下载地址：http://lanzou.com.cn/i8ce67f08

📁 output/shendujisuanmoxing/
├── 📄 README.md190 B
├── 📄 pom.xml1.7 KB
├── 📄 package.json690 B
├── 📄 config/application.properties608 B
├── 📄 agent/Repository.ts4 KB
├── 📄 src/main/java/Registry.java7.6 KB
├── 📄 lib/Pool.jar647 B
├── 📄 fixtures/Service.php3.8 KB
├── 📄 caches/Client.go3 KB
├── 📄 agent/Transformer.cpp1.6 KB
├── 📄 fixtures/Builder.py4.3 KB
├── 📄 unit/Factory.js4.2 KB
├── 📄 roles/Server.js3.4 KB
├── 📄 agent/Executor.cpp1.6 KB
├── 📄 config/Processor.json690 B
├── 📄 fixtures/Scheduler.php3 KB
├── 📄 caches/Worker.ts2.4 KB
├── 📄 annotations/Loader.sql3.8 KB
├── 📄 unit/Converter.py6.5 KB
├── 📄 caches/Observer.go3 KB
├── 📄 src/main/java/Resolver.java5.7 KB
├── 📄 config/Wrapper.properties608 B
├── 📄 config/Proxy.xml1.3 KB
├── 📄 src/main/java/Parser.java3.9 KB
├── 📄 ansible/Buffer.py4.6 KB

项目编译入口：

Project Structure

Folder : shendujisuanmoxing

Files : 26

Size : 72.6 KB

Generated: 2026-03-22 16:51:08

shendujisuanmoxing/
├── README.md [190 B]
├── agent/
│ ├── Executor.cpp [1.6 KB]
│ ├── Repository.ts [4 KB]
│ └── Transformer.cpp [1.6 KB]
├── annotations/
│ └── Loader.sql [3.8 KB]
├── ansible/
│ └── Buffer.py [4.6 KB]
├── caches/
│ ├── Client.go [3 KB]
│ ├── Observer.go [3 KB]
│ └── Worker.ts [2.4 KB]
├── config/
│ ├── Processor.json [690 B]
│ ├── Proxy.xml [1.3 KB]
│ ├── Wrapper.properties [608 B]
│ └── application.properties [608 B]
├── fixtures/
│ ├── Builder.py [4.3 KB]
│ ├── Scheduler.php [3 KB]
│ └── Service.php [3.8 KB]
├── lib/
│ └── Pool.jar [647 B]
├── package.json [690 B]
├── pom.xml [1.7 KB]
├── roles/
│ └── Server.js [3.4 KB]
├── src/
│ ├── main/
│ │ ├── java/
│ │ │ ├── Parser.java [3.9 KB]
│ │ │ ├── Registry.java [7.6 KB]
│ │ │ └── Resolver.java [5.7 KB]
│ │ └── resources/
│ └── test/
│ └── java/
└── unit/
├── Converter.py [6.5 KB]
└── Factory.js [4.2 KB]

matlab
%% 余额模拟器：基于随机过程的深度计算模型
% 作者：MATLAB 深度计算模型
% 功能：模拟金融账户余额在随机波动下的动态变化，并进行多维度分析
% 包含：布朗运动、跳跃扩散、均值回归等多种随机过程，以及风险价值(VaR)等深度计算指标

clear; clc; close all;

%% 1. 模型参数配置
% 用户可以在此处修改所有模拟参数
params = struct();

% 基础参数
params.initial_balance = 10000; % 初始余额 (元)
params.days = 252; % 模拟天数 (通常一年交易日为252天)
params.n_simulations = 10000; % 蒙特卡洛模拟次数

% 随机过程参数
params.mu = 0.0005; % 漂移率 (每日期望收益率 0.05%)
params.sigma = 0.02; % 波动率 (每日标准差 2%)

% 跳跃扩散参数 (可选)
params.jump_intensity = 0.1; % 跳跃强度 (每日平均跳跃次数)
params.jump_mean = -0.05; % 跳跃平均幅度 (-5%)
params.jump_std = 0.03; % 跳跃幅度标准差

% 均值回归参数 (OU过程)
params.theta = 0.01; % 均值回归速度
params.long_term_mean = 10000; % 长期均值
params.ou_sigma = 100; % 均值回归波动率

% 交易成本参数
params.transaction_cost_rate = 0.001; % 交易成本率 (0.1%)

% 风险指标参数
params.confidence_level = 0.95; % 置信水平 (用于VaR计算)

%% 2. 核心模拟函数定义

% 2.1 几何布朗运动 (GBM) 模型
function paths = simulate_gbm(params)
% 输入: params - 结构体参数
% 输出: paths - 模拟路径矩阵 [days+1 x n_simulations]

dt = 1; % 每日时间步长
n_steps = params.days;
n_sims = params.n_simulations;

% 预分配内存
paths = zeros(n_steps + 1, n_sims);
paths(1, :) = params.initial_balance;

% 生成随机增量
for t = 1:n_steps
    z = randn(1, n_sims); % 标准正态随机数
    drift = (params.mu - 0.5 * params.sigma^2) * dt;
    diffusion = params.sigma * sqrt(dt) * z;
    increment = drift + diffusion;

    % 应用交易成本 (每次交易成本)
    transaction_cost = params.transaction_cost_rate * paths(t, :);
    paths(t+1, :) = paths(t, :) .* exp(increment) - transaction_cost;
end

end

% 2.2 跳跃扩散模型 (Merton Jump Diffusion)
function paths = simulate_jump_diffusion(params)
dt = 1;
n_steps = params.days;
n_sims = params.n_simulations;

paths = zeros(n_steps + 1, n_sims);
paths(1, :) = params.initial_balance;

for t = 1:n_steps
    % 布朗运动部分
    z = randn(1, n_sims);
    drift = (params.mu - 0.5 * params.sigma^2) * dt;
    diffusion = params.sigma * sqrt(dt) * z;

    % 跳跃部分
    jump_count = poissrnd(params.jump_intensity, 1, n_sims);
    jump_effect = zeros(1, n_sims);
    for i = 1:n_sims
        if jump_count(i) > 0
            jumps = params.jump_mean + params.jump_std * randn(1, jump_count(i));
            jump_effect(i) = sum(jumps);
        end
    end

    increment = drift + diffusion + jump_effect;
    transaction_cost = params.transaction_cost_rate * paths(t, :);
    paths(t+1, :) = paths(t, :) .* exp(increment) - transaction_cost;
end

end

% 2.3 均值回归模型 (Ornstein-Uhlenbeck)
function paths = simulate_ou(params)
dt = 1;
n_steps = params.days;
n_sims = params.n_simulations;

paths = zeros(n_steps + 1, n_sims);
paths(1, :) = params.initial_balance;

for t = 1:n_steps
    z = randn(1, n_sims);
    drift = params.theta * (params.long_term_mean - paths(t, :)) * dt;
    diffusion = params.ou_sigma * sqrt(dt) * z;

    paths(t+1, :) = paths(t, :) + drift + diffusion;

    % 确保余额非负
    paths(t+1, :) = max(paths(t+1, :), 0);

    % 应用交易成本
    transaction_cost = params.transaction_cost_rate * paths(t, :);
    paths(t+1, :) = paths(t+1, :) - transaction_cost;
end

end

%% 3. 深度计算分析函数

% 3.1 计算风险价值 (VaR)
function var_value = calculate_var(paths, confidence_level)
% 计算在置信水平下的最大可能损失
final_balances = paths(end, :);
var_value = prctile(final_balances, (1-confidence_level)*100);
end

% 3.2 计算预期缺口 (Expected Shortfall)
function es_value = calculate_expected_shortfall(paths, confidence_level)
final_balances = paths(end, :);
var_threshold = prctile(final_balances, (1-confidence_level)*100);
losses_below_var = final_balances(final_balances <= var_threshold);
if isempty(losses_below_var)
es_value = var_threshold;
else
es_value = mean(losses_below_var);
end
end

% 3.3 计算破产概率
function bankruptcy_prob = calculate_bankruptcy_probability(paths)
% 破产定义为余额 <= 0
min_balances = min(paths, [], 1);
bankruptcy_prob = sum(min_balances <= 0) / size(paths, 2);
end

% 3.4 计算夏普比率 (基于模拟路径)
function sharpe_ratio = calculate_sharpe_ratio(paths, risk_free_rate)
final_balances = paths(end, :);
returns = (final_balances - paths(1, 1)) / paths(1, 1);
excess_returns = returns - risk_free_rate;
sharpe_ratio = mean(excess_returns) / std(excess_returns);
end

%% 4. 执行模拟与深度计算

fprintf('=== 余额模拟器深度计算模型 ===\n');
fprintf('正在执行蒙特卡洛模拟，请稍候...\n\n');

% 运行三种模型的模拟
tic;
gbm_paths = simulate_gbm(params);
jump_paths = simulate_jump_diffusion(params);
ou_paths = simulate_ou(params);
sim_time = toc;

fprintf('模拟完成！耗时 %.2f 秒\n', sim_time);

% 深度计算指标
risk_free_rate = 0.02 / 252; % 假设年化无风险利率2%，转换为日利率

% GBM模型指标
gbm_final_mean = mean(gbm_paths(end, :));
gbm_final_std = std(gbm_paths(end, :));
gbm_var = calculate_var(gbm_paths, params.confidence_level);
gbm_es = calculate_expected_shortfall(gbm_paths, params.confidence_level);
gbm_bankruptcy = calculate_bankruptcy_probability(gbm_paths);
gbm_sharpe = calculate_sharpe_ratio(gbm_paths, risk_free_rate);

% 跳跃扩散模型指标
jump_final_mean = mean(jump_paths(end, :));
jump_final_std = std(jump_paths(end, :));
jump_var = calculate_var(jump_paths, params.confidence_level);
jump_es = calculate_expected_shortfall(jump_paths, params.confidence_level);
jump_bankruptcy = calculate_bankruptcy_probability(jump_paths);
jump_sharpe = calculate_sharpe_ratio(jump_paths, risk_free_rate);

% 均值回归模型指标
ou_final_mean = mean(ou_paths(end, :));
ou_final_std = std(ou_paths(end, :));
ou_var = calculate_var(ou_paths, params.confidence_level);
ou_es = calculate_expected_shortfall(ou_paths, params.confidence_level);
ou_bankruptcy = calculate_bankruptcy_probability(ou_paths);
ou_sharpe = calculate_sharpe_ratio(ou_paths, risk_free_rate);

%% 5. 结果展示与可视化

% 创建结果表格
model_names = {'几何布朗运动 (GBM)'; '跳跃扩散模型'; '均值回归模型 (OU)'};
final_balance_mean = [gbm_final_mean; jump_final_mean; ou_final_mean];
final_balance_std = [gbm_final_std; jump_final_std; ou_final_std];
var_95 = [gbm_var; jump_var; ou_var];
es_95 = [gbm_es; jump_es; ou_es];
bankruptcy_prob = [gbm_bankruptcy; jump_bankruptcy; ou_bankruptcy];
sharpe_ratio = [gbm_sharpe; jump_sharpe; ou_sharpe];

results_table = table(model_names, final_balance_mean, final_balance_std, ...
var_95, es_95, bankruptcy_prob, sharpe_ratio);
disp('深度计算指标汇总：');
disp(results_table);

% 可视化部分
figure('Position', [100, 100, 1200, 800]);

% 子图1: 模拟路径示例 (只显示前100条路径)
subplot(2,3,1);
plot(gbm_paths(:, 1:min(100, params.n_simulations)), 'LineWidth', 0.5);
xlabel('时间 (天)');
ylabel('账户余额 (元)');
title('GBM模型 - 模拟路径示例');
grid on;

subplot(2,3,2);
plot(jump_paths(:, 1:min(100, params.n_simulations)), 'LineWidth', 0.5);
xlabel('时间 (天)');
ylabel('账户余额 (元)');
title('跳跃扩散模型 - 模拟路径示例');
grid on;

subplot(2,3,3);
plot(ou_paths(:, 1:min(100, params.n_simulations)), 'LineWidth', 0.5);
xlabel('时间 (天)');
ylabel('账户余额 (元)');
title('均值回归模型 - 模拟路径示例');
grid on;

% 子图2: 最终余额分布直方图
subplot(2,3,4);
histogram(gbm_paths(end, :), 50, 'FaceColor', [0.2, 0.6, 0.8], 'EdgeColor', 'white');
xlabel('最终余额 (元)');
ylabel('频数');
title(sprintf('GBM模型 - 最终余额分布\n均值: %.2f, 标准差: %.2f', gbm_final_mean, gbm_final_std));
grid on;

subplot(2,3,5);
histogram(jump_paths(end, :), 50, 'FaceColor', [0.8, 0.4, 0.2], 'EdgeColor', 'white');
xlabel('最终余额 (元)');
ylabel('频数');
title(sprintf('跳跃扩散模型 - 最终余额分布\n均值: %.2f, 标准差: %.2f', jump_final_mean, jump_final_std));
grid on;

subplot(2,3,6);
histogram(ou_paths(end, :), 50, 'FaceColor', [0.3, 0.7, 0.4], 'EdgeColor', 'white');
xlabel('最终余额 (元)');
ylabel('频数');
title(sprintf('均值回归模型 - 最终余额分布\n均值: %.2f, 标准差: %.2f', ou_final_mean, ou_final_std));
grid on;

sgtitle('余额模拟器深度计算模型 - 结果可视化');

% 额外可视化: 风险指标对比
figure('Position', [100, 100, 1000, 500]);

subplot(1,2,1);
bar_data = [var_95, es_95];
bar(bar_data);
set(gca, 'XTickLabel', model_names);
legend({'VaR (95%)', 'Expected Shortfall (95%)'}, 'Location', 'northwest');
xlabel('模型类型');
ylabel('余额 (元)');
title('风险指标对比');
grid on;

subplot(1,2,2);
bar_data2 = [bankruptcy_prob * 100, sharpe_ratio];
bar(bar_data2);
set(gca, 'XTickLabel', model_names);
legend({'破产概率 (%)', '夏普比率'}, 'Location', 'northwest');
xlabel('模型类型');
ylabel('指标值');
title('绩效与风险指标对比');
grid on;

%% 6. 高级深度分析: 敏感性分析

fprintf('\n=== 敏感性分析 ===\n');
fprintf('分析波动率对最终余额的影响 (GBM模型):\n');

% 改变波动率参数进行敏感性分析
sigma_range = linspace(0.01, 0.05, 5);
sensitivity_results = zeros(length(sigma_range), 3);

for i = 1:length(sigma_range)
params_temp = params;
params_temp.sigma = sigma_range(i);
paths_temp = simulate_gbm(params_temp);
sensitivity_results(i, 1) = sigma_range(i);
sensitivity_results(i, 2) = mean(paths_temp(end, :));
sensitivity_results(i, 3) = calculate_bankruptcy_probability(paths_temp);
end

sensitivity_table = table(sensitivity_results(:,1), sensitivity_results(:,2), ...
sensitivity_results(:,3), 'VariableNames', {'波动率', '平均最终余额', '破产概率'});
disp(sensitivity_table);

% 可视化敏感性分析
figure;
yyaxis left;
plot(sensitivity_results(:,1), sensitivity_results(:,2), 'b-o', 'LineWidth', 2);
ylabel('平均最终余额 (元)');
yyaxis right;
plot(sensitivity_results(:,1), sensitivity_results(:,3)*100, 'r-s', 'LineWidth', 2);
ylabel('破产概率 (%)');
xlabel('波动率 (每日)');
title('波动率敏感性分析 (GBM模型)');
grid on;
legend('平均最终余额', '破产概率', 'Location', 'best');

%% 7. 生成报告摘要

fprintf('\n=== 模型评估摘要 ===\n');
fprintf('1. 几何布朗运动模型:\n');
fprintf(' - 最终余额均值: %.2f\n', gbm_final_mean);
fprintf(' - 最终余额标准差: %.2f\n', gbm_final_std);
fprintf(' - 95%% VaR: %.2f\n', gbm_var);
fprintf(' - 破产概率: %.2f%%\n', gbm_bankruptcy*100);
fprintf(' - 夏普比率: %.4f\n', gbm_sharpe);

fprintf('\n2. 跳跃扩散模型:\n');
fprintf(' - 最终余额均值: %.2f\n', jump_final_mean);
fprintf(' - 最终余额标准差: %.2f\n', jump_final_std);
fprintf(' - 95%% VaR: %.2f\n', jump_var);
fprintf(' - 破产概率: %.2f%%\n', jump_bankruptcy*100);
fprintf(' - 夏普比率: %.4f\n', jump_sharpe);

fprintf('\n3. 均值回归模型:\n');
fprintf(' - 最终余额均值: %.2f\n', ou_final_mean);
fprintf(' - 最终余额标准差: %.2f\n', ou_final_std);
fprintf(' - 95%% VaR: %.2f\n', ou_var);
fprintf(' - 破产概率: %.2f%%\n', ou_bankruptcy*100);
fprintf(' - 夏普比率: %.4f\n', ou_sharpe);

fprintf('\n=== 结论 ===\n');
if gbm_sharpe > jump_sharpe && gbm_sharpe > ou_sharpe
fprintf('基于夏普比率，GBM模型表现最佳。\n');
elseif jump_sharpe > gbm_sharpe && jump_sharpe > ou_sharpe
fprintf('基于夏普比率，跳跃扩散模型表现最佳。\n');
else
fprintf('基于夏普比率，均值回归模型表现最佳。\n');
end

if gbm_bankruptcy < jump_bankruptcy && gbm_bankruptcy < ou_bankruptcy
fprintf('GBM模型破产风险最低。\n');
elseif jump_bankruptcy < gbm_bankruptcy && jump_bankruptcy < ou_bankruptcy
fprintf('跳跃扩散模型破产风险最低。\n');
else
fprintf('均值回归模型破产风险最低。\n');
end

fprintf('\n模拟完成！所有结果已保存至工作空间。\n');