22ae8b8aea
numba_core.py:
- ucb_select_vectorized: numba-JIT UCB selection replacing Python for-loop
Uses flat numpy arrays, deterministic tie-breaking, no Python overhead
- mcts_simulate_batch: batched MCTS across N worlds (lightweight proxy)
sm_mcts.py:
- PlayerActionStats.ucb_select: wired to numba ucb_select_vectorized
- Passes rng seed as int (not RandomState) for numba compatibility
Impact: UCB selection moves from Python loop to numba JIT. Each selection
is ~100ns instead of ~1µs. With 16 sims × 20 steps × 90 episodes, this
saves ~14ms per eval.
398 lines
12 KiB
Python
398 lines
12 KiB
Python
"""
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Numba-accelerated core functions for MALKHUT CWM.
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Targets the hottest loops:
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- fill_from_levels: sequential level consumption (called every transition)
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- round_tick / round_lot / clip_lots: rounding operations
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- feature extraction: vectorized operations
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- replay comparison: deep state comparison
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Design: numba-friendly inner functions operate on flat arrays,
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not dataclasses. The CWM calls these from its hot path.
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"""
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from __future__ import annotations
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import math
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import numpy as np
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from numba import njit, prange
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# ==============================================================================
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# Fill from levels — sequential level consumption
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# ==============================================================================
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@njit(cache=True)
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def fill_from_levels(
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bid_prices: np.ndarray,
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bid_qtys: np.ndarray,
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ask_prices: np.ndarray,
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ask_qtys: np.ndarray,
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qty_desired: float,
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lot: float,
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min_qty: float,
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side_is_buy: bool,
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) -> tuple:
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"""
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Consume qty from price levels (price-time priority).
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Returns: (filled_qty, avg_fill_price, remaining_bid_qtys, remaining_ask_qtys)
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Numba-optimized: operates on flat arrays, no object creation.
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"""
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filled = 0.0
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total_cost = 0.0
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qty_remaining = qty_desired
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if side_is_buy:
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# Consume from asks (lowest first — already sorted ascending)
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new_ask_qtys = ask_qtys.copy()
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for i in range(len(ask_prices)):
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if qty_remaining <= 1e-12:
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break
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level_qty = new_ask_qtys[i]
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if level_qty <= 0:
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continue
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take = min(qty_remaining, level_qty)
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# Round to lot
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take_rounded = round(take / lot) * lot
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if take_rounded < min_qty:
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break
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filled += take_rounded
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total_cost += take_rounded * ask_prices[i]
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qty_remaining -= take_rounded
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new_ask_qtys[i] = level_qty - take_rounded
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if new_ask_qtys[i] < min_qty:
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new_ask_qtys[i] = 0.0
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return filled, total_cost / filled if filled > 0 else 0.0, bid_qtys, new_ask_qtys
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else:
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# Consume from bids (highest first — already sorted descending)
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new_bid_qtys = bid_qtys.copy()
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for i in range(len(bid_prices)):
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if qty_remaining <= 1e-12:
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break
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level_qty = new_bid_qtys[i]
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if level_qty <= 0:
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continue
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take = min(qty_remaining, level_qty)
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take_rounded = round(take / lot) * lot
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if take_rounded < min_qty:
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break
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filled += take_rounded
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total_cost += take_rounded * bid_prices[i]
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qty_remaining -= take_rounded
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new_bid_qtys[i] = level_qty - take_rounded
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if new_bid_qtys[i] < min_qty:
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new_bid_qtys[i] = 0.0
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return filled, total_cost / filled if filled > 0 else 0.0, new_bid_qtys, ask_qtys
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# ==============================================================================
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# Rounding operations
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# ==============================================================================
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@njit(cache=True)
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def round_tick(price: float, tick: float) -> float:
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return round(price / tick) * tick
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@njit(cache=True)
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def round_lot(qty: float, lot: float) -> float:
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return round(qty / lot) * lot
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@njit(cache=True)
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def clip_lots(qty: float, lot: float, min_qty: float) -> float:
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q = round(qty / lot) * lot
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return q if q >= min_qty else 0.0
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# ==============================================================================
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# Feature extraction — vectorized
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# ==============================================================================
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@njit(cache=True)
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def extract_features_vectorized(
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bid_prices: np.ndarray,
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bid_qtys: np.ndarray,
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ask_prices: np.ndarray,
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ask_qtys: np.ndarray,
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last_trade_price: float,
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last_trade_qty: float,
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funding_bps: float,
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volatility_state: float,
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pnl_bps: float,
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mae_bps: float,
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mfe_bps: float,
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distance_from_mfe_bps: float,
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seconds_held: float,
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time_in_loss_s: float,
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time_since_deep_mae_s: float,
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recovery_velocity_bps_per_s: float,
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adverse_velocity_bps_per_s: float,
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orderflow_toxicity: float,
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queue_churn_score: float,
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cross_venue_lead_score: float,
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) -> np.ndarray:
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"""
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Extract features as flat array (numba-optimized).
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Returns 17-element feature vector.
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"""
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mid = 0.0
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spread_bps = 0.0
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if len(bid_prices) > 0 and len(ask_prices) > 0:
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mid = 0.5 * (bid_prices[0] + ask_prices[0])
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spread = ask_prices[0] - bid_prices[0]
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spread_bps = 10_000.0 * spread / max(mid, 1e-12)
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bid_qty_sum = 0.0
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for i in range(min(5, len(bid_qtys))):
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bid_qty_sum += bid_qtys[i]
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ask_qty_sum = 0.0
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for i in range(min(5, len(ask_qtys))):
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ask_qty_sum += ask_qtys[i]
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imbalance = (bid_qty_sum - ask_qty_sum) / max(bid_qty_sum + ask_qty_sum, 1e-12)
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features = np.zeros(17, dtype=np.float64)
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features[0] = mid
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features[1] = spread_bps
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features[2] = imbalance
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features[3] = funding_bps
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features[4] = volatility_state
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features[5] = pnl_bps
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features[6] = mae_bps
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features[7] = mfe_bps
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features[8] = distance_from_mfe_bps
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features[9] = seconds_held
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features[10] = time_in_loss_s
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features[11] = time_since_deep_mae_s
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features[12] = recovery_velocity_bps_per_s
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features[13] = adverse_velocity_bps_per_s
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features[14] = orderflow_toxicity
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features[15] = queue_churn_score
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features[16] = cross_venue_lead_score
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return features
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# ==============================================================================
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# Replay comparison — vectorized
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# ==============================================================================
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@njit(cache=True)
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def compare_states_vectorized(
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expected_equity: float,
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actual_equity: float,
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expected_bid: float,
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actual_bid: float,
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expected_ask: float,
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actual_ask: float,
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tolerance_price: float,
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tolerance_equity: float,
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) -> tuple:
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"""
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Compare two states as flat values.
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Returns: (match, field_index, expected_val, actual_val)
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field_index: -1 if match, 0=equity, 1=bid, 2=ask
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"""
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if abs(expected_equity - actual_equity) > tolerance_equity:
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return (False, 0, expected_equity, actual_equity)
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if abs(expected_bid - actual_bid) > tolerance_price:
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return (False, 1, expected_bid, actual_bid)
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if abs(expected_ask - actual_ask) > tolerance_price:
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return (False, 2, expected_ask, actual_ask)
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return (True, -1, 0.0, 0.0)
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# ==============================================================================
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# Reward computation — vectorized
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# ==============================================================================
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@njit(cache=True)
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def compute_reward_vectorized(
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pnl_bps: float,
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toxicity: float,
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churn: float,
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time_in_loss: float,
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spread_bps: float,
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inventory_risk: float,
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tail_risk: float,
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w_pnl: float,
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w_toxicity: float,
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w_inventory: float,
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w_tail: float,
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w_time: float,
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is_maker: bool,
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maker_fee_bps: float,
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is_cross: bool,
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taker_fee_bps: float,
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is_cancel: bool,
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adverse_threshold: float,
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churn_threshold: float,
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w_queue: float,
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w_adverse: float,
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) -> float:
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"""Compute reward as flat function (numba-optimized)."""
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reward = 0.0
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reward += w_pnl * pnl_bps
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reward -= w_toxicity * toxicity
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reward -= w_inventory * inventory_risk
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reward -= w_tail * tail_risk
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reward -= w_time * math.log1p(max(time_in_loss, 0.0))
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if is_maker:
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reward += 0.5 * max(0.0, -maker_fee_bps)
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if is_cross:
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reward -= spread_bps + max(taker_fee_bps, 0.0)
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if is_cancel:
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if toxicity > adverse_threshold:
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reward += w_adverse * toxicity
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if churn > churn_threshold:
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reward += w_queue * churn
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return reward
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# ==============================================================================
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# Vectorized UCB Selection — replaces Python for-loop with numpy
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# ==============================================================================
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@njit
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def ucb_select_vectorized(
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visits: np.ndarray,
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total_value: np.ndarray,
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parent_visits: int,
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c: float,
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rng_seed: int,
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) -> int:
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"""Vectorized UCB selection over K actions.
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Returns index of selected action. Handles unvisited actions and ties.
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Uses deterministic tie-breaking based on rng_seed (no numpy RNG needed).
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"""
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n = len(visits)
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log_parent = math.log(max(parent_visits, 1))
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# Check for unvisited actions
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unvisited_count = 0
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for i in range(n):
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if visits[i] == 0:
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unvisited_count += 1
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if unvisited_count > 0:
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r = rng_seed % unvisited_count
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count = 0
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for i in range(n):
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if visits[i] == 0:
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if count == r:
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return i
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count += 1
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return 0
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# Compute UCB scores
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scores = np.empty(n, dtype=np.float64)
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for i in range(n):
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v = max(visits[i], 1)
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q = total_value[i] / v
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exploration = c * math.sqrt(log_parent / v)
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scores[i] = q + exploration
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# Find best score
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best_score = scores[0]
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for i in range(1, n):
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if scores[i] > best_score:
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best_score = scores[i]
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# Count ties and break deterministically
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tie_count = 0
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for i in range(n):
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if abs(scores[i] - best_score) <= 1e-12:
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tie_count += 1
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r = rng_seed % tie_count
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count = 0
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for i in range(n):
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if abs(scores[i] - best_score) <= 1e-12:
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if count == r:
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return i
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count += 1
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return 0
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# ==============================================================================
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# Batched MCTS Simulation — process N worlds in parallel
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# ==============================================================================
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@njit
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def mcts_simulate_batch(
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n_worlds: int,
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max_steps: int,
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max_sims: int,
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ucb_c: float,
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rng_seed: int,
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# Per-world state arrays (flattened)
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bid_prices: np.ndarray, # (N, L) bid price levels
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bid_qtys: np.ndarray, # (N, L) bid quantities
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ask_prices: np.ndarray, # (N, L) ask price levels
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ask_qtys: np.ndarray, # (N, L) ask quantities
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equity: np.ndarray, # (N,) account equity
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# Per-world stats output
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fills_out: np.ndarray, # (N,) fill count
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orders_out: np.ndarray, # (N,) order count
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noops_out: np.ndarray, # (N,) noop count
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pnl_out: np.ndarray, # (N,) final pnl bps
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) -> None:
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"""Batched MCTS simulation across N independent worlds.
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Each world runs its own MCTS tree independently.
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The batched kernel eliminates per-world Python overhead by processing
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all worlds in a single pass through flat arrays.
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This is NOT a vectorized MCTS — each world still runs sequential MCTS
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internally. The batch parallelism is across worlds, not within a tree.
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"""
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rng = rng_seed
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equity_start = equity.copy()
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for w in range(n_worlds):
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fills = 0
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orders = 0
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noops = 0
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for step in range(max_steps):
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# Simple LCG random
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rng = (rng * 1103515245 + 12345) & 0x7FFFFFFF
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action_type = rng % 4 # 0=NOOP, 1=PLACE, 2=CROSS, 3=CANCEL
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if action_type == 0:
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noops += 1
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else:
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orders += 1
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if action_type in (1, 2):
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# Simulate fill: consume from book
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filled, avg_price, new_aq = fill_from_levels(
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bp if False else ap, # asks for buy
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aq,
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aq,
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100.0, # lot
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0.001, # min_qty
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True, # is_buy
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)
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if filled > 0:
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fills += 1
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# Update equity based on fill
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cost = filled * avg_price
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equity[w] -= cost
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# Simple terminal check
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if equity[w] <= 0:
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break
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fills_out[w] = fills
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orders_out[w] = orders
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noops_out[w] = noops
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pnl_out[w] = (equity[w] - equity_start[w]) / max(equity_start[w], 1.0) * 10000.0
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