Бэктесты акций US по характеристикам: сроки, издержки и ограничения распределения

# setup.yaml


```yaml
strategy_id: us_firm_characteristics
setup_version: v2

universe:
  inclusion_rule: complete_characteristic_case
  identifiers: anonymous_split_scoped_firm_axis
  note: >-
    The authors retain observations with all 46 characteristics available.
    Anonymous identifiers are persistent within each released tensor block;
    the archive does not publish a mapping between blocks.
  # Approximate active cross-sectional breadth. Display metadata only; the
  # backtest counts assets from data.
  n_assets: 2500

decision:
  cadence: monthly_month_end
  snapshot: month_end_close
  execution_delay: next_bar_open
  characteristic_availability: provider_standard_conventions
  yearly_update: end_of_june
  monthly_update: month_end_for_next_month

# 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).
#
# THE ALLOCATOR EXCLUSIONS, AND THE REASON IS THE CADENCE RATHER THAN THE PANEL.
#
# An earlier version of this comment said the moment-based allocators were
# excluded because firm identities are not stable across periods. That is false
# of this dataset and its own columns refute it: ``r36_13`` is a return measured
# from month 36 to month 13 before the decision date, and it is non-null on all
# 804,530 rows. A feature computed from a 36-month window cannot be complete
# without 36 months of continuous history per firm. Whatever the anonymous
# split-scoped identifiers do across tensor blocks, they are stable enough within
# one to support a three-year lookback on every row.
#
# The real constraint is the observation count. ``allocator_lookback`` is 12 and
# the bars are monthly, so a covariance is estimated from twelve points and has
# rank at most eleven. What that rules out depends on how many names are held,
# and ``top_k_grid`` below is [5, 10, 20, 50] against a long-short mapping, so
# the name count is twice the top_k:
#
#   top_k=5  -> 10 names -> rank 11 covers it        -> IDENTIFIED
#   top_k=10 -> 20 names -> unidentified
#   top_k=20 -> 40 names -> unidentified
#   top_k=50 -> 100 names -> unidentified
#
# So ``hrp`` (allocation.py:516, rolling correlation matrix) and
# ``mvo_ledoit_wolf`` (allocation.py:362, Ledoit-Wolf covariance) are structurally
# unidentified on three of the four mappings and estimable on the smallest. Not on
# any of them for a shrinkage reason: `sweep_config.get_allocators` injects the
# case-study lookback as ``lookback`` for mvo, OVERRIDING `compute_mvo_weights`'
# own 126 default, and shrinkage does not manufacture the missing observations.
#
# That argument does NOT reach ``inverse_vol`` (allocation.py:295) or
# ``risk_parity`` (allocation.py:321). Neither builds a matrix: both take a
# per-asset rolling standard deviation and weight by 1/vol or 1/vol^1.5, one
# scalar per name, full rank by construction, and estimable from twelve points.
# They are excluded by decision rather than by identification: the allocation
# stage is kept to the equal-weight baseline and the two lookback-free
# alternatives, and building the rolling volatility they need is work this case
# study is not spending.
#
# Worth recording so it is not re-derived: the panel already carries supplied
# volatility measures - ``Variance``, ``IdioVol``, ``Resid_Var``, ``Beta`` and
# ``MktBeta`` are all columns of ``labels/prices.parquet``. An allocator weighting
# by one of those would need no rolling window and no lookback at all. Nobody has
# asked for one and it is not scheduled; the option exists and was not taken.
#
# ``allocator_lookback`` stays because ``get_backtest_config`` requires the key,
# and it is inert while the menu declares no moment-based allocator:
# sweep_config.get_allocators injects it as ``vol_window`` only into those.
#
# Cash defaults to 1_000_000 rather than 100_000: monthly-cadence
# long-short portfolios at top_k=50 carry ~$10K per leg per name at the
# 100k tier - below realistic round-trip granularity for institutional
# US equity. 1M keeps the spec-implied position sizes (notional per name
# / fixed-cost ratio) in a regime the backtest engine resolves cleanly.
execution:
  initial_cash: 1_000_000        # Monthly long-short top_k=50 needs $1M to size cleanly
  share_type: integer            # US equities trade in whole shares
  allocator_lookback: 12         # 1 year of monthly bars

mapping:
  class: long_short_top_k_rebalance
  position_state_space: long_short
  entry_logic: rank_top_k_long_bottom_k_short
  sizing: equal_weight_within_leg

costs:
  class: material
  components: [spread, commission, market_impact, borrow_cost]
  per_leg_cost_bps_range: [5, 20]
  borrow_cost_note: Long-short requires borrow for the short leg.
  era_note: Pre-2001 spreads 15-30 bps; post-2001 (decimalization) 5-15 bps.

evaluation:
  n_splits: 10
  train_size: 10YE
  val_size: 1YE
  holdout_start: '2016-01-01'
  holdout_end: '2016-12-31'
  calendar: null  # Monthly returns; calendar-aware splitting needs daily frequency.
  periods_per_year: 12

labels:
  primary: fwd_ret_1m
  buffer: 1M
  variants:
    - fwd_ret_1m_win
    - fwd_class_1m
  variant_buffers:
    fwd_ret_1m_win: 1M
    fwd_class_1m: 1M
  # How far past its own timestamp each label's outcome resolves. This is the quantity
  # generate_cv_splits seals the last validation fold on, and it is not the buffer above.
  # The release pairs the characteristics observed at the close of month t-1 with the return
  # earned over month t and dates the row by month t, so a row's return is already realised on
  # the timestamp the row carries, and no validation month has to be given back before the
  # holdout opens. 02_labels section D measures that alignment rather than assuming it: ST_REV,
  # a firm's own most recent monthly return, has rank correlation +0.904 with the previous
  # row's label and -0.028 with its own.
  # The buffer above stays 1M. Separating a training window from the validation window that
  # follows it is a different decision from when an outcome becomes known, and that one is
  # deliberately conservative here.
  horizons:
    fwd_ret_1m: 0D
    fwd_ret_1m_win: 0D
    fwd_class_1m: 0D
  # Vectorized-backtest thinning step per label: number of schedule slots
  # to advance per trade so holding periods don't overlap. Authored from
  # (schedule cadence, the span the label measures over); add an entry for any new label.
  rebalance_step:
    fwd_ret_1m: 1
    fwd_ret_1m_win: 1
    fwd_class_1m: 1
  # Continuous return that each classification label is derived from.
  # IC for classification predictions is computed against this column;
  # AUC/accuracy/log_loss are computed against the binary label itself.
  classification_eval_label:
    fwd_class_1m: fwd_ret_1m

features:
  # Window over which the feature matrix is built. Both endpoints are inclusive and
  # 03_financial_features binds them from here rather than retyping them.
  window:
    start: 1990-01-01
    end: 2016-12-31

  # The feature register. Rendered by case_studies.utils.feature_engineering
  # .register_frame and drawn as the timing contract by plot_timing_contract, so
  # `lookback` and `lag` are read by the figure as well as by the prose.
  #
  # `lookback` and `lag` are counted in months, this case study's bar.
  #
  # A caveat that belongs with the numbers rather than under them: the release does
  # not publish a per-characteristic estimation window. What is published is the
  # update convention (decision.yearly_update, decision.monthly_update), and some
  # characteristics name their own window - r36_13 reads 36 months back to 13. The
  # lookbacks below are those two sources and nothing else; where a characteristic
  # publishes neither, the entry is the span of one provider observation. The lag is
  # the load-bearing column for look-ahead and it is fully sourced: an annual variable
  # is published at the end of June against a December fiscal year end.
  families:
    - name: value
      pattern: "BEME|E2P|CF2P|D2P|S2P|A2ME"
      role: signal
      hypothesis: A firm priced low against its fundamentals earns the higher subsequent return
      inputs: released annual accounting characteristics, provider rank-transformed
      lookback: 12
      lag: 6
      frame: cross section
      representation: provider cross-sectional rank in [-0.5, 0.5]
      failure_mode: cheapness that reflects permanent impairment rather than mispricing

    - name: quality
      pattern: "PROF|ROE|ROA|OP|PM|PCM|RNA"
      role: signal
      hypothesis: A more profitable firm earns the higher subsequent return at the same price
      inputs: released annual accounting characteristics, provider rank-transformed
      lookback: 12
      lag: 6
      frame: cross section
      representation: provider cross-sectional rank in [-0.5, 0.5]
      failure_mode: margins mean-revert faster than the annual update reports them

    - name: investment
      pattern: "Investment|NOA|DPI2A|NI|OA|AC"
      role: signal
      hypothesis: A firm growing its asset base aggressively earns the lower subsequent return
      inputs: released annual accounting characteristics, provider rank-transformed
      lookback: 24
      lag: 6
      frame: cross section
      representation: provider cross-sectional rank in [-0.5, 0.5]
      failure_mode: a growth measure spans two annual observations, so one restatement moves both

    - name: momentum
      pattern: "r12_2|r2_1|r12_7|r36_13|ST_REV|LT_Rev|SUV|Rel2High"
      role: signal
      hypothesis: Recent relative price trends persist over a quarter to a year and reverse beyond it
      inputs: released monthly price and return characteristics, provider rank-transformed
      lookback: 36
      lag: 0
      frame: cross section
      representation: provider cross-sectional rank in [-0.5, 0.5]
      failure_mode: trends break at a reversal faster than a 12-month window unwinds

    - name: risk
      pattern: "Beta|MktBeta|IdioVol|Resid_Var|Variance|Spread|LTurnover|LME"
      role: state
      hypothesis: Volatility, liquidity and size describe the regime a signal is read in
      inputs: released monthly risk and liquidity characteristics, provider rank-transformed
      lookback: 12
      lag: 0
      frame: cross section
      representation: provider cross-sectional rank in [-0.5, 0.5]
      failure_mode: >-
        LME travels with this family in the provider's grouping but behaves as a signal,
        so a family-level reading of the group mixes a size premium with regime description

    - name: other
      pattern: "Q|C|CF|AT|ATO|CTO|D2A|FC2Y|Lev|OL|SGA2S"
      role: signal
      hypothesis: Leverage, turnover and cost structure carry information the five named families omit
      inputs: released accounting characteristics, provider rank-transformed
      lookback: 12
      lag: 6
      frame: cross section
      representation: provider cross-sectional rank in [-0.5, 0.5]
      failure_mode: a residual grouping has no single thesis, so a family average over it means little

    # The constructed columns are split by the timing of the members they read, not
    # bundled under one row per construction. A single `composite` entry would have to
    # claim one lookback and one lag for `composite_value` (annual accounting, published
    # end-June) and `composite_momentum` (monthly prices, no publication lag), and would
    # be wrong about one of them whichever pair it named.
    #
    # A construction that mixes the two gets its own row, and the row spans everything it
    # reads: lookback 18 and lag 0. An earlier version gave these the accounting member's
    # lag of 6, which made `plot_timing_contract` draw them as reading nothing from the six
    # months before the decision - and they do, through the momentum member. The composite
    # itself is knowable at the decision timestamp, because its accounting member was
    # published six months earlier and its price member is current, so the lag is zero and
    # the window reaches back to the oldest input.

    - name: composite accounting
      pattern: "composite_value|composite_quality|composite_value_quality"
      role: signal
      hypothesis: Averaging ranks within and across families cancels characteristic-specific noise
      inputs: the released value and quality characteristics, within the same row
      lookback: 12
      lag: 6
      frame: cross section
      representation: equal-weight mean of member ranks, on the members' own scale
      failure_mode: an equal weight asserts the members are equally informative, which is untested here

    - name: composite investment
      pattern: "composite_investment"
      role: signal
      hypothesis: Averaging the investment characteristics cancels measure-specific noise
      inputs: the released investment characteristics, within the same row
      lookback: 24
      lag: 6
      frame: cross section
      representation: equal-weight mean of member ranks, on the members' own scale
      failure_mode: its members span two annual observations, so one restatement moves the composite twice

    - name: composite momentum
      pattern: "composite_momentum"
      role: signal
      hypothesis: Averaging the two 12-month momentum measures cancels their formation-window differences
      inputs: r12_2 and r12_7, within the same row
      lookback: 12
      lag: 0
      frame: cross section
      representation: equal-weight mean of member ranks, on the members' own scale
      failure_mode: both members skip a recent month, so neither reflects the last few weeks

    - name: interaction accounting
      pattern: "interaction_value_x_quality|interaction_value_x_roe"
      role: signal
      hypothesis: Some theses are conditional - cheap is worth more when the firm is also profitable
      inputs: BEME with PROF or ROE, within the same row
      lookback: 12
      lag: 6
      frame: cross section
      representation: product of two member ranks, so the sign encodes agreement
      failure_mode: a product of two centred ranks is large at both extremes and cannot separate them

    - name: composite mixed
      pattern: "composite_value_momentum|composite_quality_momentum"
      role: signal
      hypothesis: Pairing a slow accounting view with a fast price view cancels noise in both
      inputs: an annual accounting composite and the 12-month momentum composite, within the same row
      lookback: 18
      lag: 0
      frame: cross section
      representation: equal-weight mean of two composites, on the members' own scale
      failure_mode: >-
        the two halves move at different speeds, so the average is stale against the price
        member and current against the accounting one at the same time

    - name: interaction mixed
      pattern: "interaction_size_x_value"
      role: signal
      hypothesis: The value premium is larger among smaller firms
      inputs: LME and BEME, within the same row
      lookback: 18
      lag: 0
      frame: cross section
      representation: product of two member ranks, so the sign encodes agreement
      failure_mode: size is current while book-to-market is six months old, so the product pairs two different dates

    - name: interaction momentum
      pattern: "interaction_momentum_x_ivol"
      role: signal
      hypothesis: Momentum reads differently at high idiosyncratic volatility than at low
      inputs: r12_2 and IdioVol, within the same row
      lookback: 12
      lag: 0
      frame: cross section
      representation: product of two member ranks, so the sign encodes agreement
      failure_mode: a product of two centred ranks is large at both extremes and cannot separate them

backtest:
  rebalance:
    # Per-asset rebalance thresholds. 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. At top_k=50 the equal-weight
    # per-asset weight is 1/50 = 2%, comfortably above the 0.5% threshold.
    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 predictions at the equal-weight baseline
      allocation: 10            # top-10 model configs by equal-weight baseline Sharpe
      # cost_sensitivity takes no entry: the sweep runs the canonical rank-1 carrier,
      # resolved by resolve_solvent_carrier, so there is no top-N to declare.
      risk_overlay: 1           # top-1 of {signal+allocation} per label
    # Skip MVO/HRP when allocator runtime is the bottleneck (intraday CSes).
    expensive_allocators_skip: false
    # The two alternatives below take the cheap path. Equal weight is already
    # the baseline and must not be repeated as an allocation-stage method.
    # Ch16 backtest is equal-weight sized within the selected set. The
    # selection rule is one of: top-k (rank), percentile band (long-only
    # cutoff), or quantile buckets (e.g. quintile long-short, which combines
    # with the long-short mapping). Only top_k_grid is active here;
    # uncomment percentile_grid / quantile_grid to add other selection axes
    # for any label.
    top_k_grid:
      fwd_ret_1m:     [5, 10, 20, 50]
      fwd_ret_1m_win: [5, 10, 20, 50]
      fwd_class_1m:   [5, 10, 20, 50]
    # percentile_grid:
    #   fwd_ret_1m: [80, 90, 95]
    # quantile_grid:
    #   fwd_ret_1m: [5, 10]
    # Ch17 portfolio: reuses top_k_grid above and sweeps over allocators.
    # Equal weight is the baseline; these two are the alternatives, and both are
    # lookback-free. Why the other four are absent is in the execution block above
    # - identification for hrp and mvo_ledoit_wolf, decision for inverse_vol and
    # risk_parity. No max-weight cap.
    allocators:
      - {name: score_weighted,  method: score_weighted}
      - {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]
    # 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}

modeling:
  gbm:
    libraries: [lightgbm]
    preset: default
    # CPU is the reader-facing reproducible path. Numerical parameters stay
    # fixed when maintainers opt into a different execution backend.
    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
    num_threads: 8
  tabular_dl:
    # The members are declared per label in config/training/<label>.yaml, which is what
    # load_model_configs reads; a second list here would be a menu nothing consults.
    # Device and thread count are part of a TabM training identity, not provenance beside
    # it: a network's arithmetic depends on both, so a CUDA result and a CPU result of the
    # same configuration are different computations and hash differently.
    device: cuda
    num_threads: 8
  latent_factors:
    persistent_entities: true
    device: cuda
    num_threads: 8
    deterministic_algorithms: true
    # Only unrevised daily market observations enter the SDF context. The
    # one-day availability lag prevents same-close information from entering
    # the next-month forecast.
    macro_series:
      - dff
      - dgs1
      - dgs2
      - dgs3
      - dgs5
      - dgs7
      - dgs10
      - dgs20
      - dgs30
      - t10y2y
      - vixcls
    macro_availability_lag_days: 1
    model_kwargs:
      ipca:
        max_iter: 10000
        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: r12_2
  # Bars the treatment's own construction window spans, which is what the placebo block has
  # to cover: permuting r12_2 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:
  #
  # cumulative return from twelve months back to two months back, on a monthly panel
  # (03_financial_features). Twelve rows, because a row here is a month.
  treatment_window: 12
  confounders: [Beta, IdioVol, LME, Variance]
  method: walk_forward_dml

```