├── .DS_Store ├── .github └── workflows │ └── nightly_test.yml ├── .gitignore ├── LICENSE ├── README.md ├── colab_notebooks ├── .DS_Store ├── Part12_All_Weather_Portfolio__optimisation_with_Crypto.ipynb ├── Part14_Exploring_Tech_Indicators_For_Stocks_and_Crypto.ipynb ├── Part17_SNP500_Fundamental_Analysis_using_AlphaVantage.ipynb ├── Part2_Finance_APIs_overview.ipynb ├── Part3_NLP_Sentiment_Analysis_for_Stocks.ipynb ├── Part4_Scrapping_EPS_Q2_20_and_Daily_Returns.ipynb ├── Part4_[2024]_Scrapping_EPS_Q2_20_and_Daily_Returns.ipynb ├── Part5_All_history_EPS_estimates_vs_actual_and_long_term_returns.ipynb ├── Part6_IPO_2020_Bubble_or_a_new_reality_[13Dec].ipynb ├── Part6_IPO_2020_Bubble_or_a_new_reality_[15Dec_by_DiKor].ipynb ├── Part7_Rerun_100_recent_IPOs_2020_2021_[5_Mar].ipynb ├── Part8_Practical_Portfolio_Optimisation.ipynb └── Part9_Macro_Indicators_vs_Stock_Indexes_Growth.ipynb ├── mlops_stocks_day_trading ├── LICENCE.md ├── README.md ├── notebooks │ ├── full_ml_flow.ipynb │ └── test_load_sqlite3.ipynb └── src │ ├── __init__.py │ ├── common │ ├── data_sources.py │ └── utils.py │ ├── load_data_mysql.py │ ├── prefect_flow.py │ └── test_load_data_sqlite3.py ├── static ├── .DS_Store └── part6_graphs │ ├── chart_1.JSON │ ├── chart_10.JSON │ ├── chart_11.JSON │ ├── chart_12.JSON │ ├── chart_2.JSON │ ├── chart_3.JSON │ ├── chart_4.JSON │ ├── chart_5.JSON │ ├── chart_6.JSON │ ├── chart_7.JSON │ ├── chart_7_1.JSON │ ├── chart_8.JSON │ ├── chart_9.JSON │ └── chart_nyse.JSON └── yt_videos_colabs ├── .DS_Store ├── PythonInvest_com_1_Python_Financial_Data_APIs_and_visualisation.ipynb ├── PythonInvest_com_2_2_EveryDay_Sentiment_Analysis_of_Financial_News_.ipynb ├── PythonInvest_com_2_Sentiment_Analysis_of_Financial_News_.ipynb ├── PythonInvest_com_3_Scraping_financial_data_Earnings_per_share_.ipynb ├── PythonInvest_com_4_Practical_Portfolio_Optimisation_(PyPortfolioOpt).ipynb ├── PythonInvest_com_5_Get_EU_ETFs_with_Selenium.ipynb └── PythonInvest_com_6_OpenAI's_for_News_Summarization.ipynb /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/realmistic/PythonInvest-basic-fin-analysis/832cafddbac0c2d97023db2f35bb531b601c0bd3/.DS_Store -------------------------------------------------------------------------------- /.github/workflows/nightly_test.yml: -------------------------------------------------------------------------------- 1 | name: NightlyTestJob 2 | on: workflow_dispatch 3 | #on: [push] 4 | #on: 5 | # schedule: 6 | # - cron: '30 1 * * *' 7 | jobs: 8 | Test-GitHUb-Actions: 9 | runs-on: ubuntu-latest 10 | steps: 11 | - run: echo "The job was automatically triggered by a ${{ github.event_name }} event." 12 | - run: echo "This job is now running on a ${{ runner.os }} server hosted by GitHub!" 13 | - run: echo "The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}." 14 | - uses: actions/checkout@v2 15 | - uses: actions/setup-python@v2 16 | with: 17 | python-version: '3.8' 18 | - name: Install python modules 19 | run: pip install nbconvert jupyter yfinance pandas-datareader matplotlib nltk 20 | - name: List files in the repository 21 | run: | 22 | ls ${{ github.workspace }}/colab_notebooks 23 | - name: Run notebooks 24 | run: jupyter nbconvert --to notebook --execute ${{ github.workspace }}/yt_videos_colabs/PythonInvest_com_2_2_EveryDay_Sentiment_Analysis_of_Financial_News_.ipynb 25 | - name: Push news 26 | run: | 27 | git config user.name github-actions 28 | git config user.email github-actions@github.com 29 | find . -name "*TSLA_news_sentiments_*.csv" -exec git add {} \; && git commit -m "update news sentiment" 30 | git push 31 | - run: echo "The ${{ github.repository }} repository has been cloned to the runner." 32 | - run: echo "The workflow is now ready to test your code on the runner." 33 | - run: echo "🍏 This job's status is ${{ job.status }}." 34 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | data/ 12 | build/ 13 | develop-eggs/ 14 | dist/ 15 | downloads/ 16 | eggs/ 17 | .eggs/ 18 | lib/ 19 | lib64/ 20 | parts/ 21 | sdist/ 22 | var/ 23 | wheels/ 24 | pip-wheel-metadata/ 25 | share/python-wheels/ 26 | *.egg-info/ 27 | .installed.cfg 28 | *.egg 29 | MANIFEST 30 | 31 | # PyInstaller 32 | # Usually these files are written by a python script from a template 33 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 34 | *.manifest 35 | *.spec 36 | 37 | # Installer logs 38 | pip-log.txt 39 | pip-delete-this-directory.txt 40 | 41 | # Unit test / coverage reports 42 | htmlcov/ 43 | .tox/ 44 | .nox/ 45 | .coverage 46 | .coverage.* 47 | .cache 48 | nosetests.xml 49 | coverage.xml 50 | *.cover 51 | *.py,cover 52 | .hypothesis/ 53 | .pytest_cache/ 54 | 55 | # Translations 56 | *.mo 57 | *.pot 58 | 59 | # Django stuff: 60 | *.log 61 | local_settings.py 62 | db.sqlite3 63 | db.sqlite3-journal 64 | 65 | # Flask stuff: 66 | instance/ 67 | .webassets-cache 68 | 69 | # Scrapy stuff: 70 | .scrapy 71 | 72 | # Sphinx documentation 73 | docs/_build/ 74 | 75 | # PyBuilder 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | .python-version 87 | 88 | # pipenv 89 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 90 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 91 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 92 | # install all needed dependencies. 93 | #Pipfile.lock 94 | 95 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 96 | __pypackages__/ 97 | 98 | # Celery stuff 99 | celerybeat-schedule 100 | celerybeat.pid 101 | 102 | # SageMath parsed files 103 | *.sage.py 104 | 105 | # Environments 106 | .env 107 | .venv 108 | env/ 109 | venv/ 110 | ENV/ 111 | env.bak/ 112 | venv.bak/ 113 | 114 | # Spyder project settings 115 | .spyderproject 116 | .spyproject 117 | 118 | # Rope project settings 119 | .ropeproject 120 | 121 | # mkdocs documentation 122 | /site 123 | 124 | # mypy 125 | .mypy_cache/ 126 | .dmypy.json 127 | dmypy.json 128 | 129 | # Pyre type checker 130 | .pyre/ 131 | 132 | .vscode/ -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020-2022 Ivan Brigida 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # PythonInvest-basic-fin-analysis 2 | :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: 3 | 4 | # Use Your Computer to Make Informed Decisions in Stock Trading 5 | 6 | **INTRO** 7 | ------------------------------------- 8 | These articles are intended to guide a delevoper, analyst, or an individual investor with some programming experience on how to use Python and Google Colab to get a financial data, analyse it, and test the investment hypothesis at scale 9 | ------------------------------------- 10 | 11 | The Colab code is used in the blog [PythonInvest](https://pythoninvest.com/blog). Please refer to the Website or posts on Medium to get the most detailed explanation. 12 | 13 | :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: :sparkles: 14 | 15 | **MEDIUM POSTS** 16 | * :one:  Part 1: [Python Environment](https://pythoninvest.com/long-read/python-environment) 17 | * :two:  Part 2: [Exploring Finance APIs](https://pythoninvest.com/long-read/exploring-finance-apis) 18 | * :three:  Part 3: [Sentiment Analysis of Financial News](https://pythoninvest.com/long-read/sentiment-analysis-of-financial-news) 19 | * :four:  Part 4: [Scraping Earnings Per Share (EPS)](https://pythoninvest.com/long-read/scraping-earnings-per-share-eps) 20 | * :five:  Part 5: [Developing a Short Term Investment Strategy Based on Earnings-Per-Share(EPS) Data](https://pythoninvest.com/long-read/developing-short-term-investment-strategy-based-on-esp) 21 | * :six:  Part 6: [Boom of IPOs in 2020](https://pythoninvest.com/long-read/boom-of-ipos-in-2020) 22 | * 7️⃣  Part 7: [Comparing IPOs from late 2020 vs. early 2021](https://pythoninvest.com/long-read/comparing-ipos-from-late-2020-2021) 23 | * 8️⃣  Part 8: [Practical Portfolio Optimisation](https://pythoninvest.com/long-read/practical-portfolio-optimisation) 24 | * 9️⃣  Part 9: [Macroeconomic Indicators Affecting Stock Market](https://pythoninvest.com/long-read/macro-indicators-affecting-stock-market) 25 | * :one::zero:  Part 10: [Passive Investing with ETFs in Europe](https://pythoninvest.com/long-read/passive-investing-with-etfs-europe) 26 | * :one::one:  Part 11: [2023 Update - Passive Investing with ETFs in Europe](https://pythoninvest.com/long-read/2023-passive-investing-with-etfs-europe) 27 | * :one::two:  Part 12: [All Weather Portfolio (of ETFs) with Crypto](https://pythoninvest.com/long-read/all-weather-portfolio-with-crypto) 28 | * :one::three:  Part 13: [Leveraging OpenAI's API for Financial News Summarization](https://pythoninvest.com/long-read/chatgpt-api-for-financial-news-summarization) 29 | * :one::four:  Part 14: [Exploring Tech Indicators For Stocks and Crypto](https://pythoninvest.com/long-read/tech-indicators-on-crypto-and-stocks) 30 | * :one::five:  Part 15: [2024 U.S. Economy Highlights & Dashboard Insights for Retail Investors](https://pythoninvest.com/long-read/2024-year-wrap-and-automatic-dashboard) 31 | * :one::six:  Part 16: [Long-term Impactful Financial News with LLMs](https://pythoninvest.com/long-read/long-term-financial-news-using-llms) 32 | * :one::seven:  Part 17: [Stock Screening Using Paid Data](https://pythoninvest.com/long-read/stock-screening-using-paid-data) 33 | 34 | **FOLDERS** 35 | 36 | **colab_notebooks** 37 | All notebooks with the code are published in the colab_notebooks folder. Please copy them and try to run. 38 | I'm happy to hear any idea to improve the code or just that you find this work useful. 39 | 40 | **static** 41 | Some colab notebooks have dumps of graphs to the JSON format (Plotly), which is inserted to the website. 42 | 43 | **yt_videos_colabs** 44 | Sometimes there is a redacted version (normally, easier and smaller) of a main colab from an article, that I use in videos on the [PythonInvest YouTube channel](https://www.youtube.com/channel/UCZb99NG1IE75c5qH2mCuqVg). 45 | 46 | 47 | Cheers,
48 | PythonInvest team 49 | -------------------------------------------------------------------------------- /colab_notebooks/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/realmistic/PythonInvest-basic-fin-analysis/832cafddbac0c2d97023db2f35bb531b601c0bd3/colab_notebooks/.DS_Store -------------------------------------------------------------------------------- /mlops_stocks_day_trading/LICENCE.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/realmistic/PythonInvest-basic-fin-analysis/832cafddbac0c2d97023db2f35bb531b601c0bd3/mlops_stocks_day_trading/LICENCE.md -------------------------------------------------------------------------------- /mlops_stocks_day_trading/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/realmistic/PythonInvest-basic-fin-analysis/832cafddbac0c2d97023db2f35bb531b601c0bd3/mlops_stocks_day_trading/README.md -------------------------------------------------------------------------------- /mlops_stocks_day_trading/notebooks/full_ml_flow.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 1 IMPORTS" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 35, 13 | "metadata": {}, 14 | "outputs": [ 15 | { 16 | "name": "stdout", 17 | "output_type": "stream", 18 | "text": [ 19 | "The autoreload extension is already loaded. To reload it, use:\n", 20 | " %reload_ext autoreload\n" 21 | ] 22 | } 23 | ], 24 | "source": [ 25 | "# Autoreload to see the changes in the Python proj files: https://stackoverflow.com/questions/5364050/reloading-submodules-in-ipython\n", 26 | "%load_ext autoreload\n", 27 | "%autoreload 2" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 36, 33 | "metadata": {}, 34 | "outputs": [], 35 | "source": [ 36 | "import pandas as pd\n", 37 | "import numpy as np\n", 38 | "import sys\n", 39 | "import os" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 37, 45 | "metadata": {}, 46 | "outputs": [ 47 | { 48 | "name": "stdout", 49 | "output_type": "stream", 50 | "text": [ 51 | "/home/realmistic/PythonInvest-basic-fin-analysis/mlops_stocks_day_trading\n" 52 | ] 53 | } 54 | ], 55 | "source": [ 56 | "# There is a problem with import in Jupiter notebook vs. the standard Python proj \n", 57 | "# https://stackoverflow.com/questions/4383571/importing-files-from-different-folder\n", 58 | "# https://github.com/microsoft/vscode-jupyter/issues/3605\n", 59 | "sys.path.append('/home/realmistic/PythonInvest-basic-fin-analysis/mlops_stocks_day_trading/')\n", 60 | "\n", 61 | "os.chdir ('//home/realmistic/PythonInvest-basic-fin-analysis/mlops_stocks_day_trading/')\n", 62 | "!pwd" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": {}, 68 | "source": [ 69 | "# 2) TEST" 70 | ] 71 | }, 72 | { 73 | "cell_type": "code", 74 | "execution_count": 38, 75 | "metadata": {}, 76 | "outputs": [], 77 | "source": [ 78 | "from dotenv import load_dotenv\n", 79 | "import os " 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 39, 85 | "metadata": {}, 86 | "outputs": [ 87 | { 88 | "data": { 89 | "text/plain": [ 90 | "True" 91 | ] 92 | }, 93 | "execution_count": 39, 94 | "metadata": {}, 95 | "output_type": "execute_result" 96 | } 97 | ], 98 | "source": [ 99 | "# get env vars for the DB connection\n", 100 | "load_dotenv()" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 40, 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [ 109 | "# print(os.environ.get('mysql_user'))\n", 110 | "# print(os.environ.get('mysql_pwd'))\n", 111 | "# print(os.environ.get('mysql_db'))\n" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 41, 117 | "metadata": {}, 118 | "outputs": [ 119 | { 120 | "name": "stdout", 121 | "output_type": "stream", 122 | "text": [ 123 | "Connected\n" 124 | ] 125 | } 126 | ], 127 | "source": [ 128 | "import MySQLdb\n", 129 | "\n", 130 | "user = os.environ.get('mysql_user')\n", 131 | "pwd = os.environ.get('mysql_pwd')\n", 132 | "db = os.environ.get('mysql_db')\n", 133 | "\n", 134 | "try:\n", 135 | " db_connection = MySQLdb.connect(\"127.0.0.1\", user, pwd, db)\n", 136 | " # If connection is not successful\n", 137 | "except:\n", 138 | " print(\"Can't connect to database\")\n", 139 | " # If Connection Is Successful\n", 140 | "print(\"Connected\")" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": 42, 146 | "metadata": {}, 147 | "outputs": [ 148 | { 149 | "name": "stdout", 150 | "output_type": "stream", 151 | "text": [ 152 | "(('8.0.30-0ubuntu0.22.04.1',),)\n" 153 | ] 154 | } 155 | ], 156 | "source": [ 157 | "# TRY EXECUTE SOME SQL_STATEMENTS\n", 158 | "\n", 159 | "cursor = db_connection.cursor()\n", 160 | "# cursor.execute(\"SELECT CURDATE();\")\n", 161 | "cursor.execute(\"SELECT VERSION()\")\n", 162 | "# cursor.execute(\"DESC top90;\")\n", 163 | "# cursor.execute(\"SHOW INDEXES stocks.top90;\")\n", 164 | "result = cursor.fetchall()\n", 165 | "cursor.close()\n", 166 | "\n", 167 | "print(result)" 168 | ] 169 | }, 170 | { 171 | "cell_type": "code", 172 | "execution_count": 43, 173 | "metadata": {}, 174 | "outputs": [], 175 | "source": [ 176 | "# Another way via sqlalchemy\n", 177 | "\n", 178 | "from sqlalchemy import create_engine\n", 179 | "import pymysql\n", 180 | "import pandas as pd" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 44, 186 | "metadata": {}, 187 | "outputs": [], 188 | "source": [ 189 | "sqlEngine = create_engine(f'mysql+pymysql://{user}:{pwd}@localhost/{db}',\n", 190 | " connect_args= dict(host='localhost', port=3306))" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 45, 196 | "metadata": {}, 197 | "outputs": [], 198 | "source": [ 199 | "conn = sqlEngine.connect()" 200 | ] 201 | }, 202 | { 203 | "cell_type": "code", 204 | "execution_count": 46, 205 | "metadata": {}, 206 | "outputs": [ 207 | { 208 | "data": { 209 | "text/plain": [ 210 | "[('8.0.30-0ubuntu0.22.04.1',)]" 211 | ] 212 | }, 213 | "execution_count": 46, 214 | "metadata": {}, 215 | "output_type": "execute_result" 216 | } 217 | ], 218 | "source": [ 219 | "conn.execute(\"SELECT VERSION()\").fetchall()" 220 | ] 221 | }, 222 | { 223 | "cell_type": "code", 224 | "execution_count": 47, 225 | "metadata": {}, 226 | "outputs": [ 227 | { 228 | "data": { 229 | "text/plain": [ 230 | "[('Date', 'datetime', 'YES', 'MUL', None, ''),\n", 231 | " ('Open', 'double', 'YES', '', None, ''),\n", 232 | " ('High', 'double', 'YES', '', None, ''),\n", 233 | " ('Low', 'double', 'YES', '', None, ''),\n", 234 | " ('Close', 'double', 'YES', '', None, ''),\n", 235 | " ('Adj Close', 'double', 'YES', '', None, ''),\n", 236 | " ('Volume', 'bigint', 'YES', '', None, '')]" 237 | ] 238 | }, 239 | "execution_count": 47, 240 | "metadata": {}, 241 | "output_type": "execute_result" 242 | } 243 | ], 244 | "source": [ 245 | "conn.execute(\"DESC top90;\").fetchall()\n", 246 | "# conn.execute(\"SHOW FULL COLUMNS FROM top90;\").fetchall()" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 48, 252 | "metadata": {}, 253 | "outputs": [ 254 | { 255 | "name": "stdout", 256 | "output_type": "stream", 257 | "text": [ 258 | "name 'data' is not defined\n" 259 | ] 260 | } 261 | ], 262 | "source": [ 263 | "tableName = 'top90'\n", 264 | "try:\n", 265 | " frame = data.to_sql(tableName, conn, if_exists='fail');\n", 266 | "except ValueError as vx:\n", 267 | " print(vx)\n", 268 | "except Exception as ex: \n", 269 | " print(ex)\n", 270 | "else:\n", 271 | " print(\"Table %s created successfully.\"%tableName); " 272 | ] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "execution_count": 49, 277 | "metadata": {}, 278 | "outputs": [ 279 | { 280 | "data": { 281 | "text/plain": [ 282 | "[('Date', 'datetime', 'YES', 'MUL', None, ''),\n", 283 | " ('Open', 'double', 'YES', '', None, ''),\n", 284 | " ('High', 'double', 'YES', '', None, ''),\n", 285 | " ('Low', 'double', 'YES', '', None, ''),\n", 286 | " ('Close', 'double', 'YES', '', None, ''),\n", 287 | " ('Adj Close', 'double', 'YES', '', None, ''),\n", 288 | " ('Volume', 'bigint', 'YES', '', None, '')]" 289 | ] 290 | }, 291 | "execution_count": 49, 292 | "metadata": {}, 293 | "output_type": "execute_result" 294 | } 295 | ], 296 | "source": [ 297 | "conn.execute(\"DESC top90\").fetchall()" 298 | ] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": 50, 303 | "metadata": {}, 304 | "outputs": [ 305 | { 306 | "data": { 307 | "text/plain": [ 308 | "[(datetime.datetime(2021, 1, 4, 0, 0), 87.87699890136719, 88.03250122070312, 85.39250183105469, 86.41200256347656, 86.41200256347656, 38038000),\n", 309 | " (datetime.datetime(2021, 1, 5, 0, 0), 86.25, 87.38349914550781, 85.90074920654297, 87.0459976196289, 87.0459976196289, 22906000),\n", 310 | " (datetime.datetime(2021, 1, 6, 0, 0), 85.13150024414062, 87.4000015258789, 84.94999694824219, 86.7645034790039, 86.7645034790039, 52042000),\n", 311 | " (datetime.datetime(2021, 1, 7, 0, 0), 87.00299835205078, 89.41999816894531, 86.85250091552734, 89.36250305175781, 89.36250305175781, 45300000),\n", 312 | " (datetime.datetime(2021, 1, 8, 0, 0), 89.39900207519531, 90.49199676513672, 88.67675018310547, 90.3604965209961, 90.3604965209961, 41012000),\n", 313 | " (datetime.datetime(2021, 1, 11, 0, 0), 89.30349731445312, 89.71575164794922, 88.0260009765625, 88.33599853515625, 88.33599853515625, 24194000),\n", 314 | " (datetime.datetime(2021, 1, 12, 0, 0), 87.69599914550781, 88.9020004272461, 86.2655029296875, 87.32749938964844, 87.32749938964844, 27140000),\n", 315 | " (datetime.datetime(2021, 1, 13, 0, 0), 86.92900085449219, 88.25174713134766, 86.90049743652344, 87.72000122070312, 87.72000122070312, 21882000),\n", 316 | " (datetime.datetime(2021, 1, 14, 0, 0), 87.68099975585938, 88.75050354003906, 86.66999816894531, 87.00900268554688, 87.00900268554688, 23590000),\n", 317 | " (datetime.datetime(2021, 1, 15, 0, 0), 86.90950012207031, 87.80000305175781, 86.07749938964844, 86.80950164794922, 86.80950164794922, 26844000)]" 318 | ] 319 | }, 320 | "execution_count": 50, 321 | "metadata": {}, 322 | "output_type": "execute_result" 323 | } 324 | ], 325 | "source": [ 326 | "conn.execute(\"SELECT * FROM top90 limit 10\").fetchall()" 327 | ] 328 | } 329 | ], 330 | "metadata": { 331 | "kernelspec": { 332 | "display_name": "Python 3 (ipykernel)", 333 | "language": "python", 334 | "name": "python3" 335 | }, 336 | "language_info": { 337 | "codemirror_mode": { 338 | "name": "ipython", 339 | "version": 3 340 | }, 341 | "file_extension": ".py", 342 | "mimetype": "text/x-python", 343 | "name": "python", 344 | "nbconvert_exporter": "python", 345 | "pygments_lexer": "ipython3", 346 | "version": "3.9.12" 347 | }, 348 | "vscode": { 349 | "interpreter": { 350 | "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" 351 | } 352 | } 353 | }, 354 | "nbformat": 4, 355 | "nbformat_minor": 2 356 | } 357 | -------------------------------------------------------------------------------- /mlops_stocks_day_trading/src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/realmistic/PythonInvest-basic-fin-analysis/832cafddbac0c2d97023db2f35bb531b601c0bd3/mlops_stocks_day_trading/src/__init__.py -------------------------------------------------------------------------------- /mlops_stocks_day_trading/src/common/data_sources.py: -------------------------------------------------------------------------------- 1 | # All companies >=$50b market cap (15-Aug-2021) https://www.nasdaq.com/market-activity/stocks/screener 2 | TICKERS = {'AAPL', 'MSFT', 'AMZN', 'GOOG', 'FB', 'PTR', 'TSLA', 'BABA', 'TSM', 'BRK-B', 'V', 'JPM', 3 | 'JNJ', 'WMT', 'NVDA', 'MA', 'UNH', 'HD', 'BAC', 'DIS', 'PG', 'PYPL', 'ASML', 'CMCSA', 'XOM', 4 | 'ADBE', 'KO', 'VZ', 'INTC', 'TM', 'ORCL', 'CSCO', 'NFLX', 'PFE', 'NKE', 'T', 'ABT', 'CRM', 5 | 'PEP', 'ABBV', 'CVX', 'MRK', 'LLY', 'WFC', 'ACN', 'AVGO', 'UPS', 'TMO', 'DHR', 'TXN', 'MCD', 6 | 'MDT', 'COST', 'SAP', 'PDD', 'C', 'UL', 'LIN', 'HON', 'SHOP', 'PM', 'AZN', 'QCOM', 'BMY', 'UNP', 7 | 'RY', 'NEE', 'NVO', 'BA', 'MS', 'AMGN', 'HDB', 'LOW', 'SNY', 'BLK', 'TD', 'SBUX', 'CHTR', 'RTX', 8 | 'CAT', 'SCHW', 'HSBC', 'IBM', 'AXP', 'AMAT', 'BUD', 'SONY', 'INTU', 'GE', 'MMM', 'AMT', 'CVS', 9 | 'TGT', 'DEO', 'DE', 'EL', 'BX', 'LMT', 'NVS', 'TMUS', 'BHP', 'BBL', 'SE', 'RDS-A', 'RIO', 'MRNA', 'GS', 10 | 'AMD', 'SONY', 'SQ', 'ISRG', 'TTE', 'SNAP', 'NOW', 'JD', 'SPGI', 'GSK', 'ZM', 'VALE', 'INFY', 'SYK', 11 | 'PLD', 'PLD', 'ZTS', 'BAM', 'MELI', 'BNTX', 'ABNB', 'ADP', 'BKNG', 'MO', 'ANTM', 'GILD', 'MDLZ', 'SNOW', 'USB', 'BTI', 12 | 'TJX', 'BP', 'TEAM', 'CCI', 'FIS', 'LRCX', 'DUK', 'PNC', 'SHW', 'CB', 'MU', 'UBER', 'ENB', 'COF', 'BNS', 'GM', 'HCA', 'MMC', 13 | 'TFC', 'DELL', 'CSX', 'CNI', 'ILMN', 'ABB', 'COP', 'CME', 'FDX', 'PBR', 'FISV', 'ITW', 'ADSK', 'EQIX', 'CI', 'MUFG', 'MCO', 14 | 'BDX', 'EW', 'WBK', 'SO', 'EQNR', 'STLA', 'NIO', 'IBN', 'CL', 'SAN', 'REGN', 'ETN', 'BMO', 'NSC', 'VMW', 'ICE', 'ATVI', 'TWLO', 15 | 'DASH', 'WM', 'ECL', 'BSX', 'D', 'ADI', 'AON', 'CVNA', 'EMR', 'APD', 'NTES', 'UBS', 'CPNG', 'RELX', 'WDAY', 'NOC', 'BSBR', 'DOCU', 16 | 'ITUB', 'IDXX', 'NXPI', 'AMX', 'AMOV', 'TRI', 'PGR', 'HMC', 'RACE', 'SNP', 'FCX', 'DG', 'GD', 'PUK', 'LULU', 'PSA', 'CRWD', 'ALGN', 17 | 'COIN', 'F', 'ING', 'MET', 'CM', 'CMG', 'BIDU', 'HUM', 'TAK', 'SCCO', 'MSCI', 'MNST', 'JCI', 'TWTR', 'BIIB', 'ROP', 'FTNT', 'EBAY', 18 | 'VRTX', 'GPN', 'KLAC', 'TEL'} 19 | 20 | 21 | # Macro economic indicators (mostly US) from the FRED database 22 | # Detailed info on each indicator check on web: https://fred.stlouisfed.org/series/ 23 | # DOC with the metrics and external exploratory Colab: https://docs.google.com/document/d/1Cf4C3Xz4_yitlzPaLEknHoDlw7KMXey4c49kZ7ucQEE/edit?usp=sharing 24 | 25 | FRED_INDICATORS = ['GDP', 'GDPC1', 'GDPPOT', 'NYGDPMKTPCDWLD', # 1. Growth 26 | 'CPIAUCSL', 'CPILFESL', 'GDPDEF', # 2. Prices and Inflation 27 | 'M1SL', 'WM1NS', 'WM2NS', 'M1V', 'M2V', 'WALCL', # 3. Money Supply 28 | 'DFF', 'DTB3', 'DGS5', 'DGS10', 'DGS30', 'T5YIE', # 4. Interest Rates 29 | 'T10YIE', 'T5YIFR', 'TEDRATE', 'DPRIME', # 4. Interest Rates 30 | 'UNRATE', 'NROU', 'CIVPART', 'EMRATIO', # 5. Employment 31 | 'UNEMPLOY', 'PAYEMS', 'MANEMP', 'ICSA', 'IC4WSA', # 5. Employment 32 | 'CDSP', 'MDSP', 'FODSP', 'DSPIC96', 'PCE', 'PCEDG', # 6. Income and Expenditure 33 | 'PSAVERT', 'DSPI', 'RSXFS', # 6. Income and Expenditure 34 | 'INDPRO', 'TCU', 'HOUST', 'GPDI', 'CP', 'STLFSI2', # 7. Other indicators 35 | 'DCOILWTICO', 'DTWEXAFEGS', 'DTWEXBGS', # 7. Other indicators 36 | 'GFDEBTN', 'GFDEGDQ188S', # 8. Gov-t debt 37 | # 9. Additional indicators from IVAN 38 | 'DEXUSEU', 'GVZCLS', 'VIXCLS', 'DIVIDEND', 39 | # 9. Additional indicators from IVAN 40 | 'MORTGAGE30US', 'SPCS20RSA' 41 | ] 42 | 43 | # Macro Indicators from QUANDL 44 | QUANDL_INDICATORS = {'BCHAIN/MKPRU', 'USTREASURY/YIELD', 'USTREASURY/REALYIELD', # 9. Additional indicators from IVAN 45 | # 9. Additional indicators from IVAN 46 | 'MULTPL/SHILLER_PE_RATIO_MONTH', 'LBMA/GOLD' 47 | } 48 | 49 | # update period for each ind (Y=Yearly, Q=Quarterly, M=Monthly, W=Weekly, D=Daily) 50 | INDICATORS_PERIODS = { # 1. Growth 51 | 'GDP': 'Q', 'GDPC1': 'Q', 'GDPPOT': 'Q', 'NYGDPMKTPCDWLD': 'Y', 52 | # 2. Prices and Inflation 53 | 'CPIAUCSL': 'M', 'CPILFESL': 'M', 'GDPDEF': 'Q', 54 | # 3. Money Supply 55 | 'M1SL': 'M', 'WM1NS': 'W', 'WM2NS': 'W', 'M1V': 'Q', 'M2V': 'Q', 'WALCL': 'W', 56 | # 4. Interest Rates 57 | 'DFF': 'D', 'DTB3': 'D', 'DGS5': 'D', 'DGS10': 'D', 'DGS30': 'D', 'T5YIE': 'D', 58 | # 4. Interest Rates 59 | 'T10YIE': 'D', 'T5YIFR': 'D', 'TEDRATE': 'D', 'DPRIME': 'D', 60 | # 5. Employment 61 | 'UNRATE': 'M', 'NROU': 'Q', 'CIVPART': 'M', 'EMRATIO': 'M', 62 | # 5. Employment 63 | 'UNEMPLOY': 'M', 'PAYEMS': 'M', 'MANEMP': 'M', 'ICSA': 'W', 'IC4WSA': 'W', 64 | # 6. Income and Expenditure 65 | 'CDSP': 'Q', 'MDSP': 'Q', 'FODSP': 'Q', 'DSPIC96': 'M', 'PCE': 'M', 'PCEDG': 'M', 66 | # 6. Income and Expenditure 67 | 'PSAVERT': 'M', 'DSPI': 'M', 'RSXFS': 'M', 68 | # 7. Other indicators 69 | 'INDPRO': 'M', 'TCU': 'M', 'HOUST': 'M', 'GPDI': 'Q', 'CP': 'Q', 'STLFSI2': 'W', 70 | # 7. Other indicators 71 | 'DCOILWTICO': 'D', 'DTWEXAFEGS': 'D', 'DTWEXBGS': 'D', 72 | # 8. Gov-t debt 73 | 'GFDEBTN': 'Q', 'GFDEGDQ188S': 'Q', 74 | # 9. FRED: Additional indicators from IVAN 75 | 'DEXUSEU': 'D', 'GVZCLS': 'D', 'VIXCLS': 'D', 'DIVIDEND': 'Q', 76 | # 9. FRED: Additional indicators from IVAN 77 | 'MORTGAGE30US': 'W', 'SPCS20RSA': 'M', 78 | # 9.QUANDL: Additional indicators from IVAN 79 | 'BCHAIN_MKPRU': 'D', 'USTREASURY_YIELD': 'D', 'USTREASURY_REALYIELD': 'D', 80 | 'MULTPL_SHILLER_PE_RATIO_MONTH': 'M', 'LBMA_GOLD': 'D' 81 | } 82 | -------------------------------------------------------------------------------- /mlops_stocks_day_trading/src/common/utils.py: -------------------------------------------------------------------------------- 1 | # retry func() call for n times (default is 5) 2 | def retry(func, times=5): 3 | for _ in range(times): 4 | try: 5 | return func() 6 | except Exception as e: 7 | print(e) 8 | continue 9 | -------------------------------------------------------------------------------- /mlops_stocks_day_trading/src/load_data_mysql.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import datetime 3 | 4 | # API finance data 5 | import yfinance as yf 6 | 7 | # db connectivity 8 | from sqlalchemy import create_engine 9 | import pymysql 10 | 11 | import os 12 | 13 | # FOR loading local secrets in .env files: 14 | # https://www.realpythonproject.com/3-ways-to-store-and-read-credentials-locally-in-python/ 15 | from dotenv import load_dotenv 16 | 17 | # data_sources and utils 18 | import common.data_sources 19 | import common.utils 20 | 21 | # Data storage class 22 | class DataRepositoryMySQL: 23 | tickers_df: pd.DataFrame = None # main in-memory storage of tickers stats 24 | ALL_TICKERS: data_sources.TICKERS 25 | 26 | # CLASS CONSTRUCTOR 27 | def __init__( 28 | self, db_user: str, db_pwd: str, db_database: str, db_port: int = 3306 29 | ): 30 | self.ticker_df = None 31 | self.TICKERS = data_sources.TICKERS 32 | 33 | # MYSQL : make a conn string for SQLAlchemy engine 34 | conn_string = f"mysql+pymysql://{db_user}:{db_pwd}@localhost/{db_database}" 35 | sqlEngine = create_engine( 36 | conn_string, connect_args=dict(host="localhost", port=db_port) 37 | ) 38 | 39 | # SQLLITE3 40 | self.db_conn_sqlite = None 41 | self.db_cur_sqlite = None 42 | # list of existing db files 43 | self.existing_db_files = [f for f in os.listdir("data/") if f.endswith(".db")] 44 | print(self.existing_db_files) 45 | # Create a db file if not there : https://docs.python.org/3/library/sqlite3.html#sqlite3-tutorial 46 | self.db_conn_sqlite = sqlite3.connect(db_path) 47 | self.db_cur_sqlite = self.db_conn_sqlite.cursor() 48 | 49 | # if there is a table ==> let's load its contents to df_tickers, otherwise let's create it 50 | if self._db_check_table_exists("tickers"): 51 | self._db_read_all_records_from_db() 52 | else: # not self._db_check_table_exists('tickers') ==TRUE 53 | self._db_create_table_tickers() 54 | 55 | # MYSQL 56 | load_dotenv() # load env vars 57 | 58 | try: 59 | self.db_conn_mysql = mysql.connector.connect( 60 | user=os.environ.get("mysql_user"), database=os.environ.get("mysql_db") 61 | ) 62 | except mysql.connector.Error as err: 63 | if err.errno == errorcode.ER_ACCESS_DENIED_ERROR: 64 | print("Something is wrong with your user name or password") 65 | elif err.errno == errorcode.ER_BAD_DB_ERROR: 66 | print("Database does not exist") 67 | else: 68 | print(err) 69 | else: 70 | self.db_conn_mysql.close() 71 | 72 | # CLASS DESTRUCTOR 73 | def __del__(self): 74 | # close cursor 75 | self.db_conn_sqlite.close() 76 | self.db_conn_mysql.close() 77 | 78 | # PROTECTED: CHECK IF A TABLE EXISTS 79 | def _db_check_table_exists(self, table_name: str = "tickers"): 80 | sql = f""" SELECT count(name) 81 | FROM sqlite_master 82 | WHERE type='table' 83 | AND name='{table_name}' """ 84 | 85 | self.db_cur_sqlite.execute(sql) 86 | if self.db_cur_sqlite.fetchone()[0] == 1: 87 | return True 88 | else: 89 | return False 90 | 91 | # PROTECTED: CREATE A TABLE IF NOT EXISTS (SQLLITE3) 92 | def _db_create_table_tickers(self): 93 | # https://stackoverflow.com/questions/734689/sqlite-primary-key-on-multiple-columns 94 | sql = """CREATE TABLE "tickers" ( 95 | "Date" TIMESTAMP, 96 | "Ticker" TEXT, 97 | "Open" REAL, 98 | "High" REAL, 99 | "Low" REAL, 100 | "Close" REAL, 101 | "Adj Close" REAL, 102 | "Volume" INTEGER, 103 | PRIMARY KEY (Date, Ticker) 104 | )""" 105 | self.db_cur_sqlite.execute(sql) 106 | self.db_conn_sqlite.commit() 107 | 108 | def _db_read_all_records_from_db(self): 109 | try: 110 | self.tickers_df = pd.read_sql("select * from tickers", self.db_conn_sqlite) 111 | self.tickers_df["Date"] = pd.to_datetime( 112 | self.tickers_df["Date"] 113 | ) # need correct type 'datetime' and not object 114 | except Exception as e: 115 | print(f"FAILED TO READ ALL RECORDS FROM DB: {e}") 116 | 117 | # PROTECTED: fetch stats from the API for 1 ticker and store to the DB 118 | def _fetch_ticker_incremental(self, ticker: str, period_days=1): 119 | max_data_date = None # by default let's assume there is no data in DB 120 | incremental_df: pd.DataFrame = None 121 | 122 | if self.tickers_df is not None: 123 | max_data_date = self.tickers_df.Date.max() 124 | 125 | if max_data_date is None: 126 | max_data_date = datetime.datetime.strptime("2021-01-01", "%Y-%m-%d") 127 | 128 | start_date = max_data_date + datetime.timedelta(days=1) 129 | end_date = max_data_date + datetime.timedelta(days=1 + period_days) 130 | 131 | # try 5 times to get the incr. data for 1 ticker 132 | incremental_df = retry( 133 | lambda: yf.download(ticker, start=start_date, end=end_date) 134 | ) 135 | incremental_df["Ticker"] = ticker # define a new field 136 | 137 | print( 138 | f"downloaded records {incremental_df.shape} with min_date {incremental_df.index.min()} and max_date {incremental_df.index.max()}" 139 | ) 140 | 141 | # https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_sql.html 142 | try: 143 | incremental_df.to_sql( 144 | name="tickers", 145 | index=True, 146 | # index_label='', 147 | if_exists="append", 148 | con=self.db_conn_sqlite, 149 | ) 150 | self.db_conn_sqlite.commit() 151 | 152 | incremental_df["Date"] = incremental_df.index 153 | # append_local_df with the new data 154 | # self._db_read_all_records_from_db() 155 | self.tickers_df = self.tickers_df.append(incremental_df, ignore_index=True) 156 | 157 | except Exception as e: 158 | print(f"SAVE TO DB IS NOT SUCCESSFUL: {e}") 159 | 160 | def fetch_ticker_incremental(self, ticker: str, period_days=1): 161 | self._fetch_ticker_incremental(ticker, period_days) 162 | print(f"Successfully fetched {ticker} for more {period_days}") 163 | 164 | -------------------------------------------------------------------------------- /mlops_stocks_day_trading/src/prefect_flow.py: -------------------------------------------------------------------------------- 1 | from prefect import flow, task 2 | from prefect.task_runners import SequentialTaskRunner 3 | 4 | 5 | from load_data import DataRepository 6 | 7 | @task (retries=3) 8 | def get_new_tickers_data(): 9 | repo = DataRepository() 10 | repo.fetch_ticker_incremental(ticker='GOOG', period_days=7) 11 | 12 | @flow(name = "Data Flow", 13 | task_runner = SequentialTaskRunner()) 14 | def full_cycle(): 15 | """Call all data APIs to store the stats to DB""" 16 | get_new_tickers_data() 17 | return 18 | 19 | if __name__ == "__main__": 20 | full_cycle() 21 | -------------------------------------------------------------------------------- /mlops_stocks_day_trading/src/test_load_data_sqlite3.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | 3 | import os 4 | 5 | # databases 6 | import sqlite3 7 | 8 | import datetime 9 | import yfinance as yf 10 | 11 | # data_sources CONSTANTS : TICKERS, etc. 12 | import data_sources 13 | 14 | # retry func() call for n times (default is 5) 15 | def retry(func, times=5): 16 | for _ in range(times): 17 | try: 18 | return func() 19 | except Exception as e: 20 | print(e) 21 | continue 22 | 23 | 24 | # Data storage class 25 | class DataRepositorySqlLite: 26 | tickers_df: pd.DataFrame = None # main in-memory storage of tickers stats 27 | ALL_TICKERS: data_sources.TICKERS 28 | 29 | # CLASS CONSTRUCTOR 30 | def __init__(self, db_path="data/tickers.db"): 31 | self.ticker_df = None 32 | self.TICKERS = data_sources.TICKERS 33 | 34 | # SQLLITE3 35 | self.db_conn_sqlite = None 36 | self.db_cur_sqlite = None 37 | # list of existing db files 38 | self.existing_db_files = [f for f in os.listdir("data/") if f.endswith(".db")] 39 | print(self.existing_db_files) 40 | # Create a db file if not there : https://docs.python.org/3/library/sqlite3.html#sqlite3-tutorial 41 | self.db_conn_sqlite = sqlite3.connect(db_path) 42 | self.db_cur_sqlite = self.db_conn_sqlite.cursor() 43 | 44 | # if there is a table ==> let's load its contents to df_tickers, otherwise let's create it 45 | if self._db_check_table_exists("tickers"): 46 | self._db_read_all_records_from_db() 47 | else: # not self._db_check_table_exists('tickers') ==TRUE 48 | self._db_create_table_tickers() 49 | 50 | # CLASS DESTRUCTOR 51 | def __del__(self): 52 | # close cursor 53 | self.db_conn_sqlite.close() 54 | 55 | # PROTECTED: CHECK IF A TABLE EXISTS 56 | def _db_check_table_exists(self, table_name: str = "tickers"): 57 | sql = f""" SELECT count(name) 58 | FROM sqlite_master 59 | WHERE type='table' 60 | AND name='{table_name}' """ 61 | 62 | self.db_cur_sqlite.execute(sql) 63 | if self.db_cur_sqlite.fetchone()[0] == 1: 64 | return True 65 | else: 66 | return False 67 | 68 | # PROTECTED: CREATE A TABLE IF NOT EXISTS (SQLLITE3) 69 | def _db_create_table_tickers(self): 70 | # https://stackoverflow.com/questions/734689/sqlite-primary-key-on-multiple-columns 71 | sql = """CREATE TABLE "tickers" ( 72 | "Date" TIMESTAMP, 73 | "Ticker" TEXT, 74 | "Open" REAL, 75 | "High" REAL, 76 | "Low" REAL, 77 | "Close" REAL, 78 | "Adj Close" REAL, 79 | "Volume" INTEGER, 80 | PRIMARY KEY (Date, Ticker) 81 | )""" 82 | self.db_cur_sqlite.execute(sql) 83 | self.db_conn_sqlite.commit() 84 | 85 | def _db_read_all_records_from_db(self): 86 | try: 87 | self.tickers_df = pd.read_sql("select * from tickers", self.db_conn_sqlite) 88 | self.tickers_df["Date"] = pd.to_datetime( 89 | self.tickers_df["Date"] 90 | ) # need correct type 'datetime' and not object 91 | except Exception as e: 92 | print(f"FAILED TO READ ALL RECORDS FROM DB: {e}") 93 | 94 | # PROTECTED: fetch stats from the API for 1 ticker and store to the DB 95 | def _fetch_ticker_incremental(self, ticker: str, period_days=1): 96 | max_data_date = None # by default let's assume there is no data in DB 97 | incremental_df: pd.DataFrame = None 98 | 99 | if self.tickers_df is not None: 100 | max_data_date = self.tickers_df.Date.max() 101 | 102 | if max_data_date is None: 103 | max_data_date = datetime.datetime.strptime("2021-01-01", "%Y-%m-%d") 104 | 105 | start_date = max_data_date + datetime.timedelta(days=1) 106 | end_date = max_data_date + datetime.timedelta(days=1 + period_days) 107 | 108 | # try 5 times to get the incr. data for 1 ticker 109 | incremental_df = retry( 110 | lambda: yf.download(ticker, start=start_date, end=end_date) 111 | ) 112 | incremental_df["Ticker"] = ticker # define a new field 113 | 114 | print( 115 | f"downloaded records {incremental_df.shape} with min_date {incremental_df.index.min()} and max_date {incremental_df.index.max()}" 116 | ) 117 | 118 | # https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_sql.html 119 | try: 120 | incremental_df.to_sql( 121 | name="tickers", 122 | index=True, 123 | # index_label='', 124 | if_exists="append", 125 | con=self.db_conn_sqlite, 126 | ) 127 | self.db_conn_sqlite.commit() 128 | 129 | incremental_df["Date"] = incremental_df.index 130 | # append_local_df with the new data 131 | # self._db_read_all_records_from_db() 132 | self.tickers_df = self.tickers_df.append(incremental_df, ignore_index=True) 133 | 134 | except Exception as e: 135 | print(f"SAVE TO DB IS NOT SUCCESSFUL: {e}") 136 | 137 | def fetch_ticker_incremental(self, ticker: str, period_days=1): 138 | self._fetch_ticker_incremental(ticker, period_days) 139 | print(f"Successfully fetched {ticker} for more {period_days}") 140 | 141 | -------------------------------------------------------------------------------- /static/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/realmistic/PythonInvest-basic-fin-analysis/832cafddbac0c2d97023db2f35bb531b601c0bd3/static/.DS_Store -------------------------------------------------------------------------------- /static/part6_graphs/chart_1.JSON: -------------------------------------------------------------------------------- 1 | var graphs = {"data": [{"domain": {"x": [0.0, 1.0], "y": [0.0, 1.0]}, "hovertemplate": "Sector=%{label}
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