Stock_analysis_project.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import yfinance as yf \n",
    "from datetime import datetime\n"
   ]
  },
  {
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   "execution_count": 4,
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      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "                         Open        High         Low       Close   Adj Close  \\\n",
      "Ticker Date                                                                     \n",
      "AAPL   2024-01-30  190.940002  191.800003  187.470001  188.039993  187.800354   \n",
      "       2024-01-31  187.039993  187.100006  184.350006  184.399994  184.164993   \n",
      "       2024-02-01  183.990005  186.949997  183.820007  186.860001  186.621872   \n",
      "       2024-02-02  179.860001  187.330002  179.250000  185.850006  185.613159   \n",
      "       2024-02-05  188.149994  189.250000  185.839996  187.679993  187.440811   \n",
      "\n",
      "                      Volume  \n",
      "Ticker Date                   \n",
      "AAPL   2024-01-30   55859400  \n",
      "       2024-01-31   55467800  \n",
      "       2024-02-01   64885400  \n",
      "       2024-02-02  102518000  \n",
      "       2024-02-05   69668800  \n"
     ]
    }
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   "source": [
    "# Specify the start and end date of your monitoring period\n",
    "start_date = datetime.now() - pd.DateOffset(months= 3)\n",
    "end_date = datetime.now()\n",
    "\n",
    "tickers = ['AAPL', 'TSLA', 'HSBA.L', 'MSFT']\n",
    "\n",
    "df_list= []\n",
    "\n",
    "for ticker in tickers:\n",
    "    data = yf.download (ticker, start= start_date, end= end_date)\n",
    "    df_list.append(data)\n",
    "\n",
    "df= pd.concat(df_list, keys=tickers, names= ['Ticker', 'Date'])\n",
    "print(df.head())\n",
    "\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In the above cell codes, we first import relevant python libraries and downloaded historical stock price data for four companies: Apple, Tesla,\n",
    "# Microsoft and HSBC Holdings PLC ORD for the last three months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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     "text": [
      "  Ticker       Date        Open        High         Low       Close  \\\n",
      "0   AAPL 2024-01-30  190.940002  191.800003  187.470001  188.039993   \n",
      "1   AAPL 2024-01-31  187.039993  187.100006  184.350006  184.399994   \n",
      "2   AAPL 2024-02-01  183.990005  186.949997  183.820007  186.860001   \n",
      "3   AAPL 2024-02-02  179.860001  187.330002  179.250000  185.850006   \n",
      "4   AAPL 2024-02-05  188.149994  189.250000  185.839996  187.679993   \n",
      "\n",
      "    Adj Close     Volume  \n",
      "0  187.800354   55859400  \n",
      "1  184.164993   55467800  \n",
      "2  186.621872   64885400  \n",
      "3  185.613159  102518000  \n",
      "4  187.440811   69668800  \n"
     ]
    }
   ],
   "source": [
    "# In the downloaded historical price dataset, the Date column is the index column in the DataFrame, therefore we need to reset the index before moving forward\n",
    "# with our analysis.\n",
    "\n",
    "df = df.reset_index()\n",
    "print(df.head())"
   ]
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   ],
   "source": [
    "# Lets check the performance of all the companies in the stock market\n",
    "\n",
    "import plotly.express as px \n",
    "fig = px.line (df, x= 'Date',\n",
    "               y= 'Close',\n",
    "               color = 'Ticker',\n",
    "               title= \"Stock Market performance for the last 3 months\")\n",
    "fig.show()"
   ]
  },
  {
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    "# To make it easy to understand and compare the performance of these companies and identify similarities or differences in their stock price movement,\n",
    "#let's look at the facet area chart. \n",
    "\n",
    "fig = px.area(df, x = 'Date', y = 'Close', color = 'Ticker',\n",
    "              facet_col='Ticker',\n",
    "              labels= {'Date':'Date', 'Close':'Closing Price', 'Ticker':'Company'},\n",
    "              title = 'Stock prices for Apple, Tesla, HSBA Holdings and Microsoft')\n",
    "fig.show()\n"
   ]
  },
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   "execution_count": 10,
   "metadata": {},
   "outputs": [
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     "text": [
      "Moving Average for AAPL\n",
      "          MA10        MA20\n",
      "0          NaN         NaN\n",
      "1          NaN         NaN\n",
      "2          NaN         NaN\n",
      "3          NaN         NaN\n",
      "4          NaN         NaN\n",
      "..         ...         ...\n",
      "58  169.421999  169.687999\n",
      "59  169.545999  169.653499\n",
      "60  169.031000  169.482499\n",
      "61  168.306000  169.373499\n",
      "62  168.387000  169.546999\n",
      "\n",
      "[63 rows x 2 columns]\n",
      "Moving Average for HSBA.L\n",
      "           MA10        MA20\n",
      "126         NaN         NaN\n",
      "127         NaN         NaN\n",
      "128         NaN         NaN\n",
      "129         NaN         NaN\n",
      "130         NaN         NaN\n",
      "..          ...         ...\n",
      "185  650.759998  642.845001\n",
      "186  652.279999  644.575003\n",
      "187  653.299994  647.005002\n",
      "188  654.999994  649.460001\n",
      "189  661.539795  653.244901\n",
      "\n",
      "[64 rows x 2 columns]\n",
      "Moving Average for MSFT\n",
      "           MA10        MA20\n",
      "190         NaN         NaN\n",
      "191         NaN         NaN\n",
      "192         NaN         NaN\n",
      "193         NaN         NaN\n",
      "194         NaN         NaN\n",
      "..          ...         ...\n",
      "248  412.506998  417.479999\n",
      "249  411.086996  416.850499\n",
      "250  408.197998  415.731000\n",
      "251  406.639999  415.011000\n",
      "252  405.500998  413.895000\n",
      "\n",
      "[63 rows x 2 columns]\n",
      "Moving Average for TSLA\n",
      "           MA10        MA20\n",
      "63          NaN         NaN\n",
      "64          NaN         NaN\n",
      "65          NaN         NaN\n",
      "66          NaN         NaN\n",
      "67          NaN         NaN\n",
      "..          ...         ...\n",
      "121  157.515999  165.227499\n",
      "122  156.553000  164.450500\n",
      "123  156.110999  163.967999\n",
      "124  155.834998  163.592999\n",
      "125  159.091998  164.534499\n",
      "\n",
      "[63 rows x 2 columns]\n"
     ]
    }
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   "source": [
    "# The next step is to identify trends and patterns in each company’s stock price movements over a period of time:\n",
    "#To do so, the best way is to analyze the moving average (MA) and specify the number of days for these average(the window period).\n",
    "\n",
    "df['MA10'] = df.groupby('Ticker')['Close'].rolling(window=10).mean().reset_index(0, drop=True)\n",
    "df['MA20'] = df.groupby('Ticker')['Close'].rolling(window=20).mean().reset_index(0, drop=True)\n",
    "\n",
    "for ticker, group in df.groupby('Ticker'):\n",
    "    print(f'Moving Average for {ticker}')\n",
    "    print(group[['MA10', 'MA20']])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
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    "# Visualize the moving averages of all companies:\n",
    "for ticker, group in df.groupby('Ticker'):\n",
    "    fig = px.line(group, x= 'Date', y=['Close', 'MA10', 'MA20'],\n",
    "                  title= f\"{ticker} Moving Averages\")\n",
    "    fig.show()"
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    "#Closing Prices (Blue Line): TSLA (Tesla.Inc)\n",
    "#The blue line represents the actual closing prices of TSLA stock over time. When the blue line goes up, it means the stock price increased on that day, and vice versa.\n",
    "#The overall trend appears to be upward, especially after early April.\n",
    "#MA10 (Red Line):\n",
    "#The red line represents the 10-day moving average (MA10). MA10 is calculated by averaging the closing prices over the past 10 trading days.\n",
    "#When the red line crosses above the blue line, it suggests a potential bullish signal. Around early April, the red line crosses above the blue line, indicating a positive trend.\n",
    "#MA20 (Green Line):\n",
    "#The green line represents the 20-day moving average (MA20). MA20 considers the average closing prices over the past 20 trading days.\n",
    "#When the green line crosses above the red line, it confirms a bullish sentiment. Similarly, around early April, the green line crosses above the red line, supporting the positive trend.\n",
    "\n",
    "#In summary:\n",
    "#The stock price has been generally rising.\n",
    "#Both MA10 and MA20 confirm the bullish trend after early April."
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   "execution_count": 13,
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    "#For all companies graphs, this is the interpretation.\n",
    "\n",
    "#The output shows four separate graphs for each company. When the MA10 crosses above the MA20, it is considered a bullish signal indicating that the stock price will continue to rise.\n",
    "#Conversely, when the MA10 crosses below the MA20, it is a bearish signal that the stock price will continue falling.\n"
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    "# The next move is to analyze the volatility of all four companies stock, and our first question is what is Volatility.\n",
    "# Volatility is a measure of how much and how often the stock prices or market fluctuates over a given period of time. Here's the volatility of all four companies:\n",
    "\n",
    "df['volatility'] = df.groupby('Ticker')['Close'].pct_change().rolling(window=10).std().reset_index(0, drop=True)\n",
    "fig = px.line(df, x='Date', y= 'volatility',\n",
    "              color = 'Ticker',\n",
    "              title=\"Volatility of all four companies\")\n",
    "fig.show()"
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    "#High volatility indicates that the stock or market experiences large and frequent price movements,\n",
    "#while low volatility indicates that the market experiences smaller or less frequent price movements.\n",
    "\n",
    "#Graphical interpretation\n",
    "#AAPL (Apple):\n",
    "#The blue line represents AAPL’s volatility.\n",
    "#AAPL’s volatility remains relatively stable throughout the period. There are minor fluctuations, but no significant spikes.\n",
    "\n",
    "#HSBA.L (HSBC Holdings):\n",
    "#The green line represents TSLA’s volatility.\n",
    "#HSBA.L’s volatility shows moderate fluctuations. It remains within a reasonable range without extreme spikes.\n",
    "\n",
    "#TSLA (Tesla):\n",
    "#The red line represents Tesla’s volatility.\n",
    "#Tesla’s volatility is generally stable until around April 21.\n",
    "#On April 21, there’s a sharp spike in volatility, indicating a sudden increase in market uncertainty. \n",
    "\n",
    "#MSFT (Microsoft):\n",
    "#The purple line represents MSFT’s volatility.\n",
    "#MSFT’s volatility remains relatively stable throughout the period. There are minor fluctuations, but no significant deviations.\n",
    "\n",
    "#In summary:\n",
    "\n",
    "#AAPL and MSFT exhibit stable volatility.\n",
    "#HSBA.L has moderate fluctuations.\n",
    "#TSLA experiences a sudden spike in volatility around April 21."
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   "source": [
    "# Now lets analyze the correlation between companies: Apple and Microsoft stock prices\n",
    "\n",
    "Apple = df.loc[df['Ticker']== 'AAPL', ['Date', 'Close']].rename(columns= {'Close': 'AAPL'})\n",
    "Microsoft = df.loc[df['Ticker']== 'MSFT', ['Date', 'Close']].rename(columns= {'Close': 'MSFT'})\n",
    "df_corr = pd.merge(Apple, Microsoft, on='Date')\n",
    "\n",
    "# create a scatter plot to visualize the correlation\n",
    "fig = px.scatter(df_corr, x='AAPL', y='MSFT',\n",
    "                 trendline='ols',\n",
    "                 title='Correlation between Apple and Microsoft')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The downward-sloping trend line suggests a negative correlation. When AAPL stock price increases, MSFT stock price tends to decrease (and vice versa).\n",
    "#The strength of this relationship is not extreme, but it’s noticeable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date        AAPL        MSFT\n",
      "0  2024-01-30  188.039993  408.589996\n",
      "1  2024-01-31  184.399994  397.579987\n",
      "2  2024-02-01  186.860001  403.779999\n",
      "3  2024-02-02  185.850006  411.220001\n",
      "4  2024-02-05  187.679993  405.649994\n",
      "..        ...         ...         ...\n",
      "58 2024-04-23  166.899994  407.570007\n",
      "59 2024-04-24  169.020004  409.059998\n",
      "60 2024-04-25  169.889999  399.040009\n",
      "61 2024-04-26  169.300003  406.320007\n",
      "62 2024-04-29  173.500000  402.250000\n",
      "\n",
      "[63 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df_corr)"
   ]
  }
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