ffd-sip-strategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import sys\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "plt.rcParams['figure.figsize'] = [14, 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Config options\n",
    "cwd = 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "dateparse = lambda x: pd.datetime.strptime(x, '%d-%b-%Y')\n",
    "nifty = pd.concat([pd.read_csv(cwd + '/' + f, parse_dates=['Date'], date_parser=dateparse) for f in sorted(os.listdir(cwd))], ignore_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Shares Traded</th>\n",
       "      <th>Turnover (Rs. Cr)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>4903.000000</td>\n",
       "      <td>4903.000000</td>\n",
       "      <td>4903.000000</td>\n",
       "      <td>4903.000000</td>\n",
       "      <td>4.903000e+03</td>\n",
       "      <td>4903.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>5028.744983</td>\n",
       "      <td>5062.516990</td>\n",
       "      <td>4989.049154</td>\n",
       "      <td>5026.400959</td>\n",
       "      <td>1.498470e+08</td>\n",
       "      <td>6290.407073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>3138.698027</td>\n",
       "      <td>3146.534063</td>\n",
       "      <td>3123.482854</td>\n",
       "      <td>3134.811321</td>\n",
       "      <td>9.859852e+07</td>\n",
       "      <td>4425.010146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>853.000000</td>\n",
       "      <td>877.000000</td>\n",
       "      <td>849.950000</td>\n",
       "      <td>854.200000</td>\n",
       "      <td>1.394931e+06</td>\n",
       "      <td>40.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1919.050000</td>\n",
       "      <td>1942.400000</td>\n",
       "      <td>1901.650000</td>\n",
       "      <td>1918.425000</td>\n",
       "      <td>7.620246e+07</td>\n",
       "      <td>2958.745000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>5005.350000</td>\n",
       "      <td>5057.500000</td>\n",
       "      <td>4956.450000</td>\n",
       "      <td>5003.950000</td>\n",
       "      <td>1.345816e+08</td>\n",
       "      <td>5647.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7633.225000</td>\n",
       "      <td>7676.725000</td>\n",
       "      <td>7580.650000</td>\n",
       "      <td>7619.150000</td>\n",
       "      <td>1.909097e+08</td>\n",
       "      <td>8186.865000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>12052.650000</td>\n",
       "      <td>12103.050000</td>\n",
       "      <td>12005.850000</td>\n",
       "      <td>12088.550000</td>\n",
       "      <td>7.411532e+08</td>\n",
       "      <td>35131.190000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open          High           Low         Close  Shares Traded  \\\n",
       "count   4903.000000   4903.000000   4903.000000   4903.000000   4.903000e+03   \n",
       "mean    5028.744983   5062.516990   4989.049154   5026.400959   1.498470e+08   \n",
       "std     3138.698027   3146.534063   3123.482854   3134.811321   9.859852e+07   \n",
       "min      853.000000    877.000000    849.950000    854.200000   1.394931e+06   \n",
       "25%     1919.050000   1942.400000   1901.650000   1918.425000   7.620246e+07   \n",
       "50%     5005.350000   5057.500000   4956.450000   5003.950000   1.345816e+08   \n",
       "75%     7633.225000   7676.725000   7580.650000   7619.150000   1.909097e+08   \n",
       "max    12052.650000  12103.050000  12005.850000  12088.550000   7.411532e+08   \n",
       "\n",
       "       Turnover (Rs. Cr)  \n",
       "count        4903.000000  \n",
       "mean         6290.407073  \n",
       "std          4425.010146  \n",
       "min            40.120000  \n",
       "25%          2958.745000  \n",
       "50%          5647.400000  \n",
       "75%          8186.865000  \n",
       "max         35131.190000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nifty.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date                 datetime64[ns]\n",
       "Open                        float64\n",
       "High                        float64\n",
       "Low                         float64\n",
       "Close                       float64\n",
       "Shares Traded                 int64\n",
       "Turnover (Rs. Cr)           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nifty.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Shares Traded</th>\n",
       "      <th>Turnover (Rs. Cr)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>1482.15</td>\n",
       "      <td>1592.90</td>\n",
       "      <td>1482.15</td>\n",
       "      <td>1592.2</td>\n",
       "      <td>25358322</td>\n",
       "      <td>884.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>1594.40</td>\n",
       "      <td>1641.95</td>\n",
       "      <td>1594.40</td>\n",
       "      <td>1638.7</td>\n",
       "      <td>38787872</td>\n",
       "      <td>1973.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>1634.55</td>\n",
       "      <td>1635.50</td>\n",
       "      <td>1555.05</td>\n",
       "      <td>1595.8</td>\n",
       "      <td>62153431</td>\n",
       "      <td>3084.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>1595.80</td>\n",
       "      <td>1639.00</td>\n",
       "      <td>1595.80</td>\n",
       "      <td>1617.6</td>\n",
       "      <td>51272875</td>\n",
       "      <td>2531.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>1616.60</td>\n",
       "      <td>1628.25</td>\n",
       "      <td>1597.20</td>\n",
       "      <td>1613.3</td>\n",
       "      <td>54315945</td>\n",
       "      <td>1914.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date     Open     High      Low   Close  Shares Traded  \\\n",
       "0 2000-01-03  1482.15  1592.90  1482.15  1592.2       25358322   \n",
       "1 2000-01-04  1594.40  1641.95  1594.40  1638.7       38787872   \n",
       "2 2000-01-05  1634.55  1635.50  1555.05  1595.8       62153431   \n",
       "3 2000-01-06  1595.80  1639.00  1595.80  1617.6       51272875   \n",
       "4 2000-01-07  1616.60  1628.25  1597.20  1613.3       54315945   \n",
       "\n",
       "   Turnover (Rs. Cr)  \n",
       "0             884.15  \n",
       "1            1973.69  \n",
       "2            3084.79  \n",
       "3            2531.18  \n",
       "4            1914.63  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nifty.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "nifty = nifty.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-01-03 00:00:00\n",
      "2000-01-04 00:00:00\n",
      "2000-01-05 00:00:00\n",
      "2000-01-06 00:00:00\n",
      "2000-01-07 00:00:00\n",
      "2000-01-10 00:00:00\n",
      "equal\n",
      "2000-01-11 00:00:00\n",
      "2000-01-12 00:00:00\n",
      "2000-01-13 00:00:00\n",
      "2000-01-14 00:00:00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Timestamp('2019-09-19 00:00:00')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in nifty.index[:10]:\n",
    "    if (i.month==1 and i.day==11):\n",
    "        print('equal')\n",
    "    print(i)\n",
    "nifty[datetime(2001,1,3):datetime(2001,1,3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "nifty[\"52 week high\"] = pd.Series.rolling(nifty.High, window=200, min_periods=1).max()\n",
    "nifty[\"52 week low\"] = pd.Series.rolling(nifty.Low, window=200, min_periods=1).min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff98aa50358>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nifty[[\"52 week high\", \"52 week low\", \"Close\"]].plot()\n",
    "# nifty[[\"High\"]].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of times market is at highs: 0.11054456455231491\n",
      "Fraction of times market is at lows: 0.016724454415663878\n"
     ]
    }
   ],
   "source": [
    "highs = (nifty[\"52 week high\"] == nifty.High).value_counts()\n",
    "print(\"Fraction of times market is at highs:\", highs[True]/(highs[True] + highs[False]))\n",
    "lows = (nifty[\"52 week low\"] == nifty.Low).value_counts()\n",
    "print(\"Fraction of times market is at lows:\", lows[True]/(lows[True] + lows[False]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Parameters:\n",
    "    def __init__(self, **kwds):\n",
    "        self.__dict__.update(kwds)\n",
    "params = Parameters(bear_delta=5, bear_percent=4, bear_ndays=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Variables almost remaining constants\n",
    "debt_rate = 0.074 # percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DebtCorpus with both a deposit() and a withdraw() function \n",
    "class DebtCorpus: \n",
    "    def __init__(self): \n",
    "        self.balance = 0\n",
    "        self.date = datetime(1990, 1, 1)\n",
    "\n",
    "    def Check(self, date):\n",
    "        if (date < self.date):\n",
    "            print(\"Date cannot be less than last account operation date\")\n",
    "            return False\n",
    "        return True\n",
    "  \n",
    "    def Deposit(self, date, amount):\n",
    "        if (self.Check(date) == False):\n",
    "            return False\n",
    "        delta = (date - self.date).days / 365.25\n",
    "        self.balance = amount + self.balance * pow(1 + debt_rate, delta)\n",
    "        self.date = date\n",
    "        return True\n",
    "  \n",
    "    def Withdraw(self, date, amount): \n",
    "        if (self.Check(date) == False):\n",
    "            return False\n",
    "        delta = (date - self.date).days / 365.25\n",
    "        self.balance = self.balance * pow(1 + debt_rate, delta)\n",
    "        self.date = date\n",
    "        fulfilled = min(self.balance, amount)\n",
    "        if self.balance <= amount:\n",
    "            print('Balance is lesser than requested', self.balance, amount)\n",
    "        self.balance -= fulfilled \n",
    "        return fulfilled\n",
    "  \n",
    "    def Get(self, date):\n",
    "        delta = (date - self.date).days / 365.25\n",
    "        self.balance = self.balance * pow(1 + debt_rate, delta)\n",
    "        self.date = date\n",
    "        return self.balance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "212.77761431538931"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# debt = DebtCorpus()\n",
    "# print(debt.Deposit(datetime(2000, 1, 1), 100))\n",
    "# print(debt.Withdraw(datetime(2010, 1, 1), 100))\n",
    "# debt.Get(datetime(2020, 1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "def EvaluateStrategy(df, params, pa = 100):\n",
    "    naive_sips, curr_year = [], 0\n",
    "    equity_investments = []\n",
    "    debt_corpus = DebtCorpus()\n",
    "    total_invested = 0\n",
    "    min_chkpts = []\n",
    "    bear_delta = params.bear_delta\n",
    "    bear_percent = params.bear_percent\n",
    "    nifty[\"bear_ndays\"] = pd.Series.rolling(nifty.Low, window=params.bear_ndays, min_periods=1).min()\n",
    "    for ind in df.index:\n",
    "        if (naive_sips == [] or curr_year < ind.year):\n",
    "            total_invested += pa\n",
    "            naive_sips.append((ind, df.Close[0], pa))\n",
    "            debt_corpus.Deposit(ind, pa)\n",
    "            curr_year = ind.year\n",
    "        if (df[\"bear_ndays\"][ind] == df.Low[ind]):\n",
    "            if (min_chkpts == []):\n",
    "                min_chkpts = [ind]\n",
    "            if (df.Close[ind] < (1 - bear_percent/100.0) * df.Close[min_chkpts[-1]]):\n",
    "                min_chkpts.append(ind)\n",
    "                if (len(min_chkpts)%2 == 0):\n",
    "                    bear_delta = bear_delta * 2\n",
    "                # Make investment\n",
    "                amount = debt_corpus.Withdraw(ind, bear_delta)\n",
    "                if (amount>0):\n",
    "                    equity_investments.append((df.Close[ind], amount))\n",
    "                    print (ind, df.Close[ind], amount, (1 - bear_percent/100.0))\n",
    "    print(\"total_invested\", total_invested)\n",
    "    # Calculate the final profits\n",
    "    index_close = df.Close.iloc[-1]\n",
    "    equity_amount, equity_invested = 0, 0\n",
    "    for i in equity_investments:\n",
    "        equity_invested += i[1]\n",
    "        equity_amount += i[1] * index_close / i[0]\n",
    "    print('equity_amount:', equity_amount)\n",
    "    print('equity_invested:', equity_invested)\n",
    "    print(\"Equity returns:\", equity_amount/equity_invested)\n",
    "    debt_amount = debt_corpus.Get(df.index[-1])\n",
    "    print('debt_amount:', debt_amount)\n",
    "    overall_amount = debt_amount + equity_amount\n",
    "    print(\"Overall returns:\", overall_amount/total_invested)\n",
    "    sip_amount = 0\n",
    "    for i in naive_sips:\n",
    "        print(i[2], index_close, i[1])\n",
    "        sip_amount += i[2] * index_close / i[1]\n",
    "    print(\"Naive SIP returns:\", sip_amount / total_invested)\n",
    "    # CAGR = [ (Ending value/Beginning Value)^(1/N) ] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000-04-04 00:00:00 1428.1 10 0.96\n",
      "2000-04-25 00:00:00 1359.45 10 0.96\n",
      "2000-05-15 00:00:00 1299.25 20 0.96\n",
      "2000-05-23 00:00:00 1224.4 20 0.96\n",
      "2000-10-17 00:00:00 1158.05 40 0.96\n",
      "2001-04-10 00:00:00 1103.05 40 0.96\n",
      "Balance is lesser than requested 65.96764728036544 80\n",
      "2001-04-12 00:00:00 1024.9 65.96764728036544 0.96\n",
      "Balance is lesser than requested 0.0 80\n",
      "Balance is lesser than requested 0.0 160\n",
      "Balance is lesser than requested 0.0 160\n",
      "total_invested 2000\n",
      "equity_amount: 1940.2998688468788\n",
      "equity_invested: 205.96764728036544\n",
      "Equity returns: 9.420410896890623\n",
      "debt_amount: 3717.814819874003\n",
      "Overall returns: 2.8290573443604408\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "100 10704.8 1592.2\n",
      "Naive SIP returns: 6.723275970355481\n"
     ]
    }
   ],
   "source": [
    "EvaluateStrategy(nifty, params)"
   ]
  }
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