Best posts made by eprice122

@balibou Currently I have it set up so the timeframe I am testing is pulled from a DB (similar to pulling the data from a csv) and turned into the benchmark.

"""
    You can assume parsed is a dict with datetime keys and close values
    parsed = {
        datetime.datetime(2014, 2, 3, 9, 30):176.74
        datetime.datetime(2014, 2, 3, 9, 31): 177.01
        ...
    }
    """
    benchmark = (
        pd.DataFrame.from_dict(parsed, orient="index")
        .pct_change()
        .fillna(0)
        .replace([np.inf, -np.inf], float("NaN"))
        .dropna()
    ) # All these functions on the datafame were involved in the quantstat util functions I mentioned

If your's isn't working as expected, I found it helpful to call _prepare_benchmark("SPY")
then turn it to a dict and see how the benchmark dataframe would look with TimeFrame.Days.

Also your df_values dataframe should have the same length, start & end dates as the benchmark.

Hopefully this helps.

I did some debugging and found that a reason some functions don't work in quantstats is because the 'benchmark' it uses is yahoo finance data with a one day timeframe.

If you try and run this on another timeframe, the returns and benchmark dataframes are different sizes. I had success with these functions by making my own benchmark dataframe with my timeframe.

Look at the functions: _prepare_benchmark and download_returns in the quantstats utils file to accomplish this.