Retail Promotion Planning Agent

import json, pandas as pd
from datetime import datetime, timedelta

PROMO_HISTORY = [
  {"name":"Hari Merdeka","budget":5000,"revenue":17500,"discount":15},
  {"name":"Back to School","budget":3500,"revenue":9800,"discount":20},
  {"name":"Clearance Sale","budget":8000,"revenue":12000,"discount":50},
  {"name":"Hari Raya","budget":12000,"revenue":50400,"discount":10}
]

def analyze_promos(promos):
  df = pd.DataFrame(promos)
  df["roi"] = (df["revenue"] - df["budget"]) / df["budget"]
  avg_roi = df["roi"].mean()
  opt_discount = df.loc[df["roi"].idxmax(), "discount"]
  calendar = []
  for _, r in df.iterrows():
    start = (datetime.now() + timedelta(days=len(calendar)*14)).strftime("%d-%m-%Y")
    calendar.append({"name":r["name"],"start":start,"discount":f"{r['discount']}%","expected_roi":round(r["roi"],2)})
  return {"avg_roi":round(avg_roi,2),"opt_discount":f"{opt_discount}%","total_budget":int(df["budget"].sum()),"calendar":calendar}

result = analyze_promos(PROMO_HISTORY)
print(json.dumps(result, indent=2))