321 lines
14 KiB
Python
321 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Complete backtest analysis for kch123's Polymarket trading strategy
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Demonstrates copy-trading viability with realistic projections
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"""
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import json
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import time
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from datetime import datetime
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from collections import defaultdict
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from typing import Dict, List, Tuple
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import statistics
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class PolynMarketBacktester:
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def __init__(self, initial_bankroll: float = 10000):
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self.initial_bankroll = initial_bankroll
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self.markets = {} # conditionId -> market data
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self.trades_by_market = defaultdict(list)
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def parse_sample_data(self):
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"""
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Use the sample trades we've collected to demonstrate the methodology
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This represents the approach we'd use on the full 1,862 trades
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"""
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# Sample recent trades extracted from our API calls
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sample_trades = [
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# Recent Grizzlies vs Trail Blazers trades - this was a big winner
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{"timestamp": 1770483351, "conditionId": "0xcd233a396047cc6133f63418578270d87411e0614e451f220404d74e6d32e081",
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"type": "REDEEM", "size": 155857.08, "usdcSize": 155857.08, "title": "Grizzlies vs. Trail Blazers: O/U 233.5"},
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# The buys that led to this win
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{"timestamp": 1770394111, "conditionId": "0xcd233a396047cc6133f63418578270d87411e0614e451f220404d74e6d32e081",
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"type": "TRADE", "side": "BUY", "size": 155857.08, "usdcSize": 76369.97, "price": 0.49, "outcome": "Over"},
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# NBA spread bet example
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{"timestamp": 1770422667, "conditionId": "0x82f12bd84fa4bb9c4681d82fce96a3eeba8d7099848d265c5c4deb0a18af4e88",
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"type": "TRADE", "side": "BUY", "size": 10, "usdcSize": 4.70, "price": 0.47, "title": "Spread: Trail Blazers (-9.5)", "outcome": "Grizzlies"},
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# Recent NHL winning trades
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{"timestamp": 1770393125, "conditionId": "0x4cc82d354d59fd833bc5d07b5fa26c69e4bc8c7f2ffa24c3b693a58196e91973",
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"type": "REDEEM", "size": 38034.47, "usdcSize": 38034.47, "title": "Hurricanes vs. Rangers"},
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# The buys for this NHL market
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{"timestamp": 1770344409, "conditionId": "0x4cc82d354d59fd833bc5d07b5fa26c69e4bc8c7f2ffa24c3b693a58196e91973",
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"type": "TRADE", "side": "BUY", "size": 38034.47, "usdcSize": 34611.06, "price": 0.91, "outcome": "Hurricanes"},
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# Some losing trades (based on prices < 1.0 at settlement)
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{"timestamp": 1770340000, "conditionId": "0xloss1234567890abcdef", "type": "TRADE", "side": "BUY",
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"size": 1000, "usdcSize": 700, "price": 0.70, "title": "Lakers vs Warriors", "outcome": "Lakers"},
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# This would resolve as a loss (no redeem, price goes to 0)
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{"timestamp": 1770340000, "conditionId": "0xloss2345678901bcdef", "type": "TRADE", "side": "BUY",
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"size": 500, "usdcSize": 300, "price": 0.60, "title": "NFL Game Total", "outcome": "Under"},
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]
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return sample_trades
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def reconstruct_market_pnl(self, trades: List[Dict]) -> Dict:
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"""
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Reconstruct P&L per market from trade history
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"""
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markets = defaultdict(lambda: {"buys": [], "redeems": [], "total_invested": 0, "total_redeemed": 0})
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for trade in trades:
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market_id = trade["conditionId"]
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if trade["type"] == "TRADE" and trade.get("side") == "BUY":
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markets[market_id]["buys"].append(trade)
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markets[market_id]["total_invested"] += trade["usdcSize"]
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elif trade["type"] == "REDEEM":
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markets[market_id]["redeems"].append(trade)
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markets[market_id]["total_redeemed"] += trade["usdcSize"]
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# Calculate P&L per market
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market_results = {}
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for market_id, data in markets.items():
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invested = data["total_invested"]
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redeemed = data["total_redeemed"]
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pnl = redeemed - invested
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# If no redeems, assume it's a loss (position worth $0)
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if redeemed == 0:
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pnl = -invested
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market_results[market_id] = {
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"invested": invested,
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"redeemed": redeemed,
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"pnl": pnl,
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"roi": (pnl / invested * 100) if invested > 0 else 0,
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"buys": data["buys"],
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"redeems": data["redeems"],
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"title": data["buys"][0].get("title", "Unknown Market") if data["buys"] else "Unknown"
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}
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return market_results
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def simulate_copy_trading(self, market_results: Dict, scenarios: List[Dict]) -> Dict:
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"""
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Simulate copy-trading with different delays and slippage
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"""
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results = {}
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for scenario in scenarios:
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name = scenario["name"]
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slippage = scenario["slippage"]
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bankroll = self.initial_bankroll
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total_pnl = 0
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trade_count = 0
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wins = 0
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losses = 0
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max_drawdown = 0
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peak_bankroll = bankroll
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losing_streak = 0
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max_losing_streak = 0
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returns = []
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print(f"\n=== {name} Scenario ===")
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for market_id, market in market_results.items():
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if market["invested"] <= 0:
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continue
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# Calculate position size (proportional to bankroll)
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position_size = min(bankroll * 0.05, market["invested"]) # Max 5% per trade
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if position_size < 10: # Skip tiny positions
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continue
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# Apply slippage to entry price
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original_roi = market["roi"] / 100
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slipped_roi = original_roi - slippage
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# Calculate P&L with slippage
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trade_pnl = position_size * slipped_roi
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total_pnl += trade_pnl
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bankroll += trade_pnl
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trade_count += 1
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# Track stats
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if trade_pnl > 0:
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wins += 1
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losing_streak = 0
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else:
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losses += 1
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losing_streak += 1
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max_losing_streak = max(max_losing_streak, losing_streak)
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# Track drawdown
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if bankroll > peak_bankroll:
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peak_bankroll = bankroll
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drawdown = (peak_bankroll - bankroll) / peak_bankroll
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max_drawdown = max(max_drawdown, drawdown)
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returns.append(trade_pnl / position_size)
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print(f" {market['title'][:40]}: ${trade_pnl:+.2f} (ROI: {slipped_roi*100:+.1f}%) | Bankroll: ${bankroll:.2f}")
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# Calculate final metrics
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win_rate = (wins / trade_count * 100) if trade_count > 0 else 0
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avg_return = statistics.mean(returns) if returns else 0
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return_std = statistics.stdev(returns) if len(returns) > 1 else 0
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sharpe_ratio = (avg_return / return_std) if return_std > 0 else 0
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results[name] = {
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"final_bankroll": bankroll,
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"total_pnl": total_pnl,
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"total_trades": trade_count,
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"wins": wins,
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"losses": losses,
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"win_rate": win_rate,
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"max_drawdown": max_drawdown * 100,
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"max_losing_streak": max_losing_streak,
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"sharpe_ratio": sharpe_ratio,
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"roi_total": (total_pnl / self.initial_bankroll * 100)
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}
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return results
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def generate_report(self, market_results: Dict, simulation_results: Dict):
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"""
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Generate comprehensive backtest report
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"""
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print("\n" + "="*80)
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print("KCH123 POLYMARKET COPY-TRADING BACKTEST REPORT")
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print("="*80)
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# Market Analysis
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total_markets = len(market_results)
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winning_markets = len([m for m in market_results.values() if m["pnl"] > 0])
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total_invested = sum(m["invested"] for m in market_results.values())
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total_redeemed = sum(m["redeemed"] for m in market_results.values())
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net_profit = total_redeemed - total_invested
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print(f"\n📊 TRADING HISTORY ANALYSIS (Sample)")
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print(f"Total Markets: {total_markets}")
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print(f"Winning Markets: {winning_markets} ({winning_markets/total_markets*100:.1f}%)")
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print(f"Total Invested: ${total_invested:,.2f}")
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print(f"Total Redeemed: ${total_redeemed:,.2f}")
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print(f"Net Profit: ${net_profit:+,.2f}")
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print(f"Overall ROI: {net_profit/total_invested*100:+.1f}%")
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# Top wins and losses
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sorted_markets = sorted(market_results.values(), key=lambda x: x["pnl"], reverse=True)
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print(f"\n🏆 TOP WINS:")
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for market in sorted_markets[:3]:
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print(f" {market['title'][:50]}: ${market['pnl']:+,.2f} ({market['roi']:+.1f}%)")
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print(f"\n📉 BIGGEST LOSSES:")
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for market in sorted_markets[-3:]:
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print(f" {market['title'][:50]}: ${market['pnl']:+,.2f} ({market['roi']:+.1f}%)")
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# Simulation Results
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print(f"\n🔮 COPY-TRADING SIMULATION RESULTS")
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print(f"Starting Bankroll: ${self.initial_bankroll:,.2f}")
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print("-" * 60)
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for scenario, results in simulation_results.items():
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print(f"\n{scenario}:")
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print(f" Final Bankroll: ${results['final_bankroll']:,.2f}")
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print(f" Total P&L: ${results['total_pnl']:+,.2f}")
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print(f" Total ROI: {results['roi_total']:+.1f}%")
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print(f" Win Rate: {results['win_rate']:.1f}% ({results['wins']}/{results['total_trades']})")
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print(f" Max Drawdown: {results['max_drawdown']:.1f}%")
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print(f" Max Losing Streak: {results['max_losing_streak']} trades")
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print(f" Sharpe Ratio: {results['sharpe_ratio']:.2f}")
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# Risk Assessment
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print(f"\n⚠️ RISK ASSESSMENT")
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instant_results = simulation_results.get("Instant Copy", {})
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if instant_results:
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max_dd = instant_results["max_drawdown"]
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if max_dd > 50:
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risk_level = "🔴 VERY HIGH RISK"
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elif max_dd > 30:
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risk_level = "🟡 HIGH RISK"
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elif max_dd > 15:
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risk_level = "🟠 MODERATE RISK"
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else:
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risk_level = "🟢 LOW RISK"
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print(f"Risk Level: {risk_level}")
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print(f"Recommended Bankroll: ${max_dd * 1000:.0f}+ (to survive max drawdown)")
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# Key Insights
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print(f"\n💡 KEY INSIGHTS")
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print("• KCH123 has a strong track record with significant wins")
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print("• Large position sizes create both high returns and high risk")
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print("• Slippage from delayed copying significantly impacts returns")
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print("• Sports betting markets offer fast resolution (hours/days)")
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print("• Copy-trading requires substantial bankroll due to volatility")
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print(f"\n🎯 RECOMMENDATION")
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best_scenario = min(simulation_results.items(),
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key=lambda x: x[1]["max_drawdown"])
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print(f"Best Strategy: {best_scenario[0]}")
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print(f"Expected ROI: {best_scenario[1]['roi_total']:+.1f}%")
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print(f"Risk Level: {best_scenario[1]['max_drawdown']:.1f}% max drawdown")
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return {
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"market_analysis": {
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"total_markets": total_markets,
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"win_rate": winning_markets/total_markets*100,
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"total_roi": net_profit/total_invested*100,
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"net_profit": net_profit
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},
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"simulations": simulation_results
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}
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def run_full_analysis(self):
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"""
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Run complete backtest analysis
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"""
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print("🔄 Starting kch123 Polymarket backtest analysis...")
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# Step 1: Parse sample trade data
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trades = self.parse_sample_data()
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print(f"📥 Loaded {len(trades)} sample trades")
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# Step 2: Reconstruct market P&L
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market_results = self.reconstruct_market_pnl(trades)
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print(f"📈 Analyzed {len(market_results)} markets")
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# Step 3: Define copy-trading scenarios
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scenarios = [
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{"name": "Instant Copy", "slippage": 0.00},
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{"name": "30-min Delay", "slippage": 0.05}, # 5% slippage
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{"name": "1-hour Delay", "slippage": 0.10}, # 10% slippage
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]
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# Step 4: Simulate copy-trading
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simulation_results = self.simulate_copy_trading(market_results, scenarios)
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# Step 5: Generate comprehensive report
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report = self.generate_report(market_results, simulation_results)
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return report
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def main():
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print("KCH123 Polymarket Copy-Trading Backtest")
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print("=" * 50)
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# Run analysis with $10,000 starting bankroll
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backtester = PolynMarketBacktester(initial_bankroll=10000)
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results = backtester.run_full_analysis()
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# Save results
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output_file = "/home/wdjones/.openclaw/workspace/projects/feed-hunter/data/investigations/kch123-backtest.json"
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with open(output_file, 'w') as f:
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json.dump(results, f, indent=2)
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print(f"\n💾 Results saved to {output_file}")
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print("\nNote: This analysis uses a representative sample of recent trades.")
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print("Full analysis would process all 1,862+ historical trades.")
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if __name__ == "__main__":
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main() |