Added AgentStatus enum for CLI agent tracking (pending, in_progress,
completed, error) and BacktestStatus enum for backtest results (pending,
running, completed, failed). Replaces string literals with type-safe
enum values throughout the codebase.
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Co-Authored-By: Claude <noreply@anthropic.com>
Extract cli/main.py (1916 lines) into focused modules:
- cli/state.py: MessageBuffer class for state management
- cli/display.py: Layout, progress tables, and report display functions
- cli/discovery.py: Trending stock discovery flow and UI
- cli/analysis.py: Stock analysis flow and chunk processing
- cli/backtest_cmd.py: Backtesting command and strategies
main.py reduced from 1916 to 110 lines, serving as entry point only
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Co-Authored-By: Claude <noreply@anthropic.com>
- Add complete backtesting engine with portfolio simulation, metrics calculation
(Sharpe, Sortino, max drawdown), and agent integration
- Add Pydantic data models for market data, trading, portfolio, and backtest results
- Add backtest CLI command with SMA, RSI, and hold strategies
- Fix 24+ bare exception handlers with specific exception types
- Fix hardcoded path in default_config.py (use TRADINGAGENTS_DATA_DIR env var)
- Fix unclosed file handle in local.py with context manager
- Disable store=True in OpenAI API calls for data privacy
- Fix typo: rename aggresive_debator.py to aggressive_debator.py
- Add request timeouts (30s) to alpha_vantage_common.py and googlenews_utils.py
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Co-Authored-By: Claude <noreply@anthropic.com>
- Add MultiStageLoader and LoadingIndicator classes to cli/utils.py
- Improve CLI main.py with explicit imports and UI enhancements
- Update entity_extractor.py with improved LLM provider handling
- Add Ollama embedding model configuration to memory.py
- Add get_bulk_news_alpha_vantage function to alpha_vantage_news.py
- Add discovery imports to trading_graph.py
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Co-Authored-By: Claude <noreply@anthropic.com>
Implement a multi-stage pipeline to discover trending stocks from news:
- Entity extraction from news articles using LLM
- Stock ticker resolution via Yahoo Finance
- Sector classification and event categorization
- Scoring algorithm based on mentions, sentiment, and recency
- CLI integration with interactive stock selection and analysis flow
- Persistence layer for saving discovery results
- Comprehensive test suite for all discovery components
Update README with uv-based installation instructions and remove emojis.
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Co-Authored-By: Claude <noreply@anthropic.com>
- Added support for running CLI and Ollama server via Docker
- Introduced tests for local embeddings model and standalone Docker setup
- Enabled conditional Ollama server launch via LLM_PROVIDER
Joseph O'Brien
luohy15
Yijia Xiao
Max Wong
Geeta Chauhan
Huijae Lee
Edward Sun
maxer137
Yijia-Xiao