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NVombatNVombat
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Merge pull request #106 from NVombat/nv/backend
🗑️ Cleanup
2 parents 95ec473 + 7395ab7 commit 4543999

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-5535
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‎analysis/Dynamic Analysis.ipynb

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‎analysis/dynamic_analysis.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Imports\n",
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"import yfinance as yf\n",
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"import datetime as dt\n",
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"import pandas as pd\n",
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"import requests\n",
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"import time\n",
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"import io\n",
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"\n",
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"# Read data from URL containing NASDAQ listings\n",
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"url = \"https://pkgstore.datahub.io/core/nasdaq-listings/nasdaq-listed_csv/data/7665719fb51081ba0bd834fde71ce822/nasdaq-listed_csv.csv\"\n",
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"data = requests.get(url).content\n",
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"df = pd.read_csv(io.StringIO(data.decode(\"utf-8\")))\n",
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"df.head()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"symbols = df[\"Symbol\"].to_list()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"stock_id = input(\"Enter Stock ID:\")\n",
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"stock_id = stock_id.upper()\n",
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"\n",
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"if stock_id in symbols:\n",
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" df_stock = yf.download(stock_id, start=\"1950-01-01\", period=\"1d\")\n",
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" print(df_stock)\n",
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"else:\n",
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" print(\"Incorrect Stock Symbol. Please Enter Valid Symbols\")"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_stock.drop(\"Adj Close\", axis=\"columns\", inplace=True)\n",
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"df_stock.head()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Reset index makes sure the dataframe has indexing of its own and converts the date index to a column\n",
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"df_stock.reset_index(inplace=True)\n",
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"df_stock.head()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert the date to a datetime object (gets converted to a specialised type of datetime object)\n",
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"df_stock[\"Date\"] = pd.to_datetime(df_stock[\"Date\"])\n",
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"df_stock.Date.dtype"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert date to epoch datetime format\n",
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"df_stock[\"Date\"] = (df_stock[\"Date\"] - dt.datetime(1970, 1, 1)).dt.total_seconds()\n",
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"df_stock.head()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df_stock.Date.dtype"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Format for plotting requires specific size for date so multiply by 1000\n",
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"df_stock[\"Date\"] = df_stock[\"Date\"] * 1000\n",
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"df_stock.head()"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert to json format and make sure its converted as json with arrays thus orient = values\n",
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"df_stock.to_json(\"data/\" + stock_id + \"_mod.json\", orient=\"values\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎analysis/Formatting.ipynb renamed to ‎analysis/formatting.ipynb

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}
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],
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"source": [
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"# Drop the 'Adj Close' column\n",
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"df.drop(\"Adj Close\", axis=\"columns\", inplace=True)\n",
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"df.head()"
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]
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"df.Date.dtype"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Select Data from a specific date range\n",
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"# from pytz import utc\n",
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"# df_2020 = df[df['Date'] > dt.datetime(2019, 12, 31)]\n",
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"\n",
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"# Reset Indexing so index starts from 0 and it not duplicated\n",
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"# df_2020.reset_index(inplace=True, drop=True)\n",
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"# df_2020"
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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": 8,

‎analysis/Stock Price Substitute.ipynb renamed to ‎analysis/stockprice_substitute.ipynb

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{
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"cells": [
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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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"import yfinance as yf\n",
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"import json"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"symbol = \"GOOGL\"\n",
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"ticker = yf.Ticker(symbol)"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"yfinance.Ticker object <GOOGL>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ticker"
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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": 7,

‎analysis/Stock Price Variation.ipynb renamed to ‎analysis/stockprice_variation.ipynb

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