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earnings_analysis.py
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earnings_analysis.py
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import pandas as pd
from openaicall import get_predictions
from fetcherv6 import net_income_direction, get_company_name
import logging
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
def analyze_earnings(cvm_code):
# Get predictions
predictions = get_predictions(cvm_code)
# Get actual earnings direction
actual_earnings = net_income_direction(cvm_code)
# Get company name
company_name = get_company_name(cvm_code)
logging.info(f"Company name: {company_name}")
# Prepare data for CSV
csv_data = []
for index, row in predictions.iterrows():
year = row['Year']
logging.info(f"Processing year: {year}")
try:
year = int(year)
except ValueError:
logging.warning(f"Invalid year value: {year}")
continue
if year in actual_earnings.index:
actual_direction = 'increase' if actual_earnings[year] > 0 else 'decrease'
csv_data.append({
'Company Name': company_name,
'Year': year,
'Actual Earnings Direction': actual_direction,
'Predicted Earnings Direction': row['earnings direction'],
'Magnitude': row['magnitude'],
'Confidence Score': row['confidence score'],
'Summary of Rationale': row['summary of rationale']
})
else:
logging.warning(f"Year {year} not found in actual earnings data. Skipping.")
# Create DataFrame
df = pd.DataFrame(csv_data)
logging.info(f"Final DataFrame shape: {df.shape}")
logging.info(f"Final DataFrame head:\n{df.head()}")
if df.empty:
logging.warning("No matching years found between predictions and actual earnings.")
# Save to CSV
csv_filename = f"{company_name.replace(' ', '_')}_earnings_analysis.csv"
df.to_csv(csv_filename, index=False)
logging.info(f"Analysis saved to {csv_filename}")
return df
if __name__ == "__main__":
cvm_code = input("Enter the CVM code: ")
result_df = analyze_earnings(cvm_code)
print(result_df)