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完整条件
初始条件
- 一线城市房价：550 万
- 二线城市房价：150 万
路径 A (继续在一线城市工作)：
- 初始薪资：25,000 元/月
- 无房，存款老家房子，折现 40 万
路径 B (到二线城市工作)：
- 初始薪资：14,000 元/月
- 存款老家房子，折现 40 万
路径 C (回老家)：
- 初始薪资：5,000 元/月
- 有房贷 60 万
事件概率和影响
- 路径 A：
- 好事：薪资每年上涨 10%，概率为 30%
- 坏事：被裁员，失去固定薪资 3 个月，概率为 10%
- 期望事件进度：5 年后买车，买房（首付 35%），结婚
- 路径 B：
- 好事：薪资为一线城市的 90%，概率为 60%；薪资增长概率为 30%
- 坏事：水土不服，长期待业或只有原薪资 50%，概率为 10%
- 期望事件进度：买房，5 年后买车，结婚
- 路径 C：
- 好事：薪资为一线城市的 50%，概率为 30%；正常为 20%-30%
- 坏事：老家房子贬值 30%-40%，概率为 40%
- 期望事件进度：还清房贷，结婚
模拟的关键变量
- 每月薪资的 65%可用于支出房子和车子
- 存款的累积和更新
- 每月薪资的变化
- 特定事件（如买车、买房、结婚）对总进度的影响
- 随机事件的发生概率
import random # 定义基本参数 initial_salary_a = 25000 # 路径 A 初始薪资 initial_salary_b = 14000 # 路径 B 初始薪资 initial_salary_c = 5000 # 路径 C 初始薪资 savings_a = 400000 # 路径 A 初始存款 savings_b = 400000 # 路径 B 初始存款 savings_c = 0 # 路径 C 初始存款 mortgage_c = 600000 # 路径 C 房贷 first_tier_price = 5500000 # 一线城市房价 second_tier_price = 1500000 # 二线城市房价 salary_increase_prob_a = 0.3 # 一线城市薪资增长概率 salary_increase_prob_b = 0.3 # 二线城市薪资增长概率 layoff_prob = 0.1 # 被裁员概率 house_depreciation_prob_c = 0.4 # 老家房子贬值概率 salary_usable_ratio = 0.65 # 薪资中可用于支出的比例 duration = 15 * 12 # 模拟时间（月） # 模拟函数 def simulate_path_with_negatives(path): import random # 基本参数 initial_salary_a = 25000 initial_salary_b = 14000 initial_salary_c = 5000 savings_a = 400000 savings_b = 400000 savings_c = 0 mortgage_c = 600000 first_tier_price = 5500000 second_tier_price = 1500000 duration = 15 * 12 salary_usable_ratio = 0.65 # 事件概率 salary_increase_prob_a = 0.3 salary_increase_prob_b = 0.3 layoff_prob = 0.1 house_depreciation_prob_c = 0.4 # 模拟函数 def simulate_path(path): salary = initial_salary_a if path == 'A' else initial_salary_b if path == 'B' else initial_salary_c savings = savings_a if path == 'A' else savings_b if path == 'B' else savings_c mortgage_remaining = mortgage_c if path == 'C' else 0 progress = 0 married = False house_bought = False car_bought = False for month in range(duration): if path == 'A' and random.random() < salary_increase_prob_a: salary *= 1.1 elif path == 'B' and random.random() < salary_increase_prob_b: salary *= 1.1 if path == 'A' and random.random() < layoff_prob: salary = 0 elif path == 'B' and random.random() < layoff_prob: salary *= 0.5 usable_salary = salary * salary_usable_ratio savings += usable_salary if not car_bought and month >= 60: car_bought = True progress += 10 if not house_bought and (savings >= first_tier_price * 0.35 if path == 'A' else savings >= second_tier_price * 0.35): house_bought = True savings -= first_tier_price * 0.35 if path == 'A' else second_tier_price * 0.35 progress += 30 if not married and (house_bought or path == 'C'): married = True progress += 20 if path == 'C' and random.random() < house_depreciation_prob_c: mortgage_remaining *= (1 - 0.35) if path == 'C' and mortgage_remaining > 0: repayment = min(mortgage_remaining, usable_salary) mortgage_remaining -= repayment savings -= repayment if mortgage_remaining == 0: progress += 20 if salary == 0 and month % 3 == 0: salary = initial_salary_a if path == 'A' else initial_salary_b if path == 'B' else initial_salary_c return progress progress_a = simulate_path('A') progress_b = simulate_path('B') progress_c = simulate_path('C') # a=60% b=60% c=50% 
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