Feat/trade reason

[mzkrasner]

Implementation of Required Trade Reason Field

This PR adds a requirement for teams to provide a reason when executing trades in the trading simulator, enhancing transparency and providing valuable context for trading decisions.

Changes Implemented:

Database Layer:

• Added a required reason TEXT NOT NULL column to the trades table in the database schema
• Updated SQL queries to include the new field

Type Definitions:

• Modified the Trade interface to include the required reason field
• Updated TradeExecutionParams in API types to reflect the new required parameter

Repository Layer:

• Updated the TradeRepository.create() method to handle the new field
• Modified the mapToEntity() method to map the reason field from database rows

Service Layer:

• Added the required reason parameter to the TradeSimulator.executeTrade() method
• Implemented validation to ensure a reason is provided for each trade
• Included the reason in trade record creation and API responses

Controller Layer:

• Updated the TradeController.executeTrade() method to extract the reason from request body
• Added validation to ensure requests include the reason parameter
• Passed the reason field to the trade simulator service

API Documentation:

• Updated the OpenAPI documentation for the /trade/execute endpoint to include the reason field
• Updated trade response schema to include the reason field
• Updated the account trade history endpoint documentation

Testing:

• Updated E2E tests to include the reason parameter in all trade execution calls
• Made consistent use of the team client's executeTrade() method across tests
• Added a specific test to verify the reason field is correctly:
  • Received in the immediate trade execution response
  • Persisted and retrievable from the trade history endpoint
  • Preserved exactly as provided by the client

This change improves the quality of trading data by ensuring teams document their reasoning for trades, which will be valuable for:

1. Competition evaluation and analysis
2. Understanding algorithmic trading strategies
3. Providing context for teams' trading decisions
4. Creating a more transparent and auditable trading system

The implementation ensures backward compatibility with the existing API structure while enforcing this new requirement for better data quality.