def plot_returns(tickers):
if len(tickers) > 1 and type(tickers) != str:
data = yf.Tickers(tickers)
df = data.history(period=start_date)
returns = df['Close'].resample('M').ffill().pct_change()
returns1 = (returns + 1).cumprod()
fig = go.Figure()
for column in returns1:
fig.add_trace(go.Scatter(x=returns1.index, y=returns1[column], mode='lines', name=column))
fig.update_layout(
title='Cumulative Returns on $1 Investment'
)
st.write(fig)
elif len(tickers) == 1 and type(tickers) != str:
ticker = tickers[0]
data = yf.Ticker(ticker)
df = data.history(period=start_date)
returns = df['Close'].resample('M').ffill().pct_change()
returns1 = (returns + 1).cumprod()
returns1 = returns1.to_frame('Close')
fig = px.line(returns1, x=returns1.index, y=returns1['Close'])
st.write(fig)
elif type(tickers) == str:
ticker = tickers
data = yf.Ticker(ticker)
df = data.history(period=start_date)
returns = df['Close'].resample('M').ffill().pct_change()
returns1 = (returns + 1).cumprod()
returns1 = returns1.to_frame('Close')
fig = px.line(returns1, x=returns1.index, y=returns1['Close'], title='Cumulative Returns on $1 Investment')
st.write(fig)
def getData():
stocks = yf.Tickers(['FB', 'AMZN', 'NFLX', 'GOOGL', 'AAPL'])
time_period = 'max' #can change this
df = stocks.history(period=time_period)
df = df['Close']
df = df.reset_index()
df = df[df.notna().all(axis=1)]
df.to_csv("prices.csv", index=False)
# df = pd.read_csv("prices.csv")
df["Date"] = pd.to_datetime(
df["Date"]) # convert string to datetime format
stocks = ['FB', 'AMZN', 'NFLX', 'GOOGL', 'AAPL']
scaler = StandardScaler()
df[stocks] = scaler.fit_transform(df[stocks])
train_size = int(df.shape[0] * 0.80)
val_size = int(df.shape[0] * 0.90)
a = df.iloc[:train_size, :]
b = df.iloc[train_size:val_size, :]
c = df.iloc[val_size:, :]
a = a.drop("Date", axis=1)
b = b.drop("Date", axis=1)
c = c.drop("Date", axis=1)
# dfRecommendations = pd.read_csv("recommendations.csv")# Read from cache to reduce time taken
stocks = ['FB', 'AMZN', 'NFLX', 'GOOGL', 'AAPL']
masterlist = []
for each in stocks:
x = yf.Ticker(each)
y = x.recommendations
y['stock_symbol'] = each
y = y.reset_index()
masterlist.append(y)
dfRecommendations = pd.concat(masterlist, axis=0)
dfRecommendations["Date"] = pd.to_datetime(
dfRecommendations["Date"]) # convert string to datetime format
return df, a, b, c, dfRecommendations
def stock_info_all_at_once():
tick_list = new_scraper()
request_string = ''
for x in tick_list:
request_string = request_string + x + ' '
all_info = yf.Tickers(request_string)
stockInfos = []
for tk in tick_list:
strCMD = 'all_info.tickers.' + tk + '.info'
print(tk)
stockInfos.append(exec(strCMD))
if len(stockInfos) < 450:
print('NOT ENOUGH INFO')
return stockInfos
else:
print("DONE!")
#PUT TO EXCEL
stockInfos = sorted(stockInfos,
key=lambda x: x['marketCap'],
reverse=True)
df = pandas.DataFrame(stockInfos)
df.to_excel(stock_info_excel_path)
#DUMP TO FILE
with open('Stock Picker/Results/stockInfo.json', 'w') as fp:
fp.write(json.dumps(stockInfos, indent=4))
return stockInfos
def load_data():
stocks = yf.Tickers(['FB','AMZN','NFLX','GOOGL','AAPL'])
time_period = 'max'#can change this
df = stocks.history(period = time_period)
df = df['Close']
df = df.reset_index()
df = df[df.notna().all(axis = 1)]
# df.to_csv("prices.csv", index=False)
# df = pd.read_csv("prices.csv")
df["Date"] = pd.to_datetime(df["Date"]) # convert string to datetime format
# stocks = ['FB','AMZN','NFLX','GOOGL','AAPL']
# masterlist = []
# for each in stocks:
# x = yf.Ticker(each)
# y = x.recommendations
# y['stock_symbol'] = each
# y = y.reset_index()
# masterlist.append(y)
# dfRecommendations = pd.concat(masterlist,axis= 0)
# dfRecommendations["Date"] = pd.to_datetime(dfRecommendations["Date"]) # convert string to datetime format
# dfRecommendations.to_csv('recommendations.csv',index = False)
dfRecommendations = pd.read_csv("recommendations.csv")# Read from cache to reduce time taken
dfRecommendations["Date"] = pd.to_datetime(dfRecommendations["Date"]) # convert string to datetime format
dfRecommendations["Date"] = dfRecommendations["Date"].dt.date
return df, dfRecommendations
def get_data(tickers_list, period):
"""
:param tickers: str: stock ticker string
:param period: str: valid date period for comparison
:return: temp_string, delta: str, float: stock printing statements and ratio are returned
"""
pairs = dict()
temp_string = ""
tickers = " ".join([x.upper() for x in tickers_list]).strip()
stocks = yf.Tickers(tickers)
data = stocks.history(env.get('PERIOD', period))
for ticker in tickers_list:
close = data.Close[ticker][-1]
close_date = data.index[-1]
temp_string += "{} Close {}: {}\n".format(ticker, str(close_date),
str(close))
high = max(data.Close[ticker])
temp_string += "{} {}-High: {}\n".format(ticker,
env.get('PERIOD', period),
str(high))
delta = calc_stock(high, close)
pairs[ticker] = delta
temp_string += "{} Delta: {}\n".format(ticker, str(delta)) + "\n"
return temp_string, pairs
def main():
symbols_list = ['SPY','QQQ','IWM','GLD','UVXY','TLT','TSLA','NVDA','NFLX','AMC']
#df=pd.read_csv(url)
#symbols_list.extend(list(df.Symbol.values))
final_list = []
for x in np.arange(0,len(symbols_list),100):
try:
symbols = symbols_list[x:x+100]
print(symbols)
ticker_list = yf.Tickers(' '.join(symbols))
history = ticker_list.history(period="max")
for ticker in ticker_list.tickers:
try:
mydf = history[[('Close',ticker),('Volume',ticker)]]
arr = etl(mydf)
if arr.shape[0] > total_days:
tmp_list = chunckify(arr)
final_list.extend(tmp_list)
except:
traceback.print_exc()
raise ValueError()
time.sleep(2)
except:
traceback.print_exc()
raise ValueError()
Y = np.array([x[1].T for x in final_list])
X = np.array([x[0] for x in final_list])
np.save('X.npy', X)
np.save('Y.npy', Y)
def lambda_handler(event, context):
stocks = [
'FB', 'SHOP', 'BYND', 'NFLX', 'PINS', 'SQ', 'TTD', 'OKTA', 'SNAP',
'DDOG'
]
data = yf.Tickers(stocks)
hist = data.history(start="2020-05-14",
end="2020-05-15",
interval="1m",
group_by='tickers')
output = []
for stock in stocks:
for index, price in hist[stock].iterrows():
output.append({
'high': price['High'],
'low': price['Low'],
'ts': index.strftime('%Y-%m-%d %H:%M:%S'),
'name': stock + "Cg=="
})
as_jsonstr = json.dumps(output)
fh = boto3.client("firehose", "us-east-2")
fh.put_record(DeliveryStreamName="delivery-system",
Record={"Data": as_jsonstr.encode('utf-8')})
return {'statusCode': 200, 'body': json.dumps(f'Done: {as_jsonstr}')}
def lambda_handler(event, context):
symbols = [
'FB', 'SHOP', 'BYND', 'NFLX', 'PINS', 'SQ', 'TTD', 'OKTA', 'SNAP',
'DDOG'
]
tickers = yf.Tickers(' '.join(symbols))
res = tickers.history(start="2020-05-14",
end="2020-05-15",
period='1d',
interval='1m',
group_by='row')
records = []
for st in symbols:
for i in range(len(res)):
ss = res.iloc[i].loc[st, ['High', 'Low']]
ss.reset_index(level=0, drop=True, inplace=True)
ss.rename({'High': 'high', "Low": 'low'}, inplace=True)
ss['ts'] = str(res.index[i])
ss['name'] = st
records.append(ss.to_json())
fh = boto3.client("firehose", "us-east-2")
for r in records:
fh.put_record(DeliveryStreamName="DataTransformer",
Record={"Data": r.encode('utf-8')})
return {'statusCode': 200, 'body': json.dumps(f'Done! Records')}
def lambda_handler(event, context):
# initialize boto3 client
fh = boto3.client("firehose", "us-east-2")
#leverage the power of yfinance.history
stock_symbol = ['FB','SHOP','BYND','NFLX','PINS','SQ','TTD','OKTA','SNAP','DDOG']
data = yf.Tickers(stock_symbol)
history = data.history(start="2020-05-14", end="2020-05-15",interval="1m",group_by = 'tickers')
req = []
for stock in stock_symbol:
for index, value in history[stock].iterrows():
dataf = {'high':value['High'],'low':value['Low'],'ts':index.strftime('%Y-%m-%d %H:%M:%S'),'name':stock}
#convert it into JSON
as_jsonstr = json.dumps(dataf)
# this actually pushed to our firehose datastream
# we must "encode" in order to convert it into the
# bytes datatype as all of AWS libs operate over
# bytes not strings
fh.put_record(
DeliveryStreamName="STA9760-delivery-yfinance-stream",
Record={"Data": as_jsonstr.encode('utf-8')})
req.append(dataf)
# return
return {
'statusCode': 200,
'body': json.dumps(f'Done! Recorded: {as_jsonstr}')
}
def allStocks():
global all_stocks
symbolList = getSymbols()
#query = buildQuery(symbolList)
#response = json.loads(urlopen(query).read())
# return jsonify(response["query"])
response = yf.Tickers(symbolList)
#TODO: parse safely
#quotes = response["query"]["results"]["quote"]
for ticker in symbolList:
#symbol = quote["Symbol"]
#price = quote["LastTradePriceOnly"]
#name = quote["Name"]
#change = quote["Change"]
#time = quote["LastTradeTime"]
name = getattr(response.tickers, ticker).info["longName"]
symbol = getattr(response.tickers, ticker).info["symbol"]
price = getattr(response.tickers, ticker).info["regularMarketPrice"]
change = getattr(response.tickers, ticker).info["SandP52WeekChange"]
time = getattr(response.tickers,
ticker).info["exchangeTimezoneShortName"]
all_stocks[symbol] = {
"name": name,
"symbol": symbol,
"price": price,
"change": change,
"time": time
}
#return render_template("index.html",mainController.stocks=flask.jsonify(all_stocks))
return flask.jsonify(all_stocks)
def get_data(tickers_list, period, company_names=all_company_names):
"""
:param tickers: str: stock ticker string
:param period: str: valid date period for comparison
:return: temp_string, delta: str, float: stock printing statements and ratio are returned
"""
pairs = dict()
temp_string = ""
tickers = " ".join([x.upper() for x in tickers_list]).strip()
stocks = yf.Tickers(tickers)
data = stocks.history(env.get('PERIOD', period))['Close']
print(data.head())
print(data.tail())
for ticker in tickers_list:
try:
df = data[ticker]
df.dropna(inplace=True)
close = df[-1]
high = max(df)
delta = calc_stock(high, close)
pairs[ticker] = delta
except Exception as e:
print(f"Couldn't find {ticker} in data")
return pairs
def analyze(self):
# Load data from file, generate data by running the `ticker_counts.py` script
data_directory = Path('./data')
input_path = data_directory / f'{dt.date.today()}_tick_df.csv'
df_tick = pd.read_csv(input_path).sort_values(
by=['Mentions', 'Ticker'], ascending=False)
columns = [
'Ticker', 'Name', 'Industry', 'PreviousClose', 'Low5d', 'High5d',
'ChangePercent1d', 'ChangePercent5d', 'ChangePercent1mo'
]
# Activate all tickers' info in parallel
self.tickers = yf.Tickers(df_tick['Ticker'].tolist())
with concurrent.futures.ThreadPoolExecutor() as executor:
executor.map(lambda t: t.info, self.tickers.tickers)
self.data = self.tickers.download(period='1mo',
group_by='ticker',
progress=True)
fin_data = [self.get_ticker_info(tick) for tick in df_tick['Ticker']]
df_best = pd.DataFrame(fin_data, columns=columns)
# Save to file to load into yahoo analysis script
output_path = data_directory / f'{dt.date.today()}_financial_df.csv'
df_best.to_csv(output_path, index=False)
def current_price():
companies = get_companies()
# TODO: remember to remove '.SA' if the source file already
# records codes with it
company_codes = list(
map(lambda c: companies[c]['codes'] + '.SA', companies))
company_codes_str = ' '.join(company_codes)
tickers = yfinance.Tickers(company_codes_str).tickers
current_prices = {}
i = 0 # to iterate over company_codes
for ticker in tickers:
LOG.info('Obtaining data from %s', company_codes[i])
try:
ticker_current_price = ticker.info['previousClose']
LOG.info('Price: %s', ticker_current_price)
except KeyError as e:
if e.args[0] == 'regularMarketOpen':
LOG.error('No regularMarketOpen found, skipping it.')
continue
except ValueError as e:
LOG.error(e)
continue
current_prices[company_codes[i]] = ticker_current_price
i += 1
LOG.info('Number of obtained last close price: %d', len(current_prices))
LOG.info(current_prices)
return current_prices
def load_data_history(tickers, start_date):
if len(tickers) > 1 and type(tickers) != str:
ticker_data = yf.Tickers(tickers)
data = ticker_data.history(period=start_date)
for ticker in tickers:
fig = go.Figure()
fig = px.line(data, x=data.index, y=data['Close'][ticker])
fig.update_xaxes(title='Date')
fig.update_yaxes(title='Price')
st.subheader(ticker + "'s Price History")
st.plotly_chart(fig)
elif len(tickers) == 1 and type(tickers) != str:
ticker = tickers[0]
ticker_data = yf.Ticker(ticker)
data = ticker_data.history(period=start_date)
fig = go.Figure()
fig = px.line(data, x=data.index, y=data['Close'])
fig.update_xaxes(title='Date')
fig.update_yaxes(title='Price')
st.subheader(ticker + "'s Price History")
st.plotly_chart(fig)
elif type(tickers) == str:
ticker = tickers
ticker_data = yf.Ticker(ticker)
data = ticker_data.history(period=start_date)
fig = go.Figure()
fig = px.line(data, x=data.index, y=data['Close'])
fig.update_xaxes(title='Date')
fig.update_yaxes(title='Price')
st.subheader(ticker + "'s Price History")
st.plotly_chart(fig)
def getDetail():
symbol = flask.request.get_json() #.encode('ascii')
symbol = symbol[10:-1]
print(symbol)
response = yf.Tickers(symbol)
hist = getattr(response.tickers, symbol).history('3mo')
open_data = hist.iloc[:, 0]
model = AR(open_data)
model_fit = model.fit(disp=0)
print(model_fit.summary())
predictions = model_fit.predict(start=len(open_data),
end=len(open_data) + 9,
dynamic=False)
dates = open_data.index.tolist()
final_date = dates[-1].to_pydatetime()
dates = date_unix(dates)
open_data = list(open_data)
l = []
for i in range(1, 11):
l.append(final_date + timedelta(days=i))
predict_dates = list(map(lambda x: time.mktime(x.timetuple()), l))
predict_open = list(predictions)
actual = list(zip(dates, open_data))
predict = list(zip(predict_dates, predict_open))
data = {"actual": actual, "predict": predict}
return flask.jsonify(data)
def analyze(self, best_n=25):
# Load data from file, generate data by running the `ticker_counts.py` script
data_directory = Path('./data')
input_path = data_directory / f'{dt.date.today()}_tick_df.csv'
df_tick = pd.read_csv(input_path).sort_values(
by=['Mentions', 'Ticker'], ascending=False)
columns = [
'Name', 'Industry', 'Previous Close', '5d Low', '5d High',
'1d Change (%)', '5d Change (%)', '1mo Change (%)'
]
df_best = df_tick.head(best_n)
# Add ASX code to tickers
df_best['Ticker'] = [v + '.AX' for v in df_best['Ticker'].tolist()]
# Activate all tickers' info in parallel
self.tickers = yf.Tickers(df_best['Ticker'].tolist())
with concurrent.futures.ThreadPoolExecutor() as executor:
executor.map(lambda t: t.info, self.tickers.tickers)
#self.data = self.tickers.download(period='1mo', group_by='ticker', progress=True)
self.data = yf.download(
[v.info['symbol'] for v in self.tickers.tickers],
period='1mo',
group_by='ticker',
progress=True)
df_best[columns] = df_best['Ticker'].apply(self.get_ticker_info)
# Save to file to load into yahoo analysis script
output_path = data_directory / f'df_best_{best_n}.csv'
df_best.to_csv(output_path, index=False)
print(df_best.head())
def allSelected(tickerSymbol, date, close, volume, dividens):
if len(tickerSymbol) == 0:
st.info(
"Use the menu on the left to select the stocks that you want to compare"
)
return
elif len(tickerSymbol) == 1:
tickerData = yf.Ticker(tickerSymbol[0])
else:
tickerData = yf.Tickers(tickerSymbol)
st.write("Data range: " + date)
tickerDf = tickerData.history(date)
# Open High Low Close Volume Dividends Stock Splits
if close:
st.write("""
## Close price""")
st.area_chart(tickerDf.Close)
if volume:
st.write("""
## Volume""")
st.line_chart(tickerDf.Volume)
if dividens:
st.write("""
## Dividends""")
st.line_chart(tickerDf.Dividends)
if not close and not volume and not dividens:
st.info("Select the data to be shown in the left bar")
def returnStockTickers():
if WEEKDAY < 5:
tickers = yf.Tickers("YEXT FCN ACN")
finalString = "How did ye ole house do on the market today? \n\n" + str(
tickers.history(period="1d"))
return finalString
else:
return False
def home():
tickers = yf.Tickers('msft aapl goog nok tlry pltr')
# tickers = yf.Tickers('aapl')
data = []
for key in tickers.tickers:
data.append(tickers.tickers[key].info)
return render_template("homepage.html", data=data)
def get_yf_tickers(self, tickers: {str, list}):
"""
Returns an iterable of yfinance.Ticker objects
:param tickers: str, list
:return: yfinance.Tickers.tickers
"""
return yf.Tickers(
tickers=self.get_multiple_ticker_str(tickers)).tickers
def fetch_historicals(
self,
symbols: str or list = None,
time_period: str = "ytd",
aggregation="Close",
):
"""
Returns a dictionary mapping each ticker (in the user's holdings) to a
pandas dataframe-interpreted dictionary of the stock's historical data
"""
logger.info("Logged Into Robinhood")
# Fetch build holding symbols/tickers
if symbols is None:
if self.build_holdings is None:
self.build_holdings = self.fetch_build_holdings()
symbols = [
x.replace(".", "-") for x in list(self.build_holdings.keys())
]
# Fetch historical data from yfinance
# could also get from robinhood, but yfinance is faster and more accurate
response = yf.Tickers(" ".join(symbols))
# fetch_instrument_to_stock_map()
rh_instruments = self.rh_conn.stocks.get_instruments_by_symbols(
symbols)
instrument_id_to_symbol = {}
for instrument_data in rh_instruments:
instrument_id_to_symbol[
instrument_data["id"]] = instrument_data["symbol"]
tickers = response.tickers
historical_data = {}
macd_data = {}
for ticker, ticker_obj in tickers.items():
df = None
try:
df = ticker_obj.history(period=time_period)
except Exception:
try:
df = ticker_obj.history(period="ytd")
except Exception as e:
logger.error(f"{e.__class__.__name__}: {e}")
logger.error(f"No historical data found for {ticker}")
if df is not None and not df.empty:
logger.info(f"Historical DF ================== \n{df}")
macd_d3_data = self.generate_macd(df, aggregation)
df.index = df.index.map(str)
ticker_historicals = df.to_dict("index")
historical_data[ticker.replace("-", ".")] = ticker_historicals
macd_data[ticker.replace("-", ".")] = macd_d3_data
else:
logger.info(f"Skipping {ticker} historical data")
return historical_data, macd_data, instrument_id_to_symbol
def getReferrer():
tickers = yf.Tickers('FB SHOP BYND NFLX PINS SQ TTD OKTA SNAP DDOG')
hist = tickers.history(start="2020-12-01", end="2020-12-02", interval = "5m")
hist=hist.stack()
hist.reset_index(inplace=True)
hist['Datetime']= hist['Datetime'].astype(str)
hist=hist.rename(columns={'High': 'high', 'Low': 'low', 'Datetime': 'ts', 'level_1':'name'})
hist=hist[['high', 'low', 'ts', 'name']]
return hist.to_json(orient="records", lines = True)
def get_multi_price_history(self, stocks, period='1mo'):
tickers = yf.Tickers(' '.join(stocks))
res = {}
for t in tickers.tickers:
his = t.history(period=period)
if len(his) == 0:
continue
res[t.ticker] = his
return res
def multiple_tickers():
'''
using Tickers module
to obtain info about several companies at the same time
'''
tickers = yf.Tickers('bmbl afrm')
company_info = tickers.tickers.BMBL.info
print(company_info)
def see_all(stock_list, chat_id, last_prices):
current = 0
tickers = yf.Tickers(stock_list).tickers
new_prices = []
for t in tickers:
stock = t.get_info()
new_prices.append(
make_alert_if_needed(stock, last_prices[current], chat_id))
current += 1
return new_prices
def get_port_info_values(syms):
names = syms if type(syms) == list else syms.tolist()
tickers = yf.Tickers(names)
dict_list = []
for n in tqdm.tqdm(names):
d = tickers.tickers[n].get_info()
d['symbol'] = n
dict_list.append(d)
df_info_values = pd.DataFrame(dict_list)
return df_info_values
def __init__(self, keyword_list: list) -> None:
"""Initialization of DesignerFinanceCollector
Args:
keyword_list (list): Keyword-list with the tickers to search for.
"""
self.keyword_list = keyword_list
self.tickers = yf.Tickers(" ".join(self.keyword_list))
self.df = pd.DataFrame()
self.dict = {}
def get_stock_price(tickers):
stocks = yf.Tickers(tickers).tickers
prices = {
ticker: stock.info["regularMarketPrice"]
for ticker, stock in stocks.items()
if stock.info["regularMarketPrice"] != None
}
return dumps(prices, indent=4)
def multiple_tickets():
tickers = yf.Tickers('msft aapl goog')
# ^ returns a named tuple of Ticker objects
# access each ticker using (example)
tickers.tickers.MSFT.info
tickers.tickers.AAPL.history(period="1mo")
tickers.tickers.GOOG.actions
data = yf.download("SPY AAPL", start="2017-01-01", end="2017-04-30")
def get(self, request):
symbols = []
dict = {}
queryset = Stock.objects.all()
for query in queryset:
symbols.append(query.name)
tickers = yf.Tickers(symbols)
for s in symbols:
dict[s] = tickers.tickers[s].info['regularMarketPrice']
return Response(dict)