If you want to consistently earn money with your investments, backtesting is one of the best ways to assess the effectiveness of your trading strategies — of course, assuming that you implement it properly. The idea is that you can experiment with the different parameters for your chosen strategies, and see which combinations give you the best returns for your investment.

如果您想通过投资来持续赚钱，回测是评估交易策略有效性的最佳方法之一-当然，前提是您正确实施了该策略。 这个想法是，您可以为所选策略尝试不同的参数，并查看哪种组合可以为您的投资带来最佳的回报。

However, this can easily start getting tedious as you would have to run hundreds or even thousands of parameter combinations manually.

但是，这很容易开始变得乏味，因为您必须手动运行数百甚至数千个参数组合。

To solve this problem, we can use fastquant to implement a technique called “grid search”, which basically allows you to run a backtest across each of the parameter combinations that you want to run for your strategy.

为了解决这个问题，我们可以使用fastquant实现一种称为“网格搜索”的技术，该技术基本上允许您对要为策略运行的每个参数组合进行回测。

For the rest of the article, I’ll be demonstrating how to apply automated grid search when backtesting your trading strategies.

对于本文的其余部分，我将演示在回测交易策略时如何应用自动网格搜索。

Note: If you’re not yet familiar with how to do basic backtesting with fastquant, you may want to check out my previous article on how to do this in 3 lines of code.

注意：如果您还不熟悉如何使用fastquant进行基本的回测，则可能需要查看我的前 一篇文章 ，其中包含3行代码。

Let’s start by installing fastquant via pip!

让我们开始通过pip安装fastquant！

# Run this on your terminalpip install fastquant# Alternatively, you can run this from jupyter this way!pip install fastquant

For example, let’s use Jollibee Food Corp. (JFC) stock from 2018–01–01 to 2019–12–31 as our sample data.

例如，让我们使用2018年1月1日至2019年12月31日之间的Jollibee Food Corp.(JFC)库存作为样本数据。

Note: get_stock_data supports all of the companies accessible from Yahoo Finance and PSE.

注意： get_stock_data 支持可从 Yahoo Finance 和PSE 访问的所有公司 。

from fastquant import get_stock_datadf = get_stock_data("JFC", "2018-01-01", "2019-12-31")df.head()#         dt  close# 2018-01-03  255.4# 2018-01-04  255.0# 2018-01-05  255.0# 2018-01-08  256.0# 2018-01-09  255.8

Say you want to backtest a simple moving average crossover (SMAC) strategy on JFC stock with a fast period of 15 days, and slow period of 40 days.

假设您要以15天的快速周期和40天的慢周期对JFC股票进行简单的移动平均交叉(SMAC)策略进行回测。

from fastquant import backtestbacktest("smac", df, fast_period=15, slow_period=40)# Starting Portfolio Value: 100000.00# Final Portfolio Value: 68742.36

You’ll notice that your final portfolio value went down by a lot (~31%), and by looking at the chart below, you’ll see that this is because the strategy was still recommending trades (green and red arrows) when the stock was on an overall down trend.

您会注意到您的最终投资组合价值下降了很多(〜31％)，并且通过查看下表，您会发现这是因为该策略仍在建议交易时(绿色和红色箭头)。库存总体呈下降趋势。

So now, we’ll want to see if there’s a combination of “slow period” and “fast period” that will lead to an SMAC strategy that knows not to make trades during the overall downtrend.

因此，现在，我们要查看是否存在“慢速期”和“快速期”的组合，这将导致SMAC策略在整个下降趋势中都不进行交易。

One way to do this is to try out a large number of possible fast period and slow period combinations. For this example, we’ll set the slow period to take values within the range 20 to 241 (skipping every 5), while the fast period can take values within the range 1 to 20.

一种方法是尝试大量可能的快速周期和慢速周期组合。 在此示例中，我们将慢速周期设置为采用20到241范围内的值(每5个跳跃一次)，而快周期可以采用1到20范围内的值。

This means fast period can take 20 possible values, while slow period can take 45 possible values. Together, that translates to 900 (20 x 45) possible combinations! Imagine doing this one by one right.

这意味着快周期可以采用20个可能的值，而慢周期可以采用45个可能的值。 总之，这意味着900(20 x 45)种可能的组合！ 想象一下，一步一步地做到这一点。

Thankfully, we can easily automate this by using fastquant’s built-in grid search capabilities by using iterators (e.g. list , range ) as inputs, rather than single numbers.

值得庆幸的是，我们可以使用fastquant的内置网格搜索功能(通过将迭代器(例如list ， range ))作为输入而不是单个数字来轻松实现此自动化。

results = backtest("smac", df, fast_period=range(1, 21), slow_period=range(20, 241, 5))results[["fast_period", "slow_period", "final_value"]].head()# fast_period slow_period final_value#0          3         105   107042.02#1          8         205   103774.66#2         11         170   103774.66#3          8         200   103774.66#4          9          95   103388.12

Note that “backtest” returns a pandas dataframe (“results”) which contains the results for each iteration on each row. Each column of the dataframe corresponds to either a parameter that was used (e.g. fast_period), or a performance metric for the strategy (e.g. final_value).

请注意，“ backtest”返回一个熊猫数据框(“结果”)，其中包含每一行每次迭代的结果。 数据帧的每一列都对应于所使用的参数(例如fast_period )，或者对应于该策略的性能指标(例如final_value )。

Conveniently, the rows are sorted in descending order based on each iteration’s portfolio return, and so the “best” parameter combinations are the ones at the top of the dataframe.

方便地，这些行基于每次迭代的投资组合收益按降序排序，因此“最佳”参数组合是数据框顶部的参数组合。

In this case, it seems that the top parameter combination is: fast_period=3 , and slow_period=105 , with a portfolio return of 7,042.02. This indicates that we may want to use this parameter combination when utilizing the SMAC strategy on JFC stock.

在这种情况下，似乎最上面的参数组合是：fast_period = 3和slow_period = 105，投资组合回报为7,042.02。 这表明在对JFC股票使用SMAC策略时，我们可能要使用此参数组合。

Note: It is very possible that this parameter combination is still overfitting to the chosen period. See the caveats and safeguards of backtesting in this previous article.

注意：此参数组合很可能仍会过度适合所选时间段。 请参阅上 一篇文章中 的回测注意事项和保护措施 。

Now, let’s run our optimal strategy!

现在，让我们运行最佳策略！

from fastquant import backtestbacktest("smac", df, fast_period=3, slow_period=105)# Starting Portfolio Value: 100000.00# Final Portfolio Value: 107042.02

As shown in the plot below, the parameter combination above would have recommended us to sell our stock right before the overall downtrend — see the red downward arrow right before the drop. This explains how it was able to avoid the losses that we saw during our initial implementation of the SMAC strategy at the beginning of this article.

如下图所示，上面的参数组合建议我们在总体下降趋势之前就卖出股票-看到下跌之前的红色向下箭头。 这说明了如何避免本文开头介绍的SMAC战略的最初实施过程中出现的损失。

With the results dataframe, you can even visualize the effectiveness of each parameter combination with a heatmap like below!

使用结果数据框，您甚至可以通过如下所示的热图直观地看到每个参数组合的有效性！

Congratulations! By now, you should be familiar with how to automatically optimize your trading strategy with fastquant’s built in grid search capabilities. Next time you catch yourself plugging in parameter combinations manually to backtest, do consider using this automated approach!

恭喜你！ 到目前为止，您应该已经熟悉如何使用fastquant的内置网格搜索功能自动优化交易策略。 下一次你发现自己堵漏参数组合手动backtest ，就可以考虑使用这种自动化的方法！

It’s also important to note that this is a capability that was adapted from the backtrader package, which fastquant is built on top of. The difference is that fastquant allows you to do this in much fewer lines of code (as few as 3), so that you can spend more time testing your hypotheses, rather than setting up code.

还必须注意，这是从backtrader包改编而成的功能 ，而fastquant是在backtrader包的基础上构建的。 不同之处在于， fastquant允许您用更少的代码行(最多3行)来执行此操作，这样您就可以花更多的时间测试假设，而无需设置代码。

To see a more detailed version of the examples above with full code, including a demo on how the code would have looked without the “built-in grid search”, do check out this technical tutorial by Jerome de Leon.

要查看带有完整代码的上述示例的更详细版本，包括有关没有“内置网格搜索”的情况下代码外观的演示，请查阅Jerome de Leon撰写的本技术教程 。

If you have any questions about this material, please feel free to comment below, or message me on Twitter, or Linkedin.

如果您对此材料有任何疑问，请在下面发表评论，或在Twitter或Linkedin上给我发消息。

Lastly, in case you want to contribute to the package, or learn more about how others are using fastquant, do join our community Slack workspace, where we have bi-weekly meet ups with fellow quants!

最后，如果您想为该软件包做出贡献，或者想了解更多有关其他人如何使用fastquant的信息，请加入我们的社区Slack工作区 ，在这里我们每两周与其他量子成员见面！