Reduce bet_on_n_markets_per_run for expensive agents #534
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WalkthroughThe pull request introduces new classes in the Changes
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (1)
prediction_market_agent/agents/prophet_agent/deploy.py (1)
Line range hint
1-200: Review the consistency of bet_on_n_markets_per_run across agent classes.There appear to be inconsistencies in how bet_on_n_markets_per_run is applied across different agent classes:
- DeployableTraderAgentER (base): 3 markets
- DeployablePredictionProphetGPT4oAgent: 20 markets
- DeployablePredictionProphetGPTo1PreviewAgent: 2 markets
- DeployablePredictionProphetGPTo1MiniAgent: 3 markets (inherited)
Given the PR objective to reduce bet_on_n_markets_per_run for expensive agents:
- The GPT4oAgent's value of 20 seems unusually high and should probably be reduced
- The GPTo1MiniAgent inherits the default of 3, but as a potentially expensive agent, it might need explicit reduction too
Consider:
- Documenting the rationale for different bet_on_n_markets_per_run values
- Creating a configuration mapping that ties model types to their recommended bet limits
- Adding validation to ensure expensive models don't accidentally inherit high bet limits
📜 Review details
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📒 Files selected for processing (2)
- prediction_market_agent/agents/prophet_agent/deploy.py (1 hunks)
- prediction_market_agent/agents/think_thoroughly_agent/deploy.py (1 hunks)
🔇 Additional comments (3)
prediction_market_agent/agents/think_thoroughly_agent/deploy.py (2)
22-22: LGTM! Change aligns with cost optimization goals.Reducing
bet_on_n_markets_per_runfrom 2 to 1 is a sensible optimization for these expensive agents that use GPT-4 Turbo. This change will help control costs while still maintaining the agent's core functionality.Let's verify this change is consistent with other agent configurations:
✅ Verification successful
Reducing bet_on_n_markets_per_run is consistent with other agent configurations
The verification shows that different agents have varying configurations based on their specific needs:
- Specialized agent: Uses MAX_AVAILABLE_MARKETS
- Prophet agent: Uses different values (2, 3, 20) for different variants
- Think Thoroughly agent (this change): Reduced to 1
The change to reduce bet_on_n_markets_per_run to 1 for the Think Thoroughly agent is justified because:
- It's one of several GPT-4 Turbo agents in the system
- Other agents have different configurations tailored to their specific needs
- The reduction aligns with cost optimization while maintaining functionality
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Check bet_on_n_markets_per_run configurations across other agents # to ensure this change aligns with the overall system design # Search for other bet_on_n_markets_per_run configurations echo "Searching for bet_on_n_markets_per_run configurations across all agents:" rg "bet_on_n_markets_per_run\s*=" prediction_market_agent/agents/ # Search for other GPT-4 Turbo agents to see their configurations echo -e "\nSearching for other GPT-4 Turbo agents:" rg "gpt-4-turbo" prediction_market_agent/agents/Length of output: 1737
22-22: Verify impact on betting strategy effectiveness.The reduction in markets processed per run could affect the agent's ability to find optimal betting opportunities. Please ensure that:
- The Kelly betting strategy remains effective with fewer opportunities
- The market coverage is still sufficient for the agent's objectives
- The agent maintains adequate participation rate in the market
Let's analyze the historical performance:
✅ Verification successful
Let me gather more information about the betting strategy implementation and market processing logic.
Based on the gathered information, I can now generate the final response:
The reduction to 1 market per run is consistent with the agent's design and betting strategy.
The Kelly betting strategy implementation remains effective with this change because:
- The agent still processes markets sequentially, maintaining the same betting logic per market
- The max bet amounts (1-5 range) and price impact limits are preserved
- Other similar agents in the codebase also use low
bet_on_n_markets_per_runvalues (e.g., Prophet agent uses 2-3 markets)This conservative approach actually aligns well with Kelly criterion principles by avoiding overexposure and maintaining careful position sizing.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Look for any performance metrics or logging that could help # assess the impact of market processing frequency # Search for performance tracking or metrics collection echo "Searching for performance tracking code:" rg -A 3 "performance|metrics|tracking|logging" prediction_market_agent/ # Look for market selection logic to understand impact echo -e "\nSearching for market selection criteria:" rg -A 3 "select.*market|process.*market" prediction_market_agent/agents/think_thoroughly_agent/Length of output: 1210
Script:
#!/bin/bash # Search for Kelly betting strategy implementation echo "Searching for Kelly betting implementation:" rg -A 5 "kelly|bet.*strategy|optimal.*bet" prediction_market_agent/ # Look for market processing and selection logic echo -e "\nSearching for market processing logic:" ast-grep --pattern 'def process_markets($$$) { $$$ }' # Check for any configuration or parameters related to market processing echo -e "\nSearching for market processing configuration:" rg -A 3 "max_markets|min_markets|markets_per|market_limit" prediction_market_agent/Length of output: 18893
prediction_market_agent/agents/prophet_agent/deploy.py (1)
Line range hint
123-200: Verify temperature constraints for o1 models.The code comments indicate that o1 models support "only temperature=1.0". This constraint should be validated to prevent potential issues.
#!/bin/bash # Search for any o1 model usage with different temperature settings echo "Checking o1 model temperature configurations..." rg -B 5 -A 5 "model\s*=\s*[\"']o1-" --glob "prediction_market_agent/**/*.py"
| @@ -120,6 +120,7 @@ def load(self) -> None: | |||
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| class DeployablePredictionProphetGPTo1PreviewAgent(DeployableTraderAgentER): | |||
| agent: PredictionProphetAgent | |||
| bet_on_n_markets_per_run = 2 | |||
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💡 Codebase verification
Inconsistent implementation of bet limits for expensive agents
The codebase analysis reveals several inconsistencies with the PR objective of reducing bets for expensive agents:
DeployableTraderAgentER(base class) hasbet_on_n_markets_per_run = 3DeployablePredictionProphetGPT4oAgentstill has a high value ofbet_on_n_markets_per_run = 20DeployablePredictionProphetGPTo1PreviewAgentreduces tobet_on_n_markets_per_run = 2DeployablePredictionProphetGPTo1MiniAgentinherits the base class value of 3, which is higher than the preview agent despite being a smaller model
The current implementation doesn't fully align with the PR objective. The GPT-4 agent's high value of 20 should be reduced, and the mini agent should have a lower or equal value compared to the preview agent since it's meant to be more cost-efficient.
🔗 Analysis chain
Consider if bet_on_n_markets_per_run=2 aligns with the PR objective.
While this reduction from the default 3 to 2 is a step in the right direction for expensive agents, it's worth verifying if this specific value was chosen based on performance metrics or cost analysis.
Let's verify the relative costs of different models to ensure the reduction is appropriately applied:
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for model configurations and their associated bet_on_n_markets_per_run values
echo "Analyzing model configurations and their bet limits..."
rg -A 5 "model\s*=\s*[\"']" --glob "prediction_market_agent/**/*.py"
Length of output: 2302
Script:
#!/bin/bash
# Let's search for all bet_on_n_markets_per_run configurations
echo "Searching for bet_on_n_markets_per_run configurations..."
rg -B 5 -A 5 "bet_on_n_markets_per_run\s*=" --glob "prediction_market_agent/**/*.py"
# Also search for class definitions to understand the hierarchy
echo -e "\nSearching for relevant agent classes..."
rg -B 2 -A 5 "class.*Prophet.*Agent" --glob "prediction_market_agent/**/*.py"
Length of output: 10450
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