AI For Trading: Kalman Filter (25)

与蒙特卡洛定位方法最主要的区别是:卡尔曼滤波对一个连续的状态进行估计,而蒙特卡洛定位方法中,得把世界分割成离散的小块。作为结果,卡尔曼滤波给我们一个单峰分布。而蒙特卡洛定位方法是多峰分布。这两种方法都适用于机器人定位和对其他车辆的跟踪。

Kalman Filters for Pairs Trading

One way Kalman Filters are used in trading is for choosing the hedge ratio in pairs trading. We will get into pairs trading and hedge ratios in lesson 13 of this module, but for now, imagine that there’s a magic number that you can estimate from a model, such as a regression model, based on time series data of two stocks.
卡尔曼滤波器用于交易的一种方法是选择成对交易中的套期保值比率。我们将在本单元的第13课中讨论成对交易和套期保值比率,但是现在,想象一下,您可以根据两个股票的时间序列数据从模型中估计出一个神奇的数字,例如回归模型。

Every day when you get another data point, you can run another regression and get an updated estimate for this number. So do you take the most recent number every time? Do you take a moving average? If so, how many days will you average together? Will you give each day the same weight when taking the average?
每当您获得另一个数据点时,您可以运行另一个回归并获得此数字的更新估计值。所以你每次都拿最新号码吗?你采取移动平均线吗?如果是这样,你会在一起平均多少天?在取平均值时,你会给每天相同的体重吗?

All of these kinds of decisions are meant to smooth an estimate of a number that is based on noisy data. The Kalman Filter is designed to provide this estimate based on both past information and new observations. So instead of taking a moving average of this estimate, we can use a Kalman Filter.
所有这些类型的决定都是为了平滑基于噪声数据的数字的估计。卡尔曼滤波器旨在根据过去的信息和新的观察结果提供这种估计。因此,我们可以使用卡尔曼滤波器,而不是采用此估计值的移动平均值。

The Kalman Filter takes the time series of two stocks, and generate its “smoothed” estimate for this magic number at each new time period. Kalman Filters are often used in control systems for vehicles such as cars, planes, rockets, and robots. They’re similar to the application in pairs trading because they take noisy indirect measurements at each new time period in order to estimate state variables (location, direction, speed) of a system .
卡尔曼滤波器采用两个股票的时间序列,并在每个新时间段生成对该幻数的“平滑”估计。卡尔曼滤波器通常用于汽车,飞机,火箭和机器人等车辆的控制系统。它们与成对交易中的应用类似,因为它们在每个新时间段进行噪声间接测量,以估计系统的状态变量(位置,方向,速度)

Kalman Filters are not used in this module’s project, but if you want to learn more, please check out the extracurricular content section: "Machine Learning": Introduction to Kalman Filters.
卡尔曼滤波器不在此模块的项目中使用,但如果您想了解更多信息,请查看课外内容部分:“机器学习”:卡尔曼滤波器简介。

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