논문 리뷰 및 구현 - Option-Expiration Week Effect

논문리뷰

• 저자 : Licheng Sun

• 

• Licheng Sun 교수님이 발표하신 " Returns and Option Activity over the Option-Expiration Week for S&P 100 Stocks "(2013) 을 리뷰해 보겠다.

핵심내용

• The long position is opened at the start of the expiration week.
• The algorithm stays in cash during days out of the options expiration week.

논문구현

python 으로 코드구현을 해보았다

사용한 라이브러리 : custom messari, backtrader, empyrical, pandas, numpy, matplotlib

사용한 환경 : colab

 

핵심 포인트 : 만료하는 주에 월요일 거래, 일주일 홀딩후 판매

 

환경설정

from messari.messari import Messari
import backtrader as bt
import matplotlib.pyplot as plt
from pylab import rcParams
import empyrical as ep
import pandas as pd
import numpy as np
%matplotlib inline
rcParams['figure.figsize'] = 16,9
rcParams['figure.facecolor'] = '#eeeeee'
plt.title('dummy')
plt.plot([1,3,2,4])
plt.close()
messari = Messari('your api')

 

Data 준비

class DataCrawling():
    def __init__(self, start, end, messari = messari):
        self.start = start 
        self.end = end
        self.messari = messari

    def get_data(self) :
        assets = 'btc'
        metric = 'price'
        df_btc = self.messari.get_metric_timeseries(asset_slugs=assets, asset_metric=metric, start=dt.strptime(self.start, "%Y-%m-%d"), end=dt.strptime(self.end, "%Y-%m-%d"))
        df_btc.columns = df_btc.columns.get_level_values(1)
        df_btc = df_btc.reset_index()
        df_btc.columns = ['Time', 'Open','High','Low','Close','Volume']
        df_btc.set_index('Time',inplace=True)
        df_btc['openinterest'] = 0

        # return bt.feeds.PandasData(dataname=df_btc)
        return df_btc
        
 data = DataCrawling('2020-01-01','2020-12-30').get_data()

만료일 설정

expire_list = []
for i,row in data.iterrows() :
  if row.name.isoweekday() == 1 :
    if (15<= row.name.day) and (row.name.day <= 21) :
      expire_list.append(1)
    elif (len(expire_list) >= 7) and (expire_list[-7] == 1) :
      expire_list.append(-1)
    else : expire_list.append(0)
  else : expire_list.append(0)

data['Expire_data'] = expire_list

backtrader 적용

class pandasDataFeed(bt.feeds.PandasData):
    lines = ('Expire_data',)
    params = (
        ('datetime', None),
        ('high', 'High'),
        ('low', 'Low'),
        ('open', 'Open'),
        ('close', 'Close'),
        ('volume', None),
        ('Expire_data', 'Expire_data')
    )

df=pandasDataFeed(dataname=data)

class Test_Strategy(bt.Strategy):

    def next(self):

        if not self.position:

            # Not yet ... we MIGHT BUY if ...
            if self.data.Expire_data == 1 :
                # Keep track of the created order to avoid a 2nd order
                self.order = self.buy()

        else:

            if self.data.Expire_data == -1 :

                # Keep track of the created order to avoid a 2nd order
                self.order = self.sell()
                
 cerebro = bt.Cerebro()
cerebro.adddata(df, name = 'ok')
cerebro.addstrategy(Test_Strategy)

# strats = cerebro.optstrategy(KnnStrategy, maperiod=range(10, 11))  # [10]
cerebro.addanalyzer(bt.analyzers.PyFolio)

# Set our desired cash start
cerebro.broker.setcash(1000000)

# Add a FixedSize sizer according to the stake
cerebro.addsizer(bt.sizers.FixedSize, stake=5)

# Set the commission
cerebro.broker.setcommission(commission=0.0)

# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())

bt.position.Position(size=10, price=0.0)
# Run over everything
results = cerebro.run()  # [15]
strat = results[0]  # [16]

결과값 확인

strat = results[0]
pyfoliozer = strat.analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()

SIMPLE_STAT_FUNCS = [
    ep.annual_return,
    ep.cum_returns_final,
    ep.annual_volatility,
    ep.sharpe_ratio,
    ep.calmar_ratio,
    ep.stability_of_timeseries,
    ep.max_drawdown,
    ep.omega_ratio,
    ep.sortino_ratio,
    ep.tail_ratio,
]

STAT_FUNC_NAMES = {
    'annual_return': 'Annual return',
    'cum_returns_final': 'Cumulative returns',
    'annual_volatility': 'Annual volatility',
    'sharpe_ratio': 'Sharpe ratio',
    'calmar_ratio': 'Calmar ratio',
    'stability_of_timeseries': 'Stability',
    'max_drawdown': 'Max drawdown',
    'omega_ratio': 'Omega ratio',
    'sortino_ratio': 'Sortino ratio',
    'skew': 'Skew',
    'kurtosis': 'Kurtosis',
    'tail_ratio': 'Tail ratio',
    'common_sense_ratio': 'Common sense ratio',
    'value_at_risk': 'Daily value at risk',
    'alpha': 'Alpha',
    'beta': 'Beta',
}

stats = pd.Series()
for i,stat_func in enumerate(SIMPLE_STAT_FUNCS):
    stats[STAT_FUNC_NAMES[stat_func.__name__]] = stat_func(returns)
    stats.iloc[i] = str(np.round(stats.iloc[i]  * 100,3)) + '%'

stats
perf_stats = pd.DataFrame(stats, columns=['Backtest'])
print(perf_stats)

 

 

 

Reference :

https://www.researchgate.net/publication/228290860_Returns_and_Option_Activity_over_the_Option-Expiration_Week_for_SP_100_Stocks

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