Merge 9c49595806 into 13b826a31d
This commit is contained in:
bmigette
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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, including a new **Macro Analyst** agent.
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## Files Added
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### 1. `tradingagents/dataflows/macro_utils.py`
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New module providing FRED API integration with the following functions:
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- **`get_fred_api_key()`** - Get FRED API key from config or environment
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- **`get_fred_data(series_id, start_date, end_date)`** - Core FRED API wrapper
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- **`get_treasury_yield_curve(curr_date)`** - Treasury yield curve data with inversion analysis
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- **`get_economic_indicators_report(curr_date, lookback_days=90)`** - Comprehensive economic indicators
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- Federal Funds Rate
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- Consumer Price Index (CPI)
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- Producer Price Index (PPI)
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- Unemployment Rate
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- 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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- **`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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### 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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```python
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from .macro_utils import get_economic_indicators_report, get_treasury_yield_curve, get_fed_calendar_and_minutes
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```
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**Updated VENDOR_LIST:**
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```python
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VENDOR_LIST = [
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"local",
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"yfinance",
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"openai",
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"google",
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"fred" # New
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]
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```
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**New TOOLS_CATEGORIES Entry:**
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```python
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"macro_data": {
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"description": "Macroeconomic indicators and Federal Reserve data",
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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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}
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```
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**New VENDOR_METHODS Entries:**
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```python
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# macro_data
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"get_economic_indicators": {
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"fred": get_economic_indicators_report,
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},
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"get_yield_curve": {
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"fred": get_treasury_yield_curve,
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},
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"get_fed_calendar": {
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"fred": get_fed_calendar_and_minutes,
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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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1. **Environment Variable:**
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```bash
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export FRED_API_KEY="your_api_key_here"
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```
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2. **Or via Config System:**
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The `get_fred_api_key()` function will check:
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- Config system via `get_api_key("fred_api_key", "FRED_API_KEY")`
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- Environment variable `FRED_API_KEY`
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3. **Get a Free API Key:**
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- Visit: https://fred.stlouisfed.org/
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- Register for a free account
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- Generate API key under "My Account" → "API Keys"
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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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from tradingagents.dataflows.interface import route_to_vendor
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# Get economic indicators
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indicators = route_to_vendor(
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"get_economic_indicators",
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curr_date="2025-10-06",
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lookback_days=90
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)
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# Get yield curve
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yield_curve = route_to_vendor(
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"get_yield_curve",
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curr_date="2025-10-06"
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)
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# Get Fed calendar
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fed_calendar = route_to_vendor(
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"get_fed_calendar",
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curr_date="2025-10-06"
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)
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```
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### Direct Function Calls
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```python
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from tradingagents.dataflows.macro_utils import (
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get_economic_indicators_report,
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get_treasury_yield_curve,
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get_fed_calendar_and_minutes,
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get_macro_economic_summary
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)
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# Complete macro analysis
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summary = get_macro_economic_summary(
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curr_date="2025-10-06",
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lookback_days=90
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)
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# Individual components
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indicators = get_economic_indicators_report("2025-10-06", 90)
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yield_curve = get_treasury_yield_curve("2025-10-06")
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fed_data = get_fed_calendar_and_minutes("2025-10-06")
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```
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## Integration with Macro Analyst
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The macro analyst can now use these tools through the vendor routing system. The tools are automatically available through the `macro_data` category:
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```python
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# In agent configuration
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config = {
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"data_vendors": {
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"macro_data": "fred" # Use FRED for macro data
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}
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}
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```
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## Data Returned
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All functions return formatted markdown strings suitable for LLM analysis:
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- **Economic Indicators**: Markdown tables with current values, changes, and analysis
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- **Yield Curve**: Markdown table with maturities, yields, and inversion warnings
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- **Fed Calendar**: FOMC meeting schedule and policy trajectory
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- **Trading Implications**: Actionable insights for different economic scenarios
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## PR Compatibility Notes
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Changes were made with minimal modifications to existing code:
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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 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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To verify the integration works:
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```python
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# Test FRED API connection
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from tradingagents.dataflows.macro_utils import get_fred_data
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result = get_fred_data("FEDFUNDS", "2025-01-01", "2025-10-06")
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print(result)
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# Test vendor routing
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from tradingagents.dataflows.interface import route_to_vendor
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indicators = route_to_vendor(
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"get_economic_indicators",
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curr_date="2025-10-06",
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lookback_days=30
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)
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print(indicators)
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```
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## Dependencies
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No new dependencies required. Uses existing dependencies:
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- `requests` - For FRED API calls
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- `pandas` - For data manipulation
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- `datetime` - For date handling
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- Existing config system for API key management
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## Future Enhancements
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Potential improvements:
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- Add caching for FRED API responses (similar to YFinanceDataProvider)
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- Add more FRED series (housing data, commodity prices, etc.)
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- Add international economic indicators
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- Add custom FRED series ID support for advanced users
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|
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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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|
|
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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."
|
||||
" 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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|
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if len(result.tool_calls) == 0:
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report = result.content
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|
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return {
|
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"messages": [result],
|
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"macro_report": report,
|
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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
|
||||
)
|
||||
from tradingagents.agents.utils.macro_data_tools import (
|
||||
get_economic_indicators,
|
||||
get_yield_curve,
|
||||
get_fed_calendar
|
||||
)
|
||||
|
||||
def create_msg_delete():
|
||||
def delete_messages(state):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,82 @@
|
|||
from typing import Annotated
|
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from langchain_core.tools import tool
|
||||
|
||||
|
||||
@tool
|
||||
def get_economic_indicators(
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
lookback_days: Annotated[int, "How many days to look back for data"] = 90,
|
||||
):
|
||||
"""
|
||||
Retrieve comprehensive economic indicators report from FRED including:
|
||||
- Federal Funds Rate
|
||||
- Consumer Price Index (CPI) and Producer Price Index (PPI)
|
||||
- Unemployment Rate and Nonfarm Payrolls
|
||||
- GDP Growth Rate
|
||||
- ISM Manufacturing PMI
|
||||
- Consumer Confidence
|
||||
- VIX (Market Volatility)
|
||||
|
||||
Args:
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
lookback_days (int): How many days to look back for data
|
||||
|
||||
Returns:
|
||||
str: Comprehensive economic indicators report with analysis
|
||||
"""
|
||||
from tradingagents.dataflows.interface import route_to_vendor
|
||||
|
||||
result = route_to_vendor(
|
||||
"get_economic_indicators",
|
||||
curr_date=curr_date,
|
||||
lookback_days=lookback_days
|
||||
)
|
||||
return str(result)
|
||||
|
||||
|
||||
@tool
|
||||
def get_yield_curve(
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
):
|
||||
"""
|
||||
Retrieve US Treasury yield curve data from FRED with inversion analysis.
|
||||
Includes yields for 1M, 3M, 6M, 1Y, 2Y, 3Y, 5Y, 7Y, 10Y, 20Y, and 30Y maturities.
|
||||
Provides 2Y-10Y spread analysis and yield curve interpretation.
|
||||
|
||||
Args:
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
|
||||
Returns:
|
||||
str: Treasury yield curve data with analysis and recession signals
|
||||
"""
|
||||
from tradingagents.dataflows.interface import route_to_vendor
|
||||
|
||||
result = route_to_vendor(
|
||||
"get_yield_curve",
|
||||
curr_date=curr_date
|
||||
)
|
||||
return str(result)
|
||||
|
||||
|
||||
@tool
|
||||
def get_fed_calendar(
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
):
|
||||
"""
|
||||
Retrieve Federal Reserve meeting calendar and recent policy updates.
|
||||
Includes FOMC meeting schedule, recent Fed Funds rate history,
|
||||
and key policy considerations.
|
||||
|
||||
Args:
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
|
||||
Returns:
|
||||
str: Fed calendar, meeting schedule, and policy trajectory
|
||||
"""
|
||||
from tradingagents.dataflows.interface import route_to_vendor
|
||||
|
||||
result = route_to_vendor(
|
||||
"get_fed_calendar",
|
||||
curr_date=curr_date
|
||||
)
|
||||
return str(result)
|
||||
|
|
@ -16,6 +16,7 @@ from .alpha_vantage import (
|
|||
get_news as get_alpha_vantage_news
|
||||
)
|
||||
from .alpha_vantage_common import AlphaVantageRateLimitError
|
||||
from .macro_utils import get_economic_indicators_report, get_treasury_yield_curve, get_fed_calendar_and_minutes
|
||||
|
||||
# Configuration and routing logic
|
||||
from .config import get_config
|
||||
|
|
@ -51,6 +52,14 @@ TOOLS_CATEGORIES = {
|
|||
"get_insider_sentiment",
|
||||
"get_insider_transactions",
|
||||
]
|
||||
},
|
||||
"macro_data": {
|
||||
"description": "Macroeconomic indicators and Federal Reserve data",
|
||||
"tools": [
|
||||
"get_economic_indicators",
|
||||
"get_yield_curve",
|
||||
"get_fed_calendar"
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -58,7 +67,8 @@ VENDOR_LIST = [
|
|||
"local",
|
||||
"yfinance",
|
||||
"openai",
|
||||
"google"
|
||||
"google",
|
||||
"fred"
|
||||
]
|
||||
|
||||
# Mapping of methods to their vendor-specific implementations
|
||||
|
|
@ -114,6 +124,16 @@ VENDOR_METHODS = {
|
|||
"yfinance": get_yfinance_insider_transactions,
|
||||
"local": get_finnhub_company_insider_transactions,
|
||||
},
|
||||
# macro_data
|
||||
"get_economic_indicators": {
|
||||
"fred": get_economic_indicators_report,
|
||||
},
|
||||
"get_yield_curve": {
|
||||
"fred": get_treasury_yield_curve,
|
||||
},
|
||||
"get_fed_calendar": {
|
||||
"fred": get_fed_calendar_and_minutes,
|
||||
},
|
||||
}
|
||||
|
||||
def get_category_for_method(method: str) -> str:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,352 @@
|
|||
import requests
|
||||
import json
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Annotated, Dict, List, Optional
|
||||
from .config import get_api_key, DATA_DIR
|
||||
import os
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def get_fred_api_key():
|
||||
"""Get FRED API key from config or environment"""
|
||||
try:
|
||||
api_key = get_api_key("fred_api_key", "FRED_API_KEY")
|
||||
except:
|
||||
api_key = None
|
||||
if not api_key:
|
||||
api_key = os.getenv("FRED_API_KEY")
|
||||
return api_key
|
||||
|
||||
|
||||
def get_fred_data(series_id: str, start_date: str, end_date: str) -> Dict:
|
||||
"""
|
||||
Get economic data from FRED API
|
||||
|
||||
Args:
|
||||
series_id: FRED series ID (e.g., 'FEDFUNDS', 'CPIAUCSL')
|
||||
start_date: Start date in YYYY-MM-DD format
|
||||
end_date: End date in YYYY-MM-DD format
|
||||
|
||||
Returns:
|
||||
Dictionary with FRED data
|
||||
"""
|
||||
api_key = get_fred_api_key()
|
||||
if not api_key:
|
||||
return {"error": "FRED API key not found. Please set FRED_API_KEY environment variable."}
|
||||
|
||||
url = "https://api.stlouisfed.org/fred/series/observations"
|
||||
params = {
|
||||
'series_id': series_id,
|
||||
'api_key': api_key,
|
||||
'file_type': 'json',
|
||||
'observation_start': start_date,
|
||||
'observation_end': end_date,
|
||||
'sort_order': 'desc',
|
||||
'limit': 100
|
||||
}
|
||||
|
||||
try:
|
||||
response = requests.get(url, params=params)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
except Exception as e:
|
||||
return {"error": f"Failed to fetch FRED data for {series_id}: {str(e)}"}
|
||||
|
||||
|
||||
def get_treasury_yield_curve(curr_date: str) -> str:
|
||||
"""
|
||||
Get current Treasury yield curve data
|
||||
|
||||
Args:
|
||||
curr_date: Current date in YYYY-MM-DD format
|
||||
|
||||
Returns:
|
||||
Formatted string with yield curve data
|
||||
"""
|
||||
# Treasury yield series IDs
|
||||
yield_series = {
|
||||
"1 Month": "DGS1MO",
|
||||
"3 Month": "DGS3MO",
|
||||
"6 Month": "DGS6MO",
|
||||
"1 Year": "DGS1",
|
||||
"2 Year": "DGS2",
|
||||
"3 Year": "DGS3",
|
||||
"5 Year": "DGS5",
|
||||
"7 Year": "DGS7",
|
||||
"10 Year": "DGS10",
|
||||
"20 Year": "DGS20",
|
||||
"30 Year": "DGS30"
|
||||
}
|
||||
|
||||
start_date = (datetime.strptime(curr_date, "%Y-%m-%d") - timedelta(days=30)).strftime("%Y-%m-%d")
|
||||
|
||||
result = f"## Treasury Yield Curve as of {curr_date}\n\n"
|
||||
|
||||
yield_data = []
|
||||
for maturity, series_id in yield_series.items():
|
||||
data = get_fred_data(series_id, start_date, curr_date)
|
||||
|
||||
if "error" in data:
|
||||
continue
|
||||
|
||||
observations = data.get("observations", [])
|
||||
if observations:
|
||||
latest = observations[0]
|
||||
if latest.get("value") != ".":
|
||||
yield_data.append({
|
||||
"maturity": maturity,
|
||||
"yield": float(latest["value"]),
|
||||
"date": latest["date"]
|
||||
})
|
||||
|
||||
if yield_data:
|
||||
result += "| Maturity | Yield (%) | Date |\n"
|
||||
result += "|----------|-----------|------|\n"
|
||||
|
||||
for item in yield_data:
|
||||
result += f"| {item['maturity']} | {item['yield']:.2f}% | {item['date']} |\n"
|
||||
|
||||
# Calculate yield curve analysis
|
||||
result += "\n### Yield Curve Analysis\n"
|
||||
|
||||
# Find 2Y and 10Y for inversion check
|
||||
two_year = next((item for item in yield_data if item["maturity"] == "2 Year"), None)
|
||||
ten_year = next((item for item in yield_data if item["maturity"] == "10 Year"), None)
|
||||
|
||||
if two_year and ten_year:
|
||||
spread = ten_year["yield"] - two_year["yield"]
|
||||
result += f"- **2Y-10Y Spread**: {spread:.2f} basis points\n"
|
||||
|
||||
if spread < 0:
|
||||
result += "- **⚠️ INVERTED YIELD CURVE**: Potential recession signal\n"
|
||||
elif spread < 50:
|
||||
result += "- **📊 FLAT YIELD CURVE**: Economic uncertainty\n"
|
||||
else:
|
||||
result += "- **📈 NORMAL YIELD CURVE**: Healthy economic expectations\n"
|
||||
else:
|
||||
result += "No recent yield curve data available.\n"
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def get_economic_indicators_report(curr_date: str, lookback_days: int = 90) -> str:
|
||||
"""
|
||||
Get comprehensive economic indicators report
|
||||
|
||||
Args:
|
||||
curr_date: Current date in YYYY-MM-DD format
|
||||
lookback_days: How many days to look back for data
|
||||
|
||||
Returns:
|
||||
Formatted string with economic indicators
|
||||
"""
|
||||
start_date = (datetime.strptime(curr_date, "%Y-%m-%d") - timedelta(days=lookback_days)).strftime("%Y-%m-%d")
|
||||
|
||||
# Key economic indicators
|
||||
indicators = {
|
||||
"Federal Funds Rate": {
|
||||
"series": "FEDFUNDS",
|
||||
"description": "Federal Reserve's target interest rate",
|
||||
"unit": "%"
|
||||
},
|
||||
"Consumer Price Index (CPI)": {
|
||||
"series": "CPIAUCSL",
|
||||
"description": "Inflation measure based on consumer goods",
|
||||
"unit": "Index",
|
||||
"yoy": True
|
||||
},
|
||||
"Producer Price Index (PPI)": {
|
||||
"series": "PPIACO",
|
||||
"description": "Inflation measure at producer level",
|
||||
"unit": "Index",
|
||||
"yoy": True
|
||||
},
|
||||
"Unemployment Rate": {
|
||||
"series": "UNRATE",
|
||||
"description": "Percentage of labor force unemployed",
|
||||
"unit": "%"
|
||||
},
|
||||
"Nonfarm Payrolls": {
|
||||
"series": "PAYEMS",
|
||||
"description": "Monthly change in employment",
|
||||
"unit": "Thousands",
|
||||
"mom": True
|
||||
},
|
||||
"GDP Growth Rate": {
|
||||
"series": "GDP",
|
||||
"description": "Gross Domestic Product growth",
|
||||
"unit": "Billions",
|
||||
"qoq": True
|
||||
},
|
||||
"ISM Manufacturing PMI": {
|
||||
"series": "NAPM",
|
||||
"description": "Manufacturing sector health indicator",
|
||||
"unit": "Index"
|
||||
},
|
||||
"Consumer Confidence": {
|
||||
"series": "CSCICP03USM665S",
|
||||
"description": "Consumer sentiment indicator",
|
||||
"unit": "Index"
|
||||
},
|
||||
"VIX": {
|
||||
"series": "VIXCLS",
|
||||
"description": "Market volatility index",
|
||||
"unit": "Index"
|
||||
}
|
||||
}
|
||||
|
||||
result = f"## Economic Indicators Report ({start_date} to {curr_date})\n\n"
|
||||
|
||||
for indicator_name, config in indicators.items():
|
||||
data = get_fred_data(config["series"], start_date, curr_date)
|
||||
|
||||
if "error" in data:
|
||||
result += f"### {indicator_name}\n**Error**: {data['error']}\n\n"
|
||||
continue
|
||||
|
||||
observations = data.get("observations", [])
|
||||
if not observations:
|
||||
result += f"### {indicator_name}\n**No data available**\n\n"
|
||||
continue
|
||||
|
||||
# Filter out missing values
|
||||
valid_obs = [obs for obs in observations if obs.get("value") != "."]
|
||||
if not valid_obs:
|
||||
result += f"### {indicator_name}\n**No valid data available**\n\n"
|
||||
continue
|
||||
|
||||
latest = valid_obs[0]
|
||||
latest_value = float(latest["value"])
|
||||
latest_date = latest["date"]
|
||||
|
||||
result += f"### {indicator_name}\n"
|
||||
result += f"- **Latest Value**: {latest_value:.2f} {config['unit']} (as of {latest_date})\n"
|
||||
result += f"- **Description**: {config['description']}\n"
|
||||
|
||||
# Calculate changes if we have enough data
|
||||
if len(valid_obs) >= 2:
|
||||
previous = valid_obs[1]
|
||||
previous_value = float(previous["value"])
|
||||
change = latest_value - previous_value
|
||||
change_pct = (change / previous_value) * 100 if previous_value != 0 else 0
|
||||
|
||||
result += f"- **Change**: {change:+.2f} {config['unit']} ({change_pct:+.2f}%)\n"
|
||||
result += f"- **Previous**: {previous_value:.2f} {config['unit']} (as of {previous['date']})\n"
|
||||
|
||||
# Calculate year-over-year change for inflation indicators
|
||||
if config.get("yoy") and len(valid_obs) >= 12:
|
||||
year_ago = valid_obs[11] if len(valid_obs) > 11 else valid_obs[-1]
|
||||
year_ago_value = float(year_ago["value"])
|
||||
yoy_change = ((latest_value - year_ago_value) / year_ago_value) * 100
|
||||
result += f"- **Year-over-Year**: {yoy_change:+.2f}%\n"
|
||||
|
||||
# Add interpretation
|
||||
if indicator_name == "Federal Funds Rate":
|
||||
if latest_value > 4.0:
|
||||
result += "- **💡 Analysis**: Restrictive monetary policy stance\n"
|
||||
elif latest_value < 2.0:
|
||||
result += "- **💡 Analysis**: Accommodative monetary policy stance\n"
|
||||
else:
|
||||
result += "- **💡 Analysis**: Neutral monetary policy stance\n"
|
||||
|
||||
elif "CPI" in indicator_name or "PPI" in indicator_name:
|
||||
if len(valid_obs) >= 12:
|
||||
if yoy_change > 3.0:
|
||||
result += "- **💡 Analysis**: Above Fed's 2% inflation target\n"
|
||||
elif yoy_change < 1.0:
|
||||
result += "- **💡 Analysis**: Below Fed's 2% inflation target\n"
|
||||
else:
|
||||
result += "- **💡 Analysis**: Near Fed's 2% inflation target\n"
|
||||
|
||||
elif indicator_name == "Unemployment Rate":
|
||||
if latest_value < 4.0:
|
||||
result += "- **💡 Analysis**: Very low unemployment, tight labor market\n"
|
||||
elif latest_value > 6.0:
|
||||
result += "- **💡 Analysis**: Elevated unemployment, loose labor market\n"
|
||||
else:
|
||||
result += "- **💡 Analysis**: Moderate unemployment levels\n"
|
||||
|
||||
elif "PMI" in indicator_name:
|
||||
if latest_value > 50:
|
||||
result += "- **💡 Analysis**: Expanding manufacturing sector\n"
|
||||
else:
|
||||
result += "- **💡 Analysis**: Contracting manufacturing sector\n"
|
||||
|
||||
elif indicator_name == "VIX":
|
||||
if latest_value > 30:
|
||||
result += "- **💡 Analysis**: High market volatility/fear\n"
|
||||
elif latest_value < 15:
|
||||
result += "- **💡 Analysis**: Low market volatility/complacency\n"
|
||||
else:
|
||||
result += "- **💡 Analysis**: Moderate market volatility\n"
|
||||
|
||||
result += "\n"
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def get_fed_calendar_and_minutes(curr_date: str) -> str:
|
||||
"""
|
||||
Get Federal Reserve meeting calendar and recent minutes
|
||||
|
||||
Args:
|
||||
curr_date: Current date in YYYY-MM-DD format
|
||||
|
||||
Returns:
|
||||
Formatted string with Fed calendar information
|
||||
"""
|
||||
result = f"## Federal Reserve Calendar & Policy Updates\n\n"
|
||||
|
||||
# Get recent Fed Funds rate data to show policy trajectory
|
||||
start_date = (datetime.strptime(curr_date, "%Y-%m-%d") - timedelta(days=365)).strftime("%Y-%m-%d")
|
||||
fed_data = get_fred_data("FEDFUNDS", start_date, curr_date)
|
||||
|
||||
if "error" not in fed_data:
|
||||
observations = fed_data.get("observations", [])
|
||||
valid_obs = [obs for obs in observations if obs.get("value") != "."]
|
||||
|
||||
if valid_obs and len(valid_obs) >= 2:
|
||||
result += "### Recent Federal Funds Rate History\n"
|
||||
result += "| Date | Rate (%) | Change |\n"
|
||||
result += "|------|----------|--------|\n"
|
||||
|
||||
for i, obs in enumerate(valid_obs[:6]): # Show last 6 observations
|
||||
rate = float(obs["value"])
|
||||
if i < len(valid_obs) - 1:
|
||||
prev_rate = float(valid_obs[i + 1]["value"])
|
||||
change = rate - prev_rate
|
||||
change_str = f"{change:+.2f}%" if change != 0 else "No change"
|
||||
else:
|
||||
change_str = "-"
|
||||
|
||||
result += f"| {obs['date']} | {rate:.2f}% | {change_str} |\n"
|
||||
|
||||
result += "\n"
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def get_macro_economic_summary(curr_date: str, lookback_days: int = 90) -> str:
|
||||
"""
|
||||
Get comprehensive macro economic summary combining economic indicators, yield curves, and Fed data
|
||||
|
||||
Args:
|
||||
curr_date: Current date in YYYY-MM-DD format
|
||||
lookback_days: How many days to look back for data
|
||||
|
||||
Returns:
|
||||
Complete macro economic analysis
|
||||
"""
|
||||
result = f"# Macro Economic Analysis - {curr_date}\n\n"
|
||||
|
||||
# Get all components
|
||||
indicators_report = get_economic_indicators_report(curr_date, lookback_days)
|
||||
yield_curve = get_treasury_yield_curve(curr_date)
|
||||
fed_calendar = get_fed_calendar_and_minutes(curr_date)
|
||||
|
||||
# Combine all reports
|
||||
result += indicators_report + "\n"
|
||||
result += yield_curve + "\n"
|
||||
result += fed_calendar + "\n"
|
||||
|
||||
return result
|
||||
|
|
@ -48,6 +48,7 @@ class GraphSetup:
|
|||
- "social": Social media analyst
|
||||
- "news": News analyst
|
||||
- "fundamentals": Fundamentals analyst
|
||||
- "macro": Macro economic analyst
|
||||
"""
|
||||
if len(selected_analysts) == 0:
|
||||
raise ValueError("Trading Agents Graph Setup Error: no analysts selected!")
|
||||
|
|
@ -85,6 +86,13 @@ class GraphSetup:
|
|||
delete_nodes["fundamentals"] = create_msg_delete()
|
||||
tool_nodes["fundamentals"] = self.tool_nodes["fundamentals"]
|
||||
|
||||
if "macro" in selected_analysts:
|
||||
analyst_nodes["macro"] = create_macro_analyst(
|
||||
self.quick_thinking_llm
|
||||
)
|
||||
delete_nodes["macro"] = create_msg_delete()
|
||||
tool_nodes["macro"] = self.tool_nodes["macro"]
|
||||
|
||||
# Create researcher and manager nodes
|
||||
bull_researcher_node = create_bull_researcher(
|
||||
self.quick_thinking_llm, self.bull_memory
|
||||
|
|
|
|||
|
|
@ -33,7 +33,10 @@ from tradingagents.agents.utils.agent_utils import (
|
|||
get_news,
|
||||
get_insider_sentiment,
|
||||
get_insider_transactions,
|
||||
get_global_news
|
||||
get_global_news,
|
||||
get_economic_indicators,
|
||||
get_yield_curve,
|
||||
get_fed_calendar
|
||||
)
|
||||
|
||||
from .conditional_logic import ConditionalLogic
|
||||
|
|
@ -155,6 +158,14 @@ class TradingAgentsGraph:
|
|||
get_income_statement,
|
||||
]
|
||||
),
|
||||
"macro": ToolNode(
|
||||
[
|
||||
# Macroeconomic analysis tools
|
||||
get_economic_indicators,
|
||||
get_yield_curve,
|
||||
get_fed_calendar,
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
def propagate(self, company_name, trade_date):
|
||||
|
|
|
|||
Loading…
Reference in New Issue