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，乙個字讚！於是整理一下和大家分享。(如有錯歡迎指教！)
支援向量機/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?
複製**
好吧，故事是這樣子的：
程式是 基於發明者量化平台的，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 **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
複製**
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("**次數", n, "成功次數", success, "準確率:", '%.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):
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
if none in sets:
log("樣本不足, 無法** ...")
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("**當前bar結束:", bar.time, ['漲', '跌', '橫'][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)
```[閱讀原文](複製**
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