python支持向量机分类器怎么用_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://www.botvs.com/bbs-topic/680
我们来用Python 实现一个 SVM 分类器 预测 买卖
程序是 基于 BotVS 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
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, 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)