DS_ALGOTRADING_ASSIGN_#2.ipynb

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  {
   "cell_type": "markdown",
   "id": "breathing-capability",
   "metadata": {},
   "source": [
    "# Assignment 2:  YINGWEI CHEN\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alternate-genius",
   "metadata": {},
   "source": [
    "# Question 1\n",
    "Get 1-day trading data (open, close, high, low and volume) with 1 minute intveral for Therma Bright Inc. (THRM.V).\n",
    "Plot candle stick plot and VWAP in one figure using the 1-day data above. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "miniature-cardiff",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "import finnhub\n",
    "import yfinance as yf\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas_datareader import data as pdr\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "stock_name='THRM.V'\n",
    "#stock_name='AMZN'\n",
    "stock_candle=yf.Ticker(stock_name).history(interval='1m', period='1d')\n",
    "stock_candle=stock_candle.reset_index()\n",
    "stock_candle['trading_date']=stock_candle['Datetime'].apply(lambda x: x.date())\n",
    "stock_candle['trading_time']=stock_candle['Datetime'].apply(lambda x: x.time().strftime('%H:%M'))\n",
    "stock_candle['volume_cumsum']=stock_candle['Volume'].cumsum()\n",
    "\n",
    "range_high=stock_candle['High'].max()*1.01\n",
    "range_low=stock_candle['Low'].min()*0.99\n",
    "\n",
    "price_list=['Open','High','Low','Close']\n",
    "price_vwap=['VWAP_'+x for x in price_list]\n",
    "for price_type, price_vwap in zip(price_list,price_vwap):\n",
    "    stock_candle[price_vwap]=(stock_candle[price_type]*stock_candle['Volume']).cumsum()/stock_candle['volume_cumsum']\n",
    "\n",
    "raw_trading_data=go.Candlestick(x=stock_candle['trading_time'], open=stock_candle['Open'], high=stock_candle['High'], low=stock_candle['Low'], close=stock_candle['Close'])\n",
    "vwap_trading_data=go.Scatter(x=stock_candle['trading_time'], y=stock_candle['VWAP_Close'])\n",
    "\n",
    "fig=go.Figure(data=[raw_trading_data,vwap_trading_data])\n",
    "fig.update_layout(title=f'Stock Candle and VWAP ---- {stock_name} ---- Intraday ', title_font_size=30, showlegend=True, \n",
    "                  width=1200, height=600, yaxis_title=f'Stock Price', xaxis_title=f'Trading Time')\n",
    "\n",
    "fig.update_yaxes(range=(range_low,range_high))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blocked-syndicate",
   "metadata": {},
   "source": [
    "# Question 2\n",
    "Get daily trading data (pen, close, high, low and volume) from 20180-01-01 to 2020-12-31 for Therma Bright Inc. (THRM.V)\n",
    "Plot candle stick plot and 30-day and 100 day expenential moving averages on one figure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "structural-superintendent",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "0.0851666676501433\n",
      "0.06315000016242266\n"
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   "source": [
    "stock_name='THRM.V'\n",
    "hist_2018_2020=yf.download(stock_name, start='2018-01-01', end='2021-01-01' ).reset_index()\n",
    "alpha_30=2.0/(30+1)\n",
    "alpha_100=2.0/(100+1) \n",
    "hist_2018_2020.loc[:,'sma_30'] =hist_2018_2020['Close'].rolling(30).mean()\n",
    "hist_2018_2020.loc[:,'sma_100']=hist_2018_2020['Close'].rolling(100).mean()\n",
    "\n",
    "# Comment out 2 lines code are not correct and keep here for reference \n",
    "#hist_2018_2020['EMA_30']=np.where(hist_2018_2020['EMA_30'].shift(periods=1)>0, \n",
    "#    alpha_30*hist_2018_2020['Close']+hist_2018_2020['EMA_30'].shift(periods=1)*(1-alpha_30), hist_2018_2020['EMA_30']) \n",
    "\n",
    "def ema_from_df(array_df, ema_cnt):\n",
    "    alpha=2.0/(1+ema_cnt)\n",
    "    if len(array_df)<=ema_cnt:\n",
    "        print(f\"The input columns is shorter than {ema_cnt}\")\n",
    "    else:\n",
    "        array_size=array_df.size\n",
    "        temp_arr=np.zeros(array_size)\n",
    "        copy_arr=np.copy(array_df)\n",
    "        temp_arr[ema_cnt-1]=copy_arr[:ema_cnt].mean()\n",
    "        print( temp_arr[ema_cnt-1])\n",
    "        for i in range(ema_cnt, array_size):\n",
    "            temp_arr[i]=temp_arr[i-1]*(1-alpha)+copy_arr[i]*alpha\n",
    "        return temp_arr\n",
    "\n",
    "hist_2018_2020.loc[:,'ema_30']=ema_from_df(hist_2018_2020['Close'], 30)\n",
    "hist_2018_2020.loc[:,'ema_100']=ema_from_df(hist_2018_2020['Close'], 100)\n",
    "\n",
    "daily_trading_data=go.Candlestick(x=hist_2018_2020['Date'], open=hist_2018_2020['Open'], high=hist_2018_2020['High'], \n",
    "                                  low=hist_2018_2020['Low'], close=hist_2018_2020['Close'])\n",
    "ema30_trading_data=go.Scatter(x=hist_2018_2020['Date'], y=hist_2018_2020['ema_30'])\n",
    "ema100_trading_data=go.Scatter(x=hist_2018_2020['Date'], y=hist_2018_2020['ema_100'])\n",
    "\n",
    "fig=go.Figure(data=[daily_trading_data,ema30_trading_data, ema100_trading_data])\n",
    "fig.update_layout(title=f'{stock_name} Stock Candle and EMA 30 & 100', title_font_size=30, showlegend=True, \n",
    "                  width=1200, height=600, xaxis_title='Trading Date', yaxis_title=f'{stock_name} Stock')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "korean-trial",
   "metadata": {},
   "source": [
    "# Question 3: (1)\n",
    "Use the same data in Question 2.\n",
    "    (1) Calculate daily return (return=log(today close/previous close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "exact-isolation",
   "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>Close</th>\n",
       "      <th>daily_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.075</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.070</td>\n",
       "      <td>-0.068993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.065</td>\n",
       "      <td>-0.074108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.080</td>\n",
       "      <td>0.207639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.100</td>\n",
       "      <td>0.223144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>0.210</td>\n",
       "      <td>-0.023531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>0.225</td>\n",
       "      <td>0.068993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>750</th>\n",
       "      <td>0.225</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>751</th>\n",
       "      <td>0.225</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>0.215</td>\n",
       "      <td>-0.045462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>752 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Close  daily_return\n",
       "1    0.075      0.000000\n",
       "2    0.070     -0.068993\n",
       "3    0.065     -0.074108\n",
       "4    0.080      0.207639\n",
       "5    0.100      0.223144\n",
       "..     ...           ...\n",
       "748  0.210     -0.023531\n",
       "749  0.225      0.068993\n",
       "750  0.225      0.000000\n",
       "751  0.225      0.000000\n",
       "752  0.215     -0.045462\n",
       "\n",
       "[752 rows x 2 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_2018_2020.loc[:,'daily_return']=np.log(hist_2018_2020['Close']/hist_2018_2020['Close'].shift(periods=1))\n",
    "hist_2018_2020=hist_2018_2020[hist_2018_2020['daily_return'].notnull()]\n",
    "hist_2018_2020[['Close','daily_return']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specialized-flush",
   "metadata": {},
   "source": [
    "# Question 3: (2)\n",
    "Use the same data in Question 2.\n",
    "    (2) Conduct the hypothesis testing to check if the distribution of daily return is normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "urban-corps",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1f0dfbae248>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization can provide the first impresssion of statistic distribution\n",
    "import seaborn as sns\n",
    "from scipy import stats \n",
    "sns.displot(hist_2018_2020['daily_return'],kde=True, binwidth=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "annoying-saturday",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------------------------------------------------\n",
      "The sample Mean: 0.001400465281235082 \n",
      "The sample standard deviation: 0.17459207289146936 \n",
      "\n",
      "Assume the sample mean and standard error are the best estimate of population mean and standard deviation\n",
      "We make Hypothesis H0 and H1 as follow:\n",
      "H0: the stock daily return follow normal distribution of N(0.001400465281235082,0.17459207289146936)\n",
      "H1: the stock daily return does not follow normal distribution\n"
     ]
    }
   ],
   "source": [
    "# Calculate the sample mean and standard error\n",
    "# Make assumption of using sample mean and standard error as the mean and stardard deviation of normal distribution.\n",
    "# Print out H0 and H1 content\n",
    "\n",
    "from scipy import stats \n",
    "hist_2018_2020.columns\n",
    "#print(hist_2018_2020.head())\n",
    "sample_mean=hist_2018_2020['daily_return'].mean()\n",
    "sample_std=hist_2018_2020['daily_return'].std()\n",
    "print(\"-------------------------------------------------------------------------------------------\")\n",
    "print(f\"The sample Mean: {sample_mean} \\nThe sample standard deviation: {sample_std} \\n\")\n",
    "print(f\"Assume the sample mean and standard error are the best estimate of population mean and standard deviation\")\n",
    "print(\"We make Hypothesis H0 and H1 as follow:\")\n",
    "print(f\"H0: the stock daily return follow normal distribution of N({sample_mean},{sample_std})\")\n",
    "print(f\"H1: the stock daily return does not follow normal distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "renewable-faculty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KS test result: \n",
      " *** Test Statistic: 0.29841278994676507 *** P-Value: 7.165538917336539e-60 \n",
      "\n",
      "Conclusion: (when set critical value as 0.05)\n",
      "The pvalue of KS test is 7.165538917336539e-60 and less than 0.05, We REJECT H0 and conclude that daily return does NOT folow normal distribution!\n"
     ]
    }
   ],
   "source": [
    "# There are multiple testing methodologies avaialbe in Python Scipy package \n",
    "# Kolmogorov-Smirnov test is presented as follow\n",
    "\n",
    "stats_ks, pvalue_ks=stats.kstest(hist_2018_2020['daily_return'],'norm',(sample_mean,sample_std))\n",
    "print(f\"KS test result: \\n *** Test Statistic: {stats_ks} *** P-Value: {pvalue_ks} \\n\")\n",
    "print (\"Conclusion: (when set critical value as 0.05)\")\n",
    "if pvalue_ks<0.05:\n",
    "    print(f\"The pvalue of KS test is {pvalue_ks} and less than 0.05, We REJECT H0 and conclude that daily return does NOT folow normal distribution!\")\n",
    "else:\n",
    "   print(f\"The pvalue of KS test is {pvalue_ks} and large than 0.05, We ACCEPT H0 and conclude that daily return folow normal distribution!\" ) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "placed-remainder",
   "metadata": {},
   "source": [
    "# Question 4\n",
    "\n",
    "Take 10 stocks in S&P 500 and collect daily close price from 2020-06-01 to 2020-12-31 for slected stocks\n",
    "(1) Calculate daily return for each of 10 stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "united-fruit",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "            return_MMM  return_ABT  return_ABBV  return_ABMD  return_ACN  \\\n",
      "Date                                                                       \n",
      "2020-06-01         NaN         NaN          NaN          NaN         NaN   \n",
      "2020-06-02    0.013154   -0.004202     0.003742     0.019791    0.011731   \n",
      "2020-06-03    0.022394   -0.013260    -0.001649     0.031494    0.007263   \n",
      "2020-06-04    0.008278   -0.035072     0.023380    -0.008522   -0.020372   \n",
      "2020-06-05    0.029460    0.018079     0.008668     0.056784    0.026193   \n",
      "...                ...         ...          ...          ...         ...   \n",
      "2020-12-23   -0.004530   -0.007695     0.004658     0.005673   -0.007854   \n",
      "2020-12-24    0.003042    0.008341    -0.000194    -0.002107   -0.000544   \n",
      "2020-12-28    0.001088   -0.005182     0.001838     0.030831    0.009207   \n",
      "2020-12-29   -0.004935    0.004997     0.012011     0.025307   -0.007421   \n",
      "2020-12-30    0.001494    0.001015     0.005429     0.009274   -0.000854   \n",
      "\n",
      "            return_ATVI  return_ADBE  return_AMD  return_AAP  return_AES  \n",
      "Date                                                                      \n",
      "2020-06-01          NaN          NaN         NaN         NaN         NaN  \n",
      "2020-06-02    -0.000137     0.001948   -0.001680   -0.002148    0.043655  \n",
      "2020-06-03    -0.042834    -0.001333   -0.015245    0.020085    0.038898  \n",
      "2020-06-04    -0.001574    -0.010623   -0.001898   -0.000844    0.015290  \n",
      "2020-06-05    -0.005458     0.018236    0.008891    0.027057    0.032697  \n",
      "...                 ...          ...         ...         ...         ...  \n",
      "2020-12-23     0.008796    -0.014505   -0.017433   -0.000690    0.028937  \n",
      "2020-12-24     0.008280     0.005919    0.002836    0.008688   -0.009410  \n",
      "2020-12-28     0.005154    -0.001822   -0.002290   -0.011769    0.001288  \n",
      "2020-12-29    -0.000656     0.006313   -0.010756   -0.013376    0.000429  \n",
      "2020-12-30     0.002296    -0.009324    0.018261    0.006171   -0.022999  \n",
      "\n",
      "[149 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "df_sp500=pd.read_csv(r\"C:\\Finnhub_Data\\finnhub_data\\S_P550_Symbol_list.csv\")\n",
    "#yf_stock=yf.download(sp500_10, start='2020-06-01', end='2020-12-31')\n",
    "stock_list=list(df_sp500.loc[:9,'Symbol'])\n",
    "stock_daily=[]\n",
    "for stock_name in stock_list:\n",
    "    yf_stock=yf.download(stock_name,  start='2020-06-01', end='2020-12-31')\n",
    "    yf_stock['symbol']=stock_name\n",
    "    yf_stock[f'return_{stock_name}']=np.log(yf_stock['Close']/yf_stock['Close'].shift(periods=1))\n",
    "    stock_daily.append(yf_stock.loc[:,[f'return_{stock_name}']])\n",
    "    \n",
    "stock_10sp=pd.concat(stock_daily,axis=1)\n",
    "print(stock_10sp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continuous-shanghai",
   "metadata": {},
   "source": [
    "# Question 4 - (2)\n",
    "Run PCA on calculated daily return and find the first principal component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "minus-boating",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148\n",
      "            return_MMM_standard  return_ABT_standard  return_ABBV_standard  \\\n",
      "Date                                                                         \n",
      "2020-06-02             0.796493            -0.314648              0.179834   \n",
      "2020-06-03             1.390323            -0.858644             -0.174605   \n",
      "2020-06-04             0.483121            -2.168655              1.471036   \n",
      "2020-06-05             1.844451             1.023528              0.503759   \n",
      "2020-06-08            -0.256501             1.715702              0.450222   \n",
      "...                         ...                  ...                   ...   \n",
      "2020-12-23            -0.340003            -0.524430              0.240107   \n",
      "2020-12-24             0.146605             0.438671             -0.078914   \n",
      "2020-12-28             0.021067            -0.373500              0.054686   \n",
      "2020-12-29            -0.365994             0.237842              0.723529   \n",
      "2020-12-30             0.047177            -0.001333              0.290801   \n",
      "\n",
      "            return_ABMD_standard  return_ACN_standard  return_ATVI_standard  \\\n",
      "Date                                                                          \n",
      "2020-06-02              0.731912             0.580179             -0.090747   \n",
      "2020-06-03              1.225266             0.324406             -2.410885   \n",
      "2020-06-04             -0.461599            -1.257373             -0.168862   \n",
      "2020-06-05              2.291365             1.407912             -0.379902   \n",
      "2020-06-08              0.482501             0.669569              0.924742   \n",
      "...                          ...                  ...                   ...   \n",
      "2020-12-23              0.136794            -0.540884              0.394664   \n",
      "2020-12-24             -0.191175            -0.122449              0.366605   \n",
      "2020-12-28              1.197303             0.435707              0.196752   \n",
      "2020-12-29              0.964475            -0.516083             -0.118976   \n",
      "2020-12-30              0.288575            -0.140185              0.041443   \n",
      "\n",
      "            return_ADBE_standard  return_AMD_standard  return_AAP_standard  \\\n",
      "Date                                                                         \n",
      "2020-06-02              0.013218            -0.162424            -0.182895   \n",
      "2020-06-03             -0.131998            -0.574458             1.190362   \n",
      "2020-06-04             -0.543147            -0.169066            -0.102310   \n",
      "2020-06-05              0.734071             0.158643             1.620974   \n",
      "2020-06-08              0.473301            -0.185861             0.055395   \n",
      "...                          ...                  ...                  ...   \n",
      "2020-12-23             -0.714958            -0.640938            -0.092844   \n",
      "2020-12-24              0.188950            -0.025268             0.486436   \n",
      "2020-12-28             -0.153659            -0.180964            -0.777135   \n",
      "2020-12-29              0.206396            -0.438128            -0.876393   \n",
      "2020-12-30             -0.485681             0.443263             0.330980   \n",
      "\n",
      "            return_AES_standard  \n",
      "Date                             \n",
      "2020-06-02             1.757103  \n",
      "2020-06-03             1.546187  \n",
      "2020-06-04             0.499357  \n",
      "2020-06-05             1.271207  \n",
      "2020-06-08             1.851732  \n",
      "...                         ...  \n",
      "2020-12-23             1.104521  \n",
      "2020-12-24            -0.595859  \n",
      "2020-12-28            -0.121487  \n",
      "2020-12-29            -0.159587  \n",
      "2020-12-30            -1.198447  \n",
      "\n",
      "[148 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "# Method 1: Self-Implementation Notice: set STD(ddof=0) is to be consistent with the PYTHON preprocessing package. ddof=1 is default could make more sense here.   \n",
    "\n",
    "stock_10sp=stock_10sp[stock_10sp.index>'2020-06-01']\n",
    "print(len(stock_10sp))\n",
    "#adjust_std=np.sqrt(len(stock_10sp)/(len(stock_10sp)-1))  \n",
    "#print(adjust_std)\n",
    "for stock_name in stock_list:\n",
    "    stock_10sp[f'return_{stock_name}_u']=stock_10sp[f'return_{stock_name}'].mean()\n",
    "    stock_10sp[f'return_{stock_name}_s']=stock_10sp[f'return_{stock_name}'].std()\n",
    "    stock_10sp[f'return_{stock_name}_standard']=(stock_10sp[f'return_{stock_name}']-stock_10sp[f'return_{stock_name}_u'])/stock_10sp[f'return_{stock_name}'].std(ddof=0)\n",
    "    #stock_10sp[f'return_{stock_name}_standard']=stock_10sp[f'return_{stock_name}_standard']*adjust_std\n",
    "col_return_standard=[f'return_{x}_standard' for x in stock_list]\n",
    "self_preprocessing_df=stock_10sp[col_return_standard]\n",
    "print(self_preprocessing_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "decimal-service",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Method 2 Standardize return using Python preprocessing package\n",
    "# For demonstration purpose only\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "raw_feature=['return_'+x for x in stock_list]\n",
    "sklearn_preprocessing=stock_10sp.loc[:,raw_feature]\n",
    "#print(sklearn_preprocessing)\n",
    "sklearn_preprocessing=StandardScaler().fit_transform(sklearn_preprocessing)\n",
    "sklearn_preprocessing_df=pd.DataFrame(sklearn_preprocessing)\n",
    "sklearn_preprocessing_df.columns=col_return_standard\n",
    "#print(sklearn_preprocessing_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "brown-equity",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Return_Close_PCA1  Return_Close_PCA2  return_MMM_standard  \\\n",
      "Date                                                                    \n",
      "2020-06-02          -0.939493          -1.233198             0.796493   \n",
      "2020-06-03          -0.385581          -3.067722             1.390323   \n",
      "2020-06-04           1.089927          -0.831744             0.483121   \n",
      "2020-06-05          -3.356692          -1.840471             1.844451   \n",
      "2020-06-08          -1.973215          -0.220244            -0.256501   \n",
      "...                       ...                ...                  ...   \n",
      "2020-12-23           0.447512          -0.589730            -0.340003   \n",
      "2020-12-24          -0.239738           0.307374             0.146605   \n",
      "2020-12-28          -0.153035           0.190966             0.021067   \n",
      "2020-12-29           0.072545           0.307427            -0.365994   \n",
      "2020-12-30           0.090792           0.394585             0.047177   \n",
      "\n",
      "            return_ABT_standard  return_ABBV_standard  return_ABMD_standard  \\\n",
      "Date                                                                          \n",
      "2020-06-02            -0.314648              0.179834              0.731912   \n",
      "2020-06-03            -0.858644             -0.174605              1.225266   \n",
      "2020-06-04            -2.168655              1.471036             -0.461599   \n",
      "2020-06-05             1.023528              0.503759              2.291365   \n",
      "2020-06-08             1.715702              0.450222              0.482501   \n",
      "...                         ...                   ...                   ...   \n",
      "2020-12-23            -0.524430              0.240107              0.136794   \n",
      "2020-12-24             0.438671             -0.078914             -0.191175   \n",
      "2020-12-28            -0.373500              0.054686              1.197303   \n",
      "2020-12-29             0.237842              0.723529              0.964475   \n",
      "2020-12-30            -0.001333              0.290801              0.288575   \n",
      "\n",
      "            return_ACN_standard  return_ATVI_standard  return_ADBE_standard  \\\n",
      "Date                                                                          \n",
      "2020-06-02             0.580179             -0.090747              0.013218   \n",
      "2020-06-03             0.324406             -2.410885             -0.131998   \n",
      "2020-06-04            -1.257373             -0.168862             -0.543147   \n",
      "2020-06-05             1.407912             -0.379902              0.734071   \n",
      "2020-06-08             0.669569              0.924742              0.473301   \n",
      "...                         ...                   ...                   ...   \n",
      "2020-12-23            -0.540884              0.394664             -0.714958   \n",
      "2020-12-24            -0.122449              0.366605              0.188950   \n",
      "2020-12-28             0.435707              0.196752             -0.153659   \n",
      "2020-12-29            -0.516083             -0.118976              0.206396   \n",
      "2020-12-30            -0.140185              0.041443             -0.485681   \n",
      "\n",
      "            return_AMD_standard  return_AAP_standard  return_AES_standard  \n",
      "Date                                                                       \n",
      "2020-06-02            -0.162424            -0.182895             1.757103  \n",
      "2020-06-03            -0.574458             1.190362             1.546187  \n",
      "2020-06-04            -0.169066            -0.102310             0.499357  \n",
      "2020-06-05             0.158643             1.620974             1.271207  \n",
      "2020-06-08            -0.185861             0.055395             1.851732  \n",
      "...                         ...                  ...                  ...  \n",
      "2020-12-23            -0.640938            -0.092844             1.104521  \n",
      "2020-12-24            -0.025268             0.486436            -0.595859  \n",
      "2020-12-28            -0.180964            -0.777135            -0.121487  \n",
      "2020-12-29            -0.438128            -0.876393            -0.159587  \n",
      "2020-12-30             0.443263             0.330980            -1.198447  \n",
      "\n",
      "[148 rows x 12 columns]\n"
     ]
    }
   ],
   "source": [
    "# Princial Component Analysis on Standardized Data\n",
    "# Merge back with standardized return columns\n",
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(n_components=2)\n",
    "principal_components=pca.fit_transform(self_preprocessing_df)\n",
    "PCA_Final_df=pd.concat([pd.DataFrame(principal_components,columns=['Return_Close_PCA1','Return_Close_PCA2']),\n",
    "                        self_preprocessing_df.reset_index()],axis=1).set_index('Date')\n",
    "print(PCA_Final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loose-amber",
   "metadata": {},
   "source": [
    "# Question 4 (3)  Note: Using Two Y-axis to handle different scale\n",
    "Plot first principal compoent and daily return of S&P500 in one figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "ahead-contributor",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "          Date  sp500_return_pct  sp500_return  Return_Close_PCA1\n",
      "0   2020-06-02          0.817731      0.008177          -0.939493\n",
      "1   2020-06-03          1.355667      0.013557          -0.385581\n",
      "2   2020-06-04         -0.337439     -0.003374           1.089927\n",
      "3   2020-06-05          2.587401      0.025874          -3.356692\n",
      "4   2020-06-08          1.196966      0.011970          -1.973215\n",
      "..         ...               ...           ...                ...\n",
      "143 2020-12-23          0.074553      0.000746           0.447512\n",
      "144 2020-12-24          0.353035      0.003530          -0.239738\n",
      "145 2020-12-28          0.868471      0.008685          -0.153035\n",
      "146 2020-12-29         -0.222986     -0.002230           0.072545\n",
      "147 2020-12-30          0.134065      0.001341           0.090792\n",
      "\n",
      "[148 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'PCA of 10 Stock vs. SP500 Index Daily Return - Using 2 Axis')"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500=yf.download('^GSPC',  start='2020-06-01', end='2020-12-31')\n",
    "sp500['sp500_return']=np.log(sp500['Close']/sp500['Close'].shift(periods=1))\n",
    "sp500['sp500_return_pct']=sp500['sp500_return']*100\n",
    "sp500_pca=pd.concat([sp500.loc[sp500.index>'2020-06-01',['sp500_return_pct','sp500_return']],PCA_Final_df.loc[:,'Return_Close_PCA1'] ], axis=1).reset_index()\n",
    "print(sp500_pca)\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "fig,ax1=plt.subplots()\n",
    "color_ax1='tab:red'\n",
    "color_ax2='tab:blue'\n",
    "ax1.set_xlabel('Date')\n",
    "ax1.set_ylabel('S&P500 Daily Return', color=color_ax1)\n",
    "ax1.plot('Date', 'sp500_return', data=sp500_pca,color=color_ax1)\n",
    "ax1.tick_params(axis='y',color='red',labelcolor=color_ax1)\n",
    "plt.legend()\n",
    "ax2=ax1.twinx()\n",
    "ax2.set_ylabel('1ST PCA of 10 Stocks',color=color_ax2)\n",
    "ax2.plot('Date', 'Return_Close_PCA1',data=sp500_pca, color=color_ax2)\n",
    "ax2.tick_params(axis='y',labelcolor=color_ax2)\n",
    "fig.tight_layout()\n",
    "#plt.plot('Date', 'sp500_return_pct', data=sp500_pca)\n",
    "#plt.plot('Date', 'Return_Close_PCA1',data=sp500_pca)\n",
    "\n",
    "ax1.xaxis.grid(which='major',linestyle='--')\n",
    "plt.legend()\n",
    "plt.title(\"PCA of 10 Stock vs. SP500 Index Daily Return - Using 2 Axis\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legal-puppy",
   "metadata": {},
   "source": [
    "# Question 4 (4) \n",
    "Calculate the correlation coefficient between first principal component and daily return of S&P500 index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "mechanical-delicious",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------------------------------------------\n",
      "The correlation Coefficient is: -0.8835144305792828 and pvalue is: 6.061556245048064e-50\n",
      "------------------------------------------------------------------------------------\n",
      "Conclusion:The 1st PCA of selected 10 stocks and SP500 Index Daily Return are negative correlated when threshold set as 0.5 and significant level 0.05!\n",
      "------------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import pearsonr\n",
    "corr,pvalue=pearsonr(sp500_pca['sp500_return'],sp500_pca['Return_Close_PCA1'])\n",
    "print('------------------------------------------------------------------------------------')\n",
    "print(f\"The correlation Coefficient is: {corr} and pvalue is: {pvalue}\")\n",
    "print('------------------------------------------------------------------------------------')\n",
    "significant_level=0.05\n",
    "corr_threshold=0.5\n",
    "if pvalue<significant_level:\n",
    "    if corr>corr_threshold:\n",
    "        print(f\"Conclusion:The 1st PCA of selected 10 Stocks and SP500 Index Daily Return are positive correlated when threshold set as {corr_threshold} and significant level {significant_level}!\")\n",
    "    if corr<corr_threshold*(-1):\n",
    "        print(f\"Conclusion:The 1st PCA of selected 10 stocks and SP500 Index Daily Return are negative correlated when threshold set as {corr_threshold} and significant level {significant_level}!\")\n",
    "print('------------------------------------------------------------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "minimal-details",
   "metadata": {},
   "source": [
    "# Extra Credit\n",
    "Pick 2 companies in the same indsutry, collect close, open, high and low for each company from 2020-06 to 202012.\n",
    "Variable set 1 - company A's close, open, high and low\n",
    "Variable set 2 - company B's close, open, high and low\n",
    "(1) Run canonical analysis using data stated above\n",
    "(2) Give some conclusion or finding from your analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "useful-tournament",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "                Open      High       Low     Close\n",
      "Date                                              \n",
      "2020-06-01 -1.348561 -1.212318 -1.281585 -1.200969\n",
      "2020-06-02 -1.081040 -1.168388 -1.107746 -1.152838\n",
      "2020-06-03 -1.015646 -0.844901 -0.954121 -0.739716\n",
      "2020-06-04 -0.369634 -0.004235 -0.444732  0.044416\n",
      "2020-06-05  1.003637  0.880362  0.410314  0.401386\n",
      "...              ...       ...       ...       ...\n",
      "2020-12-23  1.419779  1.525338  1.568567  1.620701\n",
      "2020-12-24  1.641722  1.519347  1.653465  1.518423\n",
      "2020-12-28  1.611998  1.587240  1.736342  1.602652\n",
      "2020-12-29  1.724951  1.599221  1.714107  1.578586\n",
      "2020-12-30  1.558494  1.613199  1.712086  1.684876\n",
      "\n",
      "[149 rows x 4 columns]\n",
      "The correlation between first components of two CCA is 0.9623537744088362\n",
      "The correlation between second components of two CCA is 0.9063151681785587\n"
     ]
    }
   ],
   "source": [
    "# (1) Frun canonical analysis using data stated above\n",
    "from sklearn.cross_decomposition import CCA\n",
    "stock_symbol1='DAL'\n",
    "stock_symbol2='UAL'\n",
    "stock_same_industry=[stock_symbol1,stock_symbol2]\n",
    "stock_df=['stock_'+x for x in stock_same_industry]\n",
    "start_date='2020-06-01'\n",
    "end_date='2020-12-31'\n",
    "stock_1=yf.download(stock_symbol1,'2020-06-01','2020-12-31')\n",
    "stock_2=yf.download(stock_symbol2,'2020-06-01','2020-12-31')\n",
    "column_keep=['Open','High','Low','Close']\n",
    "stock_1=stock_1[column_keep]\n",
    "stock_2=stock_2[column_keep]\n",
    "stock_1=(stock_1-stock_1.mean())/stock_1.std()\n",
    "stock_2=(stock_2-stock_2.mean())/stock_2.std()\n",
    "print(stock_1)\n",
    "ca=CCA()\n",
    "ca.fit(stock_1, stock_2)\n",
    "stock1_cca, stock2_cca=ca.transform(stock_1, stock_2)\n",
    "corr_first=np.corrcoef(stock1_cca[:,0],stock2_cca[:,0])\n",
    "corr_second=np.corrcoef(stock1_cca[:,1],stock2_cca[:,1])\n",
    "print(f\"The correlation between first components of two CCA is {corr_first[0,1]}\")\n",
    "print(f\"The correlation between second components of two CCA is {corr_second[0,1]}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "viral-signature",
   "metadata": {},
   "source": [
    "#(2) Give some conclusion or findings from your analysis\n",
    "Conclusion:\n",
    "    UAL and DAL are two companies in the Airlines industry which were highly negative \n",
    "    affected by the covid-19 and positively affected by government support and vaccine information.\n",
    "    The volatility from above reason would dominate over other factors such as financial performance during\n",
    "    this special periods. Thus it is not surprise to see the first component show high correlation of 0.96.\n",
    "    Since our attributes are limited to stock prices (Open, High,Low,Close) thus the variability among attributes \n",
    "    are very limit. This leads to the unusually high correlation coefficience of the second components. \n",
    "    Following heatmap shows the correlation matrix of stock prices (Open, High, Low, Close) using stock 1 (DAL) as example. \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "respected-cherry",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix=stock_1.corr()\n",
    "sns.heatmap(correlation_matrix, annot=True)"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}