planning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Callable, List, Tuple, Dict, Any\n",
    "import yfinance as yf\n",
    "from pandas import DataFrame, Series\n",
    "from datetime import datetime\n",
    "\n",
    "class Backtester:\n",
    "  def __init__(self, strategy: Callable):\n",
    "    self.strategy = strategy\n",
    "\n",
    "\n",
    "  def testTickerReport(self, ticker: str, start: str, end: str, startingAmount: float = 1000000.00) -> Dict[str, Any]: # returns a tuple of a dictionary of dates and prices, and a list of returns\n",
    "    stock = yf.Ticker(ticker)\n",
    "    data = stock.history(start=start, end=end)\n",
    "    data = data['Close'].to_list()\n",
    "    return self.testCustomReport(data, start, end, startingAmount)\n",
    "\n",
    "  @staticmethod\n",
    "  def getRatios(pctChanges: List[float], maxDrawdown: float, overallReturn: float, n: int) -> List[float]:\n",
    "    pct = Series(pctChanges)\n",
    "    riskFreeRate = yf.download('^IRX', start=pct.index[0], end=pct.index[-1])['Close'].pct_change().mean()\n",
    "\n",
    "    sharpe = (overallReturn-riskFreeRate)/pct.std()\n",
    "    sortino = (overallReturn-riskFreeRate)/pct[pct < 0].std()\n",
    "    calmar = overallReturn/maxDrawdown\n",
    "    return [sharpe, sortino, calmar]\n",
    "\n",
    "\n",
    "  def testCustomReport(self, data: List[float], start: str, end: str, startingAmount: float = 1000000.00) -> float:\n",
    "    signals = self.strategy(data)\n",
    "    amt = startingAmount\n",
    "    histArr = []\n",
    "    trades = []\n",
    "    drawdowns = []\n",
    "    bought = False\n",
    "    boughtPrice = 0\n",
    "    shares = 0\n",
    "    prev = 0\n",
    "    for price, signal in zip(data, signals):\n",
    "      if prev != signal:\n",
    "        if signal: # Buying a stock\n",
    "          bought = True\n",
    "          boughtPrice = minPrice = price\n",
    "          currentDuration = 0\n",
    "          currentDrawdownDuration = 0\n",
    "          shares = int(amt/price)\n",
    "          amt -= shares * price\n",
    "        else: # Selling a stock\n",
    "          bought = False\n",
    "          trades.append((currentDuration, (price-boughtPrice)/boughtPrice))\n",
    "          drawdowns.append((minPrice-price)/price)\n",
    "          amt += price * shares\n",
    "          shares = 0\n",
    "      if bought:\n",
    "        if price < boughtPrice:\n",
    "          currentDrawdownDuration += 1\n",
    "        else:\n",
    "          drawdowns.append((currentDrawdownDuration, (minPrice-price)/price))\n",
    "          currentDrawdownDuration = 0\n",
    "\n",
    "        currentDuration += 1\n",
    "        minPrice = min(minPrice, price)\n",
    "      prev = signal\n",
    "      histArr.append(shares*price+amt)\n",
    "    totals = histArr\n",
    "    \n",
    "    report = {}\n",
    "    report['Start'] = start\n",
    "    report['End'] = end\n",
    "    report['Duration'] = len(totals)\n",
    "    report['Exposure Time'] = sum([x[0] for x in trades])/len(trades)\n",
    "    report['Net Worth'] = totals\n",
    "\n",
    "    report['Equity Final'] = totals[-1]\n",
    "    report['Equity Peak'] = max(totals)\n",
    "\n",
    "    report['Return'] = (totals[-1]-startingAmount)/startingAmount\n",
    "    report['Buy and Hold Return'] = (data[-1]-data[0])/data[0]\n",
    "\n",
    "    report['Max Drawdown'] = min(d[1] for d in drawdowns)\n",
    "    report['Avg Drawdown'] = sum(d[1] for d in drawdowns)/len(drawdowns)\n",
    "    report[\"Max Drawdown Duration\"] = max(d[0] for d in drawdowns)\n",
    "    report[\"Avg Drawdown Duration\"] = sum(d[0] for d in drawdowns)/len(drawdowns)\n",
    "    \n",
    "    report[\"# Trades\"] = len(trades)\n",
    "    report[\"Win Rate\"] = sum(1 for t in trades if t[1] > 0)/len(trades)\n",
    "    report[\"Best Trade\"] = max(t[1] for t in trades)\n",
    "    report[\"Worst Trade\"] = min(t[1] for t in trades)\n",
    "\n",
    "    report[\"Max Trade Duration\"] = max(t[0] for t in trades)\n",
    "    report[\"Avg Trade Duration\"] = sum(t[0] for t in trades)/len(trades)\n",
    "\n",
    "    report[\"Sharpe Ratio\"], report[\"Sortino Ratio\"], report[\"Calmar Ratio\"] = self.getRatios([x[1] for x in trades], report[\"Max Drawdown\"], report[\"Return\"], len(trades))\n",
    "    \n",
    "    return report\n",
    "\n",
    "  def graphTicker(self, ticker: str, start: str, end: str) -> None:\n",
    "    pass\n",
    "\n",
    "  def graphCustom(self, data: List[float]) -> None:\n",
    "    pass\n",
    "\n",
    "# How to factor in a look back period for a potential strategy\n",
    "# Data clean up and organization between different sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Callable, List, Tuple, Dict, Any\n",
    "import yfinance as yf\n",
    "from pandas import DataFrame\n",
    "from datetime import datetime\n",
    "\n",
    "class Backtester:\n",
    "  def __init__(self, strategy: Callable):\n",
    "    self.strategy = strategy\n",
    "\n",
    "  def testTicker(self, ticker: str, start: str, end: str, startingAmount: float = 1000000.00) -> List[float]: # takes in ticker symbol, start date, end date\n",
    "    stock = yf.Ticker(ticker)\n",
    "    data = stock.history(start=start, end=end)\n",
    "    data = data['Close'].to_list()\n",
    "    signals = self.strategy(data)\n",
    "\n",
    "    amt = startingAmount\n",
    "    histArr = []\n",
    "    shares = 0\n",
    "    prev = 0\n",
    "    for price, signal in zip(data, signals):\n",
    "      if prev != signal:\n",
    "        if signal:\n",
    "          shares = int(amt/price)\n",
    "          amt -= shares * price\n",
    "        else:\n",
    "          amt += price * shares\n",
    "          shares = 0\n",
    "      prev = signal\n",
    "      histArr.append(shares*price+amt)\n",
    "\n",
    "    return histArr\n",
    "\n",
    "\n",
    "  def testTickerReport(self, ticker: str, start: str, end: str, startingAmount: float = 1000000.00) -> Dict[str, Any]: # returns a tuple of a dictionary of dates and prices, and a list of returns\n",
    "    stock = yf.Ticker(ticker)\n",
    "    data = stock.history(start=start, end=end)\n",
    "    data = data['Close'].to_list()\n",
    "    signals = self.strategy(data)\n",
    "\n",
    "    amt = startingAmount\n",
    "    histArr = []\n",
    "    trades = []\n",
    "    drawdowns = []\n",
    "    bought = False\n",
    "    boughtPrice = 0\n",
    "    shares = 0\n",
    "    prev = 0\n",
    "    for price, signal in zip(data, signals):\n",
    "      if prev != signal:\n",
    "        if signal: # Buying a stock\n",
    "          bought = True\n",
    "          boughtPrice = minPrice = price\n",
    "          currentDuration = 0\n",
    "          currentDrawdownDuration = 0\n",
    "          shares = int(amt/price)\n",
    "          amt -= shares * price\n",
    "        else: # Selling a stock\n",
    "          bought = False\n",
    "          trades.append((currentDuration, (price-boughtPrice)/boughtPrice))\n",
    "          drawdowns.append((minPrice-price)/price)\n",
    "          amt += price * shares\n",
    "          shares = 0\n",
    "      if bought:\n",
    "        if price < boughtPrice:\n",
    "          currentDrawdownDuration += 1\n",
    "        else:\n",
    "          drawdowns.append((currentDrawdownDuration, (minPrice-price)/price))\n",
    "          currentDrawdownDuration = 0\n",
    "\n",
    "        currentDuration += 1\n",
    "        minPrice = min(minPrice, price)\n",
    "      prev = signal\n",
    "      histArr.append(shares*price+amt)\n",
    "    totals = histArr\n",
    "    \n",
    "    report = {}\n",
    "    report['Start'] = start\n",
    "    report['End'] = end\n",
    "    report['Duration'] = len(totals)\n",
    "    report['Exposure Time'] = sum([x[0] for x in trades])/len(trades)\n",
    "    report['Net Worth'] = totals\n",
    "\n",
    "    report['Equity Final'] = totals[-1]\n",
    "    report['Equity Peak'] = max(totals)\n",
    "\n",
    "    report['Return'] = (totals[-1]-startingAmount)/startingAmount\n",
    "    report['Buy and Hold Return'] = (data[-1]-data[0])/data[0]\n",
    "\n",
    "    report['Max Drawdown'] = min(d[1] for d in drawdowns)\n",
    "    report['Avg Drawdown'] = sum(d[1] for d in drawdowns)/len(drawdowns)\n",
    "    report[\"Max Drawdown Duration\"] = max(d[0] for d in drawdowns)\n",
    "    report[\"Avg Drawdown Duration\"] = sum(d[0] for d in drawdowns)/len(drawdowns)\n",
    "    \n",
    "    report[\"# Trades\"] = len(trades)\n",
    "    report[\"Win Rate\"] = sum(1 for t in trades if t[1] > 0)/len(trades)\n",
    "    report[\"Best Trade\"] = max(t[1] for t in trades)\n",
    "    report[\"Worst Trade\"] = min(t[1] for t in trades)\n",
    "\n",
    "    report[\"Max Trade Duration\"] = max(t[0] for t in trades)\n",
    "    report[\"Avg Trade Duration\"] = sum(t[0] for t in trades)/len(trades)\n",
    "    \n",
    "    return report\n",
    "\n",
    "\n",
    "  def testCustom(self, data: List[float], start: str, end: str, startingAmount: float = 1000000.00) -> float:\n",
    "    signals = self.strategy(data)\n",
    "\n",
    "    amt = startingAmount\n",
    "    histArr = []\n",
    "    shares = 0\n",
    "    prev = 0\n",
    "    for price, signal in zip(data, signals):\n",
    "      if prev != signal:\n",
    "        if signal:\n",
    "          shares = int(amt/price)\n",
    "          amt -= shares * price\n",
    "        else:\n",
    "          amt += price * shares\n",
    "          shares = 0\n",
    "      prev = signal\n",
    "      histArr.append(shares*price+amt)\n",
    "\n",
    "      return histArr\n",
    "\n",
    "  def testCustomReport(self, data: List[float]) -> Tuple[Dict[str, float], List[float]]:\n",
    "    pass\n",
    "\n",
    "  def graphTicker(self, ticker: str, start: str, end: str) -> None:\n",
    "    pass\n",
    "\n",
    "  def graphCustom(self, data: List[float]) -> None:\n",
    "    pass\n",
    "\n",
    "# How to factor in a look back period for a potential strategy\n",
    "# Data clean up and organization between different sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim = yf.download(\"AAPL\", start=datetime.datetime(y, m, d), end=datetime.date.today())[-period-window_size:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def momentum(ma1, ma2, df):\n",
    "  pass\n",
    "\n",
    "def wrapper(df):\n",
    "  return momentum(50, 200, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02 00:00:00-05:00</th>\n",
       "      <td>72.246685</td>\n",
       "      <td>73.310001</td>\n",
       "      <td>71.990615</td>\n",
       "      <td>73.249031</td>\n",
       "      <td>135480400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-03 00:00:00-05:00</th>\n",
       "      <td>72.468595</td>\n",
       "      <td>73.305097</td>\n",
       "      <td>72.310075</td>\n",
       "      <td>72.536880</td>\n",
       "      <td>146322800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06 00:00:00-05:00</th>\n",
       "      <td>71.649176</td>\n",
       "      <td>73.153905</td>\n",
       "      <td>71.395540</td>\n",
       "      <td>73.114883</td>\n",
       "      <td>118387200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07 00:00:00-05:00</th>\n",
       "      <td>73.124647</td>\n",
       "      <td>73.383158</td>\n",
       "      <td>72.549096</td>\n",
       "      <td>72.771027</td>\n",
       "      <td>108872000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08 00:00:00-05:00</th>\n",
       "      <td>72.471061</td>\n",
       "      <td>74.246499</td>\n",
       "      <td>72.471061</td>\n",
       "      <td>73.941650</td>\n",
       "      <td>132079200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-23 00:00:00-05:00</th>\n",
       "      <td>130.037521</td>\n",
       "      <td>130.303174</td>\n",
       "      <td>128.679679</td>\n",
       "      <td>128.856796</td>\n",
       "      <td>88223700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-24 00:00:00-05:00</th>\n",
       "      <td>129.211031</td>\n",
       "      <td>131.316662</td>\n",
       "      <td>128.994563</td>\n",
       "      <td>129.850586</td>\n",
       "      <td>54930100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28 00:00:00-05:00</th>\n",
       "      <td>131.838131</td>\n",
       "      <td>135.134321</td>\n",
       "      <td>131.365829</td>\n",
       "      <td>134.494766</td>\n",
       "      <td>124486200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-12-29 00:00:00-05:00</th>\n",
       "      <td>135.832963</td>\n",
       "      <td>136.561069</td>\n",
       "      <td>132.182538</td>\n",
       "      <td>132.704025</td>\n",
       "      <td>121047300</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-12-30 00:00:00-05:00</th>\n",
       "      <td>133.402609</td>\n",
       "      <td>133.806028</td>\n",
       "      <td>131.257611</td>\n",
       "      <td>131.572479</td>\n",
       "      <td>96452100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Date                                                                        \n",
       "2020-01-02 00:00:00-05:00   72.246685   73.310001   71.990615   73.249031   \n",
       "2020-01-03 00:00:00-05:00   72.468595   73.305097   72.310075   72.536880   \n",
       "2020-01-06 00:00:00-05:00   71.649176   73.153905   71.395540   73.114883   \n",
       "2020-01-07 00:00:00-05:00   73.124647   73.383158   72.549096   72.771027   \n",
       "2020-01-08 00:00:00-05:00   72.471061   74.246499   72.471061   73.941650   \n",
       "...                               ...         ...         ...         ...   \n",
       "2020-12-23 00:00:00-05:00  130.037521  130.303174  128.679679  128.856796   \n",
       "2020-12-24 00:00:00-05:00  129.211031  131.316662  128.994563  129.850586   \n",
       "2020-12-28 00:00:00-05:00  131.838131  135.134321  131.365829  134.494766   \n",
       "2020-12-29 00:00:00-05:00  135.832963  136.561069  132.182538  132.704025   \n",
       "2020-12-30 00:00:00-05:00  133.402609  133.806028  131.257611  131.572479   \n",
       "\n",
       "                              Volume  Dividends  Stock Splits  \n",
       "Date                                                           \n",
       "2020-01-02 00:00:00-05:00  135480400        0.0           0.0  \n",
       "2020-01-03 00:00:00-05:00  146322800        0.0           0.0  \n",
       "2020-01-06 00:00:00-05:00  118387200        0.0           0.0  \n",
       "2020-01-07 00:00:00-05:00  108872000        0.0           0.0  \n",
       "2020-01-08 00:00:00-05:00  132079200        0.0           0.0  \n",
       "...                              ...        ...           ...  \n",
       "2020-12-23 00:00:00-05:00   88223700        0.0           0.0  \n",
       "2020-12-24 00:00:00-05:00   54930100        0.0           0.0  \n",
       "2020-12-28 00:00:00-05:00  124486200        0.0           0.0  \n",
       "2020-12-29 00:00:00-05:00  121047300        0.0           0.0  \n",
       "2020-12-30 00:00:00-05:00   96452100        0.0           0.0  \n",
       "\n",
       "[252 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import Callable, List, Tuple, Dict\n",
    "from random import randrange\n",
    "\n",
    "# from pandas import DataFrame\n",
    "\n",
    "def testTicker(ticker: str, start: str, end: str) -> List[float]:\n",
    "    stock = yf.Ticker(ticker)\n",
    "    data = stock.history(start=start, end=end)\n",
    "    prices = data['Close'].to_list()\n",
    "    arr = [1000000]\n",
    "    for i in range(1, len(prices)):\n",
    "        arr.append(arr[i - 1] * (1 + (randrange(-100, 100) / 1000)))\n",
    "    return arr\n",
    "\n",
    "\n",
    "def graphTicker(ticker: str, start: str, end: str) -> None:\n",
    "    stock = yf.Ticker(ticker)\n",
    "    data = stock.history(start=start, end=end)\n",
    "    prices = data['Close'].to_list()\n",
    "    dates = data.index.to_list()\n",
    "    backtests = testTicker(ticker, start, end)\n",
    "\n",
    "    fig, ax1 = plt.subplots()\n",
    "    ax2 = ax1.twinx()\n",
    "\n",
    "    ax2.set_ylim([0, 2_000_000])\n",
    "    ax2.set_ylabel('Backtesting Balance')\n",
    "    ax1.plot(dates, prices)\n",
    "    ax2.plot(dates, backtests, color='red')\n",
    "\n",
    "    display(data)\n",
    "graphTicker(\"AAPL\", \"2020-01-01\", \"2020-12-31\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "arr = [True, True, False]\n",
    "print(sum(arr))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}