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06ba83e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | from agency_swarm.tools import BaseTool
from pydantic import Field
import pandas as pd
import numpy as np
class FallbackDataTool(BaseTool):
"""
This tool provides a fallback mechanism to use GPT-based datasets for market data analysis
if the finance APIs fail. It accesses pre-trained datasets and returns data in a format
compatible with the DataAnalysisTool.
"""
symbol: str = Field(
..., description="The stock symbol for which to retrieve fallback market data."
)
num_days: int = Field(
30, description="The number of days of data to generate for the fallback dataset."
)
def run(self):
"""
Generates a fallback dataset for the specified stock symbol.
Returns the data as a pandas DataFrame compatible with the DataAnalysisTool.
"""
# Simulate a pre-trained dataset using random data generation
dates = pd.date_range(end=pd.Timestamp.today(), periods=self.num_days)
data = {
'Open': np.random.uniform(low=100, high=200, size=self.num_days),
'High': np.random.uniform(low=100, high=200, size=self.num_days),
'Low': np.random.uniform(low=100, high=200, size=self.num_days),
'Close': np.random.uniform(low=100, high=200, size=self.num_days),
'Volume': np.random.randint(low=1000, high=10000, size=self.num_days)
}
fallback_data = pd.DataFrame(data, index=dates)
# Ensure DataFrame operations are handled correctly
# Check if DataFrame is empty
if fallback_data.empty:
raise ValueError("The generated fallback data is empty.")
# Check for any missing values in the DataFrame
if fallback_data.isnull().any().any():
raise ValueError("The generated fallback data contains missing values.")
# Return the fallback data as a pandas DataFrame
return fallback_data
# Example usage:
# tool = FallbackDataTool(symbol="AAPL", num_days=30)
# fallback_data = tool.run()
# print(fallback_data) |