def compute(self, today, assets, out, data): drawdowns = fmax.accumulate(data, axis=0) - data drawdowns[isnan(drawdowns)] = NINF drawdown_ends = nanargmax(drawdowns, axis=0) # TODO: Accelerate this loop in Cython or Numba. for i, end in enumerate(drawdown_ends): peak = nanmax(data[:end + 1, i]) out[i] = (peak - data[end, i]) / data[end, i]
def compute(self, today, assets, out, data): drawdowns = fmax.accumulate(data, axis=0) - data drawdowns[isnan(drawdowns)] = NINF drawdown_ends = nanargmax(drawdowns, axis=0) # TODO: Accelerate this loop in Cython or Numba. for i, end in enumerate(drawdown_ends): peak = nanmax(data[:end + 1, i]) out[i] = (peak - data[end, i]) / data[end, i]