wehub-resource-sync
469 lines
14 KiB
YAML
469 lines
14 KiB
YAML
strategy_id: nasdaq100_microstructure
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setup_version: v1
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universe:
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symbols:
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- AAL
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- AAPL
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- ADBE
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- ADI
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- ADP
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- ADSK
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- AEP
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- ALGN
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- ALXN
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- AMAT
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- AMD
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- AMGN
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- AMZN
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- ANSS
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- ASML
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- ATVI
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- AVGO
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- BIDU
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- BIIB
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- BKNG
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- BMRN
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- CDNS
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- CDW
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- CERN
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- CHKP
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- CHTR
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- CMCSA
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- COST
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- CPRT
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- CSCO
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- CSGP
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- CSX
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- CTAS
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- CTSH
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- CTXS
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- DLTR
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- DOCU
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- DXCM
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- EA
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- EBAY
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- EXC
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- EXPE
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- FAST
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- FB
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- FISV
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- FOX
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- FOXA
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- GILD
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- GOOG
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- GOOGL
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- IDXX
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- ILMN
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- INCY
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- INTC
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- INTU
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- ISRG
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- JD
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- KDP
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- KHC
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- KLAC
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- LBTYA
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- LBTYK
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- LRCX
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- LULU
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- MAR
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- MCHP
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- MDLZ
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- MELI
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- MNST
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- MRNA
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- MRVL
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- MSFT
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- MTCH
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- MU
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- MXIM
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- NFLX
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- NTAP
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- NTES
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- NVDA
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- NXPI
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- OKTA
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- ORLY
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- PAYX
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- PCAR
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- PDD
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- PEP
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- PTON
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- PYPL
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- QCOM
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- REGN
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- ROST
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- SBUX
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- SGEN
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- SIRI
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- SNPS
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- SPLK
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- SWKS
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- TEAM
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- TMUS
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- TSLA
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- TTWO
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- TXN
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- UAL
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- ULTA
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- VRSK
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- VRSN
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- VRTX
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- WBA
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- WDAY
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- WDC
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- WLTW
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- XEL
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- XLNX
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- ZM
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n_assets: 114
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eligibility_rule: nasdaq100_membership
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# Cost-feasible universe: the cheapest-to-trade names by round-trip cost,
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# frozen per split. Provenance: _build_cost_feasible_universe.py
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# (round-trip proxy 2*(per_share/mean_price)*1e4 + 2*median_half_spread_bps).
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# The validation list is profiled on quote bars strictly before the
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# validation window (2020-01-01 -> 2020-06-30); the holdout list from the
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# pre-holdout liquidity profile (< 2021-07-01). No look-ahead. Selected via
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# strategy.signal.universe_filter='cost_feasible'; the split-specific list is
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# chosen at backtest time by resolving the prediction set's split (NOT a
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# spec-hash input — like the 'liquid' filter, the hash carries only the
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# filter name). The full 114 universe collapses out of sample (featured
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# config holdout -0.42) while the cost-feasible universe is positive; the
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# screen is load-bearing. See 16_costs.py for the full-vs-screened contrast.
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cost_feasible:
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validation:
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- AAPL
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- MSFT
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- FB
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- SBUX
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- PEP
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- ATVI
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- INTC
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- PYPL
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- MDLZ
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- AMD
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- QCOM
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- TXN
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- GILD
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- MU
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- NVDA
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- AMAT
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- CSCO
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- TMUS
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- WBA
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- COST
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- JD
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- CSX
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- XEL
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- CMCSA
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- EXC
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- EBAY
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- MNST
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- FISV
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- FAST
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- EA
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- CTSH
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- NFLX
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- XLNX
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- AMZN
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- ADBE
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- PAYX
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- CDNS
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- AMGN
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- MXIM
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- BIDU
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- PCAR
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- CERN
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- UAL
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- ADP
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- KHC
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- FOXA
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- WDC
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- TSLA
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- FOX
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- CTXS
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holdout:
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- AAPL
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- MSFT
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- FB
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- SBUX
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- PEP
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- GILD
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- AMD
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- QCOM
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- INTC
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- MDLZ
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- ATVI
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- AMAT
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- MU
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- PYPL
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- AEP
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- TXN
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- JD
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- CSX
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- MRVL
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- COST
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- TMUS
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- CMCSA
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- EBAY
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- CSCO
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- NVDA
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- XEL
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- FISV
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- WBA
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- EXC
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- FAST
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- TSLA
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- CTSH
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- MXIM
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- ADI
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- AMZN
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- NFLX
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- AMGN
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- EA
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- PAYX
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- CERN
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- ADBE
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- ADP
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- KHC
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- CDNS
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- XLNX
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- MNST
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- KDP
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- BIDU
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- DLTR
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- UAL
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decision:
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bar_frequency: 15_minute
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decision_snapshot: bar_close
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execution_delay: 1_bar
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execution_price_assumption: next_bar_open_or_vwap
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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. 15-min bars (26 bars/day);
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# 520 bars ≈ 20 trading days ≈ 1 month of intraday coverage. ``periods_per_year``
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# annualizes Sharpe at daily-equivalent grain (252); allocator windows are
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# measured in raw 15-min bars regardless.
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#
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# Expensive allocators (RP/HRP/MVO_LW) are dropped via expensive_allocators_skip
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# below — covariance estimation at 15-min cadence is prohibitive and
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# empirically degenerate. MVO_LW carries no per-allocator lookback override
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# here for that reason; revisit if expensive allocators are reintroduced
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# (a 6-month equivalent at 15-min would need ~3,300 bars).
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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 catastrophic degenerate output here
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# (avg Sharpe -11 across 339 signal runs; integer-share rounding × the
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# nasdaq-100 high-priced tail (BKNG≈$5k, MSTR/NFLX/AVGO $700-1.5k)). See
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# 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: 520 # ~20 trading days of 15-min bars
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mapping:
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class: intraday_rank_and_trade
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position_state_space: long_short
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entry_logic: rank_or_threshold
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sizing: dollar_neutral_or_beta_neutral
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costs:
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class: dominant
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# Per-share commission plus measured per-asset half-spread slippage from
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# AlgoSeek NBBO close quotes over the training window. Integer share
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# sizing. The asset_spreads_source parquet is loaded by the backtest
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# cost-preset builder and joined per symbol; default_half_spread_usd is
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# the universe p75 fallback for symbols not in the profile.
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model: per_share_plus_spread
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per_share: 0.0035 # IBKR Pro Tiered top tier (<=300K shares/mo)
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minimum: 0.0
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asset_spreads_source: liquidity_profile.parquet
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asset_spreads_column: median_half_spread_usd
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default_half_spread_usd: 0.025
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spread_convention: half_spread
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source: AlgoSeek minute-bar NBBO close quotes, training window 2020-01-02 to 2021-07-01.
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friction_floor_bps: 5
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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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# Canonical strategy universe. The 15-min round-trip cost on the
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# cost-expensive tail of the 114-name panel consumes the intraday edge:
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# the featured config collapses out of sample on the full universe
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# (holdout -0.42) but is positive on the cost-feasible subset (the
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# cheapest-to-trade names, frozen per split — see universe.cost_feasible).
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# The full-universe variant is NOT a canonical rank-1 / cohort / DSR
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# candidate; it lives only in the 16_costs.py full-vs-screened comparison,
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# mirroring sp500_options' liquid screen. Read by
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# case_studies.utils.sweep_config.get_universe_filters_for.
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universe_filter: cost_feasible
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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: 0 # nasdaq has no expensive allocators (see expensive_allocators below)
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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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# No expensive allocators on nasdaq. At 15-min frequency the 1.3M-bar
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# panel makes covariance estimation on the rebalance schedule
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# prohibitive; empirically every expensive allocator (risk_parity,
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# mvo_ledoit_wolf, hrp) produces deeply negative Sharpes (-7 to -10)
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# on every nasdaq label and equal_weight ties at the top. The 15-min
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# cadence is a costs-dominated regime where signal-stage selection
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# carries the entire story.
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expensive_allocators_skip: true
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expensive_allocators: []
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# Ch16 signal-stage selection. Long-short equal-weight top-k on the
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# 15-min schedule across four labels (5m/15m/60m continuous + 15m
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# direction). The classification label fwd_dir_15m is a first-class
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# signal alongside the continuous labels and carries the same top_k
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# grid.
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top_k_grid:
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fwd_ret_5m: [5, 10, 20]
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fwd_ret_15m: [5, 10, 20]
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fwd_ret_60m: [5, 10, 20]
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fwd_dir_15m: [5, 10, 20]
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# Ch17 portfolio: reuses top_k_grid above and sweeps over allocators.
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# Cheap tier only on nasdaq (see ``expensive_allocators`` above).
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# No max_weight cap on inverse_vol — see
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# memory/feedback_max_weight_caps_intentionally_absent.md (would force
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# equal-weight at top_k=5 and defeat the moment-allocator comparison).
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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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# Ch18 cost sensitivity (bps regime).
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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 cost regime: dollars per share half-spread. Swept
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# alongside cost_grid_bps for CSes whose declared cost model is
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# per_share_plus_spread. 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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# Alternative rebalance cadences explored by Ch18's cadence × cost
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# heatmap (16_costs.py Section 4). The first entry must be the CS's
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# default cadence (``decision.bar_frequency``). Each cadence reuses the
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# ``cost_grid_half_spread_usd`` grid above. Tokens are the same cadence
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# vocabulary recognized by the engine; the notebook pulls the list from
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# here, never hardcoded.
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cadence_sweep: [15_minute, 30_minute, 1_hour, 4_hour]
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# nasdaq100 v4 slot-mechanism sweep (Ch16 signal stage replacement).
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#
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# Adds two selection methods to the canonical Ch16 expansion:
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# - slot_persistent_signal_exit: per-symbol rolling-percentile entry,
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# fixed weight-per-slot allocation (slots ARE the allocation —
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# Ch17 allocator stage is skipped for these rows), max-hold +
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# optional signal-exit (rolling lower quantile crossover) for
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# position lifecycle. See case_studies/utils/slot_strategy.py for
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# the mechanism. Sub-params live under selection_method_config in
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# the flattened scheme dict.
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# - eq_w_topk: the canonical equal-weight top-K baseline, kept as
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# a within-sweep comparison. Direction grid covers long_only,
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# long_short.
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#
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# slot × long_short is intentionally dropped — slot books are
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# single-direction by construction (one symbol cannot occupy a long
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# AND short slot simultaneously). The cross-asset long-short story
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# belongs to eq_w_topk / quintile_long_short.
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#
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# Per-prediction cardinality:
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# slot: long_q(3) × max_slots(3) × hold_bars(3) × exit_signal_q(4)
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# × bars_per_day_grid(1) = 108 (long_only only)
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# eq_w_topk: top_k(3) × direction(2) = 6
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# ~114 per prediction × ~12 representative preds ≈ ~1,400 backtests
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# for the signal stage on val (less than the original 2,800 estimate
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# because slot × long_short is dropped).
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signal_nasdaq100:
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selection_method: [slot_persistent_signal_exit, eq_w_topk]
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long_q: [0.90, 0.95, 0.99]
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direction: [long_only, long_short]
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max_slots: [5, 10, 20]
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hold_bars: [8, 16, 32] # 2h, 4h, 8h at 15-min grid
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exit_signal_q: [null, 0.30, 0.50, 0.70] # null disables signal-exit
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pred_freshness_max_min: 14 # variant-A: freshest 1m pred per 15m bar
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bars_per_day_grid: [14] # 15m only; 1m cadence failed comprehensively
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top_k_grid: [5, 10, 20]
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lookback_days: 21
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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: 6M
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val_size: 6M
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holdout_start: '2021-07-01'
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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 (daily MTM despite intraday signal cadence)
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labels:
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primary: fwd_ret_15m
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buffer: 15min
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variants:
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- fwd_ret_5m
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- fwd_ret_60m
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- fwd_dir_15m
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variant_buffers:
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fwd_ret_5m: 5min
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fwd_ret_60m: 60min
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fwd_dir_15m: 15min
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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_15m: 1
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fwd_ret_5m: 1
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fwd_ret_60m: 4 # 15min schedule, 60min horizon -> ceil(60/15) = 4
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fwd_dir_15m: 1
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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_15m: fwd_ret_15m
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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: cpu
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causal:
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treatment: signed_vol_share
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confounders: [rel_spread_close, rv_5m, r1m]
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method: walk_forward_dml
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