风险模型

1import json2from wi_actuarial.methods.evt import rainfall_return_level34result = rainfall_return_level.rainfall_return_level(5    annual_max=[126.8, 68.8, 173.1, 49.9, 167.9, 72.2, 102.7, 141.1, 100, 109.3, 118.4, 98.2, 161.2, 113.9, 78.1, 90.9, 42, 79.5, 107.6, 158.6, 147.8, 74.5, 224.8, 104.3, 185.4, 108, 110.1, 77.7, 88, 174, 105.1, 104.2, 82.9, 179.1, 128.4, 127.9, 143.7, 99.7, 88.4, 112.2, 74, 176.8, 87.1, 81.6, 132.1, 204.8, 75.6, 163.3, 81.2, 150.2, 63.5, 73.1, 124.1, 162.3, 125.7, 73, 79.8, 84.3, 84, 71.3, 65, 98.9],6    AreaCode="350624",7)8print(json.dumps(result, ensure_ascii=False))

1import json2from wi_actuarial import cat34result = cat.ep_curve(5    AreaCode="350624",6    BuildingTIV=2350184,7    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 = 1.226993   # 巨灾模型 cat.ep_curve：年平均损失率（‰）3amount = 2350184           # 保险金额：合同金额（元）4T = 10                          # 暴雨重现期（设防标准，年）5months = 6                     # 工期（月）6season = 0.6438            # 施工各月的常年降水占全年的比例之和7share = (0.5, 0.8)          # 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")