def check_code():
input_keyword = input('Please input keyword for the stock searching for:')
all_ticker = data.get_nasdaq_symbols(retry_count=3, timeout=30, pause=None)
all_ticker = all_ticker.drop(columns=[
'Nasdaq Traded', 'Listing Exchange', 'Market Category', 'ETF',
'Round Lot Size', 'Test Issue', 'Financial Status', 'CQS Symbol',
'NASDAQ Symbol', 'NextShares'
])
name_list = all_ticker['Security Name'].values.tolist()
name_list = [x.lower() for x in name_list]
l = []
num = 0
for item in name_list:
if input_keyword in item:
l.insert(num, item)
num = num + 1
all_ticker = all_ticker.reset_index()
all_ticker = all_ticker.set_index('Security Name')
code_dic = all_ticker.to_dict()
new_dic = code_dic.get('Symbol')
new_dic = {k.lower(): v for k, v in new_dic.items()}
print('\nBelow are code for security name containing: ' + input_keyword +
'\n')
for item in l:
p = str(item) + ' ---- ' + str(new_dic.get(item))
print(p)
next = next_request()
return next
def retrieve_stock_data(symbol):
"""
Implements pandas_datareader to extract data from RobinHood's API into a pandas DataFrame.
:param symbol: String, ticker-symbol of the stock who's data is to be retrieved.
:return: pandas DataFrame object of stock data
"""
data = pdr.DataReader(symbol, 'robinhood')
nasdaq = pdr.get_nasdaq_symbols()
name = nasdaq["Security Name"][symbol]
return data, name
def read_stock_data() -> pd.DataFrame:
"""
A simple function to retrieve the stock data
Returns
-------
pandas.DataFrame
A Pandas DataFrame that stock data
"""
try:
data = pd.read_csv(stocks_path)
except FileNotFoundError:
data = pdr.get_nasdaq_symbols()
save(data, stocks_path)
return data
import logging
def save_nasdaq():
nasdaq = web.get_nasdaq_symbols()
nasdaq.to_pickle("./data/nasdaq_symbols.pkl")
return nasdaq
import tkinter as tk
import tkinter.ttk as ttk
from autocompletebox import AutocompleteEntry
from autocompletebox import NO_RESULTS_MESSAGE
import pandas_datareader.data as data
from scraper import getIncomeAnalysis
from scraper import getExpenseAnalysis
from scraper import getLiabilityAnalysis
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
matplotlib.use("TkAgg")
all_ticker = data.get_nasdaq_symbols(retry_count=3, timeout=30, pause=None)
all_ticker = all_ticker.drop(columns=[
'Nasdaq Traded', 'Listing Exchange', 'Market Category', 'ETF',
'Round Lot Size', 'Test Issue', 'Financial Status', 'CQS Symbol',
'NASDAQ Symbol', 'NextShares'
])
all_ticker = all_ticker.reset_index()
all_ticker = all_ticker.set_index('Security Name')
code_dic = all_ticker.to_dict()
code_dic = code_dic.get('Symbol')
class Application(tk.Frame, object):
"""Main Class of the application
Methods:
__init__ -- Set up the UI
def get_all_stock_code() -> []:
sym = pdr.get_iex_symbols()
pdr.get_nasdaq_symbols()
return sym['symbol'].to_list()
import os
import pandas as pd
from pandas_datareader import data as web # Package and modules for importing data; this code may change depending on pandas version
import datetime
from sklearn.linear_model import LinearRegression
import numpy as np
# Setting up the model
linreg = LinearRegression()
# Collecting stock tickers we are interested in
t = web.get_nasdaq_symbols()
t = t[t['Financial Status'] == 'N']
t = t[t['Listing Exchange'] == 'Q']
t = t[t['Test Issue'] == False]
tickers = t.index.values
money = 10000
portfolio = dict((t, 0) for t in tickers)
currPrice = dict((t, 0) for t in tickers)
def evaulateForTrade(ticker, start, end):
# First argument is the series we want, second is the source ("yahoo" for Yahoo! Finance), third is the start date, fourth is the end date
try:
stock = web.DataReader(ticker, "yahoo", start, end)
type(stock)
close = stock['Adj Close'].values[-10:]
x = np.arange(1, len(close) + 1).reshape(-1, 1)
linreg.fit(x, close)
if (linreg.coef_ > 0):
return True
except Exception as e: