chemavx
9add52ab05
CI/CD / build-and-push (push) Successful in 2m24s
Republicans (plural) previously didn't match _PARTY_RE because the pattern was r"\bRepublican\b" (no optional s). Added Republicans? for symmetry with Democrats?. The general-election family fix already handles this case via etype_m, but the plural match is needed for the party-only fallback branch. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
479 lines
20 KiB
Python
479 lines
20 KiB
Python
"""
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Polymarket CLOB API client.
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Docs: https://docs.polymarket.com
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"""
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import asyncio
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import logging
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import os
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import re
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from dataclasses import dataclass, field
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from datetime import datetime, timezone, timedelta
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from typing import Optional
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import httpx
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log = logging.getLogger(__name__)
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POLYMARKET_API = "https://clob.polymarket.com"
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GAMMA_API = "https://gamma-api.polymarket.com"
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# ─────────────────────────────────────────────────────────────────────────────
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# Phase 2 — Market family classification helpers
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# Used by market_family_key() below.
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# ─────────────────────────────────────────────────────────────────────────────
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_YEAR_RE = re.compile(r"\b(202\d|203\d)\b")
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_MONTH_RE = re.compile(
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r"\b(january|february|march|april|may|june|july|august|"
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r"september|october|november|december)\b",
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re.IGNORECASE,
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)
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_FED_TRIGGER_RE = re.compile(
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r"\b(federal reserve|interest rate|bps|basis point|fed\s+(rate|meeting|decision))",
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re.IGNORECASE,
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)
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_US_STATE_RE = re.compile(
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r"\b(Alabama|Alaska|Arizona|Arkansas|California|Colorado|Connecticut|"
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r"Delaware|Florida|Georgia|Hawaii|Idaho|Illinois|Indiana|Iowa|Kansas|"
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r"Kentucky|Louisiana|Maine|Maryland|Massachusetts|Michigan|Minnesota|"
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r"Mississippi|Missouri|Montana|Nebraska|Nevada|New\s+Hampshire|"
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r"New\s+Jersey|New\s+Mexico|New\s+York|North\s+Carolina|North\s+Dakota|"
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r"Ohio|Oklahoma|Oregon|Pennsylvania|Rhode\s+Island|South\s+Carolina|"
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r"South\s+Dakota|Tennessee|Texas|Utah|Vermont|Virginia|Washington|"
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r"West\s+Virginia|Wisconsin|Wyoming)\b",
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re.IGNORECASE,
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)
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_PARTY_RE = re.compile(r"\b(Republicans?|Democrats?|Democratic|GOP)\b", re.IGNORECASE)
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_ELECTION_TYPE_RE = re.compile(
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r"\b(presidential|president|mayoral|mayor|gubernatorial|governor|"
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r"senate|congress(?:ional)?|primary|election)\b",
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re.IGNORECASE,
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)
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# Ordered list of (pattern, place_slug) for named non-US locations.
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# Checked after US-state patterns so US city/state names don't shadow these.
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_NAMED_PLACES: list[tuple[re.Pattern, str]] = [
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(re.compile(r"\bColomb", re.IGNORECASE), "colombia"),
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(re.compile(r"\bSeoul\b", re.IGNORECASE), "seoul"),
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(re.compile(r"\bBusan\b", re.IGNORECASE), "busan"),
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(re.compile(r"\bGyeonggi\b", re.IGNORECASE), "gyeonggi"),
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(re.compile(r"\bChungcheong", re.IGNORECASE), "chungcheong"),
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(re.compile(r"\bSouth\s+Korean?\b", re.IGNORECASE), "south-korea"),
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(re.compile(r"\bLos\s+Angeles\b", re.IGNORECASE), "los-angeles"),
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(re.compile(r"\bCuba\b", re.IGNORECASE), "cuba"),
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(re.compile(r"\bLebanon\b", re.IGNORECASE), "lebanon"),
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(re.compile(r"\bIsrael\b", re.IGNORECASE), "israel"),
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(re.compile(r"\bUkraine\b", re.IGNORECASE), "ukraine"),
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(re.compile(r"\bRussia\b", re.IGNORECASE), "russia"),
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]
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# Ordered list of (pattern, company_slug) for tech/company markets.
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_NAMED_COMPANIES: list[tuple[re.Pattern, str]] = [
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(re.compile(r"\bopenai\b", re.IGNORECASE), "openai"),
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(re.compile(r"\banthropic\b", re.IGNORECASE), "anthropic"),
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(re.compile(r"\bnvidia\b", re.IGNORECASE), "nvidia"),
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(re.compile(r"\bapple\b", re.IGNORECASE), "apple"),
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(re.compile(r"\bmicrosoft\b", re.IGNORECASE), "microsoft"),
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(re.compile(r"\bgoogle\b", re.IGNORECASE), "google"),
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(re.compile(r"\btesla\b", re.IGNORECASE), "tesla"),
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# \bmeta\b does NOT match MetaMask (no word boundary mid-compound-word)
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(re.compile(r"\bmeta\b", re.IGNORECASE), "meta"),
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]
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def _end_month(market: "Market") -> str:
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"""Return market end_date formatted as YYYY-MM, or '' if unparseable."""
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raw = market.end_date
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if not raw:
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return ""
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try:
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dt = datetime.fromisoformat(raw.replace("Z", "+00:00"))
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return dt.strftime("%Y-%m")
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except (ValueError, TypeError):
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return ""
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def market_family_key(market: "Market") -> str:
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"""
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Return a stable slug that groups related markets together.
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Markets in the same family share an underlying event (same election,
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same Fed meeting decision, same company). The bot allows at most one
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open position per family per cycle to avoid correlated exposure.
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Priority order (first match wins):
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1. Fed / interest-rate decision → fed-{month}-{year}
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2. US state + party election → {state}-{party}-{year}
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3. Named non-US city/country → {place}-{event_type}-{year}
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4. Named tech company → {company}-{year}
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5. Fallback → {category}-{end_YYYY-MM}
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Examples:
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"Will Ken Paxton win the 2026 Texas Republican Primary"
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→ texas-republican-2026
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"Will the Fed decrease rates by 25 bps after April 2026 meeting"
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→ fed-april-2026
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"Will OpenAI IPO by December 31 2026?"
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→ openai-2026
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"""
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q = market.question
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# Prefer year from question text; fall back to end_date year if absent
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year_m = _YEAR_RE.search(q)
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if year_m:
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year = year_m.group(1)
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else:
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end_m = _end_month(market) # e.g. "2026-06"
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year = end_m[:4] if end_m else "unknown"
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# 1. Fed / interest-rate meeting
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if _FED_TRIGGER_RE.search(q):
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month_m = _MONTH_RE.search(q)
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if month_m:
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return f"fed-{month_m.group(1).lower()}-{year}"
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return f"fed-{year}"
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# 2. US state + election event
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# Key design: general elections group by office, not by party, so
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# "Republicans win Ohio governor" and "Democrats win Ohio governor"
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# share the same family (ohio-gubernatorial-2026) and the bot can only
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# hold one position. Primaries keep the party because each party runs
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# its own primary (texas-republican-primary is distinct from texas-democrat-primary).
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state_m = _US_STATE_RE.search(q)
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party_m = _PARTY_RE.search(q)
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etype_m = _ELECTION_TYPE_RE.search(q)
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if state_m and (party_m or etype_m):
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state = re.sub(r"\s+", "-", state_m.group(1).lower())
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is_primary = etype_m is not None and "primary" in etype_m.group(1).lower()
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if party_m and is_primary:
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# Primary race: party is the disambiguation (each party has its own primary)
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raw_party = party_m.group(1).lower()
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party = "democrat" if "democrat" in raw_party else "republican"
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return f"{state}-{party}-{year}"
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if etype_m:
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# General election: family = office, not party
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# "Republicans win Ohio governor" == "Democrats win Ohio governor" → same race
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raw_etype = etype_m.group(1).lower()
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etype = {
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"president": "presidential",
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"mayor": "mayoral",
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"governor": "gubernatorial",
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}.get(raw_etype, raw_etype)
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return f"{state}-{etype}-{year}"
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# Has party but no election type — preserve old behaviour (e.g. "Texas Republican")
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raw_party = party_m.group(1).lower() # type: ignore[union-attr]
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party = "democrat" if "democrat" in raw_party else "republican"
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return f"{state}-{party}-{year}"
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# 3. Named non-US city / country
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for place_re, place_slug in _NAMED_PLACES:
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if place_re.search(q):
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if etype_m is None:
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etype_m = _ELECTION_TYPE_RE.search(q)
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if etype_m:
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raw_etype = etype_m.group(1).lower()
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# Normalise synonyms
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etype = {
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"president": "presidential",
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"mayor": "mayoral",
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"governor": "gubernatorial",
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}.get(raw_etype, raw_etype)
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else:
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etype = "event"
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return f"{place_slug}-{etype}-{year}"
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# 4. Named tech company
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for company_re, company_slug in _NAMED_COMPANIES:
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if company_re.search(q):
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return f"{company_slug}-{year}"
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# 5. Fallback: category + end_date month
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end_month = _end_month(market)
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base = market.category if market.category else "misc"
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return f"{base}-{end_month}" if end_month else f"{base}-{year}"
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@dataclass
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class Market:
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id: str
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condition_id: str
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question: str
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yes_token_id: str
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no_token_id: str
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yes_price: float # 0-1, current best ask for YES
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no_price: float
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volume_24h: float
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end_date: str
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active: bool
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category: str = ""
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@dataclass
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class OrderBook:
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market_id: str
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yes_bids: list[tuple[float, float]] = field(default_factory=list) # (price, size)
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yes_asks: list[tuple[float, float]] = field(default_factory=list)
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mid_price: float = 0.5
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class PolymarketClient:
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"""
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Async Polymarket client.
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In paper mode, API key is not needed — only public data.
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API key required for placing real orders.
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"""
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def __init__(self) -> None:
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self.api_key = os.getenv("POLYMARKET_API_KEY", "")
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self.secret = os.getenv("POLYMARKET_SECRET", "")
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self.passphrase = os.getenv("POLYMARKET_PASSPHRASE", "")
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self._client = httpx.AsyncClient(timeout=30)
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# Keywords that identify crypto / finance markets.
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# Short tickers are padded with spaces to avoid false substring matches
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# (e.g. " eth " won't match "Hegseth"; " sol " won't match "solar").
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_CRYPTO_FINANCE_KEYWORDS: list[str] = [
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"bitcoin", "btc", " eth ", "ethereum", " sol ", "solana",
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"xrp", "ripple", "dogecoin", "doge", "litecoin", "ltc",
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"coinbase", "binance", "kraken", "bybit", "okx",
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"usdc", "usdt", "stablecoin",
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"defi", "nft", "blockchain", "crypto",
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" fdv", "airdrop", "token launch", "token listing",
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"microstrategy", "mstr", "saylor",
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"nasdaq", "sp500", "s&p 500", "s&p500",
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"federal reserve", "fed rate", "interest rate",
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"inflation", "tariff", "treasury yield",
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"recession", " gdp ", "unemployment", "trade war", "trade deal",
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" ipo ", "sec ", "cftc",
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]
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_POLITICS_KEYWORDS: list[str] = [
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"election", "president", "congress", "senate", "vote", "war",
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"trump", "biden", "ukraine", "russia", "israel", "nato",
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]
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_TECH_KEYWORDS: list[str] = [
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" ai ", "openai", "apple", "google", "microsoft", "meta",
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"nvidia", "regulation", "antitrust",
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"tesla", "elon", "nuclear", "quantum", "chip",
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]
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_EVENTS_KEYWORDS: list[str] = [
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"world cup", "oscar", "nobel", "spacex", "nasa",
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]
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# Sports markets are excluded entirely — BTC/F&G/GNews have no edge there.
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# Checked before any category match so sports don't bleed into politics/events.
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_SPORTS_EXCLUSIONS: list[str] = [
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" nba ", " nfl ", " mlb ", " nhl ",
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"basketball", "football", "baseball", "hockey", "soccer",
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" mvp ", "rookie of the", "championship", "super bowl", "world series",
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"playoffs", "playoff", "tournament",
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"tennis", " golf ", " ufc ", "boxing", "wrestler", "wrestling",
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"slam dunk", "home run", "touchdown",
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# European / international football leagues
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"la liga", "premier league", "bundesliga", "serie a", "ligue 1",
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"champions league", "europa league", "conference league",
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"copa del rey", "fa cup", "dfb pokal",
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"relegation", "golden boot", "top scorer",
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" liga ", "eredivisie", "primeira liga",
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]
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@classmethod
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def _is_sports(cls, question: str) -> bool:
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q = f" {question.lower()} "
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return any(kw in q for kw in cls._SPORTS_EXCLUSIONS)
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@classmethod
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def _is_crypto_finance(cls, question: str) -> bool:
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q = f" {question.lower()} " # pad so edge keywords match cleanly
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return any(kw in q for kw in cls._CRYPTO_FINANCE_KEYWORDS)
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@classmethod
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def _is_politics(cls, question: str) -> bool:
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q = f" {question.lower()} "
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return any(kw in q for kw in cls._POLITICS_KEYWORDS)
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@classmethod
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def _is_tech(cls, question: str) -> bool:
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q = f" {question.lower()} "
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return any(kw in q for kw in cls._TECH_KEYWORDS)
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@classmethod
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def _is_events(cls, question: str) -> bool:
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q = f" {question.lower()} "
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return any(kw in q for kw in cls._EVENTS_KEYWORDS)
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@classmethod
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def _detect_category(cls, question: str) -> str:
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"""Return the category label for a market question, or '' if unsupported."""
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if cls._is_sports(question):
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return "" # exclude sports regardless of other keyword matches
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if cls._is_politics(question):
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return "politics"
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# Tech checked before crypto/finance: company-specific markets (OpenAI IPO,
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# NVIDIA earnings, Apple antitrust) should be "tech" even when they contain
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# generic finance keywords like "ipo" or "sec".
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if cls._is_tech(question):
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return "tech"
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if cls._is_crypto_finance(question):
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return "crypto/finance"
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if cls._is_events(question):
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return "events"
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return ""
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async def get_active_markets(
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self,
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min_volume: float = 500,
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pages: int = 3,
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page_size: int = 200,
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max_days_to_resolution: int = 90,
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) -> list[Market]:
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"""Fetch active markets from Gamma API (no auth needed).
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Fetches events without tag filtering (tag= param is unreliable),
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then keeps only markets whose question matches any supported category
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(crypto/finance, politics, tech, events) and that:
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- have NOT already expired (end_dt >= now)
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- resolve within max_days_to_resolution days
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"""
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seen: set[str] = set()
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markets: list[Market] = []
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now = datetime.now(timezone.utc)
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cutoff = now + timedelta(days=max_days_to_resolution)
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for page in range(pages):
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try:
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resp = await self._client.get(
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f"{GAMMA_API}/events",
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params={
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"active": True,
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"closed": False,
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"limit": page_size,
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"offset": page * page_size,
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},
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)
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resp.raise_for_status()
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events = resp.json()
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if not events:
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break # no more pages
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for event in events:
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event_title = event.get("title", "")
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for m in event.get("markets", []):
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try:
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if not m.get("active") or m.get("closed"):
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continue
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question = m.get("question", "")
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# Detect category from question or event title
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category = self._detect_category(question)
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if not category:
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category = self._detect_category(event_title)
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if not category:
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continue
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# Filter: skip already-expired and far-future markets
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# Gamma API may return endDate or end_date (snake_case)
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raw_end = m.get("endDate") or m.get("end_date") or m.get("endDateIso", "")
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if raw_end:
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try:
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end_dt = datetime.fromisoformat(
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raw_end.replace("Z", "+00:00")
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)
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# Make naive datetimes UTC-aware before comparing
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if end_dt.tzinfo is None:
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end_dt = end_dt.replace(tzinfo=timezone.utc)
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if end_dt < now:
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log.debug("Skipping expired market: %s", question[:60])
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continue
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if end_dt > cutoff:
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continue
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except (ValueError, TypeError):
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pass # keep market if date unparseable
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market_id = str(m["id"])
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if market_id in seen:
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continue
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vol = float(m.get("volume24hr", 0))
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if vol < min_volume:
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continue
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raw_prices = m.get("outcomePrices", ["0.5", "0.5"])
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if isinstance(raw_prices, str):
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import json as _json
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raw_prices = _json.loads(raw_prices)
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yes_price = float(raw_prices[0])
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raw_tokens = m.get("clobTokenIds", ["", ""])
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if isinstance(raw_tokens, str):
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import json as _json
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raw_tokens = _json.loads(raw_tokens)
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seen.add(market_id)
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markets.append(Market(
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id=market_id,
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condition_id=m.get("conditionId", ""),
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question=question,
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yes_token_id=raw_tokens[0] if raw_tokens else "",
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no_token_id=raw_tokens[1] if len(raw_tokens) > 1 else "",
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yes_price=yes_price,
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no_price=1 - yes_price,
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volume_24h=vol,
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end_date=m.get("endDate", ""),
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active=True,
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category=category,
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))
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except (KeyError, ValueError, IndexError) as e:
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log.debug("Skipping malformed market: %s", e)
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except httpx.HTTPError as e:
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|
log.error("Polymarket API error (page=%d): %s", page, e)
|
|
break
|
|
|
|
by_cat: dict[str, int] = {}
|
|
for mkt in markets:
|
|
by_cat[mkt.category] = by_cat.get(mkt.category, 0) + 1
|
|
log.info(
|
|
"Loaded %d markets (min_vol=%.0f, resolving within %dd): %s",
|
|
len(markets), min_volume, max_days_to_resolution,
|
|
", ".join(f"{k}={v}" for k, v in sorted(by_cat.items())),
|
|
)
|
|
return markets
|
|
|
|
async def get_order_book(self, token_id: str) -> Optional[OrderBook]:
|
|
"""Get order book for a specific token."""
|
|
try:
|
|
resp = await self._client.get(
|
|
f"{POLYMARKET_API}/book",
|
|
params={"token_id": token_id},
|
|
)
|
|
resp.raise_for_status()
|
|
data = resp.json()
|
|
|
|
bids = [(float(b["price"]), float(b["size"])) for b in data.get("bids", [])]
|
|
asks = [(float(a["price"]), float(a["size"])) for a in data.get("asks", [])]
|
|
|
|
mid = 0.5
|
|
if bids and asks:
|
|
mid = (bids[0][0] + asks[0][0]) / 2
|
|
|
|
return OrderBook(
|
|
market_id=token_id,
|
|
yes_bids=bids,
|
|
yes_asks=asks,
|
|
mid_price=mid,
|
|
)
|
|
except Exception as e:
|
|
log.warning("Order book fetch failed for %s: %s", token_id, e)
|
|
return None
|
|
|
|
async def close(self) -> None:
|
|
await self._client.aclose()
|