Simple-Risk-Modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48993fde",
   "metadata": {},
   "source": [
    "# Simple Risk Forecasting"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50e7816",
   "metadata": {},
   "source": [
    "This code demonstrates a simple risk forecasting simulation using Python. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "63b3d400",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scipy import stats\n",
    "from statistics import NormalDist\n",
    "\n",
    "from sklearn.preprocessing import scale\n",
    "import math\n",
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e18ada92",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f354d666",
   "metadata": {},
   "source": [
    "***\n",
    "### Define Parameters\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794d7abf",
   "metadata": {},
   "source": [
    "Define the minimum and maximum values for the probability of an event, the number of simulation trials, and the parameters for log-normal distribution.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5bcdafc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the minimum and maximum values for probability of event\n",
    "MIN = .07\n",
    "MAX = .17\n",
    "# Define the number of trials\n",
    "NUM_SAMPLES = 1000\n",
    "# Define the log loss mean and standard deviation\n",
    "LOG_MEAN = 12.55\n",
    "LOG_STD = 3.06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a278e886",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize lists to store trial results\n",
    "trial_results = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74a70099",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure we always get the same numbers\n",
    "# random.seed(42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b94637",
   "metadata": {},
   "source": [
    "***\n",
    "### Generate Random Probabilities\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ff4fcb06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random numbers for each trial\n",
    "for trial in range(NUM_SAMPLES):\n",
    "    # Generate a random number between MIN and MAX, not assuming any underlying distribution \n",
    "    random_number = MIN + (MAX-MIN) * random.random()\n",
    "    trial_results.append(random_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7bdd6707",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataFrame to store the trial results\n",
    "data = {'Frequency': np.arange(1, NUM_SAMPLES + 1), 'Probability_of_event': trial_results}\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "440e4f0b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability_of_event</th>\n",
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       "      <td>0.072783</td>\n",
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       "      <th>1</th>\n",
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       "      <td>0.107784</td>\n",
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       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <th>995</th>\n",
       "      <td>996</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>0.147615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>0.119150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>0.133528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.091581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Frequency  Probability_of_event\n",
       "0            1              0.072783\n",
       "1            2              0.107784\n",
       "2            3              0.121950\n",
       "3            4              0.118135\n",
       "4            5              0.141986\n",
       "..         ...                   ...\n",
       "995        996              0.139416\n",
       "996        997              0.147615\n",
       "997        998              0.119150\n",
       "998        999              0.133528\n",
       "999       1000              0.091581\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the results\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9506b84a",
   "metadata": {},
   "source": [
    "***\n",
    "### Simulate Events\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b37ab68d",
   "metadata": {},
   "source": [
    "Let's simulate events based on the generated probabilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c74be64c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Each of the probabilities may lead to a simulated event\n",
    "df['Simulated_event'] = df['Probability_of_event'].apply(lambda x : int(round(random.random() < x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b2c4833",
   "metadata": {},
   "outputs": [
    {
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       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>0.119150</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>0.133528</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.091581</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
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      ],
      "text/plain": [
       "     Frequency  Probability_of_event  Simulated_event\n",
       "0            1              0.072783                0\n",
       "1            2              0.107784                0\n",
       "2            3              0.121950                0\n",
       "3            4              0.118135                0\n",
       "4            5              0.141986                1\n",
       "..         ...                   ...              ...\n",
       "995        996              0.139416                0\n",
       "996        997              0.147615                0\n",
       "997        998              0.119150                0\n",
       "998        999              0.133528                1\n",
       "999       1000              0.091581                0\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the results\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58514ae9",
   "metadata": {},
   "source": [
    "***\n",
    "### Simulate Losses\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a065979d",
   "metadata": {},
   "source": [
    "Let's simulate losses based on a log-normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a510d17",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = NormalDist(mu=LOG_MEAN, sigma=LOG_STD).inv_cdf(random.uniform(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd54cdb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Losses are log-normally distributed, around given mean and standard deviation\n",
    "dist = []\n",
    "for i in range(NUM_SAMPLES):\n",
    "    res = NormalDist(mu=LOG_MEAN, sigma=LOG_STD).inv_cdf(random.uniform(0, 1))\n",
    "    dist.append(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8cc53dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Possible_loss'] = pd.Series(dist, index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6599564c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frequency</th>\n",
       "      <th>Probability_of_event</th>\n",
       "      <th>Simulated_event</th>\n",
       "      <th>Possible_loss</th>\n",
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       "      <td>0.141986</td>\n",
       "      <td>1</td>\n",
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       "      <th>995</th>\n",
       "      <td>996</td>\n",
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       "      <td>0</td>\n",
       "      <td>9.127598</td>\n",
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       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>0.147615</td>\n",
       "      <td>0</td>\n",
       "      <td>10.357502</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>0.119150</td>\n",
       "      <td>0</td>\n",
       "      <td>8.054368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>0.133528</td>\n",
       "      <td>1</td>\n",
       "      <td>13.917978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.091581</td>\n",
       "      <td>0</td>\n",
       "      <td>10.466208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Frequency  Probability_of_event  Simulated_event  Possible_loss\n",
       "0            1              0.072783                0      13.271678\n",
       "1            2              0.107784                0      11.725442\n",
       "2            3              0.121950                0       9.371626\n",
       "3            4              0.118135                0      10.923560\n",
       "4            5              0.141986                1       6.662659\n",
       "..         ...                   ...              ...            ...\n",
       "995        996              0.139416                0       9.127598\n",
       "996        997              0.147615                0      10.357502\n",
       "997        998              0.119150                0       8.054368\n",
       "998        999              0.133528                1      13.917978\n",
       "999       1000              0.091581                0      10.466208\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the results\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab4f279d",
   "metadata": {},
   "source": [
    "***\n",
    "### Calculate Simulated Loss\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1bf9457",
   "metadata": {},
   "source": [
    "Calculate simulated loss for each trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0a53dea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Simulated_loss'] = df['Simulated_event'] * df['Possible_loss']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8990200f",
   "metadata": {},
   "source": [
    "***\n",
    "### Logarithm Transformation\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23589a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "def logarithmbase10(x):\n",
    "    try:\n",
    "        return math.log10(x)\n",
    "    except:\n",
    "        return 0.0001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f20eda37",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Log_simulated_loss'] = df['Simulated_loss'].apply(lambda x : logarithmbase10(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e00a1cd",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.121950</td>\n",
       "      <td>0</td>\n",
       "      <td>9.371626</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.118135</td>\n",
       "      <td>0</td>\n",
       "      <td>10.923560</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.141986</td>\n",
       "      <td>1</td>\n",
       "      <td>6.662659</td>\n",
       "      <td>6.662659</td>\n",
       "      <td>0.823648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>0.139416</td>\n",
       "      <td>0</td>\n",
       "      <td>9.127598</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>0.147615</td>\n",
       "      <td>0</td>\n",
       "      <td>10.357502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>0.119150</td>\n",
       "      <td>0</td>\n",
       "      <td>8.054368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>0.133528</td>\n",
       "      <td>1</td>\n",
       "      <td>13.917978</td>\n",
       "      <td>13.917978</td>\n",
       "      <td>1.143576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.091581</td>\n",
       "      <td>0</td>\n",
       "      <td>10.466208</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Frequency  Probability_of_event  Simulated_event  Possible_loss  \\\n",
       "0            1              0.072783                0      13.271678   \n",
       "1            2              0.107784                0      11.725442   \n",
       "2            3              0.121950                0       9.371626   \n",
       "3            4              0.118135                0      10.923560   \n",
       "4            5              0.141986                1       6.662659   \n",
       "..         ...                   ...              ...            ...   \n",
       "995        996              0.139416                0       9.127598   \n",
       "996        997              0.147615                0      10.357502   \n",
       "997        998              0.119150                0       8.054368   \n",
       "998        999              0.133528                1      13.917978   \n",
       "999       1000              0.091581                0      10.466208   \n",
       "\n",
       "     Simulated_loss  Log_simulated_loss  \n",
       "0          0.000000            0.000100  \n",
       "1          0.000000            0.000100  \n",
       "2          0.000000            0.000100  \n",
       "3          0.000000            0.000100  \n",
       "4          6.662659            0.823648  \n",
       "..              ...                 ...  \n",
       "995        0.000000            0.000100  \n",
       "996        0.000000            0.000100  \n",
       "997        0.000000            0.000100  \n",
       "998       13.917978            1.143576  \n",
       "999        0.000000            0.000100  \n",
       "\n",
       "[1000 rows x 6 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the results\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "804d3cc5",
   "metadata": {},
   "source": [
    "***\n",
    "### Visualization\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fe4141b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar plot for simulated events\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "sns.barplot(data = df, x = df['Simulated_event'].value_counts().keys(), y = df['Simulated_event'].value_counts()/len(df)*100, \n",
    "            palette=['darkred', 'lightblue'])\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('Simulated years without/with events', size=12, color='#4f4e4e')\n",
    "plt.xticks(size=8, color='#4f4e4e')\n",
    "plt.yticks([], [])\n",
    "# plt.text(x=1, y=90, s='N events simulated in most years', \n",
    "#                  color='darkgrey', fontsize=8, horizontalalignment='center')\n",
    "plt.text(x=0, y=2, s=\" \".join([(round(df['Simulated_event'].value_counts()[0]/len(df)*100, 2)).astype('str'), \"%\"]),  \n",
    "                 color='white', fontsize=8, horizontalalignment='center')\n",
    "plt.text(x=1, y=2, s=\" \".join([(round(df['Simulated_event'].value_counts()[1]/len(df)*100, 2)).astype('str'), \"%\"]),  \n",
    "                 color='white', fontsize=8, horizontalalignment='center')\n",
    "sns.despine(left=True, bottom=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6a25e386",
   "metadata": {},
   "outputs": [],
   "source": [
    "actual_losses = [x for x in df['Log_simulated_loss'] if x > 0.0001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "38dd4890",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram for simulated losses\n",
    "\n",
    "plt.figure(figsize=(20, 10))\n",
    "sns.distplot(actual_losses, bins=30, color='darkred')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.title('Simulated Losses [$ MM]', size=12, color='#4f4e4e')\n",
    "plt.xticks(size=8, color='#4f4e4e')\n",
    "plt.yticks([], [])\n",
    "sns.despine(left=True, bottom=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "183dad28",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}