sentiment-engine/MALKHUT/malkhut/cwm/latency_model.py

"""
Latency Model — simulate realistic feed and order latencies.

Essential for:
  - Realistic fill simulation
  - Latency arbitrage defense
  - Optimal order timing
"""
from __future__ import annotations

import math
from dataclasses import dataclass
from typing import Optional

import numpy as np
from numba import njit


@dataclass(frozen=True, slots=True)
class LatencyState:
    """Latency state for the CWM."""
    feed_latency_ms: float
    order_latency_ms: float
    feed_jitter_ms: float
    order_jitter_ms: float


@njit(cache=True)
def simulate_feed_latency(
    base_latency_ms: float,
    jitter_ms: float,
    rng_seed: int,
) -> float:
    """
    Simulate feed latency with jitter.

    Model: base_latency + uniform(-jitter, +jitter)
    Returns latency in milliseconds.
    """
    # Simple deterministic jitter using seed
    jitter = jitter_ms * (2.0 * ((rng_seed % 1000) / 1000.0) - 1.0)
    return max(0.0, base_latency_ms + jitter)


@njit(cache=True)
def simulate_order_latency(
    base_latency_ms: float,
    jitter_ms: float,
    queue_position: int,
    recent_trade_rate: float,
    rng_seed: int = 0,
) -> float:
    """
    Simulate order latency with queue dynamics.

    Model:
      - Base latency + jitter
      - Additional latency from queue position (longer queue = slower fill)
      - Reduced latency when trade rate is high (faster queue consumption)

    Returns latency in milliseconds.
    """
    jitter = jitter_ms * (2.0 * ((rng_seed % 1000) / 1000.0) - 1.0)
    queue_delay = queue_position / max(recent_trade_rate, 0.01) * 1000.0
    return max(0.0, base_latency_ms + jitter + queue_delay * 0.1)


@njit(cache=True)
def compute_latency_impact(
    feed_latency_ms: float,
    order_latency_ms: float,
    price_change_per_ms: float,
) -> float:
    """
    Compute the cost of latency in basis points.

    Model:
      - Feed latency: price moves before we see it
      - Order latency: price moves before our order arrives
      - Total cost = (feed_latency + order_latency) * price_change_per_ms

    Returns cost in basis points.
    """
    total_latency_ms = feed_latency_ms + order_latency_ms
    # Assume price moves ~1bp per 10ms in volatile markets
    cost_bps = total_latency_ms * price_change_per_ms
    return cost_bps


class LatencyModel:
    """
    Latency model for the CWM.

    Simulates realistic feed and order latencies.
    Used by CWM to make fill simulation realistic.
    """

    def __init__(
        self,
        feed_latency_ms: float = 10.0,
        order_latency_ms: float = 50.0,
        feed_jitter_ms: float = 2.0,
        order_jitter_ms: float = 10.0,
    ) -> None:
        self._feed_latency = feed_latency_ms
        self._order_latency = order_latency_ms
        self._feed_jitter = feed_jitter_ms
        self._order_jitter = order_jitter_ms
        self._rng_seed = 0

    def simulate_feed_latency(self) -> float:
        """Simulate current feed latency."""
        self._rng_seed += 1
        return simulate_feed_latency(self._feed_latency, self._feed_jitter, self._rng_seed)

    def simulate_order_latency(self, queue_position: int = 0, recent_trade_rate: float = 0.5) -> float:
        """Simulate current order latency."""
        self._rng_seed += 1
        return simulate_order_latency(self._order_latency, self._order_jitter, queue_position, recent_trade_rate, self._rng_seed)

    def compute_latency_cost(self, price_change_per_ms: float = 0.001) -> float:
        """Compute latency cost in basis points."""
        return compute_latency_impact(self._feed_latency, self._order_latency, price_change_per_ms)

    @property
    def feed_latency_ms(self) -> float:
        return self._feed_latency

    @property
    def order_latency_ms(self) -> float:
        return self._order_latency