199 lines
6.7 KiB
Python
199 lines
6.7 KiB
Python
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"""NegRisk opportunity engine.
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On every book update for a token that belongs to a known event, recompute the
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sum of VWAP best-asks across all outcomes of that event and emit an
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`Opportunity` when the depth-clipped net edge exceeds the threshold.
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The math deliberately walks the book rather than trusting top-of-book — the
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executable edge on a 100-share basket is often smaller than the quoted top.
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"""
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from __future__ import annotations
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import asyncio
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from collections.abc import AsyncIterator
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from dataclasses import dataclass, field
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from datetime import UTC, datetime
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from decimal import Decimal
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from loguru import logger
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from ..book.l2 import BookRegistry, BookUpdate, LiveBook
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from ..config import settings
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from ..models import Event, Opportunity, OpportunityLeg
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BPS = Decimal(10_000)
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@dataclass(frozen=True, slots=True)
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class EngineConfig:
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min_net_edge_bps: int
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fees_per_share_usd: Decimal
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gas_per_basket_usd: Decimal
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max_basket_usd: Decimal
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min_basket_count: Decimal = Decimal(1)
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size_grid: tuple[Decimal, ...] = (
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Decimal(10),
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Decimal(25),
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Decimal(50),
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Decimal(100),
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Decimal(250),
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Decimal(500),
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Decimal(1000),
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)
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@classmethod
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def from_settings(cls) -> EngineConfig:
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return cls(
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min_net_edge_bps=settings.min_net_edge_bps,
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# CLOB taker fee is typically 0 on Polymarket; keep a hook for non-zero.
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fees_per_share_usd=Decimal("0.0"),
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# Approx sum of gas for split/buy legs + redeem on Polygon, USD-denominated.
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# Sized conservatively at ~$0.10 until we wire a real gas oracle.
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gas_per_basket_usd=Decimal("0.10"),
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max_basket_usd=settings.max_basket_usd,
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)
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@dataclass
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class EventIndex:
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"""Registry mapping token_id -> Event so engine can look up siblings."""
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by_event_id: dict[str, Event] = field(default_factory=dict)
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by_token_id: dict[str, str] = field(default_factory=dict)
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def upsert(self, event: Event) -> None:
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self.by_event_id[event.id] = event
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for o in event.outcomes:
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self.by_token_id[o.token_id] = event.id
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def event_for_token(self, token_id: str) -> Event | None:
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eid = self.by_token_id.get(token_id)
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return self.by_event_id.get(eid) if eid else None
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class OpportunityEngine:
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def __init__(
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self,
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*,
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books: BookRegistry,
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index: EventIndex,
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config: EngineConfig | None = None,
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) -> None:
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self._books = books
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self._index = index
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self._config = config or EngineConfig.from_settings()
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self._out: asyncio.Queue[Opportunity] = asyncio.Queue(maxsize=256)
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@property
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def config(self) -> EngineConfig:
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return self._config
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async def run(self) -> None:
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async for update in self._books.updates():
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try:
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self._handle(update)
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except Exception as exc:
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logger.exception("engine handler failed: {}", exc)
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def _handle(self, update: BookUpdate) -> None:
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event = self._index.event_for_token(update.token_id)
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if event is None:
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return
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opp = self.evaluate(event)
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if opp is not None:
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self._out.put_nowait(opp)
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async def opportunities(self) -> AsyncIterator[Opportunity]:
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while True:
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yield await self._out.get()
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def evaluate(self, event: Event) -> Opportunity | None:
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legs_books: list[tuple[int, str, str, LiveBook]] = []
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for o in event.outcomes:
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book = self._books.get(o.token_id)
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if book is None or not book.asks:
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return None
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legs_books.append((o.outcome_index, o.token_id, o.name, book))
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best_opp: Opportunity | None = None
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best_profit = Decimal("-1")
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for candidate_k in self._candidate_sizes(legs_books):
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legs, cost_sum = self._walk_legs(legs_books, candidate_k)
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if legs is None:
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continue
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gross = Decimal(1) * candidate_k - cost_sum # per-basket gross = 1 - Σ vwap
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gross_per_basket = gross / candidate_k
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n = Decimal(len(legs))
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fee_cost_per_basket = self._config.fees_per_share_usd * n
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gas_amortized = self._config.gas_per_basket_usd / candidate_k
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net_per_basket = gross_per_basket - fee_cost_per_basket - gas_amortized
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if net_per_basket <= 0:
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continue
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bps = int((net_per_basket / Decimal(1)) * BPS)
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if bps < self._config.min_net_edge_bps:
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continue
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expected_profit = net_per_basket * candidate_k
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if expected_profit <= best_profit:
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continue
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candidate = Opportunity.from_legs(
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detected_at=datetime.now(UTC),
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event=event,
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legs=tuple(legs),
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fees_per_share=self._config.fees_per_share_usd,
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gas_per_basket_usd=gas_amortized,
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max_baskets=candidate_k,
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)
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best_opp = candidate
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best_profit = expected_profit
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return best_opp
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def _candidate_sizes(
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self, legs_books: list[tuple[int, str, str, LiveBook]]
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) -> list[Decimal]:
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depth = min(sum(lb.asks.values()) for *_, lb in legs_books)
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if depth <= 0:
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return []
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est_cost_per_share = sum(
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(lb.asks.keys()[0] for *_, lb in legs_books), Decimal(0)
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)
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budget_cap = (
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self._config.max_basket_usd / est_cost_per_share
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if est_cost_per_share > 0
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else depth
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)
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ceiling = min(depth, budget_cap)
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if ceiling < self._config.min_basket_count:
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return []
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candidates = [s for s in self._config.size_grid if s <= ceiling]
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if not candidates or candidates[-1] != ceiling:
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candidates.append(ceiling)
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return candidates
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def _walk_legs(
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self,
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legs_books: list[tuple[int, str, str, LiveBook]],
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size: Decimal,
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) -> tuple[list[OpportunityLeg] | None, Decimal]:
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legs: list[OpportunityLeg] = []
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total_cost = Decimal(0)
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for idx, token_id, name, book in legs_books:
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res = book.vwap_buy(size)
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if res is None:
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return None, Decimal(0)
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vwap, filled, levels = res
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if filled < size:
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return None, Decimal(0)
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total_cost += vwap * size
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legs.append(
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OpportunityLeg(
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token_id=token_id,
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outcome_name=name,
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outcome_index=idx,
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vwap_price=vwap,
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size=size,
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levels_consumed=levels,
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)
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)
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return legs, total_cost
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