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Bill 2b040537b1 docs: update changelog for v0.5.0 release

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2025-11-07 21:04:00 -05:00

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Changelog

All notable changes to the AI-Trader-Server project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

Unreleased

[0.5.0] - 2025-11-07

Added

Comprehensive Test Coverage Improvements - Increased coverage from 61% to 84.81% (+23.81 percentage points)
- 406 passing tests (up from 364 with 42 failures)
- Added 57 new tests across 7 modules
- New test suites:
  - tools/general_tools.py: 26 tests (97% coverage) - config management, conversation extraction
  - tools/price_tools.py: 11 tests - NASDAQ symbol validation, weekend date handling
  - api/price_data_manager.py: 12 tests (85% coverage) - date expansion, prioritization, progress callbacks
  - api/routes/results_v2.py: 3 tests (98% coverage) - validation, deprecated parameters
  - agent/reasoning_summarizer.py: 2 tests (87% coverage) - trade formatting, error handling
  - api/routes/period_metrics.py: 2 tests (100% coverage) - edge cases
  - agent/mock_provider: 1 test (100% coverage) - string representation
Database Connection Management - Context manager pattern to prevent connection leaks
- New db_connection() context manager for guaranteed cleanup
- Updated 16+ test files to use context managers
- Fixes 42 test failures caused by SQLite database locking
Date Range Support in /results Endpoint - Query multiple dates in single request with period performance metrics
- start_date and end_date parameters replace deprecated date parameter
- Returns lightweight format with daily portfolio values and period metrics for date ranges
- Period metrics: period return %, annualized return %, calendar days, trading days
- Default to last 30 days when no dates provided (configurable via DEFAULT_RESULTS_LOOKBACK_DAYS)
- Automatic edge trimming when requested range exceeds available data
- Per-model results grouping
Environment Variable: DEFAULT_RESULTS_LOOKBACK_DAYS - Configure default lookback period (default: 30)

Changed

BREAKING: /results endpoint parameter date removed - use start_date/end_date instead
- Single date: ?start_date=2025-01-16 or ?end_date=2025-01-16
- Date range: ?start_date=2025-01-16&end_date=2025-01-20
- Old ?date=2025-01-16 now returns 422 error with migration instructions
Database schema improvements:
- Added CHECK constraint for action_type field (must be 'buy', 'sell', or 'hold')
- Added ON DELETE CASCADE to trading_days foreign key
- Updated drop_all_tables() to match new schema (trading_days, actions vs old positions, trading_sessions)

Fixed

Critical: Database connection leaks causing 42 test failures
- Root cause: Tests opened SQLite connections but didn't close them on failures
- Solution: Created db_connection() context manager with guaranteed cleanup in finally block
- All test files updated to use context managers
Test suite SQL statement errors:
- Updated INSERT statements with all required fields (config_path, date_range, models, created_at)
- Fixed SQL binding mismatches in test fixtures
API integration test failures:
- Fixed date parameter handling for new results endpoint
- Updated test assertions for API field name changes

Migration Guide

Before:

GET /results?date=2025-01-16&model=gpt-4

After:

# Option 1: Use start_date only
GET /results?start_date=2025-01-16&model=gpt-4

# Option 2: Use both (same result for single date)
GET /results?start_date=2025-01-16&end_date=2025-01-16&model=gpt-4

# New: Date range queries
GET /results?start_date=2025-01-16&end_date=2025-01-20&model=gpt-4

Python Client:

# OLD (will break)
results = client.get_results(date="2025-01-16")

# NEW
results = client.get_results(start_date="2025-01-16")
results = client.get_results(start_date="2025-01-16", end_date="2025-01-20")

[0.4.3] - 2025-11-07

Fixed

Critical: Fixed cross-job portfolio continuity bug where subsequent jobs reset to initial position
- Root cause: Two database query functions (get_previous_trading_day() and get_starting_holdings()) filtered by job_id, preventing them from finding previous day's position when queried from a different job
- Impact: New jobs on consecutive dates would start with $10,000 cash and empty holdings instead of continuing from previous job's ending position (e.g., Job 2 on 2025-10-08 started with $10,000 instead of $329.825 cash and lost all stock holdings from Job 1 on 2025-10-07)
- Solution: Removed job_id filters from SQL queries to enable cross-job position lookups, matching the existing design in get_current_position_from_db() which already supported cross-job continuity
- Fix ensures complete portfolio continuity (both cash and holdings) across jobs for the same model
- Added comprehensive test coverage with test_get_previous_trading_day_across_jobs and test_get_starting_holdings_across_jobs
- Locations: api/database.py:622-630 (get_previous_trading_day), api/database.py:674-681 (get_starting_holdings), tests/unit/test_database_helpers.py:133-169,265-316

[0.4.2] - 2025-11-07

Fixed

Critical: Fixed negative cash position bug where trades calculated from initial capital instead of accumulating
- Root cause: MCP tools return CallToolResult objects with position data in structuredContent field, but ContextInjector was checking isinstance(result, dict) which always failed
- Impact: Each trade checked cash against initial $10,000 instead of cumulative position, allowing over-spending and resulting in negative cash balances (e.g., -$8,768.68 after 11 trades totaling $18,768.68)
- Solution: Updated ContextInjector to extract position dict from CallToolResult.structuredContent before validation
- Fix ensures proper intra-day position tracking with cumulative cash checks preventing over-trading
- Updated unit tests to mock CallToolResult objects matching production MCP behavior
- Locations: agent/context_injector.py:95-109, tests/unit/test_context_injector.py:26-53
Enabled MCP service logging by redirecting stdout/stderr from /dev/null to main process for better debugging
- Previously, all MCP tool debug output was silently discarded
- Now visible in docker logs for diagnosing parameter injection and trade execution issues
- Location: agent_tools/start_mcp_services.py:81-88

Fixed

Critical: Fixed stale jobs blocking new jobs after Docker container restart
- Root cause: Jobs with status 'pending', 'downloading_data', or 'running' remained in database after container shutdown, preventing new job creation
- Solution: Added cleanup_stale_jobs() method that runs on FastAPI startup to mark interrupted jobs as 'failed' or 'partial' based on completion percentage
- Intelligent status determination: Uses existing progress tracking (completed/total model-days) to distinguish between failed (0% complete) and partial (>0% complete)
- Detailed error messages include original status and completion counts (e.g., "Job interrupted by container restart (was running, 3/10 model-days completed)")
- Incomplete job_details automatically marked as 'failed' with clear error messages
- Deployment-aware: Skips cleanup in DEV mode when database is reset, always runs in PROD mode
- Comprehensive test coverage: 6 new unit tests covering all cleanup scenarios
- Locations: api/job_manager.py:702-779, api/main.py:164-168, tests/unit/test_job_manager.py:451-609
Fixed Pydantic validation errors when using DeepSeek models via OpenRouter
- Root cause: LangChain's parse_tool_call() has a bug where it sometimes returns args as JSON string instead of parsed dict object
- Solution: Added ToolCallArgsParsingWrapper that:
  1. Patches parse_tool_call() to detect and fix string args by parsing them to dict
  2. Normalizes non-standard tool_call formats (e.g., {name, args, id} → {function: {name, arguments}, id})
- The wrapper is defensive and only acts when needed, ensuring compatibility with all AI providers
- Fixes validation error: tool_calls.0.args: Input should be a valid dictionary [type=dict_type, input_value='...', input_type=str]

[0.4.1] - 2025-11-06

Fixed

Fixed "No trading" message always displaying despite trading activity by initializing IF_TRADE to True (trades expected by default)
Root cause: IF_TRADE was initialized to False in runtime config but never updated when trades executed

Note

ChatDeepSeek integration was reverted as it conflicts with OpenRouter unified gateway architecture
System uses OPENAI_API_BASE (OpenRouter) with single OPENAI_API_KEY for all providers
Sporadic DeepSeek validation errors appear to be transient and do not require code changes

[0.4.0] - 2025-11-05

BREAKING CHANGES

Schema Migration: Old Tables Removed

The following database tables have been removed and replaced with new schema:

Removed Tables:

trading_sessions → Replaced by trading_days
positions (old action-centric version) → Replaced by trading_days + actions + holdings
reasoning_logs → Replaced by trading_days.reasoning_full (JSON column)

Migration Required:

If you have existing data in old tables, export it before upgrading
New installations automatically use new schema
Old data cannot be automatically migrated (different data model)

Database Path:

Production: data/trading.db
Development: data/trading_dev.db

To migrate existing production database:

# Run migration script to drop old tables
PYTHONPATH=. python api/migrations/002_drop_old_schema.py

API Endpoint Removed: /reasoning

The /reasoning endpoint has been removed and replaced by /results with reasoning parameter.

Migration Guide:

Old Endpoint	New Endpoint
`GET /reasoning?job_id=X`	`GET /results?job_id=X&reasoning=summary`
`GET /reasoning?job_id=X&include_full_conversation=true`	`GET /results?job_id=X&reasoning=full`

Benefits of New Endpoint:

Day-centric structure (easier to understand portfolio progression)
Daily P&L metrics included
AI-generated reasoning summaries (2-3 sentences)
Unified data model

Response Structure Changes:

Old /reasoning returned:

{
  "sessions": [
    {
      "session_id": 1,
      "positions": [{"action_id": 0, "cash_after": 10000, ...}],
      "conversation": [...]
    }
  ]
}

New /results?reasoning=full returns:

{
  "results": [
    {
      "date": "2025-01-15",
      "starting_position": {"holdings": [], "cash": 10000},
      "daily_metrics": {"profit": 0.0, "return_pct": 0.0},
      "trades": [{"action_type": "buy", "symbol": "AAPL", ...}],
      "final_position": {"holdings": [...], "cash": 8500},
      "reasoning": [...]
    }
  ]
}

Removed

/reasoning endpoint (use /results?reasoning=full instead)
Old database tables: trading_sessions, positions, reasoning_logs
Pydantic models: ReasoningMessage, PositionSummary, TradingSessionResponse, ReasoningResponse
Old-schema tests for deprecated tables

Added

Daily P&L Calculation System - Accurate profit/loss tracking with normalized database schema
- New trading_days table for day-centric trading results with daily P&L metrics
- holdings table for portfolio snapshots (ending positions only)
- actions table for trade execution ledger
- DailyPnLCalculator calculates P&L by valuing previous holdings at current prices
- Weekend/holiday gap handling with days_since_last_trading tracking
- First trading day properly handled with zero P&L
- Auto-initialization of schema on database creation
AI Reasoning Summaries - Automated trading decision documentation
- ReasoningSummarizer generates 2-3 sentence AI-powered summaries of trading sessions
- Fallback to statistical summary if AI generation fails
- Summaries generated during simulation and stored in database
- Full reasoning logs preserved for detailed analysis
Day-Centric Results API - Unified endpoint for trading results
- New /results endpoint with query parameters: job_id, model, date, reasoning
- Three reasoning levels: none (default), summary, full
- Response structure: starting_position, daily_metrics, trades, final_position, metadata
- Holdings chain validation across trading days
- Replaced old positions-based endpoint
BaseAgent P&L Integration - Complete integration of P&L calculation into trading sessions
- P&L calculated at start of each trading day after loading current prices
- Trading day records created with comprehensive metrics
- Holdings saved to database after each session
- Reasoning summaries generated and stored automatically
- Database helper methods for clean data access

Changed

Reduced Docker healthcheck frequency from 30s to 1h to minimize log noise while maintaining startup verification
Database schema migrated from action-centric to day-centric model
Results API now returns normalized day-centric data structure
Trade tools (buy(), sell()) now write to actions table instead of old positions table
model_day_executor simplified - removed duplicate writes to old schema tables
get_current_position_from_db() queries new schema (trading_days + holdings) instead of positions table

Improved

Database helper methods with 7 new functions for trading_days schema operations
Test coverage increased with 36+ new comprehensive tests
Documentation updated with complete API reference and database schema details

Fixed

Critical: Intra-day position tracking for sell-then-buy trades (e20dce7)
- Sell proceeds now immediately available for subsequent buy orders within same trading session
- ContextInjector maintains in-memory position state during trading sessions
- Position updates accumulate after each successful trade
- Enables agents to rebalance portfolios (sell + buy) in single session
- Added 13 comprehensive tests for position tracking
Critical: Tool message extraction in conversation history (462de3a, abb9cd0)
- Fixed bug where tool messages (buy/sell trades) were not captured when agent completed in single step
- Tool extraction now happens BEFORE finish signal check
- Reasoning summaries now accurately reflect actual trades executed
- Resolves issue where summarizer saw 0 tools despite multiple trades
Reasoning summary generation improvements (6d126db)
- Summaries now explicitly mention specific trades executed (symbols, quantities, actions)
- Added TRADES EXECUTED section highlighting tool calls
- Example: 'sold 1 GOOGL and 1 AMZN to reduce exposure' instead of 'maintain core holdings'
Final holdings calculation accuracy (a8d912b)
- Final positions now calculated from actions instead of querying incomplete database records
- Correctly handles first trading day with multiple trades
- New _calculate_final_position_from_actions() method applies all trades to calculate final state
- Holdings now persist correctly across all trading days
- Added 3 comprehensive tests for final position calculation
Holdings persistence between trading days (aa16480)
- Query now retrieves previous day's ending position as current day's starting position
- Changed query from date <= to date < to prevent returning incomplete current-day records
- Fixes empty starting_position/final_position in API responses despite successful trades
- Updated tests to verify correct previous-day retrieval
Context injector trading_day_id synchronization (05620fa)
- ContextInjector now updated with trading_day_id after record creation
- Fixes "Trade failed: trading_day_id not found in runtime config" error
- MCP tools now correctly receive trading_day_id via context injection
Schema migration compatibility fixes (7c71a04)
- Updated position queries to use new trading_days schema instead of obsolete positions table
- Removed obsolete add_no_trade_record_to_db function calls
- Fixes "no such table: positions" error
- Simplified _handle_trading_result logic
Database referential integrity (9da65c2)
- Corrected Database default path from "data/trading.db" to "data/jobs.db"
- Ensures all components use same database file
- Fixes FOREIGN KEY constraint failures when creating trading_day records
Debug logging cleanup (1e7bdb5)
- Removed verbose debug logging from ContextInjector for cleaner output

[0.3.1] - 2025-11-03

Fixed

Critical: Fixed position tracking bugs causing cash reset and positions lost over weekends
- Removed redundant ModelDayExecutor._write_results_to_db() that created corrupt records with cash=0 and holdings=[]
- Fixed profit calculation to compare against start-of-day portfolio value instead of previous day's final value
- Positions now correctly carry over between trading days and across weekends
- Profit/loss calculations now accurately reflect trading gains/losses without treating trades as losses

Changed

Position tracking now exclusively handled by trade tools (buy(), sell()) and add_no_trade_record_to_db()
Daily profit calculation compares to start-of-day (action_id=0) portfolio value for accurate P&L tracking

Added

Standardized testing scripts for different workflows:
- scripts/test.sh - Interactive menu for all testing operations
- scripts/quick_test.sh - Fast unit test feedback (~10-30s)
- scripts/run_tests.sh - Main test runner with full configuration options
- scripts/coverage_report.sh - Coverage analysis with HTML/JSON/terminal reports
- scripts/ci_test.sh - CI/CD optimized testing with JUnit/coverage XML output
Comprehensive testing documentation in docs/developer/testing.md
Test coverage requirement: 85% minimum (currently at 89.86%)

0.3.0 - 2025-11-03

Added - Development & Testing Features

Development Mode - Mock AI provider for cost-free testing
- DEPLOYMENT_MODE=DEV enables mock AI responses with deterministic stock rotation
- Isolated dev database (trading_dev.db) separate from production data
- PRESERVE_DEV_DATA=true option to prevent dev database reset on startup
- No AI API costs during development and testing
- All API responses include deployment_mode field
- Startup warning displayed when running in DEV mode
Config Override System - Docker configuration merging
- Place custom configs in user-configs/ directory
- Startup merges user config with default config
- Comprehensive validation with clear error messages
- Volume mount: ./user-configs:/app/user-configs

Added - Enhanced API Features

Async Price Download - Non-blocking data preparation
- POST /simulate/trigger no longer blocks on price downloads
- New job status: downloading_data during data preparation
- Warnings field in status response for download issues
- Better user experience for large date ranges
Resume Mode - Idempotent simulation execution
- Jobs automatically skip already-completed model-days
- Safe to re-run jobs without duplicating work
- status="skipped" for already-completed executions
- Error-free job completion when partial results exist
Reasoning Logs API - Access AI decision-making history
- GET /reasoning endpoint for querying reasoning logs
- Filter by job_id, model_name, date, include_full_conversation
- Includes conversation history and tool usage
- Database-only storage (no JSONL files)
- AI-powered summary generation for reasoning sessions
Job Skip Status - Enhanced job status tracking
- New status: skipped for already-completed model-days
- Better differentiation between pending, running, and skipped
- Accurate job completion detection

Added - Price Data Management & On-Demand Downloads

SQLite Price Data Storage - Replaced JSONL files with relational database
- price_data table for OHLCV data (replaces merged.jsonl)
- price_data_coverage table for tracking downloaded date ranges
- simulation_runs table for soft-delete position tracking
- Comprehensive indexes for query performance
On-Demand Price Data Downloads - Automatic gap filling via Alpha Vantage
- Priority-based download strategy (maximize date completion)
- Graceful rate limit handling (no pre-configured limits needed)
- Smart coverage gap detection
- Configurable via AUTO_DOWNLOAD_PRICE_DATA (default: true)
Date Range API - Simplified date specification
- Single date: {"start_date": "2025-01-20"}
- Date range: {"start_date": "2025-01-20", "end_date": "2025-01-24"}
- Automatic validation (chronological order, max range, not future)
- Configurable max days via MAX_SIMULATION_DAYS (default: 30)
Migration Tooling - Script to import existing merged.jsonl data
- scripts/migrate_price_data.py for one-time data migration
- Automatic coverage tracking during migration

Added - API Service Transformation

REST API Service - Complete FastAPI implementation for external orchestration
- POST /simulate/trigger - Trigger simulation jobs with config, date range, and models
- GET /simulate/status/{job_id} - Query job progress and execution details
- GET /results - Retrieve simulation results with filtering (job_id, date, model)
- GET /health - Service health check with database connectivity verification
SQLite Database - Complete persistence layer replacing JSONL files
- Jobs table - Job metadata and lifecycle tracking
- Job details table - Per model-day execution status
- Positions table - Trading position records with P&L
- Holdings table - Portfolio holdings breakdown
- Reasoning logs table - AI decision reasoning history
- Tool usage table - MCP tool usage statistics
Backend Components
- JobManager - Job lifecycle management with concurrent job prevention
- RuntimeConfigManager - Isolated runtime configs for thread-safe execution
- ModelDayExecutor - Single model-day execution engine
- SimulationWorker - Job orchestration with date-sequential, model-parallel execution
Comprehensive Test Suite
- 175 unit and integration tests
- 19 database tests (98% coverage)
- 23 job manager tests (98% coverage)
- 10 model executor tests (84% coverage)
- 20 API endpoint tests (81% coverage)
- 20 Pydantic model tests (100% coverage)
- 10 runtime manager tests (89% coverage)
- 22 date utilities tests (100% coverage)
- 33 price data manager tests (85% coverage)
- 10 on-demand download integration tests
- 8 existing integration tests
Docker Deployment - Persistent REST API service
- API-only deployment (batch mode removed for simplicity)
- Single docker-compose service (ai-trader-server)
- Health check configuration (30s interval, 3 retries)
- Volume persistence for SQLite database and logs
- Configurable API_PORT for flexible deployment
- System dependencies (curl, procps) for health checks and debugging
Validation & Testing Tools
- scripts/validate_docker_build.sh - Docker build and startup validation with port awareness
- scripts/test_api_endpoints.sh - Complete API endpoint testing suite with port awareness
- TESTING_GUIDE.md - Comprehensive testing procedures and troubleshooting (including port conflicts)
Documentation
- DOCKER_API.md - API deployment guide with examples
- TESTING_GUIDE.md - Validation procedures and troubleshooting
- API endpoint documentation with request/response examples
- Windmill integration patterns and examples

Changed

Project Rebrand - AI-Trader renamed to AI-Trader-Server
- Updated all documentation for new project name
- Updated Docker images to ghcr.io/xe138/ai-trader-server
- Updated GitHub Actions workflows
- Updated README, CHANGELOG, and all user guides
Architecture - Transformed from batch-only to API-first service with database persistence
Data Storage - Migrated from JSONL files to SQLite relational database
- Price data now stored in price_data table instead of merged.jsonl
- Tools/price_tools.py updated to query database
- Position data fully migrated to database-only storage (removed JSONL dependencies)
- Trade tools now read/write from database tables with lazy context injection
Deployment - Simplified to single API-only Docker service (REST API is new in v0.3.0)
Logging - Removed duplicate MCP service log files for cleaner output
Configuration - Simplified environment variable configuration
- Added: DEPLOYMENT_MODE (PROD/DEV) for environment control
- Added: PRESERVE_DEV_DATA (default: false) to keep dev data between runs
- Added: AUTO_DOWNLOAD_PRICE_DATA (default: true) - Enable on-demand downloads
- Added: MAX_SIMULATION_DAYS (default: 30) - Maximum date range size
- Added: API_PORT for host port mapping (default: 8080, customizable for port conflicts)
- Removed: RUNTIME_ENV_PATH (API dynamically manages runtime configs)
- Removed: MCP service ports (MATH_HTTP_PORT, SEARCH_HTTP_PORT, TRADE_HTTP_PORT, GETPRICE_HTTP_PORT)
- Removed: WEB_HTTP_PORT (web UI not implemented)
- MCP services use fixed internal ports (8000-8003) and are no longer exposed to host
- Container always uses port 8080 internally for API
- Only API port (8080) is exposed to host
- Reduces configuration complexity and attack surface
Requirements - Added fastapi>=0.120.0, uvicorn[standard]>=0.27.0, pydantic>=2.0.0
Docker Compose - Single service (ai-trader-server) instead of dual-mode
Dockerfile - Added system dependencies (curl, procps) and port 8080 exposure
.env.example - Simplified configuration with only essential variables
Entrypoint - Unified entrypoint.sh with proper signal handling (exec uvicorn)

Technical Implementation

Test-Driven Development - All components written with tests first
Mock-based Testing - Avoid heavy dependencies in unit tests
Pydantic V2 - Type-safe request/response validation
Foreign Key Constraints - Database referential integrity with cascade deletes
Thread-safe Execution - Isolated runtime configs per model-day
Background Job Execution - ThreadPoolExecutor for parallel model execution
Automatic Status Transitions - Job status updates based on model-day completion

Performance & Quality

Test Suite - 175 tests, all passing
- Unit tests: 155 tests
- Integration tests: 18 tests
- API tests: 20+ tests
Code Coverage - High coverage for new modules
- Date utilities: 100%
- Price data manager: 85%
- Database layer: 98%
- Job manager: 98%
- Pydantic models: 100%
- Runtime manager: 89%
- Model executor: 84%
- FastAPI app: 81%
Test Execution - Fast test suite (~12 seconds for full suite)

Integration Ready

Windmill.dev - HTTP-based integration with polling support
External Orchestration - RESTful API for workflow automation
Monitoring - Health checks and status tracking
Persistence - SQLite database survives container restarts

Fixed

Context Injection - Runtime parameters correctly injected into MCP tools
- ContextInjector always overrides AI-provided parameters (defense-in-depth)
- Hidden context parameters from AI tool schema to prevent hallucination
- Resolved database locking issues with concurrent tool calls
- Proper async handling of tool reloading after context injection
Simulation Re-runs - Prevent duplicate execution of completed model-days
- Fixed job hanging when re-running partially completed simulations
- _execute_date() now skips already-completed model-days
- Job completion status correctly reflects skipped items
Agent Initialization - Correct parameter passing in API mode
- Fixed BaseAgent initialization parameters in ModelDayExecutor
- Resolved async execution and position storage issues
Database Reliability - Various improvements for concurrent access
- Fixed column existence checks before creating indexes
- Proper database path resolution in dev mode (prevents recursive _dev suffix)
- Module-level database initialization for uvicorn reliability
- Fixed database locking during concurrent writes
- Improved error handling in buy/sell functions
Configuration - Improved config handling
- Use enabled field from config to determine which models run
- Use config models when empty models list provided
- Correct handling of merged runtime configs in containers
- Proper get_db_path() usage to pass base database path
Docker - Various deployment improvements
- Removed non-existent data scripts from Dockerfile
- Proper respect for dev mode in entrypoint database initialization
- Correct closure usage to capture db_path in lifespan context manager

Breaking Changes

Batch Mode Removed - All simulations now run through REST API
- v0.2.0 used sequential batch execution via Docker entrypoint
- v0.3.0 introduces REST API for external orchestration
- Migration: Use POST /simulate/trigger endpoint instead of direct script execution
Data Storage Format Changed - Price data moved from JSONL to SQLite
- Run python scripts/migrate_price_data.py to migrate existing merged.jsonl data
- merged.jsonl no longer used (replaced by price_data table)
- Automatic on-demand downloads eliminate need for manual data fetching
Configuration Variables Changed
- Added: DEPLOYMENT_MODE, PRESERVE_DEV_DATA, AUTO_DOWNLOAD_PRICE_DATA, MAX_SIMULATION_DAYS, API_PORT
- Removed: RUNTIME_ENV_PATH, MCP service ports, WEB_HTTP_PORT
- MCP services now use fixed internal ports (not exposed to host)

0.2.0 - 2025-10-31

Added

Complete Docker deployment support with containerization
Docker Compose orchestration for easy local deployment
Multi-stage Dockerfile with Python 3.10-slim base image
Automated CI/CD pipeline via GitHub Actions for release builds
Automatic draft release creation with version tagging
Docker images published to GitHub Container Registry (ghcr.io)
Comprehensive Docker documentation (docs/DOCKER.md)
Release process documentation (docs/RELEASING.md)
Data cache reuse design documentation (docs/DESIGN_DATA_CACHE_REUSE.md)
CLAUDE.md repository guidance for development
Docker deployment section in main README
Environment variable configuration via docker-compose
Sequential startup script (entrypoint.sh) for data fetch, MCP services, and trading agent
Volume mounts for data and logs persistence
Pre-built image support from ghcr.io/xe138/ai-trader-server
Configurable volume path for persistent data
Configurable web interface host port
Automated merged.jsonl creation during price fetching
API key registration URLs in .env.example

Changed

Updated .env.example with Docker-specific configuration, API key URLs, and paths
Updated .gitignore to exclude git worktrees directory
Removed deprecated version tag from docker-compose.yml
Updated repository URLs to Xe138/AI-Trader-Server fork
Docker Compose now uses pre-built image by default
Simplified Docker config file selection with convention over configuration
Fixed internal ports with configurable host ports
Separated data scripts from volume mount directory
Reduced log flooding during data fetch
OPENAI_API_BASE can now be left empty in configuration

Fixed

Docker Compose configuration now follows modern best practices (version-less)
Prevent restart loop on missing API keys with proper validation
Docker tag generation now converts repository owner to lowercase
Validate GITHUB_REF is a tag in docker-release workflow
Correct Dockerfile FROM AS casing
Module import errors for MCP services resolved with PYTHONPATH
Prevent price data overwrite on container restart
Merge script now writes to current directory for volume compatibility

0.1.0 - Initial Release

Added

AI trading competition platform for NASDAQ 100 stocks
Support for multiple AI models (GPT, Claude, Qwen, DeepSeek, Gemini)
MCP (Model Context Protocol) toolchain integration
- Mathematical calculation tools
- Market intelligence search via Jina AI
- Trading execution tools
- Price query tools
Historical replay architecture with anti-look-ahead controls
Alpha Vantage API integration for price data
Autonomous AI decision-making with zero human intervention
Real-time performance analytics and leaderboard
Position tracking and trading logs
Web-based performance dashboard
Complete NASDAQ 100 stock universe support
Initial capital: $10,000 per AI model
Configurable date range for backtesting
Multi-model concurrent trading support
Automatic data fetching and merging
Comprehensive README with quick start guide

Technical Details

Python 3.10+ support
LangChain framework integration
FastMCP for MCP service implementation
JSONL format for position and log storage
Weekday-only trading simulation
Configurable agent parameters (max_steps, max_retries, initial_cash)

Release Notes Template

For future releases, use this template:

## [X.Y.Z] - YYYY-MM-DD

### Added
- New features

### Changed
- Changes to existing functionality

### Deprecated
- Soon-to-be removed features

### Removed
- Removed features

### Fixed
- Bug fixes

### Security
- Security improvements

33 KiB Raw Permalink Blame History

Changelog

Unreleased

[0.5.0] - 2025-11-07

Added

Changed

Fixed

Migration Guide

[0.4.3] - 2025-11-07

Fixed

[0.4.2] - 2025-11-07

Fixed

Fixed

[0.4.1] - 2025-11-06

Fixed

Note

[0.4.0] - 2025-11-05

BREAKING CHANGES

Schema Migration: Old Tables Removed

API Endpoint Removed: /reasoning

Removed

Added

Changed

Improved

Fixed

[0.3.1] - 2025-11-03

Fixed

Changed

Added

0.3.0 - 2025-11-03

Added - Development & Testing Features

Added - Enhanced API Features

Added - Price Data Management & On-Demand Downloads

Added - API Service Transformation

Changed

Technical Implementation

Performance & Quality

Integration Ready

Fixed

Breaking Changes

0.2.0 - 2025-10-31

Added

Changed

Fixed

0.1.0 - Initial Release

Added

Technical Details

Release Notes Template

33 KiB

Raw Permalink Blame History