abhay.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame, Series\n",
    "\n",
    "\n",
    "def momentum_trading_strategy(data, short_window=50, long_window=200, entry_threshold=0.02, exit_threshold=0.01):\n",
    "    \"\"\"\n",
    "    Parameters:\n",
    "    - data: DataFrame with 'Date', 'Price', and any additional columns needed for analysis.\n",
    "    - short_window: Short-term moving average window.\n",
    "    - long_window: Long-term moving average window.\n",
    "    - entry_threshold: Minimum price change required to trigger a buy/sell signal (percentage).\n",
    "    - exit_threshold: Minimum price change required to exit a position (percentage).\n",
    "\n",
    "    Returns:\n",
    "    - signals: DataFrame with buy/sell signals (1 for buy, -1 for sell, 0 for hold).\n",
    "    \"\"\"\n",
    "\n",
    "    shorts = data.rolling(window=short_window, min_periods=1).mean()\n",
    "    longs = data.rolling(window=long_window, min_periods=1).mean()\n",
    "\n",
    "    signals = [0] * len(data)\n",
    "\n",
    "    returns = data.pct_change()\n",
    "\n",
    "    for i in range(1, len(data)):\n",
    "        if (\n",
    "            shorts[i] > longs[i] and\n",
    "            returns[i] > entry_threshold\n",
    "        ):\n",
    "            signals[i] = 1  \n",
    "\n",
    "        elif (\n",
    "            shorts[i] < longs[i] and\n",
    "            returns[i] < -entry_threshold\n",
    "        ):\n",
    "            signals[i] = -1  \n",
    "\n",
    "    for i in range(1, len(data)):\n",
    "        if (\n",
    "            signals[i] == 1 and\n",
    "            returns[i] < -exit_threshold\n",
    "        ):\n",
    "            signals[i] = 0  \n",
    "\n",
    "        elif (\n",
    "            signals[i] == -1 and\n",
    "            returns[i] > exit_threshold\n",
    "        ):\n",
    "            signals[i] = 0  \n",
    "\n",
    "    return signals\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Callable, List, Tuple, Dict, Any\n",
    "import yfinance as yf\n",
    "from pandas import Series, Timestamp\n",
    "from datetime import datetime, timedelta\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']\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",
    "\n",
    "    current_date = Timestamp(datetime.today())\n",
    "    if current_date.weekday() == 5:  # Saturday\n",
    "      current_date -= timedelta(days=1)\n",
    "    elif current_date.weekday() == 6:  # Sunday\n",
    "      current_date -= timedelta(days=2)\n",
    "    riskFreeRate = yf.download('^IRX', start=current_date, end=current_date)['Adj Close'][0]/100\n",
    "    print(riskFreeRate)\n",
    "\n",
    "    sharpe = (overallReturn-riskFreeRate)/pct.std()\n",
    "    sortino = (overallReturn-riskFreeRate)/pct[pct < 0].std()\n",
    "    calmar = (overallReturn-riskFreeRate)/abs(maxDrawdown)\n",
    "    return [sharpe, sortino, calmar]\n",
    "\n",
    "\n",
    "  def testCustomReport(self, data: Series, start: str, end: str, startingAmount: float = 1000000.00) -> float:\n",
    "    signals = self.strategy(data) # Should return list of -1, 0, 1\n",
    "    amt = startingAmount\n",
    "    histArr = []\n",
    "    trades = []\n",
    "    drawdowns = []\n",
    "    bought = False\n",
    "    boughtPrice = 0\n",
    "    shares = 0\n",
    "    for price, signal in zip(data, signals):\n",
    "      if signal == 1 and not bought: # Buying a stock\n",
    "        bought = True\n",
    "        boughtPrice = minPrice = price\n",
    "        currentDuration = 0\n",
    "        currentDrawdownDuration = 0\n",
    "        shares = amt // price\n",
    "        amt -= (shares * price)\n",
    "      elif signal == -1 and bought: # Selling a stock\n",
    "        bought = False\n",
    "        trades.append((currentDuration, (price-boughtPrice)/boughtPrice))\n",
    "        if currentDrawdownDuration:drawdowns.append((currentDrawdownDuration, (minPrice-price)/price))\n",
    "        amt += (shares * price)\n",
    "        shares = 0\n",
    "      if bought:\n",
    "        if price < boughtPrice:\n",
    "          currentDrawdownDuration += 1\n",
    "        else:\n",
    "          if currentDrawdownDuration:drawdowns.append((currentDrawdownDuration, (minPrice-price)/price))\n",
    "          currentDrawdownDuration = 0\n",
    "\n",
    "        currentDuration += 1\n",
    "        minPrice = min(minPrice, price)\n",
    "      histArr.append(shares*price+amt)\n",
    "    if bought:\n",
    "      trades.append((currentDuration, (price-boughtPrice)/boughtPrice))\n",
    "      if currentDrawdownDuration:drawdowns.append((currentDrawdownDuration, (minPrice-price)/price))\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": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "0.05320000171661377\n",
      "Start 2010-01-01\n",
      "End 2020-01-01\n",
      "Duration 2516\n",
      "Exposure Time 470.5\n",
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      "Equity Final 8166774.230371475\n",
      "Equity Peak 8166774.230371475\n",
      "Return 7.166774230371475\n",
      "Buy and Hold Return 10.038871419853216\n",
      "Max Drawdown -0.1029208755830342\n",
      "Avg Drawdown -0.09627259509420738\n",
      "Max Drawdown Duration 19\n",
      "Avg Drawdown Duration 8.857142857142858\n",
      "# Trades 4\n",
      "Win Rate 1.0\n",
      "Best Trade 0.984095270845883\n",
      "Worst Trade 0.48189821881601236\n",
      "Max Trade Duration 669\n",
      "Avg Trade Duration 470.5\n",
      "Sharpe Ratio 33.99413578326285\n",
      "Sortino Ratio nan\n",
      "Calmar Ratio 69.11692296006356\n"
     ]
    }
   ],
   "source": [
    "def wrapper(data):\n",
    "  return momentum_trading_strategy(data, short_window=50, long_window=200, entry_threshold=0.02, exit_threshold=0.01)\n",
    "\n",
    "bt = Backtester(wrapper)\n",
    "test = bt.testTickerReport('AAPL', '2010-01-01', '2020-01-01')\n",
    "for key in test:\n",
    "  print(key, test[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>2010-01-04 00:00:00-05:00</th>\n",
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       "      <th>2010-01-06 00:00:00-05:00</th>\n",
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       "      <th>2019-12-24 00:00:00-05:00</th>\n",
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       "      <td>69.478665</td>\n",
       "      <td>68.998223</td>\n",
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       "      <th>2019-12-26 00:00:00-05:00</th>\n",
       "      <td>69.461584</td>\n",
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       "      <th>2019-12-27 00:00:00-05:00</th>\n",
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      "text/plain": [
       "                                Open       High        Low      Close  \\\n",
       "Date                                                                    \n",
       "2010-01-04 00:00:00-05:00   6.469950   6.502386   6.438120   6.487532   \n",
       "2010-01-05 00:00:00-05:00   6.505420   6.535431   6.464495   6.498751   \n",
       "2010-01-06 00:00:00-05:00   6.498750   6.524517   6.388709   6.395379   \n",
       "2010-01-07 00:00:00-05:00   6.419023   6.426602   6.337174   6.383555   \n",
       "2010-01-08 00:00:00-05:00   6.375070   6.426604   6.337481   6.425997   \n",
       "...                              ...        ...        ...        ...   \n",
       "2019-12-24 00:00:00-05:00  69.429886  69.478665  68.998223  69.327454   \n",
       "2019-12-26 00:00:00-05:00  69.461584  70.720000  69.432320  70.702927   \n",
       "2019-12-27 00:00:00-05:00  70.998008  71.693064  70.266372  70.676086   \n",
       "2019-12-30 00:00:00-05:00  70.593176  71.380907  69.559132  71.095566   \n",
       "2019-12-31 00:00:00-05:00  70.707814  71.622360  70.607823  71.615044   \n",
       "\n",
       "                              Volume  Dividends  Stock Splits  \n",
       "Date                                                           \n",
       "2010-01-04 00:00:00-05:00  493729600        0.0           0.0  \n",
       "2010-01-05 00:00:00-05:00  601904800        0.0           0.0  \n",
       "2010-01-06 00:00:00-05:00  552160000        0.0           0.0  \n",
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       "2010-01-08 00:00:00-05:00  447610800        0.0           0.0  \n",
       "...                              ...        ...           ...  \n",
       "2019-12-24 00:00:00-05:00   48478800        0.0           0.0  \n",
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       "2019-12-27 00:00:00-05:00  146266000        0.0           0.0  \n",
       "2019-12-30 00:00:00-05:00  144114400        0.0           0.0  \n",
       "2019-12-31 00:00:00-05:00  100805600        0.0           0.0  \n",
       "\n",
       "[2516 rows x 7 columns]"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "ticker = yf.Ticker('AAPL')\n",
    "data = ticker.history(start='2010-01-01', end='2020-01-01')\n",
    "display(data)"
   ]
  }
 ],
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