def yahoo_finance_functions():
si.get_data("AAPL")
si.get_day_gainers()
si.get_day_losers()
si.get_day_most_active()
si.get_analysts_info("AAPL")
stock = si.get_live_price("AAPL")
def import_yahoofinance2():
# import stock_info module from yahoo_fin
from yahoo_fin import stock_info as si
# get live price of Apple
si.get_live_price("aapl")
# or Amazon
si.get_live_price("amzn")
# or any other ticker
si.get_live_price(ticker)
# get quote table back as a data frame
si.get_quote_table("aapl", dict_result = False)
# or get it back as a dictionary (default)
si.get_quote_table("aapl")
si.get_top_crypto()
# get most active stocks on the day
si.get_day_most_active()
# get biggest gainers
si.get_day_gainers()
# get worst performers
si.get_day_losers()
async def perdedores(ctx):
await ctx.send("espera.. OwO")
temp = si.get_day_losers().head(10).drop(
["Market Cap", "Volume", "Avg Vol (3 month)"], axis=1)
await ctx.send(embed=crearEmbed(
ctx, temp, 'Perdedores', 'Lista de top 10 perdedores',
'https://pbs.twimg.com/media/ES9L6NIXQAAFlnO.jpg', 0xFF0000))
def post_market(self):
root = {}
root["market"] = {}
""" Market Node """
day_gainers = si.get_day_gainers().to_dict(orient="dict")
day_losers = si.get_day_losers().to_dict(orient="dict")
top_crypto = si.get_top_crypto().to_dict(orient="dict")
most_active = si.get_day_most_active().to_dict(orient="dict")
new_node = {"Day_Gainers" : day_gainers,
"Day_Losers" : day_losers,
"Top_Crypto" : top_crypto,
"Most_Active" : most_active}
for node_key in new_node.keys():
now = str(datetime.date(datetime.now()))
key_var = now
self.post(key_var, new_node[node_key], "Market", node_key)
time.sleep(.5)
print('Sucess\n')
def loser():
losers=get_day_losers()
day_loser1=losers['Symbol']
price_day_loser1=losers['Price (Intraday)']
change_day_loser1=losers['% Change']
dl1_tick=(day_loser1[0])
dl1_price=('${:,.2f}'.format(price_day_loser1[0]))
dl1_change=('{:.2%}'.format(change_day_loser1[0]/100))
day_loser2=losers['Symbol']
price_day_loser2=losers['Price (Intraday)']
change_day_loser2=losers['% Change']
dl2_tick=(day_loser2[1])
dl2_price=('${:,.2f}'.format(price_day_loser2[1]))
dl2_change=('{:.2%}'.format(change_day_loser2[1]/100))
day_loser3=losers['Symbol']
price_day_loser3=losers['Price (Intraday)']
change_day_loser3=losers['% Change']
dl3_tick=(day_loser3[2])
dl3_price=('${:,.2f}'.format(price_day_loser3[2]))
dl3_change=('{:.2%}'.format(change_day_loser3[2]/100))
tickers=(f"The top losers are {dl1_tick}, {dl2_tick}, and {dl3_tick}.\n")
prices=(f"Their prices are currently {dl1_price}, {dl2_price}, and {dl3_price}.\n")
change=(f"Their prices dropped by {dl1_change}, {dl2_change}, and {dl3_change}.")
return(tickers+prices+change)
def submit():
if request.method == 'POST':
if request.form.get("TopGainers"):
top_gainers = si.get_day_gainers()
file_name = "TopGainers-"+ str(datetime.today()).split()[0] + '.csv'
return Response(
top_gainers.to_csv(),
mimetype="text/csv",
headers={"Content-disposition":
f"attachment; filename={file_name}"})
elif request.form.get("TopLosers"):
top_losers = si.get_day_losers()
file_name = "TopLosers-"+ str(datetime.today()).split()[0] + '.csv'
return Response(
top_losers.to_csv(),
mimetype="text/csv",
headers={"Content-disposition":
f"attachment; filename={file_name}"})
elif request.form.get("MostActives"):
most_active_stock = si.get_day_most_active()
file_name = "MostActives-" + str(datetime.today()).split()[0] + '.csv'
return Response(
most_active_stock.to_csv(),
mimetype="text/csv",
headers={"Content-disposition":
f"attachment; filename={file_name}.csv"})
def getDayMovers():
global gainers, losers, activity, gainerdata, loserdata, activedata
gainerdata = pd.DataFrame(si.get_day_gainers())[['Symbol', 'Price (Intraday)', '% Change']]
gainerdata['Price (Intraday)'] = "$" + gainerdata['Price (Intraday)'].astype(str)
gainerdata['% Change'] = "+" + gainerdata['% Change'].astype(str) + "%"
gainerdata['Combined'] = gainerdata[gainerdata.columns[0:]].apply(
lambda x: ':'.join(x.dropna().astype(str)),
axis=1
)
loserdata = pd.DataFrame(si.get_day_losers())[['Symbol', 'Price (Intraday)', '% Change']]
loserdata['Price (Intraday)'] = "$" + loserdata['Price (Intraday)'].astype(str)
loserdata['% Change'] = loserdata['% Change'].astype(str) + "%"
loserdata['Combined'] = loserdata[loserdata.columns[0:]].apply(
lambda x: ':'.join(x.dropna().astype(str)),
axis=1
)
activedata = pd.DataFrame(si.get_day_most_active())[['Symbol', 'Price (Intraday)', '% Change']]
activedata['Price (Intraday)'] = "$" + activedata['Price (Intraday)'].astype(str)
activedata['% Change'] = activedata['% Change'].astype(str) + "%"
activedata['Combined'] = activedata[activedata.columns[0:]].apply(
lambda x: ':'.join(x.dropna().astype(str)),
axis=1
)
# sets the list box data
gainers.set('\n'.join(gainerdata['Combined']))
losers.set('\n'.join(loserdata['Combined']))
activity.set('\n'.join(activedata['Combined']))
def Daily_info():
win = yf.get_day_gainers()
win = win.sort_values(by='% Change',ascending = False).head(5)
lose = yf.get_day_losers()
lose = lose.sort_values(by='% Change',ascending = True).head(5)
active = yf.get_day_most_active()
active = active.head(5)
return win,lose,active
def biggestloser():
"""Returns: stock with the the biggest losses in a given day and
its stock price with format: 'TICKER': 'PRICE'."""
day_losers = si.get_day_losers()
output = str(day_losers.at[0, 'Symbol']) + ': ' + str(
round(si.get_live_price(day_losers.at[0, 'Symbol']), roundNumber))
return output
def get_bottom_tickers():
bottom_tickers = si.get_day_losers()
bottom_array = []
for x in range(10):
bottom_array.append((str(bottom_tickers['Symbol'][x])))
return jsonify(bottom_array)
def topl(self):
toploss = si.get_day_losers()
self.listBox.delete(*self.listBox.get_children())
for x in range(25):
self.listBox.insert(
"",
"end",
values=(toploss.loc[x, 'Symbol'], toploss.loc[x, 'Name'],
'$' + str(toploss.loc[x, 'Price (Intraday)']),
toploss.loc[x, 'Change'], toploss.loc[x, '% Change']))
def worst(update, context):
stock_list = si.get_day_losers()
output = "Stock Code \t\t\t TTM\n\n"
for i in range(0, 10):
if math.isnan(stock_list.iloc[i, -1]):
ttm_val = "---"
else:
ttm_val = str(format(stock_list.iloc[i, -1], '.2f'))
output += str(i + 1) + ". " + str(
stock_list.iloc[i, 0]).upper() + " \t\t\t " + ttm_val + ' \n'
print(output)
context.bot.send_message(chat_id=update.effective_chat.id, text=output)
def get_stock_movers(number_of):
gainers = get_day_gainers()
gainers = gainers.head(number_of)
gainers = gainers[["Name", "Symbol", "% Change"]]
gainers["type"] = "gainer"
losers = get_day_losers()
losers = losers.head(number_of)
losers = losers[["Name", "Symbol", "% Change"]]
losers["type"] = "loser"
losers = losers.reindex(index=losers.index[::-1])
movers = gainers.append(losers, ignore_index=True)
movers = movers.rename(columns={"% Change": "Change"})
return movers
def functions_type():
#VERIF TAILLE DU TABLEAU
if var_text.get() > 0 and var_text.get() < 101:
if type_ == "stock_gainers":
tab = si.get_day_gainers()
elif type_ == "stock_losers":
tab = si.get_day_losers()
elif type_ == "crypto":
tab = si.get_top_crypto()
else:
print("error1")
#convert in table
tab = np.array(tab)
affichage_tab(tab)
def Download_pulse(self):
self.pulse_result = {}
# get most active stocks on the day
if asyncio.get_event_loop().is_running(
): # Only patch if needed (i.e. running in Notebook, Spyder, etc)
import nest_asyncio
nest_asyncio.apply()
self.pulse_result['most_active'] = si.get_day_most_active()
# get biggest gainers
self.pulse_result['gainer'] = si.get_day_gainers()
# get worst performers
self.pulse_result['loser'] = si.get_day_losers()
print('_____________Market Pulse Download Done________________')
def days_loser(self):
loser = get_day_losers()
df = pd.DataFrame(
loser,
columns=['Symbol', 'Price (Intraday)', 'Change', '% Change'])
df1 = df.head(5)
print(df1)
data = []
for ind in df1.index:
dic = {
'symbol': df1['Symbol'][ind],
'price': df1['Price (Intraday)'][ind],
'change': df1['Change'][ind],
'per_change': df1['% Change'][ind]
}
data.append(dic)
return data
def overview(request):
if not Profile.objects.filter(email=request.user.email).exists():
ins = Profile(email=request.user.email)
ins.save()
if request.user.is_authenticated:
if (Buystock.objects.filter(username=request.user.username).exists()):
all = Buystock.objects.filter(username=request.user.username)
for i in all:
from yahoo_fin import stock_info as si
r = si.get_live_price(i.ticker)
s = Stockd.objects.get(ticker=i.ticker,
username=request.user.username)
s.lastprice = r
s.change = (s.lastprice / s.price) * 100
s.save()
if (s.lastprice > s.price):
s.type = 'Profit'
s.profit = s.quantity * (s.lastprice - s.price)
s.change -= 100
elif (s.lastprice < s.price):
s.type = 'Loss'
s.profit = s.quantity * (s.price - s.lastprice)
s.change = 100 - s.change
else:
s.type = 'Stable'
s.profit = 0.00
s.change = 0.00
s.profit = round(s.profit, 2)
print(s.profit)
s.save()
else:
messages.warning(request, "Till Now No Stocks are bought")
from yahoo_fin import stock_info as si
r = si.get_day_most_active().head(5)
r.rename(columns={
'Market Cap': 'Market',
'Price (Intraday)': 'Price'
},
inplace=True)
json_records = r.reset_index().to_json(orient='records')
data = []
data = json.loads(json_records)
g = si.get_day_gainers().head(5)
g.rename(columns={
'Market Cap': 'Market',
'Price (Intraday)': 'Price',
'% Change': 'change'
},
inplace=True)
json_records = g.reset_index().to_json(orient='records')
datag = []
datag = json.loads(json_records)
lost = si.get_day_losers().head(5)
lost.rename(columns={
'Market Cap': 'Market',
'Price (Intraday)': 'Price',
'% Change': 'change'
},
inplace=True)
json_records = lost.reset_index().to_json(orient='records')
datal = []
datal = json.loads(json_records)
if Stockd.objects.filter(username=request.user.username).exists(
) and Buystock.objects.filter(username=request.user.username).exists():
st = Stockd.objects.filter(username=request.user.username)
po = Profile.objects.get(email=request.user.email)
return render(request, 'Stock/overview.html', {
'd': st,
'r': data,
'g': datag,
'l': datal,
'pho': po
})
else:
po = Profile.objects.get(email=request.user.email)
return render(request, 'Stock/overview.html', {
'r': data,
'g': datag,
'l': datal,
'pho': po
})
else:
auth.logout(request)
return render(request, 'login/login.html')
########################################################################################
# import stock_info module from yahoo_fin
from yahoo_fin import stock_info as si
# get live price of Apple
si.get_live_price("aapl")
# or Amazon
si.get_live_price("amzn")
# or any other ticker
si.get_live_price(ticker)
# get quote table back as a data frame
si.get_quote_table("aapl", dict_result=False)
# or get it back as a dictionary (default)
si.get_quote_table("aapl")
# get most active stocks on the day
si.get_day_most_active()
# get biggest gainers
si.get_day_gainers()
# get worst performers
si.get_day_losers()
############################################################################################
async def day_losers(ctx):
await ctx.send(si.get_day_losers())
from yahoo_fin import stock_info as si
from datetime import date, datetime
import pandas as pd
# gainer
si_day_gainer = si.get_day_gainers()
si_day_gainer['day_gainer'] = 'day_gainer'
si_day_gainer['modified_date'] = datetime.today().strftime('%Y-%m-%d')
si_day_gainer.to_csv('data/day_gainer/day_gainer_' +
datetime.today().strftime('%Y-%m-%d') + '.csv')
# loser
si_day_loser = si.get_day_losers()
si_day_loser['day_loser'] = 'day_loser'
si_day_loser['modified_date'] = datetime.today().strftime('%Y-%m-%d')
si_day_loser.to_csv('data/day_loser/day_loser_' +
datetime.today().strftime('%Y-%m-%d') + '.csv')
def main():
st.title("Welcome to Predict Future of Stocks.")
menu = ["Home", "Stock Prediction using ML"]
choice = st.sidebar.selectbox("Menu", menu)
if choice == "Home":
st.subheader("Recommendations")
si.get_day_most_active()
st.subheader("Today's Most Active Users")
st.write(si.get_day_most_active())
si.get_day_gainers()
st.subheader("Today's Top Gainers")
st.write(si.get_day_gainers())
si.get_day_losers()
st.subheader("Today's Top Losers")
st.write(si.get_day_losers())
elif choice == "Stock Prediction using ML":
st.subheader("Stock Prediction using ML")
START = "2015-01-01"
TODAY = date.today().strftime("%Y-%m-%d")
selected_stock = st.text_input("Type Stocks's name...")
submit = st.button('Search')
if submit:
si.get_live_price(selected_stock)
st.write("Live Price : ", si.get_live_price(selected_stock))
si.get_market_status()
st.write("Market state : ", si.get_market_status())
n_years = st.slider("Years of prediction:", 1, 10)
period = n_years * 365
def load_data(ticker):
data = yf.download(ticker, START, TODAY)
data.reset_index(inplace=True)
return data
data_load_state = st.text('Loading data...')
data = load_data(selected_stock)
data_load_state.text('Loading data... done!')
st.subheader('Raw data')
st.write(data.tail())
# Plot raw data
def plot_raw_data():
fig = go.Figure()
fig.add_trace(
go.Scatter(x=data['Date'],
y=data['Open'],
name="stock_open"))
fig.add_trace(
go.Scatter(x=data['Date'],
y=data['Close'],
name="stock_close"))
fig.layout.update(
title_text='Time Series data with Rangeslider',
xaxis_rangeslider_visible=True)
st.plotly_chart(fig)
plot_raw_data()
# Predict forecast with Prophet.
df_train = data[['Date', 'Close']]
df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})
m = Prophet()
m.fit(df_train)
future = m.make_future_dataframe(periods=period)
forecast = m.predict(future)
# Show and plot forecast
st.subheader('Forecast data')
st.write(forecast.tail())
st.write(f'Forecast plot for {n_years} years')
fig1 = plot_plotly(m, forecast)
st.plotly_chart(fig1)
st.write("Forecast components")
fig2 = m.plot_components(forecast)
st.write(fig2)
import yfinance as yf
from yahoo_fin import stock_info as si
import re
# def getData(ticker):
# while True:
# data = yf.Ticker(ticker)
# print("Current Price: " + str(data.info['regularMarketPrice']))
# print("Market Open: " + str(data.info['regularMarketOpen']))
# print("Market Close: " + str(data.info['previousClose']))
# print("50 Day Average: " + str(data.info['fiftyDayAverage']))
# print("200 Day Average: " + str(data.info['twoHundredDayAverage']))
# print(data.info)
#getData('GME')
print("Enter stock ticker: ")
ticker = input()
print("Current Price: " + str(si.get_live_price(ticker)))
# get most active stocks on the day
print("Most Active:\n " + str(si.get_day_most_active()))
# get biggest gainers
print("Biggest Gainers:\n " + str(si.get_day_gainers()))
# get worst performers
print("Worst performers:\n " + str(si.get_day_losers()))
def getLosers():
x = si.get_day_losers()
print(x)
import matplotlib.pyplot as plt
from datetime import date
import yfinance as yf
import pandas as pd
from yahoo_fin import stock_info as si
import mplcursors
symbol1 = "AAPL"
stockOne = yf.Ticker(symbol1)
dailyData = pd.DataFrame(stockOne.history(period="1d", interval="1m"))['Open']
dailyData.index = dailyData.index.strftime("%H:%M:%S")
gainers = pd.DataFrame(si.get_day_gainers())[['Price (Intraday)', '% Change']]
gainers.index = pd.DataFrame(si.get_day_gainers())['Symbol']
losers = pd.DataFrame(si.get_day_losers())[['Price (Intraday)', '% Change']]
losers.index = pd.DataFrame(si.get_day_losers())['Symbol']
active = pd.DataFrame(
si.get_day_most_active())[['Price (Intraday)', '% Change']]
active.index = pd.DataFrame(si.get_day_most_active())['Symbol']
print(pd.DataFrame(si.tickers_dow()))
async def on_message(message):
print(message.content)
if message.content.find("!") != -1:
content = message.content.strip('!').lower().split()
print(content)
print(content[0])
if content[0] == "day":
if content[1] == "gain":
await message.channel.send(
si.get_day_gainers().head(10).iloc[:, :3])
await message.channel.send(
"-------------------------------------------")
await message.channel.send(
si.get_day_gainers().head(10).iloc[:, 4:7])
elif content[1] == "lose":
await message.channel.send(
si.get_day_losers().head(10).iloc[:, :3])
await message.channel.send(
"-------------------------------------------")
await message.channel.send(
si.get_day_losers().head(10).iloc[:, 4:7])
elif content[1] == "active":
await message.channel.send(
si.get_day_most_active().head(10).iloc[:, :3])
await message.channel.send(
"-------------------------------------------")
await message.channel.send(
si.get_day_most_active().head(10).iloc[:, 4:7])
elif content[0] == "crypto":
await message.channel.send(si.get_top_crypto().head(10).iloc[:, :3]
)
await message.channel.send(
"-------------------------------------------")
await message.channel.send(si.get_top_crypto().head(10).iloc[:,
4:5])
elif content[0] == "help":
embedVar = discord.Embed(title="List of functioning commands",
description="",
colour=0x00ff00)
embedVar.add_field(name="\u200b",
value="!tsla\n!day gain\n!day loss",
inline=True)
embedVar.add_field(
name="\u200b",
value="!calls tlsa 03/19/2021\n!puts tlsa 03/19/2021",
inline=True)
await message.channel.send(embed=embedVar)
elif content[0] == "calls":
await message.channel.send(
op.get_calls(content[1], content[2]).iloc[:, 2:8])
elif content[0] == "puts":
await message.channel.send(
op.get_puts(content[1], content[2]).iloc[:, 2:8])
else:
temp = si.get_quote_table(content[0])
change = round(temp["Quote Price"] - temp["Previous Close"], 2)
percentage = round(change / temp["Previous Close"] * 100, 2)
displayQuote = str(round(temp["Quote Price"], 2))
displayChange = str(change)
displayPercentage = str(percentage)
displayTicker = content[0].upper()
displayClose = str(round(temp["Previous Close"], 2))
dayRange = temp["Day's Range"].replace('-', '').split()
dayLow = dayRange[0]
dayHigh = dayRange[1]
open = temp["Open"]
close = temp["Previous Close"]
volume = str(round(temp["Volume"] / 1000000, 2))
volume = volume + "M"
avgVolume = str(round(temp["Avg. Volume"] / 1000000, 2))
avgVolume = avgVolume + "M"
bid = temp["Bid"]
ask = temp["Ask"]
if change >= 0:
rgb = 0x00ff00
displayChange = "+" + displayChange
displayPercentage = "+" + displayPercentage
else:
rgb = 0xff0000
embedVar = discord.Embed(
title=
f"${displayTicker}\n${displayQuote} {displayChange} ({displayPercentage}%)",
description="",
colour=rgb)
embedVar.add_field(
name="\u200b",
value=
f"High: {dayHigh}\nLow: {dayLow}\n\nAsk: {ask}\nBid: {bid}",
inline=True)
embedVar.add_field(name="\u200b",
value=f"Open: {open}\nPrev.: {close}",
inline=True)
embedVar.add_field(
name="\u200b",
value=f"Volume: {volume}\nAvg. Vol.: {avgVolume}",
inline=True)
await message.channel.send(embed=embedVar)
def getTopLosers(self):
topLosers = si.get_day_losers()[0:10]
topLosers['volume_change'] = self.yahooFinanceDataModification.getPercentigeChangeInVolume(topLosers)
return self.yahooFinanceDataModification.formatTopGainersOrLoosersOrActive(topLosers)
import yahoo_fin.stock_info as si import ftplib, io, json, pandas, requests, requests_html #Top 100 winners today winners = si.get_day_gainers() #Top 100 losers today losers = si.get_day_losers()
def get_worst_performers():
return si.get_day_losers()
def main():
#Initialize
userInput = ''
#Program will run until 5 is entered
while userInput != "5":
userInput = mainMenu()
#Look up Stock
if userInput == "1":
#Initialize
subSelect = ''
#Get symbol, validate, print current price, opening price, previous close
symbol = getStock()
if symbol != 'Q':
quoteTable = si.get_quote_table(symbol)
print("Current price: $", round(si.get_live_price(symbol), 3),
" as of", currentTime())
print("Opening price: $", quoteTable['Open'], \
"\nPrevious closing price: $", quoteTable['Previous Close'])
#Stay on submenu until 5 is entered
while subSelect != '5':
subSelect = subMenu()
#Get date range and plot historical price
if subSelect == '1':
selection = 'Y'
while selection == 'Y':
plot(getStockData(symbol), "index", "close",
"Price ($)")
selection = input(
"Would you like to enter a new set of dates? (Y/N) "
)
#Get date range and plot historical volume
if subSelect == '2':
selection = 'Y'
while selection == 'Y':
plot(getStockData(), "index", "volume", "Volume ")
selection = input(
"Would you like to enter a new set of dates? (Y/N) "
)
#Add stock to a watchlist
if subSelect == '3':
columns = ['Name']
#Try to open existing watchlist and add stock to it
try:
watchlist = pd.read_csv("watchlist.csv",
header=0,
delim_whitespace=True)
add = pd.DataFrame([symbol],
index=None,
columns=columns)
watchlist = watchlist.append(add, True)
watchlist.to_csv('watchlist.csv', index=False)
#Create new watchlist file and add stock to it
except:
add = pd.DataFrame([symbol],
index=None,
columns=columns)
add.to_csv('watchlist.csv', index=False)
print("This stock has been added to your watchlist\n")
#Update the live price
if subSelect == '4':
print("Current price: $",
round(si.get_live_price(symbol), 3), " as of",
currentTime())
#Print Watchlist
if userInput == "2":
#Try to open watchlist. Look up live price for each stock on the list. print the resulting Dataframe
try:
watchlist = pd.read_csv("watchlist.csv",
header=0,
delim_whitespace=True)
npWatchlist = watchlist.to_numpy()
price = []
for i in range(0, len(npWatchlist)):
price.append(round(si.get_live_price(npWatchlist[i][0]),
3))
watchlist['price'] = price
watchlist = watchlist.set_index('Name')
print(watchlist)
#Error message if watchlist does not exist
except:
print(
"Watchlist is currently empty. Feel free to add stocks to your watchlist under the Look up Stock option in the main menu"
)
#Prints top 10 gainers and losers
if userInput == "3": #Gainers Losers
gainers = si.get_day_gainers()
deleteData = [
'Change', 'Volume', 'Avg Vol (3 month)', 'Market Cap',
'PE Ratio (TTM)'
]
print("Top Gainers\n")
dfTrimmer(gainers, deleteData)
losers = si.get_day_losers()
print("\nTop Losers\n")
dfTrimmer(losers, deleteData)
#Prints top 10 crypto by market cap
if userInput == "4":
crypto = si.get_top_crypto()
delete = [
"Market Cap", "Volume in Currency (Since 0:00 UTC)",
'Volume in Currency (24Hr)',
'Total Volume All Currencies (24Hr)', 'Circulating Supply'
]
print("The top 10 Cryptocurrencies by Market Cap:")
dfTrimmer(crypto, delete)
#If user enters invalid input
elif userInput != '5' and userInput != '1' and userInput != '2' and userInput != '3' and userInput != '4':
print("Selection not recognized try again")
