I have a list of booleans that correspond to buy and sell signals that I would like to backtest. To achieve this, I calculated the return ret of a security and when the signal is False I modify the corresponding return to 0 (corresponds to a cash position), and when the signal is True I kept the return. The result is a Pandas series like this:

> signal
2018-01-01 00:00:00+00:00         NaN
2018-01-01 00:05:00+00:00        True
2018-01-01 00:10:00+00:00       False
2018-01-01 00:15:00+00:00       False
2018-01-01 00:20:00+00:00        True

> ret 
2018-01-01 00:00:00+00:00         NaN
2018-01-01 00:05:00+00:00   -0.003664
2018-01-01 00:10:00+00:00   -0.002735
2018-01-01 00:15:00+00:00   -0.005104
2018-01-01 00:20:00+00:00    0.000366

> ret_backtest = ret.loc[signal[~signal].index] = 0
> ret_backtest
2018-01-01 00:00:00+00:00         NaN
2018-01-01 00:05:00+00:00   -0.003664
2018-01-01 00:10:00+00:00           0
2018-01-01 00:15:00+00:00           0
2018-01-01 00:20:00+00:00    0.000366

Then I reconstruct a price from ret_backtest, which give me a simplified result of the backtest.

result = ret_backtest.add(1).cumprod().mul(100)

My question concerns the trading fees. Usually, these fees are calculated based on the volumes bought or sold. But how can I take into account these transaction costs from a list of returns? for example, by selecting the periods when signal have changed, and applying the fees on the performance of these periods?

t = signal.shift(1) != signal
trades_timestamp = (t.loc[t]).index
  • $\begingroup$ It would be relatively easy to add bid/ask spreads to the backtest. Price impact is more complicated and depends on several factors, I would probably consult someone with experience trading in that particular market. Another more complicated issue is that the optimal trading strategy can be different once you account for transaction costs. $\endgroup$
    – fes
    Aug 6 at 6:15


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