Most Python backtests for exotic pairs rely on for-loops over candles. It works for a quick sanity check, but it becomes painful when you wa

Most Python backtests for exotic pairs rely on for-loops over candles. It works for a quick sanity check, but it becomes painful when you want to test multiple parameter sets, add transaction costs, or journal every trade. Pandas vectorization - operating on entire columns at once - removes the bottleneck and makes your code less error-prone.The core idea: instead of iterating row by row to check entry conditions, build boolean masks for your signals. A long entry might be (df['close'] df['ma'].shift(1)) & (df['rsi'] Vectorization also makes journaling trivial. Filter your trade DataFrame by entry and exit timestamps, then write it...