{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting streamlit\n",
      "  Using cached streamlit-1.26.0-py2.py3-none-any.whl (8.1 MB)\n",
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      "Installing collected packages: referencing, mdurl, MarkupSafe, importlib-metadata, gitdb, click, cachetools, blinker, tzlocal, markdown-it-py, jsonschema-specifications, jinja2, gitpython, rich, pydeck, jsonschema, altair, streamlit\n",
      "Successfully installed MarkupSafe-2.1.3 altair-5.1.1 blinker-1.6.2 cachetools-5.3.1 click-8.1.7 gitdb-4.0.10 gitpython-3.1.36 importlib-metadata-6.8.0 jinja2-3.1.2 jsonschema-4.19.0 jsonschema-specifications-2023.7.1 markdown-it-py-3.0.0 mdurl-0.1.2 pydeck-0.8.0 referencing-0.30.2 rich-13.5.2 streamlit-1.26.0 tzlocal-4.3.1\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
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      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -illow (c:\\users\\edison yi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.10_qbz5n2kfra8p0\\localcache\\local-packages\\python310\\site-packages)\n",
      "\n",
      "[notice] A new release of pip available: 22.3.1 -> 23.2.1\n",
      "[notice] To update, run: C:\\Users\\Edison Yi\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install streamlit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy_financial as npf\n",
    "from scipy.stats import norm, skew\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "matplotlib.rcParams[\"axes.formatter.limits\"] = (-99, 99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_percentiles(arr, capital_invested):\n",
    "    \"\"\"\n",
    "    Calculate the 10th, 25th, 50th (median), 75th, 90th, and the percentile of the value closest to 0 in an array.\n",
    "    Also, add a column that represents the values as a percentage of capital invested.\n",
    "\n",
    "    Args:\n",
    "    arr (list or numpy.ndarray): Input array.\n",
    "    capital_invested (float): The amount of capital invested.\n",
    "\n",
    "    Returns:\n",
    "    pandas.DataFrame: A DataFrame with percentiles and value as a percentage of capital invested.\n",
    "    \"\"\"\n",
    "    if not isinstance(arr, (list, np.ndarray)):\n",
    "        raise ValueError(\"Input must be a list or numpy.ndarray\")\n",
    "\n",
    "    percentiles = [10, 25, 50, 75, 90]\n",
    "    percentile_values = np.percentile(arr, percentiles)\n",
    "\n",
    "    # Find the value closest to 0\n",
    "    closest_value = min(arr, key=lambda x: abs(x - 0))\n",
    "\n",
    "    # Calculate the percentile of the closest value\n",
    "    sorted_arr = np.sort(arr)\n",
    "    index_of_closest = np.where(sorted_arr == closest_value)[0][0]\n",
    "    closest_percentile = (index_of_closest / (len(sorted_arr) - 1)) * 100\n",
    "\n",
    "    # Create the DataFrame with the \"Value as % of Capital\" column\n",
    "    data = {\n",
    "        'Percentile': percentiles + [closest_percentile],\n",
    "        'NPV': np.append(percentile_values, closest_value)\n",
    "    }\n",
    "\n",
    "    df = pd.DataFrame(data)\n",
    "    df['% return'] = (df['NPV'] / capital_invested) * 100\n",
    "\n",
    "    return df\n",
    "\n",
    "def bin_continuous_features(df, bin_config):\n",
    "    \"\"\"\n",
    "    Encode continuous features into bins and add them as new columns to the DataFrame.\n",
    "\n",
    "    Parameters:\n",
    "    - df: pandas DataFrame\n",
    "        The DataFrame containing the continuous features.\n",
    "    - bin_config: dict\n",
    "        A dictionary specifying the binning configuration for each feature.\n",
    "        Example: {'feature1': [0, 10, 20, 30], 'feature2': [0, 5, 10]}\n",
    "\n",
    "    Returns:\n",
    "    - df: pandas DataFrame\n",
    "        The DataFrame with binned features added as new columns.\n",
    "    \"\"\"\n",
    "\n",
    "    for feature, bins in bin_config.items():\n",
    "        # Create a new column with the binned values\n",
    "        df[f'{feature}_bin'] = pd.cut(df[feature], bins=bins, labels=False)\n",
    "\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stamp_duty_next_home(HOUSE_PRICE):\n",
    "    if HOUSE_PRICE <=250000:\n",
    "        return 0\n",
    "    elif HOUSE_PRICE <=925000:\n",
    "        return (HOUSE_PRICE-250000) * 0.05\n",
    "    elif HOUSE_PRICE <=1500000:\n",
    "        return (HOUSE_PRICE-925000) * 0.10 + (925000-250000) * 0.05\n",
    "    else:\n",
    "        return (HOUSE_PRICE-1500000) * 0.12 + (925000-250000) * 0.05 + (1500000-925000) * 0.10\n",
    "    \n",
    "def annuity_pv(payment, discount_rate, n_periods, growth_rate):\n",
    "    pv = payment * (1- (1+growth_rate)**n_periods*(1+discount_rate)**(-1*n_periods)) / (discount_rate-growth_rate)\n",
    "    return pv\n",
    "\n",
    "def annuity_fv(payment, discount_rate, n_periods, growth_rate):\n",
    "    pv = 0\n",
    "    for i in range(1, n_periods+1):\n",
    "        pv += payment*(1+growth_rate)**(i-1) *(1+discount_rate)**(n_periods-i)\n",
    "    return pv\n",
    "\n",
    "def annuity_fv_new(payment, discount_rate, n_periods, growth_rate):\n",
    "    fv = payment * ((1+discount_rate)**n_periods - (1+growth_rate)**n_periods) / (discount_rate-growth_rate)\n",
    "    return fv\n",
    "\n",
    "def annuity_payment(pv, discount_rate, n_periods, growth_rate):\n",
    "    return pv* (discount_rate - growth_rate) / (1- (1+growth_rate)**n_periods * (1+discount_rate)**(-1*n_periods))\n",
    "\n",
    "\n",
    "def pv_future_payment(payment, discount_rate, n_periods):\n",
    "    return payment/(1+discount_rate)**(n_periods)\n",
    "\n",
    "def fv_present_payment(payment, discount_rate, n_periods):\n",
    "    return payment*(1+discount_rate)**(n_periods)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1143.7229458355264"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annuity_fv(100,0.02,10,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1143.722945835528"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annuity_fv_new(100,0.02,10,0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Buy_or_Rent_Model():\n",
    "    def __init__(self) -> None:\n",
    "        # PARAMS\n",
    "        # Fixed (in expectation)\n",
    "        self.HOUSE_PRICE = 800000 #including upfront repairs and renovations \n",
    "        self.RENTAL_YIELD = 0.043 # assumed rent as a proportion of house price https://www.home.co.uk/company/press/rental_yield_heat_map_london_postcodes.pdf\n",
    "        self.DEPOSIT_MULT = 0.5\n",
    "        self.MORTGAGE_LENGTH = 30\n",
    "        self.BUYING_COST_FLAT = 3000 #https://www.movingcostscalculator.co.uk/calculator/\n",
    "        self.SELLING_COST_MULT = 0.02 #https://www.movingcostscalculator.co.uk/calculator/\n",
    "        self.ONGOING_COST_MULT = 0.006 # service charge + repairs, council tax and bills are omitted since they are the same whether buying or renting\n",
    "        # Probability distribution\n",
    "        self.rent_increase = 0.01325 # historical: https://www.ons.gov.uk/economy/inflationandpriceindices/bulletins/indexofprivatehousingrentalprices/april2023\n",
    "        self.property_price_growth_annual = 0.025 # historical average = 0.034 over the last 8 year, adjusted down due to end to abnormally low interest rates; source for historical data: https://www.statista.com/statistics/620414/monthly-house-price-index-in-london-england-uk/\n",
    "        self.mortgage_interest_annual = 0.05\n",
    "        self.investment_return_annual = 0.06\n",
    "        self.years_until_sell = 20\n",
    "        # financial modelling params\n",
    "        self.inflation = 0.02 #on ongoing costs, also for converting fv\n",
    "\n",
    "    def run_calculations(self):\n",
    "        self.monthly_rent = self.HOUSE_PRICE * self.RENTAL_YIELD /12\n",
    "        self.STAMP_DUTY = get_stamp_duty_next_home(self.HOUSE_PRICE)\n",
    "        self.discount_rate = self.investment_return_annual\n",
    "        self.DEPOSIT = self.HOUSE_PRICE * self.DEPOSIT_MULT\n",
    "        self.future_house_price = self.HOUSE_PRICE * (1+self.property_price_growth_annual)**self.years_until_sell\n",
    "        self.mortgage_calculations()\n",
    "        self.get_house_buying_npv()\n",
    "        self.get_house_buying_fv()\n",
    "        self.get_renting_fv()\n",
    "\n",
    "    def mortgage_calculations(self):\n",
    "        self.mortgage_amount = self.HOUSE_PRICE * (1 - self.DEPOSIT_MULT)\n",
    "        self.annual_mortgage_payment = annuity_payment(self.mortgage_amount, self.mortgage_interest_annual,self.MORTGAGE_LENGTH,0)\n",
    "        self.pv_mortage_payments = annuity_pv(self.annual_mortgage_payment, self.discount_rate, self.MORTGAGE_LENGTH, 0)\n",
    "        self.fv_mortgage_payments = pv_future_payment(annuity_fv(self.annual_mortgage_payment, self.discount_rate, self.MORTGAGE_LENGTH, 0), self.discount_rate, self.MORTGAGE_LENGTH - self.years_until_sell)#annuity_fv(self.annual_mortgage_payment, self.discount_rate, self.MORTGAGE_LENGTH, 0)\n",
    "\n",
    "    def get_house_buying_npv(self):\n",
    "        pv_of_future_house_price = pv_future_payment(self.future_house_price, self.discount_rate, self.years_until_sell)\n",
    "        pv_of_selling_cost = pv_future_payment(self.future_house_price * self.SELLING_COST_MULT, self.discount_rate, self.years_until_sell)\n",
    "        pv_ongoing_cost = annuity_pv(self.HOUSE_PRICE * self.ONGOING_COST_MULT,self.discount_rate, self.years_until_sell, self.inflation)\n",
    "        # rent saved\n",
    "        self.pv_rent_saved = annuity_pv(self.HOUSE_PRICE*self.RENTAL_YIELD, self.discount_rate, self.years_until_sell, self.rent_increase)\n",
    "        # sum it up\n",
    "        self.buying_npv = pv_of_future_house_price + self.pv_rent_saved - self.pv_mortage_payments- pv_ongoing_cost - self.DEPOSIT  - self.BUYING_COST_FLAT - self.STAMP_DUTY - pv_of_selling_cost\n",
    "\n",
    "    def get_house_buying_fv(self): # not accounting for deposit,  immediate costs, and rent saved. ongoing costs and mortgage are rolled up and deducted from fv\n",
    "        fv_ongoing_cost = annuity_fv(self.HOUSE_PRICE * self.ONGOING_COST_MULT,self.discount_rate, self.years_until_sell, self.inflation)\n",
    "        self.rent_fv = annuity_fv(self.HOUSE_PRICE*self.RENTAL_YIELD, self.discount_rate, self.years_until_sell, self.rent_increase)\n",
    "        self.buying_fv = self.future_house_price + self.rent_fv - self.future_house_price * self.SELLING_COST_MULT - fv_ongoing_cost -  self.fv_mortgage_payments\n",
    "        self.buying_fv_inflation_adjusted = pv_future_payment(self.buying_fv, self.inflation, self.years_until_sell)\n",
    "\n",
    "    def get_renting_fv(self): # assumes that buying costs and stamp duty are invested, rent is rolled up and deducted\n",
    "        fv_buying_cost = fv_present_payment(self.BUYING_COST_FLAT, self.discount_rate, self.years_until_sell)\n",
    "        fv_STAMP_DUTY = fv_present_payment(self.STAMP_DUTY, self.discount_rate, self.years_until_sell)\n",
    "        deposit_fv = fv_present_payment(self.DEPOSIT, self.discount_rate, self.years_until_sell)\n",
    "        self.renting_fv = deposit_fv + fv_buying_cost + fv_STAMP_DUTY #- self.rent_fv\n",
    "        self.renting_fv_inflation_adjusted = pv_future_payment(self.renting_fv, self.inflation, self.years_until_sell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-130816.84599957103\n",
      "1396810.3375295922\n",
      "2006542.0504271272\n",
      "-609731.712897535\n",
      "-130816.84599957093\n"
     ]
    }
   ],
   "source": [
    "model = Buy_or_Rent_Model()\n",
    "model.rent_increase = 0.01325\n",
    "model.property_price_growth_annual = 0.025 \n",
    "model.mortgage_interest_annual = 0.05\n",
    "model.investment_return_annual = 0.08\n",
    "model.years_until_sell = 20\n",
    "model.run_calculations()\n",
    "print(model.buying_npv)\n",
    "print(model.buying_fv)\n",
    "print(model.renting_fv)\n",
    "print(model.buying_fv-model.renting_fv)\n",
    "print(pv_future_payment(model.buying_fv-model.renting_fv, model.discount_rate, model.years_until_sell))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.MORTGAGE_LENGTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3684023.3331377506"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pv_future_payment(annuity_fv(model.annual_mortgage_payment, model.discount_rate, model.MORTGAGE_LENGTH, 0), model.discount_rate,model.MORTGAGE_LENGTH - model.years_until_sell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "957161.0831163041"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annuity_fv(26020, 0.06, 20, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "437296.2722069741"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.pv_rent_saved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_param_distribution(mean, std, samples, bins,plot=True, as_int = False):\n",
    "    s = np.random.normal(mean, std, samples)\n",
    "    if plot:\n",
    "        plt.hist(s, bins, density=False)\n",
    "        plt.show()\n",
    "    if as_int:\n",
    "        s=s.astype(int) \n",
    "    return s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_combinations_and_calculate_npv(\n",
    "        n_combinations,\n",
    "        model,\n",
    "        mortgage_interest_annual_list=[0.05],\n",
    "        property_price_growth_annual_list=[0.026],\n",
    "        rent_increase_list=[0.01325],\n",
    "        investment_return_annual_list=[0.06],\n",
    "        years_until_sell_list=[20]\n",
    "        ):\n",
    "        buying_npv_list = []\n",
    "        mortgage_interest_annual_list_chosen=[]\n",
    "        property_price_growth_annual_list_chosen=[]\n",
    "        rent_increase_list_chosen=[]\n",
    "        investment_return_annual_list_chosen=[]\n",
    "        years_until_sell_list_chosen=[]\n",
    "\n",
    "        for n in range(n_combinations):\n",
    "\n",
    "                model.rent_increase = np.random.choice(rent_increase_list)\n",
    "                model.property_price_growth_annual = np.random.choice(property_price_growth_annual_list)\n",
    "                model.mortgage_interest_annual = np.random.choice(mortgage_interest_annual_list)\n",
    "                model.investment_return_annual = np.random.choice(investment_return_annual_list)\n",
    "                model.years_until_sell = np.random.choice(years_until_sell_list)\n",
    "                \n",
    "                model.run_calculations()\n",
    "                buying_npv_list.append(model.buying_npv)\n",
    "                mortgage_interest_annual_list_chosen.append(model.mortgage_interest_annual)\n",
    "                property_price_growth_annual_list_chosen.append(model.property_price_growth_annual)\n",
    "                rent_increase_list_chosen.append(model.rent_increase)\n",
    "                investment_return_annual_list_chosen.append(model.investment_return_annual)\n",
    "                years_until_sell_list_chosen.append(model.years_until_sell)\n",
    "\n",
    "        results_dict = {'buying_npv':buying_npv_list,\n",
    "                        'mortgage_interest_annual':mortgage_interest_annual_list_chosen,\n",
    "                        'property_price_growth_annual':property_price_growth_annual_list_chosen,\n",
    "                        'rent_increase':rent_increase_list_chosen,\n",
    "                        'investment_return_annual':investment_return_annual_list_chosen,\n",
    "                        'years_until_sell':years_until_sell_list_chosen}\n",
    "        results_df = pd.DataFrame(results_dict)\n",
    "        print(f'Capital Invested: £{model.DEPOSIT:.2f}')\n",
    "        print(f'NPV mean: £{np.mean(buying_npv_list):.2f}')\n",
    "        print(f'NPV mean (as % of invested capital): {np.mean(buying_npv_list)/model.DEPOSIT*100:.2f}%')\n",
    "        print(f'NPV std: £{np.std(buying_npv_list):.2f}')\n",
    "        print(f'NPV std (as % of invested capital): {np.std(buying_npv_list)/model.DEPOSIT*100:.2f}%')\n",
    "        print(f'NPV skew: {skew(buying_npv_list):.2f}')\n",
    "        percentiles_df = calculate_percentiles(buying_npv_list,model.DEPOSIT)\n",
    "        print(f'Make money: {100-percentiles_df.loc[5,\"Percentile\"]:.2f}% of the time')\n",
    "        display(percentiles_df)\n",
    "        \n",
    "        # plt.hist(buying_npv_list,30,normed=True)\n",
    "        plt.figure(figsize=(10,7))\n",
    "        sns.kdeplot(data=buying_npv_list)\n",
    "        # ax.ticklabel_format(useOffset=False)\n",
    "        plt.show()\n",
    "        return results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_samples = 10000\n",
    "n_bins = 30\n",
    "\n",
    "plt.title('mortgage_interest_annual (long term average)')\n",
    "mortgage_interest_annual_list = get_param_distribution(0.038, 0.012, n_samples,n_bins)\n",
    "plt.title('property_price_growth (long term average)')\n",
    "# mortgage_interest_annual_list = get_param_distribution(0.02, 0.01, 10000,30)\n",
    "property_price_growth_annual_list = get_param_distribution(0.024, 0.01, n_samples,n_bins)\n",
    "plt.title('rent_increase (long term average)')\n",
    "rent_increase_list = get_param_distribution(0.01325 , 0.01, n_samples,n_bins)\n",
    "plt.title('investment_return_annual (long term average)')\n",
    "investment_return_annual_list = get_param_distribution(0.055, 0.02, n_samples,n_bins)\n",
    "plt.title('years_until_sell')\n",
    "years_until_sell_list =  [40]# get_param_distribution(15, 5, n_samples,n_bins, as_int = True) #[5,10,15,20,25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capital Invested: £400000.00\n",
      "NPV mean: £171540.04\n",
      "NPV mean (as % of invested capital): 42.89%\n",
      "NPV std: £483889.88\n",
      "NPV std (as % of invested capital): 120.97%\n",
      "NPV skew: 3.01\n",
      "Make money: 56.26% of the time\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Percentile</th>\n",
       "      <th>NPV</th>\n",
       "      <th>% return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>-220679.077244</td>\n",
       "      <td>-55.169769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>-110805.415498</td>\n",
       "      <td>-27.701354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>43093.718040</td>\n",
       "      <td>10.773430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>289636.103815</td>\n",
       "      <td>72.409026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>678892.109640</td>\n",
       "      <td>169.723027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>43.743744</td>\n",
       "      <td>89.036774</td>\n",
       "      <td>0.022259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Percentile            NPV    % return\n",
       "0   10.000000 -220679.077244  -55.169769\n",
       "1   25.000000 -110805.415498  -27.701354\n",
       "2   50.000000   43093.718040   10.773430\n",
       "3   75.000000  289636.103815   72.409026\n",
       "4   90.000000  678892.109640  169.723027\n",
       "5   43.743744      89.036774    0.022259"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Buy_or_Rent_Model()\n",
    "model.DEPOSIT_MULT = 0.5\n",
    "\n",
    "results_df=generate_combinations_and_calculate_npv( \n",
    "                                                1000,\n",
    "                                                model,\n",
    "                                                mortgage_interest_annual_list=mortgage_interest_annual_list,\n",
    "                                                property_price_growth_annual_list=property_price_growth_annual_list,\n",
    "                                                rent_increase_list=rent_increase_list,\n",
    "                                                investment_return_annual_list=investment_return_annual_list,\n",
    "                                                years_until_sell_list=years_until_sell_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>buying_npv</th>\n",
       "      <th>mortgage_interest_annual</th>\n",
       "      <th>property_price_growth_annual</th>\n",
       "      <th>rent_increase</th>\n",
       "      <th>investment_return_annual</th>\n",
       "      <th>years_until_sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>buying_npv</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.288163</td>\n",
       "      <td>0.441658</td>\n",
       "      <td>0.137310</td>\n",
       "      <td>-0.693374</td>\n",
       "      <td>0.121079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mortgage_interest_annual</th>\n",
       "      <td>-0.288163</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003983</td>\n",
       "      <td>0.027695</td>\n",
       "      <td>-0.004116</td>\n",
       "      <td>-0.006385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property_price_growth_annual</th>\n",
       "      <td>0.441658</td>\n",
       "      <td>-0.003983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.035141</td>\n",
       "      <td>-0.017027</td>\n",
       "      <td>0.037720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rent_increase</th>\n",
       "      <td>0.137310</td>\n",
       "      <td>0.027695</td>\n",
       "      <td>-0.035141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032061</td>\n",
       "      <td>-0.001103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>investment_return_annual</th>\n",
       "      <td>-0.693374</td>\n",
       "      <td>-0.004116</td>\n",
       "      <td>-0.017027</td>\n",
       "      <td>-0.032061</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.012999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years_until_sell</th>\n",
       "      <td>0.121079</td>\n",
       "      <td>-0.006385</td>\n",
       "      <td>0.037720</td>\n",
       "      <td>-0.001103</td>\n",
       "      <td>0.012999</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              buying_npv  mortgage_interest_annual  \\\n",
       "buying_npv                      1.000000                 -0.288163   \n",
       "mortgage_interest_annual       -0.288163                  1.000000   \n",
       "property_price_growth_annual    0.441658                 -0.003983   \n",
       "rent_increase                   0.137310                  0.027695   \n",
       "investment_return_annual       -0.693374                 -0.004116   \n",
       "years_until_sell                0.121079                 -0.006385   \n",
       "\n",
       "                              property_price_growth_annual  rent_increase  \\\n",
       "buying_npv                                        0.441658       0.137310   \n",
       "mortgage_interest_annual                         -0.003983       0.027695   \n",
       "property_price_growth_annual                      1.000000      -0.035141   \n",
       "rent_increase                                    -0.035141       1.000000   \n",
       "investment_return_annual                         -0.017027      -0.032061   \n",
       "years_until_sell                                  0.037720      -0.001103   \n",
       "\n",
       "                              investment_return_annual  years_until_sell  \n",
       "buying_npv                                   -0.693374          0.121079  \n",
       "mortgage_interest_annual                     -0.004116         -0.006385  \n",
       "property_price_growth_annual                 -0.017027          0.037720  \n",
       "rent_increase                                -0.032061         -0.001103  \n",
       "investment_return_annual                      1.000000          0.012999  \n",
       "years_until_sell                              0.012999          1.000000  "
      ]
     },
     "execution_count": 494,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graph_kde_plots(results_df, FEATURES, num_cols = 3):\n",
    "    \n",
    "    # Calculate the number of rows and columns needed for subplots\n",
    "    num_features = len(FEATURES)\n",
    "    \n",
    "    num_rows = (num_features + num_cols - 1) // num_cols\n",
    "\n",
    "    # Create a figure and axis for subplots\n",
    "    fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 5 * num_rows))\n",
    "\n",
    "    # Flatten the axes if necessary (in case there's only one row)\n",
    "    if num_rows == 1:\n",
    "        axes = axes.reshape(1, -1)\n",
    "\n",
    "    # Loop through each feature and plot it\n",
    "    for i, feature in enumerate(FEATURES):\n",
    "        row = i // num_cols\n",
    "        col = i % num_cols\n",
    "        ax = axes[row, col]\n",
    "        \n",
    "        sns.kdeplot(data=results_df, y=\"buying_npv\", x=feature, ax=ax, fill=True)\n",
    "        ax.set_title(f\"{feature} vs. buying_npv\")\n",
    "        ax.set_ylabel(\"buying_npv\")\n",
    "        ax.set_xlabel(feature)\n",
    "         # Calculate the 95th percentile for x and y axes\n",
    "        x_low_percentile = np.percentile(results_df[feature], 0.5)\n",
    "        y_low_percentile = np.percentile(results_df['buying_npv'], 0.5)\n",
    "        x_high_percentile = np.percentile(results_df[feature], 99.5)\n",
    "        y_high_percentile = np.percentile(results_df['buying_npv'], 99.5)\n",
    "        \n",
    "        # Set the axis limits based on the 95th percentile\n",
    "        ax.set_xlim(x_low_percentile, x_high_percentile)\n",
    "        ax.set_ylim(y_low_percentile, y_high_percentile)\n",
    "        \n",
    "        # ax.set_xticklabels(ax.get_xticklabels(), rotation=45)  # Adjust the rotation angle as needed\n",
    "        \n",
    "    # Remove any empty subplots\n",
    "    for i in range(len(FEATURES), num_rows * num_cols):\n",
    "        fig.delaxes(axes.flatten()[i])\n",
    "\n",
    "    # Adjust spacing between subplots\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # Show the plots\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Edison Yi\\AppData\\Local\\Temp\\ipykernel_9544\\2758553482.py:21: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.\n",
      "  sns.kdeplot(data=results_df, y=\"buying_npv\", x=feature, ax=ax, fill=True)\n",
      "C:\\Users\\Edison Yi\\AppData\\Local\\Temp\\ipykernel_9544\\2758553482.py:32: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n",
      "  ax.set_xlim(x_low_percentile, x_high_percentile)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FEATURES = ['mortgage_interest_annual', 'property_price_growth_annual', 'rent_increase', 'investment_return_annual', 'years_until_sell']\n",
    "graph_kde_plots(results_df, FEATURES, num_cols = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FEATURES = ['mortgage_interest_annual', 'property_price_growth_annual', 'rent_increase', 'investment_return_annual', 'years_until_sell']\n",
    "\n",
    "# Calculate the number of rows and columns needed for subplots\n",
    "num_features = len(FEATURES)\n",
    "num_cols = 3\n",
    "num_rows = (num_features + num_cols - 1) // num_cols\n",
    "\n",
    "# Create a figure and axis for subplots\n",
    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 5 * num_rows))\n",
    "\n",
    "# Flatten the axes if necessary (in case there's only one row)\n",
    "if num_rows == 1:\n",
    "    axes = axes.reshape(1, -1)\n",
    "\n",
    "# Loop through each feature and plot it\n",
    "for i, feature in enumerate(FEATURES):\n",
    "    row = i // num_cols\n",
    "    col = i % num_cols\n",
    "    ax = axes[row, col]\n",
    "    \n",
    "    sns.kdeplot(data=results_df, y=\"buying_npv\", x=feature, ax=ax, fill=True)\n",
    "    ax.set_title(f\"{feature} vs. buying_npv\")\n",
    "    ax.set_ylabel(\"buying_npv\")\n",
    "    ax.set_xlabel(feature)\n",
    "    ax.set_ylim(-1000000,1000000)\n",
    "    # ax.set_xticklabels(ax.get_xticklabels(), rotation=45)  # Adjust the rotation angle as needed\n",
    "    \n",
    "# Remove any empty subplots\n",
    "for i in range(len(FEATURES), num_rows * num_cols):\n",
    "    fig.delaxes(axes.flatten()[i])\n",
    "\n",
    "# Adjust spacing between subplots\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_config = {\n",
    "    'mortgage_interest_annual':3, \n",
    "    'property_price_growth_annual':3, \n",
    "    'rent_increase':3, \n",
    "    'investment_return_annual':3, \n",
    "    'years_until_sell':3\n",
    "}\n",
    "results_df=bin_continuous_features(results_df,bin_config)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}