pySVM

import math
import numpy as np
#from sklearn import preprocessing, cross_validation, svm
from sklearn import preprocessing, svm
import matplotlib.pyplot as plt
from matplotlib import style
import sklearn
import time
from datetime import datetime
import cPickle as pickle
def initialize(context):
g.train=True
# year_date is for get_fundamentals
def train_data(year_date, index_date):
Valuation=[]
price=[]
status=[]
SZ1=get_index_stocks('399008.XSHE', date=index_date)
SZ2=get_index_stocks('399012.XSHE', date=index_date)
SH=get_index_stocks('399905.XSHE', date=index_date)
tem_index=SZ1+SZ2+SH
unix_30d=60*60*24*30
unix_weekend=60*60*24*3
q=query(
income.code, income.pubDate, income.total_operating_revenue,
income.total_operating_cost, income.administration_expense,
income.operating_profit, income.non_operating_revenue,
income.total_profit, income.net_profit, income.basic_eps,
income.diluted_eps, income.total_composite_income
).filter(
valuation.code.in_(tem_index))
incm = get_fundamentals(q, statDate=year_date)
date=incm['pubDate']
index=incm['code']
q=query(
indicator
).filter(
valuation.code.in_(index))
indictor=get_fundamentals(q, statDate=year_date)
del(indictor['code'], indictor['statDate'], indictor['pubDate'])
for each in range(0,len(date)):
q=query(
valuation.pe_ratio, valuation.pb_ratio, valuation.circulating_market_cap
).filter(
valuation.code==(index[each]))
each_valuation=get_fundamentals(q, date=date[each])
date_stamp = datetime.strptime(date[each], '%Y-%m-%d')
unix=time.mktime(date_stamp.timetuple())
unix_30_late=unix+unix_30d
Valuation.append(each_valuation.iloc[0].tolist())
p1=get_price(index[each], start_date=date[each],
end_date=date[each], frequency='daily', fields='close')
if not p1.empty:
pass
else:
p1_weekend=datetime.fromtimestamp(unix-unix_weekend).strftime('%Y-%m-%d')
p1=get_price(index[each], start_date=p1_weekend,
end_date=p1_weekend, frequency='daily', fields='close')
p1_30d=datetime.fromtimestamp(unix_30_late).strftime('%Y-%m-%d')
p2=get_price(index[each], start_date=p1_30d,
end_date=p1_30d, frequency='daily', fields='close')
if not p2.empty:
pass
else:
date_stamp2 = datetime.strptime(p1_30d, '%Y-%m-%d')
unix2=time.mktime(date_stamp2.timetuple())
unix2_weekend=unix2-unix_weekend
p2_weekend=datetime.fromtimestamp(unix2_weekend).strftime('%Y-%m-%d')
p2=get_price(index[each], start_date=p2_weekend,
end_date=p2_weekend, frequency='daily', fields='close')
dif = p2.values / p1.values
if dif > 1.1:
s=1
else:
s=0
status.append(s)
price.append(p1.iloc[0].tolist())
Valuation=pd.DataFrame(Valuation, columns=['pe','pb','cir_mkt_cap'])
price=pd.DataFrame(price, columns=['price'])
status=pd.DataFrame(status, columns=['status'])
df=pd.concat([incm,Valuation,price,indictor,status], axis=1)
del(df['pubDate'], df['statDate.1'], df['code'])
#y=df['status'].values.tolist()
#df=np.random.permutation(df)
#del(df['status'], df['code'])
#X=np.array(df.replace('NaN', 9999).values.tolist())
#X=preprocessing.scale(X)
return df
def fundamental(index):
Valuation=[]
price=[]
status=[]
q=query(
income.total_operating_revenue,
income.total_operating_cost, income.administration_expense,
income.operating_profit, income.non_operating_revenue,
income.total_profit, income.net_profit, income.basic_eps,
income.diluted_eps, income.total_composite_income
).filter(
valuation.code.in_(index))
incm = get_fundamentals(q)
q=query(
valuation.pe_ratio, valuation.pb_ratio, valuation.circulating_market_cap
).filter(
valuation.code.in_(index))
q=query(
indicator
).filter(
valuation.code.in_(index))
indictor=get_fundamentals(q)#.values.tolist()
index2=indictor['code']
del(indictor['code'], indictor['statDate'], indictor['pubDate'], indictor['day'])
for each in index2:
p=attribute_history(each, 1, unit='1d', fields=['close'], skip_paused=True)
price.append(p.iloc[0].tolist())
price=pd.DataFrame(price, columns=['price'])
df=pd.concat([incm,Valuation,price,indictor], axis=1)
X=np.array(df.replace('NaN', 9999).values.tolist())
X=preprocessing.scale(X)
return X, index2
def handle_data(context, data):
if g.train:
index_date=str('2014-03-01')
df1=train_data(str('2014q1'),index_date)
df2=train_data(str('2014q2'),index_date)
df3=train_data(str('2014q3'),index_date)
df4=train_data(str('2014q4'),index_date)
df=pd.concat([df1,df2,df3,df4], axis=0)
df.iloc[np.random.permutation(len(df))]
y=df['status'].values.tolist()
del(df['status'])
log.info("<===== shape of training dataset @ %s", str(df.shape))
X=np.array(df.replace('NaN', 9999).values.tolist())
X=preprocessing.scale(X)
clf = svm.SVC(kernel=str("linear"), C=1.0)
clf.fit(X, y)
filename = "temp.pkl"
pickle_file = open(filename, 'wb')
pickle.dump(clf, pickle_file)
pickle_file.close()
g.train=False
filename = "temp.pkl"
pickle_file = open(filename, 'rb')
clf = pickle.load(pickle_file)
year=context.current_dt.year
month=context.current_dt.month
day=context.current_dt.day
index_date=str(year)+'-'+str(month)+'-'+str(day)
SZ1=get_index_stocks('399008.XSHE', date=index_date)
SZ2=get_index_stocks('399012.XSHE', date=index_date)
SH=get_index_stocks('399905.XSHE', date=index_date)
index=SZ1+SZ2+SH
X, index2=fundamental(index)
for each in range(0, len(index2)):
if clf.predict(X[each].reshape(1,X.shape[1]))[0] == 1 and index2[each] not in context.portfolio.positions.keys():
log.info("===================Buying:", index2[each])
order_target_value(index2[each], context.portfolio.cash/5)
if clf.predict(X[each].reshape(1,X.shape[1]))[0] == 0 and index2[each] in context.portfolio.positions.keys():
log.info("<<<<<<<<<<<<<<<<< log.info("-------------------selling:", index2[each])
order_target(index2[each], 0)
# 止损
if context.portfolio.positions:
for stock in context.portfolio.positions.keys():
cur_price = data[stock].close
position=context.portfolio.positions[stock]
if cur_price > position.avg_cost * (1 + 0.5) or cur_price < position.avg_cost * (1 - 0.2):
order_target(stock, 0)

log.info("<<<<<<<<<<<", stock, "%s lose:", 1-cur_price/position.avg_cost)

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