├── Explainer_Notebook.ipynb
├── README.md
├── portfolio_overview.py
├── portfolio_overview.xlsm
└── requirements.txt


/Explainer_Notebook.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# 📈 Pull Data From Yahoo Finance"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {
 13 |     "heading_collapsed": true
 14 |    },
 15 |    "source": [
 16 |     "# Imports"
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "code",
 21 |    "execution_count": 2,
 22 |    "metadata": {
 23 |     "hidden": true
 24 |    },
 25 |    "outputs": [],
 26 |    "source": [
 27 |     "!pip install pip install yahoofinancials --quiet\n",
 28 |     "!pip install pandas --quiet"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 3,
 34 |    "metadata": {
 35 |     "hidden": true
 36 |    },
 37 |    "outputs": [],
 38 |    "source": [
 39 |     "from yahoofinancials import YahooFinancials\n",
 40 |     "import pandas as pd"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "markdown",
 45 |    "metadata": {
 46 |     "heading_collapsed": true
 47 |    },
 48 |    "source": [
 49 |     "# Basic Example"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "markdown",
 54 |    "metadata": {
 55 |     "hidden": true
 56 |    },
 57 |    "source": [
 58 |     "**`YahooFinancials`** is a powerful financial data module used for pulling both fundamental and technical data from Yahoo Finance.\n",
 59 |     ">Documentation: https://github.com/JECSand/yahoofinancials\n",
 60 |     "\n",
 61 |     "**Examples of Module Methods:**<br>\n",
 62 |     "get_daily_low()<br>\n",
 63 |     "get_daily_high()<br>\n",
 64 |     "get_currency()<br>\n",
 65 |     "get_yearly_high()<br>\n",
 66 |     "get_yearly_low()<br>\n",
 67 |     "get_dividend_yield()<br>\n",
 68 |     "...<br>"
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "code",
 73 |    "execution_count": 4,
 74 |    "metadata": {
 75 |     "hidden": true
 76 |    },
 77 |    "outputs": [],
 78 |    "source": [
 79 |     "ticker =  'TSLA'"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": 5,
 85 |    "metadata": {
 86 |     "hidden": true
 87 |    },
 88 |    "outputs": [
 89 |     {
 90 |      "data": {
 91 |       "text/plain": [
 92 |        "641.87"
 93 |       ]
 94 |      },
 95 |      "execution_count": 5,
 96 |      "metadata": {},
 97 |      "output_type": "execute_result"
 98 |     }
 99 |    ],
100 |    "source": [
101 |     "data = YahooFinancials(ticker)\n",
102 |     "data.get_open_price()"
103 |    ]
104 |   },
105 |   {
106 |    "cell_type": "code",
107 |    "execution_count": 6,
108 |    "metadata": {
109 |     "hidden": true
110 |    },
111 |    "outputs": [
112 |     {
113 |      "data": {
114 |       "text/plain": [
115 |        "{'TSLA': {'previousClose': 640.39,\n",
116 |        "  'regularMarketOpen': 641.87,\n",
117 |        "  'twoHundredDayAverage': 602.2559,\n",
118 |        "  'trailingAnnualDividendYield': None,\n",
119 |        "  'payoutRatio': 0,\n",
120 |        "  'volume24Hr': None,\n",
121 |        "  'regularMarketDayHigh': 643.82,\n",
122 |        "  'navPrice': None,\n",
123 |        "  'averageDailyVolume10Day': 37012214,\n",
124 |        "  'totalAssets': None,\n",
125 |        "  'regularMarketPreviousClose': 640.39,\n",
126 |        "  'fiftyDayAverage': 713.1397,\n",
127 |        "  'trailingAnnualDividendRate': None,\n",
128 |        "  'open': 641.87,\n",
129 |        "  'toCurrency': None,\n",
130 |        "  'averageVolume10days': 37012214,\n",
131 |        "  'expireDate': '-',\n",
132 |        "  'yield': None,\n",
133 |        "  'algorithm': None,\n",
134 |        "  'dividendRate': None,\n",
135 |        "  'exDividendDate': '-',\n",
136 |        "  'beta': 2.06013,\n",
137 |        "  'circulatingSupply': None,\n",
138 |        "  'startDate': '-',\n",
139 |        "  'regularMarketDayLow': 599.9,\n",
140 |        "  'priceHint': 2,\n",
141 |        "  'currency': 'USD',\n",
142 |        "  'trailingPE': 966.73444,\n",
143 |        "  'regularMarketVolume': 33332909,\n",
144 |        "  'lastMarket': None,\n",
145 |        "  'maxSupply': None,\n",
146 |        "  'openInterest': None,\n",
147 |        "  'marketCap': 593871306752,\n",
148 |        "  'volumeAllCurrencies': None,\n",
149 |        "  'strikePrice': None,\n",
150 |        "  'averageVolume': 35782108,\n",
151 |        "  'priceToSalesTrailing12Months': 18.831535,\n",
152 |        "  'dayLow': 599.9,\n",
153 |        "  'ask': 621.5,\n",
154 |        "  'ytdReturn': None,\n",
155 |        "  'askSize': 900,\n",
156 |        "  'volume': 33332909,\n",
157 |        "  'fiftyTwoWeekHigh': 900.4,\n",
158 |        "  'forwardPE': 112.08515,\n",
159 |        "  'maxAge': 1,\n",
160 |        "  'fromCurrency': None,\n",
161 |        "  'fiveYearAvgDividendYield': None,\n",
162 |        "  'fiftyTwoWeekLow': 89.28,\n",
163 |        "  'bid': 621,\n",
164 |        "  'tradeable': False,\n",
165 |        "  'dividendYield': None,\n",
166 |        "  'bidSize': 3000,\n",
167 |        "  'dayHigh': 643.82}}"
168 |       ]
169 |      },
170 |      "execution_count": 6,
171 |      "metadata": {},
172 |      "output_type": "execute_result"
173 |     }
174 |    ],
175 |    "source": [
176 |     "data.get_summary_data()"
177 |    ]
178 |   },
179 |   {
180 |    "cell_type": "markdown",
181 |    "metadata": {
182 |     "heading_collapsed": true
183 |    },
184 |    "source": [
185 |     "# Create Your Own DataFrame"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 7,
191 |    "metadata": {
192 |     "hidden": true
193 |    },
194 |    "outputs": [
195 |     {
196 |      "data": {
197 |       "text/html": [
198 |        "<div>\n",
199 |        "<style scoped>\n",
200 |        "    .dataframe tbody tr th:only-of-type {\n",
201 |        "        vertical-align: middle;\n",
202 |        "    }\n",
203 |        "\n",
204 |        "    .dataframe tbody tr th {\n",
205 |        "        vertical-align: top;\n",
206 |        "    }\n",
207 |        "\n",
208 |        "    .dataframe thead th {\n",
209 |        "        text-align: right;\n",
210 |        "    }\n",
211 |        "</style>\n",
212 |        "<table border=\"1\" class=\"dataframe\">\n",
213 |        "  <thead>\n",
214 |        "    <tr style=\"text-align: right;\">\n",
215 |        "      <th></th>\n",
216 |        "    </tr>\n",
217 |        "  </thead>\n",
218 |        "  <tbody>\n",
219 |        "  </tbody>\n",
220 |        "</table>\n",
221 |        "</div>"
222 |       ],
223 |       "text/plain": [
224 |        "Empty DataFrame\n",
225 |        "Columns: []\n",
226 |        "Index: []"
227 |       ]
228 |      },
229 |      "execution_count": 7,
230 |      "metadata": {},
231 |      "output_type": "execute_result"
232 |     }
233 |    ],
234 |    "source": [
235 |     "# Create an empty DataFrame\n",
236 |     "df = pd.DataFrame()\n",
237 |     "df"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "code",
242 |    "execution_count": 8,
243 |    "metadata": {
244 |     "hidden": true
245 |    },
246 |    "outputs": [],
247 |    "source": [
248 |     "tickers = ['TSLA', 'GOOG', 'MSFT']\n",
249 |     "for ticker in tickers:\n",
250 |     "    # Pull data from YahooFinance\n",
251 |     "    data = YahooFinancials(ticker)\n",
252 |     "    open_price = data.get_open_price()\n",
253 |     "    currency = data.get_currency()\n",
254 |     "    yearly_high = data.get_yearly_high()\n",
255 |     "    \n",
256 |     "    # Create Dictonary \n",
257 |     "    new_row = {\n",
258 |     "        \"ticker\": ticker,\n",
259 |     "        \"open_price\": open_price,\n",
260 |     "        \"currency\": currency,\n",
261 |     "        \"yearly_high\": yearly_high,\n",
262 |     "    }\n",
263 |     "    \n",
264 |     "    # Append data (new row) to DataFrame\n",
265 |     "    df = df.append(new_row, ignore_index=True)\n"
266 |    ]
267 |   },
268 |   {
269 |    "cell_type": "code",
270 |    "execution_count": 9,
271 |    "metadata": {
272 |     "hidden": true
273 |    },
274 |    "outputs": [
275 |     {
276 |      "data": {
277 |       "text/html": [
278 |        "<div>\n",
279 |        "<style scoped>\n",
280 |        "    .dataframe tbody tr th:only-of-type {\n",
281 |        "        vertical-align: middle;\n",
282 |        "    }\n",
283 |        "\n",
284 |        "    .dataframe tbody tr th {\n",
285 |        "        vertical-align: top;\n",
286 |        "    }\n",
287 |        "\n",
288 |        "    .dataframe thead th {\n",
289 |        "        text-align: right;\n",
290 |        "    }\n",
291 |        "</style>\n",
292 |        "<table border=\"1\" class=\"dataframe\">\n",
293 |        "  <thead>\n",
294 |        "    <tr style=\"text-align: right;\">\n",
295 |        "      <th></th>\n",
296 |        "      <th>currency</th>\n",
297 |        "      <th>open_price</th>\n",
298 |        "      <th>ticker</th>\n",
299 |        "      <th>yearly_high</th>\n",
300 |        "    </tr>\n",
301 |        "  </thead>\n",
302 |        "  <tbody>\n",
303 |        "    <tr>\n",
304 |        "      <th>0</th>\n",
305 |        "      <td>USD</td>\n",
306 |        "      <td>641.87</td>\n",
307 |        "      <td>TSLA</td>\n",
308 |        "      <td>900.40</td>\n",
309 |        "    </tr>\n",
310 |        "    <tr>\n",
311 |        "      <th>1</th>\n",
312 |        "      <td>USD</td>\n",
313 |        "      <td>2038.86</td>\n",
314 |        "      <td>GOOG</td>\n",
315 |        "      <td>2152.68</td>\n",
316 |        "    </tr>\n",
317 |        "    <tr>\n",
318 |        "      <th>2</th>\n",
319 |        "      <td>USD</td>\n",
320 |        "      <td>231.55</td>\n",
321 |        "      <td>MSFT</td>\n",
322 |        "      <td>246.13</td>\n",
323 |        "    </tr>\n",
324 |        "  </tbody>\n",
325 |        "</table>\n",
326 |        "</div>"
327 |       ],
328 |       "text/plain": [
329 |        "  currency  open_price ticker  yearly_high\n",
330 |        "0      USD      641.87   TSLA       900.40\n",
331 |        "1      USD     2038.86   GOOG      2152.68\n",
332 |        "2      USD      231.55   MSFT       246.13"
333 |       ]
334 |      },
335 |      "execution_count": 9,
336 |      "metadata": {},
337 |      "output_type": "execute_result"
338 |     }
339 |    ],
340 |    "source": [
341 |     "df"
342 |    ]
343 |   }
344 |  ],
345 |  "metadata": {
346 |   "kernelspec": {
347 |    "display_name": "Python 3",
348 |    "language": "python",
349 |    "name": "python3"
350 |   },
351 |   "language_info": {
352 |    "codemirror_mode": {
353 |     "name": "ipython",
354 |     "version": 3
355 |    },
356 |    "file_extension": ".py",
357 |    "mimetype": "text/x-python",
358 |    "name": "python",
359 |    "nbconvert_exporter": "python",
360 |    "pygments_lexer": "ipython3",
361 |    "version": "3.8.5"
362 |   }
363 |  },
364 |  "nbformat": 4,
365 |  "nbformat_minor": 4
366 | }
367 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | 
 2 | # Dividend & Portfolio Tracker in Excel by using Python [Free Template]
 3 | 
 4 | In this tutorial, I will show you the Excel Dividend & Portfolio Tracker I have built for you. All you need to do is to enter the Ticker Symbol, the number of shares you own as well as your average purchase price.
 5 | 
 6 | The Excel Spreadsheet is pulling the financial data from Yahoo Finance by using Python. It is not only limited to US stock. You could return any stock, cryptocurrency, forex, mutual fund & ETF data which is available on Yahoo Finance.
 7 | 
 8 | In this video, I will show you:
 9 | 1. how to use this template
10 | 2. how you can build your own portfolio tracker from scratch
11 | 3. and last but not least, how to customize this template to your needs and liking
12 | 
13 | 
14 | ## Screenshot
15 | 
16 | ![Excel File Screenshot](https://content.screencast.com/users/jubbel3/folders/Snagit/media/d31d9bd9-2837-4451-86b4-a37c24bcbf12/09.29.2021-10.35.jpg)
17 | 
18 | ## Video
19 | 
20 | [![YouTube Video](https://img.youtube.com/vi/4KsP5Et_aWo/0.jpg)](https://youtu.be/4KsP5Et_aWo)
21 | 
22 | 
23 | 
24 | ## 🤓 Check Out My Excel Add-ins
25 | I've developed some handy Excel add-ins that you might find useful:
26 | 
27 | - 📊 **[Dashboard Add-in](https://pythonandvba.com/grafly)**: Easily create interactive and visually appealing dashboards.
28 | - 🎨 **[Cartoon Charts Add-In](https://pythonandvba.com/cuteplots)**: Create engaging and fun cartoon-style charts.
29 | - 🤪 **[Emoji Add-in](https://pythonandvba.com/emojify)**: Add a touch of fun to your spreadsheets with emojis.
30 | - 🛠️ **[MyToolBelt Add-in](https://pythonandvba.com/mytoolbelt)**: A versatile toolbelt for Excel, featuring:
31 |   - Creation of Pandas DataFrames and Jupyter Notebooks from Excel ranges
32 |   - ChatGPT integration for advanced data analysis
33 |   - And much more!
34 | 
35 | 
36 | 
37 | ## 🤝 Connect with Me
38 | - 📺 **YouTube:** [CodingIsFun](https://youtube.com/c/CodingIsFun)
39 | - 🌐 **Website:** [PythonAndVBA](https://pythonandvba.com)
40 | - 💬 **Discord:** [Join the Community](https://pythonandvba.com/discord)
41 | - 💼 **LinkedIn:** [Sven Bosau](https://www.linkedin.com/in/sven-bosau/)
42 | - 📸 **Instagram:** [sven_bosau](https://www.instagram.com/sven_bosau/)
43 | 
44 | ## ☕ Support 
45 | If you appreciate the project and wish to encourage its continued development, consider [supporting my work](https://pythonandvba.com/coffee-donation).
46 | [![ko-fi](https://ko-fi.com/img/githubbutton_sm.svg)](https://pythonandvba.com/coffee-donation)
47 | 
48 | ## Feedback & Collaboration
49 | For feedback, suggestions, or potential collaboration opportunities, reach out at contact@pythonandvba.com.
50 | ![Logo](https://www.pythonandvba.com/banner-img)
51 | If you have any feedback, please reach out to me at contact@pythonandvba.com
52 | 
53 | 
54 | ![Logo](https://www.pythonandvba.com/banner-img)
55 | 
56 | 


--------------------------------------------------------------------------------
/portfolio_overview.py:
--------------------------------------------------------------------------------
  1 | from enum import Enum  # Standard Python Library
  2 | import time, os, sys  # Standard Python Library
  3 | import xlwings as xw  # pip install xlwings
  4 | import pandas as pd  # pip install pandas
  5 | from yahoofinancials import YahooFinancials  # pip install yahoofinancials
  6 | 
  7 | # ==============================
  8 | # Purpose:
  9 | # Returning stock, cryptocurrency, forex, mutual fund, commodity futures, ETF,
 10 | # and US Treasury financial data from Yahoo Finance & export it MS EXCEL
 11 | #
 12 | # Hints:
 13 | # In case you want to adjust/change/add more information to the worksheet,
 14 | # make sure to do your respective adjustments in the following:
 15 | #   a) class Column(Enum): change/add the column-number/name
 16 | #   b) adjust the dictonary "new_row" in the function "pull_stock_data()"
 17 | # ==============================
 18 | 
 19 | print(
 20 |     """
 21 | ==============================
 22 | Dividend & Portfolio Overview
 23 | ==============================
 24 | """
 25 | )
 26 | 
 27 | 
 28 | class Column(Enum):
 29 |     """ Column Name Translation from Excel, 1 = Column A, 2 = Column B, ... """
 30 | 
 31 |     long_name = 1
 32 |     ticker = 2
 33 |     current_price = 5
 34 |     currency = 6
 35 |     conversion_rate = 7
 36 |     open_price = 8
 37 |     daily_low = 9
 38 |     daily_high = 10
 39 |     yearly_low = 11
 40 |     yearly_high = 12
 41 |     fifty_day_moving_avg = 13
 42 |     twohundred_day_moving_avg = 14
 43 |     payout_ratio = 19
 44 |     exdividend_date = 20
 45 |     yield_rel = 21
 46 |     dividend_rate = 22
 47 | 
 48 | 
 49 | def timestamp():
 50 |     t = time.localtime()
 51 |     timestamp = time.strftime("%b-%d-%Y_%H:%M:%S", t)
 52 |     return timestamp
 53 | 
 54 | 
 55 | def clear_content_in_excel():
 56 |     """Clear the old contents in Excel"""
 57 |     if LAST_ROW > START_ROW:
 58 |         print(f"Clear Contents from row {START_ROW} to {LAST_ROW}")
 59 |         for data in Column:
 60 |             if not data.name == "ticker":
 61 |                 sht.range((START_ROW, data.value), (LAST_ROW, data.value)).options(
 62 |                     expand="down"
 63 |                 ).clear_contents()
 64 |         return None
 65 | 
 66 | 
 67 | def convert_to_target_currency(yf_retrieve_data, conversion_rate):
 68 |     """If value is not available on Yahoo finance, it will return None"""
 69 |     if yf_retrieve_data is None:
 70 |         return None
 71 |     return yf_retrieve_data * conversion_rate
 72 | 
 73 | 
 74 | def get_coversion_rate(ticker_currency):
 75 |     """
 76 |     Calculate the coversion rate between
 77 |     ticker currency & desired output currency (TARGET_CURRENCY)
 78 |     Return: conversion rate
 79 |     """
 80 |     if TARGET_CURRENCY == "TICKER CURRENCY":
 81 |         print(f"Display values in {ticker_currency}")
 82 |         conversion_rate = 1
 83 |         return conversion_rate
 84 |     conversion_rate = YahooFinancials(
 85 |         f"{ticker_currency}{TARGET_CURRENCY}=X"
 86 |     ).get_current_price()
 87 |     print(
 88 |         f"Conversion Rate from {ticker_currency} to {TARGET_CURRENCY}: {conversion_rate}"
 89 |     )
 90 |     return conversion_rate
 91 | 
 92 | 
 93 | def pull_stock_data():
 94 |     """
 95 |     Steps:
 96 |     1) Create an empty DataFrame
 97 |     2) Iterate over tickers, pull data from Yahoo Finance & add data to dictonary "new row"
 98 |     3) Append "new row" to DataFrame
 99 |     4) Return DataFrame
100 |     """
101 |     if tickers:
102 |         print(f"Iterating over the following tickers: {tickers}")
103 |         df = pd.DataFrame()
104 |         for ticker in tickers:
105 |             print(f"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
106 |             print(f"Pulling financial data for: {ticker} ...")
107 |             data = YahooFinancials(ticker)
108 |             open_price = data.get_open_price()
109 | 
110 |             # If no open price can be found, Yahoo Finance will return 'None'
111 |             if open_price is None:
112 |                 # If opening price is None, append empty dataframe (row)
113 |                 print(f"Ticker: {ticker} not found on Yahoo Finance. Please check")
114 |                 df = df.append(pd.Series(dtype=str), ignore_index=True)
115 |             else:
116 |                 try:
117 |                     try:
118 |                         long_name = data.get_stock_quote_type_data()[ticker]["longName"]
119 |                     except (TypeError, KeyError):
120 |                         long_name = None
121 |                     try:
122 |                         yield_rel = data.get_summary_data()[ticker]["yield"]
123 |                     except (TypeError, KeyError):
124 |                         yield_rel = None
125 | 
126 |                     ticker_currency = data.get_currency()
127 |                     conversion_rate = get_coversion_rate(ticker_currency)
128 | 
129 |                     new_row = {
130 |                         "ticker": ticker,
131 |                         "currency": ticker_currency,
132 |                         "long_name": long_name,
133 |                         "conversion_rate": conversion_rate,
134 |                         "yield_rel": yield_rel,
135 |                         "exdividend_date": data.get_exdividend_date(),
136 |                         "payout_ratio": data.get_payout_ratio(),
137 |                         "open_price": convert_to_target_currency(
138 |                             open_price, conversion_rate
139 |                         ),
140 |                         "current_price": convert_to_target_currency(
141 |                             data.get_current_price(), conversion_rate
142 |                         ),
143 |                         "daily_low": convert_to_target_currency(
144 |                             data.get_daily_low(), conversion_rate
145 |                         ),
146 |                         "daily_high": convert_to_target_currency(
147 |                             data.get_daily_high(), conversion_rate
148 |                         ),
149 |                         "yearly_low": convert_to_target_currency(
150 |                             data.get_yearly_low(), conversion_rate
151 |                         ),
152 |                         "yearly_high": convert_to_target_currency(
153 |                             data.get_yearly_high(), conversion_rate
154 |                         ),
155 |                         "fifty_day_moving_avg": convert_to_target_currency(
156 |                             data.get_50day_moving_avg(), conversion_rate
157 |                         ),
158 |                         "twohundred_day_moving_avg": convert_to_target_currency(
159 |                             data.get_200day_moving_avg(), conversion_rate
160 |                         ),
161 |                         "dividend_rate": convert_to_target_currency(
162 |                             data.get_dividend_rate(), conversion_rate
163 |                         ),
164 |                     }
165 |                     df = df.append(new_row, ignore_index=True)
166 |                     print(f"Successfully pulled financial data for: {ticker}")
167 | 
168 |                 except Exception as e:
169 |                     # Error Handling
170 |                     exc_type, exc_obj, exc_tb = sys.exc_info()
171 |                     fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
172 |                     print(exc_type, fname, exc_tb.tb_lineno)
173 |                     # Append Empty Row
174 |                     df = df.append(pd.Series(dtype=str), ignore_index=True)
175 |         return df
176 |     return pd.DataFrame()
177 | 
178 | 
179 | def write_value_to_excel(df):
180 |     if not df.empty:
181 |         print(f"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~")
182 |         print(f"Writing data to Excel...")
183 |         options = dict(index=False, header=False)
184 |         for data in Column:
185 |             if not data.name == "ticker":
186 |                 sht.range(START_ROW, data.value).options(**options).value = df[
187 |                     data.name
188 |                 ]
189 |         return None
190 | 
191 | 
192 | def main():
193 |     print(f"Please wait. The program is running ...")
194 |     clear_content_in_excel()
195 |     df = pull_stock_data()
196 |     write_value_to_excel(df)
197 |     print(f"Program ran successfully!")
198 |     show_msgbox("DONE!")
199 | 
200 | 
201 | # --- GET VALUES FROM EXCEL
202 | # xw.Book.caller() References the calling book
203 | # when the Python function is called from Excel via RunPython.
204 | wb = xw.Book.caller()
205 | sht = wb.sheets("Portfolio")
206 | show_msgbox = wb.macro("modMsgBox.ShowMsgBox")
207 | TARGET_CURRENCY = sht.range("TARGET_CURRENCY").value
208 | START_ROW = sht.range("TICKER").row + 1  # Plus one row after the heading
209 | LAST_ROW = sht.range(sht.cells.last_cell.row, Column.ticker.value).end("up").row
210 | sht.range("TIMESTAMP").value = timestamp()
211 | tickers = (
212 |     sht.range(START_ROW, Column.ticker.value).options(expand="down", numbers=str).value
213 | )
214 | 


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/portfolio_overview.xlsm:
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https://raw.githubusercontent.com/Sven-Bo/portfolio-tracking-excel-python/252b7c9785fd9f75b7d78c20c7aa96c228e8f7b0/portfolio_overview.xlsm


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/requirements.txt:
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1 | yahoofinancials==1.6
2 | pandas==1.2.0
3 | xlwings==0.22.2
4 | 
5 | 


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