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"""
Strategic Sandbox - Core Logic Module
Data models and simulation engine for strategy evaluation
"""
import json
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, asdict
import pandas as pd
@dataclass
class Goal:
"""Strategic goal definition with main metric"""
text: str
metric: str
baseline: float
target: float
horizon: str
unit: str = "%"
@dataclass
class Arena:
"""Market arena definition"""
market: str
category: str
competitors: List[str]
target_audience: str = ""
@dataclass
class Insight:
"""Market or consumer insight"""
id: str
text: str
evidence: List[str]
@dataclass
class Hypothesis:
"""Testable hypothesis"""
id: str
text: str
based_on: List[str] # insight IDs
metric: str
expected_change: float
@dataclass
class Move:
"""Strategic move/action"""
id: str
text: str
linked_hypothesis: str
impact: float # 0-1
fit: float # 0-1
risk: float # 0-1
cost: float
@dataclass
class Metric:
"""Success metric"""
id: str
text: str
baseline: float
target: float
unit: str
class Strategy:
"""Complete strategy model"""
def __init__(self):
self.goal: Optional[Goal] = None
self.arena: Optional[Arena] = None
self.insights: List[Insight] = []
self.hypotheses: List[Hypothesis] = []
self.moves: List[Move] = []
self.metrics: List[Metric] = []
def to_dict(self) -> Dict[str, Any]:
"""Convert strategy to dictionary"""
return {
"goal": asdict(self.goal) if self.goal else None,
"arena": asdict(self.arena) if self.arena else None,
"insights": [asdict(i) for i in self.insights],
"hypotheses": [asdict(h) for h in self.hypotheses],
"moves": [asdict(m) for m in self.moves],
"metrics": [asdict(m) for m in self.metrics]
}
def to_json(self, filepath: str):
"""Save strategy to JSON file"""
with open(filepath, 'w', encoding='utf-8') as f:
json.dump(self.to_dict(), f, indent=2, ensure_ascii=False)
@classmethod
def from_dict(cls, data: Dict[str, Any]) -> 'Strategy':
"""Load strategy from dictionary"""
strategy = cls()
if data.get("goal"):
strategy.goal = Goal(**data["goal"])
if data.get("arena"):
strategy.arena = Arena(**data["arena"])
strategy.insights = [Insight(**i) for i in data.get("insights", [])]
strategy.hypotheses = [Hypothesis(**h) for h in data.get("hypotheses", [])]
strategy.moves = [Move(**m) for m in data.get("moves", [])]
strategy.metrics = [Metric(**m) for m in data.get("metrics", [])]
return strategy
@classmethod
def from_json(cls, filepath: str) -> 'Strategy':
"""Load strategy from JSON file"""
with open(filepath, 'r', encoding='utf-8') as f:
data = json.load(f)
return cls.from_dict(data)
class SimulationEngine:
"""Strategy simulation and scoring engine"""
@staticmethod
def calculate_move_score(move: Move) -> float:
"""
Calculate move score using formula:
score = (impact × fit) × (1 - risk) / cost
"""
if move.cost == 0:
return 0
score = (move.impact * move.fit) * (1 - move.risk) / (move.cost / 100000)
return round(score, 4)
@staticmethod
def simulate_strategy(strategy: Strategy) -> Dict[str, Any]:
"""
Run simulation on complete strategy
Returns scores, rankings, and forecasts
"""
results = {
"move_scores": [],
"total_impact": 0,
"metric_forecasts": [],
"recommendations": []
}
# Calculate scores for each move
for move in strategy.moves:
score = SimulationEngine.calculate_move_score(move)
results["move_scores"].append({
"id": move.id,
"text": move.text,
"score": score,
"impact": move.impact,
"fit": move.fit,
"risk": move.risk,
"cost": move.cost,
"linked_hypothesis": move.linked_hypothesis
})
# Sort by score
results["move_scores"].sort(key=lambda x: x["score"], reverse=True)
# Calculate total impact
total_score = sum(m["score"] for m in results["move_scores"])
results["total_impact"] = round(total_score, 4)
# Forecast main metric (from Goal) first
if strategy.goal:
linked_moves = []
linked_hypotheses = []
for move in strategy.moves:
for hyp in strategy.hypotheses:
if hyp.id == move.linked_hypothesis and hyp.metric == strategy.goal.metric:
linked_moves.append(move)
if hyp.id not in linked_hypotheses:
linked_hypotheses.append(hyp.id)
# Calculate forecast and contribution breakdown
moves_breakdown = []
for move in linked_moves:
move_score = SimulationEngine.calculate_move_score(move)
moves_breakdown.append({
"id": move.id,
"text": move.text,
"score": move_score,
"hypothesis": move.linked_hypothesis
})
moves_score = sum(m["score"] for m in moves_breakdown)
forecast = strategy.goal.baseline * (1 + moves_score)
results["metric_forecasts"].append({
"id": strategy.goal.metric,
"text": f"{strategy.goal.text} (MAIN GOAL)",
"baseline": strategy.goal.baseline,
"target": strategy.goal.target,
"forecast": round(forecast, 2),
"unit": strategy.goal.unit,
"gap_to_target": round(strategy.goal.target - forecast, 2),
"linked_moves": moves_breakdown,
"linked_hypotheses": linked_hypotheses,
"is_main": True
})
# Forecast supporting metrics
for metric in strategy.metrics:
# Find moves linked to this metric through hypotheses
linked_moves = []
linked_hypotheses = []
for move in strategy.moves:
for hyp in strategy.hypotheses:
if hyp.id == move.linked_hypothesis and hyp.metric == metric.id:
linked_moves.append(move)
if hyp.id not in linked_hypotheses:
linked_hypotheses.append(hyp.id)
# Calculate forecast and contribution breakdown
moves_breakdown = []
for move in linked_moves:
move_score = SimulationEngine.calculate_move_score(move)
moves_breakdown.append({
"id": move.id,
"text": move.text,
"score": move_score,
"hypothesis": move.linked_hypothesis
})
moves_score = sum(m["score"] for m in moves_breakdown)
forecast = metric.baseline * (1 + moves_score)
results["metric_forecasts"].append({
"id": metric.id,
"text": metric.text,
"baseline": metric.baseline,
"target": metric.target,
"forecast": round(forecast, 2),
"unit": metric.unit,
"gap_to_target": round(metric.target - forecast, 2),
"linked_moves": moves_breakdown,
"linked_hypotheses": linked_hypotheses,
"is_main": False
})
# Generate recommendations
if results["move_scores"]:
top_move = results["move_scores"][0]
if top_move["risk"] > 0.7:
results["recommendations"].append(f"⚠️ Top move '{top_move['text']}' has high risk ({top_move['risk']})")
high_cost_moves = [m for m in results["move_scores"] if m["cost"] > 100000]
if high_cost_moves:
results["recommendations"].append(f"💰 {len(high_cost_moves)} move(s) exceed 100k budget")
return results
@staticmethod
def create_results_dataframe(results: Dict[str, Any]) -> pd.DataFrame:
"""Convert simulation results to pandas DataFrame"""
if not results.get("move_scores"):
return pd.DataFrame()
df = pd.DataFrame(results["move_scores"])
df = df[["id", "text", "score", "impact", "fit", "risk", "cost"]]
df.columns = ["ID", "Move", "Score", "Impact", "Fit", "Risk", "Cost"]
return df
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