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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named 'modal'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"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",
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'modal'"
]
}
],
"source": [
"from dotenv import load_dotenv\n",
"import os \n",
"from alpha_vantage.timeseries import TimeSeries\n",
"import pandas as pd\n",
"import hopsworks\n",
"import re \n",
"import modal \n",
"#prepocessing\n",
"import requests\n",
"import pandas as pd\n",
"import json\n",
"#import pandas_market_calendars as mcal\n",
"import datetime\n",
"import numpy as np\n",
"from datetime import timedelta \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 1. open 2. high 3. low 4. close 5. volume ticker\n",
"date \n",
"2024-05-03 182.10 184.78 178.4200 181.19 75491539.0 TSLA\n",
"2024-05-02 182.86 184.60 176.0200 180.01 89148041.0 TSLA\n",
"2024-05-01 182.00 185.86 179.0100 179.99 92829719.0 TSLA\n",
"2024-04-30 186.98 190.95 182.8401 183.28 127031787.0 TSLA\n",
"2024-04-29 188.42 198.87 184.5400 194.05 243869678.0 TSLA\n"
]
}
],
"source": [
"\n",
"\n",
"load_dotenv()\n",
"\n",
"api_key = os.environ.get('stocks_api') # Replace this with your actual API key\n",
"ts = TimeSeries(key=api_key, output_format='pandas')\n",
"\n",
"def fetch_stock_prices(symbol):\n",
" # Fetch daily adjusted stock prices; adjust the symbol as needed\n",
" data, meta_data = ts.get_daily(symbol=symbol, outputsize='full')\n",
" \n",
" # Add a new column named 'ticker' and fill it with the ticker name\n",
" data['ticker'] = symbol\n",
" \n",
" return data\n",
"\n",
"# Example usage\n",
"symbol = 'TSLA'\n",
"stock_data = fetch_stock_prices(symbol)\n",
"print(stock_data.head())\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def create_tsla_history():\n",
"\n",
" start_date = datetime.datetime.strptime('2015-07-16',\"%Y-%m-%d\")\n",
" end_date = datetime.datetime.strptime('2023-01-05',\"%Y-%m-%d\")\n",
"\n",
" # Get the TSLA stock data from yfinance\n",
" tsla = Ticker(\"TSLA\")\n",
" # info = tsla.info\n",
"\n",
" # get historical market data\n",
" data = tsla.history(start=start_date, end=end_date)\n",
"\n",
" # drop some columns\n",
" tesla_df = data.drop(columns=['Dividends','Stock Splits'])\n",
" tesla_df.index = tesla_df.index.strftime('%Y-%m-%d')\n",
" \n",
" print('Number of business days included in data set: ',np.shape(tesla_df))\n",
"\n",
" # Create an array of all dates in the specified period\n",
" all_dates = np.array([start_date + datetime.timedelta(days=i) for i in range((end_date - start_date).days)])\n",
" all_dates = [d.strftime('%Y-%m-%d') for d in all_dates]\n",
"\n",
" # Use setdiff1d() to find the non-business days\n",
" isBusinessDay, _ = extract_business_day(start_date='2015-07-16',end_date='2023-01-04')\n",
" non_business_days = np.setdiff1d(all_dates, isBusinessDay)\n",
"\n",
" # Add nan-values to the non-business days\n",
" print('Add {} non business days with NaN-values'.format(len(non_business_days)))\n",
" for d in non_business_days:\n",
" tesla_df.loc[d,:] = [np.nan,np.nan,np.nan,np.nan,np.nan]\n",
"\n",
" # sort index (dates)\n",
" tesla_df = tesla_df.sort_index()\n",
" \n",
" # move \"date\"-index into its own column\n",
" tesla_df = tesla_df.reset_index()\n",
" \n",
" # Rename column 'Date' to 'date'\n",
" tesla_df = tesla_df.rename(columns={'Date': 'date'})\n",
" print('Final size of dataframe',np.shape(tesla_df))\n",
" \n",
" # Write the merged dataframe to a CSV file\n",
" start_date ='2022-04-01'\n",
" end_date = '2024-04-01'\n",
" save_path = \"data/stock/tesla_{}-{}.csv\".format(start_date,end_date)\n",
" \n",
" print('Save at :',save_path)\n",
" tesla_df.to_csv(save_path, index=False)\n",
" \n",
" return tesla_df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def extract_business_day(start_date,end_date):\n",
" \"\"\"\n",
" Given a start_date and end_date.\n",
" \n",
" `Returns`:\n",
" \n",
" isBusinessDay: list of str (with all dates being business days)\n",
" is_open: boolean list\n",
" e.g is_open = [1,0,...,1] means that start_date = open, day after start_date = closed, and end_date = open\n",
" \"\"\"\n",
" \n",
" # Save for later\n",
" end_date_save = end_date\n",
" \n",
" # Get the NYSE calendar\n",
" cal = mcal.get_calendar('NYSE')\n",
"\n",
" # Get the NYSE calendar's open and close times for the specified period\n",
" schedule = cal.schedule(start_date=start_date, end_date=end_date)\n",
" \n",
" # Only need a list of dates when it's open (not open and close times)\n",
" isBusinessDay = np.array(schedule.market_open.dt.strftime('%Y-%m-%d')) \n",
" \n",
" # Go over all days: \n",
" delta = datetime.timedelta(days=1)\n",
" start_date = datetime.datetime.strptime(start_date,\"%Y-%m-%d\") #datetime.date(2015, 7, 16)\n",
" end_date = datetime.datetime.strptime(end_date,\"%Y-%m-%d\") #datetime.date(2023, 1, 4)\n",
" \n",
" # Extract days from the timedelta object\n",
" num_days = (end_date - start_date).days + 1\n",
" \n",
" # Create boolean array for days being open (1) and closed (0) \n",
" is_open = np.zeros(num_days)\n",
" \n",
" # iterate over range of dates\n",
" current_BusinessDay = isBusinessDay[0]\n",
" count_dates = 0\n",
" next_BusinessDay = 0\n",
" \n",
" while (start_date <= end_date):\n",
" \n",
" if start_date.strftime('%Y-%m-%d') == current_BusinessDay:\n",
" is_open[count_dates] = True\n",
"\n",
" if current_BusinessDay == end_date_save or current_BusinessDay==isBusinessDay[-1]:\n",
" break\n",
" else:\n",
" next_BusinessDay += 1\n",
" current_BusinessDay = isBusinessDay[next_BusinessDay]\n",
" else:\n",
" is_open[count_dates] = False\n",
"\n",
" count_dates += 1 \n",
" start_date += delta\n",
" \n",
" print(np.shape(is_open))\n",
" \n",
" return isBusinessDay, is_open"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data saved to TSLA_stock_price.csv\n"
]
}
],
"source": [
"# Define your file path and name\n",
"file_path = 'TSLA_stock_price.csv' # Customize the path and filename\n",
"\n",
"# Save the DataFrame to CSV\n",
"stock_data.to_csv(file_path)\n",
"\n",
"print(f\"Data saved to {file_path}\")"
]
}
],
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