diff --git a/prediction_market_agent/agents/arbitrage_agent/data_models.py b/prediction_market_agent/agents/arbitrage_agent/data_models.py
index d1d0e87..1f85af6 100644
--- a/prediction_market_agent/agents/arbitrage_agent/data_models.py
+++ b/prediction_market_agent/agents/arbitrage_agent/data_models.py
@@ -14,7 +14,7 @@ class CorrelatedMarketPair(BaseModel):
     related_market: AgentMarket
 
     def __repr__(self) -> str:
-        return f"main_market_question {self.main_market.question=} related_market_question {self.related_market.question=}"
+        return f"main_market {self.main_market.question} related_market_question {self.related_market.question} potential profit {self.potential_profit_per_bet_unit}"
 
     @computed_field  # type: ignore[prop-decorator]
     @property
diff --git a/prediction_market_agent/agents/arbitrage_agent/deploy.py b/prediction_market_agent/agents/arbitrage_agent/deploy.py
index 26867a2..7d093c2 100644
--- a/prediction_market_agent/agents/arbitrage_agent/deploy.py
+++ b/prediction_market_agent/agents/arbitrage_agent/deploy.py
@@ -48,6 +48,7 @@ class DeployableArbitrageAgent(DeployableTraderAgent):
     model = "gpt-4o"
     # trade amount will be divided between correlated markets.
     total_trade_amount = BetAmount(amount=0.1, currency=OmenAgentMarket.currency)
+    bet_on_n_markets_per_run = 5
 
     def run(self, market_type: MarketType) -> None:
         if market_type != MarketType.OMEN:
@@ -69,7 +70,7 @@ def get_markets(
     ) -> t.Sequence[AgentMarket]:
         return super().get_markets(
             market_type=market_type,
-            limit=limit,
+            limit=50,
             sort_by=SortBy.HIGHEST_LIQUIDITY,
             # Fetching most liquid markets since more likely they will have related markets
             filter_by=FilterBy.OPEN,