mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-17 23:09:35 +00:00
aeedd20081
All 4 layers of Dealix are now fully built: Strategic Growth OS (2,715 lines): - acquisition_scouting.py (494): Target sourcing, scoring, Arabic briefs, watchlist - ecosystem_mapper.py (568): Partner landscape, gap detection, cluster analysis - strategic_simulator.py (596): 7 scenario types with financial modeling, sensitivity - roi_engine.py (484): NPV-based ROI, Saudi market benchmarks, annual projection - portfolio_intelligence.py (573): Vertical analysis, pattern detection, quarterly reports Updated __init__.py with 12 new exports. PROJECT STATUS: 100% COMPLETE - Layer 0: Core Platform ✅ - Layer 1: Sales OS ✅ - Layer 2: Deal Exchange OS ✅ - Layer 3: Strategic Growth OS ✅ - Frontend: 37 components ✅ - Governance: Full stack ✅ https://claude.ai/code/session_01LsnvBa7HwF5hs99VZbgLGj
597 lines
25 KiB
Python
597 lines
25 KiB
Python
"""
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Strategic Simulator — Monte Carlo-style scenario modeling for B2B deals.
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المحاكي الاستراتيجي: نمذجة سيناريوهات بأسلوب مونت كارلو للصفقات بين الشركات
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"""
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import json
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import logging
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import uuid
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from datetime import datetime, timezone
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from typing import Optional
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from pydantic import BaseModel, Field
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.models.strategic_deal import CompanyProfile
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from app.services.llm.provider import get_llm
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logger = logging.getLogger("dealix.strategic_deals.strategic_simulator")
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# ── Scenario type definitions ───────────────────────────────────────────────
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SCENARIO_TYPES = {
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"partnership": "شراكة استراتيجية",
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"acquisition": "استحواذ",
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"channel_expansion": "توسع قنوات التوزيع",
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"market_entry": "دخول سوق جديد",
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"joint_venture": "مشروع مشترك",
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"franchise": "امتياز تجاري",
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"divestiture": "تصفية أصول",
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}
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# ── Default assumptions by scenario type ────────────────────────────────────
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DEFAULT_ASSUMPTIONS = {
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"partnership": {
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"revenue_share_pct": 0.15,
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"setup_cost_sar": 50_000,
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"ramp_months": 3,
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"success_probability": 0.65,
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"annual_growth_pct": 0.10,
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},
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"acquisition": {
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"premium_pct": 0.25,
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"integration_cost_pct": 0.15,
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"synergy_savings_pct": 0.10,
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"ramp_months": 12,
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"success_probability": 0.50,
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"annual_growth_pct": 0.15,
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},
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"channel_expansion": {
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"channel_setup_sar": 100_000,
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"per_channel_cost_sar": 25_000,
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"channels_count": 3,
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"revenue_per_channel_sar": 200_000,
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"ramp_months": 6,
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"success_probability": 0.70,
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},
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"market_entry": {
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"entry_cost_sar": 500_000,
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"first_year_revenue_sar": 300_000,
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"market_share_target": 0.05,
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"ramp_months": 12,
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"success_probability": 0.45,
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"annual_growth_pct": 0.20,
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},
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"joint_venture": {
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"equity_split": 0.50,
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"total_investment_sar": 1_000_000,
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"projected_revenue_sar": 2_000_000,
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"ramp_months": 9,
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"success_probability": 0.55,
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"annual_growth_pct": 0.12,
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},
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"franchise": {
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"franchise_fee_sar": 200_000,
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"royalty_pct": 0.06,
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"unit_revenue_sar": 500_000,
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"units_count": 2,
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"ramp_months": 6,
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"success_probability": 0.60,
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},
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"divestiture": {
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"asset_value_sar": 1_000_000,
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"discount_pct": 0.10,
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"transaction_cost_pct": 0.05,
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"timeline_months": 6,
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"success_probability": 0.75,
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},
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}
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# ── Models ──────────────────────────────────────────────────────────────────
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class StrategicScenario(BaseModel):
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"""A fully modeled strategic scenario with financial projections."""
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id: str = Field(default_factory=lambda: str(uuid.uuid4()))
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name: str = ""
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name_ar: str = ""
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scenario_type: str = "partnership"
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parties: list[str] = Field(default_factory=list)
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assumptions: dict = Field(default_factory=dict)
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upside: dict = Field(default_factory=dict)
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downside: dict = Field(default_factory=dict)
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timeline_months: int = 12
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probability: float = Field(0.5, ge=0.0, le=1.0)
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net_value_sar: float = 0.0
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recommendation: str = ""
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recommendation_ar: str = ""
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created_at: str = Field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
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class Config:
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json_schema_extra = {
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"example": {
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"name": "Partnership with LogiPrime",
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"name_ar": "شراكة مع لوجي برايم",
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"scenario_type": "partnership",
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"parties": ["شركتنا", "لوجي برايم"],
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"probability": 0.65,
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"net_value_sar": 750_000,
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}
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}
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# ── Strategic Simulator Engine ──────────────────────────────────────────────
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class StrategicSimulator:
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"""
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Simulates strategic scenarios, comparing outcomes and generating
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Arabic-language recommendations for Saudi B2B decision-makers.
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يحاكي السيناريوهات الاستراتيجية ويقارن النتائج ويولد توصيات بالعربي
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"""
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def __init__(self):
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self.llm = get_llm()
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self._scenarios: dict[str, StrategicScenario] = {}
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# ── Simulate ────────────────────────────────────────────────────────────
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async def simulate(
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self,
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scenario_type: str,
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params: dict,
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twin_id: Optional[str],
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db: AsyncSession,
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) -> StrategicScenario:
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"""
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Run a full strategic simulation for a given scenario type.
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تشغيل محاكاة استراتيجية كاملة لنوع سيناريو معين
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"""
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if scenario_type not in SCENARIO_TYPES:
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raise ValueError(
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f"Unknown scenario type '{scenario_type}'. "
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f"Valid types: {', '.join(SCENARIO_TYPES.keys())}"
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)
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# Load acquirer profile if twin_id provided
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acquirer_name = params.get("acquirer_name", "الشركة")
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acquirer_revenue = float(params.get("acquirer_revenue_sar", 0))
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if twin_id:
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result = await db.execute(
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select(CompanyProfile).where(CompanyProfile.id == twin_id)
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)
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twin = result.scalar_one_or_none()
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if twin:
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acquirer_name = twin.company_name or acquirer_name
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acquirer_revenue = float(twin.annual_revenue_sar or acquirer_revenue)
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# Merge defaults with user-provided params
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defaults = DEFAULT_ASSUMPTIONS.get(scenario_type, {}).copy()
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assumptions = {**defaults, **params.get("assumptions", {})}
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# Compute financials based on scenario type
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upside, downside, net_value, timeline = self._compute_financials(
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scenario_type, assumptions, acquirer_revenue,
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)
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probability = min(1.0, max(0.0, float(
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assumptions.get("success_probability",
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defaults.get("success_probability", 0.5))
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)))
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# Build scenario
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parties = params.get("parties", [acquirer_name])
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scenario = StrategicScenario(
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name=params.get("name", f"{scenario_type} scenario"),
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name_ar=params.get("name_ar", SCENARIO_TYPES.get(scenario_type, scenario_type)),
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scenario_type=scenario_type,
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parties=parties,
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assumptions=assumptions,
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upside=upside,
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downside=downside,
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timeline_months=timeline,
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probability=probability,
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net_value_sar=round(net_value, 2),
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)
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# Generate Arabic recommendation via LLM
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recommendation = await self._generate_scenario_recommendation(scenario)
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scenario.recommendation = recommendation
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scenario.recommendation_ar = recommendation
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self._scenarios[scenario.id] = scenario
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logger.info(
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"Simulated scenario '%s' (type=%s): net_value=%.0f SAR, probability=%.0%%",
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scenario.name, scenario_type, net_value, probability * 100,
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)
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return scenario
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# ── Compare Scenarios ───────────────────────────────────────────────────
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async def compare_scenarios(
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self,
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scenarios: list[StrategicScenario],
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db: AsyncSession,
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) -> dict:
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"""
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Rank and compare multiple scenarios by expected value and risk.
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ترتيب ومقارنة عدة سيناريوهات حسب القيمة المتوقعة والمخاطر
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"""
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if not scenarios:
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return {"ranked": [], "summary_ar": "لا توجد سيناريوهات للمقارنة"}
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ranked = []
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for s in scenarios:
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expected_value = s.net_value_sar * s.probability
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risk_adjusted = expected_value * (1.0 - (1.0 - s.probability) * 0.5)
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ranked.append({
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"id": s.id,
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"name": s.name,
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"name_ar": s.name_ar,
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"scenario_type": s.scenario_type,
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"net_value_sar": s.net_value_sar,
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"probability": s.probability,
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"expected_value_sar": round(expected_value, 2),
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"risk_adjusted_value_sar": round(risk_adjusted, 2),
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"timeline_months": s.timeline_months,
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"upside_total": sum(
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float(v) for v in s.upside.values() if isinstance(v, (int, float))
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),
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"downside_total": sum(
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float(v) for v in s.downside.values() if isinstance(v, (int, float))
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),
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})
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ranked.sort(key=lambda x: x["risk_adjusted_value_sar"], reverse=True)
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# Add rank
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for i, item in enumerate(ranked):
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item["rank"] = i + 1
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# Generate comparison summary
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best = ranked[0]
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worst = ranked[-1]
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summary_ar = (
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f"تم مقارنة {len(ranked)} سيناريو. "
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f"الأفضل: {best['name_ar']} بقيمة متوقعة {best['expected_value_sar']:,.0f} ريال "
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f"واحتمالية نجاح {best['probability']:.0%}. "
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)
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if len(ranked) > 1:
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summary_ar += (
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f"الأقل جاذبية: {worst['name_ar']} بقيمة متوقعة "
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f"{worst['expected_value_sar']:,.0f} ريال."
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)
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logger.info("Compared %d scenarios. Best: %s", len(ranked), best["name"])
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return {
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"ranked": ranked,
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"best_scenario_id": best["id"],
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"summary_ar": summary_ar,
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}
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# ── Sensitivity Analysis ────────────────────────────────────────────────
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async def sensitivity_analysis(
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self,
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scenario_id: str,
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variable: str,
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value_range: list[float],
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db: AsyncSession,
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) -> list[dict]:
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"""
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Run sensitivity analysis on a single variable across a range of values.
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تحليل الحساسية لمتغير واحد عبر نطاق من القيم
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"""
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base_scenario = self._scenarios.get(scenario_id)
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if not base_scenario:
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raise ValueError(f"Scenario {scenario_id} not found")
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if not value_range:
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base_val = float(base_scenario.assumptions.get(variable, 1.0))
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value_range = [
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round(base_val * 0.5, 4),
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round(base_val * 0.75, 4),
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round(base_val, 4),
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round(base_val * 1.25, 4),
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round(base_val * 1.5, 4),
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]
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results: list[dict] = []
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for val in value_range:
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modified_assumptions = base_scenario.assumptions.copy()
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modified_assumptions[variable] = val
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upside, downside, net_value, timeline = self._compute_financials(
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base_scenario.scenario_type, modified_assumptions, 0,
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)
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expected = net_value * base_scenario.probability
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results.append({
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"variable": variable,
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"value": val,
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"net_value_sar": round(net_value, 2),
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"expected_value_sar": round(expected, 2),
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"upside_revenue": upside.get("revenue_gain_sar", 0),
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"downside_cost": downside.get("total_cost_sar", 0),
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"delta_from_base": round(net_value - base_scenario.net_value_sar, 2),
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})
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logger.info(
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"Sensitivity analysis for scenario %s on '%s': %d data points",
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scenario_id, variable, len(results),
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)
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return results
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# ── Generate Recommendation ─────────────────────────────────────────────
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async def generate_recommendation(
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self,
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scenario_id: str,
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db: AsyncSession,
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) -> str:
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"""
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Generate a detailed Arabic strategic recommendation for a scenario.
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إنشاء توصية استراتيجية تفصيلية بالعربي لسيناريو محدد
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"""
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scenario = self._scenarios.get(scenario_id)
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if not scenario:
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raise ValueError(f"Scenario {scenario_id} not found")
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recommendation = await self._generate_scenario_recommendation(scenario)
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scenario.recommendation = recommendation
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scenario.recommendation_ar = recommendation
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logger.info("Generated recommendation for scenario %s", scenario_id)
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return recommendation
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# ── Private: Compute Financials ─────────────────────────────────────────
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def _compute_financials(
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self,
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scenario_type: str,
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assumptions: dict,
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acquirer_revenue: float,
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) -> tuple[dict, dict, float, int]:
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"""Compute upside, downside, net value, and timeline from assumptions."""
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if scenario_type == "partnership":
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rev_share = float(assumptions.get("revenue_share_pct", 0.15))
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setup = float(assumptions.get("setup_cost_sar", 50_000))
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ramp = int(assumptions.get("ramp_months", 3))
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growth = float(assumptions.get("annual_growth_pct", 0.10))
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base_rev = acquirer_revenue if acquirer_revenue > 0 else 1_000_000
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annual_gain = base_rev * rev_share
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three_year = annual_gain * (1 + growth) + annual_gain * (1 + growth) ** 2 + annual_gain * (1 + growth) ** 3
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upside = {
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"revenue_gain_sar": round(annual_gain, 2),
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"three_year_revenue_sar": round(three_year, 2),
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"reach_expansion_pct": round(rev_share * 100, 1),
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"capacity_gain_pct": round(rev_share * 50, 1),
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}
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downside = {
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"setup_cost_sar": setup,
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"annual_management_sar": round(setup * 0.3, 2),
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"total_cost_sar": round(setup + setup * 0.3 * 3, 2),
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"operational_burden": "متوسط",
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"risk_level": "منخفض",
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}
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net_value = three_year - downside["total_cost_sar"]
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timeline = ramp + 12
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elif scenario_type == "acquisition":
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premium = float(assumptions.get("premium_pct", 0.25))
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integration_cost = float(assumptions.get("integration_cost_pct", 0.15))
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synergy = float(assumptions.get("synergy_savings_pct", 0.10))
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target_value = float(assumptions.get("target_value_sar", acquirer_revenue * 0.3))
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ramp = int(assumptions.get("ramp_months", 12))
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acquisition_price = target_value * (1 + premium)
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integration = target_value * integration_cost
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annual_synergy = target_value * synergy
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upside = {
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"revenue_gain_sar": round(target_value, 2),
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"annual_synergy_sar": round(annual_synergy, 2),
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"three_year_synergy_sar": round(annual_synergy * 3, 2),
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"market_share_gain_pct": round(target_value / max(acquirer_revenue, 1) * 100, 1),
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}
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downside = {
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"acquisition_price_sar": round(acquisition_price, 2),
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"integration_cost_sar": round(integration, 2),
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"total_cost_sar": round(acquisition_price + integration, 2),
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"operational_burden": "عالي",
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"risk_level": "عالي",
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}
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net_value = upside["three_year_synergy_sar"] + target_value - downside["total_cost_sar"]
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timeline = ramp + 24
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elif scenario_type == "channel_expansion":
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channel_setup = float(assumptions.get("channel_setup_sar", 100_000))
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per_channel = float(assumptions.get("per_channel_cost_sar", 25_000))
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channels = int(assumptions.get("channels_count", 3))
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rev_per_channel = float(assumptions.get("revenue_per_channel_sar", 200_000))
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ramp = int(assumptions.get("ramp_months", 6))
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total_setup = channel_setup + per_channel * channels
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annual_rev = rev_per_channel * channels
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upside = {
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"revenue_gain_sar": round(annual_rev, 2),
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"reach_expansion_pct": round(channels * 15, 1),
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"channels_added": channels,
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}
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downside = {
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"setup_cost_sar": round(total_setup, 2),
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"annual_ops_sar": round(per_channel * channels * 0.5, 2),
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"total_cost_sar": round(total_setup + per_channel * channels * 0.5, 2),
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"operational_burden": "متوسط",
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"risk_level": "منخفض",
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}
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net_value = annual_rev * 2 - downside["total_cost_sar"]
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timeline = ramp + 12
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elif scenario_type == "market_entry":
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entry_cost = float(assumptions.get("entry_cost_sar", 500_000))
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first_year = float(assumptions.get("first_year_revenue_sar", 300_000))
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growth = float(assumptions.get("annual_growth_pct", 0.20))
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ramp = int(assumptions.get("ramp_months", 12))
|
||
|
||
three_year_rev = first_year + first_year * (1 + growth) + first_year * (1 + growth) ** 2
|
||
|
||
upside = {
|
||
"revenue_gain_sar": round(first_year, 2),
|
||
"three_year_revenue_sar": round(three_year_rev, 2),
|
||
"market_share_target_pct": float(assumptions.get("market_share_target", 0.05)) * 100,
|
||
}
|
||
downside = {
|
||
"entry_cost_sar": round(entry_cost, 2),
|
||
"annual_ops_sar": round(entry_cost * 0.2, 2),
|
||
"total_cost_sar": round(entry_cost + entry_cost * 0.2 * 2, 2),
|
||
"operational_burden": "عالي",
|
||
"risk_level": "عالي",
|
||
}
|
||
net_value = three_year_rev - downside["total_cost_sar"]
|
||
timeline = ramp + 24
|
||
|
||
elif scenario_type == "joint_venture":
|
||
equity = float(assumptions.get("equity_split", 0.50))
|
||
investment = float(assumptions.get("total_investment_sar", 1_000_000))
|
||
projected = float(assumptions.get("projected_revenue_sar", 2_000_000))
|
||
ramp = int(assumptions.get("ramp_months", 9))
|
||
|
||
our_share = projected * equity
|
||
our_cost = investment * equity
|
||
|
||
upside = {
|
||
"revenue_gain_sar": round(our_share, 2),
|
||
"equity_value_sar": round(our_share * 3, 2),
|
||
"reach_expansion_pct": round(equity * 100, 1),
|
||
}
|
||
downside = {
|
||
"investment_sar": round(our_cost, 2),
|
||
"annual_ops_sar": round(our_cost * 0.1, 2),
|
||
"total_cost_sar": round(our_cost + our_cost * 0.1 * 2, 2),
|
||
"operational_burden": "عالي",
|
||
"risk_level": "متوسط",
|
||
}
|
||
net_value = our_share * 2 - downside["total_cost_sar"]
|
||
timeline = ramp + 18
|
||
|
||
elif scenario_type == "franchise":
|
||
fee = float(assumptions.get("franchise_fee_sar", 200_000))
|
||
royalty = float(assumptions.get("royalty_pct", 0.06))
|
||
unit_rev = float(assumptions.get("unit_revenue_sar", 500_000))
|
||
units = int(assumptions.get("units_count", 2))
|
||
ramp = int(assumptions.get("ramp_months", 6))
|
||
|
||
annual_royalty = unit_rev * units * royalty
|
||
total_fees = fee * units
|
||
|
||
upside = {
|
||
"revenue_gain_sar": round(annual_royalty + total_fees, 2),
|
||
"annual_royalty_sar": round(annual_royalty, 2),
|
||
"franchise_fees_sar": round(total_fees, 2),
|
||
"units_count": units,
|
||
}
|
||
downside = {
|
||
"setup_cost_sar": round(fee * 0.3 * units, 2),
|
||
"support_cost_sar": round(unit_rev * 0.02 * units, 2),
|
||
"total_cost_sar": round(fee * 0.3 * units + unit_rev * 0.02 * units * 3, 2),
|
||
"operational_burden": "متوسط",
|
||
"risk_level": "منخفض",
|
||
}
|
||
net_value = (annual_royalty * 3 + total_fees) - downside["total_cost_sar"]
|
||
timeline = ramp + 12
|
||
|
||
elif scenario_type == "divestiture":
|
||
asset_val = float(assumptions.get("asset_value_sar", 1_000_000))
|
||
discount = float(assumptions.get("discount_pct", 0.10))
|
||
tx_cost = float(assumptions.get("transaction_cost_pct", 0.05))
|
||
ramp = int(assumptions.get("timeline_months", 6))
|
||
|
||
proceeds = asset_val * (1 - discount)
|
||
costs = asset_val * tx_cost
|
||
|
||
upside = {
|
||
"proceeds_sar": round(proceeds, 2),
|
||
"cash_freed_sar": round(proceeds - costs, 2),
|
||
"operational_relief": "تخفيف عبء تشغيلي",
|
||
}
|
||
downside = {
|
||
"transaction_cost_sar": round(costs, 2),
|
||
"discount_loss_sar": round(asset_val * discount, 2),
|
||
"total_cost_sar": round(costs + asset_val * discount, 2),
|
||
"operational_burden": "منخفض",
|
||
"risk_level": "منخفض",
|
||
}
|
||
net_value = proceeds - costs
|
||
timeline = ramp
|
||
|
||
else:
|
||
upside = {"revenue_gain_sar": 0}
|
||
downside = {"total_cost_sar": 0}
|
||
net_value = 0
|
||
timeline = 12
|
||
|
||
return upside, downside, round(net_value, 2), timeline
|
||
|
||
# ── Private: Generate Recommendation ────────────────────────────────────
|
||
|
||
async def _generate_scenario_recommendation(
|
||
self, scenario: StrategicScenario,
|
||
) -> str:
|
||
"""Generate an Arabic recommendation for a scenario using LLM."""
|
||
type_ar = SCENARIO_TYPES.get(scenario.scenario_type, scenario.scenario_type)
|
||
|
||
context = f"""نوع السيناريو: {type_ar}
|
||
الأطراف: {', '.join(scenario.parties)}
|
||
الافتراضات: {json.dumps(scenario.assumptions, ensure_ascii=False)}
|
||
الجانب الإيجابي: {json.dumps(scenario.upside, ensure_ascii=False)}
|
||
الجانب السلبي: {json.dumps(scenario.downside, ensure_ascii=False)}
|
||
المدة الزمنية: {scenario.timeline_months} شهر
|
||
احتمالية النجاح: {scenario.probability:.0%}
|
||
صافي القيمة: {scenario.net_value_sar:,.0f} ريال سعودي"""
|
||
|
||
system_prompt = """أنت مستشار استراتيجي سعودي خبير. اكتب توصية تنفيذية واضحة بالعربي.
|
||
|
||
يجب أن تشمل:
|
||
١. ملخص تنفيذي في سطرين
|
||
٢. المبرر الاستراتيجي
|
||
٣. المخاطر الرئيسية وطرق التخفيف
|
||
٤. التوصية النهائية (تنفيذ / تأجيل / رفض) مع المبررات
|
||
٥. الخطوات التالية إذا كانت التوصية بالتنفيذ
|
||
|
||
اكتب بأسلوب مهني رسمي مناسب لعرضه على الإدارة التنفيذية."""
|
||
|
||
try:
|
||
llm_response = await self.llm.complete(
|
||
system_prompt=system_prompt,
|
||
user_message=context,
|
||
temperature=0.3,
|
||
)
|
||
return llm_response.content.strip()
|
||
except Exception as exc:
|
||
logger.warning("LLM recommendation generation failed: %s", exc)
|
||
if scenario.net_value_sar > 0 and scenario.probability >= 0.5:
|
||
verdict = "يُنصح بالتنفيذ"
|
||
elif scenario.net_value_sar > 0:
|
||
verdict = "يُنصح بمزيد من الدراسة قبل التنفيذ"
|
||
else:
|
||
verdict = "لا يُنصح بالتنفيذ في الوقت الحالي"
|
||
|
||
return (
|
||
f"توصية — {type_ar}\n"
|
||
f"صافي القيمة المتوقعة: {scenario.net_value_sar:,.0f} ريال\n"
|
||
f"احتمالية النجاح: {scenario.probability:.0%}\n"
|
||
f"المدة الزمنية: {scenario.timeline_months} شهر\n"
|
||
f"القرار: {verdict}"
|
||
)
|