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支持向量機(SVM)是什么意思？

正好最近自己學習機器學習，看到reddit上 Please explain Support Vector Machines (SVM) like I am a 5 year old 的帖子，一個字贊！于是整理一下和大家分享。(如有錯歡迎指教！)

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什么是SVM?

支持向量機/support vector machine (SVM)。當然首先看一下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，故事是這樣子的：

在很久以前的情人節，大俠要去救他的愛人，但魔鬼和他玩了一個游戲。魔鬼在桌子上似乎有規律放了兩種顏色的球，說：“你用一根棍分開它們？要求：盡量在放更多球之后，仍然適用。” ..... 文章 詳細內容 地址：https://www.botvs.com/bbs-topic/680

• 我們來用Python 實現一個 SVM 分類器 預測 買賣

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程序是 基于發明者量化平臺的，標的物選擇為電子貨幣，因為電子貨幣適合回測。Python機器學習之SVM 預測買賣，Python入門簡單策略 sklearn 機器學習庫的使用， 回測系統自帶的庫有：

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

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實盤需要在托管者所在機器安裝策略需要的庫，策略源碼地址： https://www.botvs.com/strategy/21370

from sklearn import svm import numpy as npdef main():preTime = 0n = 0success = 0predict = NonepTime = NonemarketPosition = 0initAccount = exchange.GetAccount()Log("Running...")while True:r = exchange.GetRecords()if len(r) < 60:continuebar = r[len(r)-1]if bar.Time > preTime:preTime = bar.Timeif pTime is not None and r[len(r)-2].Time == pTime:diff = r[len(r)-2].Close - r[len(r)-3].Closeif diff > SpreadVal:success += 1 if predict == 0 else 0elif diff < -SpreadVal:success += 1 if predict == 1 else 0else:success += 1 if predict == 2 else 0pTime = NoneLogStatus("預測次數", n, "成功次數", success, "準確率:", '%.3f %%' % round(float(success) * 100 / n, 2))else:Sleep(1000)continueinputs_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 = 0diff = r[i+1].Close - r[i].Closeif diff > SpreadVal:Y = 0sets[0] = Trueelif diff < -SpreadVal:Y = 1sets[1] = Trueelse:Y = 2sets[2] = Trueoutput_Y.append(Y)if None in sets:Log("樣本不足, 無法預測 ...")continuen += 1clf = svm.LinearSVC()clf.fit(inputs_X, output_Y)predict = clf.predict(np.array([bar.Open, bar.Close]).reshape((1, -1)))pTime = bar.TimeLog("預測當前Bar結束:", bar.Time, ['漲', '跌', '橫'][predict])if marketPosition == 0:if predict == 0:exchange.Buy(initAccount.Balance/2)marketPosition = 1elif predict == 1:exchange.Sell(initAccount.Stocks/2)marketPosition = -1else:nowAccount = exchange.GetAccount()if marketPosition > 0 and predict != 0:exchange.Sell(nowAccount.Stocks - initAccount.Stocks)nowAccount = exchange.GetAccount()marketPosition = 0elif marketPosition < 0 and predict != 1:while True:dif = initAccount.Stocks - nowAccount.Stocksif dif < 0.01:breakticker = 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:breaknowAccount = exchange.GetAccount()marketPosition = 0if marketPosition == 0:LogProfit(_N(nowAccount.Balance - initAccount.Balance, 4), nowAccount)

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轉載于:https://my.oschina.net/u/3949328/blog/2994800

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