Auto-verify that buy trades wouldn't cause guaranted loss #488
Conversation
WalkthroughThe pull request introduces enhancements to the Changes
Possibly related PRs
Suggested reviewers
Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media? 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (2)
prediction_market_agent_tooling/deploy/betting_strategy.py (2)
Line range hint
192-200: Add error handling for potential division by zero.In the
calculate_price_impact_for_bet_amountmethod,total_outcome_tokensis calculated asyes + no. If bothyesandnoare zero, this will result in a division by zero when computingexpected_priceand subsequentlyprice_impact.Consider adding a check to prevent division by zero:
Apply this diff to handle the division by zero case:
total_outcome_tokens = yes + no + if total_outcome_tokens == 0: + raise ValueError("Total outcome tokens cannot be zero.") expected_price = ( no / total_outcome_tokens if buy_direction else yes / total_outcome_tokens )
Line range hint
210-221: Verify optimization success before using the result.In the
calculate_bet_amount_for_price_impactmethod, after callingminimize_scalar, the optimization might fail to converge. Usingoptimized_bet_amount.xwithout checking can lead to unexpected behavior.Ensure that the optimization was successful before proceeding.
Apply this diff to check the optimization result:
optimized_bet_amount = minimize_scalar( calculate_price_impact_deviation_from_target_price_impact, bounds=(0, 1000 * (yes_outcome_pool_size + no_outcome_pool_size)), method="bounded", tol=1e-11, options={"maxiter": 10000}, ) + if not optimized_bet_amount.success: + logger.warning("Optimization did not converge. Using original Kelly bet size.") + return kelly_bet.size return float(optimized_bet_amount.x)
There was a problem hiding this comment.
Actionable comments posted: 0
🧹 Outside diff range and nitpick comments (2)
tests/test_betting_strategy.py (2)
37-37: LGTM! Consider adding a comment explaining the liquidity amount.The introduction of
liquidity_amountenhances the test by providing a realistic liquidity context. This aligns well with the PR objective of fixing the bet amount for MaxAccuracyBettingStrategy.Consider adding a brief comment explaining why 100 xDai was chosen as the liquidity amount. This would help other developers understand the reasoning behind this specific value.
Line range hint
1-63: Overall, the changes enhance the test case effectively.The introduction of the liquidity context in the
test_rebalancefunction aligns well with the PR objective of fixing the bet amount for MaxAccuracyBettingStrategy. The changes are minimal, focused, and do not introduce unnecessary complexity to the test suite.Consider adding a new test case that specifically verifies the behavior of MaxAccuracyBettingStrategy with different liquidity scenarios. This would further strengthen the test coverage for the bet amount fix.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (1)
- tests/test_betting_strategy.py (2 hunks)
🧰 Additional context used
🔇 Additional comments (1)
tests/test_betting_strategy.py (1)
49-49: LGTM! Verify the impact on the test assertions.The update to
mock_market.get_liquidityto return theliquidity_amountis consistent with the introduction of the liquidity context. This change enhances the realism of the test scenario for theMaxAccuracyBettingStrategy.Please verify that the existing assertions in the test case (lines 56-63) still hold true with this new liquidity context. Run the following script to check if there are any assertions that might need updating:
If the script reveals any assertions that might be affected by the new liquidity context, please review and update them accordingly.
kongzii
changed the title
There was a problem hiding this comment.
Actionable comments posted: 2
🧹 Outside diff range and nitpick comments (3)
tests/test_betting_strategy.py (2)
52-52: LGTM! Consider parameterizing the liquidity amount.The addition of
liquidity_amountenhances the test by introducing a realistic liquidity constraint. This aligns well with the PR objective of auto-verifying that buy trades wouldn't cause guaranteed loss.Consider parameterizing the liquidity amount to allow for more comprehensive testing across different liquidity scenarios. This could be achieved by adding a parameter to the test function or using pytest's parametrize decorator.
Also applies to: 64-64
82-143: Excellent addition of comprehensive test cases!The new
test_attacking_marketfunction significantly enhances the test coverage by checking various edge cases related to market conditions. It directly addresses the PR objective of auto-verifying that buy trades wouldn't cause guaranteed loss.The use of parameterized testing is a great practice for covering multiple scenarios efficiently. The test cases cover both positive and negative scenarios, which is crucial for thorough testing.
Consider adding a comment or docstring to explain the reasoning behind each test case in the parametrize decorator. This would make it easier for other developers to understand the purpose of each scenario and potentially add more test cases in the future.
prediction_market_agent_tooling/markets/agent_market.py (1)
169-172: Approve the addition ofget_buy_token_amountmethod with a suggestion for improvement.The new
get_buy_token_amountmethod is a good addition to theAgentMarketclass. It provides a way to convert a bet amount to a token amount, which is essential for market operations.Consider adding a docstring to clarify the purpose of the method and explain the
directionparameter. Here's a suggested implementation:def get_buy_token_amount( self, bet_amount: BetAmount, direction: bool ) -> TokenAmount: """ Convert a bet amount to a token amount for a buy operation. Args: bet_amount (BetAmount): The amount of the bet. direction (bool): The direction of the bet (True for YES, False for NO). Returns: TokenAmount: The amount of tokens to be bought. Raises: NotImplementedError: This method should be implemented by subclasses. """ raise NotImplementedError("Subclasses must implement this method")
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
📒 Files selected for processing (3)
- prediction_market_agent_tooling/deploy/betting_strategy.py (2 hunks)
- prediction_market_agent_tooling/markets/agent_market.py (1 hunks)
- tests/test_betting_strategy.py (4 hunks)
🧰 Additional context used
🔇 Additional comments (5)
tests/test_betting_strategy.py (2)
24-30: LGTM! Imports and file structure are well-organized.The new imports for OMEN-related classes and contracts are necessary for the added functionality in the
test_attacking_marketfunction. The file structure maintains good practices by grouping related imports and keeping a clear separation between different test functions.
Line range hint
1-143: Overall, excellent additions to the test suite!The changes in this file significantly enhance the test coverage for the
MaxAccuracyBettingStrategyclass, particularly in relation to market conditions that could potentially lead to guaranteed losses. The newtest_attacking_marketfunction, along with the addition of liquidity constraints in the existingtest_rebalancefunction, directly address the PR objective of auto-verifying that buy trades wouldn't cause guaranteed loss.The code is well-structured, follows good testing practices, and effectively uses parameterized testing to cover multiple scenarios. These improvements will greatly contribute to the robustness of the betting strategy implementation.
prediction_market_agent_tooling/markets/agent_market.py (1)
Line range hint
1-324: Summary of changes and their impact.The changes made to the
AgentMarketclass are minimal but significant:
- The addition of the
get_buy_token_amountmethod provides a new abstraction layer for converting bet amounts to token amounts.- This new method aligns well with the existing structure and purpose of the class.
- There's potential for refactoring the
buy_tokensmethod to use the newget_buy_token_amountmethod, which could improve consistency and reduce duplication.Overall, these changes enhance the flexibility and extensibility of the
AgentMarketclass, allowing for more specific implementations in subclasses.prediction_market_agent_tooling/deploy/betting_strategy.py (2)
66-70: Consolidation of trade checks enhances maintainabilityThe new
check_tradesmethod effectively consolidates trade validations, improving code organization and reducing redundancy. This approach enhances maintainability and readability.
119-122: Ensurecheck_tradesdoes not introduce unintended side effectsCalling
BettingStrategy.check_trades(market, trades)after building the trades adds an extra validation step, which is beneficial for preventing unreasonable bets. However, confirm that this additional check does not introduce performance bottlenecks or alter the behavior in edge cases, especially in high-frequency trading scenarios.If needed, run performance benchmarks to assess the impact:
| def sell_tokens(self, outcome: bool, amount: TokenAmount) -> str: | ||
| raise NotImplementedError("Subclasses must implement this method") |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider refactoring buy_tokens to use get_buy_token_amount.
The buy_tokens method could potentially be refactored to use the newly added get_buy_token_amount method. This would improve consistency and reduce potential duplication of logic. Here's a suggested implementation:
def buy_tokens(self, outcome: bool, amount: TokenAmount) -> str:
bet_amount = self.get_bet_amount(amount.amount)
token_amount = self.get_buy_token_amount(bet_amount, outcome)
return self.place_bet(outcome=outcome, amount=token_amount)This refactoring assumes that get_buy_token_amount will be implemented in subclasses to handle the conversion from BetAmount to TokenAmount. Please verify if this aligns with the intended usage of get_buy_token_amount.
No description provided.