机器学习篇章，本章不过时脚本小子，机器学习最核心的是机器，是模型。
学习，无非就是找些有的没的因子扔进去，但说实话，机器学习，太过容易过拟合，容易无效化。回测好看的一笔，实盘垃圾的不行。
获取训练数据：
#获取训练数据
#用20160101到20170101一年间的数据生成训练集
df = ContextInfo.get_market_data(['open','high','low','close','volume'],stock_code=[ContextInfo.stock],start_time='20160101',end_time='20200101',dividend_type='front')
df = df.sort_index()
days = df.index.values
days_close = df['close'].values?获取训练因子：
#计算训练因子
for i in range(14, len(days) - 5):
start_day = days[i - 14]
end_day = days[i]
data = ContextInfo.get_market_data(['open','high','low','close','volume'],stock_code=[ContextInfo.stock],end_time=end_day,count=15,skip_paused=False,dividend_type='front')
data = data.sort_index()
open = data['open'].values
close = data['close'].values
max = data['high'].values
min = data['low'].values
volume = data['volume'].values
close_mean = close[-1] / np.mean(close)
volume_mean = volume[-1] / np.mean(volume)
max_mean = max[-1] / np.mean(max)
min_mean = min[-1] / np.mean(min)
vol = volume[-1]
return_now = close[-1] / close[0]
std = np.std(np.array(close), axis = 0)
#features用于存放因子
features = [close_mean, volume_mean, max_mean, min_mean, vol, return_now, std] #计算出的6个因子作为特征
x_all.append(features)
for i in range(len(days_close) - 19):
if days_close[i+19] > days_close[i+14]:
label = 1
else:
label = 0
y_all.append(label)?生成训练模型：
x_train = x_all[:-1]
y_train = y_all[:-1]
#生成训练好的模型
ContextInfo.clf = svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None)
try:
ContextInfo.clf.fit(x_train, y_train)
except:
e = traceback.format_exc()
运用模型，判断涨跌信号：
#运用模型，判断涨跌信号
try:
prediction = ContextInfo.clf.predict(features)[0] # 机器学习判断涨跌
if prediction == 1:
ContextInfo.holding = int(ContextInfo.money*0.95/(open_today))/100
order_shares(ContextInfo.stock,ContextInfo.holding*100,'fix',open_today,ContextInfo,ContextInfo.accountid)
ContextInfo.buyprice = open_today
buy_condition = True
print(today)
print('open long position to 0.95')
except :
print(('predict error occur,bar:', d))?策略代码：
#coding:gbk
#!/usr/bin/python
"""
回测模型示例（非实盘交易策略）
#单股机器学习模型，在主图下直接运行即可
#模型中以过去15个交易日数据生成特征变量以预测5个交易日后的涨跌，
#特征变量我们选取了平均收盘价，平均成交量，平均最高价，平均最低价，总收益，收盘价的标准差
#训练结束后，回测过程中在每个星期一预测本周五的涨跌，以此为据开仓
"""
import pandas as pd
import numpy as np
import time
from datetime import *
from sklearn import svm
import traceback
def init(ContextInfo):
ContextInfo.stock = ContextInfo.stockcode + '.' + ContextInfo.market
ContextInfo.set_universe([ContextInfo.stock])
ContextInfo.holding = 0
ContextInfo.days = 0
ContextInfo.money = ContextInfo.capital
ContextInfo.accountid = "testS"
def handlebar(ContextInfo):
buy_condition = False
sell_condition = False
d = ContextInfo.barpos
if ContextInfo.days == 0:
#用20160101到20170101一年间的数据生成训练集
df = ContextInfo.get_market_data(['open','high','low','close','volume'],stock_code=[ContextInfo.stock],start_time='20160101',end_time='20170101',dividend_type='front')
df = df.sort_index()
days = df.index.values
days_close = df['close'].values
print('start training SVM')
x_all = []
y_all = []
for i in range(14, len(days) - 5):
start_day = days[i - 14]
end_day = days[i]
data = ContextInfo.get_market_data(['open','high','low','close','volume'],stock_code=[ContextInfo.stock],end_time=end_day,count=15,skip_paused=False,dividend_type='front')
data = data.sort_index()
open = data['open'].values
close = data['close'].values
max = data['high'].values
min = data['low'].values
volume = data['volume'].values
close_mean = close[-1] / np.mean(close)
volume_mean = volume[-1] / np.mean(volume)
max_mean = max[-1] / np.mean(max)
min_mean = min[-1] / np.mean(min)
vol = volume[-1]
return_now = close[-1] / close[0]
std = np.std(np.array(close), axis = 0)
#features用于存放因子
features = [close_mean, volume_mean, max_mean, min_mean, vol, return_now, std] #计算出的6个因子作为特征
x_all.append(features)
for i in range(len(days_close) - 19):
if days_close[i+19] > days_close[i+14]:
label = 1
else:
label = 0
y_all.append(label)
x_train = x_all[:-1]
y_train = y_all[:-1]
ContextInfo.clf = svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=200, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None)
try:
ContextInfo.clf.fit(x_train, y_train)
except:
e = traceback.format_exc()
print(('value error, bar:', e))
print('training finish!')
timetag = ContextInfo.get_bar_timetag(d)
timetag_start = ContextInfo.get_bar_timetag(d-15)
timetag_end = ContextInfo.get_bar_timetag(d-1) #过去15个交易日的起止时间
today = timetag_to_datetime(timetag, '%Y%m%d')
start_date = timetag_to_datetime(timetag_start, '%Y%m%d')
end_date = timetag_to_datetime(timetag_end, '%Y%m%d')
weekday = datetime.strptime(today, '%Y%m%d').isoweekday()
open_today = ContextInfo.get_market_data(['open'],stock_code=[ContextInfo.stock],skip_paused=False,dividend_type='front')
close_today = ContextInfo.get_market_data(['close'],stock_code=[ContextInfo.stock],skip_paused=False,dividend_type='front')
#print ContextInfo.holding
#print weekday
if ContextInfo.holding == 0 and weekday == 1: #每个星期一判断是否开仓
data = ContextInfo.get_market_data(['open','high','low','close','volume'],stock_code=[ContextInfo.stock],end_time=end_date,count=15,skip_paused=False, dividend_type='front')
data = data.sort_index()
close = data['close'].values
max = data['high'].values
min = data['low'].values
volume = data['volume'].values
close_mean = close[-1] / np.mean(close)
volume_mean = volume[-1] / np.mean(volume)
max_mean = max[-1] / np.mean(max)
min_mean = min[-1] / np.mean(min)
vol = volume[-1]
return_now = close[-1] / close[0]
std = np.std(np.array(close), axis = 0)
features = [close_mean, volume_mean, max_mean, min_mean, vol, return_now, std]
features = np.array(features).reshape(1, -1)
try:
prediction = ContextInfo.clf.predict(features)[0]
if prediction == 1:
ContextInfo.holding = int(ContextInfo.money*0.95/(open_today))/100
order_shares(ContextInfo.stock,ContextInfo.holding*100,'fix',open_today,ContextInfo,ContextInfo.accountid)
ContextInfo.buyprice = open_today
buy_condition = True
print(today)
print('open long position to 0.95')
except :
print(('predict error occur,bar:', d))
elif ContextInfo.holding > 0 and close_today/ContextInfo.buyprice >= 1.1: #每个交易日判断止盈止损
order_shares(ContextInfo.stock,-ContextInfo.holding*100,'fix',close_today,ContextInfo,ContextInfo.accountid)
ContextInfo.holding = 0
sell_condition = True
print(today)
print('reach profit stop limit, close position')
elif ContextInfo.holding > 0 and close_today/ContextInfo.buyprice < 0.98 and weekday == 5:
order_shares(ContextInfo.stock,-ContextInfo.holding*100,'fix',close_today,ContextInfo,ContextInfo.accountid)
ContextInfo.holding = 0
sell_condition = True
print(today)
print('reach lose stop limit, close position')
ContextInfo.days += 1
ContextInfo.paint('do_buy', int(buy_condition), -1, 0)
ContextInfo.paint('do_sell', int(sell_condition), -1, 0)
