def main():
listy = gt.get_tickers()
with open('../stocklist.csv', "w") as outfile:
for entries in listy:
if getdailydata(entries) == 'cool':
outfile.write(entries)
outfile.write("\n")
def handle(self, *args, **kwargs):
list_of_tickers = gt.get_tickers()
for tickername in list_of_tickers:
print(tickername)
try:
ticker = yf.Ticker(tickername)
tags = {'longName': '',
'country': '',
'website': '',
'sector': '',
'industry': '',
'fullTimeEmployees': 0}
for tag in tags:
try:
tags[tag] = ticker.info[tag]
except KeyError:
pass
print(tags)
if tags['longName'] != '':
Company.objects.get_or_create(name=tags['longName'],
country=tags['country'],
web=tags['website'],
sector=tags['sector'],
industry=tags['industry'],
employees_count=tags['fullTimeEmployees'])
except:
pass
def buy_stock(user_id,qty,symbol,price):
curr= conn.cursor()
user= (user_id)
list_of_tickers = gt.get_tickers()
flag = True
for i in list_of_tickers:
if(i ==symbol):
flag = True
if flag == True:
curr.execute("""Select cash from user_money where user_id = ?;""",(user_id,))
um=curr.fetchall()
um =str(um)
price =str(price)
pr = price.replace(',' ,'')
p=float(pr)
qty=float(qty)
commsion = float(0.01 *(qty*p))
total =qty*p
print(um)
print(user_id)
um=float(um.strip("([,])"))
if(total +commsion > um):
print("error")
messagebox.showerror("Error", "your dont have enough balance")
else:
print("trading")
curr.execute("""Select cash from user_money where user_id = ?;""",(user_id,))
trade(conn,user_id,symbol,qty,"buy",p)
else:
messagebox.showerror("Error", "Symbol Not found")
def main():
symbols = gt.get_tickers(NYSE=True, NASDAQ=True, AMEX=True)
for i, chunk in enumerate(chunks(symbols, 200), start=1):
get_all(chunk)
sql = f"""
INSERT INTO yahoo_stock_data
(SELECT t.date,
t.low,
t.close,
t.open,
t.high,
t.volume,
t.adjclose,
t.dividends,
t.symbol,
t.splits
FROM temp_yahoo_stock_data t
WHERE NOT EXISTS(SELECT *
FROM yahoo_stock_data y
WHERE t.symbol = y.symbol
and t.date = y.date))
"""
res = pg_db.query(sql)
print(f'finished {i} batch = {i * 200}')
def stock_his():
symbol = askstring('Symbol', 'Enter Symbol')
list_of_tickers = gt.get_tickers()
flag = False
for i in list_of_tickers:
if(i ==symbol):
flag = True
if flag == True:
StockHistory(symbol)
def main():
key = 'PjeqU9zauMH9o49WYWurfZslqfY8HpF7'
#API CALL
with RESTClient(key) as client:
''' This block is for reference purposes only
# Note that Q results are off by 1 fiscal year, bug currently being worked on
resp = client.stocks_equities_daily_open_close("AAPL","2018-03-02")
print(f"on: {resp.from_} Apple opened at {resp.open} and closed at {resp.close}")
resp = client.reference_stock_financials("MSFT",limit=1,type='Q')
print(f"MSFT market cap is {resp.results[0].get('marketCapitalization')} as reported on {resp.results[0].get('reportPeriod')}.")
custom_limit=100
resp = client.reference_stock_financials("MSFT",limit=100,type='Q')
for i in range(custom_limit):
print('*'*50)
print(f"MSFT market cap is {resp.results[i].get('marketCapitalization')} as reported on {resp.results[i].get('reportPeriod')}.")
print(f"MSFT debt to equity ratio is {resp.results[i].get('debtToEquityRatio')} as reported on {resp.results[i].get('reportPeriod')}.")
print(f"MSFT divident yield is {resp.results[i].get('dividendYield')} as reported on {resp.results[i].get('reportPeriod')}.")
print(f"MSFT gross profit is {resp.results[i].get('grossProfit')} as reported on {resp.results[i].get('reportPeriod')}.")
print(f"MSFT net income is {resp.results[i].get('netIncome')} as reported on {resp.results[i].get('reportPeriod')}.")
print(f"MSFT revenues in USD is {resp.results[i].get('revenuesUSD')} as reported on {resp.results[i].get('reportPeriod')}.")
print(f"MSFT operating income is {resp.results[i].get('operatingIncome')} as reported on {resp.results[i].get('reportPeriod')}.")
print('*'*50)
print("Testing completed for stock financials. Beginning testing for databasing.")
print("="*50)
'''
#Get a list of all tickers
list_of_all_tickers = gt.get_tickers(NYSE=True,
NASDAQ=True,
AMEX=False)
#Set flags to keep track of db initializations
DB_EXISTENCE_FLAGS = [False, False, False]
#Initialize the root db
if os.path.isfile("Root_Database.csv"):
print("Root DB found, aborting init operation.")
else:
if (init_root_db(list_of_all_tickers)):
DB_EXISTENCE_FLAGS[0] = True
print("Root DB has been successfully initialized.")
else:
print("Root DB initialization has failed.")
#Initialize the info db
if os.path.isfile("Info_Database1.csv"):
print("Info DB found, aborting init operation.")
else:
if (init_info_db(key, list_of_all_tickers)):
DB_EXISTENCE_FLAGS[1] = True
print("Info DB has been successfully initialied.")
else:
print("Info DB initialization has failed.")
def mentions_by_ticker(self) -> dict:
ticker_mentions = {}
tickers = set(gt.get_tickers())
for message in self.messages:
# split message to look for tickers
message_by_parts = message["content"].split(" ")
# in_message is mentioned tickers
in_message = set(message_by_parts).intersection(tickers)
date = datetime.strptime(message["timestamp"].split('T')[0], '%Y-%m-%d')
# map by date, messages
ticker_mentions[date] = ticker_mentions.get(date, TickersByDate(date)).add_tickers(in_message, message)
return ticker_mentions
def __init__(self, tick):
"""
The constructor takes in a stock tick symbol and
initialize the member variables if the symbol is valid.
If the symbol is invalid, an exception will be raised.
"""
tickerList = set(gt.get_tickers())
if tick not in tickerList:
errorMessage = "Invalid stock tick symbol {}, please double check.".format(
tick)
raise Exception(errorMessage)
self.stock = yf.Ticker(tick)
self.tick = tick
def generate_rss_yahoo_csv(
save_to="./resource/rss_yahoo_us_stock.csv",
symbol_path=None) -> None:
if symbol_path is None:
from get_all_tickers.get_tickers import get_tickers
symbols = get_tickers()
else:
symbols = pd.read_csv(symbol_path, header=None)[0]
urls = [
f"http://finance.yahoo.com/rss/headline?s={s}" for s in symbols]
df = pd.DataFrame({
"ticker": symbols,
"url": urls,
})
df.to_csv(save_to, index=False)
def get_stock_squeeze_list():
squeeze_list = []
symbols = gt.get_tickers(NYSE=True, NASDAQ=True, AMEX=True)
for symbol in symbols:
df = MyYahoo.get_stock_data_db(symbol)
if len(df) > 120 and breaking_out_of_squeeze(df):
print(f"{symbol} is coming out the squeeze")
squeeze_list.append(dict(symbol=symbol))
if squeeze_list:
with open(
f"{Path(__file__).parent.absolute()}/results/squeeze_list.json",
'w') as fp:
recipient, message, subject = [
'*****@*****.**',
f'yahoo stock squeeze list successfully generated at {datetime.now()}',
'squeezed'
]
send_eri_mail(recipient, message, subject)
json.dump(squeeze_list, fp)
return True
def generate(self):
report = open("report.txt", "w")
input_smaDay = int(self.plainTextEdit.toPlainText())
input_rsi_high = int(self.plainTextEdit_2.toPlainText())
input_rsi_low = int(self.plainTextEdit_6.toPlainText())
input_max_days = int(self.plainTextEdit_4.toPlainText())
input_amplification = float(self.plainTextEdit_5.toPlainText())
input_checkForSell = self.plainTextEdit_3.toPlainText()
downloadStartDate = dt.datetime.now() - dt.timedelta(
days=input_smaDay * 2 + 16)
TenWeeksAgoDate = dt.datetime.now() - dt.timedelta(days=70)
downloadEndDate = dt.datetime.now()
print("Start generating Sell report ......")
report.write("--------------- SELL LIST ---------------\n")
input_checkForSellList = input_checkForSell.split(",")
sellcount = 0
for i in range(0, len(input_checkForSellList)):
try:
isSell = False
print("Checking for " + input_checkForSellList[i])
df = yf.download(input_checkForSellList[i],
downloadStartDate,
downloadEndDate,
interval='1d')
if (df.empty):
continue
lastDayPrice = df["Close"].iloc[-1]
sma = df.rolling(window=input_smaDay).mean()
if (df["High"].iloc[-1] > sma["Close"].iloc[-1]
and df["Low"].iloc[-1] < sma["Close"].iloc[-1]):
isSell = True
stock = stockstats.StockDataFrame.retype(df)
rsi = stock["rsi_14"]
if (rsi.iloc[-1] > 62):
isSell = True
if (isSell):
sellcount = sellcount + 1
print("Added to sell list :" + input_checkForSellList[i])
report.write(str(sellcount) + " ********************\n")
report.write("Stock: " + input_checkForSellList[i] + "\n")
report.write("Current Price: " + str(lastDayPrice) + "\n")
df10w = yf.download(input_checkForSellList[i],
TenWeeksAgoDate,
downloadEndDate,
interval='1d')
report.write("10 weeks high: " + str(df10w["High"].max()) +
"\n")
report.write("10 weeks low: " + str(df10w["Low"].min()) +
"\n")
report.write("RSI: " + str(rsi.iloc[-1]) + "\n")
report.write("SMA: " + str(sma["Close"].iloc[-1]) + "\n")
report.write("Today's volume: " +
str(df10w["Volume"].iloc[-1]) + "\n")
report.write("Average volume: " +
str(df10w["Volume"].mean()) + "\n")
marketCap = data.get_quote_yahoo(
input_checkForSellList[i])['marketCap']
report.write("Market capital: " +
str(millify(marketCap, precision=2)) + "\n")
report.flush()
except:
continue
print("Start generating Buy report ......")
report.write("\n--------------- BUY LIST ---------------\n")
stocksList = gt.get_tickers()
buyCount = 0
for i in range(0, len(stocksList)):
print("Checking for " + stocksList[i])
try:
marketCap = data.get_quote_yahoo(stocksList[i])['marketCap']
if (marketCap.values[0] < 2000000000):
print("Market cap below 2B BBBBBBBBBBBBBBBB")
continue
df = yf.download(stocksList[i],
downloadStartDate,
downloadEndDate,
interval='1d')
if (df.empty):
print("df is empty yyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyyy")
continue
lastDayPrice = df["Close"].iloc[-1]
sma = df.rolling(window=input_smaDay).mean()
if (df["Low"].iloc[-1] < sma["Close"].iloc[-1]):
print(
"last day lowest below SMA AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
)
continue
if (df["Close"].iloc[-1] < 6):
print(
"less than 5$ 5555555555555555555555555555555555555555555555555555555555"
)
continue
stock = stockstats.StockDataFrame.retype(df)
rsi = stock["rsi_14"]
if (rsi.iloc[-1] > input_rsi_high):
print(
"rsi too high hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh"
)
continue
if (rsi.iloc[-1] < input_rsi_low):
print(
"rsi too low lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll"
)
continue
if (rsi.iloc[-1] < rsi.iloc[-3]):
print(
"2 days ago > today rsi 22222222222222222222222222222222222222222222222222222222222222222222222222222222"
)
continue
xDaysAgoDate = dt.datetime.now() - dt.timedelta(
days=input_max_days)
dfXDays = yf.download(stocksList[i],
xDaysAgoDate,
downloadEndDate,
interval='1d')
if (dfXDays["High"].max() <
input_amplification * dfXDays["Close"].iloc[-1]):
print(
"Max < current stock MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMM"
)
continue
buyCount = buyCount + 1
print(
"Added to buy list :" + stocksList[i] +
" ///////////////////////////////////////////////////////////////////////////////////////////"
)
report.write(str(buyCount) + " ********************\n")
report.write("Stock: " + stocksList[i] + "\n")
report.write("Current Price: " + str(lastDayPrice) + "\n")
df10w = yf.download(stocksList[i],
TenWeeksAgoDate,
downloadEndDate,
interval='1d')
report.write("10 weeks high: " + str(df10w["High"].max()) +
"\n")
report.write("10 weeks low: " + str(df10w["Low"].min()) + "\n")
report.write("RSI: " + str(rsi.iloc[-1]) + "\n")
report.write("SMA: " + str(sma["Close"].iloc[-1]) + "\n")
report.write("Today's volume: " +
str(df10w["Volume"].iloc[-1]) + "\n")
report.write("Average volume: " + str(df10w["Volume"].mean()) +
"\n")
report.write("Market capital: " +
str(millify(marketCap, precision=2)) + "\n")
report.flush()
except:
print("Exception")
continue
report.close()
# import the modules
import praw
import pandas as pd
from authenticate import redditAuthenticate
from get_all_tickers import get_tickers as gt
# creating an authorized reddit instance
reddit = redditAuthenticate()
#get tickers and remove duplicates (listed on multiple exchanges?)
tickers = list(dict.fromkeys(gt.get_tickers()))
#WSB lingo
lingo = [
'I', 'A', 'DD', 'WSB', 'LOL', 'IV', 'IP', 'YOLO', 'TIL', 'EDIT', 'OTM',
'GOT', 'IPO', 'WTF', 'ATH'
]
#ignore WSB lingo
filteredTickers = [ele for ele in tickers if ele not in lingo]
#fetch daily discussion thread
#submission = next(reddit.subreddit("wallstreetbets").search("title:Daily Discussion Thread for AND flair:Daily Discussion", time_filter='day'))
submission = next(
reddit.subreddit("wallstreetbets").search(
"title:What Are Your Moves Tomorrow AND flair:Daily Discussion",
time_filter='day'))
print(submission.title)
#add limit=X to scrap first X pages only
submission.comments.replace_more()
def get_ticker_list():
ticker_filter = re.compile('^[A-Z]+$')
master_ticker_list = gt.get_tickers()
master_ticker_list = [x for x in master_ticker_list if ticker_filter.match(x)]
return master_ticker_list
import pandas as pd
import numpy as np
import logging
APCA_API_BASE_URL = "https://paper-api.alpaca.markets"
# Initializing Firestore
cred = credentials.Certificate('service_account.json')
firebase_admin.initialize_app(cred)
db = firestore.client()
# Get all Tickers
tickers = gt.get_tickers(NYSE=True, NASDAQ=True, AMEX=True)
print(tickers)
# Get Data for the past 30 days for all tickers
data = get_data_month(tickers=tickers)
print(data)
# store = pd.HDFStore('data.h5')
# # store['new_data'] = data
# data = store['new_data']
last_empty = True
def get_nyse():
nyse_tickers = get_tickers.get_tickers(NYSE=True, NASDAQ=False, AMEX=False)
return nyse_tickers
import sys
from get_all_tickers import get_tickers as gt
import subprocess
import re
EXCLUDED_SYMBOLS = [
"CBO", "CBX", "CELG~", "GIX~", "AUUD", "AUUDW", "BIOTU", "BTNB", "BCACU",
"JAQCU", "MVNR", "NLSP", "NLSPW", "SPEL"
]
fields = ["High", "Low", "Open", "Close"]
pattern = ""
for field in fields:
pattern += field + ": [\d,]*\d(\.\d+)?\s*" # Output must be a number
symbols = gt.get_tickers()
print(symbols)
errored_symbols = []
for i in range(len(symbols)):
symbol = symbols[i].strip()
if symbol in EXCLUDED_SYMBOLS:
continue
try:
out = subprocess.check_output(["py", "scrape.py", symbol]) # Expensive
if not re.match(pattern, out.decode('utf8')):
print("Output for symbol " + symbol + " is NaN", file=sys.stderr)
errored_symbols.append(symbol)
except subprocess.CalledProcessError as e:
print("Error when fetching data for symbol " + symbol, file=sys.stderr)
import stock_quality_score as qs
from get_all_tickers import get_tickers as gt
import warnings
warnings.filterwarnings("ignore")
# some example tickers
my_tickers = ['AAPL', 'MCD', 'SAP']
result = qs.quality_scores_30y(tickers=my_tickers)
# all unique tickers
all_tickers = gt.get_tickers()
all_tickers = list(set(all_tickers))
result = qs.quality_scores_30y(tickers=all_tickers) # takes long time
# calculate sum of decade scores and sort
result['score_sum'] = result.fillna(0).score_1990s + result.fillna(
0).score_2000s + result.fillna(0).score_2010s
result = result.sort_values(by='score_sum', ascending=False)
from get_all_tickers import get_tickers as gt
import pandas as pd
import csv
df = pd.read_csv("secwiki_tickers.csv")
dp = pd.read_csv("tickers.csv", names=["pTicker"])
list_of_tickers = gt.get_tickers()
# or if you want to save them to a CSV file
gt.save_tickers()
tmpTickers = []
stocks = []
for i in range(len(list_of_tickers)):
test = df[df.Ticker == list_of_tickers[i]]
if not (test.empty):
stocks.append((list_of_tickers[i], list(test.Name.values)[0]))
with open("stocks.csv", "w", newline="") as csv_file:
fieldnames = ["ticker", "name"]
writer = csv.writer(csv_file)
writer.writerows(stocks)
def allTickers(self):
list = gt.get_tickers()
return list
def mentions_by_ticker(self) -> dict:
tickers_mentions = {}
tickers = gt.get_tickers()
return tickers_mentions
def handle(self, *args, **kwargs):
list_of_tickers = gt.get_tickers()
for ticker in list_of_tickers:
print(ticker)
Ticker.objects.get_or_create(name=ticker, company=Company(id=1))
report.write("10 weeks high: " + str(df10w["High"].max()) + "\n")
report.write("10 weeks low: " + str(df10w["Low"].min()) + "\n")
report.write("RSI: " + str(rsi.iloc[-1]) + "\n")
report.write("SMA: " + str(sma["Close"].iloc[-1]) + "\n")
report.write("Today's volume: " + str(df10w["Volume"].iloc[-1]) + "\n")
report.write("Average volume: " + str(df10w["Volume"].mean()) + "\n")
marketCap = data.get_quote_yahoo(
input_checkForSellList[i])['marketCap']
report.write("Market capital: " +
str(millify(marketCap, precision=2)) + "\n")
report.flush()
print("Start generating Buy report ......")
report.write("\n--------------- BUY LIST ---------------\n")
stocksList = gt.get_tickers()
input_rsi_high = 200
input_rsi_low = 5
input_max_days = 50
input_amplification = 0.8
buyCount = 0
for i in range(0, len(stocksList)):
print("Checking for " + stocksList[i])
try:
marketCap = data.get_quote_yahoo(stocksList[i])['marketCap']
except:
print("exception")
continue
if (marketCap.values[0] < 2000000000):
print("Market cap below 2B BBBBBBBBBBBBBBBB")
continue
from flask import Flask, render_template, request, redirect
import os
import io
import base64
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import pandas as pds
import requests
import simplejson as json
#this list does not seem to be exhaustive, but is the best I could find
from get_all_tickers import get_tickers as gt
list_of_all_tickers = gt.get_tickers()
app = Flask(__name__)
@app.route('/')
def subform():
#landing page with submission form
return render_template('subform.html')
@app.route('/image', methods=['GET', 'POST'])
def image():
if request.method == 'GET':
#suggest that users hit up landing page directly
return render_template("landing_error.html")
if request.method == 'POST':
#get stock ticker symbol from submission on landing page
tickersym = request.form['Name']
#check if the input is a valid U.S. stock ticker symbol
def verify_info_database(currentTickerList):
return 1
# Update the info database
# Jump to each ticker in changeList and do operation based on opcode
# Args: changeDict<Dict>
# Returns 1 on success, else returns 0
def update_info_database(changeDict):
return 1
# Main
# Put all tickers in a list
list_of_all_tickers = gat.get_tickers(NYSE=True, NASDAQ=True, AMEX=False)
# Initialize the root DB csv file
# -Once initialized and the csv database is created,
# 1. init_root_database should never be called again
# 2. The root database should only be read from once created
# -Dynamically updating the database is too complicated and outside of scope for now
DB_EXISTENCE_FLAGS = [False, False, False]
if os.path.isfile("Root_Database.csv"):
print("Root DB found.")
else:
if (init_root_database(list_of_all_tickers)):
DB_EXISTENCE_FLAGS[0] = True
print("Root DB has been successfully initialized.")
def get_amex():
amex_tickers = get_tickers.get_tickers(NYSE=False, NASDAQ=False, AMEX=True)
return amex_tickers
def tickerList(self, dlTickers):
"""Import all NYSE tickers"""
if dlTickers:
list = gt.get_tickers()
return list
def get_nasdaq():
nasdaq_tickers = get_tickers.get_tickers(NYSE=False,
NASDAQ=True,
AMEX=False)
return nasdaq_tickers
