持有期交易策略损益——解决rolling_apply瓶颈

Question

我正在计算包​​含价格数据DataFrame中每一行的损益金额DataFrame。

逻辑如下：

• 我们在当前时间段购买/出售资产。
• 我们持有该资产的时间为holding_period。
• 如果在持有期间价格超过take_profit，则以该价格退出以获取利润。
• 如果在持有期间价格超过stop_loss，则以该价格退出，造成损失。
• 第一个take_profit或stop_loss看到的水平决定了我们是盈利还是亏损退出。
• 如果止盈或止损均未达到，则以持有期间的最后价格退出。

我实现此目的的方法是使用pandas.rolling_apply，它将提供的函数应用到 中每个系列的滚动窗口上DataFrame。

鉴于rolling_apply为 中的每个行和列组合调用一个函数DataFrame，这是一个严重的瓶颈。

我想知道是否有更好的方法使用其他 pandas/numpy 功能来实现此目的？

这是当前的实现：

def potential_pnl(prices, side, periods, take_profit=np.nan, stop_loss=np.nan):

    # set sign depending on direction of price movement required by BUY/SELL
    if side == Side.SELL:
        take_profit *= -1
    else:
        stop_loss *= -1

    def period_potential_pnl(window):
        # enter at the first price, rest of the window are possible exit prices
        entry_price = window[0]
        exit_prices = window[1:]

        take_profit_price = entry_price + take_profit
        stop_loss_price   = entry_price + stop_loss

        # calculate array of bools showing where take_profit/stop_loss is reached
        if side == Side.BUY:
            filtered = exit_prices[ (exit_prices >= take_profit_price) |
                                    (exit_prices <= stop_loss_price) ]
        else:
            filtered = exit_prices[ (exit_prices <= take_profit_price) |
                                    (exit_prices >= stop_loss_price) ]

        # if neither take_profit/stop_loss is reached, exit at the last price
        # otherwise exit at the first price which exceeds take_profit/stop_loss
        if len(filtered) == 0:
            exit_price = exit_prices[-1]
        else:
            exit_price = filtered[0]

        exit_pnl = exit_price - entry_price
        if side == Side.SELL:
            exit_pnl *= -1
        return exit_pnl

    # apply `period_potential_pnl` onto the dataframe
    pnl = pd.rolling_apply(prices, periods + 1, period_potential_pnl)

    # shift back by periods so the exit pnl is lined up with the entry price
    pnl = pnl.shift(-periods)[:-periods]
    return pnl

我尝试过的事情：

我最初使用pandas.rolling_maxandpandas.rolling_min来确定是否达到take_profitor 。stop_loss

我使用这种方法时遇到的问题有两个：

• 您不能使用最大值作为 的退出价格take_profit，因为take_profit很可能已经以较低的价格达到；不可能实时知道最长持有期限是多少。
• 您无法确定首先到达take_profit或中的哪一个。stop_loss

问题：

有没有更有效的方法来计算每个期间的损益？

Answer 1

这是解决此问题的一种方法：

from datetime import datetime, timedelta
from dateutil.relativedelta import relativedelta
from pandas_datareader.data import DataReader

样本数据：

prices = DataReader('IBM', 'yahoo', datetime(2015, 1, 1), datetime.today().utcnow())['Open'].resample('D').fillna(method='ffill')
prices.head()

Date
2015-01-02    161.309998
2015-01-03    161.309998
2015-01-04    161.309998
2015-01-05    161.270004
2015-01-06    159.669998
Freq: D, Name: Open, dtype: float64

计算函数pnl- 获取 take_profit、cut_loss 或 period_end 发生的第一个日期，并使用相应的退出价格计算损益（对于策略profit_goal则相反：cut_losssell

def get_pnl(prices, start_date, holding_period=90, profit_goal=0.10, cut_loss=.10):
    end_date = start_date + timedelta(days=holding_period)
    data = prices[start_date: end_date]

    start_price = data.iloc[0]
    take_profit = start_price * (1 + profit_goal)
    cut_loss = start_price * (1 - cut_loss)
    exit_date = end_date

    if (data > take_profit).any():
        exit_date = data[data > take_profit].index[0]
    if (data[:exit_date] < cut_loss).any():
        exit_date = data[data < cut_loss].index[0]

    exit_price = data.loc[exit_date]
    print('Entered on {0} at: {1:.2f}, exited on {2} at {3:.2f} for {4:.2f}%'.format(start_date.strftime('%Y-%b-%d'), start_price, exit_date.strftime('%Y-%b-%d'), exit_price, (exit_price/start_price-1)*100))

并测试运行：

for start_date in [datetime(2015, 1, 1) + relativedelta(months=i) for i in range(12)]:
    get_pnl(prices, start_date)

要得到：

Entered on 2015-Jan-01 at 161.31, exited on 2015-Apr-01 at 160.23 for -0.67%
Entered on 2015-Feb-01 at 153.91, exited on 2015-Apr-24 at 170.23 for 10.60%
Entered on 2015-Mar-01 at 160.87, exited on 2015-May-30 at 171.35 for 6.51%
Entered on 2015-Apr-01 at 160.23, exited on 2015-Jun-30 at 163.99 for 2.35%
Entered on 2015-May-01 at 173.20, exited on 2015-Jul-30 at 160.50 for -7.33%
Entered on 2015-Jun-01 at 170.21, exited on 2015-Aug-20 at 152.74 for -10.26%
Entered on 2015-Jul-01 at 163.97, exited on 2015-Aug-24 at 143.47 for -12.50%
Entered on 2015-Aug-01 at 161.40, exited on 2015-Aug-24 at 143.47 for -11.11%
Entered on 2015-Sep-01 at 144.91, exited on 2015-Nov-30 at 138.61 for -4.35%
Entered on 2015-Oct-01 at 145.31, exited on 2015-Dec-30 at 139.58 for -3.94%
Entered on 2015-Nov-01 at 140.44, exited on 2016-Jan-20 at 118.46 for -15.65%
Entered on 2015-Dec-01 at 139.58, exited on 2016-Jan-20 at 118.46 for -15.13%