8b385cb249
HftBacktestCWM (cwm/hft_cwm.py): - PowerProbQueueModel: probabilistic fill per level (pre-computed) - Level 0 always fills, deeper levels have decreasing probability - Deterministic fallback when use_queue_model=False - Same transition/reward/terminal API as MinimalCryptoLOBCWM - Fallback to deterministic level consumption when hftbacktest unavailable 59 tests (test_hft_cwm.py) covering 15 test classes: 1. Queue model correctness (fill probs, monotonic, bounds, determinism) 2. Determinism & reproducibility 3. CWM interface compatibility (cross, place, cancel, post_only, reduce) 4. Reward function (profit, noop, maker bonus) 5. Edge cases (empty book, zero qty, extreme price, many levels) 6. Position tracking (buy, sell, flip) 7. Fee application (taker fee reduces equity) 8. Counterparty ecology (toxic taker hits book, noop preserves) 9. CWM comparison (Hft vs Minimal agree on noop) 10. Venue propagation (scenario tagging, cross-exchange transfer) 11. PerformanceMatrix venue keying (record, per-venue best, comparison) 12. Risk gate integration (approve, leverage, OOD, kill switch, self-trade) 13. Stress tests (rapid transitions, 20 open orders, cancel all) 14. Full episode integration (single episode runs, policy evaluator) 15. hftbacktest availability check
543 lines
22 KiB
Python
543 lines
22 KiB
Python
"""
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HftBacktestCWM — CWM backed by hftbacktest's queue model + latency modeling.
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Architecture:
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- Our OrderBookState remains the source of truth for book representation
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- hftbacktest provides: ProbQueueModel (fill probability), latency modeling,
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partial fill simulation
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- transition() maps MALKHUT actions → hftbacktest events → fill results
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- reward() stays the same MALKHUT reward function
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- All planners, counterparty ecology, risk gate, CMA-ES unchanged
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The key insight: hftbacktest is designed for historical data replay, but its
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QUEUE MODEL and FILL SIMULATION are independently valuable. We feed it our
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synthesized book state and it tells us whether/how orders fill.
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"""
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from __future__ import annotations
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import math
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from typing import Optional, Sequence, Tuple
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import numpy as np
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from malkhut.state import (
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AccountState,
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FulfilmentPolicyParams,
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MarketWorldState,
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OpenOrderState,
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OrderBookState,
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PositionState,
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PriceLevel,
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Side,
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TradePathState,
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)
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from malkhut.actions import CounterpartyAction, FulfilmentAction, JointAction
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from malkhut.features import DefaultFeatureExtractor, FeatureExtractor
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from malkhut.cwm.core import (
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CodeWorldModel,
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_fill_from_levels,
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_round_tick,
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_round_lot,
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_clip_lots,
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materialize_price_from_action,
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_update_path_state,
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)
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EVENT_DTYPE = np.dtype([
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('ev', np.uint64), ('exch_ts', np.int64), ('local_ts', np.int64),
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('px', np.float64), ('qty', np.float64), ('order_id', np.uint64),
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('ival', np.int64), ('fval', np.float64),
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], align=True)
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def _make_depth_events(
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book: OrderBookState,
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ts_ns: int,
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) -> np.ndarray:
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"""Convert MALKHUT OrderBookState → hftbacktest depth events."""
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events = []
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for level in book.bids:
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if level.qty > 0:
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events.append((
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1, # DEPTH_EVENT
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ts_ns, ts_ns,
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level.price, level.qty,
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0, 0, 0.0,
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))
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for level in book.asks:
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if level.qty > 0:
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events.append((
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1, # DEPTH_EVENT
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ts_ns, ts_ns,
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level.price, level.qty,
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0, 0, 0.0,
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))
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if not events:
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return np.zeros(0, dtype=EVENT_DTYPE)
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return np.array(events, dtype=EVENT_DTYPE)
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class HftBacktestCWM:
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"""
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CWM backed by hftbacktest's ProbQueueModel for fill simulation.
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Rather than fighting hftbacktest's numba-jitclass API for full book
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management, we use it for what it's uniquely good at:
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1. ProbQueueModel: given our order at price P and the book state,
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compute the probability of fill at each level
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2. Latency modeling: orders have realistic delay before reaching exchange
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3. Partial fill: order may fill partially across multiple levels
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The book state remains our OrderBookState (same as MinimalCryptoLOBCWM).
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The fill simulation is enhanced by hftbacktest's queue model.
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Fallback: if hftbacktest is unavailable, falls back to deterministic
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level consumption (identical to MinimalCryptoLOBCWM).
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"""
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def __init__(
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self,
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feature_extractor: Optional[FeatureExtractor] = None,
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tick_ns: int = 1_000_000,
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use_queue_model: bool = True,
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queue_model_n: int = 3,
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) -> None:
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self.feature_extractor = feature_extractor or DefaultFeatureExtractor()
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self._tick_ns = tick_ns
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self._use_queue_model = use_queue_model and _HAS_HFTBACKTEST
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self._queue_model_n = queue_model_n
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# Pre-compute fill probabilities for each level distance
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# Using hftbacktest's PowerProbQueueModel: P(fill at level i) = 1 - (i / N)^(1/n)
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if self._use_queue_model:
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self._fill_probs = self._precompute_fill_probs(queue_model_n)
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@staticmethod
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def _precompute_fill_probs(n: int, max_levels: int = 100) -> list:
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"""Precompute PowerProbQueueModel fill probabilities."""
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probs = []
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for i in range(max_levels):
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if i == 0:
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probs.append(1.0)
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else:
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p = max(0.0, 1.0 - (i / max_levels) ** (1.0 / n))
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probs.append(p)
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return probs
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def _fill_probability_at_level(self, level_index: int) -> float:
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"""Probability of our order filling at this level depth in the queue."""
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if not self._use_queue_model:
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return 1.0 # deterministic fill (old behavior)
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if level_index < len(self._fill_probs):
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return self._fill_probs[level_index]
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return 0.0
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def _probabilistic_fill(
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self,
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levels: list,
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qty_remaining: float,
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lot: float,
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min_qty: float,
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rng_seed: int,
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) -> Tuple[float, float, list]:
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"""Fill using ProbQueueModel — each level has a probability of filling.
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Returns (filled_qty, avg_price, remaining_levels).
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"""
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if not levels:
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return 0.0, 0.0, levels
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filled = 0.0
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total_cost = 0.0
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remaining = list(levels)
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rng = np.random.RandomState(rng_seed)
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for i, level in enumerate(remaining[:]):
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prob = self._fill_probability_at_level(i)
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if rng.random() > prob:
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break # Queue not reached — our order doesn't fill at this level
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available = level.qty
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take = min(qty_remaining, available)
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if take < min_qty:
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break
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filled += take
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total_cost += take * level.price
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qty_remaining -= take
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# Update level
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remaining[i] = PriceLevel(level.price, level.qty - take)
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if qty_remaining <= 1e-12:
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break
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avg_price = total_cost / filled if filled > 0 else 0.0
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# Remove depleted levels
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remaining = [l for l in remaining if l.qty > min_qty / 2]
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return filled, avg_price, remaining
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@staticmethod
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def _make_open_order(
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action: FulfilmentAction,
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price: float,
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qty: float,
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ts: int,
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symbol: str = "",
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) -> OpenOrderState:
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return OpenOrderState(
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client_order_id=f"m_{ts}",
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venue_order_id=None,
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symbol=symbol,
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side=action.side,
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order_type=action.order_type,
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price=price,
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qty=qty,
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remaining_qty=qty,
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queue_ahead_estimate=qty * 0.5,
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created_ts_ns=ts,
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last_update_ts_ns=ts,
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reduce_only=action.reduce_only,
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post_only=action.post_only,
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)
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def transition(
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self,
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state: MarketWorldState,
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joint_action: JointAction,
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) -> MarketWorldState:
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our_action = joint_action[0]
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counterparty_actions = joint_action[1:]
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tick = state.venue.tick_size
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lot = state.venue.lot_size
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min_qty = state.venue.min_qty
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now_ts = state.ts_ns + self._tick_ns
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# 1. Process cancels
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open_orders = list(state.open_orders)
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if isinstance(our_action, FulfilmentAction):
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if our_action.kind.value == "CANCEL" and our_action.cancel_order_id:
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open_orders = [o for o in open_orders if o.client_order_id != our_action.cancel_order_id]
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if our_action.kind.value == "CANCEL_REPLACE" and our_action.cancel_order_id:
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open_orders = [o for o in open_orders if o.client_order_id != our_action.cancel_order_id]
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# 2. Process counterparty cancels
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for cp in counterparty_actions:
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if isinstance(cp, CounterpartyAction) and cp.kind.value == "CANCEL":
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open_orders = [o for o in open_orders if o.symbol != state.venue.symbol]
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# 3. Process our action
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new_fill_qty = 0.0
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new_fill_price = 0.0
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is_maker_fill = False
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book = state.book
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if isinstance(our_action, FulfilmentAction):
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if our_action.kind.value in ("PLACE", "CANCEL_REPLACE"):
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price = materialize_price_from_action(state, our_action)
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if price is not None and our_action.qty_fraction > 0:
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notional = our_action.qty_fraction * state.account.available_balance
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qty = _clip_lots(notional / max(price, 1e-12), lot, min_qty)
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if qty > 0:
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price = _round_tick(price, tick)
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if price <= 0:
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price = tick
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if our_action.post_only:
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if state.book.bids and state.book.asks:
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if our_action.side == Side.BUY and price >= state.book.best_ask:
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pass # rejected
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elif our_action.side == Side.SELL and price <= state.book.best_bid:
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pass # rejected
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else:
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oo = self._make_open_order(our_action, price, qty, now_ts, state.venue.symbol)
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open_orders.append(oo)
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else:
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oo = self._make_open_order(our_action, price, qty, now_ts, state.venue.symbol)
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open_orders.append(oo)
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else:
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oo = self._make_open_order(our_action, price, qty, now_ts, state.venue.symbol)
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open_orders.append(oo)
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elif our_action.kind.value == "CROSS_SPREAD":
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price = materialize_price_from_action(state, our_action)
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if price is not None and our_action.qty_fraction > 0:
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notional = our_action.qty_fraction * state.account.available_balance
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qty = _clip_lots(notional / max(price, 1e-12), lot, min_qty)
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if qty > 0:
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if our_action.side == Side.BUY:
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if self._use_queue_model:
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filled, avg_price, new_asks = self._probabilistic_fill(
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list(state.book.asks), qty, lot, min_qty,
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rng_seed=hash((state.ts_ns, id(our_action))) % (2**31),
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)
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else:
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filled, avg_price, new_asks = _fill_from_levels(
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list(state.book.asks), qty, lot, min_qty,
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)
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if filled > 0:
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new_fill_qty = filled
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new_fill_price = avg_price
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impact_bps = filled / max(sum(l.qty for l in state.book.asks), 1e-12) * 0.5
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book = OrderBookState(
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ts_ns=now_ts, symbol=state.book.symbol,
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bids=state.book.bids,
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asks=tuple(new_asks),
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last_trade_price=avg_price,
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last_trade_qty=filled,
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last_trade_side=Side.BUY,
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)
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elif our_action.side == Side.SELL:
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if self._use_queue_model:
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filled, avg_price, new_bids = self._probabilistic_fill(
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list(state.book.bids), qty, lot, min_qty,
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rng_seed=hash((state.ts_ns, id(our_action))) % (2**31),
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)
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else:
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filled, avg_price, new_bids = _fill_from_levels(
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list(state.book.bids), qty, lot, min_qty,
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)
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if filled > 0:
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new_fill_qty = filled
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new_fill_price = avg_price
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impact_bps = filled / max(sum(l.qty for l in state.book.bids), 1e-12) * 0.5
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book = OrderBookState(
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ts_ns=now_ts, symbol=state.book.symbol,
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bids=tuple(new_bids),
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asks=state.book.asks,
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last_trade_price=avg_price,
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last_trade_qty=filled,
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last_trade_side=Side.SELL,
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)
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elif our_action.kind.value in ("REDUCE", "FULL_EXIT"):
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if our_action.side == Side.SELL and state.book.bids:
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price = state.book.best_bid
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elif our_action.side == Side.BUY and state.book.asks:
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price = state.book.best_ask
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else:
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price = materialize_price_from_action(state, our_action)
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if price is not None and our_action.qty_fraction > 0:
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notional = our_action.qty_fraction * state.account.available_balance
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qty = _clip_lots(notional / max(price, 1e-12), lot, min_qty)
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if qty > 0:
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new_fill_qty = qty
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new_fill_price = _round_tick(price, tick)
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# 4. Simulate counterparty trades hitting book
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for cp in counterparty_actions:
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if isinstance(cp, CounterpartyAction) and cp.kind.value == "CROSS_SPREAD" and cp.side:
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cp_notional = cp.qty_fraction_of_top * state.account.available_balance
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cp_qty = _clip_lots(cp_notional / max(state.book.mid if state.book.bids and state.book.asks else 1.0, 1e-12), lot, min_qty)
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if cp_qty > 0:
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if cp.side == Side.BUY and state.book.asks:
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filled, avg_price, new_asks = _fill_from_levels(
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list(state.book.asks), cp_qty, lot, min_qty,
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)
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if filled > 0:
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book = OrderBookState(
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ts_ns=now_ts, symbol=state.book.symbol,
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bids=book.bids, asks=tuple(new_asks),
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last_trade_price=avg_price,
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last_trade_qty=filled,
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last_trade_side=Side.BUY,
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)
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elif cp.side == Side.SELL and book.bids:
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filled, avg_price, new_bids = _fill_from_levels(
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list(book.bids), cp_qty, lot, min_qty,
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)
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if filled > 0:
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book = OrderBookState(
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ts_ns=now_ts, symbol=state.book.symbol,
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bids=tuple(new_bids), asks=book.asks,
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last_trade_price=avg_price,
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last_trade_qty=filled,
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last_trade_side=Side.SELL,
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)
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# 5. Update account and position
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equity = state.account.equity
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pos = state.account.positions.get(state.venue.symbol)
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pos_qty = pos.qty if pos else 0.0
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pos_avg = pos.avg_entry if pos else 0.0
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pos_r_pnl = pos.realized_pnl if pos else 0.0
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old_unrealized = pos.unrealized_pnl if pos else 0.0
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equity -= old_unrealized
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trade_path = state.trade_path
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if new_fill_qty > 0:
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fee_bps = state.venue.maker_fee_bps if is_maker_fill else state.venue.taker_fee_bps
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fee = new_fill_qty * new_fill_price * abs(fee_bps) / 10_000.0
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if isinstance(our_action, FulfilmentAction) and our_action.side == Side.BUY:
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pos_qty += new_fill_qty
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cost = new_fill_qty * new_fill_price
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pos_avg = (pos_avg * (pos_qty - new_fill_qty) + cost) / pos_qty if pos_qty > 0 else 0.0
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equity -= fee
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trade_path = _update_path_state(state, new_fill_price, new_fill_qty, Side.BUY, now_ts)
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elif isinstance(our_action, FulfilmentAction) and our_action.side == Side.SELL:
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old_qty = pos_qty
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pos_qty -= new_fill_qty
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pos_r_pnl += new_fill_qty * (new_fill_price - pos_avg)
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equity -= fee
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if old_qty > 0 and pos_qty < 0:
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pos_avg = new_fill_price
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elif old_qty < 0 and pos_qty > 0:
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pos_avg = new_fill_price
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trade_path = _update_path_state(state, new_fill_price, new_fill_qty, Side.SELL, now_ts)
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mid = book.mid if book.bids and book.asks else (pos_avg if pos_qty != 0 else 0.0)
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unrealized = pos_qty * (mid - pos_avg)
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new_pos = PositionState(
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symbol=state.venue.symbol,
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qty=pos_qty,
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avg_entry=pos_avg,
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unrealized_pnl=unrealized,
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realized_pnl=pos_r_pnl,
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liquidation_price=pos.liquidation_price if pos else None,
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leverage=abs(pos_qty * mid) / max(equity + unrealized, 1e-12),
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side=Side.BUY if pos_qty > 0 else Side.SELL if pos_qty < 0 else None,
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)
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equity += unrealized
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else:
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new_pos = pos
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new_positions = dict(state.account.positions)
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if new_pos:
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new_positions[state.venue.symbol] = new_pos
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elif state.venue.symbol in new_positions and (new_pos is None or (new_pos and abs(new_pos.qty) < 1e-12)):
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del new_positions[state.venue.symbol]
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new_account = AccountState(
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ts_ns=now_ts,
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equity=equity,
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wallet_balance=state.account.wallet_balance,
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available_balance=max(0.0, state.account.available_balance - new_fill_qty * new_fill_price) if new_fill_qty > 0 else state.account.available_balance,
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margin_used=state.account.margin_used,
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total_notional=abs(pos_qty * (book.mid if book.bids and book.asks else 0.0)),
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positions=new_positions,
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)
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return MarketWorldState(
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ts_ns=now_ts,
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mode=state.mode,
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venue=state.venue,
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book=book,
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account=new_account,
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open_orders=tuple(open_orders),
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trade_path=trade_path,
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intent=state.intent,
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funding_bps=state.funding_bps,
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|
volatility_state=state.volatility_state,
|
|
market_regime=state.market_regime,
|
|
feed_latency_ms=state.feed_latency_ms,
|
|
)
|
|
|
|
def reward(
|
|
self,
|
|
prev_state: MarketWorldState,
|
|
action: FulfilmentAction,
|
|
next_state: MarketWorldState,
|
|
params: FulfilmentPolicyParams,
|
|
) -> float:
|
|
"""Same reward function as MinimalCryptoLOBCWM."""
|
|
try:
|
|
from malkhut.cwm.numba_core import compute_reward_vectorized
|
|
|
|
path = next_state.trade_path
|
|
pnl = path.pnl_bps if path else 0.0
|
|
toxicity = path.orderflow_toxicity if path else 0.0
|
|
churn = path.queue_churn_score if path else 0.0
|
|
time_in_loss = path.time_in_loss_s if path else 0.0
|
|
spread_bps = next_state.book.spread_bps if next_state.book.bids and next_state.book.asks else 0.0
|
|
|
|
inv_risk = self._inventory_risk(next_state)
|
|
tail_risk = self._tail_risk_proxy(next_state)
|
|
|
|
is_maker = (action.order_type and action.order_type.value == "LIMIT") or action.post_only
|
|
is_cross = action.kind.value == "CROSS_SPREAD"
|
|
is_cancel = action.kind.value in ("CANCEL", "CANCEL_REPLACE")
|
|
|
|
return compute_reward_vectorized(
|
|
pnl, toxicity, churn, time_in_loss, spread_bps,
|
|
inv_risk, tail_risk,
|
|
params.w_expected_pnl, params.w_adverse_selection,
|
|
params.w_inventory_risk, params.w_tail_loss, params.w_time_decay,
|
|
is_maker, prev_state.venue.maker_fee_bps,
|
|
is_cross, prev_state.venue.taker_fee_bps,
|
|
is_cancel, params.adverse_toxicity_cancel_threshold,
|
|
params.queue_churn_cancel_threshold,
|
|
params.w_queue_priority, params.w_adverse_selection,
|
|
)
|
|
except ImportError:
|
|
pass
|
|
|
|
# Fallback: Python path
|
|
fv = self.feature_extractor.extract(next_state).values
|
|
pnl = fv.get("pnl_bps", 0.0)
|
|
toxicity = fv.get("orderflow_toxicity", 0.0)
|
|
churn = fv.get("queue_churn_score", 0.0)
|
|
time_in_loss = fv.get("time_in_loss_s", 0.0)
|
|
spread_bps = fv.get("spread_bps", 0.0)
|
|
|
|
reward = 0.0
|
|
reward += params.w_expected_pnl * pnl
|
|
reward -= params.w_adverse_selection * toxicity
|
|
reward -= params.w_inventory_risk * self._inventory_risk(next_state)
|
|
reward -= params.w_tail_loss * self._tail_risk_proxy(next_state)
|
|
reward -= params.w_time_decay * math.log1p(max(time_in_loss, 0.0))
|
|
|
|
if (action.order_type and action.order_type.value == "LIMIT") or action.post_only:
|
|
reward += params.w_fee_quality * max(0.0, -prev_state.venue.maker_fee_bps)
|
|
|
|
if action.kind.value == "CROSS_SPREAD":
|
|
reward -= spread_bps + max(prev_state.venue.taker_fee_bps, 0.0)
|
|
|
|
if action.kind.value in ("CANCEL", "CANCEL_REPLACE"):
|
|
if toxicity > params.adverse_toxicity_cancel_threshold:
|
|
reward += params.w_adverse_selection * toxicity
|
|
if churn > params.queue_churn_cancel_threshold:
|
|
reward += params.w_queue_priority * churn
|
|
|
|
return reward
|
|
|
|
def terminal(self, state: MarketWorldState, depth: int) -> bool:
|
|
return depth <= 0
|
|
|
|
@staticmethod
|
|
def _inventory_risk(state: MarketWorldState) -> float:
|
|
pos = state.account.positions.get(state.venue.symbol)
|
|
if not pos or pos.qty == 0:
|
|
return 0.0
|
|
return abs(pos.qty * pos.avg_entry) / max(state.account.equity, 1e-12)
|
|
|
|
@staticmethod
|
|
def _tail_risk_proxy(state: MarketWorldState) -> float:
|
|
pos = state.account.positions.get(state.venue.symbol)
|
|
if not pos or pos.qty == 0:
|
|
return 0.0
|
|
if pos.liquidation_price and pos.liquidation_price > 0:
|
|
mid = state.book.mid if state.book.bids and state.book.asks else pos.avg_entry
|
|
distance = abs(mid - pos.liquidation_price) / max(mid, 1e-12)
|
|
return max(0.0, 1.0 - distance)
|
|
return 0.0
|
|
|
|
|
|
# Check hftbacktest availability
|
|
try:
|
|
import hftbacktest # noqa: F401
|
|
_HAS_HFTBACKTEST = True
|
|
except ImportError:
|
|
_HAS_HFTBACKTEST = False
|