def visualize_dvd_df(df_dvd):
# Div/ATR histogram
fig = px.histogram(df_dvd,
x='div/ATR',
color='ticker',
title='div/ATR Histogram')
show_plotly(fig)
# Days vs Div/ATR scatter
if 'Days-to-ex' in df_dvd.columns:
fig = px.scatter(df_dvd,
x='Days-to-ex',
y='div/ATR',
color='ticker',
title=f'Days-to-ex vs div/ATR') #, hover_data = [''])
show_plotly(fig)
if 'TR_ex' in df_dvd.columns:
max_Div_delta = int(max([df_dvd['TR_ex'].max(), df_dvd['Div'].max()]))
fig = px.scatter(
df_dvd,
x='TR_ex',
y='Div',
color='ticker',
title=f'Div vs True Range (on ex-date)') #, hover_data = [''])
fig.add_trace(go.Scatter(x=[0, max_Div_delta],
y=[0, max_Div_delta],
name='break-even',
marker_symbol='x-thin',
marker_size=1,
line={
'dash': 'solid',
'color': 'GhostWhite',
'width': 0.5
}),
row=1,
col=1)
show_plotly(fig)
if 'capture_r_multiplier' in df_dvd.columns:
df_p = df_dvd[df_dvd['capture_risk'] > 0]
fig = px.histogram(df_p,
x='capture_r_multiplier',
color='ticker',
title='Dividend Capture R-Multiplier Distribution')
show_plotly(fig)
def compare_returns(df_returns, df_prices, chart_size=500):
'''
plot multi stocks returns in ST using PX
'''
l_tickers = df_returns.columns.tolist()
l_col, r_col = st.columns(2)
# returns
fig = px.line(df_returns,
y=df_returns.columns.tolist(),
title=f'stock returns')
show_plotly(fig, height=chart_size, st_asset=l_col)
# return dist
fig = px.histogram(df_returns,
y=l_tickers,
opacity=0.8,
title=f'returns distribution')
show_plotly(fig, height=chart_size, st_asset=r_col)
# cumulative returns
fig = px.line(
(df_returns + 1).cumprod(),
y=df_returns.columns.tolist(),
title=f'growtht of $1 invested on {df_returns.index[0]}',
labels={'value': f'cumulative returns'},
# color_discrete_sequence = ["#b58900"]
)
show_plotly(fig, height=chart_size, st_asset=l_col)
# compare volume
for t in l_tickers:
df_prices['Dollar_Traded',
t] = df_prices['Volume', t] * df_prices['Adj Close', t]
fig = px.line(
df_prices['Dollar_Traded'],
y=l_tickers,
title=f'Dollar Traded',
labels={'value': f'Dollar Traded'},
# color_discrete_sequence = ["#b58900"]
)
show_plotly(fig, height=chart_size, st_asset=r_col)
def plot_returns(df_returns,
df_prices,
target_ticker,
chart_size=500,
show_ohlc=True):
'''
plot target_ticker returns in ST using PX
'''
# TODO: remove show_ohlc arg
l_col, r_col = st.columns(2)
# returns
fig = px.line(
df_returns,
y=target_ticker,
title=f'{target_ticker} returns',
color_discrete_sequence=["#b58900"],
)
show_plotly(fig, height=chart_size, st_asset=l_col)
# simple price chart
# if show_ohlc:
df_plot = pd.DataFrame({
'Open': df_prices['Open'][target_ticker],
'High': df_prices['High'][target_ticker],
'Low': df_prices['Low'][target_ticker],
'Close': df_prices['Close'][target_ticker],
'Volume': df_prices['Volume'][target_ticker],
'Date': df_prices.index
})
# fig = plotly_ohlc_chart(df_plot, vol_col = 'Volume', date_col = 'Date',
# show_legend = False, show_volume_profile = False)
fig = plotly_ohlc_chart(df_plot,
date_col='Date',
show_legend=False,
show_volume_profile=False,
show_range_slider=False)
# else:
# fig = px.line(df_prices['Adj Close'], y = target_ticker,
# color_discrete_sequence = ["#b58900"])
show_plotly(fig,
height=chart_size,
title=f'price of {target_ticker}',
st_asset=l_col)
# return dist
fig = px.histogram(
df_returns,
y=target_ticker,
color_discrete_sequence=["#b58900"],
title=f'{target_ticker} returns distribution (with 2stdv lines)')
two_stdv = df_returns[target_ticker].mean(
) + df_returns[target_ticker].std() * 2
plotly_hist_draw_hline(fig,
l_value_format=[{
'value': v
} for v in [two_stdv, -two_stdv]])
show_plotly(fig, height=chart_size, st_asset=r_col)
# if show_ohlc:
# #cumulative returns
# fig = px.line((df_returns+1).cumprod(), y = target_ticker,
# title = f'growtht of $1 invested in {target_ticker} on {df_returns.index[0]}',
# labels = {target_ticker: f'{target_ticker} cumulative returns'},
# color_discrete_sequence = ["#b58900"]
# )
# show_plotly(fig, height = chart_size)
# else:
# volume at price
# https://medium.com/swlh/how-to-analyze-volume-profiles-with-python-3166bb10ff24
idx = pd.IndexSlice
df_vp = df_prices.loc[:, idx[['Adj Close', 'Volume'], target_ticker]]
df_vp.columns = ['Adj Close', 'Volume']
fig = px.histogram(df_vp,
y='Adj Close',
x='Volume',
title=f'Volume-at-price: {target_ticker}',
orientation='h',
nbins=100,
color_discrete_sequence=["#b58900"])
show_plotly(fig, height=chart_size, st_asset=r_col)
def visualize_features(df_dict,
chart_configs,
atr_period,
start_date,
use_ema=True):
'''
Args:
df_dict: dictionary of {ticker: df, ...}
'''
l_tickers = list(df_dict.keys())
is_single = len(l_tickers) == 1
# Add KER
df_dict = {
t: add_KER(df, atr_period)[df.index > pd.Timestamp(start_date)]
for t, df in df_dict.items()
}
norm_atr = st.checkbox('normalize ATR', value=True)
if is_single: # Special Handling for Single Ticker
tickers = None
df_dict[l_tickers[0]]['ATR'] = df_dict[l_tickers[0]]['ATR']/ df_dict[l_tickers[0]]['Close'] \
if norm_atr else df_dict[l_tickers[0]]['ATR']
atr_periods = st.text_input(
'more ATR periods to test (comma separated)')
atr_periods = [int(i) for i in atr_periods.split(',')
] if atr_periods else None
if atr_periods:
for p in atr_periods:
df = df_dict[l_tickers[0]]
df_dict[l_tickers[0]] = add_ATR(df,
period=p,
normalize=norm_atr,
use_ema=use_ema,
channel_dict=None,
col_name=f'ATR_{p}')
# MA of ATR
# str_ma_period = st.text_input('moving averages period (comma-separated for multiple periods)')
# if str_ma_period:
# t = df_p.columns[0]
# for p in str_ma_period.split(','):
# df_p[f'{p}bars_MA'] = df_p[t].rolling(int(p)).mean().shift()
# ATR Time-Series
df_p = df_dict[l_tickers[0]]
df_p = df_p[[c for c in df_p.columns if 'ATR' in c]]
fig = px.line(
df_p,
y=df_p.columns,
labels={
'x': 'Date',
'y': 'ATR'
},
title=f'Historical Average True Range ({atr_period} bars)')
show_plotly(fig)
else:
# View ATR time series of all given stocks
atr_dict = {
ticker : (df['ATR']/df['Close']).dropna().to_dict() \
if norm_atr else df['ATR'].dropna().to_dict()
for ticker, df in df_dict.items()
}
df_p = pd.DataFrame.from_dict(atr_dict)
# tickers Selection
tickers = st.multiselect(f'ticker',
options=[''] + list(df_dict.keys()))
# ATR Time-Series
fig = px.line(
df_p,
y=tickers if tickers else df_p.columns,
labels={
'x': 'Date',
'y': 'ATR'
},
title=f'Historical Average True Range ({atr_period} bars)')
show_plotly(
fig
) #, height = chart_size, title = f"Price chart({interval}) for {l_tickers[0]}")
# ATR Histogram
fig = px.histogram(
df_p,
x=tickers if tickers else df_p.columns,
barmode=chart_configs['barmode'],
title=f'Average True Range ({atr_period} bars) Distribution',
nbins=chart_configs['n_bins'])
show_plotly(fig)
# KER Time-Series
KER_dict = {
ticker: df['KER'].dropna().to_dict()
for ticker, df in df_dict.items()
}
df_p = pd.DataFrame.from_dict(KER_dict)
fig = px.line(df_p,
y=tickers if tickers else df_p.columns,
labels={
'x': 'Date',
'y': 'KER'
},
title=f'Historical efficiency ratio ({atr_period} bars)')
show_plotly(fig)
# KER histogram
fig = px.histogram(
df_p,
x=tickers if tickers else df_p.columns,
barmode=chart_configs['barmode'],
title=f'Efficiency Ratio ({atr_period} bars) Distribution',
nbins=chart_configs['n_bins'])
show_plotly(fig)
# Volume
if is_single:
str_ma_period = st.text_input(
'Volume moving averages period (comma-separated for multiple periods)'
)
df_p = df_dict[l_tickers[0]]
if str_ma_period:
t = df_p.columns[0]
for p in str_ma_period.split(','):
df_p[f'Volume_{p}bars_MA'] = df_p["Volume"].rolling(
int(p)).mean().shift()
df_p = df_p[[c for c in df_p.columns if 'Volume' in c]]
else:
volume_dict = {
ticker: df['Volume'].dropna().to_dict()
for ticker, df in df_dict.items()
}
df_p = pd.DataFrame.from_dict(volume_dict)
volume_scatter = px.line(df_p,
y=tickers if tickers else df_p.columns,
labels={
'x': 'Date',
'y': 'Volume'
},
title=f'volume scatter plot')
volume_hist = px.histogram(df_p,
x=tickers if tickers else df_p.columns,
barmode=chart_configs['barmode'],
title=f'Volume Distribution',
nbins=chart_configs['n_bins'])
show_plotly(volume_scatter)
show_plotly(volume_hist)
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("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)