How to inject custom entry/exit dates?

[Vince Trost]

Hi Michael,

First off, thank you for sharing your work on this package. It's the only one out there that I've seen dedicated to backtesting options strategies.

I'm curious how I can backtest strategies with my own entry/exit dates. In your wiki, you say "but theoretically, it is possible by injecting a list of entry and exit dates created from external sources based on indicators." After poking around in the code, I struggled to see where to do this and figured asking you might be faster.

Can you point me in the right direction? Thanks

[Michael Chu]

hi @vjtrost88 thank you for trying my library!

In terms of using your own entry/exit dates, currently it is not supported, but I do plan on adding that function in the future.

If you wanted to modify the code yourself to see if it works, my suggestion is this:

In the function below, located inside core.py,

def _evaluate_options(data, **kwargs):

    # trim option chains with strikes too far out from current price
    data = data.pipe(_calculate_otm_pct).pipe(
        _trim, "otm_pct", lower=kwargs["max_otm_pct"] * -1, upper=kwargs["max_otm_pct"],
    )

    # remove option chains that are worthless, it's unrealistic to enter
    # trades with worthless options
    entries = _remove_min_bid_ask(data, kwargs["min_bid_ask"])

    # to reduce unnecessary computation, filter for options with the desired exit DTE
    exits = _get(data, "dte", kwargs["exit_dte"])

    return (
        entries.merge(
            right=exits,
            on=["underlying_symbol", "option_type", "expiration", "strike"],
            suffixes=("_entry", "_exit"),
        )
        # by default we use the midpoint spread price to calculate entry and exit costs
        .assign(entry=lambda r: (r["bid_entry"] + r["ask_entry"]) / 2)
        .assign(exit=lambda r: (r["bid_exit"] + r["ask_exit"]) / 2)
        .pipe(_remove_invalid_evaluated_options)
        .pipe(_calculate_profit_loss)
    )[evaluated_cols]

update the lines where entries( entries = _remove_min_bid_ask(data, kwargs["min_bid_ask"]) ) and exits (exits = _get(data, "dte", kwargs["exit_dte"])) dataframes are being filtered, and filter on the df's quote_dates to equal the entry/exit dates you want to look at (likely from some lists).

so for example, lets say you have a list of entry_dates = [...] and exit_dates = [...],

entries = data[data['quote_date'].isin(entry_dates)]
exits = data[data['quote_date'].isin(exit_dates)]

You may/may not have to figure out how to match up the option on entry to the correct exit on the correct exit dates however, since it's doing a merge between the two dataframes after.

Let me know if that works for you.