Pair_Selection_using_yf_lib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the data from tsm and qcom with inteval of 30 minutes\n",
    "\n",
    "tsm = yf.Ticker(\"TSM\")\n",
    "qcom = yf.Ticker(\"QCOM\")\n",
    "\n",
    "tsm_data = tsm.history(period=\"60d\", interval=\"90m\")\n",
    "qcom_data = qcom.history(period=\"60d\", interval=\"90m\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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           "#636efa",
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           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
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          "font": {
           "color": "#2a3f5f"
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          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
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          "hoverlabel": {
           "align": "left"
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          "hovermode": "closest",
          "mapbox": {
           "style": "light"
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          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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           "zaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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          "yaxis": {
           "automargin": true,
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           "linecolor": "white",
           "ticks": "",
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           "zerolinewidth": 2
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         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot both tickers on go figure\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=tsm_data.index, y=tsm_data['Close'], name='TSM'))\n",
    "fig.add_trace(go.Scatter(x=qcom_data.index, y=qcom_data['Close'], name='QCOM'))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 Open        High         Low       Close  \\\n",
      "Datetime                                                                    \n",
      "2024-02-21 09:30:00-05:00  124.430000  124.599998  122.910004  123.430000   \n",
      "2024-02-21 11:00:00-05:00  123.419998  124.779999  123.360001  124.110001   \n",
      "2024-02-21 12:30:00-05:00  124.120003  124.550003  123.849998  124.103798   \n",
      "2024-02-21 14:00:00-05:00  124.070000  124.410004  123.449997  124.410004   \n",
      "2024-02-21 15:30:00-05:00  124.419998  125.379997  124.379997  125.320000   \n",
      "...                               ...         ...         ...         ...   \n",
      "2024-05-15 09:30:00-04:00  153.360001  155.550003  152.729996  154.880005   \n",
      "2024-05-15 11:00:00-04:00  154.889999  155.160004  154.470001  154.850006   \n",
      "2024-05-15 12:30:00-04:00  154.869904  155.869995  154.830002  155.769897   \n",
      "2024-05-15 14:00:00-04:00  155.759995  156.000000  155.262802  155.675003   \n",
      "2024-05-15 15:30:00-04:00  155.679993  155.770004  154.779999  155.580002   \n",
      "\n",
      "                            Volume  Dividends  Stock Splits  \n",
      "Datetime                                                     \n",
      "2024-02-21 09:30:00-05:00  3917378        0.0           0.0  \n",
      "2024-02-21 11:00:00-05:00  1809200        0.0           0.0  \n",
      "2024-02-21 12:30:00-05:00  1121343        0.0           0.0  \n",
      "2024-02-21 14:00:00-05:00  1461359        0.0           0.0  \n",
      "2024-02-21 15:30:00-05:00  1498647        0.0           0.0  \n",
      "...                            ...        ...           ...  \n",
      "2024-05-15 09:30:00-04:00  5168840        0.0           0.0  \n",
      "2024-05-15 11:00:00-04:00  2178861        0.0           0.0  \n",
      "2024-05-15 12:30:00-04:00  1628494        0.0           0.0  \n",
      "2024-05-15 14:00:00-04:00  1649554        0.0           0.0  \n",
      "2024-05-15 15:30:00-04:00  1882200        0.0           0.0  \n",
      "\n",
      "[300 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(tsm_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# plot eith candle stick\n",
    "\n",
    "fig = go.Figure(data=[go.Candlestick(x=tsm_data.index,\n",
    "                open=tsm_data['Open'],\n",
    "                high=tsm_data['High'],\n",
    "                low=tsm_data['Low'],\n",
    "                close=tsm_data['Close'])])\n",
    "\n",
    "# plot moving average of 20 ticks and 80 ticks\n",
    "\n",
    "fig.add_trace(go.Scatter(x=tsm_data.index, y=tsm_data['Close'].rolling(window=5).mean(), name='5 Ticks MA'))\n",
    "fig.add_trace(go.Scatter(x=tsm_data.index, y=tsm_data['Close'].rolling(window=20).mean(), name='20 Ticks MA'))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "signal\n",
      " 0.0    286\n",
      " 1.0      7\n",
      "-1.0      6\n",
      "Name: count, dtype: int64\n"
     ]
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    "# count the number of times the 5 ticks MA crosses the 20 ticks MA\n",
    "\n",
    "tsm_data['5T_MA'] = tsm_data['Close'].rolling(window=5).mean()\n",
    "tsm_data['20T_MA'] = tsm_data['Close'].rolling(window=20).mean()\n",
    "\n",
    "tsm_data['signal'] = np.where(tsm_data['5T_MA'] > tsm_data['20T_MA'], 1, 0)\n",
    "tsm_data['signal'] = tsm_data['signal'].diff()\n",
    "\n",
    "\n",
    "print(tsm_data['signal'].value_counts())\n",
    "\n",
    "# plot the buy and sell signals\n",
    "\n",
    "fig = go.Figure(data=[go.Candlestick(x=tsm_data.index,\n",
    "                open=tsm_data['Open'],\n",
    "                high=tsm_data['High'],\n",
    "                low=tsm_data['Low'],\n",
    "                close=tsm_data['Close'])])\n",
    "\n",
    "fig.add_trace(go.Scatter(x=tsm_data.index, y=tsm_data['Close'].rolling(window=5).mean(), name='5 Ticks MA'))\n",
    "fig.add_trace(go.Scatter(x=tsm_data.index, y=tsm_data['Close'].rolling(window=20).mean(), name='20 Ticks MA'))\n",
    "\n",
    "buy_signals = tsm_data[tsm_data['signal'] == 1]\n",
    "sell_signals = tsm_data[tsm_data['signal'] == -1]\n",
    "\n",
    "fig.add_trace(go.Scatter(x=buy_signals.index, y=buy_signals['Close'], mode='markers', marker=dict(color='green', size=10), name='Buy Signal'))\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sell_signals.index, y=sell_signals['Close'], mode='markers', marker=dict(color='red', size=10), name='Sell Signal'))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-212.84329223632812\n"
     ]
    },
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   "source": [
    "# calculate profit and loss\n",
    "\n",
    "tsm_data['buy_price'] = np.where(tsm_data['signal'] == 1, tsm_data['Close'], np.nan)\n",
    "tsm_data['sell_price'] = np.where(tsm_data['signal'] == -1, tsm_data['Close'], np.nan)\n",
    "\n",
    "tsm_data['buy_price'] = tsm_data['buy_price'].ffill()\n",
    "tsm_data['sell_price'] = tsm_data['sell_price'].ffill()\n",
    "\n",
    "tsm_data['PnL'] = tsm_data['sell_price'] - tsm_data['buy_price']\n",
    "\n",
    "print(tsm_data['PnL'].sum())\n",
    "\n",
    "# plot the profit and loss\n",
    "\n",
    "fig = go.Figure(data=[go.Candlestick(x=tsm_data.index,\n",
    "                open=tsm_data['Open'],\n",
    "                high=tsm_data['High'],\n",
    "                low=tsm_data['Low'],\n",
    "                close=tsm_data['Close'])])\n",
    "\n",
    "fig.add_trace(go.Scatter(x=tsm_data.index, y=tsm_data['Close'].rolling(window=5).mean(), name='5 Ticks MA'))\n",
    "fig.add_trace(go.Scatter(x=tsm_data.index, y=tsm_data['Close'].rolling(window=20).mean(), name='20 Ticks MA'))\n",
    "\n",
    "buy_signals = tsm_data[tsm_data['signal'] == 1]\n",
    "sell_signals = tsm_data[tsm_data['signal'] == -1]\n",
    "\n",
    "fig.add_trace(go.Scatter(x=buy_signals.index, y=buy_signals['Close'], mode='markers', marker=dict(color='green', size=10), name='Buy Signal'))\n",
    "fig.add_trace(go.Scatter(x=sell_signals.index, y=sell_signals['Close'], mode='markers', marker=dict(color='red', size=10), name='Sell Signal'))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8238552547796001"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_0 = (tsm_data['Close'].iloc[0]) / (qcom_data['Close'].iloc[0])\n",
    "ratio_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "qcom_data['Close_NORM'] = qcom_data['Close'].multiply(ratio_0).round(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Datetime\n",
       "2024-02-21 09:30:00-05:00    3.051758e-07\n",
       "2024-02-21 11:00:00-05:00    3.009456e-01\n",
       "2024-02-21 12:30:00-05:00    1.877259e-01\n",
       "2024-02-21 14:00:00-05:00    6.553566e-02\n",
       "2024-02-21 15:30:00-05:00    6.928669e-02\n",
       "                                 ...     \n",
       "2024-05-15 09:30:00-04:00   -4.840809e+00\n",
       "2024-05-15 11:00:00-04:00   -5.645234e+00\n",
       "2024-05-15 12:30:00-04:00   -5.285571e+00\n",
       "2024-05-15 14:00:00-04:00   -4.737859e+00\n",
       "2024-05-15 15:30:00-04:00   -4.750470e+00\n",
       "Length: 300, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsm_qcom_dif = tsm_data['Close'] - qcom_data['Close_NORM']\n",
    "tsm_qcom_dif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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   "source": [
    "# Plot the difference between the two tickers\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=tsm_qcom_dif.index, y=tsm_qcom_dif, name='TSM-QCOM'))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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           "bgcolor": "#E5ECF6",
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          "xaxis": {
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           "zerolinewidth": 2
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          "yaxis": {
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     },
     "metadata": {},
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   ],
   "source": [
    "# Plot the two normalized tickers\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=tsm_data.index, y=tsm_data['Close'], name='TSM'))\n",
    "fig.add_trace(go.Scatter(x=qcom_data.index, y=qcom_data['Close_NORM'], name='QCOM'))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the difference and its moving average\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=tsm_qcom_dif.index, y=tsm_qcom_dif, name='TSM-QCOM'))\n",
    "fig.add_trace(go.Scatter(x=tsm_qcom_dif.index, y=tsm_qcom_dif.rolling(window=5).mean(), name='5 Ticks MA'))\n",
    "fig.add_trace(go.Scatter(x=tsm_qcom_dif.index, y=tsm_qcom_dif.rolling(window=20).mean(), name='20 Ticks MA'))\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsm_qcom_dif = tsm_qcom_dif.to_frame()\n",
    "tsm_qcom_dif.columns = ['Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "signal\n",
      " 0.0    286\n",
      " 1.0      7\n",
      "-1.0      6\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "name": "TSM-QCOM",
         "type": "scatter",
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     "text": [
      "TSMC Profit =>  -172.36000061035156\n",
      "QCOM Profit =>  159.28459999999995\n",
      "Pair profit =>  -13.075400610351608\n"
     ]
    }
   ],
   "source": [
    "tsm_qcom_dif['10T_MA'] = tsm_qcom_dif['Close'].rolling(window=10).mean()\n",
    "tsm_qcom_dif['60T_MA'] = tsm_qcom_dif['Close'].rolling(window=60).mean()\n",
    "\n",
    "tsm_qcom_dif['signal'] = np.where(tsm_qcom_dif['10T_MA'] > tsm_qcom_dif['60T_MA'], 1, 0)\n",
    "tsm_qcom_dif['signal'] = tsm_qcom_dif['signal'].diff()\n",
    "\n",
    "print(tsm_qcom_dif['signal'].value_counts())\n",
    "\n",
    "# Plot the signals\n",
    "\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=tsm_qcom_dif.index, y=tsm_qcom_dif['Close'], name='TSM-QCOM'))\n",
    "fig.add_trace(go.Scatter(x=tsm_qcom_dif.index, y=tsm_qcom_dif.rolling(window=10).mean()['Close'], name='10 Ticks MA'))\n",
    "fig.add_trace(go.Scatter(x=tsm_qcom_dif.index, y=tsm_qcom_dif.rolling(window=50).mean()['Close'], name='50 Ticks MA'))\n",
    "\n",
    "buy_signals = tsm_qcom_dif[tsm_qcom_dif['signal'] == -1]\n",
    "sell_signals = tsm_qcom_dif[tsm_qcom_dif['signal'] == 1]\n",
    "\n",
    "fig.add_trace(go.Scatter(x=buy_signals.index, y=buy_signals['Close'], mode='markers', marker=dict(color='green', size=10), name='Buy Signal'))\n",
    "fig.add_trace(go.Scatter(x=sell_signals.index, y=sell_signals['Close'], mode='markers', marker=dict(color='red', size=10), name='Sell Signal'))\n",
    "\n",
    " # -1 buy || 1 sell\n",
    "\n",
    "tsm_data['signal'] = -tsm_qcom_dif['signal']\n",
    "qcom_data['signal'] = tsm_qcom_dif['signal'] # opposite signal\n",
    "\n",
    "fig.show()\n",
    "\n",
    "# plot the profit and loss\n",
    "\n",
    "tsm_data['buy_price'] = np.where(tsm_data['signal'] == -1, tsm_data['Close'], np.nan)\n",
    "tsm_data['sell_price'] = np.where(tsm_data['signal'] == 1, tsm_data['Close'], np.nan)\n",
    "\n",
    "qcom_data['buy_price'] = np.where(qcom_data['signal'] == -1, qcom_data['Close_NORM'], np.nan)\n",
    "qcom_data['sell_price'] = np.where(qcom_data['signal'] == 1, qcom_data['Close_NORM'], np.nan)\n",
    "\n",
    "tsm_profit = tsm_data['sell_price'].sum() - tsm_data['buy_price'].sum()\n",
    "qcom_profit = qcom_data['sell_price'].sum() - qcom_data['buy_price'].sum()\n",
    "\n",
    "print('TSMC Profit => ', tsm_profit)\n",
    "print('QCOM Profit => ', qcom_profit)\n",
    "\n",
    "pair_tsm_qcom_profit = tsm_profit + qcom_profit\n",
    "\n",
    "print('Pair profit => ', pair_tsm_qcom_profit)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Consider TSM as the first and QCOM as the second ticker.\n",
    "#When there is a buy signal for TSM, we buy TSM and sell QCOM.\n",
    "#When there is a sell signal for TSM, we sell TSM and buy QCOM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copy signal column from difference dataframe to TSM dataframe\n",
    "tsm_data['signal'] = tsm_qcom_dif['signal']\n",
    "qcom_data['signal'] = -tsm_qcom_dif['signal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-3.77193993151737, 0.014768756562687647, array([-3.93344345, -3.35664144, -3.05866504]))\n"
     ]
    }
   ],
   "source": [
    "# calculate cointegration between the two tickers\n",
    "from statsmodels.tsa.stattools import coint\n",
    "\n",
    "cointegration = coint(tsm_data['Close'], qcom_data['Close_NORM'])\n",
    "print(cointegration)\n",
    "\n"
   ]
  }
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