<p>我明白了,这是我的<em>真实</em>代码(所以我不会把所有东西都贴出来)。这将起作用(但不确定是否以<strong>最快的方式实现)。在</p>
<p>我在用<strong><em>数据帧应用</em></strong>。这不是矢量化的方法,但应该比python中的循环快得多。有人能不能请你解释一下如何用完全矢量化的方式重写下面的内容?在</p>
<p>引用本文-<a href="https://engineering.upside.com/a-beginners-guide-to-optimizing-pandas-code-for-speed-c09ef2c6a4d6" rel="nofollow noreferrer">https://engineering.upside.com/a-beginners-guide-to-optimizing-pandas-code-for-speed-c09ef2c6a4d6</a></p>
<p>。。。我无法用矢量化的方式重写,而且考虑到查找的性质,我开始觉得下面的内容不可能是矢量化的(如果你们中有人能证明我错了,我很高兴):</p>
<pre><code>pdPnl = pd.DataFrame.from_records([ObjectUtil.objectPropertiesToDictionary(pnl) for pnl in profitLosses], columns=ObjectUtil.objectPropertiesToDictionary(profitLosses[0]).keys())
pdPnl["TM1"] = pdPnl.apply(lambda rw : rw["COB"] - timedelta(days=1) , axis=1)
pdPnl["MonthStart"] = pdPnl.apply(lambda rw : rw["COB"].replace(day=1), axis=1)
pdPnl["QuarterStart"] = pdPnl.apply(lambda rw : DateTimeUtil.getQuarterStart(rw["COB"], rw["COB"].year), axis=1)
pdPnl["YearStart"] = pdPnl.apply(lambda rw : datetime(rw["COB"].year, 1, 1), axis=1)
pdPnl["DTDRealizedPnl"] = pdPnl.apply(lambda rw : PnlCalculatorBase.computeField(pdPnl, rw["TM1"], rw["InceptionRealizedPnl"], "InceptionRealizedPnl"), axis=1)
pdPnl["DTDUnrealizedPnl"] = pdPnl.apply(lambda rw : PnlCalculatorBase.computeField(pdPnl, rw["TM1"], rw["InceptionUnrealizedPnl"], "InceptionUnrealizedPnl"), axis=1)
pdPnl["MTDRealizedPnl"] = pdPnl.apply(lambda rw : PnlCalculatorBase.computeField(pdPnl, rw["MonthStart"], rw["InceptionRealizedPnl"], "InceptionRealizedPnl"), axis=1)
pdPnl["MTDUnrealizedPnl"] = pdPnl.apply(lambda rw : PnlCalculatorBase.computeField(pdPnl, rw["MonthStart"], rw["InceptionUnrealizedPnl"], "InceptionUnrealizedPnl"), axis=1)
pdPnl["YTDRealizedPnl"] = pdPnl.apply(lambda rw : PnlCalculatorBase.computeField(pdPnl, rw["YearStart"], rw["InceptionRealizedPnl"], "InceptionRealizedPnl"), axis=1)
pdPnl["YTDUnrealizedPnl"] = pdPnl.apply(lambda rw : PnlCalculatorBase.computeField(pdPnl, rw["YearStart"], rw["InceptionUnrealizedPnl"], "InceptionUnrealizedPnl"), axis=1)
pdPnl["SharpeRatio"] = pdPnl.apply(lambda rw : PnlCalculatorBase.computeSharpeRatio(pdPnl, rw["COB"]), axis=1)
pdPnl["MaxDrawDown"] = pdPnl.apply(lambda rw : PnlCalculatorBase.computeMaxDrawDown(pdPnl, rw["COB"]), axis=1)
pnlDict = pdPnl.to_dict() # Then convert back to List of ProfitLoss (Slow...)
</code></pre>
<p>查找函数包括:</p>
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