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, 51, 17, 719581)
pd.options.display.float_format = '{:.2f}'.format
symbol = 'ptlo'
week = '2022-03-18'
n = 10
# Parameters
symbol = "oxy"
week = "2022-03-18"
n = 20
symbol = symbol.upper()
n = n * -1
get_daily_returns([symbol]).set_index('ticker').transpose()
ticker | OXY |
---|---|
name | Occidental Petroleum Corporation |
previous_close | 42.98 |
current_price | 41.46 |
percent change | -3.55 |
today_volume | 16.31 |
previous_volume | 24.45 |
get_ytd_growth_percentage([symbol]).set_index('ticker').transpose()
ticker | OXY |
---|---|
name | Occidental Petroleum Corporation |
growth | 42.95 |
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 | 40.84 | 40.84 | 38.97 | 39.35 | 39.35 | 20307500 | 39.41 | 36.78 | 35.05 | 29.60 | 64.15 |
2022-02-09 | 39.71 | 41.06 | 39.62 | 40.30 | 40.30 | 16412000 | 39.75 | 37.16 | 35.41 | 29.67 | 66.21 |
2022-02-10 | 40.07 | 41.88 | 40.02 | 40.68 | 40.68 | 17756300 | 40.13 | 37.45 | 35.79 | 29.75 | 67.01 |
2022-02-11 | 41.00 | 43.16 | 40.77 | 42.98 | 42.98 | 24447800 | 40.59 | 37.84 | 36.25 | 29.83 | 71.30 |
2022-02-14 | 42.60 | 42.75 | 40.83 | 41.42 | 41.42 | 16331311 | 40.82 | 38.18 | 36.67 | 29.91 | 65.25 |
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.