Added macro economic analyst - 2
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bastien.migette
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@ -1,7 +1,7 @@
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# FRED API Macro Data Integration
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## Summary
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Added FRED (Federal Reserve Economic Data) API support to the TradingAgents vendor methods system for macroeconomic analysis.
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Added FRED (Federal Reserve Economic Data) API support to the TradingAgents vendor methods system for macroeconomic analysis, including a new **Macro Analyst** agent.
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## Files Added
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@ -24,9 +24,22 @@ New module providing FRED API integration with the following functions:
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- **`get_fed_calendar_and_minutes(curr_date)`** - Federal Reserve meeting calendar and policy updates
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- **`get_macro_economic_summary(curr_date, lookback_days=90)`** - Complete macro economic analysis combining all components
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### 2. `tradingagents/agents/analysts/macro_analyst.py`
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New macro analyst agent that uses FRED API tools to analyze economic conditions:
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- Analyzes Federal Reserve policy and economic indicators
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- Evaluates inflation, employment, and growth trends
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- Assesses Treasury yield curve and recession signals
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- Provides market implications and trading considerations
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### 3. `tradingagents/agents/utils/macro_data_tools.py`
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Tool wrapper functions for LangChain integration:
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- **`get_economic_indicators(curr_date, lookback_days)`** - Comprehensive economic indicators
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- **`get_yield_curve(curr_date)`** - Treasury yield curve with inversion analysis
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- **`get_fed_calendar(curr_date)`** - Fed meeting calendar and policy updates
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## Files Modified
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### 2. `tradingagents/dataflows/interface.py`
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### 4. `tradingagents/dataflows/interface.py`
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Updated vendor routing system to include FRED macro data:
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**Added Imports:**
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@ -71,6 +84,76 @@ VENDOR_LIST = [
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},
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```
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### 5. `tradingagents/agents/__init__.py`
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Added macro analyst to exports:
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```python
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from .analysts.macro_analyst import create_macro_analyst
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__all__ = [
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# ... existing exports ...
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"create_macro_analyst",
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]
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```
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### 6. `tradingagents/agents/utils/agent_utils.py`
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Added macro tool imports:
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```python
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from tradingagents.agents.utils.macro_data_tools import (
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar
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)
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```
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### 7. `tradingagents/graph/setup.py`
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Added macro analyst option to graph setup:
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```python
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def setup_graph(
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self, selected_analysts=["market", "social", "news", "fundamentals"]
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):
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"""Set up and compile the agent workflow graph.
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Args:
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selected_analysts (list): List of analyst types to include. Options are:
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- "market": Market analyst
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- "social": Social media analyst
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- "news": News analyst
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- "fundamentals": Fundamentals analyst
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- "macro": Macro economic analyst # New!
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"""
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# ... existing analyst setup ...
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if "macro" in selected_analysts:
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analyst_nodes["macro"] = create_macro_analyst(
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self.quick_thinking_llm
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)
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delete_nodes["macro"] = create_msg_delete()
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tool_nodes["macro"] = self.tool_nodes["macro"]
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```
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### 8. `tradingagents/graph/trading_graph.py`
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Added macro tools import and tool node:
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```python
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from tradingagents.agents.utils.agent_utils import (
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# ... existing imports ...
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar
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)
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def _create_tool_nodes(self) -> Dict[str, ToolNode]:
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return {
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# ... existing tool nodes ...
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"macro": ToolNode(
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[
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar,
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]
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),
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}
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```
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## Configuration Required
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To use FRED API features, set the FRED API key via:
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@ -92,6 +175,22 @@ To use FRED API features, set the FRED API key via:
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## Usage Examples
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### Enable Macro Analyst in Graph
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```python
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from tradingagents.graph.trading_graph import TradingAgentsGraph
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# Create graph with macro analyst enabled
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graph = TradingAgentsGraph(
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selected_analysts=["market", "fundamentals", "macro"], # Include "macro"
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debug=True,
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config=your_config
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)
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# Run analysis
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result = graph.propagate("AAPL", "2025-10-06")
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```
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### Via Vendor Routing System
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```python
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@ -165,11 +264,12 @@ All functions return formatted markdown strings suitable for LLM analysis:
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Changes were made with minimal modifications to existing code:
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1. ✅ **New file only** - `macro_utils.py` is a new addition
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2. ✅ **Additive changes** - Only added new entries to existing dictionaries
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1. ✅ **New files only** - `macro_utils.py`, `macro_analyst.py`, `macro_data_tools.py` are new additions
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2. ✅ **Additive changes** - Only added new entries to existing dictionaries and imports
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3. ✅ **No breaking changes** - Existing functionality unchanged
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4. ✅ **Follows existing patterns** - Uses same vendor routing architecture as other data sources
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5. ✅ **Consistent naming** - Follows existing naming conventions (`get_*` pattern)
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4. ✅ **Follows existing patterns** - Uses same vendor routing and analyst architecture
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5. ✅ **Consistent naming** - Follows existing naming conventions (`get_*`, `create_*_analyst` patterns)
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6. ✅ **Optional feature** - Macro analyst is opt-in via `selected_analysts` parameter
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## Testing
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@ -6,6 +6,7 @@ from .analysts.fundamentals_analyst import create_fundamentals_analyst
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from .analysts.market_analyst import create_market_analyst
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from .analysts.news_analyst import create_news_analyst
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from .analysts.social_media_analyst import create_social_media_analyst
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from .analysts.macro_analyst import create_macro_analyst
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from .researchers.bear_researcher import create_bear_researcher
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from .researchers.bull_researcher import create_bull_researcher
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@ -29,6 +30,7 @@ __all__ = [
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"create_bull_researcher",
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"create_research_manager",
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"create_fundamentals_analyst",
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"create_macro_analyst",
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"create_market_analyst",
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"create_neutral_debator",
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"create_news_analyst",
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@ -0,0 +1,78 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_economic_indicators, get_yield_curve, get_fed_calendar
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from tradingagents.dataflows.config import get_config
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def create_macro_analyst(llm):
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def macro_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state["company_of_interest"]
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tools = [
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar,
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]
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system_message = (
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"You are a macro economic analyst tasked with analyzing Federal Reserve data, economic indicators, and macroeconomic trends. "
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"Your objective is to write a comprehensive report detailing current economic conditions, monetary policy implications, and their impact on financial markets. "
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"Analyze key indicators such as:\n"
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"- Federal Funds Rate and monetary policy trajectory\n"
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"- Inflation indicators (CPI, PPI)\n"
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"- Employment data (unemployment rate, payrolls)\n"
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"- Treasury yield curve and inversion signals\n"
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"- Economic growth indicators (GDP, PMI)\n"
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"- Market volatility (VIX)\n\n"
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"Provide detailed analysis of:\n"
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"1. Current economic cycle positioning\n"
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"2. Federal Reserve policy stance and likely direction\n"
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"3. Inflation and employment trends\n"
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"4. Yield curve implications for recession risk\n"
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"5. Market implications and trading considerations\n\n"
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"Use the available tools: `get_economic_indicators` for comprehensive economic data, "
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"`get_yield_curve` for Treasury yields and inversion analysis, and `get_fed_calendar` for FOMC schedule and policy trajectory. "
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"Make sure to provide detailed, actionable insights rather than generic summaries. "
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"Append a Markdown table at the end summarizing key macro indicators and their implications."
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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report = ""
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if len(result.tool_calls) == 0:
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report = result.content
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return {
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"messages": [result],
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"macro_report": report,
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}
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return macro_analyst_node
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@ -19,6 +19,11 @@ from tradingagents.agents.utils.news_data_tools import (
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get_insider_transactions,
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get_global_news
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)
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from tradingagents.agents.utils.macro_data_tools import (
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar
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)
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def create_msg_delete():
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def delete_messages(state):
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@ -0,0 +1,82 @@
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from typing import Annotated
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from langchain_core.tools import tool
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@tool
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def get_economic_indicators(
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curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
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lookback_days: Annotated[int, "How many days to look back for data"] = 90,
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):
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"""
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Retrieve comprehensive economic indicators report from FRED including:
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- Federal Funds Rate
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- Consumer Price Index (CPI) and Producer Price Index (PPI)
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- Unemployment Rate and Nonfarm Payrolls
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- GDP Growth Rate
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- ISM Manufacturing PMI
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- Consumer Confidence
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- VIX (Market Volatility)
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Args:
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curr_date (str): Current date in yyyy-mm-dd format
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lookback_days (int): How many days to look back for data
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Returns:
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str: Comprehensive economic indicators report with analysis
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"""
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from tradingagents.dataflows.interface import route_to_vendor
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result = route_to_vendor(
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"get_economic_indicators",
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curr_date=curr_date,
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lookback_days=lookback_days
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)
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return str(result)
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@tool
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def get_yield_curve(
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curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
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):
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"""
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Retrieve US Treasury yield curve data from FRED with inversion analysis.
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Includes yields for 1M, 3M, 6M, 1Y, 2Y, 3Y, 5Y, 7Y, 10Y, 20Y, and 30Y maturities.
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Provides 2Y-10Y spread analysis and yield curve interpretation.
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Args:
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curr_date (str): Current date in yyyy-mm-dd format
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Returns:
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str: Treasury yield curve data with analysis and recession signals
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"""
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from tradingagents.dataflows.interface import route_to_vendor
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result = route_to_vendor(
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"get_yield_curve",
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curr_date=curr_date
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)
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return str(result)
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@tool
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def get_fed_calendar(
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curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
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):
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"""
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Retrieve Federal Reserve meeting calendar and recent policy updates.
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Includes FOMC meeting schedule, recent Fed Funds rate history,
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and key policy considerations.
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Args:
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curr_date (str): Current date in yyyy-mm-dd format
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Returns:
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str: Fed calendar, meeting schedule, and policy trajectory
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"""
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from tradingagents.dataflows.interface import route_to_vendor
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result = route_to_vendor(
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"get_fed_calendar",
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curr_date=curr_date
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)
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return str(result)
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@ -48,6 +48,7 @@ class GraphSetup:
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- "social": Social media analyst
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- "news": News analyst
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- "fundamentals": Fundamentals analyst
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- "macro": Macro economic analyst
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"""
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if len(selected_analysts) == 0:
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raise ValueError("Trading Agents Graph Setup Error: no analysts selected!")
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@ -85,6 +86,13 @@ class GraphSetup:
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delete_nodes["fundamentals"] = create_msg_delete()
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tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"]
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if "macro" in selected_analysts:
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analyst_nodes["macro"] = create_macro_analyst(
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self.quick_thinking_llm
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)
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delete_nodes["macro"] = create_msg_delete()
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tool_nodes["macro"] = self.tool_nodes["macro"]
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# Create researcher and manager nodes
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bull_researcher_node = create_bull_researcher(
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self.quick_thinking_llm, self.bull_memory
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@ -33,7 +33,10 @@ from tradingagents.agents.utils.agent_utils import (
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get_news,
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get_insider_sentiment,
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get_insider_transactions,
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get_global_news
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get_global_news,
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar
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)
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from .conditional_logic import ConditionalLogic
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@ -155,6 +158,14 @@ class TradingAgentsGraph:
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get_income_statement,
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]
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),
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"macro": ToolNode(
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[
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# Macroeconomic analysis tools
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get_economic_indicators,
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get_yield_curve,
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get_fed_calendar,
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]
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),
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}
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def propagate(self, company_name, trade_date):
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