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Updated_10
Browse files- LSTM_model.keras +0 -0
- __pycache__/feature_pipeline.cpython-311.pyc +0 -0
- __pycache__/feature_view.cpython-311.pyc +0 -0
- __pycache__/news_preprocessing.cpython-311.pyc +0 -0
- __pycache__/stock_preprocessing.cpython-311.pyc +0 -0
- feature_pipeline.ipynb +221 -134
- feature_view_freddie.ipynb → feature_view.ipynb +121 -15
- feature_view.py +103 -0
- historical_news.ipynb +1 -1
- historical_stock.ipynb +18 -6
- requirements.txt +3 -1
- stock_preprocessing.py +177 -0
LSTM_model.keras
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__pycache__/feature_pipeline.cpython-311.pyc
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__pycache__/stock_preprocessing.cpython-311.pyc
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"Requirement already satisfied: great_expectations==0.18.12 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (0.18.12)\n",
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"Requirement already satisfied: Click>=7.1.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (8.1.7)\n",
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"Requirement already satisfied: cryptography>=3.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (42.0.6)\n",
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"Requirement already satisfied: Ipython>=7.16.3 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from great_expectations==0.18.12) (8.22.2)\n",
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"Requirement already satisfied: babel>=2.10 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jupyterlab-server<3,>=2.22.1->notebook>=6.4.10->great_expectations==0.18.12) (2.14.0)\n",
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"Requirement already satisfied: json5>=0.9.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jupyterlab-server<3,>=2.22.1->notebook>=6.4.10->great_expectations==0.18.12) (0.9.25)\n",
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"Requirement already satisfied: executing>=1.2.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from stack-data->Ipython>=7.16.3->great_expectations==0.18.12) (2.0.1)\n",
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"Requirement already satisfied: asttokens>=2.1.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from stack-data->Ipython>=7.16.3->great_expectations==0.18.12) (2.4.1)\n",
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"Requirement already satisfied: pure-eval in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from stack-data->Ipython>=7.16.3->great_expectations==0.18.12) (0.2.2)\n",
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"Requirement already satisfied: rfc3986-validator>=0.1.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.1.1)\n",
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"Requirement already satisfied: beautifulsoup4 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (4.12.3)\n",
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"Requirement already satisfied: bleach!=5.0.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (6.1.0)\n",
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"Requirement already satisfied: defusedxml in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.7.1)\n",
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"Requirement already satisfied: jupyterlab-pygments in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.3.0)\n",
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"Requirement already satisfied: nbclient>=0.5.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.10.0)\n",
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"Requirement already satisfied: pandocfilters>=1.4.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.5.1)\n",
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"Requirement already satisfied: tinycss2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.3.0)\n",
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| 129 |
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"Requirement already satisfied: webencodings in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from bleach!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (0.5.1)\n",
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| 130 |
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"Requirement already satisfied: fqdn in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.5.1)\n",
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| 131 |
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"Requirement already satisfied: isoduration in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (20.11.0)\n",
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"Requirement already satisfied: uri-template in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.3.0)\n",
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| 133 |
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"Requirement already satisfied: webcolors>=1.11 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.13)\n",
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| 134 |
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"Requirement already satisfied: soupsieve>1.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from beautifulsoup4->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (2.5)\n",
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| 135 |
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"Requirement already satisfied: arrow>=0.15.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (1.3.0)\n",
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"Requirement already satisfied: types-python-dateutil>=2.8.10 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from arrow>=0.15.0->isoduration->jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.9.0->jupyter-server<3,>=2.4.0->notebook>=6.4.10->great_expectations==0.18.12) (2.9.0.20240316)\n"
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| 137 |
]
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}
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],
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},
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{
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"cell_type": "code",
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+
"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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},
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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| 170 |
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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| 174 |
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"Requirement already satisfied: modal in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (0.62.141)\n",
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"Requirement already satisfied: aiohttp in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (3.9.5)\n",
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"Requirement already satisfied: aiostream~=0.5.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (0.5.2)\n",
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| 177 |
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"Requirement already satisfied: certifi in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (2024.2.2)\n",
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| 178 |
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"Requirement already satisfied: click>=8.1.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (8.1.7)\n",
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| 179 |
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"Requirement already satisfied: fastapi in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (0.111.0)\n",
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"Requirement already satisfied: grpclib==0.4.7 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (0.4.7)\n",
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"Requirement already satisfied: protobuf!=4.24.0,<5.0,>=3.19 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (4.25.3)\n",
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| 182 |
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"Requirement already satisfied: rich>=12.0.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (13.7.1)\n",
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"Requirement already satisfied: synchronicity~=0.6.6 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (0.6.7)\n",
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"Requirement already satisfied: toml in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (0.10.2)\n",
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"Requirement already satisfied: typer>=0.9 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (0.12.3)\n",
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| 186 |
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"Requirement already satisfied: types-certifi in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (2021.10.8.3)\n",
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| 187 |
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"Requirement already satisfied: types-toml in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (0.10.8.20240310)\n",
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| 188 |
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"Requirement already satisfied: watchfiles in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (0.21.0)\n",
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| 189 |
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"Requirement already satisfied: typing-extensions~=4.6 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from modal) (4.11.0)\n",
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"Requirement already satisfied: h2<5,>=3.1.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from grpclib==0.4.7->modal) (4.1.0)\n",
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"Requirement already satisfied: multidict in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from grpclib==0.4.7->modal) (6.0.5)\n",
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"Requirement already satisfied: colorama in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from click>=8.1.0->modal) (0.4.6)\n",
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"Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from rich>=12.0.0->modal) (3.0.0)\n",
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"Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from rich>=12.0.0->modal) (2.17.2)\n",
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"Requirement already satisfied: sigtools==4.0.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from synchronicity~=0.6.6->modal) (4.0.1)\n",
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"Requirement already satisfied: attrs in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from sigtools==4.0.1->synchronicity~=0.6.6->modal) (23.2.0)\n",
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"Requirement already satisfied: shellingham>=1.3.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from typer>=0.9->modal) (1.5.4)\n",
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"Requirement already satisfied: aiosignal>=1.1.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from aiohttp->modal) (1.3.1)\n",
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"Requirement already satisfied: frozenlist>=1.1.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from aiohttp->modal) (1.4.1)\n",
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"Requirement already satisfied: yarl<2.0,>=1.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from aiohttp->modal) (1.9.4)\n",
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"Requirement already satisfied: starlette<0.38.0,>=0.37.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (0.37.2)\n",
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"Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,!=2.1.0,<3.0.0,>=1.7.4 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (2.7.1)\n",
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"Requirement already satisfied: fastapi-cli>=0.0.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (0.0.2)\n",
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"Requirement already satisfied: httpx>=0.23.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (0.27.0)\n",
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"Requirement already satisfied: jinja2>=2.11.2 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (3.1.3)\n",
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"Requirement already satisfied: python-multipart>=0.0.7 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (0.0.9)\n",
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"Requirement already satisfied: ujson!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0,>=4.0.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (5.9.0)\n",
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"Requirement already satisfied: orjson>=3.2.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (3.10.3)\n",
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"Requirement already satisfied: email_validator>=2.0.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from fastapi->modal) (2.1.1)\n",
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"Requirement already satisfied: uvicorn>=0.12.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from uvicorn[standard]>=0.12.0->fastapi->modal) (0.29.0)\n",
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"Requirement already satisfied: anyio>=3.0.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from watchfiles->modal) (4.3.0)\n",
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"Requirement already satisfied: idna>=2.8 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from anyio>=3.0.0->watchfiles->modal) (3.7)\n",
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"Requirement already satisfied: sniffio>=1.1 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from anyio>=3.0.0->watchfiles->modal) (1.3.1)\n",
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"Requirement already satisfied: dnspython>=2.0.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from email_validator>=2.0.0->fastapi->modal) (2.6.1)\n",
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"Requirement already satisfied: hyperframe<7,>=6.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from h2<5,>=3.1.0->grpclib==0.4.7->modal) (6.0.1)\n",
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"Requirement already satisfied: hpack<5,>=4.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from h2<5,>=3.1.0->grpclib==0.4.7->modal) (4.0.0)\n",
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"Requirement already satisfied: httpcore==1.* in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from httpx>=0.23.0->fastapi->modal) (1.0.5)\n",
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"Requirement already satisfied: h11<0.15,>=0.13 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from httpcore==1.*->httpx>=0.23.0->fastapi->modal) (0.14.0)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\frede\\onedrive\\dokumenter\\master\\mlops\\mlops_mod-2\\.conda\\lib\\site-packages (from jinja2>=2.11.2->fastapi->modal) (2.1.5)\n",
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" date 1. open 2. high 3. low 4. close 5. volume ticker\n",
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"0 2024-05-03 182.10 184.78 178.4200 181.19 75491539.0 TSLA\n",
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"1 2024-05-02 182.86 184.60 176.0200 180.01 89148041.0 TSLA\n",
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"2 2024-05-01 182.00 185.86 179.0100 179.99 92829719.0 TSLA\n",
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"3 2024-04-30 186.98 190.95 182.8401 183.28 127031787.0 TSLA\n",
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"4 2024-04-29 188.42 198.87 184.5400 194.05 243869678.0 TSLA\n",
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| 39 |
+
"Connected. Call `.close()` to terminate connection gracefully.\n",
|
| 40 |
+
"\n",
|
| 41 |
+
"Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/693399\n",
|
| 42 |
+
"Connected. Call `.close()` to terminate connection gracefully.\n",
|
| 43 |
+
"Index(['date', 'open', 'high', 'low', 'close', 'volume', 'ticker'], dtype='object')\n",
|
| 44 |
+
"2024-05-06 13:44:59,122 WARNING: DeprecationWarning: Providing event_time as a single-element list is deprecated and will be dropped in future versions. Provide the feature_name string instead.\n",
|
| 45 |
+
"\n",
|
| 46 |
+
"Feature Group created successfully, explore it at \n",
|
| 47 |
+
"https://c.app.hopsworks.ai:443/p/693399/fs/689222/fg/787797\n"
|
| 48 |
+
]
|
| 49 |
+
},
|
| 50 |
+
{
|
| 51 |
+
"data": {
|
| 52 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 53 |
+
"model_id": "1b857e05ae714fc09a2a7fcd05f56a73",
|
| 54 |
+
"version_major": 2,
|
| 55 |
+
"version_minor": 0
|
| 56 |
+
},
|
| 57 |
+
"text/plain": [
|
| 58 |
+
"Uploading Dataframe: 0.00% | | Rows 0/3486 | Elapsed Time: 00:00 | Remaining Time: ?"
|
| 59 |
+
]
|
| 60 |
+
},
|
| 61 |
+
"metadata": {},
|
| 62 |
+
"output_type": "display_data"
|
| 63 |
+
},
|
| 64 |
+
{
|
| 65 |
+
"name": "stdout",
|
| 66 |
+
"output_type": "stream",
|
| 67 |
+
"text": [
|
| 68 |
+
"Launching job: tesla_stock_2_offline_fg_materialization\n",
|
| 69 |
+
"Job started successfully, you can follow the progress at \n",
|
| 70 |
+
"https://c.app.hopsworks.ai/p/693399/jobs/named/tesla_stock_2_offline_fg_materialization/executions\n",
|
| 71 |
+
"2024-05-06 13:45:08,516 WARNING: DeprecationWarning: Providing event_time as a single-element list is deprecated and will be dropped in future versions. Provide the feature_name string instead.\n",
|
| 72 |
+
"\n",
|
| 73 |
+
"Feature Group created successfully, explore it at \n",
|
| 74 |
+
"https://c.app.hopsworks.ai:443/p/693399/fs/689222/fg/785786\n"
|
| 75 |
+
]
|
| 76 |
+
},
|
| 77 |
+
{
|
| 78 |
+
"data": {
|
| 79 |
+
"application/vnd.jupyter.widget-view+json": {
|
| 80 |
+
"model_id": "98e7ee7cb2c943b8893d0ae2a7254104",
|
| 81 |
+
"version_major": 2,
|
| 82 |
+
"version_minor": 0
|
| 83 |
+
},
|
| 84 |
+
"text/plain": [
|
| 85 |
+
"Uploading Dataframe: 0.00% | | Rows 0/66 | Elapsed Time: 00:00 | Remaining Time: ?"
|
| 86 |
+
]
|
| 87 |
+
},
|
| 88 |
+
"metadata": {},
|
| 89 |
+
"output_type": "display_data"
|
| 90 |
+
},
|
| 91 |
+
{
|
| 92 |
+
"name": "stdout",
|
| 93 |
+
"output_type": "stream",
|
| 94 |
+
"text": [
|
| 95 |
+
"Launching job: news_sentiment_updated_2_offline_fg_materialization\n",
|
| 96 |
+
"Job started successfully, you can follow the progress at \n",
|
| 97 |
+
"https://c.app.hopsworks.ai/p/693399/jobs/named/news_sentiment_updated_2_offline_fg_materialization/executions\n"
|
| 98 |
+
]
|
| 99 |
+
}
|
| 100 |
+
],
|
| 101 |
"source": [
|
| 102 |
"from feature_pipeline import tesla_fg\n",
|
| 103 |
"from feature_pipeline import news_sentiment_fg"
|
|
|
|
| 105 |
},
|
| 106 |
{
|
| 107 |
"cell_type": "code",
|
| 108 |
+
"execution_count": 3,
|
| 109 |
"metadata": {},
|
| 110 |
"outputs": [
|
| 111 |
{
|
|
|
|
| 114 |
"True"
|
| 115 |
]
|
| 116 |
},
|
| 117 |
+
"execution_count": 3,
|
| 118 |
"metadata": {},
|
| 119 |
"output_type": "execute_result"
|
| 120 |
}
|
|
|
|
| 128 |
},
|
| 129 |
{
|
| 130 |
"cell_type": "code",
|
| 131 |
+
"execution_count": 4,
|
| 132 |
"metadata": {},
|
| 133 |
"outputs": [
|
| 134 |
{
|
|
|
|
| 136 |
"output_type": "stream",
|
| 137 |
"text": [
|
| 138 |
"Connection closed.\n",
|
| 139 |
+
"Connected. Call `.close()` to terminate connection gracefully.\n"
|
| 140 |
+
]
|
| 141 |
+
},
|
| 142 |
+
{
|
| 143 |
+
"name": "stdout",
|
| 144 |
+
"output_type": "stream",
|
| 145 |
+
"text": [
|
| 146 |
"\n",
|
| 147 |
+
"Logged in to project, explore it here https://c.app.hopsworks.ai:443/p/693399\n",
|
| 148 |
"Connected. Call `.close()` to terminate connection gracefully.\n"
|
| 149 |
]
|
| 150 |
}
|
|
|
|
| 157 |
},
|
| 158 |
{
|
| 159 |
"cell_type": "code",
|
| 160 |
+
"execution_count": 9,
|
| 161 |
"metadata": {},
|
| 162 |
"outputs": [],
|
| 163 |
"source": [
|
|
|
|
| 169 |
"\n",
|
| 170 |
" # Define the query\n",
|
| 171 |
" ds_query = tesla_fg.select(['date', 'open', 'ticker'])\\\n",
|
| 172 |
+
" .join(news_sentiment_fg.select(['sentiment']))\n",
|
| 173 |
"\n",
|
| 174 |
" # Create the feature view\n",
|
| 175 |
" feature_view = fs.create_feature_view(\n",
|
| 176 |
" name='tesla_stocks_fv',\n",
|
| 177 |
" query=ds_query,\n",
|
| 178 |
+
" labels=['open']\n",
|
| 179 |
" )\n",
|
| 180 |
"\n",
|
| 181 |
" return feature_view, tesla_fg"
|
|
|
|
| 183 |
},
|
| 184 |
{
|
| 185 |
"cell_type": "code",
|
| 186 |
+
"execution_count": 10,
|
| 187 |
"metadata": {},
|
| 188 |
"outputs": [
|
| 189 |
{
|
|
|
|
| 191 |
"output_type": "stream",
|
| 192 |
"text": [
|
| 193 |
"Feature view created successfully, explore it at \n",
|
| 194 |
+
"https://c.app.hopsworks.ai:443/p/693399/fs/689222/fv/tesla_stocks_fv/version/1\n"
|
| 195 |
]
|
| 196 |
}
|
| 197 |
],
|
|
|
|
| 205 |
},
|
| 206 |
{
|
| 207 |
"cell_type": "code",
|
| 208 |
+
"execution_count": 11,
|
| 209 |
+
"metadata": {},
|
| 210 |
+
"outputs": [],
|
| 211 |
+
"source": [
|
| 212 |
+
"def fix_data_from_feature_view(df,start_date,end_date):\n",
|
| 213 |
+
" df = df.sort_values(\"date\")\n",
|
| 214 |
+
" df = df.reset_index()\n",
|
| 215 |
+
" df = df.drop(columns=[\"index\"])\n",
|
| 216 |
+
"\n",
|
| 217 |
+
" # Create a boolean mask for rows that fall within the date range\n",
|
| 218 |
+
" mask = (pd.to_datetime(df['date']) >= pd.to_datetime(start_date)) & (pd.to_datetime(df['date']) <= pd.to_datetime(end_date))\n",
|
| 219 |
+
" len_df = np.shape(df)\n",
|
| 220 |
+
" df = df[mask] # Use the boolean mask to filter the DataFrame\n",
|
| 221 |
+
" print('From shape {} to {} after cropping to given date range: {} to {}'.format(len_df,np.shape(df),start_date,end_date))\n",
|
| 222 |
+
"\n",
|
| 223 |
+
" # Get rid off all non-business days\n",
|
| 224 |
+
" isBusinessDay, is_open = extract_business_day(start_date,end_date)\n",
|
| 225 |
+
" is_open = [not i for i in is_open] # Invert the mask to be able to drop all non-buisiness days\n",
|
| 226 |
+
"\n",
|
| 227 |
+
" filtered_df = df.drop(df[is_open].index) # Use the mask to filter the rows of the DataFrame\n",
|
| 228 |
+
" print('From shape {} to {} after removing non-business days'.format(np.shape(df),np.shape(filtered_df)))\n",
|
| 229 |
+
" print(filtered_df)\n",
|
| 230 |
+
" \n",
|
| 231 |
+
" return filtered_df"
|
| 232 |
+
]
|
| 233 |
+
},
|
| 234 |
+
{
|
| 235 |
+
"cell_type": "code",
|
| 236 |
+
"execution_count": 7,
|
| 237 |
"metadata": {},
|
| 238 |
"outputs": [],
|
| 239 |
"source": [
|
|
|
|
| 255 |
},
|
| 256 |
{
|
| 257 |
"cell_type": "code",
|
| 258 |
+
"execution_count": 8,
|
| 259 |
"metadata": {},
|
| 260 |
"outputs": [],
|
| 261 |
"source": [
|
feature_view.py
ADDED
|
@@ -0,0 +1,103 @@
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# %%
|
| 2 |
+
# Import necessary libraries
|
| 3 |
+
import pandas as pd # For data manipulation using DataFrames
|
| 4 |
+
import numpy as np # For numerical operations
|
| 5 |
+
import matplotlib.pyplot as plt # For data visualization
|
| 6 |
+
import os # For operating system-related tasks
|
| 7 |
+
import joblib # For saving and loading models
|
| 8 |
+
import hopsworks # For getting access to hopsworks
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
# Import specific modules from scikit-learn
|
| 13 |
+
from sklearn.preprocessing import StandardScaler, OneHotEncoder # For data preprocessing
|
| 14 |
+
from sklearn.metrics import accuracy_score # For evaluating model accuracy
|
| 15 |
+
|
| 16 |
+
# %%
|
| 17 |
+
from feature_pipeline import tesla_fg
|
| 18 |
+
from feature_pipeline import news_sentiment_fg
|
| 19 |
+
|
| 20 |
+
# %%
|
| 21 |
+
from dotenv import load_dotenv
|
| 22 |
+
import os
|
| 23 |
+
|
| 24 |
+
load_dotenv()
|
| 25 |
+
|
| 26 |
+
# %%
|
| 27 |
+
api_key = os.environ.get('hopsworks_api')
|
| 28 |
+
project = hopsworks.login(api_key_value=api_key)
|
| 29 |
+
fs = project.get_feature_store()
|
| 30 |
+
|
| 31 |
+
# %%
|
| 32 |
+
def create_stocks_feature_view(fs, version):
|
| 33 |
+
|
| 34 |
+
# Loading in the feature groups
|
| 35 |
+
tesla_fg = fs.get_feature_group('tesla_stock', version=1)
|
| 36 |
+
news_sentiment_fg = fs.get_feature_group('news_sentiment_updated', version=1)
|
| 37 |
+
|
| 38 |
+
# Define the query
|
| 39 |
+
ds_query = tesla_fg.select(['date', 'open', 'ticker'])\
|
| 40 |
+
.join(news_sentiment_fg.select(['sentiment']))
|
| 41 |
+
|
| 42 |
+
# Create the feature view
|
| 43 |
+
feature_view = fs.create_feature_view(
|
| 44 |
+
name='tesla_stocks_fv',
|
| 45 |
+
query=ds_query,
|
| 46 |
+
labels=['open']
|
| 47 |
+
)
|
| 48 |
+
|
| 49 |
+
return feature_view, tesla_fg
|
| 50 |
+
|
| 51 |
+
# %%
|
| 52 |
+
try:
|
| 53 |
+
feature_view = fs.get_feature_view("tesla_stocks_fv", version=1)
|
| 54 |
+
tesla_fg = fs.get_feature_group('tesla_stock', version=1)
|
| 55 |
+
except:
|
| 56 |
+
feature_view, tesla_fg = create_stocks_feature_view(fs, 1)
|
| 57 |
+
|
| 58 |
+
# %%
|
| 59 |
+
def fix_data_from_feature_view(df,start_date,end_date):
|
| 60 |
+
df = df.sort_values("date")
|
| 61 |
+
df = df.reset_index()
|
| 62 |
+
df = df.drop(columns=["index"])
|
| 63 |
+
|
| 64 |
+
# Create a boolean mask for rows that fall within the date range
|
| 65 |
+
mask = (pd.to_datetime(df['date']) >= pd.to_datetime(start_date)) & (pd.to_datetime(df['date']) <= pd.to_datetime(end_date))
|
| 66 |
+
len_df = np.shape(df)
|
| 67 |
+
df = df[mask] # Use the boolean mask to filter the DataFrame
|
| 68 |
+
print('From shape {} to {} after cropping to given date range: {} to {}'.format(len_df,np.shape(df),start_date,end_date))
|
| 69 |
+
|
| 70 |
+
# Get rid off all non-business days
|
| 71 |
+
isBusinessDay, is_open = extract_business_day(start_date,end_date)
|
| 72 |
+
is_open = [not i for i in is_open] # Invert the mask to be able to drop all non-buisiness days
|
| 73 |
+
|
| 74 |
+
filtered_df = df.drop(df[is_open].index) # Use the mask to filter the rows of the DataFrame
|
| 75 |
+
print('From shape {} to {} after removing non-business days'.format(np.shape(df),np.shape(filtered_df)))
|
| 76 |
+
print(filtered_df)
|
| 77 |
+
|
| 78 |
+
return filtered_df
|
| 79 |
+
|
| 80 |
+
# %%
|
| 81 |
+
#def create_stocks_feature_view(fs, version):
|
| 82 |
+
|
| 83 |
+
#Loading in the feature groups
|
| 84 |
+
# tesla_fg = fs.get_feature_group('tesla_stock', version = 3)
|
| 85 |
+
# news_sentiment_fg = fs.get_feature_group('news_sentiment_updated', version = 2)
|
| 86 |
+
|
| 87 |
+
# ds_query = tesla_fg.select(['date','open', 'ticker'])\
|
| 88 |
+
# .join(news_sentiment_fg.select_except(['ticker','time', 'amp_url', 'image_url']))
|
| 89 |
+
|
| 90 |
+
# return (fs.create_tesla_feature_view(
|
| 91 |
+
# name = 'tsla_stocks_fv',
|
| 92 |
+
# query = ds_query,
|
| 93 |
+
# labels=['ticker']
|
| 94 |
+
# ), tesla_fg)
|
| 95 |
+
|
| 96 |
+
# %%
|
| 97 |
+
#try:
|
| 98 |
+
# feature_view = fs.get_feature_view("tsla_stocks_fv", version=1)
|
| 99 |
+
# tesla_fg = fs.get_feature_group('tesla_stock', version=3)
|
| 100 |
+
#except:
|
| 101 |
+
# feature_view, tesla_fg = create_stocks_feature_view(fs, 1)
|
| 102 |
+
|
| 103 |
+
|
historical_news.ipynb
CHANGED
|
@@ -663,7 +663,7 @@
|
|
| 663 |
"name": "python",
|
| 664 |
"nbconvert_exporter": "python",
|
| 665 |
"pygments_lexer": "ipython3",
|
| 666 |
-
"version": "3.11.
|
| 667 |
},
|
| 668 |
"orig_nbformat": 4
|
| 669 |
},
|
|
|
|
| 663 |
"name": "python",
|
| 664 |
"nbconvert_exporter": "python",
|
| 665 |
"pygments_lexer": "ipython3",
|
| 666 |
+
"version": "3.11.9"
|
| 667 |
},
|
| 668 |
"orig_nbformat": 4
|
| 669 |
},
|
historical_stock.ipynb
CHANGED
|
@@ -4,7 +4,19 @@
|
|
| 4 |
"cell_type": "code",
|
| 5 |
"execution_count": 1,
|
| 6 |
"metadata": {},
|
| 7 |
-
"outputs": [
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 8 |
"source": [
|
| 9 |
"from dotenv import load_dotenv\n",
|
| 10 |
"import os \n",
|
|
@@ -25,7 +37,7 @@
|
|
| 25 |
},
|
| 26 |
{
|
| 27 |
"cell_type": "code",
|
| 28 |
-
"execution_count":
|
| 29 |
"metadata": {},
|
| 30 |
"outputs": [
|
| 31 |
{
|
|
@@ -67,7 +79,7 @@
|
|
| 67 |
},
|
| 68 |
{
|
| 69 |
"cell_type": "code",
|
| 70 |
-
"execution_count":
|
| 71 |
"metadata": {},
|
| 72 |
"outputs": [],
|
| 73 |
"source": [
|
|
@@ -125,7 +137,7 @@
|
|
| 125 |
},
|
| 126 |
{
|
| 127 |
"cell_type": "code",
|
| 128 |
-
"execution_count":
|
| 129 |
"metadata": {},
|
| 130 |
"outputs": [],
|
| 131 |
"source": [
|
|
@@ -198,7 +210,7 @@
|
|
| 198 |
},
|
| 199 |
{
|
| 200 |
"cell_type": "code",
|
| 201 |
-
"execution_count":
|
| 202 |
"metadata": {},
|
| 203 |
"outputs": [
|
| 204 |
{
|
|
@@ -236,7 +248,7 @@
|
|
| 236 |
"name": "python",
|
| 237 |
"nbconvert_exporter": "python",
|
| 238 |
"pygments_lexer": "ipython3",
|
| 239 |
-
"version": "3.11.
|
| 240 |
},
|
| 241 |
"orig_nbformat": 4
|
| 242 |
},
|
|
|
|
| 4 |
"cell_type": "code",
|
| 5 |
"execution_count": 1,
|
| 6 |
"metadata": {},
|
| 7 |
+
"outputs": [
|
| 8 |
+
{
|
| 9 |
+
"ename": "ModuleNotFoundError",
|
| 10 |
+
"evalue": "No module named 'modal'",
|
| 11 |
+
"output_type": "error",
|
| 12 |
+
"traceback": [
|
| 13 |
+
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
| 14 |
+
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
| 15 |
+
"Cell \u001b[1;32mIn[1], line 7\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mhopsworks\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mre\u001b[39;00m \n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmodal\u001b[39;00m \n\u001b[0;32m 8\u001b[0m \u001b[38;5;66;03m#prepocessing\u001b[39;00m\n\u001b[0;32m 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n",
|
| 16 |
+
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'modal'"
|
| 17 |
+
]
|
| 18 |
+
}
|
| 19 |
+
],
|
| 20 |
"source": [
|
| 21 |
"from dotenv import load_dotenv\n",
|
| 22 |
"import os \n",
|
|
|
|
| 37 |
},
|
| 38 |
{
|
| 39 |
"cell_type": "code",
|
| 40 |
+
"execution_count": null,
|
| 41 |
"metadata": {},
|
| 42 |
"outputs": [
|
| 43 |
{
|
|
|
|
| 79 |
},
|
| 80 |
{
|
| 81 |
"cell_type": "code",
|
| 82 |
+
"execution_count": null,
|
| 83 |
"metadata": {},
|
| 84 |
"outputs": [],
|
| 85 |
"source": [
|
|
|
|
| 137 |
},
|
| 138 |
{
|
| 139 |
"cell_type": "code",
|
| 140 |
+
"execution_count": null,
|
| 141 |
"metadata": {},
|
| 142 |
"outputs": [],
|
| 143 |
"source": [
|
|
|
|
| 210 |
},
|
| 211 |
{
|
| 212 |
"cell_type": "code",
|
| 213 |
+
"execution_count": null,
|
| 214 |
"metadata": {},
|
| 215 |
"outputs": [
|
| 216 |
{
|
|
|
|
| 248 |
"name": "python",
|
| 249 |
"nbconvert_exporter": "python",
|
| 250 |
"pygments_lexer": "ipython3",
|
| 251 |
+
"version": "3.11.9"
|
| 252 |
},
|
| 253 |
"orig_nbformat": 4
|
| 254 |
},
|
requirements.txt
CHANGED
|
@@ -16,4 +16,6 @@ python-dotenv
|
|
| 16 |
requests
|
| 17 |
alpha_vantage
|
| 18 |
textblob
|
| 19 |
-
great_expectations==0.18.12
|
|
|
|
|
|
|
|
|
| 16 |
requests
|
| 17 |
alpha_vantage
|
| 18 |
textblob
|
| 19 |
+
great_expectations==0.18.12
|
| 20 |
+
prophet
|
| 21 |
+
tensorflow
|
stock_preprocessing.py
ADDED
|
@@ -0,0 +1,177 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# %%
|
| 2 |
+
from dotenv import load_dotenv
|
| 3 |
+
import os
|
| 4 |
+
from alpha_vantage.timeseries import TimeSeries
|
| 5 |
+
import pandas as pd
|
| 6 |
+
import hopsworks
|
| 7 |
+
import re
|
| 8 |
+
import modal
|
| 9 |
+
#prepocessing
|
| 10 |
+
import requests
|
| 11 |
+
import pandas as pd
|
| 12 |
+
import json
|
| 13 |
+
#import pandas_market_calendars as mcal
|
| 14 |
+
import datetime
|
| 15 |
+
import numpy as np
|
| 16 |
+
from datetime import datetime, timedelta
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
# %%
|
| 20 |
+
load_dotenv()
|
| 21 |
+
|
| 22 |
+
api_key = os.environ.get('stocks_api') # Replace this with your actual API key
|
| 23 |
+
ts = TimeSeries(key=api_key, output_format='pandas')
|
| 24 |
+
|
| 25 |
+
# Fetch daily adjusted stock prices; adjust the symbol as needed
|
| 26 |
+
data, meta_data = ts.get_daily(symbol='TSLA', outputsize='full')
|
| 27 |
+
|
| 28 |
+
print(data.head())
|
| 29 |
+
|
| 30 |
+
# %%
|
| 31 |
+
data
|
| 32 |
+
|
| 33 |
+
# %%
|
| 34 |
+
data.info()
|
| 35 |
+
|
| 36 |
+
# %%
|
| 37 |
+
meta_data
|
| 38 |
+
|
| 39 |
+
# %%
|
| 40 |
+
#Stock market:
|
| 41 |
+
def today_is_a_business_day(today):
|
| 42 |
+
# Get the NYSE calendar
|
| 43 |
+
cal = mcal.get_calendar('NYSE')
|
| 44 |
+
schedule = cal.schedule(start_date=today, end_date=today) # Get the NYSE calendar's open and close times for the specified period
|
| 45 |
+
try:
|
| 46 |
+
isBusinessDay = schedule.market_open.dt.strftime('%Y-%m-%d')
|
| 47 |
+
return True
|
| 48 |
+
except:
|
| 49 |
+
print('Today {} is not a business day'.format(today))
|
| 50 |
+
return False
|
| 51 |
+
|
| 52 |
+
# %%
|
| 53 |
+
def next_business_day(today):
|
| 54 |
+
|
| 55 |
+
# Real tomorrow
|
| 56 |
+
tomorrow = (today + timedelta(days=1)).strftime("%Y-%m-%d")
|
| 57 |
+
|
| 58 |
+
# Get the NYSE calendar
|
| 59 |
+
cal = mcal.get_calendar('NYSE')
|
| 60 |
+
|
| 61 |
+
found_next_business_day = False
|
| 62 |
+
while not found_next_business_day:
|
| 63 |
+
schedule = cal.schedule(start_date=tomorrow, end_date=tomorrow) # Get the NYSE calendar's open and close times for the specified period
|
| 64 |
+
try:
|
| 65 |
+
isBusinessDay = schedule.market_open.dt.strftime('%Y-%m-%d') # Only need a list of dates when it's open (not open and close times)
|
| 66 |
+
found_next_business_day = True
|
| 67 |
+
except:
|
| 68 |
+
print('The date {} is not a business day'.format(tomorrow))
|
| 69 |
+
tomorrow = (datetime.datetime.strptime(tomorrow,"%Y-%m-%d") + timedelta(days=1)).strftime("%Y-%m-%d")
|
| 70 |
+
|
| 71 |
+
return isBusinessDay.to_numpy()[0]
|
| 72 |
+
|
| 73 |
+
# %%
|
| 74 |
+
def extract_business_day(start_date,end_date):
|
| 75 |
+
"""
|
| 76 |
+
Given a start_date and end_date.
|
| 77 |
+
|
| 78 |
+
`Returns`:
|
| 79 |
+
|
| 80 |
+
isBusinessDay: list of str (with all dates being business days)
|
| 81 |
+
is_open: boolean list
|
| 82 |
+
e.g is_open = [1,0,...,1] means that start_date = open, day after start_date = closed, and end_date = open
|
| 83 |
+
"""
|
| 84 |
+
|
| 85 |
+
# Save for later
|
| 86 |
+
end_date_save = end_date
|
| 87 |
+
|
| 88 |
+
# Get the NYSE calendar
|
| 89 |
+
cal = mcal.get_calendar('NYSE')
|
| 90 |
+
|
| 91 |
+
# Get the NYSE calendar's open and close times for the specified period
|
| 92 |
+
schedule = cal.schedule(start_date=start_date, end_date=end_date)
|
| 93 |
+
|
| 94 |
+
# Only need a list of dates when it's open (not open and close times)
|
| 95 |
+
isBusinessDay = np.array(schedule.market_open.dt.strftime('%Y-%m-%d'))
|
| 96 |
+
|
| 97 |
+
# Go over all days:
|
| 98 |
+
delta = datetime.timedelta(days=1)
|
| 99 |
+
start_date = datetime.datetime.strptime(start_date,"%Y-%m-%d") #datetime.date(2015, 7, 16)
|
| 100 |
+
end_date = datetime.datetime.strptime(end_date,"%Y-%m-%d") #datetime.date(2023, 1, 4)
|
| 101 |
+
|
| 102 |
+
# Extract days from the timedelta object
|
| 103 |
+
num_days = (end_date - start_date).days + 1
|
| 104 |
+
|
| 105 |
+
# Create boolean array for days being open (1) and closed (0)
|
| 106 |
+
is_open = np.zeros(num_days)
|
| 107 |
+
|
| 108 |
+
# iterate over range of dates
|
| 109 |
+
current_BusinessDay = isBusinessDay[0]
|
| 110 |
+
count_dates = 0
|
| 111 |
+
next_BusinessDay = 0
|
| 112 |
+
|
| 113 |
+
while (start_date <= end_date):
|
| 114 |
+
|
| 115 |
+
if start_date.strftime('%Y-%m-%d') == current_BusinessDay:
|
| 116 |
+
is_open[count_dates] = True
|
| 117 |
+
|
| 118 |
+
if current_BusinessDay == end_date_save or current_BusinessDay==isBusinessDay[-1]:
|
| 119 |
+
break
|
| 120 |
+
else:
|
| 121 |
+
next_BusinessDay += 1
|
| 122 |
+
current_BusinessDay = isBusinessDay[next_BusinessDay]
|
| 123 |
+
else:
|
| 124 |
+
is_open[count_dates] = False
|
| 125 |
+
|
| 126 |
+
count_dates += 1
|
| 127 |
+
start_date += delta
|
| 128 |
+
|
| 129 |
+
print(np.shape(is_open))
|
| 130 |
+
|
| 131 |
+
return isBusinessDay, is_open
|
| 132 |
+
|
| 133 |
+
# %%
|
| 134 |
+
def clean_column_name(name):
|
| 135 |
+
# Remove all non-letter characters
|
| 136 |
+
cleaned_name = re.sub(r'[^a-zA-Z]', '', name)
|
| 137 |
+
return cleaned_name
|
| 138 |
+
|
| 139 |
+
# %%
|
| 140 |
+
data.columns = [clean_column_name(col) for col in data.columns]
|
| 141 |
+
|
| 142 |
+
# %%
|
| 143 |
+
data.head()
|
| 144 |
+
|
| 145 |
+
# %%
|
| 146 |
+
data.reset_index(inplace=True)
|
| 147 |
+
|
| 148 |
+
|
| 149 |
+
# %%
|
| 150 |
+
data.head()
|
| 151 |
+
|
| 152 |
+
# %%
|
| 153 |
+
data
|
| 154 |
+
|
| 155 |
+
# %%
|
| 156 |
+
# Define the date range you're interested in
|
| 157 |
+
yesterday =datetime.now()-timedelta(days=1)
|
| 158 |
+
two_years_back = yesterday - timedelta(days=684)
|
| 159 |
+
|
| 160 |
+
# %%
|
| 161 |
+
# Filter the DataFrame to this range
|
| 162 |
+
filtered_df = data[(data['date'] >= two_years_back) & (data['date'] <= yesterday)]
|
| 163 |
+
|
| 164 |
+
# %%
|
| 165 |
+
filtered_df.head()
|
| 166 |
+
|
| 167 |
+
# %%
|
| 168 |
+
print(filtered_df['date'].min())
|
| 169 |
+
print(filtered_df['date'].max())
|
| 170 |
+
|
| 171 |
+
# %%
|
| 172 |
+
filtered_df.shape
|
| 173 |
+
|
| 174 |
+
# %%
|
| 175 |
+
|
| 176 |
+
|
| 177 |
+
|