CANDLESTICK_CHART.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![This is an image](Quant-Trading.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=\"3\">\n",
    "Please visit our website <a href=\"https://www.quant-trading.co\" target=\"_blank\">quant-trading.co</a> for more tools on quantitative finance and data science.\n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install plotly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **HOW TO MAKE A CANDLESTICK CHART FOR STOCKS' PRICES?**\n",
    "\n",
    "## **Candlestick chart**\n",
    "\n",
    "<font size=\"3\"> Candlestick charts are a very useful way to visualize financial markets data. There, you can see the low, high, open and close for a particular asset. Candlestick charts originated in Japan over 100 years before the West developed the bar and point-and-figure charts. The candlestick has a wide part called the \"real body\". This real body represents the price range between the open and close of that day's trading. When the real body is filled in or black (also red), it means the close was lower than the open. If the real body is white (or light color), it means the close was higher than the open. Just above and below the real body are often seen the vertical lines called shadows. The shadows show the high and low prices of that day's trading. If the upper shadow on a down candle is short, it indicates that the open on that day was near the day's high. A short upper shadow on an up day dictates that the close was near the high. The relationship between the days open, high, low, and close determines the look of the daily candlestick. Real bodies can be long or short and black or white. Shadows can be long or short.<br><br> \n",
    "    \n",
    "<font size=\"3\"> In this notebook we will use Yahoo Finance Data and then plot it into a CandleStick chart. If you want to know how to download the data from the yahoo finance API please take a look  <a href=\"https://quant-trading.co/how-to-download-data-from-yahoo-finance-api/\" target=\"_blank\">here</a>\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "import datetime\n",
    "import yfinance as yf "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Download the date**\n",
    "\n",
    "<font size=\"3\"> We do as usual with the yahoo finance API. \n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2024-01-08</td>\n",
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       "      <th>56</th>\n",
       "      <td>2024-03-22</td>\n",
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       "      <th>57</th>\n",
       "      <td>2024-03-25</td>\n",
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       "      <td>171.940002</td>\n",
       "      <td>169.449997</td>\n",
       "      <td>170.850006</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2024-03-26</td>\n",
       "      <td>170.000000</td>\n",
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       "      <td>169.580002</td>\n",
       "      <td>169.710007</td>\n",
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       "      <td>0.0</td>\n",
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       "         Date        Open        High         Low       Close    Volume  \\\n",
       "0  2024-01-02  186.911482  188.199846  183.655642  185.403412  82488700   \n",
       "1  2024-01-03  183.985237  185.643125  183.196235  184.015198  58414500   \n",
       "2  2024-01-04  181.917861  182.856666  180.649491  181.678177  71983600   \n",
       "3  2024-01-05  181.758077  182.527085  179.940389  180.949097  62303300   \n",
       "4  2024-01-08  181.857938  185.363474  181.268693  185.323517  59144500   \n",
       "..        ...         ...         ...         ...         ...       ...   \n",
       "56 2024-03-22  171.759995  173.050003  170.059998  172.279999  71106600   \n",
       "57 2024-03-25  170.570007  171.940002  169.449997  170.850006  54288300   \n",
       "58 2024-03-26  170.000000  171.419998  169.580002  169.710007  57388400   \n",
       "59 2024-03-27  170.410004  173.600006  170.110001  173.309998  60273300   \n",
       "60 2024-03-28  171.750000  172.229996  170.509995  171.479996  65672700   \n",
       "\n",
       "    Dividends  Stock Splits  \n",
       "0         0.0             0  \n",
       "1         0.0             0  \n",
       "2         0.0             0  \n",
       "3         0.0             0  \n",
       "4         0.0             0  \n",
       "..        ...           ...  \n",
       "56        0.0             0  \n",
       "57        0.0             0  \n",
       "58        0.0             0  \n",
       "59        0.0             0  \n",
       "60        0.0             0  \n",
       "\n",
       "[61 rows x 8 columns]"
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     "execution_count": 7,
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   "source": [
    "Ticker = 'AAPL'\n",
    "my_ticker = yf.Ticker(Ticker)\n",
    "\n",
    "#DATES\n",
    "start_date = datetime.datetime(2023, 12, 31)\n",
    "end_date = datetime.datetime(2024, 3, 31)\n",
    "\n",
    "prices_hist = my_ticker.history(start=start_date,end=end_date)\n",
    "prices_hist.reset_index(inplace=True)\n",
    "prices_hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Create the candlestick chart**\n",
    "\n",
    "   \n",
    "<font size=\"3\"> We will use the plotly library, paticularly the graph objects module. The plotly.graph_objects module provides an automatically-generated hierarchy of classes called \"graph objects\" that may be used to represent figures, with a top-level class plotly.graph_objects.Figure. Graph objects have several benefits compared to plain Python dictionaries. Below you can find an example on how to do that.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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   "source": [
    "#Gráfico de velas acción particular\n",
    "\n",
    "layout = go.Figure(data=[go.Candlestick(\n",
    "    x=prices_hist['Date'],\n",
    "    open=prices_hist['Open'], high=prices_hist['High'],\n",
    "    low=prices_hist['Low'], close=prices_hist['Close'],\n",
    "    increasing_line_color= 'royalblue', decreasing_line_color= 'darkblue'\n",
    ")])\n",
    "\n",
    "\n",
    "layout.update_layout(title=\"Candle Chart for \"+Ticker,                     \n",
    "                     title_x=0.5,\n",
    "                     xaxis_rangeslider_visible=False,\n",
    "                     paper_bgcolor='rgba(0,0,0,0)', \n",
    "                     plot_bgcolor='rgba(0,0,0,0)')\n",
    "layout.show()"
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    "If this content is helpful and you want to make a donation please click on the button\n",
    "\n",
    "[![paypal](https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif)](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=29CVY97MEQ9BY)"
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