def plot_uoa_performance(s, exp, strike):
data = r.get_option_historicals(s, exp, strike, 'call')
dates = []
prices = []
for point in data:
dates.append(
datetime.datetime.strptime(point['begins_at'],
'%Y-%m-%dT%H:%M:%SZ'))
prices.append(float(point['open_price']))
plt.plot(dates, prices)
plt.show()
def getStrikesOHLCChangesExpiration(symbol, expiration_dates, optionType, span, volume_limit=0):
df_expirationDates = pd.DataFrame()
for expirationDate in expiration_dates[0:5]:
print('{}\t'.format(expirationDate), end='')
#options = r.find_options_for_list_of_stocks_by_expiration_date([symbol], expirationDate, optionType)
options = r.find_options_by_expiration([symbol], expirationDate, optionType)
options = (filter(lambda x: x['volume'] != '', options))
dfoptions = pd.DataFrame((filter(lambda x: x['volume'] > volume_limit, options)))
if dfoptions.empty:
print('Volume is 0 for options')
else:
dfoptions[['strike_price', 'high_price', 'low_price']] = dfoptions[
['strike_price', 'high_price', 'low_price']].apply(pd.to_numeric)
dfstrike_prices = dfoptions.sort_values(by='volume', ascending=False)[0:5][['strike_price', 'volume']]
dfstrike_prices.sort_values(by='strike_price', inplace=True)
# dfstrike_prices
df_all = pd.DataFrame()
for idx, row in dfstrike_prices.iterrows():
strike_price = row.strike_price
# print('Strike price: {}'.format(strike_price))
# df = pd.DataFrame(
# r.get_option_historicals(symbol, expirationDate, strike_price, optionType, span)['data_points'])
df = pd.DataFrame(
r.get_option_historicals(symbol, expirationDate, strike_price, optionType, span))
df['strike_price'] = strike_price
df_all = pd.concat([df_all, df[
['begins_at', 'strike_price', 'open_price', 'high_price', 'low_price', 'close_price', 'volume']]])
df_all[['strike_price','open_price', 'high_price', 'low_price','close_price']] = df_all[
['strike_price', 'open_price','high_price', 'low_price','close_price']].apply(pd.to_numeric)
df_all = df_all[df_all.high_price != 0.01]
df_all['expiration_date'] = expirationDate
print()
diffMaxHighLow(df_all, dfstrike_prices)
df_expirationDates = pd.concat([df_expirationDates, df_all])
return df_expirationDates
def plotPricesForExpriation(
ohlc,
symbol,
expirationDate,
dfstrike_prices,
span='week',
optionType='call',
):
"""
:param OHLC: open_price, high_price, low_price, close_price
:param symbol:
:param expirationDate:
:param dfstrike_prices:
:param optionType:
:param span:
:return:
"""
df_all = pd.DataFrame()
for idx, row in dfstrike_prices.iterrows():
strike_price = row.strike_price
# print('Strike price: {}'.format(strike_price))
df = pd.DataFrame(
r.get_option_historicals(symbol, expirationDate, strike_price,
optionType, span))
if df.empty:
print('No options\t{}\t{}\t{}'.format(expirationDate, strike_price,
span))
return
df['strike_price'] = strike_price
df[[
'strike_price', 'open_price', 'high_price', 'low_price',
'close_price'
]] = df[[
'strike_price', 'open_price', 'high_price', 'low_price',
'close_price'
]].apply(pd.to_numeric)
ohlc_price = df[ohlc].iloc[0]
df['diff'] = df[ohlc] - ohlc_price
df['pct_change'] = 0 if ohlc_price == 0 else round(
df['diff'] / ohlc_price, 2)
df_all = pd.concat([
df_all, df[[
'begins_at', 'strike_price', 'close_price', 'high_price',
'pct_change', 'low_price', 'open_price', 'volume'
]]
])
# df_all = df_all[df_all != 0.01]
df_all = df_all[df.high_price != df.high_price[0:1]
[0]] # filter no changes form the starting date
print()
diffMaxHighLow(df_all, dfstrike_prices)
local = utcToLocal(df_all.iloc[-1:].begins_at.values[-1])
title = 'Percent change {} Expiration:{}, Time:{:{dfmt} {tfmt}}'.format(
symbol, expirationDate, local, dfmt='%Y-%m-%d', tfmt='%H:%M')
fig = px.line(df_all,
x="begins_at",
y="pct_change",
color="strike_price",
line_group="strike_price",
hover_name="strike_price",
line_shape="spline",
render_mode="svg")
fig.update_layout(title=title,
xaxis_title="Date",
yaxis_title="Pct Change",
font=dict(family="Courier New, monospace",
size=18,
color="#7f7f7f"))
fig.show()
title = 'Close Price {} Expiration:{}, Time:{:{dfmt} {tfmt}}'.format(
symbol, expirationDate, local, dfmt='%Y-%m-%d', tfmt='%H:%M')
fig = px.line(df_all,
x="begins_at",
y="close_price",
color="strike_price",
line_group="strike_price",
hover_name="strike_price",
line_shape="spline",
render_mode="svg")
fig.update_layout(title=title,
xaxis_title="Date",
yaxis_title="Close Price",
font=dict(family="Courier New, monospace",
size=18,
color="#7f7f7f"))
fig.show()
#!!! Fill out username and password
username = ''
password = ''
#!!!
login = r.login(username,password)
#!!! fill out the specific option information
symbol = 'V'
expirationDate = '2018-11-16'
strike = 150
optionType = 'call'
span = 'year' #available options are day,week,year, and 5year
#!!!
historicalData = r.get_option_historicals(symbol,expirationDate,strike,optionType,span)
dates = []
closingPrices = []
openPrices = []
for item in historicalData['data_points']:
print(item)
dates.append(item['begins_at'])
closingPrices.append(item['close_price'])
openPrices.append(item['open_price'])
#!!! I made it so it only got the last 30 days but you could delete these lines to get full year.
dates = dates[-30:]
closingPrices = closingPrices[-30:]
' break even.. $', round(float(item['break_even_price']), 3),
' current.. $', round(float(stockPrice[0])),
' change needed.. $', round(changeNeeded, 3),
' % change needed.. ', round(changeNeededPercent, 3), "%",
' \n\t\tprevious close.. $', round(float(item['previous_close_price']), 3),
' change.. $', round(changeInCost, 3),
' percent change.. ', round(changeInCostPercent, 3), "%"
' \n\t\t\timplied volatility.. ',
impliedVolatility,
' volume.. ', item['volume'],
' open interest.. ', item['open_interest'],
' \n\t\t\thigh price.. ', highPrice,
' low price.. ', lowPrice,
)
historicalData = rh.get_option_historicals(stock, date, item['strike_price'], item['type'], 'year')
dates = []
closingPrices = []
openPrices = []
for thing in historicalData['data_points']:
dates.append(thing['begins_at'])
closingPrices.append(float(thing['close_price']))
openPrices.append(float(thing['open_price']))
# Getting data to graph
dates = dates[-historicalDays:]
closingPrices = closingPrices[-historicalDays:]
openPrices = openPrices[-historicalDays:]
#
login = r.login(username, password)
#!!! fill out the specific option information
symbol = 'AAPL'
symbol_name = r.get_name_by_symbol(symbol)
expirationDate = '2020-07-02' # format is YYYY-MM-DD.
strike = 300
optionType = 'call' # available options are 'call' or 'put' or None.
interval = 'hour' # available options are '5minute', '10minute', 'hour', 'day', and 'week'.
span = 'week' # available options are 'day', 'week', 'year', and '5year'.
bounds = 'regular' # available options are 'regular', 'trading', and 'extended'.
info = None
#!!!
historicalData = r.get_option_historicals(symbol, expirationDate, strike,
optionType, interval, span, bounds,
info)
dates = []
closingPrices = []
openPrices = []
for data_point in historicalData:
dates.append(data_point['begins_at'])
closingPrices.append(data_point['close_price'])
openPrices.append(data_point['open_price'])
# change the dates into a format that matplotlib can recognize.
x = [dt.datetime.strptime(d, '%Y-%m-%dT%H:%M:%SZ') for d in dates]
# plot the data.