賣SPY末日期權勝率有多少？

之前發現美股的SPY期權居然有周1，周3過期的期權，這樣加上週5，一週有3天過期的期權了。之前嘗試賣過幾次末日期權，勝率還蠻高。來去quantopian上統計下真的歷史勝率有多少？

"""
This is a template algorithm on Quantopian for you to adapt and fill in.
"""
import quantopian.algorithm as algo
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.filters import QTradableStocksUS


def initialize(context):
    """
    Called once at the start of the algorithm.
    """
    context.enter_price = 0
    context.b_rise = False
    # Rebalance every day, 1 hour after market open.
    algo.schedule_function(
        rebalance,
        algo.date_rules.every_day(),
        algo.time_rules.market_open(minutes=20),
    )

    # Record tracking variables at the end of each day.
    algo.schedule_function(
        record_vars,
        algo.date_rules.every_day(),
        algo.time_rules.market_close(),
    )
def before_trading_start(context, data):
    """
    Called every day before market open.
    """
    pass


def rebalance(context, data):
    """
    Execute orders according to our schedule_function() timing.
    """
    
    weekday = get_datetime("US/Eastern").date().weekday() + 1
    if weekday == 1 or weekday == 3 or weekday == 5:
        log.info(weekday)
        history_data = data.history(symbol('SPY'), ['close','high','low','open'], 2, '1m')
        context.enter_price = history_data['close'][-1]
        history_data_day = data.history(symbol('SPY'), ['close','high','low','open'], 2, '1d')
        log.info("enter_price:" + str(context.enter_price))
        if history_data_day['close'][-1] > history_data_day['close'][0]:
            context.b_rise = True
        else:
            context.b_rise = False
    pass


def record_vars(context, data):
    """
    Plot variables at the end of each day.
    """
    weekday = get_datetime("US/Eastern").date().weekday() + 1
    if weekday == 1 or weekday == 3 or weekday == 5:
        history_data = data.history(symbol('SPY'), ['close','high','low','open'], 2, '1d')
        high_price = history_data['high'][-1]
        low_price = history_data['low'][-1]
        log.info("high_price:" + str(high_price))
        log.info("low_price:" + str(low_price))
        
        if context.b_rise:
            if low_price >= context.enter_price * 0.993:
                log.info("Success Put Ex")
            else:
                log.info("Fail Put Ex")
        else:
            if high_price <= context.enter_price * 1.006:
                log.info("Success Call Ex")
            else:
                log.info("Fail Call Ex")
    pass


def handle_data(context, data):
    """
    Called every minute.
    """
    pass

邏輯是開盤20分鐘後，如果SPY是漲的，就賣執行價爲當前價格 * 0.993的PUT，如果是跌的，就賣執行價爲價格* 1.006的Call。如果賣的是PUT，收盤前最低價擊穿執行價就止損，如果賣的是CALL，收盤前最高價擊穿執行價就止損。 統計Log，發現跟無腦賣的勝率差不多，都是75%左右。
估計盈虧比是1：3.所以這個策略沒法盈利。