用Python实现一个SVM分类器策略

支持向量机(SVM)是什么意思？

正好最近本身学习机器学习，看到reddit上 Please explain Support Vector Machines (SVM) like I am a 5 year old 的帖子，一个字赞！因而整理一下和你们分享。(若有错欢迎指教！)

什么是SVM?

支持向量机/support vector machine (SVM)。固然首先看一下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?
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好吧，故事是这样子的：

在好久之前的情人节，大侠要去救他的爱人，但魔鬼和他玩了一个游戏。魔鬼在桌子上彷佛有规律放了两种颜色的球，说：“你用一根棍分开它们？要求：尽可能在放更多球以后，仍然适用。” ..... 文章 详细内容 地址：www.botvs.com/bbs-topic/6…

• 咱们来用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，由于电子货币适合回测。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
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实盘须要在托管者所在机器安装策略须要的库，策略源码地址： www.botvs.com/strategy/21…

from sklearn import svm
import numpy as np

def 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, 没法预测 ...")
            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)
    ```
    
    <br>
    
[阅读原文](https://quant.la/Article/View/33/%E7%94%A8Python%E5%AE%9E%E7%8E%B0%E4%B8%80%E4%B8%AASVM%E5%88%86%E7%B1%BB%E5%99%A8%E7%AD%96%E7%95%A5.html)复制代码