{
  "id": 18392826,
  "title": "Polymarket Trading Bot :Market Maker - How to Backtest Strategies and Optimize Trading Parameters",
  "url": "https://urgent.news/2026/11/05/polymarket-trading-bot-market-maker-how-to-backtest-strategies-and",
  "topic": "finance",
  "section": "Finance & Markets",
  "published": "2026-11-05T23:00:10.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/benjamin_cup/polymarket-trading-bot-market-maker-how-to-backtest-strategies-and-optimize-trading-parameters-4elm"
  },
  "original_language": "en",
  "account": "Backtesting is a crucial step in evaluating a trading strategy before deploying it in live markets. By simulating the strategy against historical data, traders can analyze performance metrics and optimize parameters without risking real funds. This process helps identify potential issues such as order execution problems, market impact, and trading costs that may not be apparent in backtesting.",
  "summary": "Day 9 — Building a Python backtesting framework to simulate order execution, measure performance, and optimize market-making strategies. Introduction In the previous article, we built an adaptive market-making strategy that adjusts its spreads based on volatility, liquidity, and inventory. But an important question remains: How do we know whether our strategy actually works? Imagine your…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}