def test_find_tradable_options(self):
info = r.find_options_by_expiration(self.symbol, self.expiration_date)
first = info[0]
assert (first['expiration_date'] == self.expiration_date)
assert (len(info) > 100)
info = r.find_options_by_expiration(self.symbol,
self.expiration_date,
info='strike_price')
first = info[0]
assert (type(first) == str)
assert (len(info) > 100)
info = r.find_options_by_expiration(self.symbol,
self.expiration_date,
info='expiration_date')
assert (len(set(info)) == 1)
def test_find_tradable_options(self):
info = r.find_options_by_expiration(self.symbol, self.expiration_date)
first = info[0]
self.assertEqual(first['expiration_date'], self.expiration_date)
self.assertGreater(len(info), 100)
info = r.find_options_by_expiration(self.symbol,
self.expiration_date,
info='strike_price')
first = info[0]
self.assertEqual(type(first), str)
self.assertGreater(len(info), 100)
info = r.find_options_by_expiration(self.symbol,
self.expiration_date,
info='expiration_date')
self.assertEqual(len(set(info)), 1)
def get_option(ticker, date, delta_threshold, option_type=None, info=None):
options = r.find_options_by_expiration(ticker,
date,
optionType=option_type)
closest_delta = -1.0 if option_type == 'call' else 1.0
best_option = None
for o in options:
if o['delta'] is None:
continue
if abs(float(o['delta']) - delta_threshold) < abs(closest_delta -
delta_threshold):
closest_delta = float(o['delta'])
best_option = o
return best_option
def getStrikePrices(symbol, expirationDate, optionType='call', volume_limit=0):
#options = r.find_options_for_list_of_stocks_by_expiration_date([symbol], expirationDate=expirationDate,optionType=optionType)
options = r.find_options_by_expiration([symbol],
expirationDate=expirationDate,
optionType=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 = dfoptions.sort_values(
by='volume', ascending=False)[0:5][['strike_price',
'volume']].apply(pd.to_numeric)
dfstrike_prices.sort_values(by='strike_price',
ascending=True,
inplace=True)
return dfstrike_prices
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
pwd = keyring.get_password("robinhood", "my_very_own_rh_username")
login = r.login(MY_RH_USERNAME, pwd)
latest_prices = r.get_latest_price(tickers)
prices = dict(zip(tickers, list(map(float, latest_prices))))
today = datetime.date.today()
friday = today + datetime.timedelta((4 - today.weekday()) % 7)
# If it's late in the week and you want to look at next week, uncomment the below line
# friday = friday + datetime.timedelta(7)
expiration = "{}-{}-{}".format(friday.year, friday.month, friday.day)
putData = r.find_options_by_expiration(tickers,
expirationDate=expiration,
optionType='put')
putsToLookAt = defaultdict(lambda: [])
for put in putData:
tick = put["chain_symbol"]
curr_stock_price = prices[tick]
strike = float(put["strike_price"])
if strike > curr_stock_price:
# We don't sell puts for strikes above the current stock price
continue
premium = float(put["bid_price"]
) # only the bid price is guaranteed, rest is theoretical
if premium < strike * (MIN_PREMIUM_PERCENTAGE / 100):
# Not worth it
continue
