风险模型

1import json2from wi_actuarial.methods.evt import rainfall_return_level34result = rainfall_return_level.rainfall_return_level(5    annual_max=[54, 41.6, 40.3, 67, 74.2, 30.8, 78.8, 55.1, 44.5, 55.5, 62.9, 69.2, 79.1, 55.8, 115.1, 43.6, 55.3, 42.4, 62.2, 75.2, 35.9, 49.7, 58.3, 68.9, 44.6, 47.4, 75.7, 73, 71.4, 76, 59.3, 92.2, 57.8, 58.1, 66.7, 58.6, 25.3, 51.2, 42.7, 43.1, 40.8, 33.8, 55.9, 53.6, 74.8, 54.1, 47.1, 94.5, 86.9, 63.2, 43.1, 69.4, 51.1, 40.6, 42.1, 57.1, 46.4, 46.8, 46.1, 54, 57.3, 48],6    AreaCode="420626",7)8print(json.dumps(result, ensure_ascii=False))

1import json2from wi_actuarial import cat34result = cat.ep_curve(5    AreaCode="420626",6    BuildingTIV=1767131,7    LocDed6All=0,8    LocLimit6All=0,9    vulnerability="civil_works",10    design_return_period=10,11)12print(json.dumps(result, ensure_ascii=False))

1# 第 3 步：合成参考费率区间。前两步的结果来自平台的运行。2aal_permille = 0.727428   # 巨灾模型 cat.ep_curve：年平均损失率（‰）3amount = 1767131.17           # 保险金额：合同金额（元）4T = 10                          # 暴雨重现期（设防标准，年）5months = 31                     # 工期（月）6season = 2.6995            # 施工各月的常年降水占全年的比例之和7share = (0.3, 0.5)          # 汛期施工的分项占合同金额的比例（假设）8mgmt = (0.9, 1.2)           # 施工管理系数（取值待校准）9loading = 0.35                # 费用和利润占毛保费的比例（假设）1011p_hit = 1 - (1 - 1 / T) ** (months / 12)12pure = [aal_permille * season * share[k] * mgmt[k] for k in (0, 1)]13rate = [p / (1 - loading) for p in pure]          # 参考费率区间（‰）14premium = [r / 1000 * amount for r in rate]       # 参考保费区间（元）15print(f"施工期内至少遇到一次 {T} 年一遇暴雨的概率 {p_hit:.1%}")16print(f"参考费率区间 {rate[0]:.3f}‰ 到 {rate[1]:.3f}‰，参考保费 {premium[0]:,.0f} 到 {premium[1]:,.0f} 元")