wehub-resource-sync
207 lines
8.2 KiB
YAML
207 lines
8.2 KiB
YAML
strategy_id: cme_futures
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setup_version: v1
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# 30 CME products grouped by sector. Tickers are CME root symbols.
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universe:
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product_groups:
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equity_index:
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- ES # E-mini S&P 500
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- NQ # E-mini Nasdaq-100
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- YM # E-mini Dow Jones Industrial Average
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- RTY # E-mini Russell 2000
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treasuries:
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- ZN # 10-year T-Note
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- ZB # 30-year T-Bond
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- ZF # 5-year T-Note
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- ZT # 2-year T-Note
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energy:
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- CL # WTI Crude Oil
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- NG # Henry Hub Natural Gas
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- HO # NY Harbor ULSD (heating oil)
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- RB # RBOB Gasoline
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metals:
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- GC # Gold
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- SI # Silver
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- HG # Copper
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- PL # Platinum
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currencies:
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- 6E # Euro / US Dollar
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- 6J # Japanese Yen / US Dollar
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- 6B # British Pound / US Dollar
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- 6A # Australian Dollar / US Dollar
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- 6C # Canadian Dollar / US Dollar
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- 6S # Swiss Franc / US Dollar
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agriculture:
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- ZC # Corn
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- ZS # Soybeans
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- ZW # Wheat (Chicago SRW)
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- ZM # Soybean Meal
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- ZL # Soybean Oil
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livestock:
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- LE # Live Cattle
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- HE # Lean Hogs
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- GF # Feeder Cattle
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n_products: 30
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decision:
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cadence: weekly_friday_close
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snapshot: settlement_price
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execution_delay: monday_open
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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 (cash + share_type are spec-hash inputs).
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#
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# ``initial_cash`` is sized so the full 30-product CME universe can fill
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# positions under integer-share rules across the top_k_grid (5, 10) in
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# long-short mode. The binding constraint is the high-notional equity-
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# index e-minis: at top_k=10 long-short the per-position dollar budget
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# is 5% × cash, and integer-rounding zeros out any product whose notional
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# exceeds that budget. ES holdout notional ≈ $347k and NQ ≈ $513k drive
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# the floor: $10M (per-position $500k) clears ES in the holdout and NQ
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# in the validation window. NQ in the very-late holdout (Dec 2025,
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# notional $513k) still integer-rounds out for that one product at
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# top_k=10 LS — a residual integer-share footnote documented in the
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# README "Margin model" section.
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#
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# Per-product initial+maintenance margin (futures_specs.yaml::
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# {initial,maintenance}_margin_pct → ContractSpec.margin_pct) is wired
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# through Engine.from_config(contract_specs=...); the engine auto-
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# populates the broker's margin_pct_schedule so the margin draw scales
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# with each bar's notional rather than the account-wide 50% default.
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# allow_leverage is set in config/backtest/base.yaml.
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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 (inverse_vol, risk_parity, hrp,
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# mvo_ledoit_wolf). CME daily settlement bars → 63 ≈ 3 months. Keeps
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# allocators comparable.
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execution:
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initial_cash: 10_000_000 # sized for full 30-product universe at top_k_grid
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share_type: integer # Futures contracts are integer
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allocator_lookback: 63 # 3 months of daily CME settlement bars
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mapping:
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class: long_short_carry_rank
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position_state_space: long_short
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entry_logic: rank_by_carry_or_momentum
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sizing: equal_risk_or_notional
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costs:
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class: material
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components: [commission, spread, roll_slippage]
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commission_per_contract: 2.0
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spread_ticks:
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liquid: 1
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illiquid: 2
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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-short equal-weight top-k on both
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# label horizons; long-short is enforced at the backtest config level
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# (allow_short_selling=true).
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# 30-product universe with long_short=True: top_k=20 selects 20 longs ∪
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# 20 shorts (40 positions) from 30 products → degenerate (collapses to
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# ~zero trades). Cap at 10 = floor(30/3) to keep selections non-degenerate.
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top_k_grid:
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fwd_ret_5d: [5, 10]
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fwd_ret_21d: [5, 10]
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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/MVO_LW) all use the CS-level
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# ``execution.allocator_lookback`` — no per-allocator vol_window /
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# lookback fields. No max-weight cap.
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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}
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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; commission + exchange fees + slippage
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# combined).
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cost_grid_bps: [0, 1, 2, 3, 5, 7, 10, 15, 20, 30, 50]
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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: 5
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train_size: 8Y
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val_size: 1Y
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holdout_start: '2024-01-01'
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holdout_end: '2025-12-31'
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calendar: CME
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periods_per_year: 252 # CME 5d/wk
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labels:
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primary: fwd_ret_5d
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buffer: 5D
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variants:
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- fwd_ret_21d
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variant_buffers:
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fwd_ret_21d: 21D
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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. Authored from
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# (schedule cadence, label horizon); add an entry here for any new label.
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rebalance_step:
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fwd_ret_5d: 1 # weekly_friday schedule, 5d horizon <= 7d gap
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fwd_ret_21d: 3 # weekly_friday schedule, 21d horizon -> ceil(21/7) = 3
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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: carry_pct
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confounders: [vol_21d, momentum_composite, carry_rank]
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method: walk_forward_dml
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