Technical Analysis 2.ipynb

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   "source": [
    "# Provides ways to work with large multidimensional arrays\n",
    "import numpy as np \n",
    "# Allows for further data manipulation and analysis\n",
    "import pandas as pd\n",
    "\n",
    "# In Anaconda -> Environments -> Not Installed -> pandas-datareader -> Apply\n",
    "from pandas_datareader import data as web # Reads stock data \n",
    "import matplotlib.pyplot as plt # Plotting\n",
    "import matplotlib.dates as mdates # Styling dates\n",
    "%matplotlib inline\n",
    "\n",
    "import cufflinks as cf\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "# Make Plotly work in your Jupyter Notebook\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "# Use Plotly locally\n",
    "cf.go_offline()\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")\n",
    "\n",
    "# pip install yfinance in Qt Console or Powershell Prompt on Windows\n",
    "import yfinance as yf\n",
    "\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# Install the Python Technical Analysis library\n",
    "# pip install ta in Qt Console or Powershell prompt\n",
    "from ta.trend import MACD\n",
    "from ta.momentum import StochasticOscillator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download Stock Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
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       "  <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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
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       "    <tr>\n",
       "      <th>Date</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>2020-11-17</th>\n",
       "      <td>83.930000</td>\n",
       "      <td>84.809998</td>\n",
       "      <td>82.910004</td>\n",
       "      <td>83.360001</td>\n",
       "      <td>83.360001</td>\n",
       "      <td>29473500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-18</th>\n",
       "      <td>83.010002</td>\n",
       "      <td>83.959999</td>\n",
       "      <td>82.220001</td>\n",
       "      <td>82.540001</td>\n",
       "      <td>82.540001</td>\n",
       "      <td>29968000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-19</th>\n",
       "      <td>82.389999</td>\n",
       "      <td>85.639999</td>\n",
       "      <td>81.720001</td>\n",
       "      <td>85.540001</td>\n",
       "      <td>85.540001</td>\n",
       "      <td>50116800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-20</th>\n",
       "      <td>85.279999</td>\n",
       "      <td>86.099998</td>\n",
       "      <td>84.470001</td>\n",
       "      <td>84.639999</td>\n",
       "      <td>84.639999</td>\n",
       "      <td>35008400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-23</th>\n",
       "      <td>85.519997</td>\n",
       "      <td>87.540001</td>\n",
       "      <td>84.620003</td>\n",
       "      <td>85.309998</td>\n",
       "      <td>85.309998</td>\n",
       "      <td>46505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-11</th>\n",
       "      <td>142.960007</td>\n",
       "      <td>146.470001</td>\n",
       "      <td>140.839996</td>\n",
       "      <td>146.009995</td>\n",
       "      <td>146.009995</td>\n",
       "      <td>67934800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-12</th>\n",
       "      <td>146.029999</td>\n",
       "      <td>148.589996</td>\n",
       "      <td>144.250000</td>\n",
       "      <td>147.889999</td>\n",
       "      <td>147.889999</td>\n",
       "      <td>52124200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <td>148.000000</td>\n",
       "      <td>148.979996</td>\n",
       "      <td>142.860001</td>\n",
       "      <td>146.490005</td>\n",
       "      <td>146.490005</td>\n",
       "      <td>52271300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16</th>\n",
       "      <td>145.929993</td>\n",
       "      <td>153.080002</td>\n",
       "      <td>145.339996</td>\n",
       "      <td>152.449997</td>\n",
       "      <td>152.449997</td>\n",
       "      <td>52943700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-17</th>\n",
       "      <td>151.979996</td>\n",
       "      <td>154.659897</td>\n",
       "      <td>150.199997</td>\n",
       "      <td>151.959198</td>\n",
       "      <td>151.959198</td>\n",
       "      <td>31762866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>253 rows × 6 columns</p>\n",
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      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-11-17   83.930000   84.809998   82.910004   83.360001   83.360001   \n",
       "2020-11-18   83.010002   83.959999   82.220001   82.540001   82.540001   \n",
       "2020-11-19   82.389999   85.639999   81.720001   85.540001   85.540001   \n",
       "2020-11-20   85.279999   86.099998   84.470001   84.639999   84.639999   \n",
       "2020-11-23   85.519997   87.540001   84.620003   85.309998   85.309998   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-11-11  142.960007  146.470001  140.839996  146.009995  146.009995   \n",
       "2021-11-12  146.029999  148.589996  144.250000  147.889999  147.889999   \n",
       "2021-11-15  148.000000  148.979996  142.860001  146.490005  146.490005   \n",
       "2021-11-16  145.929993  153.080002  145.339996  152.449997  152.449997   \n",
       "2021-11-17  151.979996  154.659897  150.199997  151.959198  151.959198   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "2020-11-17  29473500  \n",
       "2020-11-18  29968000  \n",
       "2020-11-19  50116800  \n",
       "2020-11-20  35008400  \n",
       "2020-11-23  46505000  \n",
       "...              ...  \n",
       "2021-11-11  67934800  \n",
       "2021-11-12  52124200  \n",
       "2021-11-15  52271300  \n",
       "2021-11-16  52943700  \n",
       "2021-11-17  31762866  \n",
       "\n",
       "[253 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd_df = yf.download(tickers=\"AMD\", period=\"1y\", interval='1d')\n",
    "amd_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exponential Moving Average"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A EMA allows you to see the big picture when analyzing a stock. Unlike the Simple Moving Average it reduces the lag by putting more emphasis on recent price data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>Low</th>\n",
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       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MA20</th>\n",
       "      <th>MA50</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-17</th>\n",
       "      <td>83.930000</td>\n",
       "      <td>84.809998</td>\n",
       "      <td>82.910004</td>\n",
       "      <td>83.360001</td>\n",
       "      <td>83.360001</td>\n",
       "      <td>29473500</td>\n",
       "      <td>83.360001</td>\n",
       "      <td>83.360001</td>\n",
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       "    <tr>\n",
       "      <th>2020-11-18</th>\n",
       "      <td>83.010002</td>\n",
       "      <td>83.959999</td>\n",
       "      <td>82.220001</td>\n",
       "      <td>82.540001</td>\n",
       "      <td>82.540001</td>\n",
       "      <td>29968000</td>\n",
       "      <td>83.281905</td>\n",
       "      <td>83.327844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-19</th>\n",
       "      <td>82.389999</td>\n",
       "      <td>85.639999</td>\n",
       "      <td>81.720001</td>\n",
       "      <td>85.540001</td>\n",
       "      <td>85.540001</td>\n",
       "      <td>50116800</td>\n",
       "      <td>83.496962</td>\n",
       "      <td>83.414595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-20</th>\n",
       "      <td>85.279999</td>\n",
       "      <td>86.099998</td>\n",
       "      <td>84.470001</td>\n",
       "      <td>84.639999</td>\n",
       "      <td>84.639999</td>\n",
       "      <td>35008400</td>\n",
       "      <td>83.605823</td>\n",
       "      <td>83.462650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-23</th>\n",
       "      <td>85.519997</td>\n",
       "      <td>87.540001</td>\n",
       "      <td>84.620003</td>\n",
       "      <td>85.309998</td>\n",
       "      <td>85.309998</td>\n",
       "      <td>46505000</td>\n",
       "      <td>83.768125</td>\n",
       "      <td>83.535095</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-11</th>\n",
       "      <td>142.960007</td>\n",
       "      <td>146.470001</td>\n",
       "      <td>140.839996</td>\n",
       "      <td>146.009995</td>\n",
       "      <td>146.009995</td>\n",
       "      <td>67934800</td>\n",
       "      <td>129.745515</td>\n",
       "      <td>118.209468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-12</th>\n",
       "      <td>146.029999</td>\n",
       "      <td>148.589996</td>\n",
       "      <td>144.250000</td>\n",
       "      <td>147.889999</td>\n",
       "      <td>147.889999</td>\n",
       "      <td>52124200</td>\n",
       "      <td>131.473561</td>\n",
       "      <td>119.373410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-15</th>\n",
       "      <td>148.000000</td>\n",
       "      <td>148.979996</td>\n",
       "      <td>142.860001</td>\n",
       "      <td>146.490005</td>\n",
       "      <td>146.490005</td>\n",
       "      <td>52271300</td>\n",
       "      <td>132.903698</td>\n",
       "      <td>120.436806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16</th>\n",
       "      <td>145.929993</td>\n",
       "      <td>153.080002</td>\n",
       "      <td>145.339996</td>\n",
       "      <td>152.449997</td>\n",
       "      <td>152.449997</td>\n",
       "      <td>52943700</td>\n",
       "      <td>134.765251</td>\n",
       "      <td>121.692225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-17</th>\n",
       "      <td>151.979996</td>\n",
       "      <td>154.659897</td>\n",
       "      <td>150.199997</td>\n",
       "      <td>151.959198</td>\n",
       "      <td>151.959198</td>\n",
       "      <td>31762866</td>\n",
       "      <td>136.402769</td>\n",
       "      <td>122.879165</td>\n",
       "    </tr>\n",
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       "<p>253 rows × 8 columns</p>\n",
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       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "2020-11-17   83.930000   84.809998   82.910004   83.360001   83.360001   \n",
       "2020-11-18   83.010002   83.959999   82.220001   82.540001   82.540001   \n",
       "2020-11-19   82.389999   85.639999   81.720001   85.540001   85.540001   \n",
       "2020-11-20   85.279999   86.099998   84.470001   84.639999   84.639999   \n",
       "2020-11-23   85.519997   87.540001   84.620003   85.309998   85.309998   \n",
       "...                ...         ...         ...         ...         ...   \n",
       "2021-11-11  142.960007  146.470001  140.839996  146.009995  146.009995   \n",
       "2021-11-12  146.029999  148.589996  144.250000  147.889999  147.889999   \n",
       "2021-11-15  148.000000  148.979996  142.860001  146.490005  146.490005   \n",
       "2021-11-16  145.929993  153.080002  145.339996  152.449997  152.449997   \n",
       "2021-11-17  151.979996  154.659897  150.199997  151.959198  151.959198   \n",
       "\n",
       "              Volume        MA20        MA50  \n",
       "Date                                          \n",
       "2020-11-17  29473500   83.360001   83.360001  \n",
       "2020-11-18  29968000   83.281905   83.327844  \n",
       "2020-11-19  50116800   83.496962   83.414595  \n",
       "2020-11-20  35008400   83.605823   83.462650  \n",
       "2020-11-23  46505000   83.768125   83.535095  \n",
       "...              ...         ...         ...  \n",
       "2021-11-11  67934800  129.745515  118.209468  \n",
       "2021-11-12  52124200  131.473561  119.373410  \n",
       "2021-11-15  52271300  132.903698  120.436806  \n",
       "2021-11-16  52943700  134.765251  121.692225  \n",
       "2021-11-17  31762866  136.402769  122.879165  \n",
       "\n",
       "[253 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amd_df['MA20'] = amd_df['Adj Close'].ewm(span=20, adjust=False).mean()\n",
    "amd_df['MA50'] = amd_df['Adj Close'].ewm(span=50, adjust=False).mean()\n",
    "amd_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Moving Averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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    "fig = go.Figure()\n",
    "\n",
    "# Define candlestick and moving average lines\n",
    "candle = go.Candlestick(x=amd_df.index, open=amd_df['Open'],\n",
    "                       high=amd_df['High'], low=amd_df['Low'],\n",
    "                       close=amd_df['Close'], name='Candlestick')\n",
    "ema20 = go.Scatter(x=amd_df.index, y=amd_df['MA20'], \n",
    "                   line=dict(color='green', width=1), name=\"EMA20\")\n",
    "\n",
    "ema50 = go.Scatter(x=amd_df.index, y=amd_df['MA50'], \n",
    "                  line=dict(color='orange', width=1), name=\"EMA50\")\n",
    "\n",
    "# Add plots to the figure\n",
    "fig.add_trace(candle)\n",
    "fig.add_trace(ema20)\n",
    "fig.add_trace(ema50)      \n",
    "\n",
    "# Add title\n",
    "fig.update_layout(title=\"AMD\")\n",
    "\n",
    "# Get rid of empty dates on the weekend\n",
    "fig.update_xaxes(rangebreaks=[dict(bounds=[\"sat\", \"mon\"])])\n",
    "                                            \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Moving Average Convergence & Divergence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The MACD helps us to see buy & sell signals. It shows the difference between 2 moving averages.\n",
    "\n",
    "While these signals are derived from moving averages they occur much more quickly then with moving averages. It is important to know that since the signals occur earlier that they are also more risky. \n",
    "\n",
    "When the signal line crosses the MACD line moving upwards this is bullish and vice versa. The slope of the angle tells you how strong the trend is. "
   ]
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   "execution_count": 5,
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   "source": [
    "def plot_macd_stoch(ticker, time_period, time_int):\n",
    "    # Download stock data\n",
    "    stock_df = yf.download(tickers=ticker, period=time_period, interval=time_int)\n",
    "    \n",
    "    # Define candlestick and moving average lines\n",
    "    candle = go.Candlestick(x=stock_df.index, open=stock_df['Open'],\n",
    "                       high=stock_df['High'], low=stock_df['Low'],\n",
    "                       close=stock_df['Close'])\n",
    "    # A period of 12 and 26 are commonly used\n",
    "    stock_df['MA12'] = stock_df['Adj Close'].ewm(span=12, adjust=False).mean()\n",
    "    stock_df['MA26'] = stock_df['Adj Close'].ewm(span=26, adjust=False).mean()\n",
    "    \n",
    "    # Calculate the MACD\n",
    "    # A signal line uses a period of 9 and when it crosses the other moving\n",
    "    # average it is a sign to buy or sell\n",
    "    macd = MACD(close=stock_df['Close'],\n",
    "             window_slow=26,\n",
    "             window_fast=12,\n",
    "             window_sign=9)\n",
    "\n",
    "    # A Stochastic (stuh ka stuhk) Oscillator is also plotted because it \n",
    "    # gives us a signal of an upcoming trend reversal\n",
    "    # Values range from 0 to 100 and values over 80 are considered to be\n",
    "    # overbought while those under 20 are considered oversold\n",
    "    \n",
    "    # We calculate normally over a 14 day period\n",
    "    # We are smoothing price data\n",
    "    sto_os = StochasticOscillator(high=stock_df['High'],\n",
    "                             close=stock_df['Close'],\n",
    "                             low=stock_df['Low'],\n",
    "                             window=14, \n",
    "                             smooth_window=3)\n",
    "    \n",
    "    # Make 4 subplots on separate rows\n",
    "    fig = make_subplots(rows=4, cols=1, shared_xaxes=True, vertical_spacing=0.01)\n",
    "\n",
    "    fig.add_trace(candle, row=1, col=1)\n",
    "\n",
    "    # Add the histogram, MACD and Signal lines\n",
    "    \n",
    "    # The MacD line is a calculation made by subtracting the 26 period\n",
    "    # EMA from the 12 period EMA\n",
    "    fig.add_trace(go.Scatter(x=stock_df.index,\n",
    "                         y=macd.macd(),\n",
    "                         line=dict(color='blue', width=2)\n",
    "                        ), row=2, col=1)\n",
    "    \n",
    "    # The signal is calculated by taking the average of the last 9 values\n",
    "    # of the MACD line (The signal line is a slower more smoothed out version of\n",
    "    # the MACD line)\n",
    "    fig.add_trace(go.Scatter(x=stock_df.index,\n",
    "                         y=macd.macd_signal(),\n",
    "                         line=dict(color='orange', width=2)\n",
    "                        ), row=2, col=1)\n",
    "    \n",
    "    # The histogram shows the difference between the MACD and signal line\n",
    "    # When the MACD line is below the signal that is a negative value and vice versa\n",
    "    fig.add_trace(go.Bar(x=stock_df.index, \n",
    "                     y=macd.macd_diff()\n",
    "                    ), row=2, col=1)\n",
    "    \n",
    "    # The MacD line is a calculation made by subtracting the 26 period\n",
    "    # EMA from the 12 period EMA\n",
    "    fig.add_trace(go.Scatter(x=stock_df.index,\n",
    "                         y=macd.macd(),\n",
    "                         line=dict(color='blue', width=2)\n",
    "                        ), row=2, col=1)\n",
    "    \n",
    "    # The signal is calculated by taking the average of the last 9 values\n",
    "    # of the MACD line (The signal line is a slower more smoothed out version of\n",
    "    # the MACD line)\n",
    "    fig.add_trace(go.Scatter(x=stock_df.index,\n",
    "                         y=macd.macd_signal(),\n",
    "                         line=dict(color='orange', width=2)\n",
    "                        ), row=2, col=1)\n",
    "\n",
    "    # Plot Stochastics\n",
    "    # This is the faster of the 2 lines called the K line\n",
    "    fig.add_trace(go.Scatter(x=stock_df.index,\n",
    "                         y=sto_os.stoch(),\n",
    "                         line=dict(color='blue', width=2)\n",
    "                        ), row=3, col=1)\n",
    "    # This line is slower and is known as the D line and it is an average of the K line\n",
    "    fig.add_trace(go.Scatter(x=stock_df.index,\n",
    "                         y=sto_os.stoch_signal(),\n",
    "                         line=dict(color='orange', width=2)\n",
    "                        ), row=3, col=1)\n",
    "    \n",
    "    # Draw 20 and 80 lines\n",
    "    fig.add_hline(y=20, line_width=1, line_dash=\"dash\", line_color=\"red\", row=3, col=1)\n",
    "    fig.add_hline(y=80, line_width=1, line_dash=\"dash\", line_color=\"green\", row=3, col=1)\n",
    "\n",
    "    # Add volume \n",
    "    fig.add_trace(go.Bar(x=stock_df.index, \n",
    "                     y=stock_df['Volume']\n",
    "                    ), row=4, col=1)\n",
    "\n",
    "    # Update titles\n",
    "    fig.update_layout(title=ticker)\n",
    "    fig.update_yaxes(title_text=\"Price\", row=1, col=1)\n",
    "    fig.update_yaxes(title_text=\"MACD\", row=2, col=1)\n",
    "    fig.update_yaxes(title_text=\"StoOs\", row=3, col=1)\n",
    "    fig.update_yaxes(title_text=\"Volume\", row=4, col=1)\n",
    "\n",
    "    fig.update_layout(height=900, width=1200, \n",
    "                  showlegend=False, \n",
    "                  xaxis_rangeslider_visible=False,\n",
    "                  xaxis_rangebreaks=[dict(bounds=[\"sat\", \"mon\"]), dict(bounds=[16, 9.5], pattern=\"hour\")])\n",
    "\n",
    "    fig.show()"
   ]
  },
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     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
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    "# If we plot PG we can see a resistance line at around 140 on multiple dates\n",
    "# The volumes spike leading into uptrends\n",
    "# plot_macd_stoch(\"PG\", \"2mo\", \"1h\")\n",
    "\n",
    "# Now we will zoom in and we can see how when the MACD crosses over that these are potential \n",
    "# opportunities to buy or sell (This example would work better if we were near 140)\n",
    "plot_macd_stoch(\"PG\", \"5d\", \"5m\")\n",
    "\n",
    "# A Stochastic (stuh ka stuhk) Oscillator is also plotted because it \n",
    "# gives us a signal of an upcoming trend reversal\n",
    "# Values range from 0 to 100 and values over 80 are considered to be\n",
    "# overbought while those under 20 are considered oversold\n",
    "# This isn't a simple buy or sell plot however and it is common for stocks\n",
    "# to stay overbought or sold over long periods\n",
    "# This isn't a chart you use on its own, but instead to backup other charts\n",
    "# You use it normally when an investment is either in a down or up trend.\n",
    "# This means the stock is bouncing up and down, but consistently reaching higher highs\n",
    "# or lower lows. When in one of these trends it is common for it to remain in\n",
    "# overbought or sold trends for a long time.\n",
    "\n",
    "# Don't buy based on this chart during downtrends and don't sell during uptrends!"
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