Ideas

#Ideas

1. Linear regression via the least squares fit method

Linear regression via the least squares method is the simplest approach to performing a regression analysis of a dependent and a explanatory variable. The objective is to find the best-fitting straight line through a set of points that minimizes the sum of the squared offsets from the line.
idea: 利用历史数据计算斜率和截值，可以预测个股的走势和大盘的走势。

K-近邻算法(k-NearestNeighbor，KNN)

idea: 利用历史样本预测新加入的股票是否符合投资者编号，例如对沪深300的股票属性分类并进行训练，随机加入1只新的股票，输出相应的决策。 ##K-Means Clustering 对unsuperised数据进行分类 ##Random Forests One possible way to address over-fitting is to use an Ensemble Method: this is a meta-estimator which essentially averages the results of many individual estimators which over-fit the data. One of the most common ensemble methods is the Random Forest, in which the ensemble is made up of many decision trees which are in some way perturbed. ##Support Vector Machine Classifier Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for classification or for regression. SVMs draw a boundary between clusters of data. SVMs attempt to maximize the margin between sets of points. ##Validating Models One of the most important pieces of machine learning is model validation: that is, checking how well your model fits a given dataset. But there are some pitfalls you need to watch out for.