本策略选取了七个特征变量组成了滑动窗口长度为15天的训练集,随后训练了一个二分类(上涨/下跌)的支持向量机模型.
若没有仓位则在每个星期一的时候输入标的股票近15个交易日的特征变量进行预测,并在预测结果为上涨的时候购买标的.
若已经持有仓位则在盈利大于10%的时候止盈,在星期五损失大于2%的时候止损.
特征变量为:1.收盘价/均值2.现量/均量3.最高价/均价4.最低价/均价5.现量6.区间收益率7.区间标准差
训练数据为:SHSE.600000浦发银行,时间从2016-03-01到2017-06-30
回测时间为:2017-07-01 09:00:00到2017-10-01 09:00:00
- # coding=utf-8
- from __future__ import print_function, absolute_import, unicode_literals
- from datetime import datetime
- import numpy as np
- from gm.api import *
- import sys
- try:
- from sklearn import svm
- except:
- print('请安装scikit-learn库和带mkl的numpy')
- sys.exit(-1)
- def init(context):
- # 订阅浦发银行的分钟bar行情
- context.symbol = 'SHSE.600000'
- subscribe(symbols=context.symbol, frequency='60s')
- start_date = '2016-03-01' # SVM训练起始时间
- end_date = '2017-06-30' # SVM训练终止时间
- # 用于记录工作日
- # 获取目标股票的daily历史行情
- recent_data = history(context.symbol, frequency='1d', start_time=start_date, end_time=end_date, fill_missing='last',
- df=True)
- days_value = recent_data['bob'].values
- days_close = recent_data['close'].values
- days = []
- # 获取行情日期列表
- print('准备数据训练SVM')
- for i in range(len(days_value)):
- days.append(str(days_value[i])[0:10])
- x_all = []
- y_all = []
- for index in range(15, (len(days) - 5)):
- # 计算三星期共15个交易日相关数据
- start_day = days[index - 15]
- end_day = days[index]
- data = history(context.symbol, frequency='1d', start_time=start_day, end_time=end_day, fill_missing='last',
- df=True)
- close = data['close'].values
- max_x = data['high'].values
- min_n = data['low'].values
- amount = data['amount'].values
- volume = []
- for i in range(len(close)):
- volume_temp = amount[i] / close[i]
- volume.append(volume_temp)
- close_mean = close[-1] / np.mean(close) # 收盘价/均值
- volume_mean = volume[-1] / np.mean(volume) # 现量/均量
- max_mean = max_x[-1] / np.mean(max_x) # 最高价/均价
- min_mean = min_n[-1] / np.mean(min_n) # 最低价/均价
- vol = volume[-1] # 现量
- return_now = close[-1] / close[0] # 区间收益率
- std = np.std(np.array(close), axis=0) # 区间标准差
- # 将计算出的指标添加到训练集X
- # features用于存放因子
- features = [close_mean, volume_mean, max_mean, min_mean, vol, return_now, std]
- x_all.append(features)
- # 准备算法需要用到的数据
- for i in range(len(days_close) - 20):
- if days_close[i + 20] > days_close[i + 15]:
- label = 1
- else:
- label = 0
- y_all.append(label)
- x_train = x_all[: -1]
- y_train = y_all[: -1]
- # 训练SVM
- context.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)
- context.clf.fit(x_train, y_train)
- print('训练完成!')
- def on_bar(context, bars):
- bar = bars[0]
- # 获取当前年月日
- today = bar.bob.strftime('%Y-%m-%d')
- # 获取数据并计算相应的因子
- # 于星期一的09:31:00进行操作
- # 当前bar的工作日
- weekday = datetime.strptime(today, '%Y-%m-%d').isoweekday()
- # 获取模型相关的数据
- # 获取持仓
- position = context.account().position(symbol=context.symbol, side=PositionSide_Long)
- # 如果bar是新的星期一且没有仓位则开始预测
- if not position and weekday == 1:
- # 获取预测用的历史数据
- data = history_n(symbol=context.symbol, frequency='1d', end_time=today, count=15,
- fill_missing='last', df=True)
- close = data['close'].values
- train_max_x = data['high'].values
- train_min_n = data['low'].values
- train_amount = data['amount'].values
- volume = []
- for i in range(len(close)):
- volume_temp = train_amount[i] / close[i]
- volume.append(volume_temp)
- close_mean = close[-1] / np.mean(close)
- volume_mean = volume[-1] / np.mean(volume)
- max_mean = train_max_x[-1] / np.mean(train_max_x)
- min_mean = train_min_n[-1] / np.mean(train_min_n)
- vol = volume[-1]
- return_now = close[-1] / close[0]
- std = np.std(np.array(close), axis=0)
- # 得到本次输入模型的因子
- features = [close_mean, volume_mean, max_mean, min_mean, vol, return_now, std]
- features = np.array(features).reshape(1, -1)
- prediction = context.clf.predict(features)[0]
- # 若预测值为上涨则开仓
- if prediction == 1:
- # 获取昨收盘价
- context.price = close[-1]
- # 把浦发银行的仓位调至95%
- order_target_percent(symbol=context.symbol, percent=0.95, order_type=OrderType_Market,
- position_side=PositionSide_Long)
- print('SHSE.600000以市价单开多仓到仓位0.95')
- # 当涨幅大于10%,平掉所有仓位止盈
- elif position and bar.close / context.price >= 1.10:
- order_close_all()
- print('SHSE.600000以市价单全平多仓止盈')
- # 当时间为周五并且跌幅大于2%时,平掉所有仓位止损
- elif position and bar.close / context.price < 1.02 and weekday == 5:
- order_close_all()
- print('SHSE.600000以市价单全平多仓止损')
- if __name__ == '__main__':
- '''
- strategy_id策略ID,由系统生成
- filename文件名,请与本文件名保持一致
- mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST
- token绑定计算机的ID,可在系统设置-密钥管理中生成
- backtest_start_time回测开始时间
- backtest_end_time回测结束时间
- backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST
- backtest_initial_cash回测初始资金
- backtest_commission_ratio回测佣金比例
- backtest_slippage_ratio回测滑点比例
- '''
- run(strategy_id='strategy_id',
- filename='main.py',
- mode=MODE_BACKTEST,
- token='token_id',
- backtest_start_time='2017-07-01 09:00:00',
- backtest_end_time='2017-10-01 09:00:00',
- backtest_adjust=ADJUST_PREV,
- backtest_initial_cash=10000000,
- backtest_commission_ratio=0.0001,
- backtest_slippage_ratio=0.0001)
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