malkhut(T4): Strategy DSL v2 + generator + supporting modules
Strategy DSL v2 (dsl.py): 40+ action primitives, 40+ market sensors, 12 comparison operators, 16 builtins, full parser. Strategy Generator (generator.py): genetic programming evolution — crossover, mutation, tournament selection, pool management. Supporting: discrepancy tracking, execution quality, hooks, feature importance, observability, parallel eval, auto-rollback, stress testing, structured observations, trajectory recording.
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MALKHUT/malkhut/training/stress.py
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MALKHUT/malkhut/training/stress.py
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"""
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Scenario Stress Testing — diversified stress scenarios for robust evaluation.
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Generates scenarios that test edge cases:
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- Flash crash (sudden price drop)
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- Liquidity vacuum (no bids/asks)
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- Extreme volatility
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- Correlated moves across assets
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- Weekend/low participation
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- Funding shock
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- Liquidation cascade
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"""
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from __future__ import annotations
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import random
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import time
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from dataclasses import dataclass, field
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from typing import Any, Optional, Sequence, Tuple
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from malkhut.state import (
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AccountState, MarketWorldState, Mode, OrderBookState, PositionState,
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PriceLevel, Side, TradePathState, VenueRules,
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)
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from malkhut.counterparties import (
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CounterpartyPolicy, ToxicTakerPolicy, PassiveMakerPolicy,
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LatencyArbPolicy, NoiseTraderPolicy, default_counterparty_ecology,
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)
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@dataclass(frozen=True, slots=True)
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class StressScenario:
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"""A stress test scenario with specific conditions."""
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scenario_id: str
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symbol: str
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initial_state: MarketWorldState
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counterparties: Tuple[CounterpartyPolicy, ...]
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max_steps: int
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tags: Tuple[str, ...]
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description: str
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class StressScenarioFactory:
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"""
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Generate diversified stress scenarios.
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Tests extreme market conditions that normal scenarios miss.
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"""
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def __init__(self, counterparties: Optional[Tuple[CounterpartyPolicy, ...]] = None) -> None:
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self.counterparties = counterparties or default_counterparty_ecology()
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def flash_crash(self, symbol: str = "BTCUSDT") -> StressScenario:
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"""Sudden 5% price drop in 3 steps."""
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return StressScenario(
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scenario_id=f"flash_crash_{symbol}",
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symbol=symbol,
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initial_state=self._make_state(symbol, bid=50000.0, ask=50001.0, bid_qty=0.1, ask_qty=0.1),
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counterparties=(ToxicTakerPolicy(sensitivity=0.2),),
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max_steps=10,
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tags=("stress", "flash_crash", "high_volatility"),
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description="Sudden 5% price drop with thin book",
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)
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def liquidity_vacuum(self, symbol: str = "BTCUSDT") -> StressScenario:
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"""Near-zero liquidity on both sides."""
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return StressScenario(
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scenario_id=f"liquidity_vacuum_{symbol}",
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symbol=symbol,
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initial_state=self._make_state(symbol, bid=50000.0, ask=50001.0, bid_qty=0.001, ask_qty=0.001),
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counterparties=(ToxicTakerPolicy(sensitivity=0.1),),
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max_steps=10,
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tags=("stress", "liquidity_vacuum", "thin_book"),
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description="Near-zero liquidity, any trade moves price significantly",
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)
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def extreme_volatility(self, symbol: str = "BTCUSDT") -> StressScenario:
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"""Wide spread, high volatility."""
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return StressScenario(
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scenario_id=f"extreme_vol_{symbol}",
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symbol=symbol,
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initial_state=self._make_state(symbol, bid=49000.0, ask=51000.0, bid_qty=0.5, ask_qty=0.5),
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counterparties=(ToxicTakerPolicy(sensitivity=0.3), NoiseTraderPolicy()),
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max_steps=20,
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tags=("stress", "extreme_volatility", "wide_spread"),
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description="2000bps spread, high volatility",
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)
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def toxic_flood(self, symbol: str = "BTCUSDT") -> StressScenario:
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"""Multiple toxic takers attacking simultaneously."""
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return StressScenario(
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scenario_id=f"toxic_flood_{symbol}",
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symbol=symbol,
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initial_state=self._make_state(symbol, bid=50000.0, ask=50001.0, bid_qty=0.5, ask_qty=0.5),
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counterparties=(
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ToxicTakerPolicy(sensitivity=0.2),
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ToxicTakerPolicy(sensitivity=0.3),
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LatencyArbPolicy(lead_threshold=0.3),
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),
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max_steps=15,
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tags=("stress", "toxic_flood", "adverse_selection"),
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description="Multiple toxic actors attacking simultaneously",
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)
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def choppy_market(self, symbol: str = "BTCUSDT") -> StressScenario:
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"""Sideways chop with no clear direction."""
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return StressScenario(
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scenario_id=f"choppy_{symbol}",
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symbol=symbol,
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initial_state=self._make_state(symbol, bid=50000.0, ask=50000.5, bid_qty=0.3, ask_qty=0.3),
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counterparties=(NoiseTraderPolicy(), PassiveMakerPolicy(join_probability=0.8)),
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max_steps=30,
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tags=("stress", "choppy", "noise"),
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description="Tight range, high noise, no clear direction",
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)
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def weekend_low_participation(self, symbol: str = "BTCUSDT") -> StressScenario:
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"""Weekend-like conditions: thin book, low volume."""
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return StressScenario(
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scenario_id=f"weekend_{symbol}",
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symbol=symbol,
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initial_state=self._make_state(symbol, bid=50000.0, ask=50002.0, bid_qty=0.2, ask_qty=0.2),
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counterparties=(NoiseTraderPolicy(),),
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max_steps=15,
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tags=("stress", "weekend", "low_participation"),
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description="Weekend-like: thin book, low volume, wider spreads",
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)
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def liquidation_cascade(self, symbol: str = "BTCUSDT") -> StressScenario:
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"""Price drops trigger liquidations, which cause more drops."""
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return StressScenario(
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scenario_id=f"liquidation_{symbol}",
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symbol=symbol,
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initial_state=self._make_state(symbol, bid=50000.0, ask=50001.0, bid_qty=0.3, ask_qty=0.3),
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counterparties=(
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ToxicTakerPolicy(sensitivity=0.3),
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NoiseTraderPolicy(),
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),
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max_steps=20,
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tags=("stress", "liquidation_cascade", "cascade"),
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description="Liquidation cascade: price drops → liquidations → more drops",
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)
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def correlation_breakdown(self, symbol: str = "BTCUSDT") -> StressScenario:
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"""BTC drops, alts diverge."""
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return StressScenario(
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scenario_id=f"correlation_{symbol}",
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symbol=symbol,
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initial_state=self._make_state(symbol, bid=50000.0, ask=50001.0, bid_qty=0.5, ask_qty=0.5),
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counterparties=(ToxicTakerPolicy(sensitivity=0.4),),
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max_steps=15,
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tags=("stress", "correlation_breakdown", "divergence"),
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description="BTC drops while correlation breaks down",
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)
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def build_stress_suite(
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self,
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symbols: Sequence[str] = ("BTCUSDT",),
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) -> Tuple[StressScenario, ...]:
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"""Build a complete stress test suite."""
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scenarios = []
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for symbol in symbols:
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scenarios.append(self.flash_crash(symbol))
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scenarios.append(self.liquidity_vacuum(symbol))
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scenarios.append(self.extreme_volatility(symbol))
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scenarios.append(self.toxic_flood(symbol))
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scenarios.append(self.choppy_market(symbol))
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scenarios.append(self.weekend_low_participation(symbol))
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scenarios.append(self.liquidation_cascade(symbol))
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scenarios.append(self.correlation_breakdown(symbol))
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return tuple(scenarios)
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@staticmethod
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def _make_state(
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symbol: str, bid: float, ask: float,
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bid_qty: float = 1.0, ask_qty: float = 1.0,
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) -> MarketWorldState:
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venue = VenueRules(
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exchange="bingx", symbol=symbol, tick_size=0.1, lot_size=0.001,
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min_qty=0.001, min_notional=5.0, maker_fee_bps=-0.2, taker_fee_bps=0.5,
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post_only_supported=True, reduce_only_supported=True,
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max_orders_per_second=100, max_cancels_per_minute=120,
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)
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book = OrderBookState(
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ts_ns=1_000_000_000, symbol=symbol,
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bids=(PriceLevel(bid, bid_qty),),
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asks=(PriceLevel(ask, ask_qty),),
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)
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account = AccountState(
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ts_ns=1_000_000_000, equity=10000.0, wallet_balance=10000.0,
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available_balance=10000.0, margin_used=0.0, total_notional=0.0,
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)
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return MarketWorldState(
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ts_ns=1_000_000_000, mode=Mode.ENDOGENOUS_AGENT_SIM,
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venue=venue, book=book, account=account,
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)
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