1、前置准备
数据来源使用tushare pro，具体操作请看链接，注册就可以使用了 Tushare金融大数据开放社区
2、直接放源码
封装没有做好，很多重复代码，凑合看吧，python不太会，有大佬看到麻烦指教下是否有问题
# -*- coding: utf-8 -*-import datetimeimport osimport timeimport numpy as npimport tushare as tsfrom sklearn import svmimport joblibclass SvmUtil(object):def __init__(self):self.pro = ts.pro_api('此处放tushare的token')def svm_learning(self, stockCode):end_time = time.strftime('%Y%m%d', time.localtime(time.time()))start_year = int(time.strftime('%Y', time.localtime(time.time()))) - 2month_day = time.strftime('%m%d', time.localtime(time.time()))start_time = '{}{}'.format(start_year, month_day)# 获取数据df = self.pro.daily(ts_code=stockCode, start_date=start_time, end_date=end_time)days_value = df['trade_date'].values[::-1]days_close = df['close'].values[::-1]days = []# 获取行情日期列表for i in range(len(days_value)):days.append(str(days_value[i]))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 = self.pro.daily(ts_code=stockCode, start_date=start_day, end_date=end_day)open = data['open'].values[::-1]close = data['close'].values[::-1]max_x = data['high'].values[::-1]min_n = data['low'].values[::-1]amount = data['amount'].values[::-1]volume = []for i in range(len(close)):volume_temp = amount[i] / close[i]volume.append(volume_temp)open_mean = open[-1] / np.mean(open) # 开盘价/均值close_mean = close[-1] / np.mean(close) # 收盘价/均值diff_close_open_mean = close_mean - open_mean # 收盘价均值-开盘价均值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diff_max_min_mean = max_mean - min_mean # 最高价均值-最低价均值vol = volume[-1]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]features = [open_mean, close_mean, diff_close_open_mean, volume_mean, max_mean, min_mean, diff_max_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 = 1else:label = 0y_all.append(label)x_train = x_all[: -1]y_train = y_all[: -1]# 训练SVMmodel = svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False,tol=0.001, cache_size=400, verbose=False, max_iter=-1,decision_function_shape='ovr', random_state=None)model.fit(x_train, y_train)joblib.dump(model, stockCode[:-3] + "_model.m")def svm_predict(self, stockCode):if not (os.path.exists(stockCode[:-3] + "_model.m")):self.svm_learning(stockCode)today = datetime.date.today()first = today.replace(day=1)last_month = first - datetime.timedelta(days=15)start_time = last_month.strftime("%Y%m%d")end_time = time.strftime('%Y%m%d', time.localtime(time.time()))model = joblib.load(stockCode[:-3] + "_model.m")df = self.pro.daily(ts_code=stockCode, start_date=start_time, end_date=end_time)open = df['open'].values[::-1]close = df['close'].values[::-1]train_max_x = df['high'].values[::-1]train_min_n = df['low'].values[::-1]train_amount = df['amount'].values[::-1]volume = []for i in range(len(close)):volume_temp = train_amount[i] / close[i]volume.append(volume_temp)open_mean = open[-1] / np.mean(open)close_mean = close[-1] / np.mean(close)diff_close_open_mean = close_mean - open_meanvolume_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)diff_max_min_mean = max_mean - min_meanvol = 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 = [open_mean, close_mean, diff_close_open_mean, volume_mean, max_mean, min_mean, diff_max_min_mean,vol, return_now, std]features = np.array(features).reshape(1, -1)prediction = model.predict(features)[0]return predictionif __name__ == '__main__':code = '002277.SZ'# SvmUtil().svm_learning(code)SvmUtil().svm_predict(code)
本文由博客一文多发平台 OpenWrite 发布！
