![根据股票当日涨幅和换手率预测其下一交易日的涨跌情况[Python实现]](https://1500zi.500zi.com/uploadfile/img/15/68/7317a360b5def5ea3c9fbe1fdd0a73b2.jpg)
前些天在某网站看到了一个比较有意思的股票交易策略，即在尾盘[02:30~03:00]挑选出当日换手率3%~5%，涨幅5%左右的股票买入，下一交易日伺机卖出。因为股票涨跌存在一定的延续性，个人感觉该策略似乎有一点道理，后经过连续几天的测试，认为该策略有进一步研究和验证的价值。于是打算通过python对其进行一个初步的验证，思路大概如下：
1、获取沪深A股部分股票(60XXXX和00XXXX)过去一个月(-06-01~-07-01)涨幅和换手率的历史数据
2、以连续两个交易日的数据构建数据集，其中前一日的涨幅和换手率取整后作为X，后一日的涨跌作为y
3、各种机器学习模型对数据进行拟合，这里主要基于sklearn并且不考虑堆叠
数据获取部分
要获取沪深股票历史数据，首先需要获取股票代码部分，然后根据股票代码再到相应接口获取历史数据，这两部分均可通过网易财经获得
import urllib.requestimport reimport timefrom random import randomdef CodeGet():'''在http://quotes./old/#query=EQA_EXCHANGE_CNSESH&DataType=HS_RANK&sort=PERCENT&order=desc&count=24&page=0通过点击翻页查看开发者工具(F12)→Network找到了沪深A股的代码接口,分别是:沪:http://quotes./hs/service/diyrank.php?host=http%3A%2F%2Fquotes.%2Fhs%2Fservice%2Fdiyrank.php&page=0&query=STYPE%3AEQA%3BEXCHANGE%3ACNSESH&fields=NO%2CSYMBOL%2CNAME%2CPRICE%2CPERCENT%2CUPDOWN%2CFIVE_MINUTE%2COPEN%2CYESTCLOSE%2CHIGH%2CLOW%2CVOLUME%2CTURNOVER%2CHS%2CLB%2CWB%2CZF%2CPE%2CMCAP%2CTCAP%2CMFSUM%2CMFRATIO.MFRATIO2%2CMFRATIO.MFRATIO10%2CSNAME%2CCODE%2CANNOUNMT%2CUVSNEWS&sort=PERCENT&order=desc&count=24&type=query深:http://quotes./hs/service/diyrank.php?host=http%3A%2F%2Fquotes.%2Fhs%2Fservice%2Fdiyrank.php&page=0&query=STYPE%3AEQA%3BEXCHANGE%3ACNSESZ&fields=NO%2CSYMBOL%2CNAME%2CPRICE%2CPERCENT%2CUPDOWN%2CFIVE_MINUTE%2COPEN%2CYESTCLOSE%2CHIGH%2CLOW%2CVOLUME%2CTURNOVER%2CHS%2CLB%2CWB%2CZF%2CPE%2CMCAP%2CTCAP%2CMFSUM%2CMFRATIO.MFRATIO2%2CMFRATIO.MFRATIO10%2CSNAME%2CCODE%2CANNOUNMT%2CUVSNEWS&sort=PERCENT&order=desc&count=24&type=query其中沪88页,深110页,提取出其中符合条件的股票代码'''p = pile('"CODE":"[0-9]*"')code_lst = []# 沪代码提取for i in range(88):print('CodeGet-', i)try:req = urllib.request.urlopen(f'http://quotes./hs/service/diyrank.php?host=http%3A%2F%2Fquotes.%2Fhs%2Fservice%2Fdiyrank.php&page={i}&query=STYPE%3AEQA%3BEXCHANGE%3ACNSESH&fields=NO%2CSYMBOL%2CNAME%2CPRICE%2CPERCENT%2CUPDOWN%2CFIVE_MINUTE%2COPEN%2CYESTCLOSE%2CHIGH%2CLOW%2CVOLUME%2CTURNOVER%2CHS%2CLB%2CWB%2CZF%2CPE%2CMCAP%2CTCAP%2CMFSUM%2CMFRATIO.MFRATIO2%2CMFRATIO.MFRATIO10%2CSNAME%2CCODE%2CANNOUNMT%2CUVSNEWS&sort=PERCENT&order=desc&count=24&type=query').read().decode()except:print('failure-', i)continuefor j in p.findall(req):t = j[-7:-1]if t.startswith('60'):code_lst.append(t) # 毕竟68XXXX也买不了if random() < .7:time.sleep(5)# 深代码提取for i in range(110):print('CodeGet--', i)try:req = urllib.request.urlopen(f'http://quotes./hs/service/diyrank.php?host=http%3A%2F%2Fquotes.%2Fhs%2Fservice%2Fdiyrank.php&page={i}&query=STYPE%3AEQA%3BEXCHANGE%3ACNSESZ&fields=NO%2CSYMBOL%2CNAME%2CPRICE%2CPERCENT%2CUPDOWN%2CFIVE_MINUTE%2COPEN%2CYESTCLOSE%2CHIGH%2CLOW%2CVOLUME%2CTURNOVER%2CHS%2CLB%2CWB%2CZF%2CPE%2CMCAP%2CTCAP%2CMFSUM%2CMFRATIO.MFRATIO2%2CMFRATIO.MFRATIO10%2CSNAME%2CCODE%2CANNOUNMT%2CUVSNEWS&sort=PERCENT&order=desc&count=24&type=query').read().decode()except:print('failure--', i)continuefor j in p.findall(req):t = j[-7:-1]if t.startswith('00'):code_lst.append(t)if random() < .7:time.sleep(5)return code_lstdef DataGet(code: list):'''这里还是用网易财经的接口http://quotes./service/chddata.html?code={}&start=0601&end=0702&fields=TCLOSE;HIGH;LOW;TOPEN;LCLOSE;CHG;PCHG;TURNOVER;VOTURNOVER;TCAP;MCAP因为只需要涨跌幅和换手率,所以只查询PCHG和TURNOVER两个字段就行通过urllib.request.urlretrieve将数据保存于本地data文件夹'''for i in code:print(i)try:if i[0] == '0':urllib.request.urlretrieve(f'http://quotes./service/chddata.html?code=1{i}&start=0601&end=0702&fields=PCHG;TURNOVER', f'data\\{i}.csv')elif i[0] == '6':urllib.request.urlretrieve(f'http://quotes./service/chddata.html?code=0{i}&start=0601&end=0702&fields=PCHG;TURNOVER', f'data\\{i}.csv')except:passif __name__ == '__main__':DataGet(CodeGet())
写到这里先运行一下，发现请求频率有点过快，加个睡眠随机时间，再次运行共获得3015个文件
数据分析部分
首先看一下，换手率3%~5%且涨幅3%~7%的股票下一个交易日的涨跌幅情况，单个数据形式是这样的
import matplotlib.pyplot as pltimport pandas as pdimport oslst = []for i in os.listdir('data'):try:data = pd.read_csv(f'data\\{i}', encoding = 'utf-8').valuesexcept UnicodeDecodeError:try:data = pd.read_csv(f'data\\{i}', encoding = 'gbk').valuesexcept:continuefor j in range(len(data) - 1, 0, -1):try:if (3 <= data[j, 3] <= 7) and (3 <= data[j, 4] <= 5):lst.append(data[j - 1, 3])except:passprint(sum(lst) / len(lst)) # -0.011plt.hist(lst, bins = 50)plt.show()
emmmmm，基本上是正态的，但可以看到涨停的数量有些异常，再看下细分的统计情况
from collections import defaultdictimport matplotlib.pyplot as pltimport pandas as pdimport os# 这里打算把当日涨跌幅分为5类[-10, -6, -2, 2, 6, 10] → [-2, -1, 0, 1, 2]# 当日换手率分为5类[0, 1, 3, 5, 10] → [0, 1, 2, 3, 4]def cal(pchg, turnover):'''接收涨跌幅和换手率返回相应类别'''if pchg <= -6:p = -2elif pchg <= -2:p = -1elif pchg <= 2:p = 0elif pchg <= 6:p = 1else:p = 2if turnover <= 1:t = 0elif turnover <= 3:t = 1elif turnover <= 5:t = 2elif turnover <= 10:t = 3else:t = 4return (p, t)dic = defaultdict(list)for i in os.listdir('data'):try:data = pd.read_csv(f'data\\{i}', encoding = 'utf-8').valuesexcept UnicodeDecodeError:try:data = pd.read_csv(f'data\\{i}', encoding = 'gbk').valuesexcept:continuefor j in range(len(data) - 1, 0, -1):try:key = cal(data[j, 3], data[j, 4])if data[j - 1, 3].__class__.__name__ == 'float':dic[key].append(data[j - 1, 3])except:passfor k, v in dic.items():print(f'{k}:{sum(v) / len(v):.2f}')'''(0, 0):0.33(-1, 0):0.76(1, 1):0.26(1, 0):0.27(-1, 1):0.79(0, 1):0.29(2, 1):1.66(1, 3):-0.33(1, 2):-0.00(-1, 2):0.49(2, 3):0.80(0, 2):0.24(-1, 3):0.31(0, 3):-0.08(2, 2):0.61(-2, 2):0.84(2, 4):0.06(0, 4):-0.20(-1, 4):-0.32(1, 4):-0.85(-2, 3):-0.35(-2, 1):0.58(-2, 4):-1.35(2, 0):4.25(-2, 0):-4.44'''fig = plt.figure()n = 1for i in (-2, -1, 0, 1, 2):for j in range(5):ax = fig.add_subplot(5, 5, n)if (i, j) in dic:ax.hist(dic[(i, j)], label = f'pchg({i})-turnover({j})')ax.legend()n += 1plt.show()
至此可以对策略进行些许修正：当日涨幅在6%以上，并且换手率小于3%时，下一交易日的期望涨幅较高
模型拟合部分
通过一系列机器学习模型对数据进行拟合，保留表现最好的模型，并根据期预测结果作图
import osimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom random import randomfrom sklearn.linear_model import SGDRegressorfrom sklearn.neighbors import KNeighborsRegressorfrom sklearn.ensemble import RandomForestRegressor, BaggingRegressor, ExtraTreesRegressor, AdaBoostRegressor, GradientBoostingRegressorfrom sklearn.tree import DecisionTreeRegressorfrom sklearn.metrics import mean_squared_errorX_train, y_train, X_test, y_test = [], [], [], []for i in os.listdir('data'):try:data = pd.read_csv(f'data\\{i}', encoding = 'utf-8').valuesexcept UnicodeDecodeError:try:data = pd.read_csv(f'data\\{i}', encoding = 'gbk').valuesexcept:passfor j in range(len(data) - 1, 0, -1):try:if data[j, 3].__class__.__name__ == 'float' and data[j, 4].__class__.__name__ == 'float' and data[j - 1, 3].__class__.__name__ == 'float':if random() < 0.7:X_train.append([data[j, 3], data[j, 4]])y_train.append(data[j - 1, 3])else:X_test.append([data[j, 3], data[j, 4]])y_test.append(data[j - 1, 3])except:passX = X_train + X_testy = y_train + y_testmodel, mse = None, np.inffor mod in (SGDRegressor, KNeighborsRegressor, RandomForestRegressor, BaggingRegressor, ExtraTreesRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor):model_tmp = mod()model_tmp.fit(X_train, y_train)mse_tmp = mean_squared_error(model_tmp.predict(X_test), y_test)if mse_tmp < mse:mse = mse_tmpmodel = model_tmppchg = np.arange(-10, 10, 0.2)turnover = np.arange(0, 10, 0.1)z = np.array([[model.predict(np.array([pchg[i], turnover[j]]).reshape(1, -1))[0] for i in range(100)] for j in range(100)])pchg_, turnover_, z_ = [], [], []for p in pchg:for t in turnover:pchg_.append(p)turnover_.append(t)z_.append(model.predict(np.array([p, t]).reshape(1, -1))[0])scatter_data = pd.DataFrame({'pchg': pchg_, 'turnover': turnover_, 'z': z_})scatter_data.plot.scatter('pchg', 'turnover', c = 'z', colormap = 'jet')plt.show() # 散点图pchg, turnover = np.meshgrid(pchg, turnover)# 表面图fig = plt.figure()ax = fig.gca(projection = '3d')ax.plot_surface(pchg, turnover, z, alpha = 0.3)plt.show()
效果一般，但仍不难看出高涨幅和低换手率带来的下一交易日的期望涨幅较大
最后
本文仅为过去一月的历史数据分析，无法明确已有规律能否在未来数据上生效(机器学习难以回答的问题)，因此并不构成投资建议。
