用Python实现一个SVM分类器策略

支持向量机(SVM)是什么意思？

正好最近自己学习机器学习，看到reddit上 Please explain Support Vector Machines (SVM) like I am a 5 year old 的帖子，一个字赞！于是整理一下和大家分享。(如有错欢迎指教！)

 

什么是SVM?

支持向量机/support vector machine (SVM)。当然首先看一下wiki.

Support Vector Machines are learning models used for classification: which individuals in a population belong where? So… how do SVM and the mysterious “kernel” work?

好吧，故事是这样子的：

在很久以前的情人节，大侠要去救他的爱人，但魔鬼和他玩了一个游戏。

魔鬼在桌子上似乎有规律放了两种颜色的球，说：“你用一根棍分开它们？要求：尽量在放更多球之后，仍然适用。”
..... 文章 详细内容 地址：https://quant.la/Article/View/33/

 

• 我们来用Python 实现一个 SVM 分类器 预测 买卖

 

程序是 基于发明者量化平台的，标的物选择为电子货币，因为电子货币适合回测。Python机器学习之SVM 预测买卖，Python入门简单策略 sklearn 机器学习库的使用，

回测系统自带的库有：

numpy pandas TA-Lib scipy statsmodels sklearn cvxopt hmmlearn pykalman arch matplotlib

 

实盘需要在托管者所在机器安装策略需要的库，策略源码地址： https://www.botvs.com/strategy/21370

from sklearn import svmimport numpy as npdef main():    preTime = 0    n = 0    success = 0    predict = None    pTime = None    marketPosition = 0    initAccount = exchange.GetAccount()    Log("Running...")    while True:        r = exchange.GetRecords()        if len(r) < 60:            continue        bar = r[len(r)-1]        if bar.Time > preTime:            preTime = bar.Time            if pTime is not None and r[len(r)-2].Time == pTime:                diff = r[len(r)-2].Close - r[len(r)-3].Close                if diff > SpreadVal:                    success += 1 if predict == 0 else 0                elif diff < -SpreadVal:                    success += 1 if predict == 1 else 0                else:                    success += 1 if predict == 2 else 0                pTime = None                LogStatus("预测次数", n, "成功次数", success, "准确率:", '%.3f %%' % round(float(success) * 100 / n, 2))        else:            Sleep(1000)            continue        inputs_X, output_Y = [], []        sets = [None, None, None]        for i in xrange(1, len(r)-2, 1):            inputs_X.append([r[i].Open, r[i].Close])            Y = 0            diff = r[i+1].Close - r[i].Close            if diff > SpreadVal:                Y = 0                sets[0] = True            elif diff < -SpreadVal:                Y = 1                sets[1] = True            else:                Y = 2                sets[2] = True            output_Y.append(Y)        if None in sets:            Log("样本不足, 无法预测 ...")            continue        n += 1        clf = svm.LinearSVC()        clf.fit(inputs_X, output_Y)        predict = clf.predict(np.array([bar.Open, bar.Close]).reshape((1, -1)))        pTime = bar.Time                Log("预测当前Bar结束:", bar.Time, ['涨', '跌', '横'][predict])        if marketPosition == 0:            if predict == 0:                exchange.Buy(initAccount.Balance/2)                marketPosition = 1            elif predict == 1:                exchange.Sell(initAccount.Stocks/2)                marketPosition = -1        else:            nowAccount = exchange.GetAccount()            if marketPosition > 0 and predict != 0:                exchange.Sell(nowAccount.Stocks - initAccount.Stocks)                nowAccount = exchange.GetAccount()                marketPosition = 0            elif marketPosition < 0 and predict != 1:                while True:                    dif = initAccount.Stocks - nowAccount.Stocks                    if dif < 0.01:                        break                    ticker = exchange.GetTicker()                    exchange.Buy(ticker.Sell + (ticker.Sell-ticker.Buy)*2, dif)                    while True:                        Sleep(1000)                        orders = exchange.GetOrders()                        for order in orders:                            exchange.CancelOrder(order.Id)                        if len(orders) == 0:                            break                    nowAccount = exchange.GetAccount()                marketPosition = 0            if marketPosition == 0:                LogProfit(_N(nowAccount.Balance - initAccount.Balance, 4), nowAccount)