def Main():
with st.sidebar.expander("DVD_HK"):
st.info(f'''
Dividends: Hong Kong Listed Stocks only (for now...)
''')
default_tickers = get_index_tickers(
st_asset=st.sidebar.expander('Load an Index', expanded=True))
tickers = tickers_parser(st.text_input(
'enter stock ticker(s) [space separated]', value=default_tickers),
return_list=True)
timeframe_params = get_timeframe_params(
st_asset=st.sidebar.expander("Timeframe"), default_tenor='50y')
if tickers:
# - div history with ex-date true range vs div amount/ true range
df = get_dvd(tickers,
ex_date_after=timeframe_params['end_date'],
aggregate=True)
show_upcoming_div(df,
st_asset=st.expander("View Upcoming Dividend",
expanded=True),
timeframe_params=timeframe_params,
atr_period=22)
df_all = get_dvd(tickers, aggregate=True)
show_past_div(df_all,
st_asset=st.expander("View Pass Dividend"),
timeframe_params=timeframe_params,
atr_period=22)
def get_ticker_data_with_technicals(
ticker:str, interval:str = '1d', trade_date = datetime.date.today(),
tenor:str = '2y', data_buffer_tenor:str = '1y',
l_ma_periods:list = [22,11],
atr_params = {'period': 13, 'use_ema': True, 'channel_dict' : None},
return_all_dates = False
):
''' Get Price Data for a Ticker and add various Technical Indicators intended for
trend following strategies backtesting/ screening
Args:
l_ma_periods: list of moving averages periods to add
return_all_dates: if False, return only data from start_date
'''
start_date = (BusinessDate(trade_date)- tenor).to_date()
data_start_date = (BusinessDate(start_date) - data_buffer_tenor).to_date()
df = get_stocks_ohlc(tickers = tickers_parser(ticker),
interval = interval,
start_date = data_start_date,
end_date = trade_date,
proxies = get_proxies()
)
for p in l_ma_periods:
df = add_moving_average(df, period = p, type = 'ema')
df = add_MACD(df)
df = add_ATR(df, **atr_params) if atr_params else df
return df if return_all_dates else df[df.index > pd.Timestamp(start_date)]
def Main():
with st.sidebar.expander("RT"):
st.info(f'''
Returns Analysis: what does your stock's return distribution look like? How fat are the tails?
* inspired by this [blog post](https://www.codingfinance.com/post/2018-04-03-calc-returns-py/)
''')
tickers = tickers_parser(
st.text_input('enter stock ticker(s) [space separated]'))
with st.sidebar.expander('settings', expanded=True):
today = datetime.date.today()
end_date = st.date_input('Period End Date', value=today)
if st.checkbox('pick start date'):
start_date = st.date_input('Period Start Date',
value=today -
datetime.timedelta(days=365))
else:
tenor = st.text_input('Period', value='250b')
start_date = (BusinessDate(end_date) - tenor).to_date()
st.info(f'period start date: {start_date}')
l_interval = [
'1d', '1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '5d',
'1wk', '1mo', '3mo'
]
interval = st.selectbox('interval', options=l_interval)
if interval.endswith(('m', 'h')):
st.warning(f'intraday data cannot extend last 60 days')
if tickers:
side_config = st.sidebar.expander('charts configure', expanded=False)
with side_config:
# show_ohlc = st.checkbox('ohlc chart', value = True)
# b_two_col = st.checkbox('two-column view', value = True)
chart_size = st.number_input('Chart Size',
value=500,
min_value=400,
max_value=1500)
# if len(tickers.split())==1:
# st.warning(f'This function works best with more than one tickers. Some features below might not render...')
data_dict = get_yf_data(tickers,
start_date=start_date,
end_date=end_date,
interval=interval)
data = data_dict['prices'].copy()
df_return = data_dict['returns'].copy()
with st.expander('raw data'):
st.subheader('Price Data')
st.write(data)
st.subheader('Returns')
st.write(df_return)
l_tickers = df_return.columns.tolist()
if len(l_tickers) != len(tickers.split(' ')):
st.warning(
f'having trouble finding the right ticker?\nCheck it out first in `DESC` :point_left:'
)
single_ticker = len(l_tickers) == 1
l_col, r_col = st.columns(2)
with l_col:
target_ticker = st.selectbox(
'Analyze',
options=l_tickers if single_ticker else [''] + l_tickers)
if target_ticker:
with r_col:
with st.expander(f'{target_ticker} descriptive stats'):
st.write(df_return[target_ticker].describe())
if single_ticker:
tmp_idx = pd.MultiIndex.from_tuples(
[(col, target_ticker) for col in data.columns],
names=['variable', 'ticker'])
data.columns = tmp_idx
plot_returns(df_returns=df_return,
df_prices=data,
target_ticker=target_ticker,
chart_size=chart_size)
else:
compare_returns(df_returns=df_return,
df_prices=data,
chart_size=chart_size)
def Main():
with st.sidebar.expander("ATR"):
st.info(f'''
[Average True Range](https://www.thebalance.com/how-average-true-range-atr-can-improve-trading-4154923):
Compare risk across multiple stocks and determine your [position sizing](https://therobusttrader.com/how-to-use-atr-in-position-sizing/)
''')
tickers = tickers_parser(
st.text_input('enter stock ticker(s) [space separated]'),
max_items=None)
with st.sidebar.expander('timeframe', expanded=False):
today = datetime.date.today()
end_date = st.date_input('Period End Date', value=today)
if st.checkbox('pick start date'):
start_date = st.date_input('Period Start Date',
value=today -
datetime.timedelta(days=365))
else:
tenor = st.text_input('Period', value='250b')
start_date = (BusinessDate(end_date) - tenor).to_date()
st.info(f'period start date: {start_date}')
# TODO: allow manual handling of data_start_date
data_start_date = (BusinessDate(start_date) - "1y").to_date()
l_interval = ['1d', '1m', '5m', '1h', '1wk']
interval = st.selectbox('interval', options=l_interval)
if interval.endswith(('m', 'h')):
st.warning(f'intraday data cannot extend last 60 days')
if tickers:
with st.sidebar.expander('ATR Configs', expanded=True):
use_ema = st.checkbox('use exponential moving aveage')
atr_period = st.number_input('ATR Period (number of bars)',
value=22)
atr_multiplier = st.number_input('ATR multiplier (stop-loss size)',
value=2,
step=1)
var = st.number_input(
'Value To Risk (max drawdown on one position)', value=1000)
data_dict = get_yf_data(tickers,
start_date=data_start_date,
end_date=end_date,
interval=interval)
data = data_dict['prices'].copy()
df_return = data_dict['returns'].copy()
with st.expander('raw data'):
st.subheader('Price Data')
st.write(data)
l_tickers = df_return.columns.tolist()
if len(l_tickers) != len(tickers.split(' ')):
st.warning(
f'having trouble finding the right ticker?\nCheck it out first in `DESC` :point_left:'
)
# l_DFs is a list of [ {ticker: df}, ...]
df_dict = { l_tickers[0]: data} if len(l_tickers) ==1 else \
get_dfs_by_tickers(data)
results = [{
**{
'ticker': t
},
**get_ATR_calc(df,
period=atr_period,
use_ema=use_ema,
atr_multiplier=atr_multiplier,
var=var,
price_col='Adj Close')
} for t, df in df_dict.items()]
# add HK lot size
for r in results:
if ".HK" in r['ticker']:
r['lot_size'] = get_lot_size(r['ticker'])
with st.expander(f'ATR Results', expanded=True):
st.write(pd.DataFrame(results))
with st.expander('Visualize ATR'):
chart_configs = get_charts_configs(
st_asset=st.sidebar.expander("Chart Configs"))
visualize_features(df_dict,
chart_configs=chart_configs,
atr_period=atr_period,
start_date=start_date)
def Main():
with st.sidebar.expander("GP"):
st.info(f'''
Graph Prices (open-high-low-close)
* inspired by this [blog post](https://towardsdatascience.com/creating-a-finance-web-app-in-3-minutes-8273d56a39f8)
and this [youtube video](https://youtu.be/OhvQN_yIgCo)
* plots by Plotly with thanks to this [kaggle notebook](https://www.kaggle.com/mtszkw/technical-indicators-for-trading-stocks)
''')
tickers = tickers_parser(st.text_input('enter stock ticker'), max_items = 1)
with st.sidebar.expander('timeframe', expanded = True):
today = datetime.date.today()
end_date = st.date_input('Period End Date', value = today)
if st.checkbox('pick start date'):
start_date = st.date_input('Period Start Date', value = today - datetime.timedelta(days = 365))
else:
tenor = st.text_input('Period', value = '6m')
start_date = (BusinessDate(end_date) - tenor).to_date()
st.info(f'period start date: {start_date}')
# TODO: allow manual handling of data_start_date
# l_interval = ['1d','1wk','1m', '2m','5m','15m','30m','60m','90m','1h','5d','1mo','3mo']
interval = st.selectbox('interval', options = ['1d', '1wk', '1mo'])
is_intraday = interval.endswith(('m','h'))
data_start_date = start_date if is_intraday else \
(BusinessDate(start_date) - "1y").to_date()
if is_intraday:
st.warning(f'''
intraday data cannot extend last 60 days\n
also, some features below might not work properly
''')
if tickers:
stock_obj = yf.Ticker(tickers)
if not valid_stock(stock_obj):
st.error(f'''
{tickers} is an invalid ticker.\n
Having trouble finding the right ticker?\n
Check it out first in `DESC` :point_left:
''')
return None
side_config = st.sidebar.expander('charts configure', expanded = False)
with side_config:
show_df = st.checkbox('show price dataframe', value = False)
chart_size = st.number_input('Chart Size', value = 1200, min_value = 400, max_value = 1500, step = 50)
side_stock_info = get_stock_info_container(stock_obj.info, st_asset= st.sidebar)
data = get_stocks_ohlc(tickers,
start_date = data_start_date, end_date = end_date,
interval = interval,
proxies = get_proxies())
with st.expander('Indicators'):
l_col, m_col , r_col = st.columns(3)
with l_col:
st.write('#### the moving averages')
ma_type = st.selectbox('moving average type', options = ['', 'ema', 'sma', 'vwap'])
periods = st.text_input('moving average periods (comma separated)', value = '22,11')
if ma_type:
for p in periods.split(','):
data = add_moving_average(data, period = int(p), type = ma_type)
st.write('#### volume-based indicators')
# do_volume_profile = st.checkbox('Volume Profile')
data = add_AD(data) if st.checkbox('Show Advance/ Decline') else data
data = add_OBV(data) if st.checkbox('Show On Balance Volume') else data
with m_col:
st.write('#### MACD')
do_MACD = st.checkbox('Show MACD?', value = True)
fast = st.number_input('fast', value = 12)
slow = st.number_input('slow', value = 26)
signal = st.number_input('signal', value = 9)
if do_MACD:
data = add_MACD(data, fast = fast, slow = slow, signal = signal )
with r_col:
st.write('#### oscillator')
do_RSI = st.checkbox('RSI')
data = add_RSI(data, n = st.number_input('RSI period', value = 13)) if do_RSI else data
tup_RSI_hilo = st.text_input('RSI chart high and low line (comma separated):', value = '70,30').split(',') \
if do_RSI else None
tup_RSI_hilo = [int(i) for i in tup_RSI_hilo] if tup_RSI_hilo else None
if do_RSI:
data_over_hilo_pct = sum(
((data['RSI']> tup_RSI_hilo[0]) | (data['RSI']< tup_RSI_hilo[1])) & (data.index > pd.Timestamp(start_date))
) / len(data[data.index > pd.Timestamp(start_date)])
st.info(f"""
{round(data_over_hilo_pct * 100, 2)}% within hilo\n
5% of peaks and valley should be within hilo
""")
st.write('#### True Range Related')
atr_period = int(st.number_input('Average True Range Period', value = 13))
atr_ema = st.checkbox('use EMA for ATR', value = True)
show_ATR = st.checkbox('show ATR?', value = False)
if ma_type:
st.write('##### ATR Channels')
atr_ma_name = st.selectbox('select moving average for ATR channel',
options = [''] + get_moving_average_col(data.columns))
atr_channels = st.text_input('Channel Lines (comma separated)', value = "1,2,3") \
if atr_ma_name else None
fill_channels = st.checkbox('Fill Channels with color', value = False) \
if atr_ma_name else None
else:
atr_ma_name = None
data = add_ATR(data, period = atr_period, use_ema = atr_ema,
channel_dict = {atr_ma_name: [float(c) for c in atr_channels.split(',')]} \
if atr_ma_name else None
)
st.write(f'##### Directional System')
do_ADX = st.checkbox('Show ADX')
data = add_ADX(data, period = st.number_input("ADX period", value = 13)) \
if do_ADX else data
with st.expander('advanced settings'):
l_col, m_col , r_col = st.columns(3)
with l_col:
st.write('#### Market Type Classification')
mkt_class_period = int(st.number_input('peroid (match your trading time domain)', value = 66))
mkt_class = market_classification(data, period = mkt_class_period,
debug = False) if mkt_class_period else None
if mkt_class:
side_stock_info.write(f'market is `{mkt_class}` for the last **{mkt_class_period} bars**')
side_stock_info.write(f'[kaufman efficiency_ratio](https://strategyquant.com/codebase/kaufmans-efficiency-ratio-ker/) ({mkt_class_period} bars): `{round(efficiency_ratio(data, period = mkt_class_period),2)}`')
st.write('#### Events')
do_div = st.checkbox('show ex-dividend dates')
if do_div:
data = add_div_col(df_price = data, df_div = stock_obj.dividends)
side_stock_info.write(
stock_obj.dividends[stock_obj.dividends.index > pd.Timestamp(start_date)]
)
do_earnings = st.checkbox('show earning dates')
if do_earnings and isinstance(stock_obj.calendar, pd.DataFrame):
data = add_event_col(df_price = data,
df_events = stock_obj.calendar.T.set_index('Earnings Date'),
event_col_name= "earnings")
side_stock_info.write(stock_obj.calendar.T)
if do_MACD and ma_type:
st.write("#### Elder's Impulse System")
impulse_ema = st.selectbox('select moving average for impulse',
options = [''] + get_moving_average_col(data.columns))
data = add_Impulse(data, ema_name = impulse_ema) if impulse_ema else data
avg_pen_data = None
with m_col:
if ma_type:
st.write("#### Average Penetration for Entry/ SafeZone")
fair_col = st.selectbox('compute average penetration below',
options = [''] + get_moving_average_col(data.columns))
avg_pen_data = add_avg_penetration(df = data, fair_col = fair_col,
num_of_bars = st.number_input('period (e.g. 4-6 weeks)', value = 30), # 4-6 weeks
use_ema = st.checkbox('use EMA for penetration', value = False),
ignore_zero = st.checkbox('ignore days without penetration', value = True),
coef = st.number_input(
'SafeZone Coefficient (stops should be set at least 1x Average Penetration)',
value = 1.0, step = 0.1),
get_df = True, debug = True
) if fair_col else None
with r_col:
if do_MACD:
st.write('#### MACD Bullish Divergence')
if st.checkbox('Show Divergence'):
data = detect_macd_divergence(data,
period = st.number_input('within number of bars (should be around 3 months)', value = 66),
threshold = st.number_input('current low threshold (% of previous major low)', value = 0.95),
debug = True
)
st.write(f'#### Detect Kangaroo Tails')
tail_type = st.selectbox('Tail Type',
options = ['', 0, 1, -1])
data = detect_kangaroo_tails(data,
atr_threshold = st.number_input('ATR Threshold', value = 2.0),
period = st.number_input('period', value = 22), tail_type = tail_type) \
if tail_type else data
beta_events_to_plot, l_events_to_color, l_col_to_scatter = [], [], []
show_beta_features(data = data, l_events_to_color=l_events_to_color,
l_col_to_scatter = l_col_to_scatter, atr_period = atr_period)
if show_df:
with st.expander(f'raw data (last updated: {data.index[-1].strftime("%c")})'):
st.write(data)
if isinstance(avg_pen_data, pd.DataFrame):
with st.expander('Buy Entry (SafeZone)'):
avg_pen_dict = {
'average penetration': avg_pen_data['avg_lp'][-1],
'ATR': avg_pen_data['ATR'][-1],
'penetration stdv': avg_pen_data['std_lp'][-1],
'number of penetrations within period': avg_pen_data['count_lp'][-1],
'last': avg_pen_data['Close'][-1],
'expected ema T+1': avg_pen_data[fair_col][-1] + (avg_pen_data[fair_col][-1] - avg_pen_data[fair_col][-2])
}
avg_pen_dict = {k:round(v,2) for k,v in avg_pen_dict.items()}
avg_pen_dict['buy target T+1'] = avg_pen_dict['expected ema T+1'] - avg_pen_dict['average penetration']
st.write(avg_pen_dict)
plot_avg_pen = st.checkbox('plot buy SafeZone and show average penetration df')
plot_target_buy = False # st.checkbox('plot target buy T+1')
# if plot_avg_pen:
# st.write(avg_pen_data)
if not(show_ATR) and 'ATR' in data.columns:
del data['ATR']
#TODO: fix tz issue for interval < 1d
# see: https://stackoverflow.com/questions/16628819/convert-pandas-timezone-aware-datetimeindex-to-naive-timestamp-but-in-certain-t
fig = plotly_ohlc_chart(
df = data if is_intraday else data[data.index > pd.Timestamp(start_date)],
vol_col = 'Volume',
tup_rsi_hilo = tup_RSI_hilo,
b_fill_channel = fill_channels if atr_ma_name else None
) #, show_volume_profile = do_volume_profile)
# SafeZone
if isinstance(avg_pen_data, pd.DataFrame):
fig = add_Scatter(fig, df = avg_pen_data[avg_pen_data.index > pd.Timestamp(start_date)], target_col = 'buy_safezone') \
if plot_avg_pen else fig
if plot_target_buy:
fig.add_hline(y = avg_pen_dict['buy target T+1'] , line_dash = 'dot', row =1, col = 1)
# Events
for d in ['MACD_Divergence', 'kangaroo_tails', 'ex-dividend', 'earnings'] + beta_events_to_plot:
if d in data.columns:
fig = add_Scatter_Event(fig, data[data.index > pd.Timestamp(start_date)],
target_col = d,
anchor_col = 'Low', textposition = 'bottom center', fontsize = 8,
marker_symbol = 'triangle-up', event_label = d[0])
# Color Events
for d in l_events_to_color:
fig = add_color_event_ohlc(fig, data[data.index > pd.Timestamp(start_date)],
condition_col = d['column'], color = d['color']
) if d['column'] in data.columns else fig
# Scatter Columns
for c in l_col_to_scatter:
fig = add_Scatter(fig, data[data.index > pd.Timestamp(start_date)],
target_col = c['column'], line_color = c['color'])
show_plotly(fig, height = chart_size,
title = f"Price chart({interval}) for {tickers} : {stock_obj.info['longName']}")
def Main():
with st.sidebar.expander("MBRS"):
st.info(f'''
Members: getting Indices members
* ETFs holdings using [etf4u](https://github.com/leoncvlt/etf4u) coming soon...
''')
showIndices(st_asset=st.sidebar)
default_tickers = get_index_tickers(
st_asset=st.sidebar.expander('Load an Index', expanded=True))
with st.sidebar.expander('settings', expanded=False):
df_height = int(
st.number_input("members' df height", value=500, min_value=200))
tickers = tickers_parser(
st.text_input("index members' tickers [space separated]",
value=default_tickers))
if tickers:
with st.expander('display keys'):
l_col, r_col = st.columns(2)
with l_col:
l_keys_des = st.multiselect('descriptive',
options=[
'longName', 'previousClose',
'sector', 'fullTimeEmployees',
'country', 'industry',
'currency',
'exchangeTimezoneName'
],
default=['longName'])
l_keys_vol = st.multiselect(
'volume',
options=[
'averageVolume10days', 'circulatingSupply',
'sharesOutstanding', 'sharesShort',
'sharesPercentSharesOut', 'floatShares', 'shortRatio',
'heldPercentInsiders', 'impliedSharesOutstanding'
])
with r_col:
l_keys_dvd = st.multiselect(
'dividend related',
options=[
'dividendRate', 'exDividendDate', 'dividendYield',
'lastDividendDate', 'exDividendDate',
'lastDividendValue'
])
l_keys_fun = st.multiselect('fundamental',
options=[
'marketCap', 'trailingPE',
'priceToSalesTrailing12Month',
'forwardPE', 'profileMargins',
'forwardEps', 'bookValue',
'priceToBook', 'payoutRatio'
])
l_keys = l_keys_des + l_keys_vol + l_keys_dvd + l_keys_fun
if len(l_keys) < 1:
st.warning(f'no key selected.')
return None
# st.subheader(f'Members of `{idx}`')
st.subheader(f'Index Members stats')
data = get_members_info_df(asset=tickers.split(),
l_keys=['symbol'] + l_keys)
st.dataframe(data, height=df_height)
def Main():
with st.sidebar.expander("BETA"):
st.info(f'''
Beta Analysis vs Benchmark Security
''')
default_tickers = get_index_tickers(
st_asset = st.sidebar.expander('Load an Index', expanded = True)
)
tickers = tickers_parser(
st.text_input('enter stock ticker(s) [space separated]',
value = default_tickers)
)
with st.sidebar.expander('settings', expanded = False):
today = datetime.date.today()
end_date = st.date_input('Period End Date', value = today)
if st.checkbox('pick start date'):
start_date = st.date_input('Period Start Date', value = today - datetime.timedelta(days = 365))
else:
tenor = st.text_input('Period', value = '250b')
start_date = (BusinessDate(end_date) - tenor).to_date()
st.info(f'period start date: {start_date}')
l_interval = ['1d','1m', '2m','5m','15m','30m','60m','90m','1h','5d','1wk','1mo','3mo']
interval = st.selectbox('interval', options = l_interval)
if tickers:
side_config = st.sidebar.expander('charts configure', expanded = False)
with side_config:
show_ohlc = st.checkbox('ohlc chart', value = True)
# b_two_col = st.checkbox('two-column view', value = True)
chart_size = st.number_input('Chart Size', value = 500, min_value = 400, max_value = 1500)
data_dict = get_yf_data(tickers, start_date = start_date, end_date = end_date, interval = interval)
df_returns = data_dict['returns'].copy()
with st.expander('view returns data'):
st.subheader('Returns')
st.write(df_returns)
l_tickers = df_returns.columns.tolist()
if len(l_tickers) != len(tickers.split(' ')):
st.warning(f'having trouble finding the right ticker?\nCheck it out first in `DESC` :point_left:')
l_col, r_col = st.columns(2)
with l_col:
benchmark_ticker = st.selectbox('benchmark security', options = tickers.split())
beta_json = get_betas(df_returns, benchmark_col = benchmark_ticker.upper())
# TODO: add dividend yield?
beta_df = pd.DataFrame.from_dict(beta_json)
with r_col:
plot_var_options = [col for col in beta_df.columns if col not in ['ticker']]
y_var = st.selectbox('y-axis variable', options = plot_var_options)
x_var = st.selectbox('x-axis variable', options = plot_var_options)
with st.expander('Betas Calcuation'):
st.write(beta_df)
fig = px.scatter(beta_df, x = x_var, y = y_var, color = 'ticker')
fig.update_layout(showlegend = False)
show_plotly(fig)
def Main():
ticker = tickers_parser(st.text_input('enter a stock ticker'), max_items=1)
with st.sidebar.expander("DESC"):
st.info(f'''
Description: get general information about the company, upcoming events, etc.
''')
if ticker:
# ticker = ticker.split('.')[0].zfill(4) + '.HK' if '.HK' in ticker.upper() else ticker
side_config = st.sidebar.expander('configure', expanded=True)
with side_config:
show_ohlc = st.checkbox('ohlc chart', value=True)
show_volume = st.checkbox('show volume', value=True)
b_two_col = st.checkbox('two-column view', value=True)
chart_size = st.number_input('Chart Size',
value=500,
min_value=400,
max_value=1500)
if b_two_col:
col1, col2 = st.columns((2, 1))
else:
col1 = col2 = st.container()
stock = yf.Ticker(ticker)
if not valid_stock(stock):
st.error(f'Cannot find `{ticker}`')
return None
#TODO:
# 1. show key keys info
# 2. add twitter & NewsAPI
# 4. Download price DF
with col1:
stock_info = stock.info
with st.expander(f'Stock Info for {stock_info["longName"]}'):
str_desc = f'[:link:]({stock_info["website"]}) ' \
if 'website' in stock_info.keys() else ''
str_desc += stock_info["longBusinessSummary"] \
if "longBusinessSummary" in stock_info.keys() else ":warning: Business Summary not available"
st.write(f'{str_desc}')
if st.checkbox('show info JSON'):
st.json(stock_info)
df_all = stock.history(period="max")
with side_config:
number_td = st.number_input("Number of trading days",
value=min(250, len(df_all)),
min_value=5,
max_value=len(df_all))
df_all = df_all.tail(number_td)
with st.expander('Price Chart', expanded=True):
if show_ohlc:
fig = plotly_ohlc_chart(
df=df_all, vol_col='Volume' if show_volume else None)
else:
fig = px.line(df_all,
y='Close',
color_discrete_sequence=["#b58900"])
# color_discrete_sequence = ['yellow'])
fig.update_layout(
height=chart_size,
title=
f'{ticker.upper()} last {number_td} trading days- ohlc chart',
template='plotly_dark')
st.plotly_chart(fig,
use_container_width=True,
height=chart_size)
with st.expander('Historical Prices'):
st.dataframe(df_all)
# High, Low, Open, Close, Volume, Adj Close
# trading_data = stock.trading_data
# st.dataframe(trading_data)
# show actions (dividends, splits)
with st.expander("Corporate Actions"):
st.dataframe(stock.actions)
# show dividends
with st.expander("Dividends"):
st.dataframe(stock.dividends)
# show splits
with st.expander("Splits"):
st.dataframe(stock.splits)
with col2:
# show financials
with st.expander("Financials"):
st.write(stock.financials)
# msft.quarterly_financials
# show major holders
with st.expander("Major Holders"):
st.dataframe(stock.major_holders)
# show institutional holders
with st.expander("Institutional Holders"):
st.dataframe(stock.institutional_holders)
# show balance sheet
with st.expander("Balance Sheet"):
st.write(stock.balance_sheet)
# msft.quarterly_balance_sheet
# show cashflow
with st.expander("Cashflow"):
st.write(stock.cashflow)
# msft.quarterly_cashflow
# show earnings
with st.expander("Earnings"):
st.write(stock.earnings)
# msft.quarterly_earnings
# show sustainability
with st.expander("Sustainability"):
# this is a df issue on ST side:
# https://discuss.streamlit.io/t/after-upgrade-to-the-latest-version-now-this-error-id-showing-up-arrowinvalid/15794/24
# print(type(stock.sustainability))
st.write(stock.sustainability)
# show analysts recommendations
with st.expander("Analysts Recommendations"):
# TODO:
# 1. set "Firm" as index
# 2. turn datetime index as a col
st.dataframe(stock.recommendations)
# show next event (earnings, etc)
with st.expander("Calendar"):
if isinstance(stock.calendar, pd.DataFrame):
st.dataframe(stock.calendar.T)