"""Comprehensive Entity Analysis - List apps, connect, show top/underperformers, and simulate divestment."""
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.application import get_application_info
from fccs_agent.tools.data import smart_retrieve
from fccs_agent.utils.cache import load_members_from_cache
import csv
async def query_entity_performance(entity_name: str, year: str = "FY24") -> Optional[float]:
"""Query Net Income for a specific 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 as e:
pass
return None
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 comprehensive_analysis():
"""Run comprehensive entity analysis."""
print("=" * 80)
print("COMPREHENSIVE ENTITY ANALYSIS")
print("=" * 80)
print()
try:
config = load_config()
await initialize_agent(config)
print("[OK] Connected to FCCS")
print()
# Step 1: List all FCCS apps
print("=" * 80)
print("STEP 1: LISTING ALL FCCS APPS")
print("=" * 80)
print()
app_info = await get_application_info()
if app_info.get("status") == "success":
apps_data = app_info.get("data", {})
apps = apps_data.get("items", [])
if apps:
print(f"Found {len(apps)} application(s):")
print()
for i, app in enumerate(apps, 1):
app_name = app.get("name", "Unknown")
app_type = app.get("type", "Unknown")
print(f" {i}. {app_name} (Type: {app_type})")
# Check if "Consol" app exists
consol_app = None
for app in apps:
app_name = app.get("name", "")
if "Consol" in app_name or "consol" in app_name.lower():
consol_app = app_name
break
if consol_app:
print()
print(f"[OK] Found 'Consol' app: {consol_app}")
else:
print()
print("[INFO] No app with 'Consol' in name found.")
if apps:
print(f"[INFO] Using first available app: {apps[0].get('name')}")
else:
print("[WARNING] No applications found")
else:
print("[ERROR] Failed to retrieve application info")
print()
print("=" * 80)
print("STEP 2: CONNECTED TO CONSOL APP")
print("=" * 80)
print()
print("[OK] Connection established (app name set during initialization)")
print()
# Step 3: Top 10 performing entities for actuals 2024
print("=" * 80)
print("STEP 3: TOP 10 PERFORMING ENTITIES - ACTUALS 2024")
print("=" * 80)
print()
# Get total first
print("Getting total Net Income for FY24...")
total_result = await smart_retrieve(
account="FCCS_Net Income",
entity="FCCS_Total Geography",
period="Dec",
years="FY24",
scenario="Actual"
)
if total_result.get("status") == "success":
data = total_result.get("data", {})
rows = data.get("rows", [])
if rows:
total_value = rows[0].get("data", [None])[0]
if total_value:
print(f"Total Net Income (FY24 YTD): ${float(total_value):,.2f}")
print()
# Get entities from cache
print("Checking for cached Entity members...")
cached_entities = load_members_from_cache("Consol", "Entity")
entities_to_query = []
entity_alias_map = {} # Map entity name to alias/description
if cached_entities and cached_entities.get("items"):
print(f"[OK] Found {len(cached_entities['items'])} entities in cache")
for item in cached_entities["items"]:
entity_name = item.get("name")
if entity_name:
entities_to_query.append(entity_name)
# Get alias or description for display
alias = item.get("alias") or item.get("description") or entity_name
entity_alias_map[entity_name] = alias
else:
print("[WARNING] No entity list available in cache")
print(" Please ensure entity cache exists at: .cache/members/Consol_Entity.json")
await close_agent()
return
# Try to load aliases from CSV file if available
csv_file = Path("Ravi_ExportedMetadata_Entity.csv")
if csv_file.exists():
try:
encodings = ["utf-8", "utf-8-sig", "latin-1", "cp1252"]
for encoding in encodings:
try:
with open(csv_file, "r", encoding=encoding) as f:
reader = csv.DictReader(f)
for row in reader:
entity_name = row.get("Entity", "").strip()
alias = row.get("Alias: Default", "").strip()
if entity_name and alias:
entity_alias_map[entity_name] = alias
break
except Exception:
if encoding == encodings[-1]:
raise
continue
except Exception:
pass # If CSV can't be read, just use cache aliases
if not entities_to_query:
print("No entities to query.")
await close_agent()
return
# Exclude total/consolidated entities
exclude_keywords = ["Total", "FCCS_Total", "FCCS_Entity Total", "Consolidated", "Segment"]
individual_entities = [
e for e in entities_to_query
if not any(keyword in e for keyword in exclude_keywords)
]
print(f"Querying Net Income for {len(individual_entities)} individual entities (FY24)...")
print("(This may take a while)")
print()
entity_performance_2024 = []
queried = 0
for entity in individual_entities:
queried += 1
if queried % 10 == 0:
print(f" Progress: {queried}/{len(individual_entities)}...")
value = await query_entity_performance(entity, "FY24")
if value is not None:
entity_performance_2024.append({
"entity": entity,
"net_income": value
})
print()
if entity_performance_2024:
# Sort by Net Income (descending) - highest first
entity_performance_2024.sort(key=lambda x: x["net_income"], reverse=True)
# Show top 10 performers
top_10_performers = entity_performance_2024[:10]
print("TOP 10 PERFORMING ENTITIES - 2024 (Net Income YTD)")
print("=" * 80)
print(f"{'Rank':<6} {'Entity':<50} {'Net Income':>20}")
print("-" * 80)
for i, perf in enumerate(top_10_performers, 1):
entity_code = perf["entity"]
# Use alias if available, otherwise use entity code
display_name = entity_alias_map.get(entity_code, entity_code)
# Format: "Alias (Code)" if alias exists and is different, otherwise just the name
if display_name != entity_code and display_name:
entity_name = f"{display_name} ({entity_code})"[:48]
else:
entity_name = entity_code[:48]
net_income = perf["net_income"]
print(f"{i:<6} {entity_name:<50} ${net_income:>19,.2f}")
print("=" * 80)
print(f"Total entities queried: {len(individual_entities)}")
print(f"Entities with data: {len(entity_performance_2024)}")
else:
print("[WARNING] No performance data retrieved for FY24")
print()
print("=" * 80)
print("STEP 4: TOP 10 UNDERPERFORMING ENTITIES - ACTUALS 2024")
print("=" * 80)
print()
if entity_performance_2024:
# Sort by Net Income (ascending) - lowest first = worst performers
entity_performance_2024.sort(key=lambda x: x["net_income"])
# Show top 10 underperformers
top_10_underperformers = entity_performance_2024[:10]
print("TOP 10 UNDERPERFORMING ENTITIES - 2024 (Net Income YTD)")
print("=" * 80)
print(f"{'Rank':<6} {'Entity':<50} {'Net Income':>20}")
print("-" * 80)
for i, perf in enumerate(top_10_underperformers, 1):
entity_code = perf["entity"]
# Use alias if available, otherwise use entity code
display_name = entity_alias_map.get(entity_code, entity_code)
# Format: "Alias (Code)" if alias exists and is different, otherwise just the name
if display_name != entity_code and display_name:
entity_name = f"{display_name} ({entity_code})"[:48]
else:
entity_name = entity_code[:48]
net_income = perf["net_income"]
print(f"{i:<6} {entity_name:<50} ${net_income:>19,.2f}")
print("=" * 80)
worst_entity_2024 = top_10_underperformers[0] if top_10_underperformers else None
print(f"Worst Performer (2024): {worst_entity_2024['entity'] if worst_entity_2024 else 'N/A'}")
else:
print("[WARNING] No performance data available")
worst_entity_2024 = None
print()
print("=" * 80)
print("STEP 5: SIMULATING DIVESTMENT - WORST PERFORMING ENTITY (MID-YEAR 2025)")
print("=" * 80)
print()
# Get worst performer for 2025 (for divestment simulation)
print("Identifying worst performer for FY25...")
print()
entity_performance_2025 = []
queried = 0
for entity in individual_entities:
queried += 1
if queried % 10 == 0:
print(f" Progress: {queried}/{len(individual_entities)}...")
value = await query_entity_performance(entity, "FY25")
if value is not None:
entity_performance_2025.append({
"entity": entity,
"net_income": value
})
print()
if not entity_performance_2025:
print("[WARNING] No FY25 performance data found")
print(" Using worst performer from 2024 for simulation")
worst_entity = worst_entity_2024
worst_entity_year = "FY24"
else:
# Sort to find worst (lowest Net Income)
entity_performance_2025.sort(key=lambda x: x["net_income"])
worst_entity = entity_performance_2025[0]
worst_entity_year = "FY25"
if not worst_entity:
print("[ERROR] No worst entity identified")
await close_agent()
return
worst_entity_code = worst_entity['entity']
worst_entity_display = entity_alias_map.get(worst_entity_code, worst_entity_code)
if worst_entity_display != worst_entity_code:
print(f"Worst Performer: {worst_entity_display} ({worst_entity_code})")
else:
print(f"Worst Performer: {worst_entity_code}")
print(f"Net Income ({worst_entity_year} YTD): ${worst_entity['net_income']:,.2f}")
print()
# Get monthly performance to find mid-year (June) data
print("Getting monthly performance data for divestment simulation...")
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 = {"Jun": worst_entity['net_income']}
# Mid-year 2025 = June
mid_year_month = "Jun"
mid_year_loss = monthly_data.get(mid_year_month, worst_entity['net_income'])
print()
print(f"Monthly Performance (YTD values):")
print(f"{'Month':<10} {'YTD Net Income':>20}")
print("-" * 30)
for month in ["Jan", "Feb", "Mar", "Apr", "May", "Jun"]:
if month in monthly_data:
print(f"{month:<10} ${monthly_data[month]:>19,.2f}")
print()
print(f"Mid-Year 2025 (June) YTD Loss: ${mid_year_loss:,.2f}")
print()
# Estimate sale price
print("Estimating Sale Price...")
annual_loss = worst_entity['net_income']
estimated_sale_price = abs(annual_loss) * 0.5 # 50% of annual loss as sale price
print(f"Entity Annual Loss: ${abs(annual_loss):,.2f}")
print(f"Estimated Sale Price: ${estimated_sale_price:,.2f}")
print(" (Assumes buyer takes on assets and some liabilities)")
print()
# Step 6: Calculate impact on consolidated group
print("=" * 80)
print("STEP 6: CALCULATING IMPACT ON CONSOLIDATED GROUP")
print("=" * 80)
print()
# Get current consolidated results for FY25
consolidated_result = await query_entity_performance("FCCS_Total Geography", "FY25")
if consolidated_result is None:
print("[WARNING] Could not get FY25 consolidated results, using FY24")
consolidated_result = await query_entity_performance("FCCS_Total Geography", "FY24")
year_label = "FY24"
else:
year_label = "FY25"
if consolidated_result is None:
print("[ERROR] Could not get consolidated results")
await close_agent()
return
current_consolidated = consolidated_result
loss_removed = mid_year_loss # Loss at mid-year (June)
sale_proceeds = estimated_sale_price
# New consolidated = current - entity loss + sale proceeds
new_consolidated = current_consolidated - loss_removed + sale_proceeds
improvement = new_consolidated - current_consolidated
print("DIVESTITURE IMPACT SIMULATION")
print("=" * 80)
print(f"{'Metric':<50} {'Amount':>20}")
print("-" * 80)
print(f"{'Current Consolidated Net Income (' + year_label + ' YTD)':<50} ${current_consolidated:>19,.2f}")
print(f"{'Entity Loss Removed (June YTD)':<50} ${loss_removed:>19,.2f}")
print(f"{'Estimated Sale Proceeds':<50} ${sale_proceeds:>19,.2f}")
print("-" * 80)
print(f"{'New Consolidated Net Income':<50} ${new_consolidated:>19,.2f}")
print(f"{'Improvement':<50} ${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("=" * 80)
print("SUMMARY & RECOMMENDATION")
print("=" * 80)
print()
worst_entity_code = worst_entity['entity']
worst_entity_display = entity_alias_map.get(worst_entity_code, worst_entity_code)
if worst_entity_display != worst_entity_code:
print(f"Entity to Divest: {worst_entity_display} ({worst_entity_code})")
else:
print(f"Entity to Divest: {worst_entity_code}")
print(f"Divestment Date: Mid-Year 2025 (June)")
print(f"YTD Loss at Divestment: ${mid_year_loss:,.2f}")
print(f"Estimated Sale Price: ${estimated_sale_price:,.2f}")
print(f"Expected Improvement: ${improvement:,.2f}")
print()
if improvement > 0:
print("[RECOMMENDATION] Proceed with divestment")
print(f" The divestment would improve consolidated results by ${improvement:,.2f}")
else:
print("[RECOMMENDATION] Review divestment terms")
print(f" Current terms show a negative impact of ${abs(improvement):,.2f}")
print(" Consider renegotiating sale price or timing")
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(comprehensive_analysis())
