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alteregoeth-ai
2026-03-03 17:40:36 -06:00
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commit e263cebc76
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config.json
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{
"entry_threshold": 0.30,
"exit_threshold": 0.45,
"max_position_pct": 0.10,
"max_trades_per_run": 5,
"min_hours_to_resolution": 2,
"locations": "NYC,Chicago,Seattle,Atlanta,Dallas,Miami"
}
polymarket_weather_bot.py
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#!/usr/bin/env python3
"""
Weather Trading Bot v2 — Polymarket
Kelly Criterion + Expected Value simulation.
Usage:
python weather_bot_v2.py # Paper mode with $1000 virtual balance
python weather_bot_v2.py --live # Real trades
python weather_bot_v2.py --positions
python weather_bot_v2.py --reset # Reset simulation balance
"""
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 (full Kelly is too aggressive)
MAX_POSITION_PCT = 0.10 # Never bet more than 10% of balance on one trade
MIN_EV = 0.05 # Minimum EV to enter (5 cents per dollar risked)
SIM_BALANCE = 1000.0 # Starting virtual balance
LOCATIONS = {
"NYC": {"lat": 40.77, "lon": -73.87, "name": "New York City"},
"Chicago": {"lat": 41.97, "lon": -87.90, "name": "Chicago"},
"Seattle": {"lat": 47.45, "lon": -122.30, "name": "Seattle"},
"Atlanta": {"lat": 33.64, "lon": -84.43, "name": "Atlanta"},
"Dallas": {"lat": 32.90, "lon": -97.04, "name": "Dallas"},
"Miami": {"lat": 25.80, "lon": -80.29, "name": "Miami"},
}
ACTIVE_LOCATIONS = _cfg.get("locations", "NYC,Chicago,Seattle,Atlanta,Dallas,Miami").split(",")
ACTIVE_LOCATIONS = [l.strip() 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) * 1
payout = (1 / market_price) - 1 (net profit per $1 if we win)
Example: our_prob=0.75, price=0.08
payout = 1/0.08 - 1 = 11.5x
EV = 0.75 * 11.5 - 0.25 = 8.375 - 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
where:
p = our probability of winning
q = 1 - p (probability of losing)
b = net odds (payout per $1 bet)
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 # net odds
p = our_prob
q = 1.0 - p
kelly = (p * b - q) / b
kelly = max(0.0, kelly) # never negative
kelly = kelly * KELLY_FRACTION # fractional Kelly
kelly = min(kelly, MAX_POSITION_PCT) # cap at max position
return round(kelly, 4)
def calculate_position_size(kelly_fraction: float, balance: float) -> float:
"""Convert Kelly fraction to dollar amount."""
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)
if os.path.exists("positions.json"):
os.remove("positions.json")
print(f"{C.GREEN} ✅ Simulation reset — balance back to ${SIM_BALANCE:.2f}{C.RESET}")
# =============================================================================
# OPEN-METEO FORECAST
# =============================================================================
def get_forecast(location: str) -> dict:
loc = LOCATIONS[location]
url = (
f"https://api.open-meteo.com/v1/forecast"
f"?latitude={loc['lat']}&longitude={loc['lon']}"
f"&daily=temperature_2m_max&temperature_unit=fahrenheit&forecast_days=4"
)
try:
r = requests.get(url, timeout=10)
data = r.json()
result = {}
for date, temp in zip(data["daily"]["time"], data["daily"]["temperature_2m_max"]):
result[date] = round(temp, 1)
return result
except Exception as e:
warn(f"Forecast error for {location}: {e}")
return {}
# =============================================================================
# POLYMARKET API
# =============================================================================
def get_polymarket_event(location_slug: str, month: str, day: int, year: int) -> dict:
slug = f"highest-temperature-in-{location_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 temp_in_range(temp: float, rng: tuple) -> bool:
return rng[0] <= temp <= rng[1]
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)
current_price = float(r.json().get("outcomePrices", ["0.5"])[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['kelly_pct']:.1%} | EV: {pos['ev']:.2f} | Cost: ${pos['cost']:.2f}")
balance_str = f"${sim['balance']:.2f}"
pnl_color = C.GREEN if total_pnl >= 0 else C.RED
print(f"\n Balance: {balance_str}")
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.RED}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)
current_price = float(r.json().get("outcomePrices", ["0.5"])[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 position — 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 loc_key in ACTIVE_LOCATIONS:
loc_key = loc_key.strip()
if loc_key not in LOCATIONS:
warn(f"Unknown location: {loc_key}")
continue
loc_data = LOCATIONS[loc_key]
loc_slug = loc_key.lower().replace(" ", "-")
if loc_key not in forecast_cache:
forecast_cache[loc_key] = get_forecast(loc_key)
forecast = forecast_cache[loc_key]
if not forecast:
continue
for i in range(0, 3):
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(loc_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
# Find matching bucket
matched = None
for market in event.get("markets", []):
question = market.get("question", "")
rng = parse_temp_range(question)
if rng and temp_in_range(forecast_temp, rng):
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 CALCULATION ──
our_prob = NOAA_ACCURACY # base accuracy
# Boost if strong trend signal
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}")
# Entry checks
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": loc_key,
"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
# Save simulation state
if not dry_run:
sim["balance"] = round(balance, 2)
sim["positions"] = positions
sim["peak_balance"] = max(sim["peak_balance"], balance)
save_sim(sim)
# Summary
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 — updates prices every N seconds, auto-exits on threshold
# =============================================================================
import time as _time
def monitor(interval: int = 10):
"""
Background monitor — fetches live prices from Polymarket every N seconds,
updates PnL in simulation.json so the dashboard stays current.
Auto-exits positions when price hits EXIT_THRESHOLD.
Run: python polymarket_weather_bot.py --monitor
Stop: Ctrl+C
"""
print(f"\n{C.BOLD}{C.CYAN}📡 Live Monitor — refreshing every {interval}s{C.RESET}")
print(f" Dashboard will update automatically")
print(f" Auto-exit threshold: ${EXIT_THRESHOLD:.2f}")
print(f" Press Ctrl+C to stop\n")
while True:
try:
sim = load_sim()
positions = sim.get("positions", {})
if not positions:
print(f"{C.GRAY} {_time.strftime('%H:%M:%S')} — No open positions{C.RESET}")
_time.sleep(interval)
continue
total_pnl = 0
for mid, pos in list(positions.items()):
# Fetch current price from Polymarket
try:
url = f"https://gamma-api.polymarket.com/markets/{mid}"
r = requests.get(url, timeout=5)
data = r.json()
prices = json.loads(data.get("outcomePrices", "[0.5,0.5]"))
current_price = float(prices[0])
except Exception:
current_price = pos.get("current_price", pos["entry_price"])
pnl = (current_price - pos["entry_price"]) * pos["shares"]
pos["current_price"] = round(current_price, 4)
pos["pnl"] = round(pnl, 2)
total_pnl += pnl
pnl_str = f"{C.GREEN}+${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}")
# Auto-exit if price hit threshold
if current_price >= EXIT_THRESHOLD:
ok(f"AUTO EXIT: {pos['question'][:50]}... PnL: +${pnl:.2f}")
sim["balance"] = round(sim["balance"] + pos["cost"] + pnl, 2)
sim["wins"] += 1 if pnl > 0 else 0
sim["losses"] += 1 if pnl <= 0 else 0
sim["trades"].append({
"type": "exit",
"question": pos["question"],
"entry_price": pos["entry_price"],
"exit_price": current_price,
"pnl": round(pnl, 2),
"cost": pos["cost"],
"kelly_pct": pos.get("kelly_pct", 0),
"ev": pos.get("ev", 0),
"location": pos.get("location", ""),
"date": pos.get("date", ""),
"our_prob": pos.get("our_prob", 0),
"closed_at": datetime.now().isoformat(),
})
del sim["positions"][mid]
sim["positions"] = {k: v for k, v in sim["positions"].items()}
sim["peak_balance"] = max(sim.get("peak_balance", sim["balance"]), sim["balance"])
total_str = f"{C.GREEN}+${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 — updates dashboard every 10s")
parser.add_argument("--interval", type=int, default=10, help="Monitor refresh interval in seconds (default: 10)")
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)
sim_dashboard_repost.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Weather Bot — Kelly Simulation</title>
<link href="https://fonts.googleapis.com/css2?family=Share+Tech+Mono&family=Orbitron:wght@400;700;900&display=swap" rel="stylesheet">
<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/4.4.0/chart.umd.min.js"></script>
<style>
:root{--bg:#000;--bg2:#020c02;--border:#0a2a0a;--green:#00ff41;--green-dim:rgba(0,255,65,.08);--green-glow:rgba(0,255,65,.4);--red:#ff2244;--red-dim:rgba(255,34,68,.08);--yellow:#ffcc00;--text:#88ff88;--text2:#2a5a2a;--mono:'Share Tech Mono',monospace;--display:'Orbitron',sans-serif;}
*{margin:0;padding:0;box-sizing:border-box;}
body{background:var(--bg);color:var(--text);font-family:var(--mono);min-height:100vh;overflow-x:hidden;}
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.chart-body{padding:16px;position:relative;}
.chart-wrap{height:220px;position:relative;}
.deltas{position:absolute;inset:0;pointer-events:none;overflow:hidden;}
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.scroll::-webkit-scrollbar-thumb{background:var(--border);}
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</style>
</head>
<body>
<div class="wrap">
<div class="header">
<div>
<div class="brand-title">WEATHER BOT — KELLY SIM</div>
<div class="brand-sub">Polymarket · Open-Meteo · Kelly Criterion · EV Analysis</div>
</div>
<div class="header-right">
<div class="live-pill"><div class="dot" id="status-dot"></div><span id="status-text">CONNECTING...</span></div>
<div class="ts" id="ts">—</div>
</div>
</div>
<div id="no-server-msg" class="no-server" style="display:none;margin-bottom:20px;">
⚠️ &nbsp;Dashboard needs a local server to read <code>simulation.json</code>.<br>
Run this command in your <code>weatherbot</code> folder, then refresh:<br><br>
<code>python -m http.server 8000</code><br><br>
Then open: <code>http://localhost:8000/sim_dashboard.html</code>
</div>
<div class="stats">
<div class="stat"><div class="stat-label">Balance</div><div class="stat-val" id="s-bal">—</div><div class="stat-sub">virtual USDC</div></div>
<div class="stat"><div class="stat-label">Total PnL</div><div class="stat-val" id="s-pnl">—</div><div class="stat-sub" id="s-pct">vs $1,000 start</div></div>
<div class="stat"><div class="stat-label">Open Positions</div><div class="stat-val" id="s-open">—</div><div class="stat-sub">active trades</div></div>
<div class="stat"><div class="stat-label">Win Rate</div><div class="stat-val yellow" id="s-wr">—</div><div class="stat-sub" id="s-wl">0W / 0L</div></div>
<div class="stat"><div class="stat-label">Total Trades</div><div class="stat-val" id="s-total">—</div><div class="stat-sub">all time</div></div>
<div class="stat"><div class="stat-label">Peak Balance</div><div class="stat-val" id="s-peak">—</div><div class="stat-sub">all time high</div></div>
</div>
<div class="layout">
<div class="chart-card">
<div class="card-head"><span class="card-title">Balance History</span><span class="card-badge">KELLY-SIZED POSITIONS</span></div>
<div class="chart-body">
<div class="chart-wrap">
<canvas id="chart"></canvas>
<div class="deltas" id="deltas"></div>
</div>
</div>
</div>
<div class="panel">
<div class="card-head"><span class="card-title">Open Positions</span><span class="card-badge" id="pos-badge">0 ACTIVE</span></div>
<div class="scroll" id="pos-list"><div class="empty">Waiting for bot data...</div></div>
</div>
</div>
<div class="bot-grid">
<div class="panel">
<div class="card-head"><span class="card-title">Trade History</span><span class="card-badge">FROM simulation.json</span></div>
<div class="scroll" id="trade-list"><div class="empty">No trades yet — run: python weather_bot_v2.py --live</div></div>
</div>
<div class="panel">
<div class="card-head"><span class="card-title">Kelly + EV Log</span><span class="card-badge">ENTRIES ONLY</span></div>
<div class="scroll" id="ev-log"><div class="empty">No entries yet</div></div>
</div>
</div>
<div class="footer">
<div class="footer-text">Weather Bot v2 · Reads from simulation.json · Refreshes every 10s</div>
<div class="sim-badge">⚡ $1,000 SIMULATION</div>
</div>
</div>
<script>
const fmt2=v=>(v>=0?'+':'')+v.toFixed(2);
const fmtUSD=v=>'$'+Math.abs(v).toFixed(2);
const now=()=>new Date().toLocaleTimeString('en-US',{hour12:false});
const rand=(a,b)=>Math.random()*(b-a)+a;
let prevBal=null, chart=null, balHistory=[], timeLabels=[];
// Init chart
chart=new Chart(document.getElementById('chart'),{
type:'line',
data:{labels:[],datasets:[{data:[],borderColor:'#00ff41',backgroundColor:'rgba(0,255,65,.06)',fill:true,tension:.45,pointRadius:0,borderWidth:2}]},
options:{responsive:true,maintainAspectRatio:false,animation:{duration:400},plugins:{legend:{display:false},tooltip:{backgroundColor:'#020c02',borderColor:'#0a2a0a',borderWidth:1,callbacks:{label:c=>'$'+c.parsed.y.toFixed(2)}}},scales:{x:{ticks:{color:'#1a3a1a',font:{family:'Share Tech Mono',size:8},maxTicksLimit:8},grid:{color:'#050f05'}},y:{ticks:{color:'#2a5a2a',font:{family:'Share Tech Mono',size:9}},grid:{color:'#050f05'}}}}
});
function spawnDelta(v){
const l=document.getElementById('deltas');
const e=document.createElement('div');
e.className='delta '+(v>=0?'pos':'neg');
e.textContent=(v>=0?'+ $':'- $')+Math.abs(v).toFixed(2);
e.style.left=rand(30,l.offsetWidth-120)+'px';
e.style.top=rand(l.offsetHeight*.2,l.offsetHeight*.7)+'px';
l.appendChild(e);
setTimeout(()=>e.remove(),2500);
}
function renderPositions(positions){
const el=document.getElementById('pos-list');
document.getElementById('pos-badge').textContent=Object.keys(positions).length+' ACTIVE';
if(!Object.keys(positions).length){el.innerHTML='<div class="empty">No open positions</div>';return;}
el.innerHTML=Object.entries(positions).map(([id,p])=>{
const pnl=p.pnl||0;
const pct=(p.current_price||p.entry_price)*100;
return`<div class="pos-item">
<div class="pos-top">
<div class="pos-q">${(p.question||'').substr(0,58)}...</div>
<div class="pos-pnl ${pnl>=0?'pos':'neg'}">${pnl>=0?'+':'-'}$${Math.abs(pnl).toFixed(2)}</div>
</div>
<div class="pos-bar"><div class="pos-fill" style="width:${Math.min(100,pct)}%"></div></div>
<div class="pos-meta">
<div>${p.location||''}</div>
<div>Kelly: <span>${((p.kelly_pct||0)*100).toFixed(1)}%</span></div>
<div>EV: <span>${fmt2(p.ev||0)}</span></div>
<div>$${(p.cost||0).toFixed(2)} in</div>
</div>
</div>`;
}).join('');
}
function renderTrades(trades){
const el=document.getElementById('trade-list');
if(!trades||!trades.length){el.innerHTML='<div class="empty">No trades yet — run: python weather_bot_v2.py --live</div>';return;}
const recent=[...trades].reverse().slice(0,15);
el.innerHTML=recent.map(t=>{
const isEntry=t.type==='entry';
const isWin=t.type==='exit'&&(t.pnl||0)>0;
const cls=isEntry?'buy':isWin?'win':'loss';
const label=isEntry?'BUY':isWin?'WIN':'LOSS';
const amtStr=isEntry?`-$${(t.cost||0).toFixed(2)}`:(t.pnl||0)>=0?`+$${(t.pnl||0).toFixed(2)}`:`-$${Math.abs(t.pnl||0).toFixed(2)}`;
const amtCls=isEntry?'neg':(t.pnl||0)>=0?'pos':'neg';
const time=(t.opened_at||t.closed_at||'').substr(11,5)||'—';
return`<div class="trade-item">
<div class="t-time">${time}</div>
<span class="t-badge ${cls}">${label}</span>
<div class="t-info">
<div class="t-q">${(t.question||'').substr(0,45)}...</div>
<div class="t-meta">Kelly ${((t.kelly_pct||0)*100).toFixed(1)}% · EV ${fmt2(t.ev||0)}</div>
</div>
<div class="t-right"><div class="t-amount ${amtCls}">${amtStr}</div></div>
</div>`;
}).join('');
}
function renderEvLog(trades){
const el=document.getElementById('ev-log');
const entries=(trades||[]).filter(t=>t.type==='entry').reverse().slice(0,15);
if(!entries.length){el.innerHTML='<div class="empty">No entries yet</div>';return;}
el.innerHTML=entries.map(t=>{
const evCol=(t.ev||0)>0?'#00ff41':'#ff2244';
const time=(t.opened_at||'').substr(11,5)||'—';
return`<div style="padding:7px 14px;border-bottom:1px solid #0a2a0a;font-size:9px;">
<div style="color:#2a5a2a">${time} · ${t.location||''} · ${t.date||''}</div>
<div style="color:#88ff88;margin-top:2px">Entry: $${(t.entry_price||0).toFixed(3)} · Our prob: ${((t.our_prob||0)*100).toFixed(0)}%</div>
<div style="margin-top:2px">EV: <span style="color:${evCol}">${fmt2(t.ev||0)}</span> per $1 &nbsp;|&nbsp; Kelly: <span style="color:#ffcc00">${((t.kelly_pct||0)*100).toFixed(1)}%</span> &nbsp;|&nbsp; Size: <span style="color:#00ff41">$${(t.cost||0).toFixed(2)}</span></div>
</div>`;
}).join('');
}
async function loadData(){
try {
const r=await fetch('simulation.json?t='+Date.now());
if(!r.ok)throw new Error('not found');
const sim=await r.json();
document.getElementById('no-server-msg').style.display='none';
document.getElementById('status-dot').className='dot';
document.getElementById('status-text').textContent='LIVE';
const bal=sim.balance||1000;
const start=sim.starting_balance||1000;
const wins=sim.wins||0;
const losses=sim.losses||0;
const totalTrades=sim.total_trades||0;
const positions=sim.positions||{};
const trades=sim.trades||[];
const peak=sim.peak_balance||bal;
const openValue = Object.values(positions).reduce((s,p) => s + (p.cost||0) + (p.pnl||0), 0);
const total = bal + openValue;
const pnl = total - start;
const pct=pnl/start*100;
// Stats
document.getElementById('ts').textContent='Updated: '+now();
document.getElementById('s-bal').textContent='$'+total.toFixed(2);
const pnlEl=document.getElementById('s-pnl');
pnlEl.textContent=(pnl>=0?'+':'')+' $'+Math.abs(pnl).toFixed(2);
pnlEl.className='stat-val'+(pnl<0?' red':'');
document.getElementById('s-pct').textContent=(pct>=0?'+':'')+pct.toFixed(1)+'% return';
document.getElementById('s-open').textContent=Object.keys(positions).length;
const wr=wins+losses>0?(wins/(wins+losses)*100).toFixed(0)+'%':'—';
document.getElementById('s-wr').textContent=wr;
document.getElementById('s-wl').textContent=wins+'W / '+losses+'L';
document.getElementById('s-total').textContent=totalTrades;
document.getElementById('s-peak').textContent='$'+peak.toFixed(2);
// Chart
if(prevBal!==null&&Math.abs(bal-prevBal)>0.1)spawnDelta(bal-prevBal);
prevBal=bal;
balHistory.push(parseFloat(bal.toFixed(2)));
timeLabels.push(now());
if(balHistory.length>60){balHistory.shift();timeLabels.shift();}
chart.data.labels=[...timeLabels];
chart.data.datasets[0].data=[...balHistory];
chart.update();
renderPositions(positions);
renderTrades(trades);
renderEvLog(trades);
} catch(e) {
document.getElementById('status-dot').className='dot gray';
document.getElementById('status-text').textContent='WAITING FOR BOT';
document.getElementById('ts').textContent='No simulation.json found';
// Show instructions if running from file://
if(location.protocol==='file:'){
document.getElementById('no-server-msg').style.display='block';
}
}
}
loadData();
setInterval(loadData, 10000);
</script>
</body>
</html>