import mplfinance as mpf
import yfinance as yf
from wallstreet import get_daily_returns, get_ytd_growth_percentage, get_moving_averages, get_relative_strength
import pandas as pd
from datetime import datetime
datetime.now()
datetime.datetime(2022, 2, 14, 13, 54, 41, 670393)
pd.options.display.float_format = '{:.2f}'.format
symbol = 'ptlo'
week = '2022-03-18'
n = 10
# Parameters
symbol = "zs"
week = "2022-03-18"
n = 20
symbol = symbol.upper()
n = n * -1
get_daily_returns([symbol]).set_index('ticker').transpose()
ticker | ZS |
---|---|
name | Zscaler, Inc. |
previous_close | 273.00 |
current_price | 274.97 |
percent change | 0.72 |
today_volume | 1.09 |
previous_volume | 2.01 |
get_ytd_growth_percentage([symbol]).set_index('ticker').transpose()
ticker | ZS |
---|---|
name | Zscaler, Inc. |
growth | -14.43 |
mav = get_moving_averages(symbol)
mav[-5:]
[*********************100%***********************] 1 of 1 completed
Open | High | Low | Close | Adj Close | Volume | SMA8 | SMA21 | SMA30 | SMA200 | RSI | |
---|---|---|---|---|---|---|---|---|---|---|---|
Date | |||||||||||
2022-02-08 | 260.57 | 273.75 | 259.08 | 270.71 | 270.71 | 2017500 | 256.50 | 252.92 | 265.75 | 258.46 | 49.58 |
2022-02-09 | 276.00 | 281.25 | 270.12 | 280.85 | 280.85 | 2173400 | 261.77 | 253.17 | 264.29 | 258.88 | 52.38 |
2022-02-10 | 276.52 | 290.47 | 274.21 | 284.10 | 284.10 | 2104400 | 265.15 | 253.48 | 262.99 | 259.33 | 53.25 |
2022-02-11 | 285.07 | 288.52 | 269.43 | 273.00 | 273.00 | 2013500 | 266.64 | 253.43 | 261.27 | 259.76 | 49.97 |
2022-02-14 | 273.52 | 283.44 | 272.50 | 274.97 | 274.97 | 1094713 | 268.82 | 254.60 | 259.72 | 260.19 | 50.54 |
mas = [mpf.make_addplot(mav[n:]['SMA8'],color='green'),
mpf.make_addplot(mav[n:]['SMA21'], color='blue'),
mpf.make_addplot(mav[n:]['SMA30'], color='orange'),
]
if ('SMA200' in mav.keys()):
mas.append(mpf.make_addplot(mav[n:]['SMA200'], color='red', linestyle='dashed'))
mpf.plot(mav[n:], type='candle',style='charles',title='\n{}'.format(symbol),volume=True,addplot=mas,figscale=1.1,figratio=(3,2))
e = yf.Ticker(symbol)
hist = e.history(period='60d', interval='1d')
# Valid intervals: [1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo]
# expiration weeks
exp_weeks = pd.DataFrame(e.options, columns=['weeks'])
# exp_weeks
hlines=None
if week in exp_weeks.weeks.values:
opt = e.option_chain(week)
c_asset = pd.DataFrame(opt.calls, columns=['strike', 'volume','openInterest', 'impliedVolatility'])
call_strike = c_asset[c_asset.openInterest == c_asset.openInterest.max()].set_index('strike').index[0]
p_asset = pd.DataFrame(opt.puts, columns=['strike', 'volume','openInterest', 'impliedVolatility'])
put_strike = p_asset[p_asset.openInterest == p_asset.openInterest.max()].set_index('strike').index[0]
hlines = dict(hlines=(put_strike, call_strike), colors=('r','g','b','b'), linewidths=(1,1),linestyle='dashed')
# Settings
kwargs = dict(figscale=1.1,figratio=(8,4),
volume=True,volume_panel=2,panel_ratios=(7,2,2) , mav=(8,21,30))
exp12 = hist['Close'].ewm(span=12, adjust=False).mean()
exp26 = hist['Close'].ewm(span=26, adjust=False).mean()
macd = exp12 - exp26
signal = macd.ewm(span=9, adjust=False).mean()
histogram = macd - signal
currentPrice = hist[-1:].set_index('Close').index[0]
apds = [#mpf.make_addplot(exp12,color='lime'),
#mpf.make_addplot(exp26,color='c'),
mpf.make_addplot(histogram,type='bar',width=0.4,panel=1,
color='blue',alpha=1,secondary_y=False),
mpf.make_addplot(macd,panel=1,color='green',secondary_y=True),
mpf.make_addplot(signal,panel=1,color='purple',secondary_y=True),
]
# mpf.plot(hist,type='candle',style='yahoo',addplot=apds,**kwargs,hlines=hlines)
if hlines:
title = '\n{} {:.2f} \nOptions Open Interest Walls\n Exp Date: {} Call: {:.2f} Put: {:.2f}'.format(symbol, currentPrice, week, call_strike, put_strike)
mpf.plot(hist,type='candle',style='default',addplot=apds,**kwargs,hlines=hlines,title=title)
else:
title = '\n{} {:.2f} \n'.format(symbol, currentPrice)
mpf.plot(hist,type='candle',style='default',addplot=apds,**kwargs,title=title)
Disclaimer: I am not a professional investment adviser and my opinions are based on my own technical analysis. Please consult an investment professional before making investment decisions.