diff --git a/README.md b/README.md
index f3b080e..b92ecf7 100644
--- a/README.md
+++ b/README.md
@@ -22,3 +22,19 @@ python3 web_app.py
 ```
 
 Then open [http://localhost:5000](http://localhost:5000) in your browser.
+
+## PredictIt Trading Agent
+
+`predictit_agent.py` fetches price data from the official PredictIt API and
+simulates buying a random contract. Subsequent price updates are logged to a
+CSV file called `trading_log.csv`.
+
+Run it with:
+
+```bash
+pip install requests
+python3 predictit_agent.py --iterations 5 --delay 2 --log mylog.csv
+```
+
+If market data can't be fetched due to network restrictions, the log will be
+empty and a warning message will be printed.
diff --git a/predictit_agent.py b/predictit_agent.py
new file mode 100644
index 0000000..ca108ac
--- /dev/null
+++ b/predictit_agent.py
@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+"""Simple bot that fetches PredictIt prices and simulates trades."""
+
+import requests
+import time
+import random
+from dataclasses import dataclass
+from typing import Dict, List, Optional
+from datetime import datetime
+
+
+class PredictItAPI:
+    """Lightweight wrapper around the PredictIt public API."""
+
+    def __init__(self, base_url: str = "https://www.predictit.org/api/marketdata"):
+        self.base_url = base_url.rstrip("/")
+        self.session = requests.Session()
+        # Use a custom user agent so requests are less likely to be blocked
+        self.session.headers.update({"User-Agent": "PredictItTradingAgent/1.0"})
+
+    def fetch(self, path: str) -> Dict:
+        url = f"{self.base_url}/{path.lstrip('/')}"
+        resp = self.session.get(url, timeout=10)
+        resp.raise_for_status()
+        return resp.json()
+
+    def all_markets(self) -> List[Dict]:
+        data = self.fetch("all/")
+        return data.get("markets", [])
+
+    def market(self, market_id: int) -> Dict:
+        return self.fetch(f"markets/{market_id}/")
+
+@dataclass
+class Trade:
+    timestamp: str
+    market: str
+    contract: str
+    side: str
+    price: float
+    profit: Optional[float] = None
+
+class PredictItTradingAgent:
+    def __init__(self, api: Optional[PredictItAPI] = None):
+        self.api = api or PredictItAPI()
+        self.positions: Dict[int, Trade] = {}
+        self.history: List[Trade] = []
+
+    def fetch_data(self) -> List[Dict]:
+        try:
+            return self.api.all_markets()
+        except Exception as e:
+            print(f"Failed to fetch market data: {e}")
+            return []
+
+    def choose_contract(self, markets: List[Dict]):
+        available = []
+        for m in markets:
+            for c in m.get("contracts", []):
+                if "bestBuyYesCost" in c or "lastTradePrice" in c:
+                    available.append((m, c))
+        if not available:
+            return None, None
+        return random.choice(available)
+
+    def trade(self) -> None:
+        markets = self.fetch_data()
+        market, contract = self.choose_contract(markets)
+        if contract is None:
+            print("No contracts available.")
+            return
+        price = contract.get("bestBuyYesCost") or contract.get("lastTradePrice")
+        if price is None:
+            print("No price for contract.")
+            return
+        trade = Trade(
+            timestamp=datetime.utcnow().isoformat(),
+            market=market.get("shortName", market.get("name", "")),
+            contract=contract.get("shortName", contract.get("name", "")),
+            side="BUY",
+            price=price,
+        )
+        self.positions[contract["id"]] = trade
+        self.history.append(trade)
+        print(f"Bought {trade.contract} at {trade.price} in market {trade.market}")
+
+    def update(self) -> None:
+        markets = self.fetch_data()
+        lookup = {c["id"]: c for m in markets for c in m.get("contracts", [])}
+        for cid, trade in list(self.positions.items()):
+            contract = lookup.get(cid)
+            if not contract:
+                continue
+            price = contract.get("bestBuyYesCost") or contract.get("lastTradePrice")
+            if price is None:
+                continue
+            profit = price - trade.price
+            update_trade = Trade(
+                timestamp=datetime.utcnow().isoformat(),
+                market=trade.market,
+                contract=trade.contract,
+                side="UPDATE",
+                price=price,
+                profit=profit,
+            )
+            self.history.append(update_trade)
+            print(
+                f"Update {trade.contract}: new price {price}, PnL {profit:.2f}"
+            )
+
+    def save_history(self, filename: str = "trading_log.csv") -> None:
+        import csv
+
+        with open(filename, "w", newline="") as f:
+            writer = csv.writer(f)
+            writer.writerow(["timestamp", "market", "contract", "side", "price", "profit"])
+            for t in self.history:
+                writer.writerow([
+                    t.timestamp,
+                    t.market,
+                    t.contract,
+                    t.side,
+                    f"{t.price:.2f}",
+                    f"{t.profit:.2f}" if t.profit is not None else "",
+                ])
+        print(f"Saved log to {filename}")
+
+
+def main() -> None:
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Simulate trades using PredictIt data")
+    parser.add_argument("--iterations", type=int, default=3, help="Number of trade/update cycles")
+    parser.add_argument("--delay", type=float, default=1.0, help="Delay between cycles in seconds")
+    parser.add_argument("--log", type=str, default="trading_log.csv", help="CSV file for trade history")
+    args = parser.parse_args()
+
+    agent = PredictItTradingAgent()
+    for _ in range(args.iterations):
+        agent.trade()
+        time.sleep(args.delay)
+        agent.update()
+        time.sleep(args.delay)
+    agent.save_history(args.log)
+
+
+if __name__ == "__main__":
+    main()