wehub-resource-sync
190 lines
8.1 KiB
YAML
190 lines
8.1 KiB
YAML
strategy_id: sp500_equity_option_analytics
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setup_version: v1
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universe:
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n_assets: 633
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eligibility_rule: sp500_with_options
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decision:
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cadence: weekly_friday_close
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snapshot: friday_16:00_et
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execution_delay: monday_open
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iv_feature_lag: 1_day
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# Engine-level execution defaults. Single source of truth — Ch16-19 notebooks
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# read these via get_backtest_config(); never declare a local INITIAL_CASH
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# or share_type constant. Changing values here invalidates every existing
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# backtest_hash for this case study.
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#
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# Spec-hash inputs (each invalidates every backtest_hash on change):
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# - execution.initial_cash
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# - execution.share_type
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# - execution.allocator_lookback (CS-level fallback for moment allocators)
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# - per-allocator overrides in backtest.sweep.allocators
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#
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# ``allocator_lookback`` is the bars-of-underlying-price window applied
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# uniformly to every moment-based allocator. Daily S&P 500 equity bars
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# → 63 ≈ 3 months. ``mvo_ledoit_wolf`` carries an explicit per-allocator
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# override below (126 bars ≈ 6 months) so N/K shrinkage degeneracy doesn't
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# collapse it to identity-target at top_k=20.
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#
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# ``initial_cash`` restored to 1_000_000 (2026-05-16) — the 2026-05-15 SSOT
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# migration drop to 100k caused near-zero rebalancing here (min num_trades
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# = 27 over 4y daily on 500-name × top_k=20: at $5k/name budget × integer
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# rounding × the S&P high-priced tail, the engine almost never trades).
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# See memory/feedback_2026_05_15_equity_sizing_invalidated.md.
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execution:
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initial_cash: 1_000_000 # restores prior validated state
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share_type: integer # US equities trade in whole shares
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allocator_lookback: 63 # 3 months of daily bars (IV/RP/HRP fallback)
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mapping:
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class: long_only_rank_and_rebalance
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position_state_space: long_only
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entry_logic: rank_by_iv_signal
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sizing: equal_weight
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costs:
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class: material
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model: percentage # Loader path: bps regime via per_leg_cost_bps_range midpoint.
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components: [spread, commission, market_impact]
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per_leg_cost_bps_range: [3, 10]
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round_trip_cost_bps: 13 # Midpoint of per_leg_cost_bps_range (6.5 bps × 2 legs).
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# per_share is the commission rate for the exploratory per-share
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# cost-sensitivity regime (read by Ch18 16_costs.py and the run_sweep
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# planner). IBKR Pro Tiered top tier. NOT used in the headline bps
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# regime — bps regime is the production cost model.
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per_share: 0.0035
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note: Trades equities (not options); S&P 500 names are liquid and weekly rebalancing keeps turnover moderate.
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backtest:
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rebalance:
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# A rebalance is skipped when the per-asset weight change is below
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# min_weight_change AND the resulting trade notional is below
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# min_trade_value. The benchmark profile disables thresholds so that
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# full-universe equal-weight (1/N per asset) rebalances at all.
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default:
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min_weight_change: 0.005
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min_trade_value: 100.0
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benchmark:
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min_weight_change: 0.0
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min_trade_value: 0.0
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sweep:
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# Iteration controls per stage. ``signal: 0`` means "all predictions";
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# downstream stages take the top-N from the upstream stage's rank-1.
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# Notebooks read these via get_top_n_predictions(case_study, stage).
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top_n_predictions:
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signal: 0 # all signal predictions (eq-weight baseline)
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allocation_cheap: 0 # score_weighted, inverse_vol — all signal preds
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allocation_expensive: 10 # risk_parity, mvo_ledoit_wolf, hrp — top-10 by signal Sharpe
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cost_sensitivity: 1 # top-1 of {signal+allocation} per label
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risk_overlay: 1 # top-1 of {signal+allocation} per label
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# Skip MVO/HRP when allocator runtime is the bottleneck (intraday CSes).
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expensive_allocators_skip: false
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# Allocators routed through ``allocation_expensive`` (top-10 by signal Sharpe).
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# All others (equal_weight, score_weighted, inverse_vol) take the cheap path.
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expensive_allocators: [risk_parity, mvo_ledoit_wolf, hrp, conformal_weighted]
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# Ch16 signal-stage selection. Long-only equal-weight top-k for every
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# label.
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top_k_grid:
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fwd_ret_5d: [5, 10, 20]
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fwd_ret_10d: [5, 10, 20]
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fwd_ret_risk_adj_5d: [5, 10, 20]
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fwd_dir_5d: [5, 10, 20]
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fwd_dir_10d: [5, 10, 20]
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# Ch17 portfolio: reuses top_k_grid above and sweeps over allocators.
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# Moment-based allocators (IV/RP/HRP) use the CS-level
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# ``execution.allocator_lookback`` (63 bars). ``mvo_ledoit_wolf`` gets
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# an explicit 126-bar override (6 months) so N/K ≥ 2.5 at top_k=20 and
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# Ledoit-Wolf shrinkage doesn't collapse to identity-target.
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# No max_weight cap — see memory/feedback_max_weight_caps_intentionally_absent.md
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# (the prior 0.40 cap was already the loosened-from-0.20 workaround;
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# restoring it would push moment allocators back toward equal-weight).
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allocators:
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- {name: equal_weight, method: equal_weight}
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- {name: score_weighted, method: score_weighted}
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- {name: inverse_vol, method: inverse_vol}
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- {name: risk_parity, method: risk_parity}
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- {name: mvo_ledoit_wolf, method: mvo_ledoit_wolf, lookback: 126}
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- {name: hrp, method: hrp}
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- {name: conformal_weighted, method: conformal_weighted}
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# Ch18 cost sensitivity (bps regime — headline). A companion per-share
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# regime is run from Ch18 cost notebooks for regime comparison; for
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# sp500_eoa it is exploratory only because flat-default half-spread on
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# split-adjusted prices conflates split adjustment with realized
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# friction.
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cost_grid_bps: [0, 1, 2, 3, 5, 7, 10, 15, 20, 30, 50]
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# Companion per-share half-spread grid (USD per share). Swept alongside
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# cost_grid_bps by the planner. Values: 0¢, 0.5¢, 1¢, 2.5¢, 5¢, 10¢.
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cost_grid_half_spread_usd: [0.0, 0.005, 0.01, 0.025, 0.05, 0.10]
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# Ch19 risk overlays.
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risk_controls:
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position:
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- {name: stop_loss_3pct, type: stop_loss, threshold: 0.03}
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- {name: stop_loss_5pct, type: stop_loss, threshold: 0.05}
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- {name: stop_loss_10pct, type: stop_loss, threshold: 0.10}
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- {name: stop_loss_15pct, type: stop_loss, threshold: 0.15}
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- {name: trailing_1pct, type: trailing_stop, threshold: 0.01}
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- {name: trailing_2pct, type: trailing_stop, threshold: 0.02}
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- {name: trailing_3pct, type: trailing_stop, threshold: 0.03}
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- {name: trailing_5pct, type: trailing_stop, threshold: 0.05}
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- {name: trailing_10pct, type: trailing_stop, threshold: 0.10}
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- {name: trailing_15pct, type: trailing_stop, threshold: 0.15}
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- {name: trailing_20pct, type: trailing_stop, threshold: 0.20}
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- {name: time_exit_10, type: time_exit, bars: 10}
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- {name: time_exit_20, type: time_exit, bars: 20}
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- {name: time_exit_40, type: time_exit, bars: 40}
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evaluation:
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n_splits: 2
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train_size: 2Y
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val_size: 1Y
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holdout_start: '2021-01-01'
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holdout_end: '2021-12-31'
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calendar: NYSE
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periods_per_year: 252 # NYSE 5d/wk
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labels:
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primary: fwd_ret_5d
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buffer: 10D
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variants:
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- fwd_ret_10d
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- fwd_ret_risk_adj_5d
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- fwd_dir_5d
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- fwd_dir_10d
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variant_buffers:
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fwd_ret_10d: 10D
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fwd_ret_risk_adj_5d: 5D
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fwd_dir_5d: 5D
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fwd_dir_10d: 10D
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# Vectorized-backtest thinning step per label: number of schedule slots
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# to advance per trade so holding periods don't overlap.
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rebalance_step:
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fwd_ret_5d: 1 # ceil(5 / 5) on the weekly_friday schedule
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fwd_ret_10d: 2 # ceil(10 / 5)
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fwd_ret_risk_adj_5d: 1 # ceil(5 / 5)
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fwd_dir_5d: 1 # ceil(5 / 5)
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fwd_dir_10d: 2 # ceil(10 / 5)
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# Continuous return that each classification label is derived from.
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classification_eval_label:
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fwd_dir_5d: fwd_ret_5d
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fwd_dir_10d: fwd_ret_10d
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modeling:
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gbm:
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libraries: [lightgbm]
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preset: default
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device: gpu
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latent_factors:
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persistent_entities: true
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model_kwargs:
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sdf:
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checkpoint_epochs: [256, 512, 768, 1024, 1280]
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beta_checkpoint_epochs: [256]
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beta_default_checkpoint: 256
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causal:
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treatment: ivrv_spread
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confounders: [rv_20, mom_21d, skew_rr_30_25d]
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method: walk_forward_dml
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