Refactor column configuration in app.py by removing unnecessary dictionary structure for 'Lineup Edge'. This change streamlines the code and enhances readability while maintaining functionality.

Add column configurations for 'Finish_percentile' and 'Lineup Edge' in app.py to enhance DataFrame display. This update includes help text, width specifications, and value constraints for improved user interaction and clarity.

Update portfolio reset functionality in app.py to restore working frame from base frame. This change ensures that the portfolio resets correctly by copying the base frame, enhancing the user experience during portfolio management.

Refactor sidebar and container layout in app.py to improve user interface. Moved trimming options to the sidebar and adjusted the export section to use a container with a border. Updated DataFrame display settings for better visibility and reduced height.

Refactor lineup generation logic in hedging_preset.py to improve processing efficiency. Introduced an initialization counter for lineup adjustments and added checks for empty DataFrames before generating lineups. Enhanced debug messages for better visibility into the lineup concatenation process.

Update lineup generation logic in hedging_preset.py to adjust lineup target calculations. Changed divisor from 3 to 4 in small_field_preset and large_field_preset functions to improve accuracy in lineup adjustments.

Update volatility_preset function to use 'Similarity Score' for row filtering instead of 'median'. This change improves the accuracy of lineup adjustments by ensuring the correct comparison for ownership type maximums.

Fix condition in volatility_preset function to correctly identify rows to drop based on median values. This change ensures accurate lineup adjustments by comparing current ownership type maximums with median values.

Add volatility preset option in app.py and implement volatility_preset function. Updated lineup generation logic to include new preset for enhanced lineup edge management.

Update preset options in app.py to improve lineup generation accuracy. Changed 'Large Field (Finish Percentile / Edge)' to 'Large Field (Manage Similarity)' and adjusted corresponding logic in the form submission process.

Enhance lineup processing in hedging_preset.py by adding checks for empty DataFrames before generating lineups. This update prevents errors when no lineups are available and improves the clarity of debug messages related to lineup generation.

Refactor lineup selection logic in large_field_preset.py to improve accuracy and efficiency. Replaced the previous iterative approach with a method that calculates evenly spaced target similarity scores, ensuring a more precise selection of lineups based on similarity while avoiding duplicates.

Enhance lineup generation in hedging_preset.py by adding debug print statements for removed and team lineups. This update improves visibility into the lineup processing and ensures all relevant lineups are concatenated for final output.

Add debug print statements in hedging_preset.py to output the count of removed and locked lineups during processing. This change enhances visibility into lineup generation and aids in debugging the lineup filtering logic.

Add team lineup filtering in hedging_preset.py to enhance lineup generation. Introduced logic to separate teams and calculate removed lineups, improving the accuracy of team-based strategies.

Update sorting and ownership calculations in large_field_preset.py to use 'median' instead of 'Finish_percentile' and 'Own'. This change improves accuracy in lineup generation by aligning calculations with the updated data structure.

Remove unused import statement from hedging_preset.py to clean up the code and improve maintainability. This change enhances clarity by eliminating unnecessary dependencies.

Add team ownership calculation in hedging_preset.py to enhance lineup generation. Introduced a new function to sum hitter ownership by team and adjusted lineup target calculations for improved accuracy. This change allows for better team-based lineup strategies.

Adjust lineup target calculations in hedging_preset.py to divide by double the list size, improving accuracy in lineup generation. This change enhances the filtering process for both removed and locked lineups.

Refactor player iteration logic in hedging_preset.py to improve clarity and accuracy in lineup filtering. Changed variable names for consistency and adjusted mask conditions to enhance player selection criteria.

Add debug print statements in hedging_preset.py to output player and lineup data during processing. Adjusted lineup target calculations to use list size directly, enhancing clarity in the lineup generation logic.

Remove sorting by 'median' in hedging_preset.py to simplify the return of the top lineups based on the specified target. This change streamlines the output and focuses on the desired number of lineups without additional sorting criteria.

Refactor hedging_preset.py to use a configurable list size for top owned players. This change enhances flexibility in player selection and adjusts lineup target calculations accordingly.

Update sorting criteria in hedging_preset.py to use 'ownership' instead of 'Own'. This change improves the accuracy of player selection by aligning with the updated data structure.

Update import statements in hedging_preset.py to reflect new module structure. This change ensures proper access to small_field_preset and large_field_preset functions, enhancing code organization and maintainability.

Replace distribute_preset with hedging_preset to manage player exposure in lineup generation. Update app.py to reflect the new preset option and remove the obsolete distribute_preset function. This change enhances the flexibility of lineup strategies by allowing users to hedge against high-exposure players while maintaining performance metrics.

Refactor player filtering and portfolio distribution logic in distribute_preset.py to streamline the process of removing high-exposure players. This update enhances the accuracy of lineup generation by ensuring that the final portfolio meets the lineup target while maintaining performance metrics, and improves the handling of player selection based on similarity scores.

Limit player removal iterations in distribute_preset.py to a maximum of 10 to prevent infinite loops during portfolio distribution. This change enhances the stability of the player filtering process while maintaining the accuracy of lineup generation.

Refactor player removal and portfolio filtering logic in distribute_preset.py to improve accuracy in lineup generation. This update introduces a mechanism to continuously remove high-exposure players and ensures that the final portfolio meets the lineup target while maintaining performance metrics.

Update portfolio concatenation logic in distribute_preset.py to limit the number of players added based on lineup target. This change improves the accuracy of the distribution process by ensuring that only a calculated subset of players is included in the final portfolio.

Update sorting order in distribute_preset.py to prioritize players with the highest median values. This change enhances the accuracy of the portfolio distribution process by ensuring that the top players are selected based on their performance metrics.

Fix indentation issue in player removal logic in distribute_preset.py to ensure proper application of the filtering function. This change improves the accuracy of player exclusion during portfolio distribution.

Fix player removal logic in distribute_preset.py to correctly filter out players not in the player_out list. This change enhances the accuracy of the portfolio distribution process by ensuring only relevant players are retained.

Add debug print statement in distribute_preset.py to display the top 10 rows of the working portfolio. This change aids in verifying the filtering process and ensures the accuracy of player selection during distribution.

Sort working portfolio by 'median' before resetting index in distribute_preset.py to enhance player selection accuracy. This change ensures that the highest median values are prioritized in the distribution process.

Update player column selection logic in distribute_preset.py to reference the correct portfolio DataFrame. This change ensures that the player filtering process accurately reflects the current portfolio structure, enhancing the overall functionality of the distribution process.

Refactor player removal logic in distribute_preset.py to utilize a list of players with the lowest lineup counts. This change enhances the portfolio distribution process by ensuring that the least favorable players are excluded from the lineup, improving overall selection accuracy.

Refactor player selection logic in distribute_preset.py to improve clarity by explicitly defining columns used for player masking. This change enhances the readability of the code and ensures that only relevant columns are considered during the player filtering process.

Enhance portfolio distribution logic in distribute_preset.py by adding player exposure calculations and refining the handling of 'Similarity Score'. This update improves the accuracy of player selection and provides a summary of player exposure in the final output, ensuring a more comprehensive analysis of the lineup distribution process.

Refactor sorting logic in small_field_preset.py to ensure accurate portfolio filtering by utilizing 'Own' for sorting. This change enhances the portfolio distribution process by maintaining consistency in data handling across teams.

Refactor portfolio distribution logic in distribute_preset.py to group by 'Stack' and update sorting criteria to 'median'. This change improves the accuracy of portfolio filtering by ensuring the correct metrics are used for both selection and final output.

Update sorting logic in distribute_preset.py to prioritize 'Finish_percentile' over 'median' for improved portfolio filtering accuracy. This change enhances the effectiveness of the distribution process by ensuring the correct metric is used for sorting.

Update portfolio distribution logic in distribute_preset.py to utilize 'Similarity Score' for maximum ownership type calculations. This change improves the accuracy of portfolio filtering by ensuring the correct comparison metric is applied, enhancing the overall trimming process.

Enhance portfolio trimming logic in trim_portfolio.py by adding calculation for maximum ownership type value when conditions are not met. This adjustment improves the accuracy of the trimming process by ensuring all relevant ownership metrics are considered.

Fix logic for 'Similarity Score' comparison in trim_portfolio.py to ensure correct filtering of portfolios based on performance thresholds. This change enhances the accuracy of the trimming process.

Add 'Similarity Score' to data handling in app.py

Enhance trimming variable options in app.py and update portfolio trimming logic in trim_portfolio.py to accommodate 'Similarity Score'. This change allows for more flexible data handling and improves the trimming process based on the selected variable.

Optimize player similarity score calculation in predict_dupes function

Refactor player similarity score calculation in predict_dupes function

Update excluded columns in app.py to include 'Similarity Score' for improved data handling in portfolio processing.