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827 lines
32 KiB
Python
827 lines
32 KiB
Python
"""Core domain models for the ACE-F backtester."""
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from __future__ import annotations
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import datetime as dt
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from enum import Enum
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from typing import Any
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from pydantic import BaseModel, ConfigDict, Field
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class PositionStatus(str, Enum):
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PLANNED = "PLANNED"
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ENTERED = "ENTERED"
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PARTIALLY_EXITED = "PARTIALLY_EXITED"
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OPEN = "OPEN"
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EXIT_PENDING = "EXIT_PENDING"
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CLOSED = "CLOSED"
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ARCHIVED = "ARCHIVED"
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class ExitReason(str, Enum):
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STOP = "STOP"
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TARGET = "TARGET"
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TIME = "TIME"
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TRAILING = "TRAILING"
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KILL_SWITCH = "KILL_SWITCH"
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MISSING_BAR = "MISSING_BAR"
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NO_FOLLOW_THROUGH = "NO_FOLLOW_THROUGH"
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EARLY_FAILURE = "EARLY_FAILURE"
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NO_PROGRESS = "NO_PROGRESS"
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GIVEBACK = "GIVEBACK"
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RECYCLE = "RECYCLE"
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class BacktestMode(str, Enum):
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RESEARCH = "research"
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LIVE = "live"
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class Candidate(BaseModel):
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"""An eligible trade candidate derived from a Parquet snapshot row."""
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model_config = ConfigDict(frozen=True)
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event_id: str
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symbol: str
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issuer_id: str | None = None
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score: float
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sector: str # "UNKNOWN" if unavailable
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event_type: str
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event_timestamp: dt.datetime # must be timezone-aware
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event_date: dt.date | None = None
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filing_time_bucket: str
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timing_class: str = "unknown" # "same_day", "after_close", "unknown"
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reaction_date: dt.date
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execution_date: dt.date # mapped from Parquet entry_date at SnapshotStore boundary
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entry_price_est: float # reaction-day close price
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avg_dollar_volume: float # 20-day mean(volume * close)
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atr_14: float | None = None
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score_bucket: str
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engine_id: str = "default"
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entry_timing_policy: str = "next_open"
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shadow_only: bool = False
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engine_min_entry_price: float | None = None
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engine_max_entry_price: float | None = None
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engine_max_holding_days: int | None = None
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engine_max_positions_per_sector: int | None = None
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engine_risk_budget_pct: float = 1.0
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engine_per_trade_risk_pct: float | None = None
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engine_target_atr_multiplier: float | None = None
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engine_stop_atr_multiplier: float | None = None
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engine_target_1_r: float | None = None
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engine_target_1_fraction: float | None = None
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engine_trailing_model: str | None = None
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engine_trailing_warmup_days: int | None = None
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engine_use_reaction_day_low_stop: bool | None = None
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engine_early_failure_close_below_entry_and_reaction_close: bool | None = None
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engine_early_failure_no_progress_days: int | None = None
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engine_early_failure_no_progress_r: float | None = None
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engine_early_failure_no_progress_fraction: float | None = None
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engine_veto_oneoff_penalty: float | None = None
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engine_allow_oneoff_downsizing: bool | None = None
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engine_oneoff_downsize_floor: float | None = None
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engine_veto_parse_confidence_min: float | None = None
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engine_allow_unknown_direction: bool | None = None
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engine_next_open_gap_cap_pct: float | None = None
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engine_add_on_max_count: int | None = None
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engine_add_on_size_fraction: float | None = None
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trade_direction: str = "long" # "long" or "short"
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parent_position_id: str | None = None
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is_add_on: bool = False
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forced_shares: int | None = None
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features: dict[str, Any] = Field(default_factory=dict)
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class PlannedOrder(BaseModel):
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"""A sized, gated order plan for a candidate."""
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model_config = ConfigDict(frozen=True)
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candidate: Candidate
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shares: int
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entry_price_limit: float
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stop_price: float
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target_price: float
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risk_dollars: float
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event_date: dt.date | None = None
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timing_class: str = "unknown"
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engine_id: str = "default"
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entry_timing_policy: str = "next_open"
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shadow_only: bool = False
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parent_position_id: str | None = None
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is_add_on: bool = False
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skip_reason: str | None = None # non-None means the order was rejected
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class FilledTrade(BaseModel):
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"""A completed (closed) trade leg."""
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model_config = ConfigDict(frozen=True)
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trade_id: str
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position_id: str
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event_id: str
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symbol: str
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event_date: dt.date | None = None
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event_type: str = ""
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score: float = 0.0
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timing_class: str = "unknown"
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engine_id: str = "default"
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entry_timing_policy: str = "next_open"
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shadow_only: bool = False
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parent_position_id: str | None = None
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is_add_on: bool = False
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entry_date: dt.date
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exit_date: dt.date
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entry_price: float
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exit_price: float
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exit_reason: ExitReason
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shares: int
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commission: float
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slippage_bps: float
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gross_pnl: float
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net_pnl: float
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pnl_pct: float
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r_multiple: float
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holding_days: int
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class OpenPosition(BaseModel):
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"""A live open position (mutable throughout its lifetime)."""
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position_id: str
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plan: PlannedOrder
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entry_date: dt.date
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entry_price: float
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entry_fill_slippage_bps: float
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current_stop: float
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target_price: float
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peak_price: float
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shares_open: int
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shares_total: int
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parent_position_id: str | None = None
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is_add_on: bool = False
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days_held: int = 0
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status: PositionStatus = PositionStatus.ENTERED
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partial_fills: list[FilledTrade] = Field(default_factory=list)
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class DailyPortfolioState(BaseModel):
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"""Immutable snapshot of portfolio state at end of a trading day."""
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model_config = ConfigDict(frozen=True)
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date: dt.date
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equity: float
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sizing_equity: float | None = None # equity used for position sizing; defaults to equity when None
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cash_available: float
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gross_exposure: float
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net_exposure: float
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reserved_risk_budget: float
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unrealized_pnl: float
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realized_pnl: float
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open_positions: list[str] = Field(default_factory=list) # position_ids
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daily_new_risk_used: float
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peak_equity: float
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current_drawdown_pct: float
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class MetricsBundle(BaseModel):
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"""21 performance metrics for a completed backtest run."""
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# Trade metrics (7)
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trade_count: int = 0
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win_rate: float | None = None
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avg_win_pct: float | None = None
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avg_loss_pct: float | None = None
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profit_factor: float | None = None
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expectancy_r: float | None = None
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avg_r_multiple: float | None = None
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# Portfolio metrics (8)
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total_return_pct: float | None = None
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annualized_return_pct: float | None = None
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max_drawdown_pct: float | None = None
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calmar_ratio: float | None = None
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sharpe_ratio: float | None = None
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sortino_ratio: float | None = None
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avg_daily_pnl: float | None = None
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avg_positions_held: float | None = None
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avg_gross_exposure_pct: float | None = None
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avg_net_exposure_pct: float | None = None
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days_in_market_pct: float | None = None
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# Stability metrics (4)
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trade_skewness: float | None = None
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trade_kurtosis: float | None = None
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monthly_win_rate: float | None = None
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equity_curve_r_squared: float | None = None
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# Practicality metrics (4)
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avg_holding_days: float | None = None
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stop_exit_rate: float | None = None
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target_exit_rate: float | None = None
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no_follow_through_exit_rate: float | None = None
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score_bucket_hit_rate: dict[str, float] = Field(default_factory=dict)
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qqq_benchmark_return_pct: float | None = None
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excess_vs_qqq_pct: float | None = None
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long_net_pnl: float | None = None
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short_net_pnl: float | None = None
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long_pnl_contribution_pct: float | None = None
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short_pnl_contribution_pct: float | None = None
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# Bootstrap confidence intervals (95%)
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bootstrap_cis: dict[str, tuple[float, float] | None] = Field(default_factory=dict)
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# ---------------------------------------------------------------------------
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# Config models (mirror JSON Schema)
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# ---------------------------------------------------------------------------
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class UniverseConfig(BaseModel):
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min_price: float = 5.0
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min_avg_dollar_volume: float = 1_000_000.0
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min_market_cap_proxy: float | None = None
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exclude_asset_types: list[str] = Field(default_factory=list)
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allowed_exchanges: list[str] | None = None
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class SignalConfig(BaseModel):
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score_threshold: float = 0.5
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max_candidates_per_day: int = 5
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execution_timing: str = "next_open"
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decision_timing: str = "reaction_close"
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ranking_fields: list[str] = Field(default_factory=list)
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ranking_model_path: str | None = None
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scoring_model: str = "default" # "default" or "pead"
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pead_reaction_threshold: float = 0.05
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pead_volume_threshold: float = 1.5
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a_tier_score_threshold: float | None = None
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prior_drift_min: float | None = None
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class RiskConfig(BaseModel):
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per_trade_risk_pct: float = 0.01 # 1% of equity per trade
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per_trade_risk_pct_a_tier: float | None = None
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max_daily_new_risk_pct: float = 0.03 # 3% of equity per day
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allow_budget_downsizing: bool = False # when True, clip order size to remaining daily/engine risk budget
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allow_oneoff_downsizing: bool = False # when True, clip risk for high one-off candidates instead of hard veto
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oneoff_downsize_floor: float = 0.25 # minimum risk scaler when oneoff_downsizing is enabled
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max_positions: int = 10
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max_positions_per_sector: int = 3
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buying_power_multiplier: float = 1.0 # max gross notional / equity for new long exposure
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max_position_value_pct: float | None = None # max fraction of equity in one position
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max_adv_fraction: float | None = None # max fraction of avg daily volume
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cooldown_after_loss_streak: int = 0 # consecutive losses to trigger cooldown
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cooldown_days: int = 0 # days to sit out after streak
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macro_regime_enabled: bool = False # block entries when SPY < SMA
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macro_regime_size_scaler: float = 1.0 # size scaler when SPY < SMA (< 1.0 = scale down instead of block)
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macro_regime_mode: str = "legacy_spy" # "legacy_spy" or "spy_qqq_scaler"
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macro_regime_neutral_size_scaler: float | None = None
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macro_regime_risk_off_size_scaler: float | None = None
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macro_regime_risk_off_a_tier_only: bool = False
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macro_sma_period: int = 20 # SMA lookback for macro regime
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stop_atr_multiplier: float = 1.5 # ATR multiplier for stop distance
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backtest_mode: str = "research" # "research" or "live"
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kill_switch_cooldown_days: int = 20 # trading days before reset (research only)
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kill_switch_log_only: bool = False # log-only mode (don't trigger, just observe)
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veto_oneoff_penalty: float = 0.5 # block if oneoff_penalty >= this
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veto_parse_confidence_min: float = 0.4 # block if parse_confidence < this
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veto_unknown_direction: bool = True # block if event_direction == "unknown"
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veto_bearish_direction: bool = True # block if event_direction == "bearish"
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fixed_capital_sizing: bool = False # when True, position sizing uses initial_capital instead of current equity
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reaction_size_cap_threshold: float | None = None # abs(reaction)% above which size scales down (e.g. 0.08)
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vix_size_scaler_low: float = 15.0 # VIX level below which sizing is 1.0 (full)
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vix_size_scaler_high: float = 30.0 # VIX level above which sizing is at minimum
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vix_size_scaler_min: float = 0.35 # minimum size scaler at high VIX
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class ExecutionConfig(BaseModel):
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entry_fill_model: str = "next_open"
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exit_fill_model: str = "daily_bar_approximation"
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slippage_bps_base: float = 10.0
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commission_per_share: float = 0.005
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same_bar_priority: str = "stop_first_conservative"
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stop_model: str | None = None
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target_model: str = "fixed_r" # "fixed_r" or "atr_multiple"
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target_1_r: float | None = None # R-multiple for first target (fixed_r model)
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target_atr_multiplier: float = 1.5 # ATR multiplier for target (atr_multiple model)
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target_1_fraction: float | None = None # fraction to exit at target_1 (partial exit)
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use_tiered_targets: bool = False
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a_tier_target_1_r: float | None = None
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a_tier_target_1_fraction: float | None = None
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non_a_tier_target_1_r: float | None = None
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non_a_tier_target_1_fraction: float | None = None
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trailing_model: str | None = None
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trailing_warmup_days: int = 0 # days after entry before trailing activates
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max_holding_days: int = 10
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no_follow_through_exit: bool = False # exit at D+1 close if close < entry price
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early_failure_close_below_entry_and_reaction_close: bool = False
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early_failure_no_progress_days: int | None = None
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early_failure_no_progress_r: float | None = None
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early_failure_no_progress_fraction: float | None = None
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early_pop_giveback_days_min: int | None = None
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early_pop_giveback_days_max: int | None = None
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early_pop_giveback_trigger_r: float | None = None
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early_pop_giveback_min_r: float | None = None
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early_pop_giveback_from_peak_pct: float | None = None
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early_pop_giveback_fraction: float | None = None
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adaptive_exit_enabled: bool = False
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adaptive_exit_exhaustion_close_min: float = 0.90
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adaptive_exit_exhaustion_max_hold: int = 7
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adaptive_exit_exhaustion_trailing_warmup: int = 1
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adaptive_exit_orderly_close_min: float = 0.70
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adaptive_exit_orderly_close_max: float = 0.88
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adaptive_exit_orderly_max_hold: int = 25
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adaptive_exit_orderly_trailing_warmup: int = 12
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class StrategyEngineConfig(BaseModel):
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"""Specialist engine routing and execution policy."""
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engine_id: str
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event_types: list[str] = Field(default_factory=list)
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entry_conventions: list[str] | None = None
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allowed_macro_regimes: list[str] | None = None
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event_directions: list[str] | None = None
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guidance_statuses: list[str] | None = None
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filing_time_buckets: list[str] | None = None
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allowed_exchanges: list[str] | None = None
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timing_class: str = "any" # "same_day", "after_close", "any"
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direction: str = "any" # "long_only", "short_only", "any"
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forced_trade_direction_override: str | None = None # "long" or "short"
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entry_timing_policy: str = "next_open" # "next_open", "reaction_close"
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max_holding_days: int | None = None
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max_positions_per_sector_override: int | None = None
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engine_risk_budget_pct: float = 1.0
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per_trade_risk_pct_override: float | None = None
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stop_atr_multiplier_override: float | None = None
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target_atr_multiplier_override: float | None = None
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target_1_r_override: float | None = None
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target_1_fraction_override: float | None = None
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recycle_on_cash_block: bool = False
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recycle_min_days_held: int | None = None
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recycle_min_score_delta: float | None = None
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recycle_allowed_victim_engine_ids: list[str] | None = None
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recycle_positive_pnl_only: bool = True
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trailing_model_override: str | None = None
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trailing_warmup_days_override: int | None = None
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use_reaction_day_low_stop_override: bool | None = None
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early_failure_close_below_entry_and_reaction_close_override: bool | None = None
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early_failure_no_progress_days_override: int | None = None
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early_failure_no_progress_r_override: float | None = None
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early_failure_no_progress_fraction_override: float | None = None
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veto_oneoff_penalty_override: float | None = None
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allow_oneoff_downsizing_override: bool | None = None
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oneoff_downsize_floor_override: float | None = None
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veto_parse_confidence_min_override: float | None = None
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score_threshold_override: float | None = None
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residual_reserve_selected: bool = False
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pead_reaction_threshold_override: float | None = None
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pead_volume_threshold_override: float | None = None
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reaction_day_return_min: float | None = None
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reaction_day_return_max: float | None = None
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close_location_min: float | None = None
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close_location_max: float | None = None
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volume_ratio_min: float | None = None
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volume_ratio_max: float | None = None
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min_entry_price_override: float | None = None
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max_entry_price_override: float | None = None
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avg_dollar_volume_min: float | None = None
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avg_dollar_volume_max: float | None = None
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gap_size_min: float | None = None
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gap_size_max: float | None = None
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reaction_day_range_pct_min: float | None = None
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reaction_day_range_pct_max: float | None = None
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upper_wick_pct_min: float | None = None
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upper_wick_pct_max: float | None = None
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min_market_cap_proxy: float | None = None
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max_market_cap_proxy: float | None = None
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document_quality_score_min: float | None = None
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document_quality_score_max: float | None = None
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signal_strength_score_min: float | None = None
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signal_strength_score_max: float | None = None
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parse_confidence_overall_min: float | None = None
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parse_confidence_overall_max: float | None = None
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weak_reaction_threshold: float | None = None
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weak_reaction_gap_max: float | None = None
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unknown_direction_reaction_min: float | None = None
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unknown_direction_close_location_min: float | None = None
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unknown_direction_close_location_max: float | None = None
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unknown_direction_gap_size_min: float | None = None
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unknown_inline_exit_close_location_min: float | None = None
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unknown_inline_exit_gap_size_max: float | None = None
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unknown_inline_early_failure_close_below_entry_and_reaction_close_override: bool | None = None
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unknown_inline_early_failure_no_progress_days_override: int | None = None
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unknown_inline_early_failure_no_progress_r_override: float | None = None
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unknown_inline_early_failure_no_progress_fraction_override: float | None = None
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mixed_inline_close_location_min: float | None = None
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mixed_inline_close_location_max: float | None = None
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mixed_inline_gap_size_max: float | None = None
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mixed_inline_early_failure_close_below_entry_and_reaction_close_override: bool | None = None
|
|
mixed_inline_early_failure_no_progress_days_override: int | None = None
|
|
mixed_inline_early_failure_no_progress_r_override: float | None = None
|
|
mixed_inline_early_failure_no_progress_fraction_override: float | None = None
|
|
next_open_gap_cap_pct: float | None = None
|
|
add_on_min_parent_days_held: int | None = None
|
|
add_on_max_parent_days_held: int | None = None
|
|
add_on_schedule_days: list[int] | None = None
|
|
add_on_close_location_min: float | None = None
|
|
add_on_progress_r_min: float | None = None
|
|
add_on_progress_r_levels: list[float] | None = None
|
|
add_on_parent_score_min: float | None = None
|
|
add_on_parent_engine_ids: list[str] | None = None
|
|
add_on_max_count: int = 1
|
|
add_on_size_fraction: float = 0.5
|
|
add_on_require_above_reaction_high: bool = False
|
|
delayed_entry_lookback_days: int | None = None # e.g. 3 = look at events from 3 trading days ago
|
|
delayed_entry_source_engine_ids: list[str] | None = None # which engines' candidates to consider
|
|
delayed_entry_min_drift_pct: float | None = None # min price change since reaction close
|
|
delayed_entry_close_location_min: float | None = None # today's close location requirement
|
|
attention_min_wiki_spike_10d: float | None = None
|
|
attention_min_wiki_zscore_20d: float | None = None
|
|
attention_max_wiki_spike_10d: float | None = None
|
|
attention_max_wiki_zscore_20d: float | None = None
|
|
attention_min_article_count_3d: int | None = None
|
|
attention_min_us_article_count_3d: int | None = None
|
|
attention_min_resolver_confidence: float | None = None
|
|
shadow_only: bool = False
|
|
synthetic_only: bool = False
|
|
enabled: bool = True
|
|
|
|
|
|
class EventTypeProfile(BaseModel):
|
|
"""Per-event-type overrides for scoring, risk, and exit parameters."""
|
|
enabled: bool = True
|
|
score_threshold_override: float | None = None
|
|
max_holding_days_override: int | None = None
|
|
stop_atr_multiplier_override: float | None = None
|
|
target_atr_multiplier_override: float | None = None
|
|
direction_filter: str = "any" # "bullish_only", "bearish_only", "any"
|
|
|
|
|
|
class ReportingConfig(BaseModel):
|
|
write_trade_blotter: bool = True
|
|
write_equity_curve: bool = True
|
|
write_metrics_summary: bool = True
|
|
generate_plots: bool = False
|
|
attribution_buckets: list[str] = Field(default_factory=list)
|
|
|
|
|
|
class BacktestConfig(BaseModel):
|
|
strategy_name: str
|
|
dataset_snapshot_id: str
|
|
universe: UniverseConfig = Field(default_factory=UniverseConfig)
|
|
signal: SignalConfig = Field(default_factory=SignalConfig)
|
|
risk: RiskConfig = Field(default_factory=RiskConfig)
|
|
execution: ExecutionConfig = Field(default_factory=ExecutionConfig)
|
|
reporting: ReportingConfig = Field(default_factory=ReportingConfig)
|
|
event_type_profiles: dict[str, EventTypeProfile] = Field(default_factory=dict)
|
|
strategy_engines: list[StrategyEngineConfig] = Field(default_factory=list)
|
|
strategy_engine_selection_mode: str = "interleave" # "interleave", "interleave_head_score", "global_score", "interleave_cap_efficiency_soft", "interleave_cap_efficiency_strict", or "interleave_cash_tiebreak"
|
|
|
|
def get_event_profile(self, event_type: str) -> EventTypeProfile | None:
|
|
"""Look up event-type-specific profile. Returns None if no override."""
|
|
return self.event_type_profiles.get(event_type)
|
|
|
|
def get_strategy_engines(self) -> list[StrategyEngineConfig]:
|
|
"""Enabled strategy engines in manifest order."""
|
|
return [engine for engine in self.strategy_engines if engine.enabled]
|
|
|
|
def get_active_strategy_engines(self) -> list[StrategyEngineConfig]:
|
|
"""Enabled engines that participate in the live portfolio."""
|
|
return [engine for engine in self.get_strategy_engines() if not engine.shadow_only]
|
|
|
|
def get_shadow_strategy_engines(self) -> list[StrategyEngineConfig]:
|
|
"""Enabled engines that run in paper/shadow mode only."""
|
|
return [engine for engine in self.get_strategy_engines() if engine.shadow_only]
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Experiment models
|
|
# ---------------------------------------------------------------------------
|
|
|
|
class SplitSpec(BaseModel):
|
|
kind: str
|
|
params: dict[str, Any] = Field(default_factory=dict)
|
|
|
|
|
|
class ExperimentManifest(BaseModel):
|
|
experiment_name: str
|
|
dataset_snapshot_id: str
|
|
description: str | None = None
|
|
base_config: str # path to base config JSON file
|
|
overrides: dict[str, Any] = Field(default_factory=dict)
|
|
strategy_engines: list[StrategyEngineConfig] = Field(default_factory=list)
|
|
splits: list[SplitSpec] = Field(default_factory=list)
|
|
tags: list[str] = Field(default_factory=list)
|
|
notes: str | None = None
|
|
|
|
|
|
class ExperimentResult(BaseModel):
|
|
model_config = ConfigDict(frozen=True)
|
|
|
|
run_id: str
|
|
manifest: ExperimentManifest
|
|
resolved_config: BacktestConfig
|
|
metrics: MetricsBundle
|
|
artifact_paths: dict[str, str] = Field(default_factory=dict)
|
|
started_at: dt.datetime
|
|
finished_at: dt.datetime
|
|
total_trading_days: int
|
|
total_candidates_seen: int
|
|
total_orders_rejected: int
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Improvement Tracking models
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class SQSWeights(BaseModel):
|
|
"""Weights for Strategy Quality Score computation."""
|
|
|
|
profitability: float = 0.40
|
|
risk: float = 0.25
|
|
consistency: float = 0.20
|
|
robustness: float = 0.15
|
|
low_trade_penalty_threshold: int = 20
|
|
low_trade_penalty_factor: float = 0.5
|
|
|
|
|
|
class SQSv2Weights(BaseModel):
|
|
"""Weights for Strategy Quality Score v2 with capital efficiency."""
|
|
|
|
profitability: float = 0.35
|
|
risk: float = 0.25
|
|
consistency: float = 0.20
|
|
robustness: float = 0.10
|
|
capital_efficiency: float = 0.10
|
|
low_trade_penalty_threshold: int = 20
|
|
low_trade_penalty_factor: float = 0.5
|
|
|
|
|
|
class PromotionScoreWeights(BaseModel):
|
|
"""Weights for promotion scoring across valid/test splits."""
|
|
|
|
valid_quality: float = 0.55
|
|
test_quality: float = 0.15
|
|
floor_quality: float = 0.30
|
|
|
|
|
|
class UnifiedScoreWeights(BaseModel):
|
|
"""Weights for a stricter single ranking score across valid/test splits."""
|
|
|
|
split_profitability: float = 0.25
|
|
split_risk: float = 0.20
|
|
split_consistency: float = 0.15
|
|
split_robustness: float = 0.20
|
|
split_capital_efficiency: float = 0.20
|
|
valid_quality: float = 0.45
|
|
test_quality: float = 0.20
|
|
floor_quality: float = 0.20
|
|
gap_quality: float = 0.15
|
|
|
|
|
|
class ReturnScoreWeights(BaseModel):
|
|
"""Weights for return-max ranking across train/valid/test splits."""
|
|
|
|
split_total_return: float = 0.28
|
|
split_annualized_return: float = 0.12
|
|
split_profitability: float = 0.12
|
|
split_sharpe: float = 0.08
|
|
split_drawdown: float = 0.12
|
|
split_return_on_gross: float = 0.18
|
|
split_gross_exposure: float = 0.05
|
|
split_days_in_market: float = 0.05
|
|
train_quality: float = 0.35
|
|
valid_quality: float = 0.30
|
|
test_quality: float = 0.35
|
|
floor_quality: float = 0.15
|
|
gap_quality: float = 0.10
|
|
missing_train_penalty: float = 0.85
|
|
low_trade_penalty_threshold: int = 10
|
|
low_trade_penalty_factor: float = 0.85
|
|
|
|
|
|
class WalkForwardScoreWeights(BaseModel):
|
|
"""Weights for walk-forward robustness scoring."""
|
|
|
|
median_return: float = 0.25
|
|
mean_return: float = 0.15
|
|
worst_return: float = 0.15
|
|
positive_fold_rate: float = 0.15
|
|
profit_factor: float = 0.10
|
|
drawdown: float = 0.10
|
|
train_test_gap: float = 0.05
|
|
fold_count: float = 0.05
|
|
low_fold_penalty_threshold: int = 6
|
|
low_fold_penalty_factor: float = 0.85
|
|
|
|
|
|
class WFQSv2Weights(BaseModel):
|
|
"""Weights for walk-forward quality score v2 with multiplicative penalties."""
|
|
|
|
median_return: float = 0.25
|
|
mean_return: float = 0.15
|
|
worst_return: float = 0.20
|
|
positive_fold_rate: float = 0.15
|
|
profit_factor: float = 0.10
|
|
drawdown: float = 0.10
|
|
fold_count: float = 0.05
|
|
low_fold_penalty_threshold: int = 6
|
|
low_fold_penalty_factor: float = 0.85
|
|
recent_fold_quality: float = 0.20
|
|
recent_lookback_days: int = 365
|
|
recent_min_folds: int = 2
|
|
|
|
|
|
class DeploymentScoreWeights(BaseModel):
|
|
"""Weights for deployment-oriented scoring."""
|
|
|
|
rqs_quality: float = 0.45
|
|
wfqs_quality: float = 0.55
|
|
|
|
|
|
class SplitResult(BaseModel):
|
|
"""Metrics for a single backtest split (train/valid/test)."""
|
|
|
|
run_id: str
|
|
trade_count: int = 0
|
|
profit_factor: float | None = None
|
|
total_return_pct: float | None = None
|
|
annualized_return_pct: float | None = None
|
|
win_rate: float | None = None
|
|
max_drawdown_pct: float | None = None
|
|
sharpe_ratio: float | None = None
|
|
monthly_win_rate: float | None = None
|
|
equity_curve_r_squared: float | None = None
|
|
avg_gross_exposure_pct: float | None = None
|
|
avg_net_exposure_pct: float | None = None
|
|
days_in_market_pct: float | None = None
|
|
|
|
|
|
class WalkForwardFoldResult(BaseModel):
|
|
"""Metrics and run metadata for one walk-forward fold."""
|
|
|
|
fold_index: int
|
|
train_start: dt.date
|
|
train_end: dt.date
|
|
test_start: dt.date
|
|
test_end: dt.date
|
|
train_run_id: str
|
|
test_run_id: str
|
|
train_metrics: SplitResult
|
|
test_metrics: SplitResult
|
|
|
|
|
|
class WalkForwardAggregate(BaseModel):
|
|
"""Aggregate statistics over walk-forward folds."""
|
|
|
|
mean_return_pct: float | None = None
|
|
median_return_pct: float | None = None
|
|
worst_return_pct: float | None = None
|
|
positive_fold_rate_pct: float | None = None
|
|
mean_profit_factor: float | None = None
|
|
mean_max_drawdown_pct: float | None = None
|
|
mean_trade_count: float | None = None
|
|
mean_win_rate: float | None = None
|
|
|
|
|
|
class WalkForwardGapStats(BaseModel):
|
|
"""Train vs test drift statistics over walk-forward folds."""
|
|
|
|
mean_train_test_return_gap_pct: float | None = None
|
|
worst_train_test_return_gap_pct: float | None = None
|
|
fold_return_cv: float | None = None
|
|
|
|
|
|
class WalkForwardSummary(BaseModel):
|
|
"""Full walk-forward validation summary."""
|
|
|
|
window_mode: str = "rolling_fixed"
|
|
train_days: int
|
|
test_days: int
|
|
step_days: int
|
|
fold_count: int
|
|
folds: list[WalkForwardFoldResult] = Field(default_factory=list)
|
|
train_aggregate: WalkForwardAggregate = Field(default_factory=WalkForwardAggregate)
|
|
test_aggregate: WalkForwardAggregate = Field(default_factory=WalkForwardAggregate)
|
|
gap_stats: WalkForwardGapStats = Field(default_factory=WalkForwardGapStats)
|
|
engine_reliability_ratio: float | None = None
|
|
|
|
|
|
class RobustnessHorizonSummary(BaseModel):
|
|
"""Aggregate statistics for one rolling horizon in the robustness matrix."""
|
|
|
|
horizon_days: int
|
|
window_count: int
|
|
mean_return_pct: float | None = None
|
|
median_return_pct: float | None = None
|
|
worst_return_pct: float | None = None
|
|
positive_window_rate_pct: float | None = None
|
|
mean_max_drawdown_pct: float | None = None
|
|
|
|
|
|
class RobustnessMatrixSummary(BaseModel):
|
|
"""Compact summary of horizon/start-date robustness validation."""
|
|
|
|
window_mode: str = "rolling_horizon"
|
|
horizons_days: list[int] = Field(default_factory=list)
|
|
step_days: int
|
|
overall_window_count: int = 0
|
|
overall_positive_window_rate_pct: float | None = None
|
|
overall_worst_return_pct: float | None = None
|
|
horizon_summaries: list[RobustnessHorizonSummary] = Field(default_factory=list)
|
|
|
|
|
|
class ConfigDelta(BaseModel):
|
|
"""Records what changed from a baseline experiment."""
|
|
|
|
base_experiment: str
|
|
changes: dict[str, str] = Field(default_factory=dict)
|
|
|
|
|
|
class JournalEntry(BaseModel):
|
|
"""One improvement cycle entry in the journal."""
|
|
|
|
entry_id: str
|
|
timestamp: str
|
|
experiment_name: str
|
|
hypothesis: str
|
|
config_delta: ConfigDelta | None = None
|
|
results: dict[str, SplitResult] = Field(default_factory=dict) # split_name → SplitResult
|
|
walk_forward_summary: WalkForwardSummary | None = None
|
|
robustness_matrix_summary: RobustnessMatrixSummary | None = None
|
|
out_of_time_robustness_summary: RobustnessMatrixSummary | None = None
|
|
sqs_score: float | None = None
|
|
sqs_breakdown: dict[str, float] = Field(default_factory=dict)
|
|
sqs_v2_score: float | None = None
|
|
sqs_v2_breakdown: dict[str, float] = Field(default_factory=dict)
|
|
promotion_score: float | None = None
|
|
promotion_breakdown: dict[str, float] = Field(default_factory=dict)
|
|
unified_score: float | None = None
|
|
unified_breakdown: dict[str, float] = Field(default_factory=dict)
|
|
rqs_score: float | None = None
|
|
rqs_breakdown: dict[str, float] = Field(default_factory=dict)
|
|
wfqs_score: float | None = None
|
|
wfqs_breakdown: dict[str, float] = Field(default_factory=dict)
|
|
wfqs_v2_score: float | None = None
|
|
wfqs_v2_breakdown: dict[str, float] = Field(default_factory=dict)
|
|
deployment_score: float | None = None
|
|
deployment_breakdown: dict[str, float] = Field(default_factory=dict)
|
|
verdict: str = "unknown" # better / worse / neutral / unknown
|
|
verdict_reasoning: str = ""
|
|
next_direction: str = ""
|
|
tags: list[str] = Field(default_factory=list)
|
|
|
|
|
|
class RegistryEntry(BaseModel):
|
|
"""A leaderboard row derived from a JournalEntry."""
|
|
|
|
entry_id: str
|
|
experiment_name: str
|
|
strategy_family: str = "other"
|
|
is_retired: bool = False
|
|
sqs_score: float | None = None
|
|
sqs_v2_score: float | None = None
|
|
promotion_score: float | None = None
|
|
unified_score: float | None = None
|
|
rqs_score: float | None = None
|
|
wfqs_score: float | None = None
|
|
wfqs_v2_score: float | None = None
|
|
deployment_score: float | None = None
|
|
walk_forward_summary: WalkForwardSummary | None = None
|
|
robustness_matrix_summary: RobustnessMatrixSummary | None = None
|
|
out_of_time_robustness_summary: RobustnessMatrixSummary | None = None
|
|
# train split metrics
|
|
train_total_return_pct: float | None = None
|
|
train_annualized_return_pct: float | None = None
|
|
# test split metrics
|
|
profit_factor: float | None = None
|
|
total_return_pct: float | None = None
|
|
annualized_return_pct: float | None = None
|
|
win_rate: float | None = None
|
|
sharpe_ratio: float | None = None
|
|
max_drawdown_pct: float | None = None
|
|
trade_count: int = 0
|
|
avg_gross_exposure_pct: float | None = None
|
|
avg_net_exposure_pct: float | None = None
|
|
days_in_market_pct: float | None = None
|
|
# valid split metrics
|
|
valid_profit_factor: float | None = None
|
|
valid_total_return_pct: float | None = None
|
|
valid_annualized_return_pct: float | None = None
|
|
valid_win_rate: float | None = None
|
|
valid_sharpe_ratio: float | None = None
|
|
valid_max_drawdown_pct: float | None = None
|
|
valid_trade_count: int = 0
|
|
valid_avg_gross_exposure_pct: float | None = None
|
|
valid_avg_net_exposure_pct: float | None = None
|
|
valid_days_in_market_pct: float | None = None
|
|
timestamp: str = ""
|
|
|
|
|
|
class ExperimentRegistry(BaseModel):
|
|
"""Full leaderboard data (regenerated from journal)."""
|
|
|
|
entries: list[RegistryEntry] = Field(default_factory=list)
|
|
updated_at: str = ""
|