def test_statements():
fstatments = Statements()
df = fstatments.get_statements('tsla')
assert (df is not None)
with pytest.raises(ValueError, match=r"Invalid chart type 'dummy'"):
finvizfinance('tsla').ticker_charts(charttype='dummy')
def test_finvizfinance_finvizfinance_chart(mocker):
mocker.patch('finvizfinance.quote.finvizfinance._checkexist',
return_value=True)
mocker.patch('finvizfinance.quote.web_scrap', return_value="")
image_scrap_mock = mocker.patch('finvizfinance.quote.image_scrap')
finvizfinance('dummy').ticker_charts()
image_scrap_mock.assert_called_with(
'https://finviz.com/chart.ashx?t=dummy&ty=c&ta=1&p=d', 'dummy', '')
def view(other_args: List[str], ticker: str):
"""View historical price with trendlines. [Source: Finviz]
Parameters
----------
other_args : List[str]
argparse other args
ticker: str
stock ticker
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="view",
description="""
View historical price with trendlines. [Source: Finviz]
""",
)
try:
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
stock = finvizfinance(ticker)
stock.TickerCharts()
img = Image.open(ticker + ".jpg")
img.show()
print("")
except SystemExit:
print("")
def sub_news(request):
"""
Show news and sentiment of ticker in /ticker?quote={TICKER}. Data from Finviz
Note: News are only available if hosted locally. Read README.md for more details
"""
ticker_selected = default_ticker(request)
ticker_fin = finvizfinance(ticker_selected)
news_df = ticker_fin.TickerNews()
news_df["Date"] = news_df["Date"].dt.date
link = news_df["Link"].to_list()
del news_df["Link"]
# Get sentiment of each news title and add it to a new column in news_df
sentiment_list = list()
all_titles = news_df['Title'].tolist()
for title in all_titles:
vs = analyzer.polarity_scores(title)
sentiment_score = vs['compound']
if sentiment_score > 0.25:
sentiment = "Bullish"
elif sentiment_score < -0.25:
sentiment = "Bearish"
else:
sentiment = "Neutral"
sentiment_list.append(sentiment)
news_df["Sentiment"] = sentiment_list
news_df = news_df.to_html(index=False)
return render(request, 'iframe_format.html', {"title": "News", "table": news_df, "link": link,
"class": "ticker_news"})
def news_sentiment():
"""
Get the news sentiment score of popular tickers from Finviz news
"""
print("-" * 100)
print("Getting News Sentiment From Finviz ...")
for ticker_selected in interested_tickers:
ticker_fin = finvizfinance(ticker_selected)
news_df = ticker_fin.TickerNews()
news_df["Date"] = news_df["Date"].dt.date
del news_df["Link"]
all_titles = news_df[news_df['Date'] == news_df["Date"].unique()
[0]]['Title'].tolist()
num_rows = 0
total_score = 0
for title in all_titles:
vs = analyzer.polarity_scores(title)
sentiment_score = vs['compound']
if sentiment_score != 0:
num_rows += 1
total_score += sentiment_score
if num_rows == 0:
avg_score = 25
else:
avg_score = round((total_score / num_rows) * 100, 2)
db.execute("INSERT INTO news_sentiment VALUES (?, ?, ?)",
(ticker_selected, avg_score, date_updated))
conn.commit()
print("News sentiment of {} completed!".format(ticker_selected))
def compare(self, ticker, compare_list, order='ticker', verbose=1):
"""Get screener table of similar property (Sector, Industry, Country)
Args:
ticker(str): the ticker to compare
compare_list(list): choice of compare property (Sector, Industry, Country) or combination.
order(str): sort the table by the choice of order
verbose(int): choice of visual the progress. 1 for visualize progress
Returns:
df(pandas.DataFrame): screener information table
"""
check_list = ['Sector', 'Industry', 'Country']
error_list = [i for i in compare_list if i not in check_list]
if len(error_list) != 0:
print('Please check: {}'.format(error_list))
raise ValueError()
stock = finvizfinance(ticker)
stock_fundament = stock.TickerFundament()
filters_dict = {}
for compare in compare_list:
filters_dict[compare] = stock_fundament[compare]
self.set_filter(filters_dict=filters_dict)
df = self.ScreenerView(order=order, verbose=verbose)
return df
def fundamental_data(ticker):
stock_info = finvizfinance(ticker)
data = {}
data = stock_info.TickerFundament()
print(data['Company'])
print(data['Industry'])
print('P/E: ' + data['P/E'])
print('EPS: ' + data['EPS (ttm)'])
print('S/O: ' + data['Shs Outstand'])
print('Beta: ' + data['Beta'])
'''
Lets define if the stock is in a positive trend or not
to do that, we will use SMA50 and SMA200
if both SMA are positive, we will say the stock is in a Strong positive trend
if SMA200 is positive but SMA50 negative, its in a Weak positive trend
if both SMA are negative, Strong negative trend
finally, if SMA200 negative but SMA50 positive, Weak negative trend
There are better ways to define trend, but this will be a useful quick way to do it
'''
sma200 = str(data['SMA200'])
sma50 = str(data['SMA50'])
print('---------------------------')
print('SMA200: ' + sma200)
print('SMA50: ' + sma50)
if sma200.startswith('-'):
if sma50.startswith('-'):
print(ticker + ' is in a Strong negative trend')
else:
print(ticker + ' is in a Weak negative trend')
else:
if sma50.startswith('-'):
print(ticker + ' is in a Weak positive trend')
else:
print(ticker + ' is in a Strong positive trend')
#Print Earnings table
stock = yf.Ticker(ticker)
cash_flows = stock.cashflow
cash_flows = cash_flows / 1000000
df = stock.earnings
df = df / 1000000
df['Growth (in %)'] = df['Earnings'].pct_change() * 100
sort_df = df.sort_values(by='Year', ascending=False)
print('---------------------------')
print('Stock earnings in $M:')
print(sort_df)
print('---------------------------')
#Lets do a Free Cash Flow calculation to see how healthy the company is
print('Free Cash Flow per year:')
ndf = pd.DataFrame()
ndf['cfo'] = cash_flows.loc['Total Cash From Operating Activities']
ndf['capex'] = cash_flows.loc['Capital Expenditures']
ndf['fcf'] = ndf['cfo'] + ndf['capex']
print(ndf)
def downSignalChart():
for signal in signals:
fticker = Ticker()
fticker.set_filter(signal=signal)
tickers = fticker.ScreenerView()
for ticker in tqdm(tickers):
stock = finvizfinance(ticker)
stock.TickerCharts(out_dir='../website/src/charts/signal/' +
signal)
def app():
st.header("Financial Forecasting for S&P 500 Stocks")
st.write(
"This section will showcase our LSTM model price prediction, Technical Analysis, News Sentiment Analysis, Analyst's Ratings of your chosen stock."
)
st.write(
"**Disclaimer: Historical data instead of real-time data are used in this section. We do not ensure the accuracy of our prediction model and shall not be liable for any financial loss for your investment."
)
st.markdown("***")
st.sidebar.header('User Input Parameters')
symbol, start, end = user_input_features()
start = pd.to_datetime(start)
end = pd.to_datetime(end)
#symbol1 = get_symbol(symbol.upper())
#st.header(f""" {symbol1} """)
stock = finvizfinance(symbol.lower())
stock_chart = stock.ticker_charts()
st.image(stock_chart)
# ## Technical Analysis
st.subheader("Technical Forecast: ")
technical(symbol)
ta_1d = pd.read_csv("ta_1d.csv")
st.dataframe(ta_1d)
st.markdown("***")
# ## News Sentiment Analysis
st.subheader("News Sentiment Analysis: ")
st.table(news_sentiment(symbol).head(5))
st.markdown("***")
# ## Analyst Ratings
st.subheader("Analyst Ratings: ")
st.table(get_analyst_price_targets(symbol))
st.markdown("***")
# ##LSTM
st.subheader("LSTM Model Prediction: ")
st.write(
"This section will show how LSTMs can be used to learn the patterns in the stock prices. Using this model you will be able to predict tomorrow’s price of your stock based on the last 10 days prices."
)
st.write(
"It will take around 5 minutes to generate the result. Please be patient."
)
lstm(symbol, start, end)
st.markdown("***")
st.write(
"Disclaimer: The data are collected from Google, Yahoo Finance and Finviz"
)
def getFinancials(self):
# get from finvizfinance
fstock = finvizfinance(self._ticker)
self.stock_fundament = fstock.TickerFundament()
# get statements
statements = Statements()
self.income_statement = statements.getStatements(self._ticker,
statement="I")
self.balance_sheet_statement = statements.getStatements(self._ticker,
statement="B")
def getData(self):
self.log.logger.debug(" About to retreive " + self.symbol)
stock = finvizfinance(self.symbol)
self.log.logger.debug(" For: " + self.symbol + " data:" +
str(stock.TickerFundament()))
df = pd.DataFrame(stock.TickerFundament().items(),
columns=['key', 'value'])
pd.set_option("display.max_rows", None, "display.max_columns", None)
self.log.logger.debug(" Values loaded: \n" + str(df))
self.log.logger.debug(" DONE - Stock Public Data Fetcher " +
str(self.symbol))
return df
def get_symbol(symbol):
try:
stock = finvizfinance(symbol)
company_name = stock.ticker_fundament()
com = list(company_name.values())[0]
return com
#url = "http://d.yimg.com/autoc.finance.yahoo.com/autoc?query={}®ion=1&lang=en".format(symbol)
#result = requests.get(url).json()
#for x in result['ResultSet']['Result']:
#if x['symbol'] == symbol:
#return x['name']
except Exception as e:
return e
def get_finviz_image(ticker: str) -> bytes:
"""Get finviz image for given ticker
Parameters
----------
ticker : str
ticker
Returns
-------
bytes
Image in byte format
"""
stock = finvizfinance(ticker)
image_url = stock.ticker_charts(urlonly=True)
r = requests.get(image_url, stream=True, headers=headers, timeout=5)
r.raise_for_status()
r.raw.decode_content = True
return r.content
def getIntradayData(self):
self.log.logger.debug(" About to retreive " + self.symbol)
stock = finvizfinance(self.symbol)
self.log.logger.debug(" For: " + self.symbol + " data:" +
str(stock.TickerFundament()))
df = pd.DataFrame()
# df = pd.DataFrame(columns=['stock', 'price', '%Change', '%Volume'])
df = df.append(
{
'stock':
self.symbol,
'price':
stock.TickerFundament().get('Price'),
'%Change':
float(stock.TickerFundament().get('Change').strip('%')) / 100,
'%Volume':
float(stock.TickerFundament().get('Rel Volume'))
},
ignore_index=True)
self.log.logger.debug(" Values loaded: \n" + str(df))
self.log.logger.debug(
" DONE - Stock Intraday Public Data Fetcher " +
str(self.symbol))
return df
import pandas as pd
import nltk
import matplotlib.pyplot as plt
from nltk.stem import WordNetLemmatizer
from nltk.sentiment.vader import SentimentIntensityAnalyzer
nltk.download('vader_lexicon')
from finvizfinance.quote import finvizfinance
stock = finvizfinance('tsla')
df = stock.TickerNews()
df = df[['Date', 'Title']]
senti = SentimentIntensityAnalyzer()
def get_sentiment_score(review):
try:
compound_score = senti.polarity_scores(review)['compound']
return compound_score
except:
return "error"
df["Sentiment_score"] = df["Title"].apply(get_sentiment_score)
df2 = df[df['Sentiment_score'] != 0]
df2 = df2.reset_index(drop=True)
plt.figure(figsize=(10, 8))
plt.plot('Date', 'Sentiment_score', data=df2)
plt.xticks(rotation=50)
plt.show()
def get_symbol(symbol):
stock = finvizfinance(symbol)
company_name = stock.ticker_fundament()
com = list(company_name.values())[0]
return com
def app():
st.write("""
# S&P 500 Stock Analyzer
Shown below are the **Fundamentals**, **News Sentiment**, **Bollinger Bands** and **Comprehensive Report (Compared with SPY as a whole as benchmark)** of your selected stock!
""")
st.markdown("***")
st.sidebar.header('User Input Parameters')
symbol, start, end = user_input_features()
start = pd.to_datetime(start)
end = pd.to_datetime(end)
#symbol1 = get_symbol(symbol.upper())
#st.subheader(symbol1)
stock = finvizfinance(symbol.lower())
stock_chart = stock.ticker_charts()
st.image(stock_chart)
# Read data
data = yf.download(symbol, start, end, threads=False)
# ## SMA and EMA
#Simple Moving Average
data['SMA'] = talib.SMA(data['Adj Close'], timeperiod=20)
# Exponential Moving Average
data['EMA'] = talib.EMA(data['Adj Close'], timeperiod=20)
# Plot
st.subheader(f"""
Simple Moving Average vs. Exponential Moving Average\n {symbol}
""")
st.line_chart(data[['Adj Close', 'SMA', 'EMA']])
# Bollinger Bands
data['upper_band'], data['middle_band'], data['lower_band'] = talib.BBANDS(
data['Adj Close'], timeperiod=20)
# Plot
st.subheader(f"""
Bollinger Bands\n {symbol}
""")
st.line_chart(
data[['Adj Close', 'upper_band', 'middle_band', 'lower_band']])
# ## MACD (Moving Average Convergence Divergence)
# MACD
data['macd'], data['macdsignal'], data['macdhist'] = talib.MACD(
data['Adj Close'], fastperiod=12, slowperiod=26, signalperiod=9)
# Plot
st.subheader(f"""
Moving Average Convergence Divergence\n {symbol}
""")
st.line_chart(data[['macd', 'macdsignal']])
# ## RSI (Relative Strength Index)
# RSI
data['RSI'] = talib.RSI(data['Adj Close'], timeperiod=14)
# Plot
st.subheader(f"""
Relative Strength Index\n {symbol}
""")
st.line_chart(data['RSI'])
st.markdown("***")
st.subheader("Fundamentals: ")
st.dataframe(get_fundamentals(symbol))
st.markdown("***")
# ## Latest News
st.subheader("Latest News: ")
st.table(get_news(symbol).head(5))
st.markdown("***")
# ## Recent Insider Trades
st.subheader("Recent Insider Trades: ")
st.table(get_insider(symbol).head(5))
st.markdown("***")
st.write("Generating comprehensive stock report...")
st.write("**please wait for some time... **")
st.write(
"This section will compare the historical performance of your selected stock with SPDR S&P 500 Trust ETF (Ticker: SPY) as benchmark."
)
stock_report(symbol)
# ## Stock report
st.subheader(f"""**{symbol} Stock Report**""")
#st.header(symbol + " Stock Report")
HtmlFile = open("report.html", 'r', encoding='utf-8')
source_code = HtmlFile.read()
#print(source_code)
components.html(source_code, height=9000)
st.write(
"Disclaimer: The data are collected from Google, Yahoo Finance and Finviz"
)
def latest_news(request):
"""
Show news and sentiment of ticker in /latest_news?quote={TICKER}. Data from Finviz
This is more detailed (graphs included) than sub_news()
Note: News are only available if hosted locally. Read README.md for more details
"""
ticker_selected = default_ticker(request)
ticker = yf.Ticker(ticker_selected)
information = check_market_hours(ticker, ticker_selected)
# To check if input is a valid ticker
if "symbol" in information:
ticker_fin = finvizfinance(ticker_selected)
news_df = ticker_fin.TickerNews()
news_df["Date"] = news_df["Date"].dt.date
link = news_df["Link"].to_list()
del news_df["Link"]
sentiment_list = list()
all_news = news_df['Title'].tolist()
for title in all_news:
vs = analyzer.polarity_scores(title)
sentiment_score = vs['compound']
if sentiment_score > 0.25:
sentiment = "Bullish"
elif sentiment_score < -0.25:
sentiment = "Bearish"
else:
sentiment = "Neutral"
sentiment_list.append(sentiment)
news_df["Sentiment"] = sentiment_list
num_rows = 0
total_score = 0
latest_date = news_df["Date"].unique()[0]
today_news = news_df[news_df['Date'] == latest_date]['Title'].tolist()
for title in today_news:
vs = analyzer.polarity_scores(title)
sentiment_score = vs['compound']
if sentiment_score != 0:
num_rows += 1
total_score += sentiment_score
if num_rows == 0:
avg_score = 25
else:
avg_score = round((total_score / num_rows) * 100, 2)
db.execute("UPDATE news_sentiment SET sentiment=? WHERE ticker=? AND date_updated=?",
(avg_score, ticker_selected, str(datetime.now()).split()[0]))
conn.commit()
db.execute("SELECT * FROM news_sentiment WHERE date_updated=?", (str(datetime.now()).split()[0],))
ticker_sentiment = db.fetchall()
days = 1
while not ticker_sentiment:
db.execute("SELECT * FROM news_sentiment WHERE date_updated=?", (str(datetime.now()-timedelta(days=days)).split()[0],))
ticker_sentiment = db.fetchall()
days += 1
ticker_sentiment = list(map(list, ticker_sentiment))
return render(request, 'news_sentiment.html', {"ticker_selected": ticker_selected,
"information": information,
"news_df": news_df.to_html(index=False),
"link": link,
"ticker_sentiment": ticker_sentiment,
"latest_date": latest_date,
"avg_score": avg_score})
else:
included_list = ", ".join(sorted(full_ticker_list()))
return render(request, 'news_sentiment.html', {"ticker_selected": ticker_selected,
"included_list": included_list,
"error": "error_true"})
def test_finvizfinance_finvizfinance():
stock = finvizfinance('tsla')
stock_info = stock.ticker_full_info()
assert (stock_info is not None)
def get_fundamentals(ticker):
return finvizfinance(ticker).TickerFundament()
import pandas as pd
from finvizfinance.quote import finvizfinance
pd.set_option('display.max_columns', None)
stocks = pd.read_csv('ownership(NASDAQ).csv')
stocks = stocks[['Ticker', 'Inst Own']]
stocks.dropna(inplace=True)
I_filter = stocks['Inst Own'] > 0.9
I_select_stocks = stocks[I_filter]['Ticker'].values
print(len(I_select_stocks), I_select_stocks)
full_news_list = []
for i in I_select_stocks:
pstock = finvizfinance(str(i))
news_df = pstock.TickerNews()
news_df['symbol'] = str(i)
print(str(i))
full_news_list.append(news_df)
full_news = pd.concat(full_news_list)
print(full_news)
# full_news.to_csv('news.csv', index=False)
def test_finvizfinance_finvizfinance_chart_invalid(mocker):
mocker.patch('finvizfinance.quote.finvizfinance._checkexist',
return_value=True)
mocker.patch('finvizfinance.quote.web_scrap', return_value="")
with pytest.raises(ValueError, match=r"Invalid timeframe 'dummy'"):
finvizfinance('tsla').ticker_charts(timeframe='dummy')
def test_finvizfinance_quote():
stock = finvizfinance('tsla')
stock_info = stock.TickerFullInfo()
assert (stock_info is not None)
def fetchChart():
for ticker in tqdm(ticker_list):
stock = finvizfinance(ticker)
stock.TickerCharts(out_dir='../website/src/charts/watchlist')
