#!/usr/bin/env python3 """ Weather Trading Bot v2 — Polymarket Kelly Criterion + Expected Value simulation. Usage: python bot_v2.py # Paper mode with $1000 virtual balance python bot_v2.py --live # Simulate trades against real prices python bot_v2.py --positions python bot_v2.py --reset # Reset simulation balance python bot_v2.py --monitor # Live price monitor, updates dashboard every 10s """ import re import json import argparse import requests from datetime import datetime, timezone, timedelta # ============================================================================= # CONFIG # ============================================================================= with open("config.json") as f: _cfg = json.load(f) ENTRY_THRESHOLD = _cfg.get("entry_threshold", 0.15) EXIT_THRESHOLD = _cfg.get("exit_threshold", 0.45) MAX_TRADES = _cfg.get("max_trades_per_run", 5) MIN_HOURS_LEFT = _cfg.get("min_hours_to_resolution", 2) PRICE_DROP_SIGNAL = _cfg.get("price_drop_threshold", 0.10) # Kelly + EV settings NOAA_ACCURACY = 0.78 # NOAA forecast accuracy for 1-3 day predictions KELLY_FRACTION = 0.25 # Use 1/4 Kelly for safety MAX_POSITION_PCT = 0.10 # Never bet more than 10% of balance on one trade MIN_EV = 0.05 # Minimum EV to enter SIM_BALANCE = 1000.0 # Starting virtual balance # Airport coordinates — match the exact stations Polymarket resolves on LOCATIONS = { "nyc": {"lat": 40.7772, "lon": -73.8726, "name": "New York City"}, # KLGA LaGuardia "chicago": {"lat": 41.9742, "lon": -87.9073, "name": "Chicago"}, # KORD O'Hare "miami": {"lat": 25.7959, "lon": -80.2870, "name": "Miami"}, # KMIA "dallas": {"lat": 32.8471, "lon": -96.8518, "name": "Dallas"}, # KDAL Love Field "seattle": {"lat": 47.4502, "lon": -122.3088, "name": "Seattle"}, # KSEA Sea-Tac "atlanta": {"lat": 33.6407, "lon": -84.4277, "name": "Atlanta"}, # KATL Hartsfield } # NWS hourly endpoints per city NWS_ENDPOINTS = { "nyc": "https://api.weather.gov/gridpoints/OKX/37,39/forecast/hourly", "chicago": "https://api.weather.gov/gridpoints/LOT/66,77/forecast/hourly", "miami": "https://api.weather.gov/gridpoints/MFL/106,51/forecast/hourly", "dallas": "https://api.weather.gov/gridpoints/FWD/87,107/forecast/hourly", "seattle": "https://api.weather.gov/gridpoints/SEW/124,61/forecast/hourly", "atlanta": "https://api.weather.gov/gridpoints/FFC/50,82/forecast/hourly", } # Station IDs for real observations STATION_IDS = { "nyc": "KLGA", "chicago": "KORD", "miami": "KMIA", "dallas": "KDAL", "seattle": "KSEA", "atlanta": "KATL", } ACTIVE_LOCATIONS = _cfg.get("locations", "nyc,chicago,miami,dallas,seattle,atlanta").split(",") ACTIVE_LOCATIONS = [l.strip().lower() for l in ACTIVE_LOCATIONS] MONTHS = ["january","february","march","april","may","june", "july","august","september","october","november","december"] # ============================================================================= # COLORS # ============================================================================= class C: GREEN = "\033[92m" YELLOW = "\033[93m" RED = "\033[91m" CYAN = "\033[96m" GRAY = "\033[90m" RESET = "\033[0m" BOLD = "\033[1m" def ok(msg): print(f"{C.GREEN} ✅ {msg}{C.RESET}") def warn(msg): print(f"{C.YELLOW} ⚠️ {msg}{C.RESET}") def info(msg): print(f"{C.CYAN} {msg}{C.RESET}") def skip(msg): print(f"{C.GRAY} ⏸️ {msg}{C.RESET}") # ============================================================================= # KELLY CRITERION + EV # ============================================================================= def calculate_ev(our_prob: float, market_price: float) -> float: """ Expected Value per $1 risked. EV = (our_prob * payout) - (1 - our_prob) payout = (1 / market_price) - 1 Example: our_prob=0.75, price=0.08 payout = 1/0.08 - 1 = 11.5x EV = 0.75 * 11.5 - 0.25 = +$8.12 per $1 risked """ if market_price <= 0 or market_price >= 1: return 0.0 payout = (1.0 / market_price) - 1.0 ev = (our_prob * payout) - (1.0 - our_prob) return round(ev, 4) def calculate_kelly(our_prob: float, market_price: float) -> float: """ Kelly Criterion: optimal fraction of bankroll to bet. f* = (p * b - q) / b We apply KELLY_FRACTION (0.25) for safety — fractional Kelly. Result is capped at MAX_POSITION_PCT (10% of balance). """ if market_price <= 0 or market_price >= 1: return 0.0 b = (1.0 / market_price) - 1.0 p = our_prob q = 1.0 - p kelly = (p * b - q) / b kelly = max(0.0, kelly) kelly = kelly * KELLY_FRACTION kelly = min(kelly, MAX_POSITION_PCT) return round(kelly, 4) def calculate_position_size(kelly_fraction: float, balance: float) -> float: return round(kelly_fraction * balance, 2) # ============================================================================= # SIMULATION STATE # ============================================================================= SIM_FILE = "simulation.json" def load_sim() -> dict: try: with open(SIM_FILE) as f: return json.load(f) except FileNotFoundError: return { "balance": SIM_BALANCE, "starting_balance": SIM_BALANCE, "positions": {}, "trades": [], "total_trades": 0, "wins": 0, "losses": 0, "peak_balance": SIM_BALANCE, } def save_sim(sim: dict): with open(SIM_FILE, "w") as f: json.dump(sim, f, indent=2) def reset_sim(): import os if os.path.exists(SIM_FILE): os.remove(SIM_FILE) print(f"{C.GREEN} ✅ Simulation reset — balance back to ${SIM_BALANCE:.2f}{C.RESET}") # ============================================================================= # NWS FORECAST # ============================================================================= def get_forecast(city_slug: str) -> dict: """ Fetch daily max temperature from NWS. Combines real station observations (past hours today) with hourly forecast (upcoming hours) to get the true daily maximum. """ forecast_url = NWS_ENDPOINTS.get(city_slug) station_id = STATION_IDS.get(city_slug) daily_max = {} headers = {"User-Agent": "weatherbot/1.0"} # Real observations — what already happened today try: obs_url = f"https://api.weather.gov/stations/{station_id}/observations?limit=48" r = requests.get(obs_url, timeout=10, headers=headers) for obs in r.json().get("features", []): props = obs["properties"] time_str = props.get("timestamp", "")[:10] temp_c = props.get("temperature", {}).get("value") if temp_c is not None: temp_f = round(temp_c * 9/5 + 32) if time_str not in daily_max or temp_f > daily_max[time_str]: daily_max[time_str] = temp_f except Exception as e: warn(f"Observations error for {city_slug}: {e}") # Hourly forecast — upcoming hours try: r = requests.get(forecast_url, timeout=10, headers=headers) periods = r.json()["properties"]["periods"] for p in periods: date = p["startTime"][:10] temp = p["temperature"] if p.get("temperatureUnit") == "C": temp = round(temp * 9/5 + 32) if date not in daily_max or temp > daily_max[date]: daily_max[date] = temp except Exception as e: warn(f"Forecast error for {city_slug}: {e}") return daily_max # ============================================================================= # POLYMARKET API # ============================================================================= def get_polymarket_event(city_slug: str, month: str, day: int, year: int) -> dict: slug = f"highest-temperature-in-{city_slug}-on-{month}-{day}-{year}" url = f"https://gamma-api.polymarket.com/events?slug={slug}" try: r = requests.get(url, timeout=10) data = r.json() if data and isinstance(data, list) and len(data) > 0: return data[0] except Exception as e: warn(f"Polymarket API error: {e}") return None def get_price_history(market_id: str) -> list: url = f"https://clob.polymarket.com/prices-history?market={market_id}&interval=1d&fidelity=60" try: r = requests.get(url, timeout=10) return r.json().get("history", []) except Exception: return [] # ============================================================================= # PARSING # ============================================================================= def parse_temp_range(question: str) -> tuple: if not question: return None if "or below" in question.lower(): m = re.search(r'(\d+)°F or below', question, re.IGNORECASE) if m: return (-999, int(m.group(1))) if "or higher" in question.lower(): m = re.search(r'(\d+)°F or higher', question, re.IGNORECASE) if m: return (int(m.group(1)), 999) m = re.search(r'between (\d+)-(\d+)°F', question, re.IGNORECASE) if m: return (int(m.group(1)), int(m.group(2))) return None def hours_until_resolution(event: dict) -> float: try: end_date = event.get("endDate") or event.get("end_date_iso") if not end_date: return 999 end_dt = datetime.fromisoformat(end_date.replace("Z", "+00:00")) delta = (end_dt - datetime.now(timezone.utc)).total_seconds() / 3600 return max(0, delta) except Exception: return 999 def detect_price_drop(history: list) -> dict: if not history or len(history) < 2: return {"dropped": False, "change": 0} recent = history[-1].get("p", 0.5) lookback = min(96, len(history) - 1) old = history[-lookback].get("p", recent) if old == 0: return {"dropped": False, "change": 0} change = (recent - old) / old return {"dropped": change < -PRICE_DROP_SIGNAL, "change": change} # ============================================================================= # SHOW POSITIONS # ============================================================================= def show_positions(): sim = load_sim() positions = sim["positions"] print(f"\n{C.BOLD}📊 Open Positions:{C.RESET}") if not positions: print(" No open positions") return total_pnl = 0 for mid, pos in positions.items(): try: url = f"https://gamma-api.polymarket.com/markets/{mid}" r = requests.get(url, timeout=5) prices = json.loads(r.json().get("outcomePrices", "[0.5,0.5]")) current_price = float(prices[0]) except Exception: current_price = pos["entry_price"] pnl = (current_price - pos["entry_price"]) * pos["shares"] total_pnl += pnl pnl_str = f"{C.GREEN}+${pnl:.2f}{C.RESET}" if pnl >= 0 else f"{C.RED}-${abs(pnl):.2f}{C.RESET}" print(f"\n • {pos['question'][:65]}...") print(f" Entry: ${pos['entry_price']:.3f} | Now: ${current_price:.3f} | " f"Shares: {pos['shares']:.1f} | PnL: {pnl_str}") print(f" Kelly used: {pos.get('kelly_pct', 0):.1%} | EV: {pos.get('ev', 0):.2f} | Cost: ${pos['cost']:.2f}") print(f"\n Balance: ${sim['balance']:.2f}") pnl_color = C.GREEN if total_pnl >= 0 else C.RED print(f" Open PnL: {pnl_color}{'+'if total_pnl>=0 else ''}{total_pnl:.2f}{C.RESET}") print(f" Total trades: {sim['total_trades']} | W/L: {sim['wins']}/{sim['losses']}") # ============================================================================= # MAIN STRATEGY # ============================================================================= def run(dry_run: bool = True): print(f"\n{C.BOLD}{C.CYAN}🌤 Weather Trading Bot v2 — Kelly + EV Edition{C.RESET}") print("=" * 55) sim = load_sim() balance = sim["balance"] positions = sim["positions"] mode = f"{C.YELLOW}PAPER MODE{C.RESET}" if dry_run else f"{C.GREEN}LIVE MODE{C.RESET}" starting = sim["starting_balance"] total_return = (balance - starting) / starting * 100 return_str = f"{C.GREEN}+{total_return:.1f}%{C.RESET}" if total_return >= 0 else f"{C.RED}{total_return:.1f}%{C.RESET}" print(f"\n Mode: {mode}") print(f" Virtual balance: {C.BOLD}${balance:.2f}{C.RESET} (started ${starting:.2f}, {return_str})") print(f" Kelly fraction: {KELLY_FRACTION:.0%} of full Kelly") print(f" Max per trade: {MAX_POSITION_PCT:.0%} of balance") print(f" Min EV: {MIN_EV:.2f} per $1 risked") print(f" NOAA accuracy: {NOAA_ACCURACY:.0%}") print(f" Trades W/L: {sim['wins']}/{sim['losses']}") forecast_cache = {} trades_executed = 0 opportunities = 0 # Check exits print(f"\n{C.BOLD}📤 Checking exits...{C.RESET}") exits_found = 0 for mid, pos in list(positions.items()): try: url = f"https://gamma-api.polymarket.com/markets/{mid}" r = requests.get(url, timeout=5) prices = json.loads(r.json().get("outcomePrices", "[0.5,0.5]")) current_price = float(prices[0]) except Exception: continue if current_price >= EXIT_THRESHOLD: exits_found += 1 pnl = (current_price - pos["entry_price"]) * pos["shares"] ok(f"EXIT: {pos['question'][:50]}...") info(f"Price ${current_price:.3f} >= exit ${EXIT_THRESHOLD:.2f} | PnL: +${pnl:.2f}") if not dry_run: balance += pos["cost"] + pnl sim["wins"] += 1 if pnl > 0 else 0 sim["losses"] += 1 if pnl <= 0 else 0 sim["trades"].append({ "type": "exit", "question": pos["question"], "entry_price": pos["entry_price"], "exit_price": current_price, "pnl": round(pnl, 2), "cost": pos["cost"], "closed_at": datetime.now().isoformat(), }) del positions[mid] ok(f"Closed — PnL: {'+'if pnl>=0 else ''}{pnl:.2f}") else: skip("Paper mode — not selling") if exits_found == 0: skip("No exit opportunities") # Scan entries print(f"\n{C.BOLD}🔍 Scanning for entry signals...{C.RESET}") for city_slug in ACTIVE_LOCATIONS: if city_slug not in LOCATIONS: warn(f"Unknown location: {city_slug}") continue loc_data = LOCATIONS[city_slug] if city_slug not in forecast_cache: forecast_cache[city_slug] = get_forecast(city_slug) forecast = forecast_cache[city_slug] if not forecast: continue for i in range(0, 4): date = datetime.now() + timedelta(days=i) date_str = date.strftime("%Y-%m-%d") month = MONTHS[date.month - 1] day = date.day year = date.year forecast_temp = forecast.get(date_str) if forecast_temp is None: continue event = get_polymarket_event(city_slug, month, day, year) if not event: continue hours_left = hours_until_resolution(event) print(f"\n{C.BOLD}📍 {loc_data['name']} — {date_str}{C.RESET}") info(f"Forecast: {forecast_temp}°F | Resolves in: {hours_left:.0f}h") if hours_left < MIN_HOURS_LEFT: skip(f"Resolves in {hours_left:.0f}h — too soon") continue matched = None for market in event.get("markets", []): question = market.get("question", "") rng = parse_temp_range(question) if rng and rng[0] <= forecast_temp <= rng[1]: try: prices = json.loads(market.get("outcomePrices", "[0.5,0.5]")) yes_price = float(prices[0]) except Exception: continue matched = {"market": market, "question": question, "price": yes_price, "range": rng} break if not matched: skip(f"No bucket found for {forecast_temp}°F") continue price = matched["price"] market_id = matched["market"].get("id", "") question = matched["question"] info(f"Bucket: {question[:60]}") info(f"Market price: ${price:.3f}") # Trend check history = get_price_history(market_id) trend = detect_price_drop(history) if trend["dropped"]: info(f"📉 Price dropped {abs(trend['change']):.0%} in 24h — stronger signal") # Kelly + EV our_prob = NOAA_ACCURACY if trend["dropped"] and abs(trend["change"]) > 0.20: our_prob = min(0.90, our_prob + 0.05) ev = calculate_ev(our_prob, price) kelly_pct = calculate_kelly(our_prob, price) position_size = calculate_position_size(kelly_pct, balance) print(f"\n {C.BOLD}📐 Kelly + EV Analysis:{C.RESET}") info(f" Our probability: {our_prob:.0%}") info(f" Market implies: {price:.1%}") info(f" Edge: {our_prob - price:.1%}") ev_color = C.GREEN if ev > 0 else C.RED print(f" {C.CYAN} EV per $1: {ev_color}{ev:+.2f}{C.RESET}") print(f" {C.CYAN} Kelly fraction: {kelly_pct:.1%} of balance{C.RESET}") print(f" {C.CYAN} Position size: ${position_size:.2f}{C.RESET}") if price >= ENTRY_THRESHOLD: skip(f"Price ${price:.3f} above threshold ${ENTRY_THRESHOLD:.2f}") continue if ev < MIN_EV: skip(f"EV {ev:.2f} below minimum {MIN_EV:.2f} — skip") continue if kelly_pct <= 0: skip("Kelly says no edge — skip") continue opportunities += 1 ok(f"ENTRY signal! EV={ev:+.2f} | Kelly={kelly_pct:.1%} | Size=${position_size:.2f}") if market_id in positions: skip("Already in this market") continue if trades_executed >= MAX_TRADES: skip(f"Max trades ({MAX_TRADES}) reached") continue if position_size < 0.50: skip(f"Position size ${position_size:.2f} too small — skip") continue shares = position_size / price info(f"Buying {shares:.1f} shares @ ${price:.3f} = ${position_size:.2f}") if not dry_run: balance -= position_size positions[market_id] = { "question": question, "entry_price": price, "shares": shares, "cost": position_size, "kelly_pct": kelly_pct, "ev": ev, "our_prob": our_prob, "date": date_str, "location": city_slug, "forecast_temp": forecast_temp, "opened_at": datetime.now().isoformat(), } sim["total_trades"] += 1 sim["trades"].append({ "type": "entry", "question": question, "entry_price": price, "shares": shares, "cost": position_size, "kelly_pct": kelly_pct, "ev": ev, "our_prob": our_prob, "opened_at": datetime.now().isoformat(), }) trades_executed += 1 ok(f"Bought {shares:.1f} shares — ${position_size:.2f} deducted from balance") else: skip("Paper mode — not buying") trades_executed += 1 if not dry_run: sim["balance"] = round(balance, 2) sim["positions"] = positions sim["peak_balance"] = max(sim["peak_balance"], balance) save_sim(sim) print(f"\n{'=' * 55}") print(f"{C.BOLD}📊 Summary:{C.RESET}") info(f"Opportunities found: {opportunities}") info(f"Trades executed: {trades_executed}") info(f"Exits found: {exits_found}") info(f"Balance: ${balance:.2f}") if dry_run: print(f"\n {C.YELLOW}[PAPER MODE — use --live to simulate trades against real prices]{C.RESET}") # ============================================================================= # LIVE MONITOR # ============================================================================= import time as _time def monitor(interval: int = 10): """ Background monitor — fetches live prices every N seconds, updates simulation.json so the dashboard stays current. Auto-exits positions when price hits EXIT_THRESHOLD. """ print(f"\n{C.BOLD}{C.CYAN}📡 Live Monitor — refreshing every {interval}s{C.RESET}") print(f" Auto-exit threshold: ${EXIT_THRESHOLD:.2f}") print(f" Press Ctrl+C to stop\n") while True: try: sim = load_sim() positions = sim.get("positions", {}) if not positions: print(f"{C.GRAY} {_time.strftime('%H:%M:%S')} — No open positions{C.RESET}") _time.sleep(interval) continue total_pnl = 0 for mid, pos in list(positions.items()): try: url = f"https://gamma-api.polymarket.com/markets/{mid}" r = requests.get(url, timeout=5) data = r.json() prices = json.loads(data.get("outcomePrices", "[0.5,0.5]")) current_price = float(prices[0]) except Exception: current_price = pos.get("current_price", pos["entry_price"]) pnl = (current_price - pos["entry_price"]) * pos["shares"] pos["current_price"] = round(current_price, 4) pos["pnl"] = round(pnl, 2) total_pnl += pnl pnl_str = f"{C.GREEN}+${pnl:.2f}{C.RESET}" if pnl >= 0 else f"{C.RED}-${abs(pnl):.2f}{C.RESET}" print(f" {C.GRAY}{_time.strftime('%H:%M:%S')}{C.RESET} " f"{pos['question'][:45]}... " f"${current_price:.3f} {pnl_str}") if current_price >= EXIT_THRESHOLD: ok(f"AUTO EXIT: {pos['question'][:50]}... PnL: +${pnl:.2f}") sim["balance"] = round(sim["balance"] + pos["cost"] + pnl, 2) sim["wins"] += 1 if pnl > 0 else 0 sim["losses"] += 1 if pnl <= 0 else 0 sim["trades"].append({ "type": "exit", "question": pos["question"], "entry_price": pos["entry_price"], "exit_price": current_price, "pnl": round(pnl, 2), "cost": pos["cost"], "kelly_pct": pos.get("kelly_pct", 0), "ev": pos.get("ev", 0), "closed_at": datetime.now().isoformat(), }) del sim["positions"][mid] sim["peak_balance"] = max(sim.get("peak_balance", sim["balance"]), sim["balance"]) total_str = f"{C.GREEN}+${total_pnl:.2f}{C.RESET}" if total_pnl >= 0 else f"{C.RED}-${abs(total_pnl):.2f}{C.RESET}" print(f" {'─'*60}") print(f" Open PnL: {total_str} | Balance: ${sim['balance']:.2f} | " f"Positions: {len(sim['positions'])}\n") save_sim(sim) except KeyboardInterrupt: print(f"\n{C.YELLOW} Monitor stopped{C.RESET}") break except Exception as e: warn(f"Monitor error: {e}") _time.sleep(interval) # ============================================================================= # CLI # ============================================================================= if __name__ == "__main__": parser = argparse.ArgumentParser(description="Weather Trading Bot v2 — Kelly + EV") parser.add_argument("--live", action="store_true", help="Execute trades (updates simulation balance)") parser.add_argument("--positions", action="store_true", help="Show open positions") parser.add_argument("--reset", action="store_true", help="Reset simulation to $1000") parser.add_argument("--monitor", action="store_true", help="Live price monitor") parser.add_argument("--interval", type=int, default=10, help="Monitor refresh interval in seconds") args = parser.parse_args() if args.reset: reset_sim() elif args.positions: show_positions() elif args.monitor: monitor(interval=args.interval) else: run(dry_run=not args.live)