{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "91c4a4fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters: mu=0.07, target_volatility=0.2, sigma=0.1732, phi=0.5\n",
      "Generate 45 periods, plot last 40 periods\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x450 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Saving example graphs...\n",
      "  ✓ Saved graph_example.png\n",
      "  ✓ Saved graph_example_r.png\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 1: Generate and Display Example Scenario\n",
    "# ============================================================================\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "# Import from the .py file\n",
    "from generate_mock_paths import (\n",
    "    generate_ar1_returns,\n",
    "    generate_signal,\n",
    "    MU,\n",
    "    SIGMA,\n",
    "    PHI,\n",
    "    TARGET_VOLATILITY,\n",
    "    N_PERIODS_GENERATE,\n",
    "    N_PERIODS_PLOT,\n",
    "    N_SCENARIOS\n",
    ")\n",
    "\n",
    "print(f\"Parameters: mu={MU}, target_volatility={TARGET_VOLATILITY}, sigma={SIGMA:.4f}, phi={PHI}\")\n",
    "print(f\"Generate {N_PERIODS_GENERATE} periods, plot last {N_PERIODS_PLOT} periods\")\n",
    "\n",
    "# Generate one independent example scenario (NOT part of the 30 scenarios)\n",
    "scenario_id = \"example\"  # Independent example with seed 156\n",
    "seed = 156  # Independent seed (30 scenarios use seeds 100, 200, ..., 3000)\n",
    "\n",
    "# Generate 46 periods total (45 + 1 realized)\n",
    "returns_full = generate_ar1_returns(\n",
    "    n_periods=N_PERIODS_GENERATE + 1,\n",
    "    mu=MU,\n",
    "    sigma=SIGMA,\n",
    "    phi=PHI,\n",
    "    seed=seed\n",
    ")\n",
    "\n",
    "signal_full = generate_signal(returns_full, mu=MU, phi=PHI)\n",
    "\n",
    "# Base: last 40 periods of the 46 generated\n",
    "returns_base = returns_full[-(N_PERIODS_PLOT + 1):-1]\n",
    "signal_base = signal_full[-(N_PERIODS_PLOT + 1):-1]\n",
    "\n",
    "# Realized: last 41 periods (40 base + 1 realized)\n",
    "returns_realized = returns_full[-N_PERIODS_PLOT - 1:]\n",
    "signal_realized = signal_full[-N_PERIODS_PLOT - 1:]\n",
    "\n",
    "\n",
    "# Plot both graphs side by side\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 4.5))\n",
    "\n",
    "# Calculate dynamic y-axis for BASE GRAPH (include next signal prediction)\n",
    "returns_pct_base = returns_base * 100\n",
    "signal_pct_base = signal_base * 100\n",
    "signal_next_pct = signal_realized[-1] * 100  # Prediction for next period\n",
    "all_values_base = np.concatenate([returns_pct_base, signal_pct_base, [signal_next_pct]])\n",
    "data_min_base = np.min(all_values_base)\n",
    "data_max_base = np.max(all_values_base)\n",
    "data_range_base = data_max_base - data_min_base\n",
    "y_min_base = data_min_base - data_range_base / 2\n",
    "y_max_base = data_max_base + data_range_base / 2\n",
    "\n",
    "# BASE GRAPH (40 periods + next signal prediction)\n",
    "time_axis_base = np.arange(-N_PERIODS_PLOT, 0)\n",
    "ax1.plot(time_axis_base, returns_pct_base, 'r-', linewidth=2, label='Asset Return')\n",
    "ax1.plot(time_axis_base, signal_pct_base, 'b--', linewidth=2, label='Signal')\n",
    "# Add the next signal prediction at time 0\n",
    "ax1.plot([time_axis_base[-1], 0], [signal_pct_base[-1], signal_next_pct], 'b--', linewidth=2)\n",
    "ax1.scatter([0], [signal_next_pct], \n",
    "           color='darkblue', s=30, zorder=5, marker='o', edgecolors='black', linewidths=1.5,\n",
    "           label='Signal Prediction')\n",
    "ax1.set_xlabel('Time Period', fontsize=10)\n",
    "ax1.set_ylabel('Return (%)', fontsize=10)\n",
    "ax1.set_ylim(y_min_base, y_max_base)\n",
    "ax1.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0f}%'))\n",
    "ax1.yaxis.tick_right()\n",
    "ax1.yaxis.set_label_position('right')\n",
    "ax1.grid(True, linestyle='-', alpha=0.3, color='gray')\n",
    "ax1.set_axisbelow(True)\n",
    "ax1.legend(loc='upper left', fontsize=10, framealpha=0.9)\n",
    "ax1.set_title(f'Base Graph (graph_{scenario_id}.png) - {N_PERIODS_PLOT} periods', fontsize=12, fontweight='bold')\n",
    "\n",
    "# Calculate dynamic y-axis for REALIZED GRAPH\n",
    "returns_pct_realized = returns_realized * 100\n",
    "signal_pct_realized = signal_realized * 100\n",
    "all_values_realized = np.concatenate([returns_pct_realized, signal_pct_realized])\n",
    "data_min_realized = np.min(all_values_realized)\n",
    "data_max_realized = np.max(all_values_realized)\n",
    "data_range_realized = data_max_realized - data_min_realized\n",
    "y_min_realized = data_min_realized - data_range_realized / 2\n",
    "y_max_realized = data_max_realized + data_range_realized / 2\n",
    "\n",
    "# REALIZED GRAPH (41 periods)\n",
    "time_axis_realized = np.arange(-(N_PERIODS_PLOT + 1), 0)\n",
    "ax2.plot(time_axis_realized, returns_pct_realized, 'r-', linewidth=2, label='Asset Return')\n",
    "ax2.plot(time_axis_realized, signal_pct_realized, 'b--', linewidth=2, label='Signal')\n",
    "ax2.scatter([time_axis_realized[-1]], [returns_pct_realized[-1]], \n",
    "           color='darkred', s=30, zorder=5, marker='o', edgecolors='black', linewidths=1.5,\n",
    "           label='Realized Return')\n",
    "ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0f}%'))\n",
    "ax2.yaxis.tick_right()\n",
    "ax2.yaxis.set_label_position('right')\n",
    "ax2.grid(True, linestyle='-', alpha=0.3, color='gray')\n",
    "ax2.set_axisbelow(True)\n",
    "ax2.legend(loc='upper left', fontsize=10, framealpha=0.9)\n",
    "ax2.set_title(f'Realized Graph (graph{scenario_id}_r.png) - {N_PERIODS_PLOT + 1} periods', fontsize=12, fontweight='bold')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Save example graphs to graphs subfolder\n",
    "print(\"\\nSaving example graphs...\")\n",
    "graphs_folder = os.path.join(os.getcwd(), \"output\", \"graphs\")\n",
    "os.makedirs(graphs_folder, exist_ok=True)\n",
    "\n",
    "# Save base example graph\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "time_axis_base = np.arange(-N_PERIODS_PLOT, 0)\n",
    "ax.plot(time_axis_base, returns_pct_base, 'r-', linewidth=2, label='Asset Return')\n",
    "ax.plot(time_axis_base, signal_pct_base, 'b--', linewidth=2, label='Signal')\n",
    "ax.plot([time_axis_base[-1], 0], [signal_pct_base[-1], signal_next_pct], 'b--', linewidth=2)\n",
    "ax.scatter([0], [signal_next_pct], color='darkblue', s=30, zorder=5, marker='o', edgecolors='black', linewidths=1.5, label='Signal Prediction')\n",
    "ax.set_xlabel('', fontsize=10)\n",
    "ax.set_ylabel('', fontsize=10)\n",
    "ax.set_ylim(y_min_base, y_max_base)\n",
    "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0f}%'))\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position('right')\n",
    "ax.grid(True, linestyle='-', alpha=0.3, color='gray')\n",
    "ax.set_axisbelow(True)\n",
    "ax.legend(loc='upper left', fontsize=10, framealpha=0.9)\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(graphs_folder, 'graph_example.png'), dpi=100, bbox_inches='tight')\n",
    "plt.close()\n",
    "print(\"  ✓ Saved graph_example.png\")\n",
    "\n",
    "# Save realized example graph\n",
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "time_axis_realized = np.arange(-(N_PERIODS_PLOT + 1), 0)\n",
    "ax.plot(time_axis_realized, returns_pct_realized, 'r-', linewidth=2, label='Asset Return')\n",
    "ax.plot(time_axis_realized, signal_pct_realized, 'b--', linewidth=2, label='Signal')\n",
    "ax.scatter([time_axis_realized[-1]], [returns_pct_realized[-1]], color='darkred', s=30, zorder=5, marker='o', edgecolors='black', linewidths=1.5, label='Realized Return')\n",
    "ax.set_xlabel('', fontsize=10)\n",
    "ax.set_ylabel('', fontsize=10)\n",
    "ax.set_ylim(y_min_realized, y_max_realized)\n",
    "ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0f}%'))\n",
    "ax.yaxis.tick_right()\n",
    "ax.yaxis.set_label_position('right')\n",
    "ax.grid(True, linestyle='-', alpha=0.3, color='gray')\n",
    "ax.set_axisbelow(True)\n",
    "ax.legend(loc='upper left', fontsize=10, framealpha=0.9)\n",
    "plt.tight_layout()\n",
    "plt.savefig(os.path.join(graphs_folder, 'graph_example_r.png'), dpi=100, bbox_inches='tight')\n",
    "plt.close()\n",
    "print(\"  ✓ Saved graph_example_r.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb1c6791",
   "metadata": {},
   "outputs": [],
   "source": [
    "# clean graphs subfolder (remove all files - any file\n",
    "graphs_folder = os.path.join(\"output\")\n",
    "if not os.path.exists(graphs_folder):\n",
    "    os.makedirs(graphs_folder)\n",
    "for filename in os.listdir(graphs_folder):\n",
    "    file_path = os.path.join(graphs_folder, filename)\n",
    "    if os.path.isfile(file_path):\n",
    "        os.remove(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "da1e2881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating 32 scenarios...\n",
      "  ✓ Scenario 1 saved\n",
      "  ✓ Scenario 2 saved\n",
      "  ✓ Scenario 3 saved\n",
      "  ✓ Scenario 4 saved\n",
      "  ✓ Scenario 5 saved\n",
      "  ✓ Scenario 6 saved\n",
      "  ✓ Scenario 7 saved\n",
      "  ✓ Scenario 8 saved\n",
      "  ✓ Scenario 9 saved\n",
      "  ✓ Scenario 10 saved\n",
      "  ✓ Scenario 11 saved\n",
      "  ✓ Scenario 12 saved\n",
      "  ✓ Scenario 13 saved\n",
      "  ✓ Scenario 14 saved\n",
      "  ✓ Scenario 15 saved\n",
      "  ✓ Scenario 16 saved\n",
      "  ✓ Scenario 17 saved\n",
      "  ✓ Scenario 18 saved\n",
      "  ✓ Scenario 19 saved\n",
      "  ✓ Scenario 20 saved\n",
      "  ✓ Scenario 21 saved\n",
      "  ✓ Scenario 22 saved\n",
      "  ✓ Scenario 23 saved\n",
      "  ✓ Scenario 24 saved\n",
      "  ✓ Scenario 25 saved\n",
      "  ✓ Scenario 26 saved\n",
      "  ✓ Scenario 27 saved\n",
      "  ✓ Scenario 28 saved\n",
      "  ✓ Scenario 29 saved\n",
      "  ✓ Scenario 30 saved\n",
      "  ✓ Scenario 31 saved\n",
      "  ✓ Scenario 32 saved\n",
      "\n",
      "✓ All 32 scenarios generated successfully!\n",
      "✓ Saved CSV: realized_returns.csv\n"
     ]
    }
   ],
   "source": [
    "# ============================================================================\n",
    "# CELL 3: Generate All Scenarios and Save to Output Folder\n",
    "# ============================================================================\n",
    "\n",
    "def plot_and_save_scenario(scenario_id, output_folder):\n",
    "    \"\"\"Generate and save both base and realized graphs for a scenario.\"\"\"\n",
    "    # Generate 46 periods (45 + 1 realized)\n",
    "    seed = scenario_id * 100\n",
    "    returns_full = generate_ar1_returns(\n",
    "        n_periods=N_PERIODS_GENERATE + 1,\n",
    "        mu=MU,\n",
    "        sigma=SIGMA,\n",
    "        phi=PHI,\n",
    "        seed=seed\n",
    "    )\n",
    "    \n",
    "    signal_full = generate_signal(returns_full, mu=MU, phi=PHI)\n",
    "    \n",
    "    # Base: last 40 periods of the 46 generated\n",
    "    returns_base = returns_full[-(N_PERIODS_PLOT + 1):-1]\n",
    "    signal_base = signal_full[-(N_PERIODS_PLOT + 1):-1]\n",
    "    \n",
    "    # Realized: last 41 periods (40 base + 1 realized)\n",
    "    returns_realized = returns_full[-N_PERIODS_PLOT - 1:]\n",
    "    signal_realized = signal_full[-N_PERIODS_PLOT - 1:]\n",
    "    \n",
    "    # Convert to percentages\n",
    "    returns_pct_base = returns_base * 100\n",
    "    signal_pct_base = signal_base * 100\n",
    "    signal_next_pct = signal_realized[-1] * 100  # Prediction for next period\n",
    "    returns_pct_realized = returns_realized * 100\n",
    "    signal_pct_realized = signal_realized * 100\n",
    "    \n",
    "    # Calculate dynamic y-axis for BASE GRAPH (include next signal prediction)\n",
    "    all_values_base = np.concatenate([returns_pct_base, signal_pct_base, [signal_next_pct]])\n",
    "    data_min_base = np.min(all_values_base)\n",
    "    data_max_base = np.max(all_values_base)\n",
    "    data_range_base = data_max_base - data_min_base\n",
    "    y_min_base = data_min_base - data_range_base / 2\n",
    "    y_max_base = data_max_base + data_range_base / 2\n",
    "    \n",
    "    # SAVE BASE GRAPH (40 periods + next signal prediction)\n",
    "    fig, ax = plt.subplots(figsize=(12, 4))\n",
    "    time_axis_base = np.arange(-N_PERIODS_PLOT, 0)\n",
    "    \n",
    "    ax.plot(time_axis_base, returns_pct_base, 'r-', linewidth=2, label='Asset Return')\n",
    "    ax.plot(time_axis_base, signal_pct_base, 'b--', linewidth=2, label='Signal')\n",
    "    # Add the next signal prediction at time 0\n",
    "    ax.plot([time_axis_base[-1], 0], [signal_pct_base[-1], signal_next_pct], 'b--', linewidth=2)\n",
    "    ax.scatter([0], [signal_next_pct], \n",
    "               color='darkblue', s=30, zorder=5, marker='o', \n",
    "               edgecolors='black', linewidths=1.5,\n",
    "               label='Signal Prediction')\n",
    "    ax.set_xlabel('', fontsize=10)\n",
    "    ax.set_ylabel('', fontsize=10)\n",
    "    ax.set_ylim(y_min_base, y_max_base)\n",
    "    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0f}%'))\n",
    "    ax.yaxis.tick_right()\n",
    "    ax.yaxis.set_label_position('right')\n",
    "    ax.grid(True, linestyle='-', alpha=0.3, color='gray')\n",
    "    ax.set_axisbelow(True)\n",
    "    ax.legend(loc='upper left', fontsize=10, framealpha=0.9)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    filename_base = f\"graph{scenario_id}.png\"\n",
    "    filepath_base = os.path.join(output_folder, filename_base)\n",
    "    plt.savefig(filepath_base, dpi=100, bbox_inches='tight')\n",
    "    plt.close()\n",
    "    \n",
    "    # Calculate dynamic y-axis for REALIZED GRAPH\n",
    "    all_values_realized = np.concatenate([returns_pct_realized, signal_pct_realized])\n",
    "    data_min_realized = np.min(all_values_realized)\n",
    "    data_max_realized = np.max(all_values_realized)\n",
    "    data_range_realized = data_max_realized - data_min_realized\n",
    "    y_min_realized = data_min_realized - data_range_realized / 2\n",
    "    y_max_realized = data_max_realized + data_range_realized / 2\n",
    "    \n",
    "    # SAVE REALIZED GRAPH (41 periods)\n",
    "    fig, ax = plt.subplots(figsize=(12, 4))\n",
    "    time_axis_realized = np.arange(-(N_PERIODS_PLOT + 1), 0)\n",
    "    \n",
    "    ax.plot(time_axis_realized, returns_pct_realized, 'r-', linewidth=2, label='Asset Return')\n",
    "    ax.plot(time_axis_realized, signal_pct_realized, 'b--', linewidth=2, label='Signal')\n",
    "    # Highlight the last realized point (smaller dot)\n",
    "    ax.scatter([time_axis_realized[-1]], [returns_pct_realized[-1]], \n",
    "               color='darkred', s=30, zorder=5, marker='o', \n",
    "               edgecolors='black', linewidths=1.5,\n",
    "               label='Realized Return')\n",
    "    ax.set_xlabel('', fontsize=10)\n",
    "    ax.set_ylabel('', fontsize=10)\n",
    "    ax.set_ylim(y_min_realized, y_max_realized)\n",
    "    ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: f'{y:.0f}%'))\n",
    "    ax.yaxis.tick_right()\n",
    "    ax.yaxis.set_label_position('right')\n",
    "    ax.grid(True, linestyle='-', alpha=0.3, color='gray')\n",
    "    ax.set_axisbelow(True)\n",
    "    ax.legend(loc='upper left', fontsize=10, framealpha=0.9)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    filename_realized = f\"graph{scenario_id}_r.png\"\n",
    "    filepath_realized = os.path.join(output_folder, filename_realized)\n",
    "    plt.savefig(filepath_realized, dpi=100, bbox_inches='tight')\n",
    "    plt.close()\n",
    "    \n",
    "    # Return data for CSV: realized return and best forecast (signal prediction)\n",
    "    realized_return = returns_full[-1]\n",
    "    best_forecast = signal_full[-1]\n",
    "    return filename_base, filename_realized, realized_return, best_forecast\n",
    "\n",
    "\n",
    "# Create output/graphs folders\n",
    "output_folder_root = os.path.join(os.getcwd(), \"output\")\n",
    "output_folder = os.path.join(output_folder_root, \"graphs\")\n",
    "os.makedirs(output_folder, exist_ok=True)\n",
    "\n",
    "print(f\"Generating {N_SCENARIOS} scenarios...\")\n",
    "csv_data = []\n",
    "for scenario_id in range(1, N_SCENARIOS + 1):\n",
    "    filename_base, filename_realized, realized_return, best_forecast = plot_and_save_scenario(scenario_id, output_folder)\n",
    "    csv_data.append({\n",
    "        'decision_id': scenario_id,\n",
    "        'realized_return': realized_return,\n",
    "        'best_forecast': best_forecast\n",
    "    })\n",
    "    print(f\"  ✓ Scenario {scenario_id} saved\")\n",
    "\n",
    "# Save CSV to output folder (not graphs subfolder)\n",
    "import csv\n",
    "csv_path = os.path.join(output_folder_root, 'realized_returns.csv')\n",
    "with open(csv_path, 'w', newline='') as csvfile:\n",
    "    fieldnames = ['decision_id', 'realized_return', 'best_forecast']\n",
    "    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "    writer.writeheader()\n",
    "    writer.writerows(csv_data)\n",
    "\n",
    "print(f\"\\n✓ All {N_SCENARIOS} scenarios generated successfully!\")\n",
    "print(f\"✓ Saved CSV: realized_returns.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "521387b6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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