def get_tech_ind():
"""
Calculate the technical indicators and cache the data
"""
cache.set(
'tech_ind',
get_techical_indicators(cache.get('comp_eod'), cache.get('sp500_eod')))
def home():
HOME_TITLE = 'Stock Dashboard'
current_cache_ticker = cache.get('comp_ticker')
if not current_cache_ticker:
current_cache_ticker = 'TSLA'
user_ticker = request.values.get('search-bar-ticker', default=current_cache_ticker)
print('CURRENT TICKER',user_ticker)
get_data = True
if user_ticker != cache.get('comp_ticker'):
get_data = cache_data(user_ticker)
if get_data:
current_cache_ticker = cache.get('comp_ticker')
get_tech_ind()
else:
flash(f"'{user_ticker}'' is an INVALID ticker symbol", 'danger')
if get_data:
get_ticker_news(current_cache_ticker)
bar = make_plot(cache.get('comp_eod'))
cache.set('eod_data_plot',bar)
return render_template('overview.html', title=HOME_TITLE, comp_name=cache.get('comp_name'),comp_ticker=current_cache_ticker, search_data=search_bar_options, plot=cache.get('eod_data_plot'), gen_info=cache.get('comp_gen_info'), curr_ticker_news=cache.get('comp_news'))
def parse_date_tech_ind():
dates_to_parse = request.get_json(force=True)
if dates_to_parse['start_date'] == "reset_dates":
return render_template('techind.html', tech_ind_data=cache.get('tech_ind'))
start_date = datetime.datetime.strptime(dates_to_parse['start_date'][:10], "%Y-%m-%d").date().strftime("%Y-%m-%d")
end_date = datetime.datetime.strptime(dates_to_parse['end_date'][:10], "%Y-%m-%d").date().strftime("%Y-%m-%d")
df = pd.DataFrame.from_dict(cache.get('tech_ind'), orient='index')
temp_data = df.loc[start_date:end_date]
output = temp_data.to_dict('index')
return render_template('techind.html', tech_ind_data=output)
def parse_date():
dates_to_parse = request.get_json(force=True)
if dates_to_parse['start_date'] == "reset_dates":
return render_template('eoddata.html', eod_data=cache.get('comp_eod'))
start_date = datetime.datetime.strptime(dates_to_parse['start_date'][:10], "%Y-%m-%d").date()
end_date = datetime.datetime.strptime(dates_to_parse['end_date'][:10], "%Y-%m-%d").date()
df = pd.DataFrame(cache.get('comp_eod'), columns =['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'AdjClose'])
temp_data = df[(df['Date'] >= start_date) & (df['Date'] <= end_date)]
output = temp_data.values.tolist()
return render_template('eoddata.html', eod_data=output)
def chartdata():
CHART_DATA_TITLE = 'Chart Data'
if not cache.get('comp_eod'):
cache_data()
return render_template('chartdata.html', title=CHART_DATA_TITLE, search_data=search_bar_options, comp_name=cache.get('comp_name'),comp_ticker=cache.get('comp_ticker'), eod_data=cache.get('comp_eod'))
def financials():
FINANCIALS_TITLE = 'Financials'
if not cache.get('comp_fins'):
cache_data()
return render_template('financials.html', title=FINANCIALS_TITLE, search_data=search_bar_options, comp_name=cache.get('comp_name'), comp_ticker=cache.get('comp_ticker'), fin_data=cache.get('comp_fins'))
def stats():
STATISTICS_TITLE = 'Statistics'
if not cache.get('comp_stats'):
cache_data()
return render_template('statistics.html', title=STATISTICS_TITLE, search_data=search_bar_options, comp_name=cache.get('comp_name'), comp_ticker=cache.get('comp_ticker'), stats_data=cache.get('comp_stats'))
def techindicators():
TECHNICAL_INDICATORS_TITLE = 'Technical Indicators'
if not cache.get('tech_ind'):
cache_data()
get_tech_ind()
return render_template('technicalindicators.html', title=TECHNICAL_INDICATORS_TITLE, search_data=search_bar_options, comp_name=cache.get('comp_name'), comp_ticker=cache.get('comp_ticker'), tech_ind_data=cache.get('tech_ind'))
def get_tech_ind_csv():
print("DOWNLOADING TECHNICAL INDICATORS CSV")
df = pd.DataFrame.from_dict(cache.get('tech_ind'), orient='index')
df.to_csv(r'outputs\technical_indicators.csv')
return send_file(r'..\outputs\technical_indicators.csv',
mimetype='text/csv',
attachment_filename='technical_indicators.csv',
as_attachment=True)
def get_daily_price_csv():
print("DOWNLOADING DAILY PRICE CSV")
df = pd.DataFrame(cache.get('comp_eod'), columns =['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'AdjClose'])
df.to_csv(r'outputs\daily_price.csv')
return send_file(r'..\outputs\daily_price.csv',
mimetype='text/csv',
attachment_filename='daily_price.csv',
as_attachment=True)
def get_all_data(ticker='tsla'):
"""
Gets the data for the specified ticker, either from the database or by webscrape
Parameters:
ticker: the ticker to get the data for
Returns:
Boolean: True, if the data exists or else false
"""
ticker = ticker.upper()
end = _get_end_date()
ticker_in_DB = ticker_exists(ticker)
if ticker_in_DB: # check if the ticker exists in the DB
_update_eod_DB(ticker, end, ticker_in_DB)
else:
added_data = _add_new_data(ticker, end)
if not added_data:
return added_data
db.session.commit()
cach_ticker = Security.query.filter(Security.ticker == ticker).first()
cach_ticker.last_updated = datetime.datetime.utcnow()
ticker_stats = get_stats_data(ticker)
cach_ticker_eod_data = clean_eod_data(cach_ticker.eod_data.all())
cach_ticker_fiancials = clean_financials(cach_ticker.financials.all()[-1])
cach_ticker_comp_info = clean_company_information(
cach_ticker.company_information.all()[-1])
cache.set('comp_eod', cach_ticker_eod_data)
cache.set('comp_fins', cach_ticker_fiancials)
cache.set('comp_gen_info', cach_ticker_comp_info)
cache.set('comp_stats', ticker_stats)
cache.set('comp_name', cach_ticker.name)
cache.set('comp_ticker', cach_ticker.ticker)
if not cache.get('sp500_eod'):
print('GETTING SP500 DATA')
_add_SP500_data("^GSPC", end)
return True
def login():
if current_user.is_authenticated:
return redirect(url_for('home'))
LOGIN_TITLE = 'Login To Your Account'
form = LoginForm()
if form.validate_on_submit():
user = user_exists(form.email.data)
if user and bcrypt.check_password_hash(user.password, form.password.data):
login_user(user, form.remember.data)
update_user_login(user)
next_page = request.args.get('next')[1:] if request.args.get('next') and request.args.get('next').startswith('/') else request.args.get('next')
if next_page == 'get_daily_price_csv':
return render_template('chartdata.html', title='Chart Data', search_data=search_bar_options, comp_name=cache.get('comp_name'), comp_ticker=cache.get('comp_ticker'), eod_data=cache.get('comp_eod'))
if next_page == 'get_tech_ind_csv':
return render_template('technicalindicators.html', title='Technical Indicators', search_data=search_bar_options, comp_name=cache.get('comp_name'), comp_ticker=cache.get('comp_ticker'), tech_ind_data=cache.get('tech_ind'))
return redirect(url_for(next_page)) if next_page else redirect(url_for('home'))
else:
flash(f'Login unsuccessful. Please try again', 'danger')
return render_template('login.html', title=LOGIN_TITLE, search_data=search_bar_options, form=form)
from flask import render_template, url_for, request, flash, redirect, jsonify, make_response, send_file, session
import concurrent.futures
import datetime
from flask_login import login_user, current_user, logout_user, login_required
import pandas as pd
from stockdashboard import app, cache, bcrypt, login_manager
from stockdashboard.utils import search_bar_data
from stockdashboard.plots.plots import make_plot
from stockdashboard.signals import technical_signal_calculations
from stockdashboard.controller import get_all_data, get_tech_ind, add_user, user_exists, get_latest_ticker_price, get_all_news, get_ticker_news, get_user_watchlist, add_user_watchlist, delete_user_watchlist, update_user_login
from stockdashboard.forms import RegistrationForm, LoginForm
search_bar_options = cache.get('search_bar_options')
if not search_bar_options:
search_bar_options = search_bar_data()
cache.set('search_bar_options', search_bar_options)
def cache_data(ticker="TSLA"):
# handle various entry points
print("GETTING DATA")
return get_all_data(ticker)
@app.template_filter('datetimeformat')
def datetimeformat(value, formatted='%A, %B %d, %Y'):
if isinstance(value, str):
value = datetime.datetime.strptime(value, "%Y-%m-%d").date()
format_time = value.strftime(formatted)