1f41be845b
ScenarioLibrary sweeps the state space (not samples) across: - spread_mult: [0.1, 0.5, 1.0, 2.0, 5.0, 10.0] - depth_fraction: [0.01, 0.05, 0.1, 0.3, 0.5, 1.0] - toxicity: [0.0, 0.3, 0.7, 1.0] - regime: [normal, crisis, recovery, transition] Default: 13 assets × 576 grid points = 7,488 scenarios. Customizable: specify symbols, dimensions, ranges. 7 tests covering: grid size, sweep output, point fields, regime coverage, custom dimensions, summary, factory function.
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
"""
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ScenarioLibrary — sweeps the state space for comprehensive Mode 1 coverage.
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Unlike ScenarioFactory which builds 30 pre-defined scenario types per asset,
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ScenarioLibrary SWEEPS across parameter dimensions to ensure coverage of
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regions the tape never visited.
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Sweep dimensions (orthogonal):
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- spread_bps: [0.1, 0.5, 1.0, 2.0, 5.0, 10.0]
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- depth_fraction: [0.01, 0.05, 0.1, 0.3, 0.5, 1.0]
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- toxicity: [0.0, 0.3, 0.7, 1.0]
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- regime: ["normal", "crisis", "recovery"]
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Total: 6 × 6 × 4 × 4 = 576 grid points per asset.
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With 3 assets = 1,728 scenarios total.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Dict, List, Optional, Sequence, Tuple
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from malkhut.training.asset_classification import ASSET_PROFILES, get_asset_profile
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# Sweep dimension defaults
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DEFAULT_SPREAD_MULTS = [0.1, 0.5, 1.0, 2.0, 5.0, 10.0]
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DEFAULT_DEPTH_FRACS = [0.01, 0.05, 0.1, 0.3, 0.5, 1.0]
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DEFAULT_TOXICITY = [0.0, 0.3, 0.7, 1.0]
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DEFAULT_REGIMES = ["normal", "crisis", "recovery", "transition"]
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@dataclass(frozen=True, slots=True)
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class SweepPoint:
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"""A single point in the swept state space."""
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symbol: str
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spread_mult: float
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depth_fraction: float
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toxicity: float
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regime: str
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label: str # human-readable: "BTC_spread2x_depth30pct_tox0.3_crisis"
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class ScenarioLibrary:
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"""Sweeps the state space for Mode 1 (EXPLORE) coverage.
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Produces grid points across spread × depth × toxicity × regime.
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These are ANCHORS that the ecology fills between and beyond.
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"""
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def __init__(
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self,
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symbols: Optional[Sequence[str]] = None,
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spread_mults: Optional[Sequence[float]] = None,
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depth_fracs: Optional[Sequence[float]] = None,
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tox_levels: Optional[Sequence[float]] = None,
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regimes: Optional[Sequence[str]] = None,
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) -> None:
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self.symbols = list(symbols or ASSET_PROFILES.keys())
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self.spread_mults = list(spread_mults or DEFAULT_SPREAD_MULTS)
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self.depth_fracs = list(depth_fracs or DEFAULT_DEPTH_FRACS)
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self.tox_levels = list(tox_levels or DEFAULT_TOXICITY)
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self.regimes = list(regimes or DEFAULT_REGIMES)
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def sweep(self) -> List[SweepPoint]:
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"""Generate full grid sweep across all dimensions."""
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points = []
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for symbol in self.symbols:
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for spread in self.spread_mults:
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for depth in self.depth_fracs:
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for tox in self.tox_levels:
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for regime in self.regimes:
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label = f"{symbol}_s{spread}_d{depth}_t{tox}_r{regime}"
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points.append(SweepPoint(
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symbol=symbol,
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spread_mult=spread,
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depth_fraction=depth,
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toxicity=tox,
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regime=regime,
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label=label,
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))
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return points
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def grid_size(self) -> int:
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return len(self.symbols) * len(self.spread_mults) * len(self.depth_fracs) * len(self.tox_levels) * len(self.regimes)
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def summary(self) -> str:
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return (f"ScenarioLibrary: {len(self.symbols)} assets × "
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f"{len(self.spread_mults)} spreads × {len(self.depth_fracs)} depths × "
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f"{len(self.tox_levels)} tox × {len(self.regimes)} regimes = "
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f"{self.grid_size()} grid points")
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def get_scenario_library(
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symbols: Optional[Sequence[str]] = None,
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) -> ScenarioLibrary:
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"""Get default scenario library for given symbols."""
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return ScenarioLibrary(symbols=symbols)
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