a96d0d8889
- growth-engine: Autonomous experiment engine (Karpathy autoresearch for marketing) - sales-pipeline: RB2B router, deal resurrector, trigger prospector, ICP learner - content-ops: Expert panel, quality gate, editorial brain, quote miner - outbound-engine: Cold outbound optimizer, lead pipeline, competitive monitor - seo-ops: Content attack briefs, GSC optimizer, trend scout - finance-ops: CFO briefing, cost estimate, scenario modeler 79 files, all sanitized - zero hardcoded credentials or internal references.
238 lines
10 KiB
Python
238 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Scenario Modeler — Models base/bull/bear cases from financial analysis.
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Takes a JSON file with financial summary data and projects 12-month scenarios.
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Outputs to stdout and saves JSON for further analysis.
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Usage:
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python3 scenario-modeler.py --input ./data/financial-latest.json
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python3 scenario-modeler.py --input ./data/financial-latest.json --output ./data/scenarios.json
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"""
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import argparse
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import json
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import os
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import sys
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from datetime import datetime
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def load_financial_data(input_path: str) -> dict:
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"""Load financial summary JSON."""
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with open(input_path) as f:
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return json.load(f)
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def model_base_case(data: dict) -> dict:
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"""Current trajectory continues. No growth, no cuts."""
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monthly_rev = data["total_revenue"] / 12
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monthly_cogs = data["total_cogs"] / 12
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monthly_opex = data["total_opex"] / 12
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monthly_other = data.get("other_expenses", 0) / 12 - data.get("other_income", 0) / 12
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monthly_net = data["net_income"] / 12
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projections = []
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for m in range(1, 13):
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projections.append({
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"month": m,
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"revenue": round(monthly_rev, 2),
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"total_costs": round(monthly_cogs + monthly_opex + monthly_other, 2),
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"net_income": round(monthly_net, 2),
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"cumulative_pl": round(monthly_net * m, 2),
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})
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monthly_burn = abs(monthly_net) if monthly_net < 0 else 0
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breakeven_monthly_cut = monthly_burn # How much to cut monthly to break even
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return {
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"name": "Base Case — Status Quo",
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"description": "Current trajectory continues. No new clients, no lost clients, no cost changes.",
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"assumptions": [
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f"Revenue stays flat at ~${monthly_rev:,.0f}/mo",
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f"COGS stays at ~${monthly_cogs:,.0f}/mo",
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f"OpEx stays at ~${monthly_opex:,.0f}/mo",
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"No new hires, no layoffs",
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],
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"monthly_burn": round(monthly_burn, 2),
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"annual_projected_loss": round(data["net_income"], 2) if data["net_income"] < 0 else 0,
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"annual_projected_profit": round(data["net_income"], 2) if data["net_income"] > 0 else 0,
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"months_to_breakeven": "N/A (already profitable)" if monthly_net > 0 else "Never (at current trajectory)",
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"key_levers": [
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f"Cut ${breakeven_monthly_cut:,.0f}/mo from costs to break even" if monthly_burn > 0 else "Maintain current discipline",
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f"Or grow revenue {round(monthly_burn / monthly_rev * 100, 1)}% while holding costs flat" if monthly_burn > 0 and monthly_rev > 0 else "Focus on margin expansion",
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"Audit subscriptions and contractor spend for quick wins",
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],
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"projections": projections,
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}
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def model_bull_case(data: dict, new_product_arr: float = 500000, new_clients: int = 3, avg_client_mrr: float = 15000) -> dict:
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"""Growth targets met: new product revenue + new agency clients."""
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monthly_rev = data["total_revenue"] / 12
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monthly_cogs = data["total_cogs"] / 12
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monthly_opex = data["total_opex"] / 12
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monthly_other = data.get("other_expenses", 0) / 12 - data.get("other_income", 0) / 12
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product_monthly = new_product_arr / 12
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new_clients_monthly = new_clients * avg_client_mrr
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# Product has SaaS margins (~80%), services have ~50% margin
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product_cogs = product_monthly * 0.20
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services_cogs = new_clients_monthly * 0.50
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projections = []
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for m in range(1, 13):
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# Ramp: product over 6 months, clients added quarterly
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product_ramp = min(m / 6, 1.0)
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client_ramp = min(m, new_clients) / new_clients
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month_rev = monthly_rev + (product_monthly * product_ramp) + (new_clients_monthly * client_ramp)
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month_costs = monthly_cogs + monthly_opex + monthly_other + (product_cogs * product_ramp) + (services_cogs * client_ramp)
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month_net = month_rev - month_costs
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projections.append({
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"month": m,
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"revenue": round(month_rev, 2),
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"total_costs": round(month_costs, 2),
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"net_income": round(month_net, 2),
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"cumulative_pl": round(sum(p["net_income"] for p in projections) + month_net, 2),
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})
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breakeven_month = None
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for p in projections:
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if p["net_income"] > 0:
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breakeven_month = p["month"]
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break
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return {
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"name": "Bull Case — Product + Growth",
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"description": f"New product hits ${new_product_arr/1000:.0f}K ARR, add {new_clients} clients at ${avg_client_mrr/1000:.0f}K/mo.",
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"assumptions": [
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f"Product ramps to ${product_monthly:,.0f}/mo over 6 months",
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f"{new_clients} new clients at ${avg_client_mrr:,.0f}/mo each, added quarterly",
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"Product has 80% gross margin (SaaS)",
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"New services clients at 50% margin",
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"No additional OpEx needed (existing team absorbs)",
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],
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"additional_annual_revenue": round((product_monthly + new_clients_monthly) * 12, 2),
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"monthly_profit_at_full_ramp": round(projections[-1]["net_income"], 2) if projections[-1]["net_income"] > 0 else 0,
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"months_to_breakeven": breakeven_month if breakeven_month else ">12 months",
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"key_levers": [
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"Product-market fit and sales execution",
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f"Services pipeline — need 1 new ${avg_client_mrr/1000:.0f}K client per quarter",
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"Keep OpEx flat during growth phase",
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"SaaS margins dramatically improve blended margin",
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],
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"projections": projections,
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}
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def model_bear_case(data: dict, pct_revenue_lost: float = 0.30) -> dict:
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"""Lose significant portion of revenue (e.g., top clients churn)."""
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monthly_rev = data["total_revenue"] / 12
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monthly_cogs = data["total_cogs"] / 12
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monthly_opex = data["total_opex"] / 12
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monthly_other = data.get("other_expenses", 0) / 12 - data.get("other_income", 0) / 12
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lost_revenue = data["total_revenue"] * pct_revenue_lost
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monthly_lost = lost_revenue / 12
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# Save ~45% of lost revenue in COGS (team partially redeployed)
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monthly_saved_cogs = monthly_lost * 0.45
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projections = []
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for m in range(1, 13):
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month_rev = monthly_rev - monthly_lost
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month_costs = (monthly_cogs - monthly_saved_cogs) + monthly_opex + monthly_other
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month_net = month_rev - month_costs
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projections.append({
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"month": m,
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"revenue": round(month_rev, 2),
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"total_costs": round(month_costs, 2),
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"net_income": round(month_net, 2),
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"cumulative_pl": round(month_net * m, 2),
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})
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return {
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"name": f"Bear Case — Lose {pct_revenue_lost*100:.0f}% Revenue",
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"description": f"Top clients churn, losing {pct_revenue_lost*100:.0f}% of revenue.",
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"assumptions": [
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f"Lose ${lost_revenue:,.0f}/yr ({pct_revenue_lost*100:.0f}% of revenue)",
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"COGS reduces ~45% of lost revenue (team partially redeployed)",
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"OpEx stays fixed (can't cut fast enough)",
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"No replacement clients in forecast period",
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],
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"lost_annual_revenue": round(lost_revenue, 2),
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"new_annual_revenue": round(data["total_revenue"] - lost_revenue, 2),
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"monthly_burn": round(abs(projections[0]["net_income"]), 2) if projections[0]["net_income"] < 0 else 0,
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"annual_projected_loss": round(projections[0]["net_income"] * 12, 2),
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"months_to_breakeven": "Requires major restructuring",
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"key_levers": [
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f"Immediate need: cut ${abs(projections[0]['net_income']):,.0f}/mo in costs",
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"Reduce headcount in affected service lines",
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"Accelerate sales pipeline to replace lost revenue",
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"Consider consolidating service lines",
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],
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"required_monthly_cost_cuts": round(abs(projections[0]["net_income"]), 2) if projections[0]["net_income"] < 0 else 0,
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"projections": projections,
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}
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def main():
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parser = argparse.ArgumentParser(description='Financial Scenario Modeler')
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parser.add_argument('--input', '-i', required=True,
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help='Path to financial summary JSON (output from cfo-analyzer history)')
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parser.add_argument('--output', '-o', default=None,
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help='Output path for scenarios JSON (default: stdout only)')
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parser.add_argument('--product-arr', type=float, default=500000,
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help='Bull case: new product ARR target (default: 500000)')
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parser.add_argument('--new-clients', type=int, default=3,
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help='Bull case: number of new clients (default: 3)')
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parser.add_argument('--client-mrr', type=float, default=15000,
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help='Bull case: average new client MRR (default: 15000)')
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parser.add_argument('--bear-loss-pct', type=float, default=0.30,
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help='Bear case: percentage of revenue lost (default: 0.30)')
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args = parser.parse_args()
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print("🔮 Scenario Modeler — Building projections...", file=sys.stderr)
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data = load_financial_data(args.input)
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scenarios = {
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"base_case": model_base_case(data),
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"bull_case": model_bull_case(data, args.product_arr, args.new_clients, args.client_mrr),
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"bear_case": model_bear_case(data, args.bear_loss_pct),
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"generated_at": datetime.now().isoformat(),
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"based_on_period": data.get("period", "Unknown"),
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}
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# Summary comparison
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scenarios["summary"] = {
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"base_monthly_burn": scenarios["base_case"]["monthly_burn"],
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"bull_monthly_profit": scenarios["bull_case"].get("monthly_profit_at_full_ramp", 0),
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"bear_monthly_burn": scenarios["bear_case"]["monthly_burn"],
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"current_net_income": data["net_income"],
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}
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if args.output:
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os.makedirs(os.path.dirname(args.output) or '.', exist_ok=True)
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with open(args.output, "w") as f:
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json.dump(scenarios, f, indent=2)
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print(f"✅ Scenarios saved to {args.output}", file=sys.stderr)
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# Print summary to stdout
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print(f"\n{'='*60}")
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for case_key in ["base_case", "bull_case", "bear_case"]:
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case = scenarios[case_key]
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print(f"\n📌 {case['name']}")
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print(f" {case['description']}")
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if case.get("monthly_burn"):
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print(f" Monthly burn: ${case['monthly_burn']:,.0f}")
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if case.get("monthly_profit_at_full_ramp"):
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print(f" Monthly profit at ramp: ${case['monthly_profit_at_full_ramp']:,.0f}")
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print(f" Breakeven: {case['months_to_breakeven']}")
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print(f" Key levers:")
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for lever in case.get("key_levers", []):
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print(f" • {lever}")
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if __name__ == "__main__":
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main()
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