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Added macro economic analyst

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bastien.migette 2025-10-06 12:06:31 +02:00
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# FRED API Macro Data Integration
## Summary
Added FRED (Federal Reserve Economic Data) API support to the TradingAgents vendor methods system for macroeconomic analysis.
## Files Added
### 1. `tradingagents/dataflows/macro_utils.py`
New module providing FRED API integration with the following functions:
- **`get_fred_api_key()`** - Get FRED API key from config or environment
- **`get_fred_data(series_id, start_date, end_date)`** - Core FRED API wrapper
- **`get_treasury_yield_curve(curr_date)`** - Treasury yield curve data with inversion analysis
- **`get_economic_indicators_report(curr_date, lookback_days=90)`** - Comprehensive economic indicators
- Federal Funds Rate
- Consumer Price Index (CPI)
- Producer Price Index (PPI)
- Unemployment Rate
- Nonfarm Payrolls
- GDP Growth Rate
- ISM Manufacturing PMI
- Consumer Confidence
- VIX (Market Volatility)
- **`get_fed_calendar_and_minutes(curr_date)`** - Federal Reserve meeting calendar and policy updates
- **`get_macro_economic_summary(curr_date, lookback_days=90)`** - Complete macro economic analysis combining all components
## Files Modified
### 2. `tradingagents/dataflows/interface.py`
Updated vendor routing system to include FRED macro data:
**Added Imports:**
```python
from .macro_utils import get_economic_indicators_report, get_treasury_yield_curve, get_fed_calendar_and_minutes
```
**Updated VENDOR_LIST:**
```python
VENDOR_LIST = [
"local",
"yfinance",
"openai",
"google",
"fred" # New
]
```
**New TOOLS_CATEGORIES Entry:**
```python
"macro_data": {
"description": "Macroeconomic indicators and Federal Reserve data",
"tools": [
"get_economic_indicators",
"get_yield_curve",
"get_fed_calendar"
]
}
```
**New VENDOR_METHODS Entries:**
```python
# 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,
},
```
## Configuration Required
To use FRED API features, set the FRED API key via:
1. **Environment Variable:**
```bash
export FRED_API_KEY="your_api_key_here"
```
2. **Or via Config System:**
The `get_fred_api_key()` function will check:
- Config system via `get_api_key("fred_api_key", "FRED_API_KEY")`
- Environment variable `FRED_API_KEY`
3. **Get a Free API Key:**
- Visit: https://fred.stlouisfed.org/
- Register for a free account
- Generate API key under "My Account" → "API Keys"
## Usage Examples
### Via Vendor Routing System
```python
from tradingagents.dataflows.interface import route_to_vendor
# Get economic indicators
indicators = route_to_vendor(
"get_economic_indicators",
curr_date="2025-10-06",
lookback_days=90
)
# Get yield curve
yield_curve = route_to_vendor(
"get_yield_curve",
curr_date="2025-10-06"
)
# Get Fed calendar
fed_calendar = route_to_vendor(
"get_fed_calendar",
curr_date="2025-10-06"
)
```
### Direct Function Calls
```python
from tradingagents.dataflows.macro_utils import (
get_economic_indicators_report,
get_treasury_yield_curve,
get_fed_calendar_and_minutes,
get_macro_economic_summary
)
# Complete macro analysis
summary = get_macro_economic_summary(
curr_date="2025-10-06",
lookback_days=90
)
# Individual components
indicators = get_economic_indicators_report("2025-10-06", 90)
yield_curve = get_treasury_yield_curve("2025-10-06")
fed_data = get_fed_calendar_and_minutes("2025-10-06")
```
## Integration with Macro Analyst
The macro analyst can now use these tools through the vendor routing system. The tools are automatically available through the `macro_data` category:
```python
# In agent configuration
config = {
"data_vendors": {
"macro_data": "fred" # Use FRED for macro data
}
}
```
## Data Returned
All functions return formatted markdown strings suitable for LLM analysis:
- **Economic Indicators**: Markdown tables with current values, changes, and analysis
- **Yield Curve**: Markdown table with maturities, yields, and inversion warnings
- **Fed Calendar**: FOMC meeting schedule and policy trajectory
- **Trading Implications**: Actionable insights for different economic scenarios
## PR Compatibility Notes
Changes were made with minimal modifications to existing code:
1. ✅ **New file only** - `macro_utils.py` is a new addition
2. ✅ **Additive changes** - Only added new entries to existing dictionaries
3. ✅ **No breaking changes** - Existing functionality unchanged
4. ✅ **Follows existing patterns** - Uses same vendor routing architecture as other data sources
5. ✅ **Consistent naming** - Follows existing naming conventions (`get_*` pattern)
## Testing
To verify the integration works:
```python
# Test FRED API connection
from tradingagents.dataflows.macro_utils import get_fred_data
result = get_fred_data("FEDFUNDS", "2025-01-01", "2025-10-06")
print(result)
# Test vendor routing
from tradingagents.dataflows.interface import route_to_vendor
indicators = route_to_vendor(
"get_economic_indicators",
curr_date="2025-10-06",
lookback_days=30
)
print(indicators)
```
## Dependencies
No new dependencies required. Uses existing dependencies:
- `requests` - For FRED API calls
- `pandas` - For data manipulation
- `datetime` - For date handling
- Existing config system for API key management
## Future Enhancements
Potential improvements:
- Add caching for FRED API responses (similar to YFinanceDataProvider)
- Add more FRED series (housing data, commodity prices, etc.)
- Add international economic indicators
- Add custom FRED series ID support for advanced users

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tradingagents/dataflows/interface.py
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@ -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:

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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"
# Fed meeting schedule (approximate - would need real Fed calendar API)
result += "### 2025 FOMC Meeting Schedule\n"
result += "- **January 28-29**: FOMC Meeting\n"
result += "- **March 18-19**: FOMC Meeting\n"
result += "- **April 29-30**: FOMC Meeting\n"
result += "- **June 10-11**: FOMC Meeting\n"
result += "- **July 29-30**: FOMC Meeting\n"
result += "- **September 16-17**: FOMC Meeting\n"
result += "- **October 28-29**: FOMC Meeting\n"
result += "- **December 16-17**: FOMC Meeting\n\n"
result += "### Key Policy Considerations\n"
result += "- **Dual Mandate**: Maximum employment and price stability\n"
result += "- **Inflation Target**: 2% annual PCE inflation\n"
result += "- **Balance Sheet**: Quantitative tightening operations\n"
result += "- **Forward Guidance**: Communication of future policy intentions\n\n"
result += "### Recent Economic Projections Summary\n"
result += "- Monitor Fed dot plot for interest rate projections\n"
result += "- Watch for changes in economic growth forecasts\n"
result += "- Track inflation expectations updates\n"
result += "- Observe unemployment rate projections\n\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"
# Add trading implications
result += "## Trading Implications\n\n"
result += "### Interest Rate Environment\n"
result += "- **Rising Rates**: Favor financials, pressure growth stocks\n"
result += "- **Falling Rates**: Support growth stocks, pressure financials\n"
result += "- **Yield Curve**: Inversion signals recession risk\n\n"
result += "### Inflation Impact\n"
result += "- **High Inflation**: Favor commodities, real assets\n"
result += "- **Low Inflation**: Support bonds, growth stocks\n"
result += "- **Deflation Risk**: Flight to quality assets\n\n"
result += "### Economic Growth\n"
result += "- **Strong Growth**: Favor cyclical sectors\n"
result += "- **Weak Growth**: Favor defensive sectors\n"
result += "- **Recession Risk**: Increase cash, quality focus\n\n"
result += "### Market Volatility\n"
result += "- **High VIX**: Opportunity for contrarian plays\n"
result += "- **Low VIX**: Risk of complacency\n"
result += "- **Vol Regime Change**: Adjust position sizing\n\n"
return result