added bot.py
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bot.py
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283
bot.py
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from __future__ import annotations
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import datetime as dt
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import json
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import time
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import uuid
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from typing import Dict, List, Optional, Tuple
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import yaml
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from fair_prob import fair_prob_threshold
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from kalshi_client import KalshiClient
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from risk import RiskManager
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from spot_feed import SpotFeed
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from storage import Storage
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def _parse_iso_z(s: str) -> dt.datetime:
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# Example: "2023-11-07T05:31:56Z"
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return dt.datetime.fromisoformat(s.replace("Z", "+00:00"))
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def discover_crypto_15m_series(client: KalshiClient, category: str, title_contains: str, symbols: List[str]) -> Dict[str, str]:
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"""
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Returns mapping symbol -> series_ticker by scanning /series?category=crypto.
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"""
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data = client.get_series_list(category=category)
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series = data.get("series", [])
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out: Dict[str, str] = {}
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for s in series:
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title = (s.get("title") or "").upper()
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if title_contains.upper() not in title:
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continue
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for sym in symbols:
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if sym.upper() in title and sym.upper() not in out:
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out[sym.upper()] = s["ticker"]
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return out
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def choose_current_market(client: KalshiClient, series_ticker: str) -> Optional[dict]:
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"""
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From open markets in a series, pick the one with the nearest close_time in the future.
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"""
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data = client.get_markets(series_ticker=series_ticker, status="open", limit=200)
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markets = data.get("markets", [])
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now = dt.datetime.now(dt.timezone.utc)
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best = None
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best_close = None
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for m in markets:
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close_time = _parse_iso_z(m["close_time"])
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if close_time <= now:
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continue
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if best_close is None or close_time < best_close:
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best = m
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best_close = close_time
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return best
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def extract_strike_and_rule(market: dict) -> Tuple[float, bool]:
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"""
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Returns (strike, resolves_yes_if_spot_ge_strike).
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For many threshold markets, Kalshi provides strike_type + floor_strike/cap_strike.
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"""
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strike_type = market.get("strike_type") # e.g., "greater" or "less"
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if strike_type == "greater":
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strike = float(market.get("floor_strike"))
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return strike, True # YES if spot >= strike
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if strike_type == "less":
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strike = float(market.get("cap_strike"))
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return strike, False # YES if spot <= strike
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# Fallback: if missing, try floor_strike then cap_strike
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if market.get("floor_strike") is not None:
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return float(market["floor_strike"]), True
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if market.get("cap_strike") is not None:
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return float(market["cap_strike"]), False
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raise RuntimeError(f"Could not determine strike from market fields: {json.dumps(market)[:500]}")
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def dollars_to_cents_price(p: float) -> int:
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# p is dollars 0.00..1.00; convert to cents 1..99
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c = int(round(p * 100))
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return max(1, min(99, c))
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def main() -> None:
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with open("config.yaml", "r") as f:
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cfg = yaml.safe_load(f)
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mode = cfg["mode"]
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client = KalshiClient.from_env()
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series_map = discover_crypto_15m_series(
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client,
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category=cfg["category"],
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title_contains=cfg["series_title_contains"],
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symbols=cfg["symbols"],
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)
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if len(series_map) == 0:
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raise RuntimeError("Could not discover any matching 15-min crypto series. Check title/category filters.")
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spot = SpotFeed(urls=cfg["coinbase"], lookback_seconds=int(cfg["vol_lookback_seconds"]))
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storage = Storage("storage.sqlite")
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risk = RiskManager(cfg["daily_loss_limit"], cfg["max_consecutive_losses"])
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last_traded_market: Dict[str, str] = {} # symbol -> market_ticker
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print(f"[init] mode={mode} series_map={series_map}")
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while True:
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if risk.trading_halted():
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print("[risk] Trading halted by risk manager. Sleeping 60s.")
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time.sleep(60)
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continue
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# refresh spot history
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try:
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spot.update()
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except Exception as e:
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print(f"[spot] Error fetching spot: {e}")
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time.sleep(5)
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continue
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now = dt.datetime.now(dt.timezone.utc)
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for sym, series_ticker in series_map.items():
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try:
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m = choose_current_market(client, series_ticker)
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if not m:
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continue
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market_ticker = m["ticker"]
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close_time = _parse_iso_z(m["close_time"])
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# Only fire in a tight window starting at (close_time - lead_seconds)
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lead = dt.timedelta(seconds=int(cfg["lead_seconds"]))
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window = dt.timedelta(seconds=int(cfg["trade_window_seconds"]))
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start = close_time - lead
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end = start + window
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if not (start <= now <= end):
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continue
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# prevent duplicate trade on same market
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if last_traded_market.get(sym) == market_ticker:
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continue
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# Pull full market details (strike fields more reliable)
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market_full = client.get_market(market_ticker)["market"]
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strike, yes_if_ge = extract_strike_and_rule(market_full)
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# Market best prices (dollars strings included)
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yes_bid = float(market_full.get("yes_bid_dollars") or 0.0)
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yes_ask = float(market_full.get("yes_ask_dollars") or 1.0)
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spread = yes_ask - yes_bid
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market_prob = yes_ask # to buy YES, you pay the ask
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# Guardrails: spread + prob bounds
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if spread > float(cfg["max_spread_dollars"]):
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reason = f"skip: spread {spread:.4f} > max"
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storage.log_decision(
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ts=time.time(),
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symbol=sym, series_ticker=series_ticker, market_ticker=market_ticker,
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close_time=m["close_time"], strike=strike, side="yes",
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market_prob=market_prob, fair_prob=0.0, edge=0.0,
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spread=spread, jump=0.0, reason=reason
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)
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continue
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if not (float(cfg["min_market_prob"]) <= market_prob <= float(cfg["max_market_prob"])):
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reason = f"skip: market_prob {market_prob:.4f} outside bounds"
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storage.log_decision(
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ts=time.time(),
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symbol=sym, series_ticker=series_ticker, market_ticker=market_ticker,
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close_time=m["close_time"], strike=strike, side="yes",
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market_prob=market_prob, fair_prob=0.0, edge=0.0,
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spread=spread, jump=0.0, reason=reason
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)
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continue
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# Jump filter (tail risk)
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jump = abs(spot.returns_over_window(sym, int(cfg["jump_lookback_seconds"])))
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if jump > float(cfg["max_abs_jump"]):
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reason = f"skip: jump {jump:.5f} > max"
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storage.log_decision(
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ts=time.time(),
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symbol=sym, series_ticker=series_ticker, market_ticker=market_ticker,
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close_time=m["close_time"], strike=strike, side="yes",
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market_prob=market_prob, fair_prob=0.0, edge=0.0,
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spread=spread, jump=jump, reason=reason
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)
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continue
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# Fair probability
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spot_px = spot.latest(sym)
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sigma = spot.realized_vol(sym)
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time_remaining = max(1.0, (close_time - now).total_seconds())
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fair = fair_prob_threshold(
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spot=spot_px,
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strike=strike,
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sigma_per_second=sigma,
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time_remaining_seconds=time_remaining,
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resolves_yes_if_spot_ge_strike=yes_if_ge,
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)
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edge = fair - market_prob
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if edge < float(cfg["min_edge"]):
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reason = f"skip: edge {edge:.4f} < min"
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storage.log_decision(
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ts=time.time(),
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symbol=sym, series_ticker=series_ticker, market_ticker=market_ticker,
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close_time=m["close_time"], strike=strike, side="yes",
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market_prob=market_prob, fair_prob=fair, edge=edge,
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spread=spread, jump=jump, reason=reason
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)
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continue
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# Determine limit price (cents)
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improve = int(cfg.get("limit_price_improve_cents", 0))
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yes_ask_cents = dollars_to_cents_price(yes_ask)
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limit_cents = max(1, yes_ask_cents - improve)
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# Size by max_cost
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max_cost_cents = int(round(float(cfg["max_cost_dollars"]) * 100))
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# worst-case cost ≈ count * price_cents
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count = max(0, max_cost_cents // limit_cents)
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if count <= 0:
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reason = "skip: count computed as 0"
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storage.log_decision(
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ts=time.time(),
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symbol=sym, series_ticker=series_ticker, market_ticker=market_ticker,
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close_time=m["close_time"], strike=strike, side="yes",
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market_prob=market_prob, fair_prob=fair, edge=edge,
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spread=spread, jump=jump, reason=reason
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)
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continue
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# Log decision
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storage.log_decision(
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ts=time.time(),
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symbol=sym, series_ticker=series_ticker, market_ticker=market_ticker,
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close_time=m["close_time"], strike=strike, side="yes",
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market_prob=market_prob, fair_prob=fair, edge=edge,
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spread=spread, jump=jump, reason="trade"
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)
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client_order_id = f"{sym}-{uuid.uuid4().hex[:12]}"
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if mode == "paper":
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print(f"[PAPER] {sym} trade {market_ticker} count={count} limit={limit_cents}c max_cost={max_cost_cents}c edge={edge:.3f}")
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storage.log_order(market_ticker, order_id=None, mode="paper", status="simulated",
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details=f"count={count} yes_price={limit_cents} buy_max_cost={max_cost_cents} edge={edge:.4f}")
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else:
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print(f"[LIVE] {sym} placing order {market_ticker} count={count} limit={limit_cents}c max_cost={max_cost_cents}c edge={edge:.3f}")
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resp = client.create_order(
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ticker=market_ticker,
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side="yes",
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action="buy",
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count=count,
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yes_price_cents=limit_cents,
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buy_max_cost_cents=max_cost_cents,
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time_in_force="fill_or_kill",
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client_order_id=client_order_id,
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)
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order = resp.get("order", {})
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order_id = order.get("order_id")
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status = order.get("status", "unknown")
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storage.log_order(market_ticker, order_id=order_id, mode="live", status=status, details=json.dumps(order)[:2000])
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last_traded_market[sym] = market_ticker
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except Exception as e:
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print(f"[loop] Error for {sym}: {e}")
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time.sleep(2)
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if __name__ == "__main__":
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main()
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