When past_memories is empty, all five agents previously injected an
empty string into their prompts while still instructing the LLM to
"address reflections and learn from past mistakes" — causing the LLM
to hallucinate fabricated lessons on first run.
Each agent now conditionally builds its memory section only when
past_memories is non-empty, so the injection and its instruction
are both absent when there is nothing to recall.
Also fixes import ordering in memory.py (logger after imports).
Tests: tests/test_hallucination_guard.py covers empty and populated
memory for all five agents (bull, bear, trader, research manager,
portfolio manager).
Companion to #563 (memory persistence).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
LLMs (especially smaller models) sometimes pass multiple indicator
names as a single comma-separated string instead of making separate
tool calls. Split and process each individually at the tool boundary.
- Replace FinnHub with Alpha Vantage API in README documentation
- Implement comprehensive Alpha Vantage modules:
- Stock data (daily OHLCV with date filtering)
- Technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands, ATR)
- Fundamental data (overview, balance sheet, cashflow, income statement)
- News and sentiment data with insider transactions
- Update news analyst tools to use ticker-based news search
- Integrate Alpha Vantage vendor methods into interface routing
- Maintain backward compatibility with existing vendor system
🤖 Generated with [Claude Code](https://claude.ai/code)
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
Zhigong Liu
Yijia-Xiao
CadeYu
luohy15
Max Wong
Yijia Xiao
Geeta Chauhan
Edward Sun
maxer137