50 KiB
50 KiB
Agent Data & Information Flows
This document describes how each agent in the TradingAgents framework collects data, processes it, and sends it to an LLM for analysis or decision-making. It also records the default model and thinking modality (quick / mid / deep) used by every agent.
Source of truth for LLM tier defaults:
tradingagents/default_config.py
Table of Contents
- Thinking-Modality Overview
- Trading Pipeline Flow
- Scanner Pipeline Flow
- Per-Agent Data Flows
- 4.1 Market Analyst
- 4.2 Fundamentals Analyst
- 4.3 News Analyst
- 4.4 Social Media Analyst
- 4.5 Bull Researcher
- 4.6 Bear Researcher
- 4.7 Research Manager
- 4.8 Trader
- 4.9 Aggressive Debator
- 4.10 Conservative Debator
- 4.11 Neutral Debator
- 4.12 Risk Manager
- 4.13 Geopolitical Scanner
- 4.14 Market Movers Scanner
- 4.15 Sector Scanner
- 4.16 Industry Deep Dive
- 4.17 Macro Synthesis
- Tool → Data-Source Mapping
- Memory System
1. Thinking-Modality Overview
The framework uses a 3-tier LLM system so that simple extraction tasks run on fast, cheap models while critical judgment calls use the most capable model.
| Tier | Config Key | Default Model | Purpose |
|---|---|---|---|
| Quick | quick_think_llm |
gpt-5-mini |
Fast extraction, summarization, debate positions |
| Mid | mid_think_llm |
None → falls back to quick | Balanced reasoning with memory |
| Deep | deep_think_llm |
gpt-5.2 |
Complex synthesis, final judgments |
Each tier can have its own _llm_provider and _backend_url overrides.
All are overridable via TRADINGAGENTS_<KEY> env vars.
Agent → Tier Assignment
| # | Agent | Tier | Has Tools? | Has Memory? | Tool Execution |
|---|---|---|---|---|---|
| 1 | Market Analyst | Quick | ✅ | — | LangGraph ToolNode |
| 2 | Fundamentals Analyst | Quick | ✅ | — | LangGraph ToolNode |
| 3 | News Analyst | Quick | ✅ | — | LangGraph ToolNode |
| 4 | Social Media Analyst | Quick | ✅ | — | LangGraph ToolNode |
| 5 | Bull Researcher | Mid | — | ✅ | — |
| 6 | Bear Researcher | Mid | — | ✅ | — |
| 7 | Research Manager | Deep | — | ✅ | — |
| 8 | Trader | Mid | — | ✅ | — |
| 9 | Aggressive Debator | Quick | — | — | — |
| 10 | Conservative Debator | Quick | — | — | — |
| 11 | Neutral Debator | Quick | — | — | — |
| 12 | Risk Manager | Deep | — | ✅ | — |
| 13 | Geopolitical Scanner | Quick | ✅ | — | run_tool_loop() |
| 14 | Market Movers Scanner | Quick | ✅ | — | run_tool_loop() |
| 15 | Sector Scanner | Quick | ✅ | — | run_tool_loop() |
| 16 | Industry Deep Dive | Mid | ✅ | — | run_tool_loop() |
| 17 | Macro Synthesis | Deep | — | — | — |
2. Trading Pipeline Flow
┌─────────────────────────┐
│ START │
│ (ticker + trade_date) │
└────────────┬─────────────┘
│
┌───────────────────────┬┴┬───────────────────────┐
▼ ▼ ▼ ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Market Analyst │ │ News Analyst │ │ Social Analyst │ │ Fundamentals │
│ (quick_think) │ │ (quick_think) │ │ (quick_think) │ │ Analyst │
│ │ │ │ │ │ │ (quick_think) │
│ Tools: │ │ Tools: │ │ Tools: │ │ Tools: │
│ • get_macro_regime│ │ • get_news │ │ • get_news │ │ • get_ttm_analysis│
│ • get_stock_data │ │ • get_global_news │ │ (sentiment) │ │ • get_fundamentals│
│ • get_indicators │ │ • get_insider_txn │ │ │ │ • get_peer_comp. │
│ │ │ │ │ │ │ • get_sector_rel. │
│ Output: │ │ Output: │ │ Output: │ │ • get_balance_sh. │
│ market_report │ │ news_report │ │ sentiment_report │ │ • get_cashflow │
│ macro_regime_rpt │ │ │ │ │ │ • get_income_stmt │
└────────┬─────────┘ └────────┬──────────┘ └────────┬──────────┘ │ Output: │
│ │ │ │ fundamentals_rpt │
└─────────────────────┼───────────────────────┘ └────────┬──────────┘
│ │
▼ │
┌─────────────────────────┐◄─────────────────────────────────┘
│ 4 analyst reports │
│ feed into debate below │
└────────────┬─────────────┘
│
┌──────────────────┴──────────────────┐
│ Investment Debate Phase │
│ │
│ ┌───────────┐ ┌───────────┐ │
│ │ Bull │◄────►│ Bear │ │
│ │ Researcher │ │ Researcher │ │
│ │ (mid_think)│ │ (mid_think)│ │
│ │ + memory │ │ + memory │ │
│ └───────────┘ └───────────┘ │
│ (max_debate_rounds = 2) │
└──────────────────┬───────────────────┘
│
▼
┌─────────────────────────┐
│ Research Manager │
│ (deep_think + memory) │
│ │
│ Reads: debate history, │
│ 4 analyst reports, │
│ macro regime │
│ │
│ Output: │
│ investment_plan │
│ (BUY / SELL / HOLD) │
└────────────┬─────────────┘
│
▼
┌─────────────────────────┐
│ Trader │
│ (mid_think + memory) │
│ │
│ Reads: investment_plan,│
│ 4 analyst reports │
│ │
│ Output: │
│ trader_investment_plan │
└────────────┬─────────────┘
│
┌──────────────────┴──────────────────┐
│ Risk Debate Phase │
│ │
│ ┌────────────┐ ┌───────────────┐ │
│ │ Aggressive │ │ Conservative │ │
│ │ (quick) │ │ (quick) │ │
│ └──────┬─────┘ └───────┬────────┘ │
│ │ ┌───────────┐│ │
│ └───►│ Neutral │◄─────────┘
│ │ (quick) │ │
│ └───────────┘ │
│ (max_risk_discuss_rounds = 2) │
└──────────────────┬────────────────────┘
│
▼
┌─────────────────────────┐
│ Risk Manager │
│ (deep_think + memory) │
│ │
│ Reads: risk debate, │
│ trader plan, 4 reports,│
│ macro regime │
│ │
│ Output: │
│ final_trade_decision │
└────────────┬─────────────┘
│
▼
┌───────────────┐
│ END │
└───────────────┘
3. Scanner Pipeline Flow
┌─────────────────────────┐
│ START │
│ (scan_date) │
└────────────┬─────────────┘
│
┌────────────────────────────┼────────────────────────────┐
▼ ▼ ▼
┌──────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Geopolitical │ │ Market Movers │ │ Sector Scanner │
│ Scanner │ │ Scanner │ │ │
│ (quick_think) │ │ (quick_think) │ │ (quick_think) │
│ │ │ │ │ │
│ Tools: │ │ Tools: │ │ Tools: │
│ • get_topic_news │ │ • get_market_ │ │ • get_sector_ │
│ │ │ movers │ │ performance │
│ Output: │ │ • get_market_ │ │ │
│ geopolitical_rpt │ │ indices │ │ Output: │
│ │ │ │ │ sector_perf_rpt │
│ │ │ Output: │ │ │
│ │ │ market_movers_rpt│ │ │
└────────┬─────────┘ └────────┬─────────┘ └────────┬─────────┘
│ │ │
└────────────────────────┼─────────────────────────┘
│ (Phase 1 → Phase 2)
▼
┌─────────────────────────────┐
│ Industry Deep Dive │
│ (mid_think) │
│ │
│ Reads: all 3 Phase-1 reports │
│ Auto-extracts top 3 sectors │
│ │
│ Tools: │
│ • get_industry_performance │
│ (called per top sector) │
│ • get_topic_news │
│ (sector-specific searches) │
│ │
│ Output: │
│ industry_deep_dive_report │
└──────────────┬───────────────┘
│ (Phase 2 → Phase 3)
▼
┌─────────────────────────────┐
│ Macro Synthesis │
│ (deep_think) │
│ │
│ Reads: all 4 prior reports │
│ No tools – pure LLM reasoning│
│ │
│ Output: │
│ macro_scan_summary (JSON) │
│ Top 8-10 stock candidates │
│ with conviction & catalysts │
└──────────────┬───────────────┘
│
▼
┌───────────────┐
│ END │
└───────────────┘
4. Per-Agent Data Flows
Each subsection follows the same structure:
Data sources → Tool calls → Intermediate processing → LLM prompt → Output
4.1 Market Analyst
| File | agents/analysts/market_analyst.py |
| Factory | create_market_analyst(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | LangGraph ToolNode (graph conditional edge) |
Data Flow:
┌─────────────────────────────────────────────────────┐
│ State Input: company_of_interest, trade_date │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ 1. get_macro_regime(curr_date) │
│ → Fetches VIX, credit spreads, yield curve, │
│ SPY breadth, sector rotation signals │
│ → Classifies: risk-on / risk-off / transition │
│ → Returns: Markdown regime report │
│ Data source: yfinance (VIX, SPY, sector ETFs) │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ 2. get_stock_data(symbol, start_date, end_date) │
│ → Fetches OHLCV price data │
│ → Returns: formatted CSV string │
│ Data source: yfinance / Alpha Vantage │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ 3. get_indicators(symbol, indicator, curr_date) │
│ → Up to 8 indicators chosen by LLM: │
│ SMA, EMA, MACD, RSI, Bollinger, ATR, VWMA, OBV │
│ → Returns: formatted indicator values │
│ Data source: yfinance / Alpha Vantage │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ LLM Prompt (quick_think): │
│ "You are a Market Analyst. Classify macro │
│ environment, select complementary indicators, │
│ frame analysis based on regime context. │
│ Provide fine-grained analysis with summary table." │
│ │
│ Context sent to LLM: │
│ • Macro regime classification │
│ • OHLCV price data │
│ • Technical indicator values │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Output: │
│ • market_report (technical analysis text) │
│ • macro_regime_report (risk-on/off classification) │
└─────────────────────────────────────────────────────┘
4.2 Fundamentals Analyst
| File | agents/analysts/fundamentals_analyst.py |
| Factory | create_fundamentals_analyst(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | LangGraph ToolNode |
Data Flow:
State Input: company_of_interest, trade_date
│
▼
1. get_ttm_analysis(ticker, curr_date)
→ Internally calls: get_income_statement, get_balance_sheet, get_cashflow
→ Computes: 8-quarter trailing metrics (revenue growth QoQ/YoY,
gross/operating/net margins, ROE trend, debt/equity, FCF)
→ Returns: Markdown TTM trend report
Data source: yfinance / Alpha Vantage
│
▼
2. get_fundamentals(ticker, curr_date)
→ Fetches: P/E, PEG, P/B, beta, 52-week range, market cap
→ Returns: formatted fundamentals report
Data source: yfinance / Alpha Vantage
│
▼
3. get_peer_comparison(ticker, curr_date)
→ Ranks company vs sector peers (1W, 1M, 3M, 6M, YTD returns)
→ Returns: ranked comparison table
Data source: yfinance
│
▼
4. get_sector_relative(ticker, curr_date)
→ Computes alpha vs sector ETF benchmark
→ Returns: alpha report (1W, 1M, 3M, 6M, YTD)
Data source: yfinance
│
▼
5. (Optional) get_balance_sheet / get_cashflow / get_income_statement
→ Raw financial statements
Data source: yfinance / Alpha Vantage
│
▼
LLM Prompt (quick_think):
"Call tools in prescribed sequence. Write comprehensive report
with multi-quarter trends, TTM metrics, relative valuation,
sector outperformance. Identify inflection points. Append
Markdown summary table with key metrics."
│
▼
Output: fundamentals_report
4.3 News Analyst
| File | agents/analysts/news_analyst.py |
| Factory | create_news_analyst(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | LangGraph ToolNode |
Data Flow:
State Input: company_of_interest, trade_date
│
▼
1. get_news(ticker, start_date, end_date)
→ Fetches company-specific news articles (past week)
→ Returns: formatted article list (title, summary, source, date)
Data source: yfinance / Finnhub / Alpha Vantage
│
▼
2. get_global_news(curr_date, look_back_days=7, limit=5)
→ Fetches broader macroeconomic / market news
→ Returns: formatted global news list
Data source: yfinance / Alpha Vantage
│
▼
3. get_insider_transactions(ticker)
→ Fetches recent insider buy/sell activity
→ Returns: insider transaction report
Data source: Finnhub (primary) / Alpha Vantage
│
▼
LLM Prompt (quick_think):
"Analyze recent news and trends over the past week.
Provide fine-grained analysis. Append Markdown table
organising key points."
│
▼
Output: news_report
4.4 Social Media Analyst
| File | agents/analysts/social_media_analyst.py |
| Factory | create_social_media_analyst(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | LangGraph ToolNode |
Data Flow:
State Input: company_of_interest, trade_date
│
▼
1. get_news(query, start_date, end_date)
→ Searches for company-related social media mentions & sentiment
→ Returns: formatted news articles related to sentiment
Data source: yfinance / Finnhub / Alpha Vantage
│
▼
LLM Prompt (quick_think):
"Analyze social media posts, recent news, public sentiment
over the past week. Look at all sources. Provide
fine-grained analysis. Append Markdown table."
│
▼
Output: sentiment_report
4.5 Bull Researcher
| File | agents/researchers/bull_researcher.py |
| Factory | create_bull_researcher(llm, memory) |
| Thinking Modality | Mid (mid_think_llm, falls back to quick_think_llm) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
┌─────────────────────────────────────────────────────┐
│ State Input: │
│ • market_report (from Market Analyst) │
│ • sentiment_report (from Social Media Analyst) │
│ • news_report (from News Analyst) │
│ • fundamentals_report (from Fundamentals Analyst) │
│ • investment_debate_state.history (debate transcript)│
│ • investment_debate_state.current_response │
│ (latest Bear argument to counter) │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Memory Retrieval (BM25): │
│ memory.get_memories(current_situation, n_matches=2) │
│ → Retrieves 2 most similar past trading situations │
│ → Returns: matched situation + recommendation │
│ (Offline, no API calls) │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ LLM Prompt (mid_think): │
│ "You are a Bull Researcher. Build evidence-based │
│ case FOR investing. Focus on growth potential, │
│ competitive advantages, positive indicators. │
│ Counter Bear's arguments with specific data. │
│ Use past reflections." │
│ │
│ Context sent: │
│ • 4 analyst reports (concatenated) │
│ • Full debate history │
│ • Bear's latest argument │
│ • 2 memory-retrieved past situations & lessons │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Output: │
│ • investment_debate_state.bull_history (appended) │
│ • investment_debate_state.current_response (latest) │
│ • investment_debate_state.count (incremented) │
└─────────────────────────────────────────────────────┘
4.6 Bear Researcher
| File | agents/researchers/bear_researcher.py |
| Factory | create_bear_researcher(llm, memory) |
| Thinking Modality | Mid (mid_think_llm, falls back to quick_think_llm) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
State Input:
• 4 analyst reports
• investment_debate_state.history
• investment_debate_state.current_response (Bull's latest argument)
│
▼
Memory Retrieval:
memory.get_memories(situation, n_matches=2)
→ 2 most relevant past situations
│
▼
LLM Prompt (mid_think):
"You are a Bear Researcher. Build well-reasoned case
AGAINST investing. Focus on risks, competitive
weaknesses, negative indicators. Critically expose
Bull's over-optimism. Use past reflections."
Context: 4 reports + debate history + Bull's argument + 2 memories
│
▼
Output:
• investment_debate_state.bear_history (appended)
• investment_debate_state.current_response (latest)
• investment_debate_state.count (incremented)
4.7 Research Manager
| File | agents/managers/research_manager.py |
| Factory | create_research_manager(llm, memory) |
| Thinking Modality | Deep (deep_think_llm, default gpt-5.2) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
┌─────────────────────────────────────────────────────┐
│ State Input: │
│ • investment_debate_state (full Bull vs Bear debate) │
│ • market_report, sentiment_report, news_report, │
│ fundamentals_report (4 analyst reports) │
│ • macro_regime_report (risk-on / risk-off) │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Memory Retrieval: │
│ memory.get_memories(situation, n_matches=2) │
│ → 2 past similar investment decisions & outcomes │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ LLM Prompt (deep_think): │
│ "Evaluate Bull vs Bear debate. Make definitive │
│ decision: BUY / SELL / HOLD. Avoid defaulting to │
│ HOLD. Account for macro regime. Summarize key │
│ points. Provide rationale and strategic actions." │
│ │
│ Context: │
│ • Full debate transcript (all rounds) │
│ • 4 analyst reports │
│ • Macro regime classification │
│ • 2 memory-retrieved past outcomes │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Output: │
│ • investment_debate_state.judge_decision │
│ • investment_plan (BUY/SELL/HOLD + detailed plan) │
└─────────────────────────────────────────────────────┘
4.8 Trader
| File | agents/trader/trader.py |
| Factory | create_trader(llm, memory) |
| Thinking Modality | Mid (mid_think_llm, falls back to quick_think_llm) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
State Input:
• company_of_interest
• investment_plan (from Research Manager)
• 4 analyst reports
│
▼
Memory Retrieval:
memory.get_memories(situation, n_matches=2)
→ 2 past similar trading decisions
│
▼
LLM Prompt (mid_think):
"Analyze investment plan. Make strategic decision:
BUY / SELL / HOLD. Must end with
'FINAL TRANSACTION PROPOSAL: BUY/HOLD/SELL'.
Leverage past decisions."
Context: investment_plan + 4 reports + 2 memories
│
▼
Output:
• trader_investment_plan (decision + reasoning)
• sender = "Trader"
4.9 Aggressive Debator
| File | agents/risk_mgmt/aggressive_debator.py |
| Factory | create_aggressive_debator(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
State Input:
• risk_debate_state.history (debate transcript)
• risk_debate_state.current_conservative_response
• risk_debate_state.current_neutral_response
• 4 analyst reports
• trader_investment_plan
│
▼
LLM Prompt (quick_think):
"Champion high-reward, high-risk opportunities.
Counter conservative and neutral analysts' points.
Highlight where caution misses critical opportunities.
Debate and persuade."
Context: trader plan + 4 reports + conservative/neutral arguments
│
▼
Output:
• risk_debate_state.aggressive_history (appended)
• risk_debate_state.current_aggressive_response
• risk_debate_state.count (incremented)
4.10 Conservative Debator
| File | agents/risk_mgmt/conservative_debator.py |
| Factory | create_conservative_debator(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
State Input:
• risk_debate_state.history
• risk_debate_state.current_aggressive_response
• risk_debate_state.current_neutral_response
• 4 analyst reports + trader_investment_plan
│
▼
LLM Prompt (quick_think):
"Protect assets, minimize volatility. Critically
examine high-risk elements. Counter aggressive and
neutral points. Emphasise downsides. Debate to
demonstrate strength of low-risk strategy."
│
▼
Output:
• risk_debate_state.conservative_history (appended)
• risk_debate_state.current_conservative_response
• risk_debate_state.count (incremented)
4.11 Neutral Debator
| File | agents/risk_mgmt/neutral_debator.py |
| Factory | create_neutral_debator(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
State Input:
• risk_debate_state.history
• risk_debate_state.current_aggressive_response
• risk_debate_state.current_conservative_response
• 4 analyst reports + trader_investment_plan
│
▼
LLM Prompt (quick_think):
"Provide balanced perspective. Challenge both
aggressive (overly optimistic) and conservative
(overly cautious). Support moderate, sustainable
strategy. Debate to show balanced view."
│
▼
Output:
• risk_debate_state.neutral_history (appended)
• risk_debate_state.current_neutral_response
• risk_debate_state.count (incremented)
4.12 Risk Manager
| File | agents/managers/risk_manager.py |
| Factory | create_risk_manager(llm, memory) |
| Thinking Modality | Deep (deep_think_llm, default gpt-5.2) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
┌─────────────────────────────────────────────────────┐
│ State Input: │
│ • risk_debate_state (Aggressive + Conservative + │
│ Neutral debate history) │
│ • 4 analyst reports │
│ • investment_plan (Research Manager's plan) │
│ • trader_investment_plan (Trader's refinement) │
│ • macro_regime_report │
└────────────────────────┬────────────────────────────┘
│
▼
Memory Retrieval:
memory.get_memories(situation, n_matches=2)
→ 2 past risk decisions & outcomes
│
▼
LLM Prompt (deep_think):
"Evaluate risk debate between Aggressive, Conservative,
Neutral analysts. Make clear decision: BUY / SELL / HOLD.
Account for macro regime. Learn from past mistakes.
Refine trader's plan. Provide detailed reasoning."
Context: full risk debate + trader plan + 4 reports +
macro regime + 2 memories
│
▼
Output:
• risk_debate_state.judge_decision
• final_trade_decision (the system's final answer)
4.13 Geopolitical Scanner
| File | agents/scanners/geopolitical_scanner.py |
| Factory | create_geopolitical_scanner(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | run_tool_loop() (inline, up to 5 rounds) |
Data Flow:
State Input: scan_date
│
▼
Tool calls via run_tool_loop():
1. get_topic_news("geopolitics", limit=10)
→ Fetches geopolitical news articles
Data source: yfinance / Alpha Vantage
2. get_topic_news("trade policy sanctions", limit=10)
→ Trade & sanctions news
3. get_topic_news("central bank monetary policy", limit=10)
→ Central bank signals
4. get_topic_news("energy oil commodities", limit=10)
→ Energy & commodity supply risks
(LLM decides which topics to search — up to 5 rounds)
│
▼
LLM Prompt (quick_think):
"Scan global news for risks and opportunities affecting
financial markets. Cover: major geopolitical events,
central bank signals, trade/sanctions, energy/commodity
risks. Include risk assessment table."
Context: all retrieved news articles
│
▼
Output: geopolitical_report
4.14 Market Movers Scanner
| File | agents/scanners/market_movers_scanner.py |
| Factory | create_market_movers_scanner(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | run_tool_loop() |
Data Flow:
State Input: scan_date
│
▼
Tool calls via run_tool_loop():
1. get_market_movers("day_gainers")
→ Top gaining stocks (symbol, price, change%, volume, market cap)
Data source: yfinance / Alpha Vantage
2. get_market_movers("day_losers")
→ Top losing stocks
3. get_market_movers("most_actives")
→ Highest-volume stocks
4. get_market_indices()
→ Major indices: SPY, DJI, NASDAQ, VIX, Russell 2000
(price, daily change, 52W high/low)
Data source: yfinance
│
▼
LLM Prompt (quick_think):
"Scan for unusual activity and momentum signals.
Cover: unusual movers & catalysts, volume anomalies,
index trends & breadth, sector concentration.
Include summary table."
Context: gainers + losers + most active + index data
│
▼
Output: market_movers_report
4.15 Sector Scanner
| File | agents/scanners/sector_scanner.py |
| Factory | create_sector_scanner(llm) |
| Thinking Modality | Quick (quick_think_llm, default gpt-5-mini) |
| Tool Execution | run_tool_loop() |
Data Flow:
State Input: scan_date
│
▼
Tool calls via run_tool_loop():
1. get_sector_performance()
→ All 11 GICS sectors with 1-day, 1-week, 1-month, YTD returns
Data source: yfinance (sector ETF proxies) / Alpha Vantage
│
▼
LLM Prompt (quick_think):
"Analyze sector rotation across all 11 GICS sectors.
Cover: momentum rankings, rotation signals (money flows),
defensive vs cyclical positioning, acceleration/deceleration.
Include ranked performance table."
Context: sector performance data
│
▼
Output: sector_performance_report
4.16 Industry Deep Dive
| File | agents/scanners/industry_deep_dive.py |
| Factory | create_industry_deep_dive(llm) |
| Thinking Modality | Mid (mid_think_llm, falls back to quick_think_llm) |
| Tool Execution | run_tool_loop() |
Data Flow:
┌─────────────────────────────────────────────────────┐
│ State Input: │
│ • scan_date │
│ • geopolitical_report (Phase 1) │
│ • market_movers_report (Phase 1) │
│ • sector_performance_report (Phase 1) │
└────────────────────────┬────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────┐
│ Pre-processing (Python, no LLM): │
│ _extract_top_sectors(sector_performance_report) │
│ → Parses Markdown table from Sector Scanner │
│ → Ranks sectors by absolute 1-month move │
│ → Returns top 3 sector keys │
└────────────────────────┬────────────────────────────┘
│
▼
Tool calls via run_tool_loop():
1. get_industry_performance("technology")
→ Top companies in sector: rating, market weight,
1D/1W/1M returns
Data source: yfinance / Alpha Vantage
2. get_industry_performance("energy")
(repeated for each top sector)
3. get_industry_performance("healthcare")
(up to 3 sector calls)
4. get_topic_news("semiconductor industry", limit=10)
→ Sector-specific news for context
5. get_topic_news("renewable energy", limit=10)
(at least 2 sector-specific news searches)
│
▼
LLM Prompt (mid_think):
"Drill into the most interesting sectors from Phase 1.
MUST call tools before writing. Explain why these
industries selected. Identify top companies, catalysts,
risks. Cross-reference geopolitical events and sectors."
Context: Phase 1 reports + industry data + sector news
│
▼
Output: industry_deep_dive_report
4.17 Macro Synthesis
| File | agents/scanners/macro_synthesis.py |
| Factory | create_macro_synthesis(llm) |
| Thinking Modality | Deep (deep_think_llm, default gpt-5.2) |
| Tool Execution | None — pure LLM reasoning |
Data Flow:
┌─────────────────────────────────────────────────────┐
│ State Input: │
│ • geopolitical_report (Phase 1) │
│ • market_movers_report (Phase 1) │
│ • sector_performance_report (Phase 1) │
│ • industry_deep_dive_report (Phase 2) │
└────────────────────────┬────────────────────────────┘
│
▼
LLM Prompt (deep_think):
"Synthesize all reports into final investment thesis.
Output ONLY valid JSON (no markdown, no preamble).
Structure:
{
executive_summary, macro_context,
key_themes (with conviction levels),
stocks_to_investigate (8-10 picks with
ticker, sector, rationale, thesis_angle,
conviction, key_catalysts, risks),
risk_factors
}"
Context: all 4 prior reports concatenated
│
▼
Post-processing (Python, no LLM):
extract_json() → strips markdown fences / <think> blocks
│
▼
Output: macro_scan_summary (JSON string)
5. Tool → Data-Source Mapping
Every tool routes through dataflows/interface.py:route_to_vendor() which
dispatches to the configured vendor.
Trading Tools
| Tool | Category | Default Vendor | Fallback | Returns |
|---|---|---|---|---|
get_stock_data |
core_stock_apis | yfinance | Alpha Vantage | OHLCV string |
get_indicators |
technical_indicators | yfinance | Alpha Vantage | Indicator values |
get_macro_regime |
(composed) | yfinance | — | Regime report |
get_fundamentals |
fundamental_data | yfinance | Alpha Vantage | Fundamentals |
get_balance_sheet |
fundamental_data | yfinance | Alpha Vantage | Balance sheet |
get_cashflow |
fundamental_data | yfinance | Alpha Vantage | Cash flow |
get_income_statement |
fundamental_data | yfinance | Alpha Vantage | Income stmt |
get_ttm_analysis |
(composed) | yfinance | — | TTM metrics |
get_peer_comparison |
(composed) | yfinance | — | Peer ranking |
get_sector_relative |
(composed) | yfinance | — | Alpha report |
get_news |
news_data | yfinance | Alpha Vantage | News articles |
get_global_news |
news_data | yfinance | Alpha Vantage | Global news |
get_insider_transactions |
(tool override) | Finnhub | Alpha Vantage | Insider txns |
Scanner Tools
| Tool | Category | Default Vendor | Fallback | Returns |
|---|---|---|---|---|
get_market_movers |
scanner_data | yfinance | Alpha Vantage | Movers table |
get_market_indices |
scanner_data | yfinance | — | Index table |
get_sector_performance |
scanner_data | yfinance | Alpha Vantage | Sector table |
get_industry_performance |
scanner_data | yfinance | — | Industry table |
get_topic_news |
scanner_data | yfinance | — | Topic news |
get_earnings_calendar |
calendar_data | Finnhub | — | Earnings cal. |
get_economic_calendar |
calendar_data | Finnhub | — | Econ cal. |
Fallback rules (ADR 011): Only 5 methods in
FALLBACK_ALLOWEDget cross-vendor fallback. All others fail-fast on error.
6. Memory System
The framework uses BM25-based lexical similarity (offline, no API calls) to retrieve relevant past trading situations.
Memory Instances
| Instance | Used By | Purpose |
|---|---|---|
bull_memory |
Bull Researcher | Past bullish analyses & outcomes |
bear_memory |
Bear Researcher | Past bearish analyses & outcomes |
trader_memory |
Trader | Past trading decisions & results |
invest_judge_memory |
Research Manager | Past investment judgments |
risk_manager_memory |
Risk Manager | Past risk decisions |
How Memory Works
Agent builds "current situation" string from:
• company ticker + trade date
• analyst report summaries
• debate context
│
▼
memory.get_memories(current_situation, n_matches=2)
→ BM25 tokenises situation and scores against stored documents
→ Returns top 2 matches:
{ matched_situation, recommendation, similarity_score }
│
▼
Injected into LLM prompt as "Past Reflections"
→ Agent uses past lessons to avoid repeating mistakes
Memory Data Flow
After trading completes → outcomes stored back:
add_situations([(situation_text, recommendation_text)])
→ Appends to document store
→ Rebuilds BM25 index for future retrieval