 is chosen against
        # that measured maximum rather than inherited: it clears every fold by
        # roughly nine times, and it still bounds a genuinely stuck fold to about
        # 13 minutes at ~0.76s per alternation, where the library default of
        # 10,000 would allow two hours. Folds that settle early pay nothing for
        # the headroom, because the alternation stops when it settles.
        max_iter: 1000
        tol: 0.00001
        factor_ridge: 0.01
        gamma_ridge: 0.01
      sdf:
        checkpoint_epochs: [256, 512, 768, 1024]  # conditional-relative; publishes global 256..1280
        beta_checkpoint_epochs: [256]
        beta_default_checkpoint: 256

causal:
  treatment: skip_recent_6_1
  # Bars the treatment's own construction window spans, which is what the placebo block has
  # to cover: permuting skip_recent_6_1 in blocks shorter than this destroys the serial
  # dependence the refutation exists to preserve, and the resulting p-value reads like a
  # refutation without being one. Declared here rather than inferred, because guessing which
  # element of a window list a column was built from puts a wrong number behind a right-looking
  # one. Derived from the construction, not chosen:
  #
  # `close.shift(21) / close.shift(6 * 21) - 1` in 03_financial_features: the six-month
  # return that stops a month short. The oldest price it reads is 126 sessions back.
  treatment_window: 126
  confounders: [vol_21d, vol_126d, regime, yield_curve_slope]
  method: walk_forward_dml

# The feature-specification register. One row per family: what it reads, how far
# back, with what delay, and whether it ranks assets against each other (signal) or
# describes the environment a ranking is formed in (state). `03_financial_features`
# builds the matrix from `windows` and audits it against `lookback`; nothing outside
# the notebook could state what this case study's feature set is until it lived here.
features:
  windows:
    momentum: [5, 10, 21, 42, 63, 126, 189, 252]
    volatility: [21, 63, 126, 252]
    skip_recent: 21
    drawdown: [63, 126]
    volume: [21, 63]
  ranked: [ret_126d, sharpe_126d, vol_63d]
  regime_threshold: 0.005  # 10y-2y spread separating the two curve regimes
  # Periods for the ml4t.engineer oscillator, trend and regime calls in C.2, and the
  # windows C.3 and C.4 read. Declared here rather than typed into the notebook so the
  # register, the warmup audit and the timing figure read one source for every window.
  oscillators:
    rsi: [7, 14]
    macd_fast: 12
    macd_slow: 26
    adx: 14
    cci: [14, 21]
    stochastic: 14
    aroon: 25
    natr: 14
    choppiness: 14
    hurst: 100
    sma: [50, 200]
    ema: 26
    bollinger: 20
  state:
    obv_zscore: 63       # on-balance-volume z-score
    positive_share: 63   # share of up sessions
    extremes: 252        # distance from the 52-week high and low
    correlation: 63      # SPY-TLT rolling correlation
    curve_zscore: 252    # yield-curve slope z-score, over trading sessions
  families:
    - name: momentum
      pattern: ret_*d|skip_recent_*|mom_accel_*
      role: signal
      hypothesis: Relative performance persists over a one-month hold
      inputs: adjusted close
      lookback: 252
      lag: 0
      frame: time series
      representation: simple return, and differences between horizons
      failure_mode: reverses at the shortest horizons
    - name: risk-adjusted momentum
      pattern: sharpe_*d
      role: signal
      hypothesis: Trend earned with less dispersion repeats more reliably
      inputs: log return
      lookback: 252
      lag: 0
      frame: time series
      representation: window return over its own dispersion
      failure_mode: unbounded when dispersion approaches zero
    - name: volatility
      pattern: vol_[0-9]*d|vol_ratio_short|vol_ratio_medium
      role: state
      hypothesis: Dispersion sets how far apart the cross-section can spread
      inputs: log return
      lookback: 252
      lag: 0
      frame: time series
      representation: annualized standard deviation, and ratios of windows
      failure_mode: lags a shock by roughly half its window
    - name: oscillator and trend
      pattern: rsi_*|macd_*|adx_*|cci_*|stoch_*|aroon_*|sma_ratio_*|ema_ratio_*|bb_pctb_*
      role: signal
      hypothesis: Price against its own recent path separates trend from exhaustion
      inputs: OHLC
      lookback: 200
      lag: 0
      frame: time series
      representation: bounded oscillator, ratio to a moving average
      failure_mode: saturates in a sustained trend
    - name: range and drawdown
      pattern: natr_*|chop_*|hurst_*|max_dd_*
      role: state
      hypothesis: Trending and mean-reverting regimes reward different signals
      inputs: OHLC
      lookback: 126
      lag: 0
      frame: time series
      representation: normalized range, Hurst exponent, share below trailing peak
      failure_mode: the exponent needs a long window to settle
    - name: volume
      pattern: vol_ratio_[0-9]*d|obv_*
      role: state
      hypothesis: Participation confirms or contradicts a price move
      inputs: share volume
      lookback: 63
      lag: 0
      frame: time series
      representation: ratio to trailing mean, z-score of on-balance volume
      failure_mode: index events put one date orders of magnitude out
    - name: extremes and consistency
      pattern: pct_positive_*|dist_52w_*
      role: signal
      hypothesis: Proximity to a 52-week extreme is itself a positioning signal
      inputs: adjusted close
      lookback: 252
      lag: 0
      frame: time series
      representation: ratio to a rolling extreme, share of positive sessions
      failure_mode: piles up at one during a sustained trend
    - name: cross-sectional position
      pattern: '*_rank'
      role: signal
      hypothesis: Only relative standing is tradable in a cross-sectional strategy
      inputs: ret_126d, sharpe_126d, vol_63d
      lookback: 126
      lag: 0
      frame: cross-section
      representation: percentile within the decision date
      failure_mode: discards the level the state families carry instead
    - name: cross-asset regime
      pattern: corr_spy_tlt_*
      role: state
      hypothesis: Rotation stops working when equities and bonds stop diversifying
      inputs: SPY and TLT log returns
      lookback: 63
      lag: 0
      frame: cross-asset
      representation: rolling correlation
      failure_mode: one equity-bond pair stands in for the whole universe
    - name: yield curve
      pattern: regime|yield_curve_*
      role: state
      hypothesis: The slope of the curve separates the macro regimes rotation depends on
      inputs: 10y and 2y constant-maturity yields
      lookback: 252
      lag: 1
      frame: macro
      representation: level, z-score, regime indicator
      failure_mode: >-
        the revised Treasury history, not the initial release, so the lag is right for
        publication timing but not for revisions

# What `04_model_based_features` decides, declared here for the same reason the
# feature windows above are: an estimation window is part of a fitted feature's
# definition, so it belongs where the definition lives rather than inside the
# notebook that runs it. Every count is in trading sessions.
#
# A fitted feature is bounded by this schedule and not by a cross-validation
# fold. The parameters behind a value are estimated from sessions strictly
# before it, refreshed on the cadence below, and the same value is produced
# whichever fold later selects the row - so the artifact carries no fold column.
model_based:
  hmm:
    # Calm and stressed. Two states is what the transition matrix and the
    # regime means below are read as, and section D reports them by name.
    n_states: 2
    # Sessions of market history spent before the first regime model is fitted.
    # Three years. A two-state chain has to see both states switch several times
    # before its transition matrix means anything, and the market series is one
    # series rather than a panel, so this is paid once for the whole notebook.
    # The folds roll a ten-year training window back one year at a time, so the
    # oldest of them begins on the second session of the panel and no burn-in
    # fits before it: this one costs the earliest folds training rows. What it
    # must not touch is a scored session, and it does not - it ends 2009-02-04,
    # 1,736 sessions before the earliest validation session any configured label
    # is scored on, 2015-12-28. 04_model_based_features asserts that and prints
    # the training rows each fold loses.
    burnin: 756
    # How often the chain is re-estimated. A quarter. Regime parameters are the
    # slowest-moving thing this notebook fits, and each estimate costs
    # `n_restarts` expectation-maximization searches.
    refit_every: 63
    # Expectation-maximization reaches a local optimum, so each fit is repeated
    # from this many starting points and the highest training likelihood is kept.
    n_restarts: 10
  garch:
    # Sessions of an ETF's own returns before its variance model is fitted. Two
    # years. Below that the persistence term is estimated from too few
    # volatility cycles, and it is the term the feature is most sensitive to.
    burnin: 504
    # Monthly. Faster than the regime model because a variance model tracks a
    # level that moves, which is the property the feature exists to report.
    refit_every: 21

```