"""Divestiture Simulation - Analyze worst performer, find best sale month, and simulate impact."""
import asyncio
import sys
from pathlib import Path
from typing import Optional
# Add parent directory to path
sys.path.insert(0, str(Path(__file__).parent.parent))
from fccs_agent.config import load_config
from fccs_agent.agent import initialize_agent, close_agent
from fccs_agent.tools.data import smart_retrieve
from fccs_agent.utils.cache import load_members_from_cache
async def get_monthly_performance(entity_name: str, year: str = "FY25") -> dict[str, float]:
"""Get monthly Net Income for an entity."""
months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
monthly_data = {}
for month in months:
try:
result = await smart_retrieve(
account="FCCS_Net Income",
entity=entity_name,
period=month,
years=year,
scenario="Actual"
)
if result.get("status") == "success":
data = result.get("data", {})
rows = data.get("rows", [])
if rows and rows[0].get("data"):
value = rows[0]["data"][0]
if value is not None:
monthly_data[month] = float(value)
except Exception:
pass
return monthly_data
async def get_ytd_performance(entity_name: str, year: str = "FY25") -> Optional[float]:
"""Get YTD Net Income for an entity."""
try:
result = await smart_retrieve(
account="FCCS_Net Income",
entity=entity_name,
period="Dec",
years=year,
scenario="Actual"
)
if result.get("status") == "success":
data = result.get("data", {})
rows = data.get("rows", [])
if rows and rows[0].get("data"):
value = rows[0]["data"][0]
return float(value) if value is not None else None
except Exception:
pass
return None
async def simulate_divestiture():
"""Simulate divestiture of worst performer."""
print("=" * 80)
print("DIVESTITURE SIMULATION - Worst Performer Analysis")
print("=" * 80)
print()
try:
config = load_config()
await initialize_agent(config)
print("[OK] Connected to FCCS")
print()
# Step 1: Identify worst performer for 2025
print("STEP 1: Identifying Worst Performer for 2025")
print("-" * 80)
cached_entities = load_members_from_cache("Consol", "Entity")
if not cached_entities or not cached_entities.get("items"):
print("[ERROR] No entities found in cache")
await close_agent()
return
entities = [item.get("name") for item in cached_entities["items"] if item.get("name")]
# Exclude total/consolidated entities
exclude_keywords = ["Total", "FCCS_Total", "FCCS_Entity Total", "Consolidated", "Segment"]
individual_entities = [
e for e in entities
if not any(keyword in e for keyword in exclude_keywords)
]
print(f"Checking {len(individual_entities)} individual entities (excluding totals)...")
entity_performance = []
for entity in individual_entities:
value = await get_ytd_performance(entity, "FY25")
if value is not None:
entity_performance.append({"entity": entity, "net_income": value})
if not entity_performance:
print("[ERROR] No performance data found")
await close_agent()
return
# Sort to find worst (lowest Net Income)
entity_performance.sort(key=lambda x: x["net_income"])
worst_entity = entity_performance[0]
print(f"Worst Performer: {worst_entity['entity']}")
print(f"Net Income (FY25 YTD): ${worst_entity['net_income']:,.2f}")
print()
# Step 2: Get monthly performance to find best sale month
print("STEP 2: Analyzing Monthly Performance to Find Best Sale Month")
print("-" * 80)
print(f"Getting monthly data for {worst_entity['entity']}...")
monthly_data = await get_monthly_performance(worst_entity['entity'], "FY25")
if not monthly_data:
print("[WARNING] No monthly data available, using YTD data")
monthly_data = {"Dec": worst_entity['net_income']}
print()
print("Monthly Net Income Performance (YTD values):")
print(f"{'Month':<10} {'YTD Net Income':>20} {'Monthly Change':>20}")
print("-" * 50)
previous_ytd = 0
best_month = None
best_ytd = float('-inf') # Start with worst possible, find least negative
for month in ["Jan", "Feb", "Mar", "Apr", "May", "Jun",
"Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]:
if month in monthly_data:
ytd_value = monthly_data[month]
monthly_change = ytd_value - previous_ytd
print(f"{month:<10} ${ytd_value:>19,.2f} ${monthly_change:>19,.2f}")
# Best month to sell: when YTD loss is smallest (least negative)
# This minimizes total loss exposure - sell early!
if ytd_value > best_ytd: # Less negative = better
best_ytd = ytd_value
best_month = month
previous_ytd = ytd_value
print()
if best_month:
best_ytd_loss = monthly_data.get(best_month, worst_entity['net_income'])
print(f"Best Month to Sell: {best_month}")
print(f" YTD Loss at that point: ${best_ytd_loss:,.2f}")
print(f" Rationale: Sell as early as possible to minimize loss exposure")
print()
# Step 3: Estimate sale price
print("STEP 3: Estimating Sale Price")
print("-" * 80)
# Simple valuation methods
annual_loss = worst_entity['net_income']
# Method 1: Asset-based (assume assets = 2x annual revenue/loss)
# Method 2: Multiple of loss (negative value - pay to get rid of it)
# Method 3: Book value estimate
# Conservative estimate: Sale at book value or small premium
# For a loss-making entity, might need to pay buyer or sell at discount
estimated_sale_price = abs(annual_loss) * 0.5 # 50% of annual loss as sale price
# Or might need to pay buyer to take it: -abs(annual_loss) * 0.2
print(f"Entity Annual Loss: ${abs(annual_loss):,.2f}")
print()
print("Valuation Scenarios:")
print(f" Scenario 1 (Asset Sale): ${estimated_sale_price:,.2f}")
print(f" Scenario 2 (Pay to Divest): -${abs(annual_loss) * 0.2:,.2f}")
print(f" Scenario 3 (Break-even): $0.00")
print()
print(f"Recommended Sale Price: ${estimated_sale_price:,.2f}")
print(" (Assumes buyer takes on assets and some liabilities)")
print()
# Step 4: Simulate impact on consolidated group
print("STEP 4: Simulating Impact on Consolidated Group")
print("-" * 80)
# Get current consolidated results
consolidated_result = await get_ytd_performance("FCCS_Total Geography", "FY25")
if consolidated_result is None:
print("[WARNING] Could not get consolidated results")
await close_agent()
return
current_consolidated = consolidated_result
entity_loss = worst_entity['net_income']
# Calculate impact
# If selling in best month, use YTD loss at that month
if best_month and best_month in monthly_data:
loss_removed = monthly_data[best_month] # YTD loss at best month
else:
loss_removed = entity_loss # Full year loss
# New consolidated = current - entity loss + sale proceeds
sale_proceeds = estimated_sale_price
new_consolidated = current_consolidated - loss_removed + sale_proceeds
improvement = new_consolidated - current_consolidated
print(f"Current Consolidated Net Income (FY25 YTD): ${current_consolidated:,.2f}")
print(f"Entity Loss to Remove: ${loss_removed:,.2f}")
print(f"Estimated Sale Proceeds: ${sale_proceeds:,.2f}")
print()
print("=" * 80)
print("DIVESTITURE IMPACT SIMULATION")
print("=" * 80)
print(f"{'Metric':<40} {'Amount':>20}")
print("-" * 80)
print(f"{'Current Consolidated Net Income':<40} ${current_consolidated:>19,.2f}")
print(f"{'Entity Loss Removed':<40} ${loss_removed:>19,.2f}")
print(f"{'Sale Proceeds':<40} ${sale_proceeds:>19,.2f}")
print("-" * 80)
print(f"{'New Consolidated Net Income':<40} ${new_consolidated:>19,.2f}")
print(f"{'Improvement':<40} ${improvement:>19,.2f}")
print("=" * 80)
print()
# Calculate percentage improvement
if current_consolidated != 0:
pct_improvement = (improvement / abs(current_consolidated)) * 100
print(f"Percentage Improvement: {pct_improvement:.1f}%")
print()
print("RECOMMENDATION:")
print(f" - Divest: {worst_entity['entity']}")
print(f" - Best Month: {best_month if best_month else 'N/A'}")
print(f" - Sale Price: ${estimated_sale_price:,.2f}")
print(f" - Expected Improvement: ${improvement:,.2f}")
print()
await close_agent()
except Exception as e:
print(f"\n[ERROR] {e}")
import traceback
traceback.print_exc()
sys.exit(1)
if __name__ == "__main__":
asyncio.run(simulate_divestiture())
