初始化

app.py

@@ -0,0 +1,175 @@
+import gradio as gr
+import pandas as pd
+from datetime import datetime
+
+# 從 bond.py 導入重構後的函數和數據
+from bond import create_bond_database, run_simulation
+
+# 1. 數據準備
+BOND_DATABASE = create_bond_database()
+BOND_CHOICES = list(BOND_DATABASE.keys())
+
+# --- Gradio 介面輔助函數 ---
+
+def get_bond_info_html(bond_name):
+    """
+    根據下拉選單選擇的債券名稱，生成用於顯示其詳細資訊的 HTML 字串。
+    
+    :param bond_name: str, 使用者從下拉選單選擇的債券名稱。
+    :return: str, 一個 HTML 格式的字串，用於在 Gradio 介面中顯示。
+    """
+    if not bond_name:
+        return "<p>請從上方選擇一個債券</p>"
+    
+    bond = BOND_DATABASE[bond_name]
+    issue_date_str = bond['issueDate'].strftime('%Y-%m-%d')
+    maturity_date_str = bond['maturity'].strftime('%Y-%m-%d')
+    
+    # **重要**: 根據資料庫中的百分比格式，調整顯示方式
+    html = f"""
+    <div style="border: 1px solid #e0e0e0; padding: 15px; border-radius: 5px;">
+        <h4 style="margin-top:0;">債券: {bond_name}</h4>
+        <table style="width:100%;">
+            <tr><td style="width:50%;"><strong>平均殖利率 (avgYld):</strong> {bond['avgYld']:.3f}%</td><td style="width:50%;"><strong>票面利率 (coupon):</strong> {bond['coupon']:.3f}%</td></tr>
+            <tr><td><strong>發行日 (issueDate):</strong> {issue_date_str}</td><td><strong>到期日 (maturity):</strong> {maturity_date_str}</td></tr>
+            <tr><td><strong>每年付息 (payPerYr):</strong> {bond['payPerYr']} 次</td><td><strong>每年複利 (compPerYr):</strong> {bond['compPerYr']} 次</td></tr>
+            <tr><td><strong>存續期間 (durT):</strong> {bond['durT']}</td><td><strong>修正存續期間 (mdurT):</strong> {bond['mdurT']}</td></tr>
+            <tr><td><strong>價格曲度 (convT):</strong> {bond['convT']}</td><td></td></tr>
+        </table>
+    </div>
+    """
+    return html
+
+def run_monte_carlo(
+    bond_name, n_simulations, dt,
+    kappa_rf, theta_rf, sigma_rf,
+    initial_spread, sigma_spread, correlation,
+    mu_r, sigma_r
+):
+    """
+    Gradio 的主要執行函數。
+    它負責收集所有來自介面的輸入參數，將百分比單位轉換為小數，
+    調用後端的 `run_simulation` 核心函式，並將計算結果回傳給介面的各個輸出元件。
+
+    :param bond_name: str, 選擇的債券名稱。
+    :param n_simulations: int, 模擬次數。
+    :param dt: float, 時間步長。
+    :param ...: 所有來自UI的模擬參數 (此處為百分比單位)。
+    :return: tuple, 包含所有結果的元組，用於更新 Gradio 介面的各個輸出元件。
+    """
+    if not bond_name:
+        raise gr.Error("請先選擇一個債券！")
+
+    bond_data = BOND_DATABASE[bond_name]
+    
+    # **重要**: 將來自UI的百分比參數轉換為小數以進行計算
+    params = {
+        'kappa_rf': kappa_rf, 
+        'theta_rf': theta_rf / 100.0, 
+        'sigma_rf': sigma_rf / 100.0,
+        'initial_spread': initial_spread / 100.0, 
+        'sigma_spread': sigma_spread / 100.0, 
+        'correlation': correlation,
+        'mu_r': mu_r / 100.0, 
+        'sigma_r': sigma_r / 100.0,
+        "n_simulations":int(n_simulations),
+    }
+    
+    rate_stats_df, price_stats_df, fig_rate_paths, fig_rate_dist, fig_price_paths, fig_price_dist, report_filepath = run_simulation(
+        bond_data=bond_data, n_simulations=int(n_simulations), dt=dt, params=params
+    )
+    
+    return (
+        rate_stats_df, price_stats_df,
+        fig_rate_paths, fig_rate_dist,
+        fig_price_paths, fig_price_dist,
+        gr.update(value=report_filepath, visible=True)
+    )
+
+# --- 建立 Gradio 介面 ---
+
+with gr.Blocks(theme=gr.themes.Soft(), title="債券利率蒙地卡羅模擬") as app:
+    gr.Markdown("# 債券利率與價格蒙地卡羅模擬")
+    
+    # --- 步驟 1 & 2: 參數設定 ---
+    with gr.Row():
+        with gr.Column(scale=2):
+            gr.Markdown("## 步驟 1: 選擇債券")
+            bond_selector = gr.Dropdown(BOND_CHOICES, label="選擇債券", info="從模擬的資料庫中選擇一個債券以載入其初始參數。", value=BOND_CHOICES[0])
+            bond_info_display = gr.HTML(get_bond_info_html(BOND_CHOICES[0]))
+        
+        with gr.Column(scale=3):
+            gr.Markdown("## 步驟 2: 設定模擬參數")
+            with gr.Tabs():
+                with gr.Tab("無風險利率 (drf)"):
+                    kappa_rf = gr.Slider(0.01, 1.0, value=0.3, step=0.01, label="均值回歸速度 (kappa_rf)")
+                    theta_rf = gr.Slider(1.0, 5.0, value=2.2, step=0.1, label="長期平均利率 (%)")
+                    sigma_rf = gr.Slider(0.1, 2.0, value=0.5, step=0.1, label="波動率 (%)")
+                with gr.Tab("有風險利率 (drs)"):
+                    initial_spread = gr.Slider(0.1, 3.0, value=0.8, step=0.1, label="初始信用利差 (%)")
+                    sigma_spread = gr.Slider(0.1, 1.0, value=0.3, step=0.05, label="利差波動率 (%)")
+                    correlation = gr.Slider(-1.0, 1.0, value=0.6, step=0.05, label="drf與利差的相關係數")
+                with gr.Tab("綜合利率 (dr)"):
+                    mu_r = gr.Slider(0.0, 0.2, value=0.05, step=0.01, label="長期漂移趨勢 (%)")
+                    sigma_r = gr.Slider(0.1, 2.0, value=0.6, step=0.1, label="年化波動率 (%)")
+            n_simulations = gr.Slider(50, 100000, value=100, step=50, label="模擬次數 (n_simulations)")
+            dt = gr.Number(value=1/252, label="時間步長 (dt)", info="以年為單位，預設為一個交易日(1/252年)。")
+
+    # --- 步驟 3: 執行 ---
+    run_button = gr.Button("開始模擬", variant="primary")
+
+    # --- 步驟 4: 結果呈現 ---
+    with gr.Tabs():
+        with gr.Tab("利率分析"):
+            gr.Markdown("## 利率模擬結果")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.Markdown("#### 利率統計數據")
+                    rate_results_table = gr.DataFrame(headers=["指標", "數值"], datatype=["str", "str"])
+                with gr.Column(scale=2):
+                    gr.Markdown("#### 利率路徑與相關性圖")
+                    plot_rate_paths = gr.Plot()
+                    gr.Markdown("#### 利率變化分佈圖")
+                    plot_rate_distribution = gr.Plot()
+        
+        with gr.Tab("債券價格分析"):
+            gr.Markdown("## 債券價格模擬結果")
+            gr.Markdown("使用修正存續期間與曲度來估算價格變化。初始價格假設為 100。")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    gr.Markdown("#### 價格統計數據 (含 VaR)")
+                    price_results_table = gr.DataFrame()
+                with gr.Column(scale=2):
+                    gr.Markdown("#### 價格模擬路徑")
+                    plot_price_paths = gr.Plot()
+                    gr.Markdown("#### 最終價格分佈")
+                    plot_price_distribution = gr.Plot()
+
+    # --- 步驟 5: 下載資料 ---
+    with gr.Column():
+        gr.Markdown("--- \n ## 下載完整報告，(若模擬次數超過1000，則不會包含模擬路徑與價格資料，以避免檔案過大)")
+        report_file = gr.File(label="下載完整報告 (Excel)", visible=False)
+
+    # --- 事件監聽 ---
+    bond_selector.change(fn=get_bond_info_html, inputs=bond_selector, outputs=bond_info_display)
+    
+    all_inputs = [
+        bond_selector, n_simulations, dt,
+        kappa_rf, theta_rf, sigma_rf,
+        initial_spread, sigma_spread, correlation,
+        mu_r, sigma_r
+    ]
+    
+    run_button.click(
+        fn=run_monte_carlo,
+        inputs=all_inputs,
+        outputs=[
+            rate_results_table, price_results_table,
+            plot_rate_paths, plot_rate_distribution,
+            plot_price_paths, plot_price_distribution,
+            report_file
+        ]
+    )
+
+if __name__ == "__main__":
+    app.launch()

bond.py

@@ -0,0 +1,303 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from datetime import datetime
+import tempfile
+import os
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+def create_bond_database():
+    """
+    創建一個包含多個債券樣本的字典，作為模擬資料庫。
+    注意：此處的 avgYld 和 coupon 以「百分比」形式儲存 (例如 2.2 代表 2.2%)。
+    
+    :return: dict，鍵為債券名稱，值為包含該債券詳細參數的字典。
+    
+    例如：
+    平均殖利率 (avgYld): 1.553%	
+    票面利率 (coupon): 2.125%
+    發行日 (issueDate): 2011-01-13	
+    到期日 (maturity): 2031-01-13
+    每年付息 (payPerYr): 1 次	
+    每年複利 (compPerYr): 1 次
+    存續期間 (durT): 5.3548	
+    修正存續期間 (mdurT): 5.273
+    價格曲度 (convT): 1762.7325	
+    """
+    bond_table = pd.read_csv(os.path.join(THIS_DIR,  "bond_db.csv"))
+    bond_table["issueDate"] = bond_table["issueDate"].apply(lambda x: pd.to_datetime(str(x)))
+    bond_table["maturity"] = bond_table["maturity"].apply(lambda x: pd.to_datetime(str(x)))
+    db = {
+        row["name"]: row 
+        for row in bond_table.to_dict("records")
+    }
+    return db
+
+# --- 模擬函數 ---
+def simulate_vasicek(r0, kappa, theta, sigma, T, dt, n_simulations, dW_shocks):
+    """
+    使用 Vasicek 模型模擬無風險利率(drf)的路徑。
+    此模型包含均值回歸特性。
+
+    :param r0: float, 初始利率 (小數形式)。
+    :param kappa: float, 均值回歸速度。
+    :param theta: float, 長期平均利率。
+    :param sigma: float, 波動率。
+    :param T: float, 總模擬時長(年)。
+    :param dt: float, 每個時間步長(年)。
+    :param n_simulations: int, 模擬路徑的數量。
+    :param dW_shocks: np.ndarray, 預先生成的隨機衝擊。
+    :return: np.ndarray, 模擬的利率路徑，形狀為 (n_simulations, num_steps + 1)。
+    """
+    num_steps = int(T / dt)
+    rates = np.zeros((n_simulations, num_steps + 1)); rates[:, 0] = r0
+    for t in range(1, num_steps + 1):
+        rates[:, t] = np.maximum(0.001, rates[:, t-1] + kappa * (theta - rates[:, t-1]) * dt + sigma * dW_shocks[:, t-1])
+    return rates
+
+def simulate_risky_rate(rf_paths, dW_rf, initial_spread, sigma_spread, correlation, T, dt, n_simulations, dW_independent):
+    """
+    在無風險利率的基礎上，模擬有風險利率(drs)的路徑。
+    主要模擬與無風險利率相關的信用利差(credit spread)的變化。
+
+    :param rf_paths: np.ndarray, 已模擬好的無風險利率路徑。
+    :param dW_rf: np.ndarray, 生成無風險利率路徑所使用的隨機衝擊。
+    :param initial_spread: float, 初始信用利差。
+    :param sigma_spread: float, 信用利差的波動率。
+    :param correlation: float, 無風險利率與信用利差變動的相關係數。
+    :param T: float, 總模擬時長(年)。
+    :param dt: float, 每個時間步長(年)。
+    :param n_simulations: int, 模擬路徑的數量。
+    :param dW_independent: np.ndarray, 獨立的隨機衝擊，用於生成利差的非系統性風險部分。
+    :return: tuple (np.ndarray, np.ndarray), 分別為模擬的有風險利率路徑和信用利差路徑。
+    """
+    num_steps = int(T / dt); spread_paths = np.zeros((n_simulations, num_steps + 1)); spread_paths[:, 0] = initial_spread
+    dW_spread = correlation * dW_rf + np.sqrt(1 - correlation**2) * dW_independent
+    for t in range(1, num_steps + 1):
+        spread_paths[:, t] = np.maximum(0.001, spread_paths[:, t-1] + sigma_spread * dW_spread[:, t-1])
+    return rf_paths + spread_paths, spread_paths
+
+def simulate_gbm(r0, mu, sigma, T, dt, n_simulations, dW_shocks):
+    """
+    使用幾何布朗運動(Geometric Brownian Motion)模型模擬綜合利率(dr)的路徑。
+    此模型常用於模擬股價，這裡用來作為一個簡化的利率模型。
+
+    :param r0: float, 初始利率 (小數形式)。
+    :param mu: float, 長期漂移率。
+    :param sigma: float, 波動率。
+    :param T: float, 總模擬時長(年)。
+    :param dt: float, 每個時間步長(年)。
+    :param n_simulations: int, 模擬路徑的數量。
+    :param dW_shocks: np.ndarray, 預先生成的隨機衝擊。
+    :return: np.ndarray, 模擬的利率路徑。
+    """
+    num_steps = int(T / dt); rates = np.zeros((n_simulations, num_steps + 1)); rates[:, 0] = r0
+    for t in range(1, num_steps + 1):
+        rates[:, t] = np.maximum(0.001, rates[:, t-1] * np.exp((mu - 0.5 * sigma**2) * dt + sigma * dW_shocks[:, t-1]))
+    return rates
+
+def calculate_price_paths(rate_paths, mdurT, convT, initial_price=100.0):
+    """
+    使用修正存續期間(mdurT)和價格曲度(convT)近似法，根據利率路徑計算債券價格路徑。
+    
+    重要：
+    此處的 convT 參數假定為您資料庫中的 "1/2價格曲度" (如 288.3137)，
+    其金融定義為 (P * C_Mod / 2)，且 P 為 initial_price (通常為 100)。
+
+    :param rate_paths: np.ndarray, 模��的利率路徑，刻度為小數點形式。
+    :param mdurT: float, 債券的修正存續期間 (D_Mod)。
+    :param convT: float, 債券的 "1/2 價格曲度" (P * C_Mod / 2)。
+    :param initial_price: float, 債券的初始價格，預設為100。
+    :return: np.ndarray, 模擬的債券價格路徑。
+    """
+    n_simulations, num_steps = rate_paths.shape[0], rate_paths.shape[1] - 1
+    price_paths = np.zeros_like(rate_paths)
+    price_paths[:, 0] = initial_price
+    
+    # --- 邏輯修正 ---
+    # 根據定義，convT = (initial_price * C_Mod / 2)
+    # 我們需要 C_Mod / 2 這一項
+    # 因此，C_Mod / 2 = convT / initial_price
+    # 
+    # 這個值在近似法中被假定為常數，不應隨 P(t-1) 變動。
+    # 【錯誤的邏輯】: convexity_factor = convT / prev_price
+    # 【修正後的邏輯】:
+    convexity_factor = convT / initial_price
+    # --- 修正結束 ---
+    
+    for t in range(1, num_steps + 1):
+        delta_y = rate_paths[:, t] - rate_paths[:, t-1]
+        
+        # 抓取前一期的價格 P(t-1)
+        prev_price = price_paths[:, t-1]
+        
+        # 1. 計算價格變動百分比 (dP/P)
+        # dP/P = -mdurT * dy + (C_Mod / 2) * (dy^2)
+        #
+        # 【注意】: 這裡的 convexity_factor 是我們在迴圈外
+        #          就已經計算好的常數 (convT / initial_price)
+        price_change_factor = -mdurT * delta_y + convexity_factor * (delta_y ** 2)
+        
+        # 2. 計算新價格 P(t) = P(t-1) * (1 + dP/P)
+        price_paths[:, t] = prev_price * (1 + price_change_factor)
+        
+    return price_paths
+
+# --- 主執行函數 ---
+def run_simulation(bond_data, n_simulations, dt, params):
+    """
+    程式核心執行函數。
+    整合所有模擬、計算、繪圖與報告生成步驟。
+
+    :param bond_data: dict, 選定債券的詳細資料。
+    :param n_simulations: int, 模擬總次數。
+    :param dt: float, 每個時間步長(年)。
+    :param params: dict, 所有模擬模型的參數。
+    :return: tuple, 包含所有結果的元組。
+    """
+    np.random.seed(42)
+    
+    # **重要**: 將來自資料庫的百分比格式轉換為小數格式以進行計算
+    initial_yield = bond_data['avgYld'] / 100.0
+    
+    time_to_maturity = (bond_data['maturity'] - datetime.now()).days / 365.25
+    T = max(time_to_maturity, 0.1)
+    num_steps = int(T / dt)
+    time_points = np.linspace(0, T, num_steps + 1)
+    
+    dW_rf = np.random.normal(0, 1, (n_simulations, num_steps)) * np.sqrt(dt)
+    dW_spread_independent = np.random.normal(0, 1, (n_simulations, num_steps)) * np.sqrt(dt)
+    dW_dr = np.random.normal(0, 1, (n_simulations, num_steps)) * np.sqrt(dt)
+
+    # --- 利率模擬 ---
+    rf_paths = simulate_vasicek(initial_yield, params['kappa_rf'], params['theta_rf'], params['sigma_rf'], T, dt, n_simulations, dW_rf)
+    drs_paths, _ = simulate_risky_rate(rf_paths, dW_rf, params['initial_spread'], params['sigma_spread'], params['correlation'], T, dt, n_simulations, dW_spread_independent)
+    dr_paths = simulate_gbm(initial_yield, params['mu_r'], params['sigma_r'], T, dt, n_simulations, dW_dr)
+
+    # --- 價格模擬 ---
+    price_rf_paths = calculate_price_paths(rf_paths, bond_data['mdurT'], bond_data['convT'])
+    price_drs_paths = calculate_price_paths(drs_paths, bond_data['mdurT'], bond_data['convT'])
+    price_dr_paths = calculate_price_paths(dr_paths, bond_data['mdurT'], bond_data['convT'])
+
+    # --- 統計數據 ---
+    drf_changes = np.diff(rf_paths, axis=1).flatten(); drs_changes = np.diff(drs_paths, axis=1).flatten(); dr_changes = np.diff(dr_paths, axis=1).flatten()
+    cov_matrix = np.cov(drf_changes, drs_changes); corr_matrix = np.corrcoef(drf_changes, drs_changes)
+    rate_stats_df = pd.DataFrame({
+        "指標": ["drf 變化量平均", "drf 變化量標準差", "drs 變化量平均", "drs 變化量標準差", "dr 變化量平均", "dr 變化量標準差", "drf-drs 共變異數", "drf-drs 相關係數"],
+        "數值": [f"{np.mean(drf_changes):.8f}", f"{np.std(drf_changes):.8f}", f"{np.mean(drs_changes):.8f}", f"{np.std(drs_changes):.8f}", f"{np.mean(dr_changes):.8f}", f"{np.std(dr_changes):.8f}", f"{cov_matrix[0, 1]:.8f}", f"{corr_matrix[0, 1]:.6f}"]
+    })
+
+    final_prices_rf = price_rf_paths[:, -1]; final_prices_drs = price_drs_paths[:, -1]; final_prices_dr = price_dr_paths[:, -1]
+    price_stats_df = pd.DataFrame({
+        "統計量": ["平均價格", "價格標準差", "最大價格", "最小價格", "95%分位數 (Q95)", "5%分位數 (Q5)", "95% VaR (價值損失)"],
+        "基於 drf": [f"{np.mean(final_prices_rf):.4f}", f"{np.std(final_prices_rf):.4f}", f"{np.max(final_prices_rf):.4f}", f"{np.min(final_prices_rf):.4f}", f"{np.percentile(final_prices_rf, 95):.4f}", f"{np.percentile(final_prices_rf, 5):.4f}", f"{100 - np.percentile(final_prices_rf, 5):.4f}"],
+        "基於 drs": [f"{np.mean(final_prices_drs):.4f}", f"{np.std(final_prices_drs):.4f}", f"{np.max(final_prices_drs):.4f}", f"{np.min(final_prices_drs):.4f}", f"{np.percentile(final_prices_drs, 95):.4f}", f"{np.percentile(final_prices_drs, 5):.4f}", f"{100 - np.percentile(final_prices_drs, 5):.4f}"],
+        "基於 dr": [f"{np.mean(final_prices_dr):.4f}", f"{np.std(final_prices_dr):.4f}", f"{np.max(final_prices_dr):.4f}", f"{np.min(final_prices_dr):.4f}", f"{np.percentile(final_prices_dr, 95):.4f}", f"{np.percentile(final_prices_dr, 5):.4f}", f"{100 - np.percentile(final_prices_dr, 5):.4f}"]
+    })
+
+    # --- Excel 報告 ---
+    fd, report_filepath = tempfile.mkstemp(suffix=".xlsx")
+    os.close(fd)
+    with pd.ExcelWriter(report_filepath) as writer:
+        pd.DataFrame(list(bond_data.items()), columns=['項目', '數值']).applymap(lambda x: x.strftime('%Y-%m-%d') if isinstance(x, datetime) else x).to_excel(writer, sheet_name='bond_info', index=False)
+        pd.DataFrame(list(params.items()), columns=['參數', '設定值']).to_excel(writer, sheet_name='simulation_parameters', index=False)
+        rate_stats_df.to_excel(writer, sheet_name='rate_statistics', index=False)
+        price_stats_df.to_excel(writer, sheet_name='price_statistics', index=False)
+        if n_simulations <= 1000:  # 避免檔案過大
+            pd.DataFrame(rf_paths.T, index=time_points).to_excel(writer, sheet_name='drf_rate_paths')
+            pd.DataFrame(drs_paths.T, index=time_points).to_excel(writer, sheet_name='drs_rate_paths')
+            pd.DataFrame(dr_paths.T, index=time_points).to_excel(writer, sheet_name='dr_rate_paths')
+            pd.DataFrame(price_rf_paths.T, index=time_points).to_excel(writer, sheet_name='drf_price_paths')
+            pd.DataFrame(price_drs_paths.T, index=time_points).to_excel(writer, sheet_name='drs_price_paths')
+            pd.DataFrame(price_dr_paths.T, index=time_points).to_excel(writer, sheet_name='dr_price_paths')
+
+    # --- 繪圖 ---
+    plt.rcParams['font.sans-serif'] = ['Microsoft JhengHei']; plt.rcParams['axes.unicode_minus'] = False
+    fig_rate_paths = plt.figure(figsize=(12, 10))
+    ax1 = fig_rate_paths.add_subplot(2, 2, 1)
+    ax1.plot(time_points, rf_paths[:10].T, alpha=0.7)
+    ax1.set_title('drf - Risk-free interest rate paths')
+    ax1.set_ylabel('Interest rate')
+    ax1.grid(True)
+
+    ax2 = fig_rate_paths.add_subplot(2, 2, 2)
+    ax2.plot(time_points, drs_paths[:10].T, alpha=0.7)
+    ax2.set_title('drs - Risky interest rate paths')
+    ax2.grid(True)
+
+    ax3 = fig_rate_paths.add_subplot(2, 2, 3)
+    ax3.plot(time_points, dr_paths[:10].T, alpha=0.7)
+    ax3.set_title('dr - Combined interest rate paths')
+    ax3.set_xlabel('Time (years)')
+    ax3.set_ylabel('Interest rate')
+    ax3.grid(True)
+
+    ax4 = fig_rate_paths.add_subplot(2, 2, 4)
+    ax4.scatter(drf_changes[::10], drs_changes[::10], alpha=0.3, s=5)
+    ax4.set_title(f'drf vs drs changes (correlation: {corr_matrix[0, 1]:.4f})')
+    ax4.set_xlabel('drf changes')
+    ax4.set_ylabel('drs changes')
+    ax4.grid(True)
+
+    fig_rate_paths.tight_layout()
+
+    fig_rate_dist = plt.figure(figsize=(12, 4))
+    ax_dist1 = fig_rate_dist.add_subplot(1, 3, 1)
+    ax_dist1.hist(drf_changes, bins=50, density=True, alpha=0.7, color='blue')
+    ax_dist1.set_title('Distribution of drf changes')
+    ax_dist1.set_xlabel('Change')
+
+    ax_dist2 = fig_rate_dist.add_subplot(1, 3, 2)
+    ax_dist2.hist(drs_changes, bins=50, density=True, alpha=0.7, color='red')
+    ax_dist2.set_title('Distribution of drs changes')
+    ax_dist2.set_xlabel('Change')
+
+    ax_dist3 = fig_rate_dist.add_subplot(1, 3, 3)
+    ax_dist3.hist(dr_changes, bins=50, density=True, alpha=0.7, color='green')
+    ax_dist3.set_title('Distribution of dr changes')
+    ax_dist3.set_xlabel('Change')
+
+    fig_rate_dist.tight_layout()
+
+    fig_price_paths = plt.figure(figsize=(12, 5))
+    ax_p1 = fig_price_paths.add_subplot(1, 3, 1)
+    ax_p1.plot(time_points, price_rf_paths[:10].T, alpha=0.7)
+    ax_p1.set_title('Price paths (based on drf)')
+    ax_p1.set_ylabel('Price')
+    ax_p1.grid(True)
+
+    ax_p2 = fig_price_paths.add_subplot(1, 3, 2)
+    ax_p2.plot(time_points, price_drs_paths[:10].T, alpha=0.7)
+    ax_p2.set_title('Price paths (based on drs)')
+    ax_p2.set_xlabel('Time (years)')
+    ax_p2.grid(True)
+
+    ax_p3 = fig_price_paths.add_subplot(1, 3, 3)
+    ax_p3.plot(time_points, price_dr_paths[:10].T, alpha=0.7)
+    ax_p3.set_title('Price paths (based on dr)')
+    ax_p3.grid(True)
+
+    fig_price_paths.tight_layout()
+
+    fig_price_dist = plt.figure(figsize=(12, 4))
+    ax_pd1 = fig_price_dist.add_subplot(1, 3, 1)
+    ax_pd1.hist(final_prices_rf, bins=50, alpha=0.7, color='blue')
+    ax_pd1.set_title('Distribution of final prices (drf)')
+    ax_pd1.set_xlabel('Price')
+
+    ax_pd2 = fig_price_dist.add_subplot(1, 3, 2)
+    ax_pd2.hist(final_prices_drs, bins=50, alpha=0.7, color='red')
+    ax_pd2.set_title('Distribution of final prices (drs)')
+    ax_pd2.set_xlabel('Price')
+
+    ax_pd3 = fig_price_dist.add_subplot(1, 3, 3)
+    ax_pd3.hist(final_prices_dr, bins=50, alpha=0.7, color='green')
+    ax_pd3.set_title('Distribution of final prices (dr)')
+    ax_pd3.set_xlabel('Price')
+
+    fig_price_dist.tight_layout()
+
+        
+    return rate_stats_df, price_stats_df, fig_rate_paths, fig_rate_dist, fig_price_paths, fig_price_dist, report_filepath

requirements.txt

@@ -0,0 +1,5 @@
+gradio
+pandas
+numpy
+matplotlib
+openpyxl