diff --git a/Beta_0.1/Data_Processor.py b/Beta_0.1/Data_Processor.py new file mode 100644 index 0000000..cbcc742 --- /dev/null +++ b/Beta_0.1/Data_Processor.py @@ -0,0 +1,98 @@ +import numpy as np +import pandas as pd +import math +from sklearn import preprocessing + +class Stock_Data(object): + # 对每一次股票数据进行处理 + def __init__(self,stock_code,input_size,num_steps,train_ratio,interval,future): + ''' + 一个Stock_Data对象对应一只股票历史数据，在Stock_count中将股票历史交易数据处理成模型需要的训练集和测试集 + :param stock_code: 股票的编码 + :param input_size: 决定target的涨跌幅是几日的平均，例如涨跌幅取一个月后五天内的平均涨跌幅，input_size=5 + :param num_steps: 决定输入LSTM神经网络的时序长度 + :param train_ratio: 训练集占所有样本的比率 + :param interval: interval用于将收盘价处理成interval天内的涨跌幅 + :param future: future表示用来预测多少天之后的涨跌幅 + ''' + self.stock_code = stock_code + self.input_size = input_size + self.num_steps = num_steps + self.train_ratio = train_ratio + self.interval = interval + # interval表示将价格换成涨跌率的时间间隔，比如将50天之内的价格换成涨跌率interval=50，就是用50天内的价格去除以50天之前的最后一天的价格 + self.future = future + # future表示取多久之后的平均值，比如预测30个交易日之后的10日平均值，future=30，input_size=10 + self.data_x = [] + self.data_y = [] + + df=pd.read_csv('Data\\交易数据\\'+stock_code+'.csv') + + '''将收盘价每interval天计算成涨跌率，''' + close=df['close'] + self.close_changed = [] + for i in range(int(math.ceil(len(close) / interval))): + if i == 0: + for j in range(interval): + self.close_changed += [(close[i * interval + j] - close[i * interval]) / close[i * interval]] + elif i != 0 and (len(close) - i * interval) > interval: + for j in range(interval): + self.close_changed += [(close[i * interval + j] - close[i * interval - 1]) / close[ + i * interval - 1]] # -1是为了取这interval天之前最后一天的 + elif i != 0 and (len(close) - i * interval) < interval: + for j in range(len(close) - i * interval): + self.close_changed += [(close[i * interval + j] - close[i * interval - 1]) / close[i * interval - 1]] + + '''数据归一化''' + self.trans_close = (self.close_changed[:] - min(self.close_changed[:])) / (max(self.close_changed[:])-min(self.close_changed[:])) #价格涨跌率归一化 + self.trans_vol = (df['vol'][:] - min(df['vol'][:])) / (max(df['vol'])-min(df['vol'])) #日成交量归一化 + + '''用未归一化的数据来构建模型targets''' + for i in range(len(self.close_changed) - num_steps - self.input_size - self.future): + change_y = 0 + for j in range(input_size): + change_y += self.close_changed[i + num_steps + self.future - j] + self.data_y += [[change_y / self.input_size]] #data_y是时间点 i 之后 future 天 input_size天内的平均涨跌幅 + self.data_y = np.array(self.data_y) + + self.data=[[self.trans_close[i],self.trans_vol[i]] for i in range(len(self.trans_close))] + self.data=preprocessing.MinMaxScaler().fit_transform(self.data) # 将data归于[0,1]之间 + self.data_x=np.array([np.array(self.data[i:i+num_steps]) for i in range(len(self.data)-num_steps-self.input_size-self.interval)]) + + '''划分训练集测试集''' + self.train_x,self.test_x = np.split(self.data_x,[int(len(self.data_x)*self.train_ratio)]) #训练集数据占所有数据的train_radio + self.train_y,self.test_y = np.split(self.data_y,[int(len(self.data_y)*self.train_ratio)]) + + def get_fluctuation(self): + # 获取每一只股票在历史数据上的涨跌幅波动 + fluctuation=max(self.close_changed[:])-min(self.close_changed[:]) + return fluctuation + + +def get_stocks(input_size,num_steps,train_ratio,interval,future): + ''' + 获取每只股票对应的Stock_Data对象，读取数据文件中的股票数据 + :return: 每只股票的训练集测试集，股票名，历史数据中涨跌幅的波动 + ''' + stocks_df=pd.DataFrame.from_csv('Data/交易数据/download_stocks_symbol.csv') + stock_name=stocks_df['code'].values.tolist() + # 处理成Stock_Data对象 + stock_data=[ + Stock_Data(stock_code=code,input_size=input_size,num_steps=num_steps,train_ratio=train_ratio,interval=interval,future=future) + for code in stock_name + ] + + '''合并每只股票的测试样本为一个统一的测试集''' + merge_test_x = [] + merge_test_y = [] + for test_data in stock_data: # 键值对 + merge_test_x += list(test_data.test_x) + merge_test_y += list(test_data.test_y) + + '''计算所有股票的平均波动''' + sum_fluctuation = 0 + for code in stock_name: + sum_fluctuation += Stock_Data(stock_code=code,input_size=input_size,num_steps=num_steps,train_ratio=train_ratio,interval=interval,future=future).get_fluctuation() + mean_fluctuation = sum_fluctuation/len(stock_name) + + return stock_name, stock_data, mean_fluctuation, merge_test_x, merge_test_y diff --git a/Beta_0.1/Data_fetcher_tushare.py b/Beta_0.1/Data_fetcher_tushare.py new file mode 100644 index 0000000..16b7dd5 --- /dev/null +++ b/Beta_0.1/Data_fetcher_tushare.py @@ -0,0 +1,55 @@ +import tushare as ts +import pandas as pd +import random,time + +'''使用tushare API需要提供的指令''' +tushare_token='9d318f4f16fc290756e756ddca50bdaabda9d3d98698345f4e505768' + +def get_stock_symbol(): + '''获取当日所有上市股票信息，并存于文件中''' + pro=ts.pro_api(tushare_token) + stocks_info=pro.query('stock_basic',exchange='',list_status='L', fields='ts_code,symbol,name,area,industry,list_date') + stocks_info.to_csv('Data\\stocks_info.csv') + return stocks_info + + +def random_download_stocks(stock_count): + ''' + 该函数用来随机下载stock_count数量的股票 + :param stock_count: 是要下载的股票数量 + ''' + ts.set_token(tushare_token) + Stocks=pd.DataFrame.from_csv('Data\\stocks_info.csv') + length=len(Stocks) + print('共有',length,'只股票，现在随机下载',stock_count,'只！') + + '''处理时间，将开始时间处理成当日的三年前的同一天''' + now = time.localtime() + year = now.tm_year - 3 + start = str(year) + if now.tm_mon < 10: + start = start + '0' + str(now.tm_mon) + else: + start = start + str(now.tm_mon) + if now.tm_mday < 10: + start = start + '0' + str(now.tm_mday) + else: + start = start + str(now.tm_mday) + '''下载股票''' + success_stocks=pd.DataFrame(columns=['code','name']) + for i in range(stock_count): + df=pd.DataFrame() + while df.empty: + index = random.randint(1, 1000000) % length + ts_code, name = Stocks.iloc[index][['ts_code', 'name']] + df = ts.pro_bar(ts_code=ts_code, start_date=start, adj='qfq') #复权可以消除由于除权除息造成的价格走势畸变，保持股价走势的连续性,便于神经网络分析，这是技术面分析常用到的数据。 + lastday =time.strptime(df.iloc[[df.shape[0]-1]]['trade_date'].values[0],'%Y%m%d') #按%Y%m%d格式，将时间字符串转化成元组 + if lastday.tm_year==year and (lastday.tm_mon==now.tm_mon or lastday.tm_mon==now.tm_mon+1): + #判断获取的股票在三年前有没有交易数据 + pass + else: + df=pd.DataFrame() + df.to_csv('Data\\交易数据\\'+ts_code+'.csv') + #存于文件中 + success_stocks.loc[success_stocks.shape[0]+1]={'code':ts_code,'name':name} + success_stocks.to_csv('Data\\交易数据\\download_stocks_symbol.csv') diff --git a/Beta_0.1/README b/Beta_0.1/README new file mode 100644 index 0000000..7d9e652 --- /dev/null +++ b/Beta_0.1/README @@ -0,0 +1 @@ +测试版0.1 diff --git a/Beta_0.1/model_lstm.py b/Beta_0.1/model_lstm.py new file mode 100644 index 0000000..46b53bf --- /dev/null +++ b/Beta_0.1/model_lstm.py @@ -0,0 +1,188 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import tensorflow as tf +import numpy as np +import pandas as pd +import Data_Processor + +class LSTM_net(): + def __init__(self, + sess, + stock_count, + lstm_shape, + num_steps, + input_size, + train_ratio, + logs_dir='', + plots_dir=''): + self.sess = sess + self.stock_count = stock_count + self.lstm_shape = lstm_shape + self.num_steps = num_steps + self.input_size = input_size + self.logs_dir = logs_dir + self.plots_dir = plots_dir + self.train_ratio=train_ratio + # 在类的构造函数里就画好图 + self.graph() + + def graph(self): + '''构建数据流图''' + '''定义需要用到的占位符''' + with tf.name_scope('inputs'): + self.inputs = tf.placeholder(tf.float32, [None,self.num_steps, 2], name='inputs') # 2 is for the change and vol + self.targets = tf.placeholder(tf.float32, [None, 1], name='targets') + with tf.name_scope('parameter'): + self.learning_rate = tf.placeholder(tf.float32, None, name='LearningRate') + self.keep_prob = tf.placeholder(tf.float32, None, name='KeepProb') # 表示在 dropout 中每个神经元被保留的几率 + + '''根据lstm_size、keep_prob、num_layers构建含Dropout包装器的LSTM神经网络''' + def _one_lstm_layer(lstm_size): + lstm_layer = tf.nn.rnn_cell.DropoutWrapper( + tf.nn.rnn_cell.LSTMCell(self.lstm_size, state_is_tuple=True), + output_keep_prob=self.keep_prob + ) + return lstm_layer + + with tf.name_scope('hidden_layer'): + if len(self.lstm_shape) > 1 : + lstm_layers = tf.nn.rnn_cell.MultiRNNCell([_one_lstm_layer(self.lstm_shape[i]) for i in range(len(self.lstm_shape))], state_is_tuple=True) + else : + lstm_layers = one_lstm_layer(self.lstm_shape[0]) #网络共num_layers层，每层的神经元结构相同 + + '''获得LSTM网络的输出和状态''' + lstm_output,_ = tf.nn.dynamic_rnn(lstm_layers, self.inputs, dtype=tf.float32) + + '''根据LSTM网络的输出计算出与target维度匹配的输出''' + # Before transpose, lstm_output.get_shape() = (batch_size, num_steps, lstm_size) + # After transpose, lstm_output.get_shape() = (num_steps, batch_size, lstm_size) + lstm_output = tf.transpose(lstm_output, [1, 0, 2], name='hidden_layer_output') + + '''构建神经网络的输出层''' + with tf.name_scope('linear_layer'): + last = tf.gather(lstm_output,int(lstm_output.get_shape()[0])-1, name='last_lstm_output') + weight = tf.Variable(tf.truncated_normal([self.lstm_size, 1]), name='weight') + bias = tf.Variable(tf.constant(0.1,shape=[1]), name='bias') + self.prediction = tf.matmul(last, weight) + bias + tf.summary.histogram('prediction', self.prediction) + + '''模型的代价函数和优化器''' + with tf.name_scope('loss'): + self.loss=tf.reduce_mean(tf.square(self.prediction-self.targets)) + tf.summary.scalar('loss', self.loss)#均方差函数 + + self.optimizer = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate).minimize(self.loss) + + '''tensorboard记录的参数mergerd, 以及文件接口''' + self.merged = tf.summary.merge_all() + self.train_writer = tf.summary.FileWriter(self.logs_dir + '/train', self.sess.graph) + self.test_writer = tf.summary.FileWriter(self.logs_dir + '/test') + + + + def train(self,max_epoch,init_learning_rate,decay_rate,decay_epoch,batch_ratio,keep_prob,interval,future): + '''获取数据，设定好batch_size与训练epoch,将数据输入进图开始训练和测试''' + + '''获取数据，初始化模型参数''' + stock_name, stock_data, mean_fluctuation, merge_test_x, merger_test_y = Data_Processor.get_stocks( + input_size=self.input_size, + num_steps=self.num_steps, + train_ratio=self.train_ratio, + interval=interval, + future=future) + + tf.global_variables_initializer().run() + + def _feed_dic(train): + if train: + feed_dic = { + self.learning_rate: learning_rate, + self.keep_prob: keep_prob, + self.inputs: batch_x, + self.targets: batch_y, + } + else: + feed_dic = { + self.learning_rate: 0.0, + self.keep_prob: 1.0, + self.inputs: merge_test_x, + self.targets: merger_test_y, + } + return feed_dic + + '''开始训练''' + for epoch in range(max_epoch): + '''每轮更新一次学习率''' + learning_rate = init_learning_rate * ( + decay_rate ** max(float(epoch + 1 - decay_epoch), 0.0) + ) + + '''按股票训练''' + for data in stock_data: + train_x = tf.placeholder(data.train_x.dtype,data.train_x.shape) + train_y = tf.placeholder(data.train_y.dtype,data.train_y.shape) + + # 取batch_size个样本的训练集 + batch_size = int(len(data.train_x)*batch_ratio) + dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y)) + dataset = dataset.batch(batch_size) + iterator = dataset.make_initializable_iterator() + # 初始化参数 + self.sess.run(iterator.initializer, feed_dict={train_x: data.train_x, train_y: data.train_y}) + next_batch = iterator.get_next() + # 获取下一个batch的数据 + batch_x, batch_y = self.sess.run(next_batch) + + # 训练 + summary, train_optimizer = self.sess.run( + [self.merged, self.optimizer], _feed_dic(True) + ) + self.train_writer.add_summary(summary, epoch) + + # 测试 + test_pred, test_loss = self.sess.run([self.prediction, self.loss], _feed_dic(False)) + print('After epoch',epoch,'the test_loss: ',test_loss) + + # 最终再测试一次 + final_pred, final_loss=self.sess.run([self.prediction, self.loss], _feed_dic(False)) + print('Final,the test_loss: ',final_loss) + + # 计算所有股票的平均预测误差 + sum_error=0 + for i in range(final_pred.shape[0]): + print('final_pred:',final_pred[i][0],' and the target:',merge_test_y[i][0]) + sum_error += (final_pred[i][0] - merge_test_y[i][0]) + mean_error=sum_error/final_pred.shape[0] + + print('所有股票涨幅的平均波动为：', mean_fluctuation) + print('在测试集上对于涨跌趋势的预测的平均误差为：', mean_error) + # 这里借助所有股票涨幅的平均波动来帮助评估模型的预测误差 + + self.train_writer.close() + self.test_writer.close() + + +def main(): + with tf.Session() as sess: + lstm_model=LSTM_net( + sess, + stock_count=5, + lstm_shape=[128], + num_steps=250, # 一年250个交易日 + input_size=10, + train_ratio=0.9, + logs_dir='./logs', + plots_dir='./plots' + ) + lstm_model.train(max_epoch=30, + init_learning_rate=0.001, + decay_rate=0.98, + decay_epoch=10, + batch_ratio=0.8, + keep_prob=0.8, + interval=30, + future=30 + ) + +main() diff --git a/Graduate_Proj_V1/Data/stocks_info.csv b/Graduate_Proj_V1/Data/stocks_info.csv new file mode 100644 index 0000000..a30ff17 --- /dev/null +++ b/Graduate_Proj_V1/Data/stocks_info.csv @@ -0,0 +1,3611 @@ +,ts_code,symbol,name,area,industry,list_date +0,000001.SZ,000001,平安银行,深圳,银行,19910403 +1,000002.SZ,000002,万科A,深圳,全国地产,19910129 +2,000004.SZ,000004,国农科技,深圳,生物制药,19910114 +3,000005.SZ,000005,世纪星源,深圳,环境保护,19901210 +4,000006.SZ,000006,深振业A,深圳,区域地产,19920427 +5,000007.SZ,000007,全新好,深圳,酒店餐饮,19920413 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"b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000735.SZ.csv" @@ -0,0 +1,722 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,000735.SZ,20190507,9.62,9.87,9.29,9.73,9.54,0.19000000000000128,1.99,517020.05,499214.001 +1,000735.SZ,20190506,10.21,10.45,9.54,9.54,10.6,-1.0600000000000005,-10.0,694600.88,693337.763 +2,000735.SZ,20190430,10.33,10.72,10.29,10.6,10.35,0.25,2.42,494771.86,521846.988 +3,000735.SZ,20190429,11.29,11.29,10.29,10.35,11.35,-1.0,-8.81,739977.12,793491.374 +4,000735.SZ,20190426,12.23,12.23,11.35,11.35,12.61,-1.2599999999999998,-9.99,1127156.57,1307020.501 +5,000735.SZ,20190425,13.11,13.56,12.6,12.61,13.31,-0.7000000000000011,-5.26,1322060.31,1743519.65 +6,000735.SZ,20190424,12.84,13.33,12.5,13.31,12.85,0.46000000000000085,3.58,1334580.01,1735809.356 +7,000735.SZ,20190423,12.66,13.29,12.26,12.85,12.75,0.09999999999999964,0.78,1081574.71,1385976.292 +8,000735.SZ,20190422,12.48,12.86,12.42,12.75,12.58,0.16999999999999993,1.35,670365.78,849905.184 +9,000735.SZ,20190419,12.65,12.75,12.38,12.58,12.65,-0.07000000000000028,-0.55,528505.7,662590.309 +10,000735.SZ,20190418,12.7,13.1,12.6,12.65,12.85,-0.1999999999999993,-1.56,637591.88,817656.198 +11,000735.SZ,20190417,12.71,13.11,12.67,12.85,12.8,0.049999999999998934,0.39,711645.5,918731.473 +12,000735.SZ,20190416,12.6,12.85,12.42,12.8,12.94,-0.1399999999999988,-1.08,719942.73,908535.009 +13,000735.SZ,20190415,13.0,13.6,12.7,12.94,13.24,-0.3000000000000007,-2.27,1067567.77,1400596.637 +14,000735.SZ,20190412,12.9,13.5,12.81,13.24,12.77,0.47000000000000064,3.68,1054367.56,1390992.301 +15,000735.SZ,20190411,12.93,13.16,12.77,12.77,13.04,-0.2699999999999996,-2.07,645086.5,833255.965 +16,000735.SZ,20190410,13.28,13.58,13.02,13.04,13.43,-0.39000000000000057,-2.9,988627.37,1310604.443 +17,000735.SZ,20190409,13.52,13.67,12.95,13.43,13.38,0.049999999999998934,0.37,1452996.6,1945656.67 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+687,000736.SZ,20160627,9.05,10.05,8.97,10.05,9.14,0.9100000000000001,9.96,66269.71,99281.0005 +688,000736.SZ,20160624,9.01,9.36,8.9,9.14,9.08,0.0600000000000005,0.66,52509.58,74155.12 +689,000736.SZ,20160623,8.7,9.4,8.63,9.08,8.76,0.3200000000000003,3.65,68937.13,95880.3674 +690,000736.SZ,20160622,8.4,8.79,8.17,8.76,8.32,0.4399999999999995,5.29,40113.25,53049.7139 +691,000736.SZ,20160621,8.72,8.72,8.19,8.32,8.56,-0.2400000000000002,-2.8,20642.95,27160.1062 +692,000736.SZ,20160620,8.66,8.77,8.46,8.56,8.68,-0.11999999999999922,-1.38,22861.75,30369.8567 +693,000736.SZ,20160617,8.53,8.92,8.42,8.68,8.56,0.11999999999999922,1.4,64795.83,87558.4243 +694,000736.SZ,20160616,8.08,9.02,8.06,8.56,8.48,0.08000000000000007,0.94,95077.96,126685.7074 +695,000736.SZ,20160615,7.79,8.67,7.63,8.48,8.15,0.33000000000000007,4.05,61545.82,77744.1385 +696,000736.SZ,20160614,8.15,8.59,8.15,8.15,9.05,-0.9000000000000004,-9.94,63787.03,81057.6808 diff --git "a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000768.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000768.SZ.csv" new file mode 100644 index 0000000..1f883dc --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000768.SZ.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,000768.SZ,20190507,14.99,15.02,14.69,14.93,14.83,0.09999999999999964,0.67,134218.06,199883.013 +1,000768.SZ,20190506,15.63,15.7,14.5,14.83,15.92,-1.0899999999999999,-6.85,237250.54,359537.887 +2,000768.SZ,20190430,15.87,16.14,15.85,15.92,15.83,0.08999999999999986,0.57,134892.75,215678.634 +3,000768.SZ,20190429,16.19,16.43,15.82,15.83,16.16,-0.33000000000000007,-2.04,155583.28,250309.54 +4,000768.SZ,20190426,16.3,16.42,16.1,16.16,16.24,-0.0799999999999983,-0.49,147574.71,240097.857 +5,000768.SZ,20190425,16.87,16.94,16.21,16.24,16.86,-0.620000000000001,-3.68,169818.38,281857.074 +6,000768.SZ,20190424,16.77,16.9,16.57,16.86,16.7,0.16000000000000014,0.96,123008.34,206311.665 +7,000768.SZ,20190423,17.11,17.2,16.7,16.7,17.11,-0.41000000000000014,-2.4,208291.09,351584.228 +8,000768.SZ,20190422,17.24,17.37,17.06,17.11,17.24,-0.129999999999999,-0.75,150492.74,258986.065 +9,000768.SZ,20190419,17.42,17.5,17.0,17.24,17.31,-0.07000000000000028,-0.4,188023.55,323736.765 +10,000768.SZ,20190418,17.45,17.63,17.27,17.31,17.44,-0.13000000000000256,-0.75,239922.68,418578.569 +11,000768.SZ,20190417,17.15,17.57,17.11,17.44,17.2,0.240000000000002,1.4,242442.29,420362.508 +12,000768.SZ,20190416,16.9,17.2,16.6,17.2,16.93,0.2699999999999996,1.59,174902.02,296382.535 +13,000768.SZ,20190415,17.16,17.36,16.9,16.93,16.93,0.0,0.0,190035.74,326440.616 +14,000768.SZ,20190412,17.03,17.16,16.79,16.93,17.02,-0.08999999999999986,-0.53,153603.23,260534.224 +15,000768.SZ,20190411,17.14,17.36,17.0,17.02,17.19,-0.1700000000000017,-0.99,196593.25,337660.51 +16,000768.SZ,20190410,17.4,17.4,17.0,17.19,17.42,-0.23000000000000043,-1.32,210586.58,361495.995 +17,000768.SZ,20190409,17.38,17.5,17.18,17.42,17.42,0.0,0.0,178076.63,309265.092 +18,000768.SZ,20190408,17.84,17.94,17.14,17.42,17.67,-0.25,-1.41,306896.72,536750.133 +19,000768.SZ,20190404,17.82,17.97,17.51,17.67,17.59,0.08000000000000185,0.45,357076.6,633050.034 +20,000768.SZ,20190403,17.41,17.67,17.25,17.59,17.49,0.10000000000000142,0.57,306881.88,535376.647 +21,000768.SZ,20190402,17.6,17.94,17.36,17.49,17.5,-0.010000000000001563,-0.06,413607.89,729026.534 +22,000768.SZ,20190401,17.23,17.67,17.15,17.5,17.05,0.4499999999999993,2.64,366770.63,641287.695 +23,000768.SZ,20190329,16.67,17.09,16.59,17.05,16.5,0.5500000000000007,3.33,315103.6,531322.325 +24,000768.SZ,20190328,16.72,17.0,16.48,16.5,16.66,-0.16000000000000014,-0.96,229017.89,383501.091 +25,000768.SZ,20190327,17.0,17.09,16.42,16.66,16.99,-0.3299999999999983,-1.94,297530.45,496132.088 +26,000768.SZ,20190326,18.0,18.0,16.98,16.99,18.0,-1.0100000000000016,-5.61,528881.54,922548.864 +27,000768.SZ,20190325,17.2,18.85,17.2,18.0,17.19,0.8099999999999987,4.71,850518.92,1522228.396 +28,000768.SZ,20190322,17.2,17.37,16.96,17.19,17.11,0.08000000000000185,0.47,287915.11,494892.189 +29,000768.SZ,20190321,17.15,17.27,16.96,17.11,17.15,-0.03999999999999915,-0.23,274575.08,469710.743 +30,000768.SZ,20190320,17.49,17.49,16.86,17.15,17.5,-0.3500000000000014,-2.0,317452.01,543732.573 +31,000768.SZ,20190319,16.93,17.56,16.86,17.5,16.97,0.5300000000000011,3.12,472645.39,820042.129 +32,000768.SZ,20190318,16.77,17.0,16.43,16.97,16.76,0.2099999999999973,1.25,348070.88,583896.156 +33,000768.SZ,20190315,17.0,17.08,16.54,16.76,16.66,0.10000000000000142,0.6,331407.92,555403.494 +34,000768.SZ,20190314,17.1,17.38,16.23,16.66,17.1,-0.4400000000000013,-2.57,482413.61,808254.547 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+55,000768.SZ,20190213,14.47,14.99,14.4,14.9,14.5,0.40000000000000036,2.76,294078.68,433245.742 +56,000768.SZ,20190212,14.36,14.59,14.3,14.5,14.37,0.13000000000000078,0.9,171924.17,248868.715 +57,000768.SZ,20190211,14.23,14.37,14.18,14.37,14.15,0.21999999999999886,1.55,136539.2,195197.232 +58,000768.SZ,20190201,13.98,14.15,13.86,14.15,13.89,0.2599999999999998,1.87,118757.72,166341.468 +59,000768.SZ,20190131,13.89,14.01,13.76,13.89,13.88,0.009999999999999787,0.07,75333.27,104715.972 +60,000768.SZ,20190130,13.88,14.05,13.7,13.88,13.88,0.0,0.0,59829.04,83469.556 +61,000768.SZ,20190129,14.11,14.15,13.7,13.88,14.15,-0.2699999999999996,-1.91,87874.01,122194.382 +62,000768.SZ,20190128,14.15,14.38,14.07,14.15,14.08,0.07000000000000028,0.5,132677.92,188955.634 +63,000768.SZ,20190125,14.11,14.28,14.02,14.08,14.1,-0.019999999999999574,-0.14,110673.04,156767.608 +64,000768.SZ,20190124,13.98,14.17,13.86,14.1,13.98,0.11999999999999922,0.86,81121.95,114033.052 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"b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000798.SZ.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,000798.SZ,20190507,5.88,6.02,5.71,6.0,5.71,0.29000000000000004,5.08,23216.66,13650.748 +1,000798.SZ,20190506,5.85,6.15,5.6,5.71,6.12,-0.41000000000000014,-6.7,26884.66,15818.774 +2,000798.SZ,20190430,6.07,6.24,6.07,6.12,6.1,0.020000000000000462,0.33,20096.43,12357.081 +3,000798.SZ,20190429,6.3,6.36,6.02,6.1,6.38,-0.28000000000000025,-4.39,32744.64,20186.551 +4,000798.SZ,20190426,6.91,6.91,6.33,6.38,7.03,-0.6500000000000004,-9.25,62775.07,40686.985 +5,000798.SZ,20190425,6.99,7.66,6.89,7.03,6.96,0.07000000000000028,1.01,90833.26,65591.53 +6,000798.SZ,20190424,7.04,7.05,6.82,6.96,7.06,-0.09999999999999964,-1.42,23836.0,16538.81 +7,000798.SZ,20190423,7.09,7.27,6.87,7.06,7.11,-0.05000000000000071,-0.7,32914.11,23332.343 +8,000798.SZ,20190422,7.1,7.22,7.07,7.11,7.11,0.0,0.0,24358.0,17395.141 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/dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000839.SZ.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,000839.SZ,20190507,4.34,4.49,4.33,4.4,4.26,0.14000000000000057,3.29,886289.33,390602.378 +1,000839.SZ,20190506,4.51,4.6,4.21,4.26,4.67,-0.41000000000000014,-8.78,1014855.53,447011.0 +2,000839.SZ,20190430,4.51,4.78,4.51,4.67,4.48,0.1899999999999995,4.24,1133712.46,528085.605 +3,000839.SZ,20190429,4.57,4.75,4.47,4.48,4.59,-0.10999999999999943,-2.4,972810.29,448771.191 +4,000839.SZ,20190426,4.7,4.82,4.58,4.59,4.74,-0.15000000000000036,-3.16,820349.85,385377.207 +5,000839.SZ,20190425,4.96,4.99,4.74,4.74,4.95,-0.20999999999999996,-4.24,1093937.85,530418.108 +6,000839.SZ,20190424,4.9,4.98,4.76,4.95,4.9,0.04999999999999982,1.02,1019052.97,498446.803 +7,000839.SZ,20190423,4.88,5.07,4.86,4.9,4.84,0.0600000000000005,1.24,1406151.72,697291.366 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"a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000917.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000917.SZ.csv" new file mode 100644 index 0000000..92453ea --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/000917.SZ.csv" @@ -0,0 +1,708 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,000917.SZ,20190507,7.27,7.38,6.96,7.28,7.19,0.08999999999999986,1.25,276492.76,199207.845 +1,000917.SZ,20190506,7.56,7.64,7.09,7.19,7.88,-0.6899999999999995,-8.76,388242.32,285449.495 +2,000917.SZ,20190430,7.62,8.14,7.62,7.88,7.61,0.2699999999999996,3.55,295985.47,233348.185 +3,000917.SZ,20190429,8.08,8.2,7.58,7.61,8.07,-0.45999999999999996,-5.7,361947.55,282864.023 +4,000917.SZ,20190426,8.4,8.43,8.04,8.07,8.43,-0.35999999999999943,-4.27,390607.17,318155.987 +5,000917.SZ,20190425,8.71,8.94,8.37,8.43,8.82,-0.39000000000000057,-4.42,364473.53,316729.673 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"a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002017.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002017.SZ.csv" new file mode 100644 index 0000000..b06e905 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002017.SZ.csv" @@ -0,0 +1,716 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002017.SZ,20190507,12.24,12.87,11.98,12.41,12.23,0.17999999999999972,1.47,241900.77,298834.801 +1,002017.SZ,20190506,13.0,13.01,12.23,12.23,13.59,-1.3599999999999994,-10.01,174064.27,218509.938 +2,002017.SZ,20190430,13.81,14.35,13.23,13.59,14.56,-0.9700000000000006,-6.66,282662.56,390278.838 +3,002017.SZ,20190429,16.03,16.32,14.56,14.56,16.18,-1.6199999999999992,-10.01,360369.44,534813.785 +4,002017.SZ,20190426,16.6,17.05,15.8,16.18,17.29,-1.1099999999999994,-6.42,388531.79,641019.154 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+713,002017.SZ,20160510,10.81,10.95,10.76,10.81,10.81,0.0,0.0,25915.03,33703.1629 +714,002017.SZ,20160509,11.24,11.24,10.74,10.81,11.31,-0.5,-4.42,42306.68,55491.5648 diff --git "a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002222.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002222.SZ.csv" new file mode 100644 index 0000000..f89f6ec --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002222.SZ.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002222.SZ,20190507,10.76,10.96,10.73,10.89,10.73,0.16000000000000014,1.49,48576.75,52712.174 +1,002222.SZ,20190506,11.41,11.47,10.67,10.73,11.86,-1.129999999999999,-9.53,109997.1,121524.888 +2,002222.SZ,20190430,11.75,11.96,11.75,11.86,11.77,0.08999999999999986,0.76,48284.13,57295.591 +3,002222.SZ,20190429,12.28,12.35,11.7,11.77,12.27,-0.5,-4.07,81045.01,96939.904 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--git "a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002244.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002244.SZ.csv" new file mode 100644 index 0000000..34f46d4 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002244.SZ.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002244.SZ,20190507,4.31,4.41,4.29,4.4,4.26,0.14000000000000057,3.29,197000.78,85793.578 +1,002244.SZ,20190506,4.5,4.52,4.17,4.26,4.61,-0.35000000000000053,-7.59,295409.19,129276.792 +2,002244.SZ,20190430,4.58,4.64,4.52,4.61,4.56,0.05000000000000071,1.1,210418.22,96500.086 +3,002244.SZ,20190429,4.6,4.65,4.53,4.56,4.61,-0.05000000000000071,-1.08,216015.94,99099.031 +4,002244.SZ,20190426,4.64,4.72,4.6,4.61,4.64,-0.02999999999999936,-0.65,255400.95,119014.286 +5,002244.SZ,20190425,4.89,4.89,4.61,4.64,4.89,-0.25,-5.11,439178.67,208228.697 +6,002244.SZ,20190424,4.96,4.97,4.78,4.89,4.89,0.0,0.0,299879.4,145407.044 +7,002244.SZ,20190423,5.03,5.04,4.87,4.89,5.0,-0.11000000000000032,-2.2,326747.0,161150.29 +8,002244.SZ,20190422,5.28,5.29,4.96,5.0,5.3,-0.2999999999999998,-5.66,501476.79,254082.254 +9,002244.SZ,20190419,5.38,5.42,5.21,5.3,5.32,-0.020000000000000462,-0.38,390434.36,206303.605 +10,002244.SZ,20190418,5.51,5.58,5.31,5.32,5.47,-0.14999999999999947,-2.74,366019.53,197189.52 +11,002244.SZ,20190417,5.58,5.72,5.46,5.47,5.55,-0.08000000000000007,-1.44,305258.33,170102.558 +12,002244.SZ,20190416,5.55,5.61,5.26,5.55,5.56,-0.009999999999999787,-0.18,358602.69,195695.527 +13,002244.SZ,20190415,5.68,5.86,5.53,5.56,5.58,-0.020000000000000462,-0.36,364251.2,206657.91 +14,002244.SZ,20190412,5.61,5.69,5.51,5.58,5.58,0.0,0.0,184482.07,102826.992 +15,002244.SZ,20190411,5.65,5.68,5.5,5.58,5.65,-0.07000000000000028,-1.24,299459.55,167548.925 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0000000..d559b92 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002288.SZ.csv" @@ -0,0 +1,700 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002288.SZ,20190507,4.68,4.84,4.5,4.74,4.68,0.0600000000000005,1.28,246622.87,115625.708 +1,002288.SZ,20190506,5.0,5.0,4.68,4.68,5.2,-0.5200000000000005,-10.0,287127.0,138150.312 +2,002288.SZ,20190430,5.02,5.44,5.0,5.2,5.25,-0.04999999999999982,-0.95,293752.01,154026.25 +3,002288.SZ,20190429,5.8,5.95,5.22,5.25,5.79,-0.54,-9.33,543804.27,300527.608 +4,002288.SZ,20190426,5.42,5.9,5.4,5.79,5.59,0.20000000000000018,3.58,496002.0,285106.475 +5,002288.SZ,20190425,5.69,5.98,5.55,5.59,5.73,-0.14000000000000057,-2.44,509369.96,294250.49 +6,002288.SZ,20190424,5.46,5.75,5.38,5.73,5.59,0.14000000000000057,2.5,407377.33,229768.391 +7,002288.SZ,20190423,6.0,6.06,5.59,5.59,6.21,-0.6200000000000001,-9.98,767656.59,442116.623 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+696,002288.SZ,20160531,9.49,9.9,9.39,9.83,9.54,0.2900000000000009,3.04,539280.91,521512.3463 +697,002288.SZ,20160530,10.74,10.74,9.38,9.54,9.76,-0.22000000000000064,-2.25,786581.55,792826.8851 +698,002288.SZ,20160527,9.76,9.76,9.76,9.76,8.87,0.8900000000000006,10.03,13132.02,12816.8515 diff --git "a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002293.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002293.SZ.csv" new file mode 100644 index 0000000..6b61ed2 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002293.SZ.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002293.SZ,20190507,10.68,10.78,10.58,10.73,10.5,0.23000000000000043,2.19,33786.54,36135.596 +1,002293.SZ,20190506,11.02,11.15,10.4,10.5,11.4,-0.9000000000000004,-7.89,64705.64,69781.919 +2,002293.SZ,20190430,11.25,11.46,11.15,11.4,11.25,0.15000000000000036,1.33,35824.56,40615.886 +3,002293.SZ,20190429,11.04,11.33,10.88,11.25,11.0,0.25,2.27,48941.38,54540.927 +4,002293.SZ,20190426,11.15,11.24,10.91,11.0,11.19,-0.1899999999999995,-1.7,47091.55,52145.799 +5,002293.SZ,20190425,11.38,11.49,11.12,11.19,11.36,-0.16999999999999993,-1.5,42352.66,48020.215 +6,002293.SZ,20190424,11.3,11.36,11.03,11.36,11.26,0.09999999999999964,0.89,49554.53,55447.71 +7,002293.SZ,20190423,11.27,11.44,11.22,11.26,11.22,0.03999999999999915,0.36,41806.53,47271.234 +8,002293.SZ,20190422,11.52,11.7,11.19,11.22,11.54,-0.3199999999999985,-2.77,77013.97,87958.572 +9,002293.SZ,20190419,11.92,12.14,11.4,11.54,11.94,-0.40000000000000036,-3.35,101510.07,117718.86 +10,002293.SZ,20190418,11.92,12.07,11.87,11.94,11.92,0.019999999999999574,0.17,25978.26,31076.043 +11,002293.SZ,20190417,11.93,12.15,11.79,11.92,11.96,-0.040000000000000924,-0.33,47304.89,56524.13 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+609,002400.SZ,20160509,10.58,10.69,10.25,10.25,10.71,-0.46000000000000085,-4.3,92249.48,164917.1587 diff --git "a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002510.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002510.SZ.csv" new file mode 100644 index 0000000..18b1fac --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002510.SZ.csv" @@ -0,0 +1,653 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002510.SZ,20190507,4.05,4.08,3.99,4.06,4.01,0.04999999999999982,1.25,114872.7,46464.765 +1,002510.SZ,20190506,4.35,4.35,4.01,4.01,4.46,-0.4500000000000002,-10.09,250392.59,103151.556 +2,002510.SZ,20190430,4.39,4.5,4.39,4.46,4.43,0.03000000000000025,0.68,125169.08,55785.35 +3,002510.SZ,20190429,4.73,4.78,4.3,4.43,4.71,-0.28000000000000025,-5.94,266283.24,119342.885 +4,002510.SZ,20190426,4.75,4.82,4.68,4.71,4.75,-0.040000000000000036,-0.84,156717.0,74461.578 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"b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002579.SZ.csv" @@ -0,0 +1,615 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002579.SZ,20190507,8.79,9.17,8.79,9.17,8.84,0.33000000000000007,3.73,59474.1,53375.742 +1,002579.SZ,20190506,9.59,9.59,8.84,8.84,9.82,-0.9800000000000004,-9.98,72515.2,66458.546 +2,002579.SZ,20190430,9.82,9.96,9.72,9.82,9.77,0.05000000000000071,0.51,43196.38,42553.279 +3,002579.SZ,20190429,10.26,10.33,9.64,9.77,10.24,-0.47000000000000064,-4.59,85557.53,84801.406 +4,002579.SZ,20190426,10.45,10.57,10.18,10.24,10.51,-0.2699999999999996,-2.57,79548.62,82635.017 +5,002579.SZ,20190425,11.25,11.38,10.4,10.51,11.3,-0.7900000000000009,-6.99,102537.44,112153.372 +6,002579.SZ,20190424,11.67,11.67,11.12,11.3,11.85,-0.5499999999999989,-4.64,130082.24,147294.931 +7,002579.SZ,20190423,11.99,12.15,11.7,11.85,12.04,-0.1899999999999995,-1.58,73668.65,87866.339 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+11,002625.SZ,20190417,12.65,12.83,12.49,12.68,12.73,-0.05000000000000071,-0.39,106557.66,134812.917 +12,002625.SZ,20190416,12.27,12.8,12.02,12.73,12.22,0.5099999999999998,4.17,138855.62,173459.818 +13,002625.SZ,20190415,12.55,12.98,12.18,12.22,12.55,-0.33000000000000007,-2.63,128711.89,161488.171 +14,002625.SZ,20190412,11.7,12.56,11.68,12.55,11.76,0.7900000000000009,6.72,162906.62,198415.934 +15,002625.SZ,20190411,12.28,12.4,11.75,11.76,12.34,-0.5800000000000001,-4.7,126437.92,151865.981 +16,002625.SZ,20190410,12.31,12.45,12.14,12.34,12.36,-0.019999999999999574,-0.16,93731.66,115534.704 +17,002625.SZ,20190409,12.21,12.48,12.15,12.36,12.36,0.0,0.0,109205.2,134378.039 +18,002625.SZ,20190408,12.59,12.86,12.2,12.36,12.67,-0.3100000000000005,-2.45,182361.12,228101.4 +19,002625.SZ,20190404,12.85,12.99,12.56,12.67,13.3,-0.6300000000000008,-4.74,298071.52,380350.846 +20,002625.SZ,20190403,13.01,13.41,12.87,13.3,12.19,1.1100000000000012,9.11,476170.12,631406.956 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+31,002625.SZ,20190319,12.0,12.2,11.9,12.08,12.07,0.009999999999999787,0.08,111869.36,134822.525 +32,002625.SZ,20190318,12.08,12.18,11.81,12.07,11.89,0.17999999999999972,1.51,94349.22,113124.681 +33,002625.SZ,20190315,11.94,12.08,11.68,11.89,11.88,0.009999999999999787,0.08,109253.95,129896.464 +34,002625.SZ,20190314,12.3,12.4,11.6,11.88,12.5,-0.6199999999999992,-4.96,166741.93,198875.658 +35,002625.SZ,20190313,12.95,13.58,12.36,12.5,12.84,-0.33999999999999986,-2.65,270307.62,351955.942 +36,002625.SZ,20190312,12.65,12.98,12.57,12.84,12.86,-0.019999999999999574,-0.16,274967.88,350501.149 +37,002625.SZ,20190311,12.0,13.12,12.0,12.86,11.93,0.9299999999999997,7.8,399969.35,513197.01 +38,002625.SZ,20190308,11.31,12.54,11.19,11.93,11.54,0.39000000000000057,3.38,317407.91,378318.191 +39,002625.SZ,20190307,11.57,11.59,11.22,11.54,11.7,-0.16000000000000014,-1.37,213966.64,244443.252 +40,002625.SZ,20190306,11.58,11.73,11.29,11.7,11.91,-0.21000000000000085,-1.76,265981.76,305929.03 +41,002625.SZ,20190305,11.48,11.91,11.34,11.91,11.47,0.4399999999999995,3.84,169807.57,197735.179 +42,002625.SZ,20190304,11.65,11.89,11.43,11.47,11.62,-0.14999999999999858,-1.29,201917.15,235516.918 +43,002625.SZ,20190301,11.91,11.92,11.32,11.62,11.89,-0.27000000000000135,-2.27,185150.79,214517.635 +44,002625.SZ,20190228,11.4,12.36,11.23,11.89,11.3,0.5899999999999999,5.22,260253.27,306233.259 +45,002625.SZ,20190227,11.6,11.79,11.06,11.3,11.4,-0.09999999999999964,-0.88,168560.72,193165.162 +46,002625.SZ,20190226,11.32,11.97,11.11,11.4,11.22,0.17999999999999972,1.6,249516.52,290709.822 +47,002625.SZ,20190225,10.81,11.29,10.81,11.22,10.8,0.41999999999999993,3.89,195590.81,216621.814 +48,002625.SZ,20190222,10.52,10.82,10.41,10.8,10.58,0.22000000000000064,2.08,81664.07,87178.604 +49,002625.SZ,20190221,10.76,10.87,10.53,10.58,10.74,-0.16000000000000014,-1.49,93288.25,100033.307 +50,002625.SZ,20190220,10.9,10.9,10.61,10.74,10.91,-0.16999999999999993,-1.56,82431.12,88381.346 +51,002625.SZ,20190219,10.44,11.38,10.42,10.91,10.42,0.4900000000000002,4.7,151474.59,165486.182 +52,002625.SZ,20190218,10.22,10.47,10.13,10.42,10.11,0.3100000000000005,3.07,78778.2,81353.911 +53,002625.SZ,20190215,10.07,10.26,10.04,10.11,10.13,-0.02000000000000135,-0.2,49905.0,50633.888 +54,002625.SZ,20190214,10.3,10.3,10.03,10.13,10.32,-0.1899999999999995,-1.84,68698.7,69730.513 +55,002625.SZ,20190213,10.24,10.36,10.0,10.32,10.12,0.20000000000000107,1.98,60766.88,62100.087 +56,002625.SZ,20190212,10.18,10.34,10.02,10.12,10.11,0.009999999999999787,0.1,51692.03,52595.537 +57,002625.SZ,20190211,9.94,10.22,9.85,10.11,9.87,0.2400000000000002,2.43,54324.47,54844.895 +58,002625.SZ,20190201,9.47,9.92,9.37,9.87,9.2,0.6699999999999999,7.28,67005.63,64914.294 +59,002625.SZ,20190131,9.77,9.89,9.2,9.2,9.82,-0.620000000000001,-6.31,83385.38,79171.312 +60,002625.SZ,20190130,10.07,10.17,9.81,9.82,10.06,-0.2400000000000002,-2.39,47829.87,47710.094 +61,002625.SZ,20190129,10.59,10.66,9.65,10.06,10.67,-0.6099999999999994,-5.72,106767.46,108126.864 +62,002625.SZ,20190128,10.95,10.95,10.55,10.67,10.79,-0.11999999999999922,-1.11,80028.39,85766.42 +63,002625.SZ,20190125,11.0,11.45,10.77,10.79,10.85,-0.0600000000000005,-0.55,125177.48,139336.812 +64,002625.SZ,20190124,10.84,10.92,10.65,10.85,10.84,0.009999999999999787,0.09,60942.02,65751.201 +65,002625.SZ,20190123,10.75,11.0,10.65,10.84,10.84,0.0,0.0,64864.48,70248.712 +66,002625.SZ,20190122,11.1,11.1,10.76,10.84,11.13,-0.2900000000000009,-2.61,80056.2,87124.743 +67,002625.SZ,20190121,10.74,11.19,10.74,11.13,10.68,0.45000000000000107,4.21,134730.34,148276.839 +68,002625.SZ,20190118,10.75,10.9,10.48,10.68,10.82,-0.14000000000000057,-1.29,110652.46,118201.919 +69,002625.SZ,20190117,11.11,11.18,10.72,10.82,11.05,-0.23000000000000043,-2.08,142875.5,155326.271 +70,002625.SZ,20190116,11.35,11.55,11.0,11.05,11.4,-0.34999999999999964,-3.07,232873.33,261421.838 +71,002625.SZ,20190115,11.27,11.89,10.91,11.4,11.15,0.25,2.24,376952.41,430118.64 +72,002625.SZ,20190114,10.66,11.15,10.45,11.15,10.14,1.0099999999999998,9.96,266226.9,291883.882 +73,002625.SZ,20190111,10.02,10.2,9.9,10.14,10.02,0.120000000000001,1.2,80311.8,80948.255 +74,002625.SZ,20190110,10.19,10.24,10.0,10.02,10.37,-0.34999999999999964,-3.38,114983.73,116083.336 +75,002625.SZ,20190109,10.53,10.99,10.31,10.37,10.48,-0.11000000000000121,-1.05,173430.05,185465.148 +76,002625.SZ,20190108,10.5,10.6,10.26,10.48,10.66,-0.17999999999999972,-1.69,100421.39,104789.422 +77,002625.SZ,20190107,10.72,10.83,10.27,10.66,10.58,0.08000000000000007,0.76,175275.71,184831.01 +78,002625.SZ,20190104,10.05,10.58,9.96,10.58,10.23,0.34999999999999964,3.42,155001.68,160912.876 +79,002625.SZ,20190103,9.9,10.52,9.88,10.23,9.81,0.41999999999999993,4.28,131480.82,135046.885 +80,002625.SZ,20190102,9.7,10.09,9.7,9.81,9.9,-0.08999999999999986,-0.91,56274.0,55827.154 +81,002625.SZ,20181228,9.85,9.96,9.69,9.9,9.82,0.08000000000000007,0.81,67946.25,66937.09 +82,002625.SZ,20181227,10.22,10.3,9.81,9.82,10.05,-0.23000000000000043,-2.29,90684.15,90892.862 +83,002625.SZ,20181226,10.45,10.63,10.05,10.05,10.54,-0.48999999999999844,-4.65,106325.3,109271.473 +84,002625.SZ,20181225,10.11,10.64,9.93,10.54,10.36,0.17999999999999972,1.74,146927.82,150263.757 +85,002625.SZ,20181224,9.97,10.4,9.78,10.36,9.98,0.379999999999999,3.81,121960.25,124722.508 +86,002625.SZ,20181221,10.04,10.2,9.92,9.98,10.15,-0.16999999999999993,-1.67,93415.21,93825.618 +87,002625.SZ,20181220,10.08,10.35,10.0,10.15,10.08,0.07000000000000028,0.69,123777.51,125864.141 +88,002625.SZ,20181219,10.3,10.37,9.91,10.08,10.2,-0.11999999999999922,-1.18,129733.69,130311.717 +89,002625.SZ,20181218,10.21,10.48,10.13,10.2,10.66,-0.46000000000000085,-4.32,170742.01,175599.662 +90,002625.SZ,20181217,10.62,10.9,10.13,10.66,10.46,0.1999999999999993,1.91,258013.1,272482.916 +91,002625.SZ,20181214,10.08,11.1,9.91,10.46,10.15,0.3100000000000005,3.05,357532.44,375191.254 +92,002625.SZ,20181213,9.88,10.43,9.8,10.15,9.64,0.5099999999999998,5.29,366517.4,370966.269 +93,002625.SZ,20181212,8.67,9.64,8.67,9.64,8.76,0.8800000000000008,10.05,194916.15,183987.613 +94,002625.SZ,20181211,8.59,8.98,8.41,8.76,8.72,0.03999999999999915,0.46,138374.9,120971.825 +95,002625.SZ,20181210,9.21,9.3,8.72,8.72,9.69,-0.9699999999999989,-10.01,220156.22,194492.03 +96,002625.SZ,20181207,8.96,9.72,8.95,9.69,8.84,0.8499999999999996,9.62,309030.72,299298.059 +97,002625.SZ,20181206,9.22,9.48,8.84,8.84,9.39,-0.5500000000000007,-5.86,66318.5,60369.326 +98,002625.SZ,20181205,9.12,9.55,9.06,9.39,9.33,0.0600000000000005,0.64,70163.1,66051.93 +99,002625.SZ,20181204,9.5,9.52,9.24,9.33,9.57,-0.2400000000000002,-2.51,54661.5,51218.648 +100,002625.SZ,20181203,9.35,9.58,9.22,9.57,9.22,0.34999999999999964,3.8,82360.18,77818.087 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+562,002625.SZ,20160512,25.22,25.27,24.1,24.92,25.75,-0.8299999999999983,-3.22,39037.0,164912.4813 +563,002625.SZ,20160511,25.73,26.19,25.57,25.75,25.72,0.030000000000001137,0.12,31470.33,138501.4943 +564,002625.SZ,20160510,25.75,26.34,25.46,25.72,25.87,-0.15000000000000213,-0.58,35654.31,156704.5116 +565,002625.SZ,20160509,27.63,27.93,25.8,25.87,28.46,-2.59,-9.1,64127.74,290777.8159 diff --git "a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002638.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002638.SZ.csv" new file mode 100644 index 0000000..252f081 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002638.SZ.csv" @@ -0,0 +1,524 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002638.SZ,20190507,2.7,2.73,2.64,2.72,2.67,0.050000000000000266,1.87,177019.05,47721.661 +1,002638.SZ,20190506,2.75,2.8,2.56,2.67,2.83,-0.16000000000000014,-5.65,285870.25,77002.715 +2,002638.SZ,20190430,2.76,2.86,2.73,2.83,2.76,0.07000000000000028,2.54,211364.15,59513.929 +3,002638.SZ,20190429,3.02,3.09,2.75,2.76,3.05,-0.29000000000000004,-9.51,519878.27,147509.815 +4,002638.SZ,20190426,3.12,3.2,3.03,3.05,3.16,-0.11000000000000032,-3.48,258809.5,80798.529 +5,002638.SZ,20190425,3.39,3.44,3.14,3.16,3.4,-0.23999999999999977,-7.06,467505.0,153489.834 +6,002638.SZ,20190424,3.38,3.44,3.33,3.4,3.35,0.04999999999999982,1.49,324296.15,109475.293 +7,002638.SZ,20190423,3.48,3.48,3.3,3.35,3.5,-0.1499999999999999,-4.29,544584.77,184415.908 +8,002638.SZ,20190422,3.54,3.73,3.47,3.5,3.58,-0.08000000000000007,-2.23,842853.75,304482.824 +9,002638.SZ,20190419,3.42,3.66,3.38,3.58,3.45,0.1299999999999999,3.77,899732.36,317708.745 +10,002638.SZ,20190418,3.52,3.64,3.35,3.45,3.52,-0.06999999999999984,-1.99,1357304.65,475008.435 +11,002638.SZ,20190417,3.17,3.52,3.16,3.52,3.2,0.31999999999999984,10.0,1004979.58,343228.63 +12,002638.SZ,20190416,3.15,3.21,3.02,3.2,3.18,0.020000000000000018,0.63,334792.57,104506.654 +13,002638.SZ,20190415,3.29,3.33,3.17,3.18,3.27,-0.08999999999999986,-2.75,370185.65,120727.544 +14,002638.SZ,20190412,3.29,3.39,3.17,3.27,3.27,0.0,0.0,514624.79,168207.741 +15,002638.SZ,20190411,3.23,3.46,3.19,3.27,3.24,0.029999999999999805,0.93,667688.51,219440.121 +16,002638.SZ,20190410,3.18,3.26,3.12,3.24,3.2,0.040000000000000036,1.25,454502.97,145897.169 +17,002638.SZ,20190409,3.21,3.23,3.13,3.2,3.25,-0.04999999999999982,-1.54,404064.93,128418.163 +18,002638.SZ,20190408,3.21,3.28,3.09,3.25,3.18,0.06999999999999984,2.2,584625.81,186961.99 +19,002638.SZ,20190404,3.21,3.27,3.11,3.18,3.2,-0.020000000000000018,-0.63,310533.49,98774.518 +20,002638.SZ,20190403,3.12,3.2,3.1,3.2,3.15,0.050000000000000266,1.59,344687.29,109396.16 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"b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002708.SZ.csv" new file mode 100644 index 0000000..26f137f --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002708.SZ.csv" @@ -0,0 +1,722 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002708.SZ,20190507,9.7,11.17,9.7,11.17,10.15,1.0199999999999996,10.05,315156.67,329460.256 +1,002708.SZ,20190506,10.09,10.86,9.81,10.15,9.92,0.23000000000000043,2.32,371772.46,385233.769 +2,002708.SZ,20190430,9.92,9.92,9.5,9.92,9.02,0.9000000000000004,9.98,174882.55,171750.399 +3,002708.SZ,20190429,9.02,9.02,9.02,9.02,8.2,0.8200000000000003,10.0,33759.44,30451.014 +4,002708.SZ,20190426,8.98,8.98,8.14,8.2,9.04,-0.8399999999999999,-9.29,123594.86,103674.06 +5,002708.SZ,20190425,9.0,9.43,8.95,9.04,9.04,0.0,0.0,105048.8,96639.604 +6,002708.SZ,20190424,9.1,9.24,8.95,9.04,9.21,-0.1700000000000017,-1.85,84445.8,76438.645 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"b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/002802.SZ.csv" @@ -0,0 +1,695 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,002802.SZ,20190507,20.3,20.55,20.1,20.48,19.97,0.5100000000000016,2.55,5055.64,10285.115 +1,002802.SZ,20190506,21.78,21.78,19.82,19.97,21.81,-1.8399999999999999,-8.44,7907.16,16285.048 +2,002802.SZ,20190430,21.51,22.08,21.48,21.81,21.67,0.13999999999999702,0.65,3698.0,8087.758 +3,002802.SZ,20190429,22.6,22.85,21.6,21.67,22.75,-1.0799999999999983,-4.75,6154.0,13569.077 +4,002802.SZ,20190426,22.63,22.9,22.41,22.75,22.63,0.120000000000001,0.53,4096.0,9321.033 +5,002802.SZ,20190425,23.3,23.38,22.59,22.63,23.37,-0.740000000000002,-3.17,7676.55,17608.748 +6,002802.SZ,20190424,23.29,23.38,23.05,23.37,23.13,0.240000000000002,1.04,5019.68,11668.994 +7,002802.SZ,20190423,23.41,23.6,23.11,23.13,23.44,-0.3100000000000023,-1.32,5490.63,12801.25 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"b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300304.SZ.csv" new file mode 100644 index 0000000..e6d6e63 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300304.SZ.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,300304.SZ,20190507,3.91,3.99,3.86,3.93,3.86,0.07000000000000028,1.81,76613.8,30085.016 +1,300304.SZ,20190506,4.17,4.17,3.85,3.86,4.28,-0.4200000000000004,-9.81,146023.76,58438.568 +2,300304.SZ,20190430,4.26,4.36,4.22,4.28,4.26,0.020000000000000462,0.47,63989.57,27533.624 +3,300304.SZ,20190429,4.47,4.49,4.18,4.26,4.47,-0.20999999999999996,-4.7,124424.4,53611.422 +4,300304.SZ,20190426,4.55,4.6,4.45,4.47,4.53,-0.0600000000000005,-1.32,101525.97,45713.799 +5,300304.SZ,20190425,4.82,4.82,4.52,4.53,4.82,-0.29000000000000004,-6.02,144605.96,67334.284 +6,300304.SZ,20190424,4.69,4.84,4.69,4.82,4.74,0.08000000000000007,1.69,93062.36,44549.508 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+1,300327.SZ,20190506,22.23,22.3,21.08,21.41,22.8,-1.3900000000000006,-6.1,43793.0,94863.313 +2,300327.SZ,20190430,22.74,23.05,22.61,22.8,22.59,0.21000000000000085,0.93,13704.01,31326.452 +3,300327.SZ,20190429,23.13,23.3,22.51,22.59,23.14,-0.5500000000000007,-2.38,24061.72,54837.545 +4,300327.SZ,20190426,23.1,23.47,23.1,23.14,23.4,-0.259999999999998,-1.11,20677.25,48050.03 +5,300327.SZ,20190425,24.38,24.44,23.31,23.4,24.56,-1.1600000000000001,-4.72,36079.64,86325.428 +6,300327.SZ,20190424,23.98,24.65,23.7,24.56,23.98,0.5799999999999983,2.42,38217.24,92280.255 +7,300327.SZ,20190423,24.7,24.72,23.98,23.98,24.56,-0.5799999999999983,-2.36,23773.9,57745.205 +8,300327.SZ,20190422,24.96,25.0,24.45,24.56,24.85,-0.2900000000000027,-1.17,23232.04,57272.17 +9,300327.SZ,20190419,24.58,24.85,24.36,24.85,24.58,0.2700000000000031,1.1,20536.66,50506.626 +10,300327.SZ,20190418,25.0,25.08,24.5,24.58,24.99,-0.41000000000000014,-1.64,24727.61,61120.622 +11,300327.SZ,20190417,25.3,25.45,24.88,24.99,25.14,-0.15000000000000213,-0.6,30151.21,75911.544 +12,300327.SZ,20190416,24.5,25.14,24.25,25.14,24.47,0.6700000000000017,2.74,30168.35,74626.08 +13,300327.SZ,20190415,25.02,25.26,24.45,24.47,24.68,-0.21000000000000085,-0.85,27719.3,69061.013 +14,300327.SZ,20190412,24.92,25.05,24.55,24.68,24.91,-0.23000000000000043,-0.92,28212.38,69586.72 +15,300327.SZ,20190411,24.91,25.19,24.51,24.91,24.91,0.0,0.0,40489.28,100440.747 +16,300327.SZ,20190410,25.15,25.42,24.9,24.91,25.35,-0.4400000000000013,-1.74,31639.85,79329.234 +17,300327.SZ,20190409,24.65,25.67,24.56,25.35,25.09,0.26000000000000156,1.04,46274.11,116750.932 +18,300327.SZ,20190408,26.18,26.18,24.93,25.09,25.95,-0.8599999999999994,-3.31,60757.09,154257.638 +19,300327.SZ,20190404,26.36,26.5,25.85,25.95,26.16,-0.21000000000000085,-0.8,45319.02,118117.91 +20,300327.SZ,20190403,26.53,26.63,25.96,26.16,26.78,-0.620000000000001,-2.32,67776.14,177451.47 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+31,300327.SZ,20190319,25.45,25.93,25.1,25.52,25.56,-0.03999999999999915,-0.16,47270.33,120631.915 +32,300327.SZ,20190318,25.58,26.1,25.32,25.56,25.36,0.1999999999999993,0.79,55088.99,141108.194 +33,300327.SZ,20190315,25.1,25.92,25.05,25.36,25.28,0.0799999999999983,0.32,63559.02,162521.41 +34,300327.SZ,20190314,24.6,25.5,24.04,25.28,24.8,0.4800000000000004,1.94,74078.7,183113.315 +35,300327.SZ,20190313,26.12,26.12,24.8,24.8,26.11,-1.3099999999999987,-5.02,66101.79,168635.441 +36,300327.SZ,20190312,26.5,26.5,25.48,26.11,26.01,0.09999999999999787,0.38,95482.21,248126.654 +37,300327.SZ,20190311,25.56,26.95,25.56,26.01,25.03,0.9800000000000004,3.92,106915.91,279101.509 +38,300327.SZ,20190308,24.45,26.58,24.05,25.03,24.93,0.10000000000000142,0.4,152804.35,393959.317 +39,300327.SZ,20190307,24.68,25.67,24.4,24.93,24.8,0.129999999999999,0.52,88374.21,221982.548 +40,300327.SZ,20190306,25.14,25.27,24.34,24.8,25.24,-0.4399999999999977,-1.74,93586.28,231461.193 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+51,300327.SZ,20190219,23.59,23.67,23.0,23.33,23.84,-0.5100000000000016,-2.14,68077.53,158926.744 +52,300327.SZ,20190218,23.43,23.97,23.12,23.84,23.08,0.7600000000000016,3.29,80658.21,190395.539 +53,300327.SZ,20190215,23.0,23.59,22.96,23.08,23.32,-0.240000000000002,-1.03,64675.94,150701.098 +54,300327.SZ,20190214,24.3,24.3,23.02,23.32,23.95,-0.629999999999999,-2.63,111804.23,262431.093 +55,300327.SZ,20190213,22.56,24.2,22.56,23.95,22.0,1.9499999999999993,8.86,144626.49,336826.358 +56,300327.SZ,20190212,20.97,22.3,20.78,22.0,21.08,0.9200000000000017,4.36,74547.24,161771.798 +57,300327.SZ,20190211,20.8,21.2,20.5,21.08,20.75,0.3299999999999983,1.59,40268.11,84040.75 +58,300327.SZ,20190201,20.27,20.75,20.1,20.75,20.07,0.6799999999999997,3.39,27175.67,55706.003 +59,300327.SZ,20190131,19.98,20.37,19.84,20.07,19.9,0.1700000000000017,0.85,22221.0,44706.373 +60,300327.SZ,20190130,20.1,20.57,19.82,19.9,20.0,-0.10000000000000142,-0.5,23258.68,47086.154 +61,300327.SZ,20190129,20.25,20.3,19.6,20.0,20.24,-0.23999999999999844,-1.19,29381.45,58610.689 +62,300327.SZ,20190128,20.83,20.83,20.1,20.24,20.64,-0.40000000000000213,-1.94,35052.06,71543.944 +63,300327.SZ,20190125,20.02,20.94,19.97,20.64,20.11,0.5300000000000011,2.64,49981.66,102682.348 +64,300327.SZ,20190124,19.84,20.65,19.68,20.11,19.7,0.41000000000000014,2.08,38985.3,78836.872 +65,300327.SZ,20190123,19.52,19.75,19.43,19.7,19.64,0.05999999999999872,0.31,18566.86,36374.883 +66,300327.SZ,20190122,19.24,20.15,19.18,19.64,19.25,0.39000000000000057,2.03,50444.86,99691.033 +67,300327.SZ,20190121,19.07,19.39,19.01,19.25,19.06,0.19000000000000128,1.0,15504.03,29821.673 +68,300327.SZ,20190118,18.94,19.1,18.83,19.06,18.83,0.23000000000000043,1.22,13131.86,24913.411 +69,300327.SZ,20190117,19.08,19.1,18.81,18.83,19.08,-0.25,-1.31,14708.17,27879.628 +70,300327.SZ,20190116,19.03,19.29,19.03,19.08,19.08,0.0,0.0,18217.26,34879.632 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0000000..6885c94 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300336.SZ.csv" @@ -0,0 +1,725 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,300336.SZ,20190507,4.36,4.41,4.28,4.39,4.32,0.0699999999999994,1.62,123831.32,53870.191 +1,300336.SZ,20190506,4.6,4.7,4.32,4.32,4.8,-0.47999999999999954,-10.0,193659.56,86168.82 +2,300336.SZ,20190430,4.71,4.84,4.7,4.8,4.71,0.08999999999999986,1.91,85371.73,40730.964 +3,300336.SZ,20190429,5.02,5.04,4.7,4.71,5.03,-0.3200000000000003,-6.36,189890.93,91717.057 +4,300336.SZ,20190426,5.01,5.09,4.99,5.03,5.04,-0.009999999999999787,-0.2,104177.5,52581.619 +5,300336.SZ,20190425,5.39,5.39,5.03,5.04,5.42,-0.3799999999999999,-7.01,200382.0,104434.935 +6,300336.SZ,20190424,5.38,5.44,5.26,5.42,5.39,0.03000000000000025,0.56,159654.1,85343.747 +7,300336.SZ,20190423,5.42,5.45,5.3,5.39,5.55,-0.16000000000000014,-2.88,244502.26,131621.744 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+721,300336.SZ,20160511,16.04,16.08,14.46,14.75,15.95,-1.1999999999999993,-7.52,202333.91,465428.3144 +722,300336.SZ,20160510,15.81,16.24,15.73,15.95,15.79,0.16000000000000014,1.01,95669.91,232796.1158 +723,300336.SZ,20160509,17.11,17.11,15.77,15.79,17.5,-1.7100000000000009,-9.77,150362.66,371892.1739 diff --git "a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300348.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300348.SZ.csv" new file mode 100644 index 0000000..be9c4b8 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300348.SZ.csv" @@ -0,0 +1,690 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,300348.SZ,20190507,20.97,21.1,19.88,20.56,20.86,-0.3000000000000007,-1.44,63719.8,130581.829 +1,300348.SZ,20190506,21.7,22.2,20.86,20.86,23.18,-2.3200000000000003,-10.01,91881.02,195834.698 +2,300348.SZ,20190430,22.89,23.52,22.72,23.18,22.9,0.28000000000000114,1.22,50903.89,117761.418 +3,300348.SZ,20190429,24.6,24.6,22.66,22.9,24.32,-1.4200000000000017,-5.84,83128.91,195050.575 +4,300348.SZ,20190426,24.25,24.85,24.21,24.32,24.4,-0.0799999999999983,-0.33,51377.42,125843.867 +5,300348.SZ,20190425,25.9,25.9,24.15,24.4,25.92,-1.5200000000000031,-5.86,88482.0,221056.341 +6,300348.SZ,20190424,25.48,26.03,25.18,25.92,25.37,0.5500000000000007,2.17,64262.0,164967.576 +7,300348.SZ,20190423,26.21,26.44,25.12,25.37,26.31,-0.9399999999999977,-3.57,94713.0,243077.965 +8,300348.SZ,20190422,27.61,27.74,26.2,26.31,27.47,-1.1600000000000001,-4.22,125128.65,332310.902 +9,300348.SZ,20190419,26.95,28.16,26.93,27.47,26.53,0.9399999999999977,3.54,161345.98,444993.726 +10,300348.SZ,20190418,26.98,26.98,26.23,26.53,27.0,-0.46999999999999886,-1.74,76412.18,203516.34 +11,300348.SZ,20190417,27.15,27.19,26.71,27.0,27.4,-0.3999999999999986,-1.46,112256.04,302417.122 +12,300348.SZ,20190416,25.5,27.5,25.37,27.4,25.7,1.6999999999999993,6.61,161816.68,434838.268 +13,300348.SZ,20190415,26.78,26.79,25.66,25.7,25.56,0.14000000000000057,0.55,97270.66,255780.572 +14,300348.SZ,20190412,25.29,25.74,25.06,25.56,25.3,0.259999999999998,1.03,63801.48,162554.901 +15,300348.SZ,20190411,25.7,26.25,25.28,25.3,26.01,-0.7100000000000009,-2.73,87590.24,225043.546 +16,300348.SZ,20190410,25.75,26.48,25.35,26.01,26.03,-0.019999999999999574,-0.08,118051.38,306736.623 +17,300348.SZ,20190409,25.85,26.39,25.22,26.03,25.9,0.13000000000000256,0.5,105862.21,274649.967 +18,300348.SZ,20190408,26.6,27.31,25.2,25.9,26.6,-0.7000000000000028,-2.63,135954.36,356045.629 +19,300348.SZ,20190404,27.13,27.39,26.46,26.6,27.1,-0.5,-1.85,135097.69,362562.16 +20,300348.SZ,20190403,27.22,27.73,26.25,27.1,27.61,-0.509999999999998,-1.85,253971.04,683302.655 +21,300348.SZ,20190402,26.1,28.56,26.1,27.61,25.96,1.6499999999999986,6.36,325732.65,905862.347 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0000000..54a0140 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300353.SZ.csv" @@ -0,0 +1,610 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,300353.SZ,20190507,12.27,13.54,12.23,13.44,12.31,1.129999999999999,9.18,437486.88,572836.673 +1,300353.SZ,20190506,13.13,13.73,12.31,12.31,13.68,-1.3699999999999992,-10.01,382593.89,497295.154 +2,300353.SZ,20190430,12.76,13.86,12.57,13.68,12.77,0.9100000000000001,7.13,416332.75,558171.571 +3,300353.SZ,20190429,12.72,13.33,12.62,12.77,12.58,0.1899999999999995,1.51,274410.2,356424.334 +4,300353.SZ,20190426,12.44,12.85,12.11,12.58,12.49,0.08999999999999986,0.72,163887.0,206407.962 +5,300353.SZ,20190425,12.87,13.09,12.38,12.49,12.93,-0.4399999999999995,-3.4,137316.03,175319.512 +6,300353.SZ,20190424,12.66,12.95,12.53,12.93,12.63,0.29999999999999893,2.38,128215.35,163695.692 +7,300353.SZ,20190423,13.27,13.28,12.61,12.63,13.27,-0.6399999999999988,-4.82,177439.0,227309.644 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+579,300353.SZ,20161207,17.47,18.23,17.47,18.14,17.75,0.39000000000000057,2.2,230808.48,417248.547 +580,300353.SZ,20161206,18.27,18.76,17.75,17.75,19.73,-1.9800000000000004,-10.04,332382.95,602805.611 +581,300353.SZ,20161205,19.73,19.73,19.73,19.73,21.92,-2.1900000000000013,-9.99,20233.0,40223.204 +582,300353.SZ,20160622,19.96,22.05,19.66,21.92,20.06,1.860000000000003,9.27,288955.63,617333.94 +583,300353.SZ,20160621,19.66,20.29,19.4,20.06,19.85,0.2099999999999973,1.06,172212.26,344185.0141 +584,300353.SZ,20160620,19.47,20.44,19.43,19.85,19.08,0.7700000000000031,4.04,224709.76,453204.1165 +585,300353.SZ,20160617,18.02,19.11,18.02,19.08,18.17,0.9099999999999966,5.01,146079.12,276459.6863 +586,300353.SZ,20160616,18.42,19.0,18.16,18.17,18.76,-0.5899999999999999,-3.14,94111.71,175680.6235 +587,300353.SZ,20160615,16.68,19.15,16.68,18.76,18.06,0.7000000000000028,3.88,145649.01,265452.3958 +588,300353.SZ,20160606,17.86,18.21,17.79,18.06,17.96,0.09999999999999787,0.56,71527.84,129605.5709 +589,300353.SZ,20160603,18.29,18.3,17.71,17.96,18.19,-0.23000000000000043,-1.26,108390.77,196157.1104 +590,300353.SZ,20160602,17.32,18.73,17.2,18.19,17.02,1.1700000000000017,6.87,161276.87,296359.7724 +591,300353.SZ,20160601,17.04,17.17,16.89,17.02,17.04,-0.019999999999999574,-0.12,61047.41,104912.124 +592,300353.SZ,20160531,16.04,17.07,16.04,17.04,16.04,1.0,6.23,68606.11,115486.7754 +593,300353.SZ,20160530,16.07,16.26,15.86,16.04,16.29,-0.25,-1.53,32540.74,52635.0724 +594,300353.SZ,20160527,16.31,16.66,16.14,16.29,16.29,0.0,0.0,59265.0,97852.4046 +595,300353.SZ,20160526,15.99,16.37,15.46,16.29,16.09,0.1999999999999993,1.24,54183.62,87083.7859 +596,300353.SZ,20160525,16.0,16.2,15.86,16.09,15.82,0.2699999999999996,1.71,53719.76,87016.349 +597,300353.SZ,20160524,16.07,16.17,15.65,15.82,16.23,-0.41000000000000014,-2.53,44745.81,71405.5312 +598,300353.SZ,20160523,16.1,16.42,16.0,16.23,15.73,0.5,3.18,51061.0,83525.7511 +599,300353.SZ,20160520,15.09,15.77,14.97,15.73,15.27,0.46000000000000085,3.01,48082.75,75099.4638 +600,300353.SZ,20160519,15.37,15.83,15.24,15.27,15.23,0.03999999999999915,0.26,44596.6,69941.4579 +601,300353.SZ,20160518,15.9,16.05,15.08,15.23,16.2,-0.9699999999999989,-5.99,55490.9,86753.5169 +602,300353.SZ,20160517,16.5,16.62,15.93,16.2,16.4,-0.1999999999999993,-1.22,43899.75,71865.3348 +603,300353.SZ,20160516,15.87,16.42,15.81,16.4,15.87,0.5299999999999994,3.34,42865.17,70049.669 +604,300353.SZ,20160513,15.77,16.22,15.69,15.87,15.97,-0.10000000000000142,-0.63,36696.69,59161.6024 +605,300353.SZ,20160512,16.08,16.08,15.37,15.97,16.39,-0.41999999999999993,-2.56,64898.2,103020.6987 +606,300353.SZ,20160511,17.15,17.15,16.36,16.39,16.83,-0.4399999999999977,-2.61,39166.35,66165.334 +607,300353.SZ,20160510,16.73,17.14,16.73,16.83,16.96,-0.13000000000000256,-0.77,31771.22,54228.9858 +608,300353.SZ,20160509,17.15,17.36,16.86,16.96,17.68,-0.7199999999999989,-4.07,68245.86,117222.7936 diff --git "a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300371.SZ.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300371.SZ.csv" new file mode 100644 index 0000000..ebfca16 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/300371.SZ.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,300371.SZ,20190507,13.67,14.08,13.67,13.9,13.64,0.2599999999999998,1.91,8591.5,11892.966 +1,300371.SZ,20190506,14.78,15.04,13.64,13.64,15.15,-1.5099999999999998,-9.97,15326.19,21968.796 +2,300371.SZ,20190430,15.13,15.29,15.01,15.15,15.15,0.0,0.0,9282.01,14070.998 +3,300371.SZ,20190429,15.88,15.98,15.02,15.15,15.97,-0.8200000000000003,-5.13,17844.62,27354.781 +4,300371.SZ,20190426,15.89,16.28,15.72,15.97,15.92,0.05000000000000071,0.31,9725.66,15622.955 +5,300371.SZ,20190425,16.68,16.68,15.91,15.92,16.68,-0.7599999999999998,-4.56,11683.55,19064.433 +6,300371.SZ,20190424,16.6,16.78,16.24,16.68,16.43,0.25,1.52,13611.12,22650.876 +7,300371.SZ,20190423,16.53,16.63,16.01,16.43,16.4,0.030000000000001137,0.18,12176.44,19946.353 +8,300371.SZ,20190422,17.03,17.07,16.4,16.4,17.11,-0.7100000000000009,-4.15,16460.0,27535.693 +9,300371.SZ,20190419,16.68,17.12,16.58,17.11,16.62,0.48999999999999844,2.95,21243.53,35879.724 +10,300371.SZ,20190418,16.68,16.69,16.34,16.62,16.61,0.010000000000001563,0.06,10214.47,16886.115 +11,300371.SZ,20190417,16.62,16.77,16.44,16.61,16.62,-0.010000000000001563,-0.06,11972.0,19900.88 +12,300371.SZ,20190416,15.93,16.7,15.9,16.62,16.0,0.620000000000001,3.88,15438.0,25158.968 +13,300371.SZ,20190415,16.52,16.79,16.0,16.0,16.03,-0.030000000000001137,-0.19,14630.72,23912.553 +14,300371.SZ,20190412,16.18,16.37,15.93,16.03,16.25,-0.21999999999999886,-1.35,9665.56,15595.668 +15,300371.SZ,20190411,16.39,16.53,16.08,16.25,16.48,-0.23000000000000043,-1.4,10366.32,16928.197 +16,300371.SZ,20190410,16.48,16.62,16.19,16.48,16.47,0.010000000000001563,0.06,12894.0,21095.215 +17,300371.SZ,20190409,16.09,16.57,16.09,16.47,16.16,0.3099999999999987,1.92,16485.12,27015.517 +18,300371.SZ,20190408,16.84,16.88,15.91,16.16,16.31,-0.14999999999999858,-0.92,16290.88,26603.344 +19,300371.SZ,20190404,16.25,16.55,16.11,16.31,16.25,0.05999999999999872,0.37,12463.74,20344.899 +20,300371.SZ,20190403,16.0,16.29,15.89,16.25,15.98,0.2699999999999996,1.69,11958.01,19291.17 +21,300371.SZ,20190402,15.91,16.21,15.88,15.98,15.97,0.009999999999999787,0.06,13201.0,21208.004 +22,300371.SZ,20190401,15.61,16.07,15.59,15.97,15.58,0.39000000000000057,2.5,16014.02,25471.803 +23,300371.SZ,20190329,14.92,15.64,14.89,15.58,15.04,0.5400000000000009,3.59,11577.39,17693.745 +24,300371.SZ,20190328,15.07,15.39,14.98,15.04,15.18,-0.14000000000000057,-0.92,9010.4,13663.491 +25,300371.SZ,20190327,15.02,15.2,14.88,15.18,15.05,0.129999999999999,0.86,8792.96,13255.31 +26,300371.SZ,20190326,15.52,15.6,14.98,15.05,15.41,-0.35999999999999943,-2.34,10453.03,16010.335 +27,300371.SZ,20190325,15.47,15.62,15.26,15.41,15.64,-0.23000000000000043,-1.47,11250.0,17372.491 +28,300371.SZ,20190322,15.8,15.95,15.32,15.64,15.67,-0.02999999999999936,-0.19,16064.0,25028.01 +29,300371.SZ,20190321,15.33,15.74,15.27,15.67,15.16,0.5099999999999998,3.36,17604.28,27352.841 +30,300371.SZ,20190320,15.42,15.47,14.92,15.16,15.3,-0.14000000000000057,-0.92,12370.33,18742.104 +31,300371.SZ,20190319,15.33,15.47,15.26,15.3,15.28,0.02000000000000135,0.13,11266.19,17295.516 +32,300371.SZ,20190318,14.96,15.35,14.96,15.28,14.89,0.3899999999999988,2.62,14475.87,21991.928 +33,300371.SZ,20190315,14.74,15.12,14.74,14.89,14.75,0.14000000000000057,0.95,17486.2,26115.346 +34,300371.SZ,20190314,15.92,15.98,14.74,14.75,16.38,-1.629999999999999,-9.95,28992.05,43739.898 +35,300371.SZ,20190313,16.21,17.03,15.66,16.38,15.93,0.4499999999999993,2.82,31186.11,51072.128 +36,300371.SZ,20190312,16.1,16.2,15.63,15.93,16.09,-0.16000000000000014,-0.99,23187.16,36849.49 +37,300371.SZ,20190311,15.23,16.12,15.23,16.09,15.17,0.9199999999999999,6.06,21029.74,32765.507 +38,300371.SZ,20190308,15.5,16.06,15.16,15.17,15.5,-0.33000000000000007,-2.13,20130.04,31378.996 +39,300371.SZ,20190307,15.5,15.77,15.39,15.5,15.5,0.0,0.0,13788.28,21498.678 +40,300371.SZ,20190306,15.1,15.5,15.1,15.5,15.2,0.3000000000000007,1.97,12515.94,19161.086 +41,300371.SZ,20190305,14.56,15.2,14.45,15.2,14.93,0.2699999999999996,1.81,10625.53,15904.139 +42,300371.SZ,20190304,14.65,15.11,14.54,14.93,14.65,0.27999999999999936,1.91,10695.25,15897.339 +43,300371.SZ,20190301,14.6,14.72,14.4,14.65,14.58,0.07000000000000028,0.48,8770.77,12754.45 +44,300371.SZ,20190228,14.47,14.78,14.47,14.58,14.44,0.14000000000000057,0.97,7224.5,10569.351 +45,300371.SZ,20190227,14.55,14.83,14.41,14.44,14.49,-0.05000000000000071,-0.35,9339.42,13630.07 +46,300371.SZ,20190226,14.6,14.77,14.32,14.49,14.6,-0.10999999999999943,-0.75,12271.0,17806.896 +47,300371.SZ,20190225,14.08,14.67,14.04,14.6,14.09,0.5099999999999998,3.62,14237.77,20548.801 +48,300371.SZ,20190222,13.75,14.15,13.52,14.09,13.79,0.3000000000000007,2.18,9660.66,13415.784 +49,300371.SZ,20190221,13.63,14.23,13.63,13.79,13.63,0.15999999999999837,1.17,11747.33,16440.811 +50,300371.SZ,20190220,13.6,13.8,13.45,13.63,13.68,-0.049999999999998934,-0.37,5156.0,7036.534 +51,300371.SZ,20190219,13.69,13.85,13.48,13.68,13.61,0.07000000000000028,0.51,8222.0,11254.751 +52,300371.SZ,20190218,13.3,13.68,13.26,13.61,13.22,0.3899999999999988,2.95,8658.0,11712.969 +53,300371.SZ,20190215,13.25,13.39,13.09,13.22,13.25,-0.02999999999999936,-0.23,5210.58,6919.296 +54,300371.SZ,20190214,13.06,13.42,13.06,13.25,13.12,0.13000000000000078,0.99,6863.3,9092.984 +55,300371.SZ,20190213,12.83,13.2,12.81,13.12,12.85,0.2699999999999996,2.1,10870.49,14161.941 +56,300371.SZ,20190212,12.71,12.94,12.58,12.85,12.68,0.16999999999999993,1.34,8506.42,10886.328 +57,300371.SZ,20190211,12.34,12.69,12.3,12.68,12.35,0.33000000000000007,2.67,9513.33,11883.891 +58,300371.SZ,20190201,12.25,12.39,12.13,12.35,12.14,0.20999999999999908,1.73,5433.49,6668.451 +59,300371.SZ,20190131,12.15,12.32,11.9,12.14,12.19,-0.049999999999998934,-0.41,4655.3,5647.492 +60,300371.SZ,20190130,12.19,12.36,12.11,12.19,12.27,-0.08000000000000007,-0.65,5513.95,6741.371 +61,300371.SZ,20190129,12.59,12.66,12.12,12.27,12.66,-0.39000000000000057,-3.08,8646.3,10659.127 +62,300371.SZ,20190128,13.21,13.21,12.47,12.66,13.21,-0.5500000000000007,-4.16,12859.0,16511.063 +63,300371.SZ,20190125,13.38,13.6,13.1,13.21,13.15,0.0600000000000005,0.46,7632.57,10162.726 +64,300371.SZ,20190124,13.23,13.3,12.99,13.15,13.09,0.0600000000000005,0.46,5015.86,6621.872 +65,300371.SZ,20190123,12.98,13.34,12.98,13.09,13.02,0.07000000000000028,0.54,4080.87,5347.952 +66,300371.SZ,20190122,13.12,13.17,12.99,13.02,13.15,-0.13000000000000078,-0.99,4846.25,6330.65 +67,300371.SZ,20190121,12.97,13.19,12.83,13.15,12.85,0.3000000000000007,2.33,8472.0,11057.796 +68,300371.SZ,20190118,12.95,13.05,12.68,12.85,12.9,-0.05000000000000071,-0.39,5834.0,7497.793 +69,300371.SZ,20190117,13.36,13.36,12.89,12.9,13.32,-0.41999999999999993,-3.15,6370.0,8302.934 +70,300371.SZ,20190116,13.27,13.43,13.11,13.32,13.25,0.07000000000000028,0.53,7741.0,10265.281 +71,300371.SZ,20190115,13.03,13.28,12.85,13.25,13.0,0.25,1.92,5925.25,7780.893 +72,300371.SZ,20190114,13.13,13.17,12.93,13.0,13.1,-0.09999999999999964,-0.76,4237.0,5514.825 +73,300371.SZ,20190111,13.09,13.18,12.95,13.1,12.92,0.17999999999999972,1.39,6576.93,8606.523 +74,300371.SZ,20190110,12.95,13.12,12.85,12.92,13.01,-0.08999999999999986,-0.69,4805.0,6263.619 +75,300371.SZ,20190109,13.03,13.19,12.94,13.01,13.04,-0.02999999999999936,-0.23,6269.07,8214.851 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+13,300446.SZ,20190415,27.05,27.47,26.18,26.2,26.98,-0.7800000000000011,-2.89,12133.0,32466.785 +14,300446.SZ,20190412,26.81,27.41,26.5,26.98,26.81,0.1700000000000017,0.63,10033.25,27155.23 +15,300446.SZ,20190411,27.25,27.8,26.64,26.81,27.27,-0.46000000000000085,-1.69,17917.57,48936.709 +16,300446.SZ,20190410,27.19,27.85,27.04,27.27,27.52,-0.25,-0.91,14768.22,40528.343 +17,300446.SZ,20190409,27.66,27.95,26.9,27.52,27.68,-0.16000000000000014,-0.58,18380.74,50248.435 +18,300446.SZ,20190408,28.24,29.53,26.5,27.68,28.54,-0.8599999999999994,-3.01,35487.19,99100.372 +19,300446.SZ,20190404,30.1,30.3,28.41,28.54,29.47,-0.9299999999999997,-3.16,29101.76,84670.435 +20,300446.SZ,20190403,29.4,29.9,29.0,29.47,29.36,0.10999999999999943,0.37,17259.01,50822.86 +21,300446.SZ,20190402,30.87,30.93,29.31,29.36,30.3,-0.9400000000000013,-3.1,29233.9,87344.359 +22,300446.SZ,20190401,27.32,30.3,27.32,30.3,27.55,2.75,9.98,46823.01,137255.651 +23,300446.SZ,20190329,27.79,27.94,26.56,27.55,27.33,0.22000000000000242,0.8,26765.06,73338.812 +24,300446.SZ,20190328,27.07,28.0,26.73,27.33,26.5,0.8299999999999983,3.13,22244.7,60925.952 +25,300446.SZ,20190327,27.55,27.67,26.05,26.5,27.13,-0.629999999999999,-2.32,33474.93,89791.633 +26,300446.SZ,20190326,28.25,28.42,26.5,27.13,28.48,-1.3500000000000014,-4.74,39878.84,108555.751 +27,300446.SZ,20190325,26.7,28.96,26.65,28.48,27.32,1.1600000000000001,4.25,32150.27,90463.726 +28,300446.SZ,20190322,27.65,28.28,27.3,27.32,28.08,-0.759999999999998,-2.71,26071.97,72117.823 +29,300446.SZ,20190321,28.0,29.58,27.15,28.08,27.97,0.10999999999999943,0.39,38920.88,111271.759 +30,300446.SZ,20190320,26.68,28.2,26.1,27.97,25.86,2.1099999999999994,8.16,51758.29,141129.572 +31,300446.SZ,20190319,26.5,26.83,25.75,25.86,26.4,-0.5399999999999991,-2.05,26078.18,68511.5 +32,300446.SZ,20190318,27.61,27.87,25.84,26.4,26.4,0.0,0.0,29682.61,78811.396 +33,300446.SZ,20190315,27.48,28.5,25.65,26.4,27.2,-0.8000000000000007,-2.94,37601.41,101339.108 +34,300446.SZ,20190314,27.54,28.1,24.81,27.2,27.49,-0.28999999999999915,-1.05,50708.2,135694.34 +35,300446.SZ,20190313,29.35,30.45,27.49,27.49,28.77,-1.2800000000000011,-4.45,75872.79,218459.711 +36,300446.SZ,20190312,26.59,28.77,26.25,28.77,26.15,2.620000000000001,10.02,84408.89,236973.692 +37,300446.SZ,20190311,25.6,27.24,23.58,26.15,24.76,1.389999999999997,5.61,73357.24,191205.681 +38,300446.SZ,20190308,24.13,26.4,23.44,24.76,24.0,0.7600000000000016,3.17,83142.78,215000.821 +39,300446.SZ,20190307,22.05,24.0,21.99,24.0,21.82,2.1799999999999997,9.99,56184.26,131979.822 +40,300446.SZ,20190306,19.8,21.82,19.68,21.82,19.84,1.9800000000000004,9.98,53354.01,112201.702 +41,300446.SZ,20190305,18.99,19.84,18.99,19.84,19.03,0.8099999999999987,4.26,26098.9,50624.074 +42,300446.SZ,20190304,19.22,19.55,18.85,19.03,18.95,0.08000000000000185,0.42,33912.9,65188.324 +43,300446.SZ,20190301,19.48,19.48,18.68,18.95,19.35,-0.40000000000000213,-2.07,24208.22,45903.124 +44,300446.SZ,20190228,18.76,19.38,18.76,19.35,18.8,0.5500000000000007,2.93,22571.46,43203.256 +45,300446.SZ,20190227,19.38,19.46,18.63,18.8,18.81,-0.00999999999999801,-0.05,43187.0,82278.783 +46,300446.SZ,20190226,18.32,19.44,18.19,18.81,17.67,1.139999999999997,6.45,74457.63,141995.297 +47,300446.SZ,20190225,16.54,17.67,16.5,17.67,16.06,1.610000000000003,10.02,53943.46,93513.657 +48,300446.SZ,20190222,15.95,16.23,15.59,16.06,15.95,0.10999999999999943,0.69,22894.08,36601.356 +49,300446.SZ,20190221,16.41,16.85,15.9,15.95,16.17,-0.22000000000000242,-1.36,30971.09,50818.59 +50,300446.SZ,20190220,16.22,16.38,15.81,16.17,16.03,0.14000000000000057,0.87,21688.76,34972.536 +51,300446.SZ,20190219,15.79,16.18,15.61,16.03,15.82,0.21000000000000085,1.33,22975.98,36603.098 +52,300446.SZ,20190218,15.31,15.82,15.31,15.82,15.31,0.5099999999999998,3.33,19964.79,31127.46 +53,300446.SZ,20190215,15.16,15.98,15.09,15.31,15.22,0.08999999999999986,0.59,18771.05,28947.1 +54,300446.SZ,20190214,15.25,15.45,15.12,15.22,15.48,-0.2599999999999998,-1.68,13182.81,20087.856 +55,300446.SZ,20190213,14.73,15.48,14.69,15.48,14.77,0.7100000000000009,4.81,21698.61,32884.814 +56,300446.SZ,20190212,14.45,14.88,14.43,14.77,14.52,0.25,1.72,8261.0,12140.817 +57,300446.SZ,20190211,14.18,14.52,14.07,14.52,14.16,0.35999999999999943,2.54,6399.02,9194.15 +58,300446.SZ,20190201,13.84,14.18,13.83,14.16,13.83,0.33000000000000007,2.39,9817.0,13819.091 +59,300446.SZ,20190131,13.82,14.14,13.68,13.83,13.89,-0.0600000000000005,-0.43,5873.51,8169.854 +60,300446.SZ,20190130,13.93,14.22,13.81,13.89,13.93,-0.03999999999999915,-0.29,5429.67,7643.082 +61,300446.SZ,20190129,14.54,14.54,13.55,13.93,14.55,-0.620000000000001,-4.26,8144.14,11397.073 +62,300446.SZ,20190128,14.78,14.79,14.43,14.55,14.44,0.11000000000000121,0.76,13068.59,19086.032 +63,300446.SZ,20190125,15.21,15.21,14.41,14.44,15.05,-0.6100000000000012,-4.05,11857.48,17535.68 +64,300446.SZ,20190124,14.66,15.08,14.66,15.05,14.79,0.26000000000000156,1.76,6162.0,9191.446 +65,300446.SZ,20190123,14.5,14.8,14.5,14.79,14.57,0.21999999999999886,1.51,5226.63,7667.446 +66,300446.SZ,20190122,14.75,14.82,14.51,14.57,14.88,-0.3100000000000005,-2.08,5670.0,8312.624 +67,300446.SZ,20190121,14.86,14.88,14.69,14.88,14.74,0.14000000000000057,0.95,5220.74,7720.415 +68,300446.SZ,20190118,14.69,14.88,14.53,14.74,14.67,0.07000000000000028,0.48,7115.75,10442.54 +69,300446.SZ,20190117,15.25,15.31,14.61,14.67,15.33,-0.6600000000000001,-4.31,10976.19,16360.179 +70,300446.SZ,20190116,15.28,15.44,15.21,15.33,15.23,0.09999999999999964,0.66,5293.62,8121.018 +71,300446.SZ,20190115,15.05,15.24,15.0,15.23,14.98,0.25,1.67,4589.41,6941.829 +72,300446.SZ,20190114,15.72,15.72,14.92,14.98,15.58,-0.5999999999999996,-3.85,10314.0,15707.406 +73,300446.SZ,20190111,15.59,15.74,15.41,15.58,15.6,-0.019999999999999574,-0.13,7241.15,11237.175 +74,300446.SZ,20190110,15.6,15.88,15.48,15.6,15.73,-0.13000000000000078,-0.83,8627.17,13467.704 +75,300446.SZ,20190109,15.85,16.08,15.73,15.73,15.74,-0.009999999999999787,-0.06,13366.53,21265.917 +76,300446.SZ,20190108,15.54,15.75,15.48,15.74,15.64,0.09999999999999964,0.64,5665.56,8846.68 +77,300446.SZ,20190107,15.29,15.72,15.26,15.64,15.27,0.370000000000001,2.42,7573.07,11733.171 +78,300446.SZ,20190104,15.0,15.28,14.57,15.27,15.1,0.16999999999999993,1.13,6602.21,9925.46 +79,300446.SZ,20190103,15.2,15.45,15.1,15.1,15.38,-0.28000000000000114,-1.82,3819.0,5816.823 +80,300446.SZ,20190102,15.3,15.51,15.1,15.38,15.34,0.040000000000000924,0.26,2811.0,4319.224 +81,300446.SZ,20181228,15.51,15.72,15.27,15.34,15.5,-0.16000000000000014,-1.03,5090.0,7836.563 +82,300446.SZ,20181227,16.0,16.18,15.5,15.5,15.91,-0.41000000000000014,-2.58,6016.84,9519.165 +83,300446.SZ,20181226,15.86,16.08,15.66,15.91,15.88,0.02999999999999936,0.19,8289.78,13176.863 +84,300446.SZ,20181225,15.49,15.89,15.18,15.88,15.59,0.2900000000000009,1.86,9522.93,14808.302 +85,300446.SZ,20181224,15.18,15.62,15.16,15.59,15.24,0.34999999999999964,2.3,5292.21,8170.0 +86,300446.SZ,20181221,15.2,15.26,15.07,15.24,15.3,-0.0600000000000005,-0.39,2756.74,4174.595 +87,300446.SZ,20181220,15.06,15.4,14.84,15.3,15.06,0.2400000000000002,1.59,5938.0,9032.961 +88,300446.SZ,20181219,15.17,15.28,14.95,15.06,15.18,-0.11999999999999922,-0.79,3658.78,5526.707 +89,300446.SZ,20181218,15.22,15.37,15.0,15.18,15.33,-0.15000000000000036,-0.98,4312.78,6549.201 +90,300446.SZ,20181217,15.06,15.33,15.02,15.33,15.06,0.2699999999999996,1.79,5984.1,9099.358 +91,300446.SZ,20181214,15.58,15.69,15.02,15.06,15.69,-0.629999999999999,-4.02,7288.24,11216.312 +92,300446.SZ,20181213,15.49,15.88,15.42,15.69,15.43,0.2599999999999998,1.69,10323.24,16198.186 +93,300446.SZ,20181212,15.42,15.64,15.42,15.43,15.45,-0.019999999999999574,-0.13,5450.39,8447.515 +94,300446.SZ,20181211,15.44,15.61,15.31,15.45,15.51,-0.0600000000000005,-0.39,6169.22,9550.682 +95,300446.SZ,20181210,15.78,15.78,15.33,15.51,15.93,-0.41999999999999993,-2.64,9079.39,14044.351 +96,300446.SZ,20181207,15.28,15.98,15.28,15.93,15.27,0.6600000000000001,4.32,9674.88,15253.973 +97,300446.SZ,20181206,15.59,15.62,15.26,15.27,15.61,-0.33999999999999986,-2.18,7393.52,11412.137 +98,300446.SZ,20181205,15.5,15.8,15.45,15.61,15.99,-0.3800000000000008,-2.38,8899.0,13885.437 +99,300446.SZ,20181204,15.68,16.02,15.68,15.99,15.76,0.23000000000000043,1.46,9953.0,15797.538 +100,300446.SZ,20181203,15.4,16.05,15.4,15.76,15.1,0.6600000000000001,4.37,23803.25,37509.427 +101,300446.SZ,20181130,14.88,15.18,14.69,15.1,14.88,0.21999999999999886,1.48,8323.29,12416.835 +102,300446.SZ,20181129,15.82,15.82,14.68,14.88,15.69,-0.8099999999999987,-5.16,7295.08,11240.454 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"b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/600532.SH.csv" new file mode 100644 index 0000000..527072b --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/600532.SH.csv" @@ -0,0 +1,717 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,600532.SH,20190507,5.08,5.09,4.82,4.95,4.94,0.009999999999999787,0.2,166415.4,82447.12 +1,600532.SH,20190506,5.19,5.36,4.94,4.94,5.49,-0.5499999999999998,-10.02,143695.11,73217.276 +2,600532.SH,20190430,5.42,5.58,5.4,5.49,5.39,0.10000000000000053,1.86,84238.96,46149.863 +3,600532.SH,20190429,5.7,5.79,5.33,5.39,5.7,-0.3100000000000005,-5.44,124120.0,68454.407 +4,600532.SH,20190426,5.81,5.86,5.67,5.7,5.81,-0.10999999999999943,-1.89,114498.01,66059.334 +5,600532.SH,20190425,6.18,6.24,5.69,5.81,6.14,-0.33000000000000007,-5.37,144765.42,86785.889 +6,600532.SH,20190424,6.0,6.2,6.0,6.14,6.07,0.0699999999999994,1.15,95099.0,58295.906 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"a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/600560.SH.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/600560.SH.csv" new file mode 100644 index 0000000..c9d7fb0 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/600560.SH.csv" @@ -0,0 +1,730 @@ +,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount +0,600560.SH,20190507,8.7,8.81,8.57,8.75,8.64,0.10999999999999943,1.27,29385.48,25612.413 +1,600560.SH,20190506,9.3,9.37,8.64,8.64,9.6,-0.9599999999999991,-10.0,52966.41,47433.468 +2,600560.SH,20190430,9.34,9.9,9.31,9.6,9.32,0.27999999999999936,3.0,41853.0,40016.693 +3,600560.SH,20190429,9.52,9.69,9.3,9.32,9.51,-0.1899999999999995,-2.0,40587.62,38538.01 +4,600560.SH,20190426,9.49,9.67,9.44,9.51,9.47,0.03999999999999915,0.42,32645.68,31187.808 +5,600560.SH,20190425,10.09,10.19,9.22,9.47,10.17,-0.6999999999999993,-6.88,50943.6,50098.266 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"a/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/download_stocks_symbol.csv" "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/download_stocks_symbol.csv" new file mode 100644 index 0000000..bd89796 --- /dev/null +++ "b/Graduate_Proj_V1/Data/\344\272\244\346\230\223\346\225\260\346\215\256/download_stocks_symbol.csv" @@ -0,0 +1,51 @@ +,code,name +1,300348.SZ,长亮科技 +2,600010.SH,包钢股份 +3,600796.SH,钱江生化 +4,600560.SH,金自天正 +5,300327.SZ,中颖电子 +6,000721.SZ,西安饮食 +7,600563.SH,法拉电子 +8,300304.SZ,云意电气 +9,002288.SZ,超华科技 +10,600292.SH,远达环保 +11,300371.SZ,汇中股份 +12,002638.SZ,勤上股份 +13,300276.SZ,三丰智能 +14,000917.SZ,电广传媒 +15,600346.SH,恒力股份 +16,000735.SZ,罗牛山 +17,600549.SH,厦门钨业 +18,600532.SH,宏达矿业 +19,300353.SZ,东土科技 +20,002625.SZ,光启技术 +21,603555.SH,贵人鸟 +22,002708.SZ,光洋股份 +23,603009.SH,北特科技 +24,600841.SH,上柴股份 +25,603885.SH,吉祥航空 +26,002400.SZ,省广集团 +27,300446.SZ,乐凯新材 +28,002293.SZ,罗莱生活 +29,000736.SZ,中交地产 +30,600463.SH,空港股份 +31,002557.SZ,洽洽食品 +32,000981.SZ,ST银亿 +33,600362.SH,江西铜业 +34,600177.SH,雅戈尔 +35,603167.SH,渤海轮渡 +36,600736.SH,苏州高新 +37,000798.SZ,中水渔业 +38,002017.SZ,东信和平 +39,002802.SZ,洪汇新材 +40,000031.SZ,大悦城 +41,002222.SZ,福晶科技 +42,000839.SZ,中信国安 +43,300099.SZ,精准信息 +44,002579.SZ,中京电子 +45,002510.SZ,天汽模 +46,000601.SZ,韶能股份 +47,000768.SZ,中航飞机 +48,002244.SZ,滨江集团 +49,000828.SZ,东莞控股 +50,300336.SZ,新文化 diff --git a/Graduate_Proj_V1/Data_Processor.py b/Graduate_Proj_V1/Data_Processor.py new file mode 100644 index 0000000..a767191 --- /dev/null +++ b/Graduate_Proj_V1/Data_Processor.py @@ -0,0 +1,99 @@ +import numpy as np +import pandas as pd +import math +from sklearn import preprocessing + + + +class Stock_Data(object): + #对每一次股票数据进行处理 + def __init__(self,stock_code,input_size,num_steps,train_ratio,interval,future): + ''' + 一个Stock_Data对象对应一只股票历史数据，在Stock_count中将股票历史交易数据处理成模型需要的训练集和测试集 + :param stock_code: 股票的编码 + :param input_size: 决定target的涨跌幅是几日的平均，例如涨跌幅取一个月后五天内的平均涨跌幅，input_size=5 + :param num_steps: 决定输入LSTM神经网络的时序长度 + :param train_ratio: 训练集占所有样本的比率 + :param interval: interval用于将收盘价处理成interval天内的涨跌幅 + :param future: future表示用来预测多少天之后的涨跌幅 + ''' + self.stock_code=stock_code + self.input_size=input_size + self.num_steps=num_steps + self.train_ratio=train_ratio + self.interval=interval + #interval表示将价格换成涨跌率的时间间隔，比如将50天之内的价格换成涨跌率interval=50，就是用50天内的价格去除以50天之前的最后一天的价格 + self.future=future + #future表示取多久之后的平均值，比如预测30个交易日之后的10日平均值，future=30，input_size=10 + self.data_x=[] + self.data_y=[] + + df=pd.read_csv('Data\\交易数据\\'+stock_code+'.csv') + + ''' + 将价格每interval天计算成涨跌率， + ''' + close=df['close'] + self.close_changed = [] + for i in range(int(math.ceil(len(close) / interval))): + if i == 0: + for j in range(interval): + self.close_changed += [(close[i * interval + j] - close[i * interval]) / close[i * interval]] + elif i != 0 and (len(close) - i * interval) > interval: + for j in range(interval): + self.close_changed += [(close[i * interval + j] - close[i * interval - 1]) / close[ + i * interval - 1]] # -1是为了取这interval天之前最后一天的 + elif i != 0 and (len(close) - i * interval) < interval: + for j in range(len(close) - i * interval): + self.close_changed += [(close[i * interval + j] - close[i * interval - 1]) / close[i * interval - 1]] + + + + '''数据归一化''' + self.trans_close = (self.close_changed[:] - min(self.close_changed[:])) / (max(self.close_changed[:])-min(self.close_changed[:])) + self.trans_vol = (df['vol'][:] - min(df['vol'][:])) / (max(df['vol'])-min(df['vol'])) + + '''用未归一化的数据来构建模型targets''' + for i in range(len(self.close_changed) - num_steps - self.input_size - self.future): + change_y = 0 + for j in range(input_size): + change_y += self.close_changed[i + num_steps + self.future - j] + self.data_y += [[change_y / self.input_size]] + self.data_y = np.array(self.data_y) + + self.data=[[self.trans_close[i],self.trans_vol[i]] for i in range(len(self.trans_close))] + self.data=preprocessing.MinMaxScaler().fit_transform(self.data) + self.data_x=np.array([np.array(self.data[i:i+num_steps]) for i in range(len(self.data)-num_steps-self.input_size-self.interval)]) + '''划分训练集测试集''' + self.train_x,self.test_x = np.split(self.data_x,[int(len(self.data_x)*self.train_ratio)]) + self.train_y,self.test_y = np.split(self.data_y,[int(len(self.data_y)*self.train_ratio)]) + + + def get_fluctuation(self): + ''' + 获取每一只股票在历史数据上的涨跌幅波动 + ''' + fluctuation=max(self.close_changed[:])-min(self.close_changed[:]) + return fluctuation + + +def get_stocks(input_size,num_steps,train_ratio,interval,future): + ''' + 获取每只股票对应的Stock_Data对象，读取数据文件中的股票数据 + :return: 每只股票的训练集测试集，股票名，历史数据中涨跌幅的波动 + ''' + #获取文件中所有股票 + stocks_df=pd.DataFrame.from_csv('Data/交易数据/download_stocks_symbol.csv') + stock_name=stocks_df['code'].values.tolist() + #处理成Stock_Data对象 + stock_data=[ + Stock_Data(stock_code=code,input_size=input_size,num_steps=num_steps,train_ratio=train_ratio,interval=interval,future=future) + for code in stock_name + ] + #计算所有股票的平均波动 + sum_fluctuation=0 + for code in stock_name: + sum_fluctuation+=Stock_Data(stock_code=code,input_size=input_size,num_steps=num_steps,train_ratio=train_ratio,interval=interval,future=future).get_fluctuation() + mean_fluctuation=sum_fluctuation/len(stock_name) + + return stock_name,stock_data,mean_fluctuation diff --git a/Graduate_Proj_V1/Data_fetcher_tushare.py b/Graduate_Proj_V1/Data_fetcher_tushare.py new file mode 100644 index 0000000..330b49b --- /dev/null +++ b/Graduate_Proj_V1/Data_fetcher_tushare.py @@ -0,0 +1,55 @@ +import tushare as ts +import pandas as pd +import random,time + +'''使用tushare API需要提供的指令''' +tushare_token='9d318f4f16fc290756e756ddca50bdaabda9d3d98698345f4e505768' + +def get_stock_symbol(): + '''获取当日所有上市股票信息，并存于文件中''' + pro=ts.pro_api(tushare_token) + stocks_info=pro.query('stock_basic',exchange='',list_status='L', fields='ts_code,symbol,name,area,industry,list_date') + stocks_info.to_csv('Data\\stocks_info.csv') + return stocks_info + + +def random_download_stocks(stock_count): + ''' + 该函数用来随机下载stock_count数量的股票 + :param stock_count: 是要下载的股票数量 + ''' + ts.set_token(tushare_token) + Stocks=pd.DataFrame.from_csv('Data\\stocks_info.csv') + length=len(Stocks) + print('共有',length,'只股票，现在随机下载',stock_count,'只！') + + '''处理时间，将开始时间处理成当日的三年前的同一天''' + now = time.localtime() + year = now.tm_year - 3 + start = str(year) + if now.tm_mon < 10: + start = start + '0' + str(now.tm_mon) + else: + start = start + str(now.tm_mon) + if now.tm_mday < 10: + start = start + '0' + str(now.tm_mday) + else: + start = start + str(now.tm_mday) + '''下载股票''' + success_stocks=pd.DataFrame(columns=['code','name']) + for i in range(stock_count): + df=pd.DataFrame() + while df.empty: + index = random.randint(1, 1000000) % length + ts_code, name = Stocks.iloc[index][['ts_code', 'name']] + df = ts.pro_bar(ts_code=ts_code, start_date=start, adj='qfq') + lastday=time.strptime(df.iloc[[df.shape[0]-1]]['trade_date'].values[0],'%Y%m%d') + if lastday.tm_year==year and (lastday.tm_mon==now.tm_mon or lastday.tm_mon==now.tm_mon+1): + #判断获取的股票在三年前有没有交易数据 + pass + else: + df=pd.DataFrame() + df.to_csv('Data\\交易数据\\'+ts_code+'.csv') + #存于文件中 + success_stocks.loc[success_stocks.shape[0]+1]={'code':ts_code,'name':name} + success_stocks.to_csv('Data\\交易数据\\download_stocks_symbol.csv') diff --git a/Graduate_Proj_V1/README.md b/Graduate_Proj_V1/README.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/Graduate_Proj_V1/README.md @@ -0,0 +1 @@ + diff --git a/Graduate_Proj_V1/__pycache__/Data_Processor.cpython-35.pyc b/Graduate_Proj_V1/__pycache__/Data_Processor.cpython-35.pyc new file mode 100644 index 0000000..5b4ad18 Binary files /dev/null and b/Graduate_Proj_V1/__pycache__/Data_Processor.cpython-35.pyc differ diff --git a/Graduate_Proj_V1/__pycache__/Data_Processor.cpython-37.pyc b/Graduate_Proj_V1/__pycache__/Data_Processor.cpython-37.pyc new file mode 100644 index 0000000..dbdea60 Binary files /dev/null and b/Graduate_Proj_V1/__pycache__/Data_Processor.cpython-37.pyc differ diff --git a/Graduate_Proj_V1/__pycache__/__init__.cpython-35.pyc b/Graduate_Proj_V1/__pycache__/__init__.cpython-35.pyc new file mode 100644 index 0000000..86e3007 Binary files /dev/null and b/Graduate_Proj_V1/__pycache__/__init__.cpython-35.pyc differ diff --git a/Graduate_Proj_V1/model_lstm.py b/Graduate_Proj_V1/model_lstm.py new file mode 100644 index 0000000..513377a --- /dev/null +++ b/Graduate_Proj_V1/model_lstm.py @@ -0,0 +1,183 @@ +import tensorflow as tf +import numpy as np +import pandas as pd +import Data_Processor + +class LSTM_net(): + def __init__(self,sess,stock_count, + lstm_size, + num_layers, + num_steps, + input_size, + embed_size, + train_ratio, + logs_dir='', + plots_dir=''): + self.sess = sess + self.stock_count = stock_count + self.lstm_size = lstm_size + self.num_layers = num_layers + self.num_steps = num_steps + self.input_size = input_size + self.embed_size = embed_size or -1 + self.logs_dir = logs_dir + self.plots_dir = plots_dir + self.train_ratio=train_ratio + self.graph() + def graph(self): + ''' + 构建数据流图 + ''' + '''定义需要用到的占位符''' + self.learning_rate = tf.placeholder(tf.float32,None,name='LearningRate') + self.keep_prob = tf.placeholder(tf.float32,None,name='KeepProb') + self.symbols_x = tf.placeholder(tf.int32,[None,self.num_steps],name='stock_labels_x') + self.inputs = tf.placeholder(tf.float32,[None,self.num_steps,2],name='inputs') + # 2 is for the change and vol,是因为有涨跌率和交易量两组数据 + self.targets = tf.placeholder(tf.float32,[None,1],name='targets') + + '''Embedding 层，用于将每只股票用embed_size大小的向量表示''' + self.embed_matrix=tf.Variable(tf.random_uniform([self.stock_count,self.embed_size],minval=-0.2,maxval=0.2),name='embed_matrix') + stock_label_embeds_x = tf.nn.embedding_lookup(self.embed_matrix, self.symbols_x) + + '''将输入和股票的Embed_size向量表示组合起来''' + self.inputs_with_embeds = tf.concat([self.inputs,stock_label_embeds_x],axis=2) + + '''根据lstm_size、keep_prob、num_layers构建含Dropout包装器的LSTM神经网络''' + def _create_one_cell(): + cell=tf.nn.rnn_cell.DropoutWrapper( + tf.nn.rnn_cell.LSTMCell(self.lstm_size,state_is_tuple=True), + output_keep_prob=self.keep_prob + ) + return cell + cell=tf.nn.rnn_cell.MultiRNNCell([_create_one_cell() for i in range(self.num_layers)],state_is_tuple=True) if self.num_layers > 1 else _create_one_cell() + + '''获得LSTM网络的输出和状态''' + val,_ = tf.nn.dynamic_rnn(cell,self.inputs_with_embeds,dtype=tf.float32) + + '''根据LSTM网络的输出计算出与target维度匹配的输出''' + # Before transpose, val.get_shape() = (batch_size, num_steps, lstm_size) + # After transpose, val.get_shape() = (num_steps, batch_size, lstm_size) + val = tf.transpose(val,[1,0,2]) + last = tf.gather(val,int(val.get_shape()[0])-1,name='last_lstm_output') + weight=tf.Variable(tf.truncated_normal([self.lstm_size,1])) + bias=tf.Variable(tf.constant(0.1,shape=[1])) + self.prediction=tf.matmul(last,weight)+bias + + '''模型的代价函数和优化器''' + self.loss=tf.reduce_mean(tf.square(self.prediction-self.targets)) + self.optimizer=tf.train.RMSPropOptimizer(learning_rate=self.learning_rate).minimize(self.loss) + + def train(self,max_epoch,init_learning_rate,decay_rate,decay_epoch,batch_ratio,keep_prob,interval,future): + ### 获取数据，设定好batch_size与训练epoch,将数据feed进图开始训练和测试 + + '''获取数据，初始化模型参数''' + stock_code,stock_data,mean_fluctuation=Data_Processor.get_stocks( + input_size=self.input_size,num_steps=self.num_steps,train_ratio=self.train_ratio,interval=interval,future=future) + tf.global_variables_initializer().run() + + '''合并每只股票的测试样本为一个统一的测试集''' + merge_test_x = [] + merge_test_y = [] + merge_test_labels_x = [] + for test_label, test_data in enumerate(stock_data): + merge_test_x += list(test_data.test_x) + merge_test_y += list(test_data.test_y) + merge_test_labels_x += [[test_label] * self.num_steps] * len(test_data.test_x) + test_feed_dic = { + self.learning_rate: 0.0, + self.keep_prob: 1.0, + self.inputs: np.array(merge_test_x), + self.targets: np.array(merge_test_y), + self.symbols_x: np.array(merge_test_labels_x), + } + + # 开始训练 + for epoch in range(max_epoch): + + '''每轮更新一次学习率''' + learning_rate = init_learning_rate * ( + decay_rate ** max(float(epoch + 1 - decay_epoch), 0.0) + ) + + for label,data in enumerate(stock_data): + # 准备训练集 + train_x = tf.placeholder(data.train_x.dtype,data.train_x.shape) + train_y = tf.placeholder(data.train_y.dtype,data.train_y.shape) + + # 取batch_size个样本的训练集 + batch_size=int(len(data.train_x)*batch_ratio) + dataset=tf.data.Dataset.from_tensor_slices((train_x,train_y)) + dataset=dataset.batch(batch_size) + iterator=dataset.make_initializable_iterator() + self.sess.run(iterator.initializer, feed_dict={train_x: data.train_x, train_y: data.train_y}) + next_batch = iterator.get_next() + batch_x, batch_y = self.sess.run(next_batch) + # 构建每个输入对应的标签 + batch_label_x = np.array([[label]*self.num_steps] * batch_x.shape[0]) + + train_feed_dic = { + self.learning_rate:learning_rate, + self.symbols_x:batch_label_x, + #self.symbols_y:batch_label_y, + self.keep_prob:keep_prob, + self.inputs:batch_x, + self.targets:batch_y, + } + # 训练 + train_loss,optimizer=self.sess.run( + [self.loss,self.optimizer],train_feed_dic + ) + print('After ', epoch, 'the train_loss: ', train_loss) + # 测试 + test_pred, test_loss = self.sess.run([self.prediction, self.loss], test_feed_dic) + print('After ',epoch,'the test_loss: ',test_loss) + + #最终再测试一次 + final_pred,final_loss=self.sess.run([self.prediction, self.loss], test_feed_dic) + print('Final,the test_loss: ',final_loss) + + #预测数据和target分别存入txt方便观察 + np.savetxt('out\pred.txt',final_pred) + np.savetxt('out\out.txt',merge_test_y) + + #保存一下模型 + Saver=tf.train.Saver() + Saver.save(sess=self.sess,save_path='.\save\params') + + # 计算平均预测误差 + sum_error=0 + for i in range(final_pred.shape[0]): + print('final_pred:',final_pred[i][0],' and the target:',merge_test_y[i][0]) + sum_error += (final_pred[i][0] - merge_test_y[i][0]) + mean_error=sum_error/final_pred.shape[0] + + print('所有股票涨幅的平均波动为：',mean_fluctuation) + print('在测试集上对于涨跌趋势的预测的平均误差为：',mean_error) + + + +def main(): + with tf.Session() as sess: + lstm_model=LSTM_net( + sess, + stock_count=50, + lstm_size=128, + num_layers=1, + num_steps=250,#一年250个交易日 + input_size=10, + embed_size=3, + train_ratio=0.9, + logs_dir='./logs', + plots_dir='. /plots' + ) + lstm_model.train(max_epoch=30, + init_learning_rate=0.001, + decay_rate=0.98, + decay_epoch=10, + batch_ratio=0.8, + keep_prob=0.8, + interval=30, + future=30 + ) +main() \ No newline at end of file diff --git a/Graduate_Proj_V1/out/out.txt b/Graduate_Proj_V1/out/out.txt new file mode 100644 index 0000000..be86367 --- /dev/null +++ b/Graduate_Proj_V1/out/out.txt @@ -0,0 +1,2000 @@ +1.000366703337000757e-01 +1.163916391639164133e-01 +1.357535753575357940e-01 +1.562522918958562657e-01 +1.741474147414741602e-01 +1.804180418041804457e-01 +1.845251191785845235e-01 +1.955262192885955097e-01 +2.026769343601026452e-01 +2.136413641364137139e-01 +2.247158049138247848e-01 +2.382471580491382634e-01 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index 0000000..034869e Binary files /dev/null and b/Graduate_Proj_V1/save/params.data-00000-of-00001 differ diff --git a/Graduate_Proj_V1/save/params.index b/Graduate_Proj_V1/save/params.index new file mode 100644 index 0000000..0a15333 Binary files /dev/null and b/Graduate_Proj_V1/save/params.index differ diff --git a/Graduate_Proj_V1/save/params.meta b/Graduate_Proj_V1/save/params.meta new file mode 100644 index 0000000..b735f04 Binary files /dev/null and b/Graduate_Proj_V1/save/params.meta differ diff --git a/README.md b/README.md new file mode 100644 index 0000000..7549975 --- /dev/null +++ b/README.md @@ -0,0 +1 @@ +包含娄瀚、朱越凡、赵一鸿三位学长的代码 diff --git a/classification/Trend b/classification/Trend new file mode 100644 index 0000000..2e55ed0 --- /dev/null +++ b/classification/Trend @@ -0,0 +1,19 @@ +# coding=utf-8 + +import xlrd +import tushare as ts +import matplotlib.pyplot as plt +stock_9 = ts.get_k_data(code='601808', start='2013-09-01', end='2018-09-01') +stock_9.to_excel('D:/stock_9.xlsx') +data = xlrd.open_workbook('D:/stock_9.xlsx') +table = data.sheet_by_name('Sheet1') +list = table.col_values(colx=3, start_rowx=1, end_rowx=-1) +# print(list) +x = [] +y = [] +# print(len(list)) +for i in range(0, 1071): + x.append(i) + y.append(list[i]) +plt.plot(x, y) +plt.show() diff --git a/classification/classification b/classification/classification new file mode 100644 index 0000000..a569759 --- /dev/null +++ b/classification/classification @@ -0,0 +1,105 @@ +# coding=UTF-8 +import numpy as np +import pandas as pd +import tushare as ts +from sklearn.covariance import GraphLassoCV, GraphicalLassoCV +from sklearn.cluster import affinity_propagation +import xlrd + + +def get_onedata(code, start, end): + df = ts.get_k_data(code, start=start, end=end) # dataframe with columns [date, open, close, high, low] + return df + + +def get_kdata(codes_list, start, end): + df = pd.DataFrame() + for code in codes_list: + # print('fetching K data of {}...'.format(code)) + df = df.append(get_onedata(code, start, end)) + return df + + +def preprocess_data(stock_df, min_K_num=1000): + df = stock_df.copy() + df['diff'] = df.close - df.open + df.drop(['open', 'close', 'high', 'low'], axis=1, inplace=True) + # print(df) + result_df = None + for name, group in df[['date', 'diff']].groupby(df.code): + if len(group.index) < min_K_num: continue + if result_df is None: + result_df = group.rename(columns={'diff': name}) + else: + result_df = pd.merge(result_df, group.rename(columns={'diff': name}), on='date', how='inner') + # 对应相同的date 排序 + + result_df.drop(['date'], axis=1, inplace=True) + # 将股票数据DataFrame转变为np.ndarray + stock_dataset = np.array(result_df).astype(np.float64) + # 数据归一化 + stock_dataset /= np.std(stock_dataset, axis=0) + return stock_dataset, result_df.columns.tolist() + + +def preprocess_data2(stock_df, min_K_num=1000): + df = stock_df.copy() + df.drop(['open', 'close', 'high', 'low'], axis=1, inplace=True) + result_df = None + for name, group in df[['date', 'volume']].groupby(df.code): + if len(group.index) < min_K_num: continue + if result_df is None: + result_df = group.rename(columns={'volume': name}) + else: + result_df = pd.merge(result_df, group.rename(columns={'volume': name}), on='date', how='inner') + result_df.drop(['date'], axis=1, inplace=True) + # 然后将股票数据DataFrame转变为np.ndarray + stock_dataset = np.array(result_df).astype(np.float64) + # 数据归一化，此处使用相关性而不是协方差的原因是在结构恢复时更高效 + stock_dataset /= np.std(stock_dataset, axis=0) + return stock_dataset, result_df.columns.tolist() + + +data = xlrd.open_workbook('nlmy.xlsx') +table = data.sheet_by_name('Animal_husbandry') +stock_list = table.col_values(colx=1, start_rowx=1, end_rowx=33) +stock_list = [str(i) for i in stock_list] + +batch_K_data = get_kdata(stock_list, start='2013-09-01', end='2018-09-01') # 查看最近五年的数据 +# print(batch_K_data.info()) + +stock_dataset, selected_stocks = preprocess_data(batch_K_data, min_K_num=1100) +stock_dataset2, selected_stocks2 = preprocess_data2(batch_K_data, min_K_num=1100) +print("The selected stocks is: ", + selected_stocks) # 这是实际使用的股票列表stock_dataset,selected_stocks=preprocess_data2(batch_K_data,min_K_num=1100) + +# 从相关性中学习其图形结构 +edge_model1 = GraphicalLassoCV(cv=3) +edge_model2 = GraphicalLassoCV(cv=3) +# edge_model.fit(stock_dataset) +edge_model1.fit(stock_dataset) +edge_model2.fit(stock_dataset2) + +# 使用近邻传播算法构建模型，并训练LassoCV graph +_, labels1 = affinity_propagation(edge_model1.covariance_) +_, labels2 = affinity_propagation(edge_model2.covariance_) + +n_labels = max(labels1) +print('Stock Clusters: {}'.format(n_labels + 1)) # 10，即得到10个类别 +sz50_df2 = stock_list +# print(sz50_df2) +for i in range(n_labels + 1): + print('Cluster: {}----> stocks: {}'.format(i, ','.join(np.array(selected_stocks)[labels1 == i]))) # 这个只有股票代码而不是股票名称 + stocks = np.array(selected_stocks)[labels1 == i].tolist() + # names = sz50_df2.loc[stocks, :].name.tolist() + # print('Cluster: {}----> stocks: {}'.format(i,','.join(names))) + +n_labels = max(labels2) +print('Stock Clusters: {}'.format(n_labels + 1)) # 10，即得到10个类别 +sz50_df2 = stock_list +# print(sz50_df2) +for i in range(n_labels + 1): + print('Cluster: {}----> stocks: {}'.format(i, ','.join(np.array(selected_stocks)[labels2 == i]))) # 这个只有股票代码而不是股票名称 + stocks = np.array(selected_stocks)[labels1 == i].tolist() + # names = sz50_df2.loc[stocks, :].name.tolist() + # print('Cluster: {}----> stocks: {}'.format(i,','.join(names))) diff --git a/classification/nlmy.xlsx b/classification/nlmy.xlsx new file mode 100644 index 0000000..50dcd2f Binary files /dev/null and b/classification/nlmy.xlsx differ diff --git a/ddd.txt b/ddd.txt deleted file mode 100644 index d800886..0000000 --- a/ddd.txt +++ /dev/null @@ -1 +0,0 @@ -123 \ No newline at end of file diff --git a/stockPricePredict/README.md b/stockPricePredict/README.md new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/stockPricePredict/README.md @@ -0,0 +1 @@ + diff 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new file mode 100644 index 0000000..2d4be9e Binary files /dev/null and b/stockPricePredict/cc.xlsx differ diff --git a/stockPricePredict/dataProcessing.py b/stockPricePredict/dataProcessing.py new file mode 100644 index 0000000..f6d0cae --- /dev/null +++ b/stockPricePredict/dataProcessing.py @@ -0,0 +1,122 @@ +''' +使用方法：修改以下变量 + example_path样本数据路径 + present_time + time + final_time + j与time有关 3月加1 6月加2 9月加3 + stock_price,basic_financial_statement,dealed_financial_statement表格路径 + 不得跨年 + ''' +import openpyxl +import pandas as pd + +# 给表格添加一行 +# path="./data_set.xlsx" +# wb = openpyxl.load_workbook(path) +# ws = wb['Sheet1'] +# ws.append(value) +# wb.save(path) + +# 取表格特定位置的值 +# excel_path1 = './1/table_first.xlsx' #会读到Refreshing +# excel_path2 = './stock_price_2015.xlsx' +# print('k') +# d = pd.read_excel(excel_path1, sheetname=None,header=None) +# d=d['Sheet2'] +# d=pd.DataFrame(d) +# print(d.shape) +# print(d.iloc[1,1]) +# print(d.loc[16,0]) +# print(d.loc[10,1]) + +#2015-03-31 +present_time="2018-06-30" +time="2018-09-30" +final_time='2018-12-31' +example_path="./data_set/data_set_2018.xlsx" +wb1=openpyxl.load_workbook(example_path) +ws1=wb1['Sheet1'] #ws1用来添加样本数据集 + +stock_price_2015=pd.read_excel("./stock_price/stock_price_2018.xlsx",sheetname=None,header=None) +stock_price_2015=stock_price_2015['Sheet2'] +stock_price_2015=pd.DataFrame(stock_price_2015) +#print(stock_price_2015) + +basic_financial_statement=pd.read_excel("./financial_statement/basic_financial_statement_2018.xlsx",sheetname=None,header=None) +basic_financial_statement=basic_financial_statement['Sheet1'] +basic_financial_statement=pd.DataFrame(basic_financial_statement) +#print(basic_financial_statement.iloc[9,1]) + +dealed_financial_statement=pd.read_excel("./financial_statement/dealed_financial_statement_2018.xlsx",sheetname=None,header=None) +dealed_financial_statement=dealed_financial_statement['Sheet2'] +dealed_financial_statement=pd.DataFrame(dealed_financial_statement) +#print(dealed_financial_statement.iloc[9,1]) + +#获取100个样本数据 +#每个样本数据的顺序为：1时间 2证券简称 3证券代码 4销售毛利率 5销售净利率 6成本费用利用率 7净资产收益率 8总资产利润率 9流动比率 +# 10速动比率 11现金比率 12资产负债率 13产权比率 14已获利息倍数 15应收账款周转率 16存货周转率 17存货周转天数 +# 18流动资产周转率 19固定资产周转率 20总资产周转率 21股东权益比率 22股东权益与固定资产比率 23每股净现金流量 +# 24经营现金流与销售收入比率 25经营现金流与净利润比率 26每股净收益 27投资收益率 28每股净资产 29当前股价 30目标股价 +# header=['时间','证券简称','证券代码','销售毛利率','销售净利率','成本费用利用率','净资产收益率','总资产利润率','流动比率', +# '速动比率','现金比率','资产负债率','产权比率','已获利息倍数','应收账款周转率','存货周转率','存货周转天数','流动资产周转率', +# '固定资产周转率','总资产周转率','股东权益比率','股东权益与固定资产比率','每股净现金流量','经营现金流与销售收入比率', +# '经营现金流与净利润比率','每股净收益','投资收益率','每股净资产','当前股价','目标股价'] + +for i in range(100): + value=[] + j=i*5+3 + value.append(time) #1时间 + value.append(stock_price_2015.iloc[2,i+1]) #2证券简称 + value.append(stock_price_2015.iloc[1,i+1]) #3证券代码 + value.append(dealed_financial_statement.iloc[53,j]) #4销售毛利率 + value.append(dealed_financial_statement.iloc[52,j]) #5销售净利率 + value.append(basic_financial_statement.iloc[53,j]/basic_financial_statement.iloc[16,j]) #6成本费用利用率 + value.append(dealed_financial_statement.iloc[39,j]) #7净资产收益率 + value.append(basic_financial_statement.iloc[58,j]/basic_financial_statement.iloc[222,j]) #8总资产利润率 + value.append(dealed_financial_statement.iloc[100,j]) #9流动比率 + value.append(dealed_financial_statement.iloc[102,j]) #10速动比率 + value.append(dealed_financial_statement.iloc[101,j]) #11现金比率 + value.append(dealed_financial_statement.iloc[88,j]) #12资产负债率 + value.append(dealed_financial_statement.iloc[104,j]) #13产权比率 + value.append(dealed_financial_statement.iloc[115,j]) #14已获利息倍数 + value.append(dealed_financial_statement.iloc[128,j]) #15应收账款周转率 + value.append(dealed_financial_statement.iloc[127,j]) #16存货周转率 + value.append(dealed_financial_statement.iloc[124,j]) #17存货周转天数 + value.append(dealed_financial_statement.iloc[130,j]) #18流动资产周转率 + value.append(dealed_financial_statement.iloc[131,j]) #19固定资产周转率 + value.append(dealed_financial_statement.iloc[132,j]) #20总资产周转率 + value.append(100-dealed_financial_statement.iloc[88,j]) #21股东权益比率 + value.append(basic_financial_statement.iloc[222,j]/basic_financial_statement.iloc[124,j]) #22股东权益与固定资产比率 + value.append(dealed_financial_statement.iloc[28,j]) #23每股净现金流量 + value.append(dealed_financial_statement.iloc[79,j]) #24经营现金流与销售收入比率 + value.append(basic_financial_statement.iloc[262,j]/basic_financial_statement.iloc[58,j]) #25经营现金流与净利润比率 + value.append(dealed_financial_statement.iloc[12,j]) #26每股净收益 + value.append(dealed_financial_statement.iloc[48,j]) #27投资收益率 + value.append(dealed_financial_statement.iloc[17,j]) #28每股净资产 + + #29当前股价 + present_stock_price=0 + sum_stock_price=0 + count=0 + shape=stock_price_2015.shape + for index in range(4,shape[0]): + if str(stock_price_2015.iloc[index, 0])>present_time and str(stock_price_2015.iloc[index, 0])<=time: + sum_stock_price+=stock_price_2015.iloc[index,i+1] + count+=1.0 + present_stock_price=sum_stock_price/count + value.append(present_stock_price) + + # 30目标股价 + target_stock_price = 0 + sum_stock_price = 0 + count = 0 + for index in range(4,shape[0]): + if str(stock_price_2015.iloc[index, 0])>time and str(stock_price_2015.iloc[index, 0])<=final_time: + sum_stock_price += stock_price_2015.iloc[index, i + 1] + count += 1.0 + target_stock_price = sum_stock_price/count + value.append(target_stock_price) + #添加一个样本 + ws1.append(value) +wb1.save(example_path) \ No newline at end of file diff --git a/stockPricePredict/dataProcessing2.py b/stockPricePredict/dataProcessing2.py new file mode 100644 index 0000000..381c05e --- /dev/null +++ b/stockPricePredict/dataProcessing2.py @@ -0,0 +1,108 @@ +#股价跨年时 + +''' +使用方法：修改以下变量 + example_path样本数据路径 + stock_price,basic_financial_statement,dealed_financial_statement表格路径 + time + + 不得跨年 + ''' +import openpyxl +import pandas as pd + +example_path="./data_set/data_set_2017.xlsx" +wb1=openpyxl.load_workbook(example_path) +ws1=wb1['Sheet1'] #ws1用来添加样本数据集 + +stock_price_2015=pd.read_excel("./stock_price/stock_price_2017.xlsx",sheetname=None,header=None) +stock_price_2015=stock_price_2015['Sheet2'] +stock_price_2015=pd.DataFrame(stock_price_2015) +#print(stock_price_2015) + +stock_price_2016=pd.read_excel("./stock_price/stock_price_2018.xlsx",sheetname=None,header=None) +stock_price_2016=stock_price_2016['Sheet2'] +stock_price_2016=pd.DataFrame(stock_price_2016) + + +basic_financial_statement=pd.read_excel("./financial_statement/basic_financial_statement_2017.xlsx",sheetname=None,header=None) +basic_financial_statement=basic_financial_statement['Sheet1'] +basic_financial_statement=pd.DataFrame(basic_financial_statement) +#print(basic_financial_statement.iloc[9,1]) + +dealed_financial_statement=pd.read_excel("./financial_statement/dealed_financial_statement_2017.xlsx",sheetname=None,header=None) +dealed_financial_statement=dealed_financial_statement['Sheet2'] +dealed_financial_statement=pd.DataFrame(dealed_financial_statement) +#print(dealed_financial_statement.iloc[9,1]) + +#获取100个样本数据 +#每个样本数据的顺序为：1时间 2证券简称 3证券代码 4销售毛利率 5销售净利率 6成本费用利用率 7净资产收益率 8总资产利润率 9流动比率 +# 10速动比率 11现金比率 12资产负债率 13产权比率 14已获利息倍数 15应收账款周转率 16存货周转率 17存货周转天数 +# 18流动资产周转率 19固定资产周转率 20总资产周转率 21股东权益比率 22股东权益与固定资产比率 23每股净现金流量 +# 24经营现金流与销售收入比率 25经营现金流与净利润比率 26每股净收益 27投资收益率 28每股净资产 29当前股价 30目标股价 +# header=['时间','证券简称','证券代码','销售毛利率','销售净利率','成本费用利用率','净资产收益率','总资产利润率','流动比率', +# '速动比率','现金比率','资产负债率','产权比率','已获利息倍数','应收账款周转率','存货周转率','存货周转天数','流动资产周转率', +# '固定资产周转率','总资产周转率','股东权益比率','股东权益与固定资产比率','每股净现金流量','经营现金流与销售收入比率', +# '经营现金流与净利润比率','每股净收益','投资收益率','每股净资产','当前股价','目标股价'] + +present_time='2017-09-30' +time='2017-12-31' +final_time='2018-03-31' +for i in range(100): + value=[] + j=i*5+4 + value.append(time) #1时间 + value.append(stock_price_2015.iloc[2,i+1]) #2证券简称 + value.append(stock_price_2015.iloc[1,i+1]) #3证券代码 + value.append(dealed_financial_statement.iloc[53,j]) #4销售毛利率 + value.append(dealed_financial_statement.iloc[52,j]) #5销售净利率 + value.append(basic_financial_statement.iloc[53,j]/basic_financial_statement.iloc[16,j]) #6成本费用利用率 + value.append(dealed_financial_statement.iloc[39,j]) #7净资产收益率 + value.append(basic_financial_statement.iloc[58,j]/basic_financial_statement.iloc[222,j]) #8总资产利润率 + value.append(dealed_financial_statement.iloc[100,j]) #9流动比率 + value.append(dealed_financial_statement.iloc[102,j]) #10速动比率 + value.append(dealed_financial_statement.iloc[101,j]) #11现金比率 + value.append(dealed_financial_statement.iloc[88,j]) #12资产负债率 + value.append(dealed_financial_statement.iloc[104,j]) #13产权比率 + value.append(dealed_financial_statement.iloc[115,j]) #14已获利息倍数 + value.append(dealed_financial_statement.iloc[128,j]) #15应收账款周转率 + value.append(dealed_financial_statement.iloc[127,j]) #16存货周转率 + value.append(dealed_financial_statement.iloc[124,j]) #17存货周转天数 + value.append(dealed_financial_statement.iloc[130,j]) #18流动资产周转率 + value.append(dealed_financial_statement.iloc[131,j]) #19固定资产周转率 + value.append(dealed_financial_statement.iloc[132,j]) #20总资产周转率 + value.append(1-dealed_financial_statement.iloc[88,j]) #21股东权益比率 + value.append(basic_financial_statement.iloc[222,j]/basic_financial_statement.iloc[124,j]) #22股东权益与固定资产比率 + value.append(dealed_financial_statement.iloc[28,j]) #23每股净现金流量 + value.append(dealed_financial_statement.iloc[79,j]) #24经营现金流与销售收入比率 + value.append(basic_financial_statement.iloc[262,j]/basic_financial_statement.iloc[58,j]) #25经营现金流与净利润比率 + value.append(dealed_financial_statement.iloc[12,j]) #26每股净收益 + value.append(dealed_financial_statement.iloc[48,j]) #27投资收益率 + value.append(dealed_financial_statement.iloc[17,j]) #28每股净资产 + + #29当前股价 + present_stock_price=0 + sum_stock_price=0 + count=0 + shape=stock_price_2015.shape + for index in range(4,shape[0]): + if str(stock_price_2015.iloc[index, 0])>present_time and str(stock_price_2015.iloc[index, 0])<=time: + sum_stock_price+=stock_price_2015.iloc[index,i+1] + count+=1.0 + present_stock_price=sum_stock_price/count + value.append(present_stock_price) + + # 30目标股价 + target_stock_price = 0 + sum_stock_price = 0 + count = 0 + shape=stock_price_2016.shape + for index in range(4,shape[0]): + if str(stock_price_2016.iloc[index, 0])>time and str(stock_price_2016.iloc[index, 0])<=final_time: + sum_stock_price += stock_price_2016.iloc[index, i + 1] + count += 1.0 + target_stock_price = sum_stock_price/count + value.append(target_stock_price) + #添加一个样本 + ws1.append(value) +wb1.save(example_path) \ No newline at end of file diff --git a/stockPricePredict/data_set/data_set_2015.xlsx b/stockPricePredict/data_set/data_set_2015.xlsx new file mode 100644 index 0000000..60ec33e Binary files /dev/null and b/stockPricePredict/data_set/data_set_2015.xlsx differ diff --git a/stockPricePredict/data_set/data_set_2016.xlsx b/stockPricePredict/data_set/data_set_2016.xlsx new file mode 100644 index 0000000..ff95ecf 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+data2015=pd.read_excel("./data_set/data_set_2015.xlsx") +data2016=pd.read_excel("./data_set/data_set_2016.xlsx") +data2017=pd.read_excel("./data_set/data_set_2017.xlsx") +data2018=pd.read_excel("./data_set/data_set_2018.xlsx") +data=data2015.append(data2016) +data=data.append(data2017) +data=data.append(data2018) + + +#剔除银行 +ex_list = list(data.销售毛利率) +#print(ex_list) +tt=ex_list[0] +for i in range(90): + ex_list.remove(tt) +data=data[data.销售毛利率.isin(ex_list)] +print(data.shape) + + + +data1=data.iloc[:,3:29] #26个特征输入 +data2=data.iloc[:,29] #1个输出结果（所要预测的股价） +X_train,X_test,y_train,y_test=train_test_split(data1,data2,test_size=0.1,random_state=0) +x_test=X_test.iloc[:,-1] #当前股价 +x_test=np.array([x_test]).reshape(-1,1) +print(x_test.shape) +print(x_test.shape[0]) +#print(x_test[0,0]) + +#砍掉一个当前股价特征 +X_train=X_train.iloc[:,0:25] +X_test=X_test.iloc[:,0:25] + + +X_train=scale(X_train) +X_test=scale(X_test) +y_train=np.array([y_train]).reshape(-1,1) +y_test=np.array([y_test]).reshape(-1,1) #目标股价 +print(y_test.shape) +#print(y_test[0:1]) + +def add_layer(inputs,input_size,output_size,activation_function=None): + with tf.variable_scope("Weights"): + Weights=tf.Variable(tf.random_normal(shape=[input_size,output_size]),name='weights') + with tf.variable_scope("biases"): + biases=tf.Variable(tf.zeros(shape=[1,output_size])+0.1,name="biases") + with tf.name_scope("Wx_plus_b"): + Wx_plus_b=tf.matmul(inputs,Weights)+biases + with tf.name_scope("dropout"): + Wx_plus_b=tf.nn.dropout(Wx_plus_b,keep_prob=keep_prob_s) + if activation_function is None: + return Wx_plus_b + else: + with tf.name_scope("activation_function"): + return activation_function(Wx_plus_b) + + +xs=tf.placeholder(shape=[None,X_train.shape[1]],dtype=tf.float32,name="inputs") +ys=tf.placeholder(shape=[None,1],dtype=tf.float32,name="y_true") +keep_prob_s=tf.placeholder(dtype=tf.float32) + +with tf.name_scope("layer_1"): + l1=add_layer(xs,25,30,activation_function=tf.nn.tanh) + l2=add_layer(l1,30,30,activation_function=tf.nn.tanh) + l3=add_layer(l2,30,10,activation_function=tf.nn.tanh) + #l4 = add_layer(l3, 30, 10, activation_function=tf.nn.tanh) + +with tf.name_scope("y_pred"): + pred=add_layer(l3,10,1) +pred=tf.add(pred,0,name="pred") + +with tf.name_scope("loss"): + loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-pred),reduction_indices=[1])) + tf.summary.scalar("loss",tensor=loss) +with tf.name_scope("train"): + train_op=tf.train.AdamOptimizer(learning_rate=0.01).minimize(loss) + +#draw pics +fig=plt.figure() +ax=fig.add_subplot(1,1,1) +l1=ax.plot(range(100),y_train[0:100],'b',label='train_real_price') +ax.set_ylim([0,50]) +plt.ion() +plt.show() + +keep_prob=0.9 +ITER=3000 + + +def fit(X,y,ax,n,keep_prob): + init=tf.global_variables_initializer() + feed_dict_train={ys:y,xs:X,keep_prob_s:keep_prob} ################################## + with tf.Session() as sess: + saver=tf.train.Saver(tf.global_variables(),max_to_keep=15) + merged=tf.summary.merge_all() + writer=tf.summary.FileWriter(logdir="bp_stock_log",graph=sess.graph) + sess.run(init) + for i in range(n): + _loss,_=sess.run([loss,train_op],feed_dict=feed_dict_train) + '''if i%100==0: + print("epoch:%d\tloss:%.5f" %(i,_loss)) + y_pred=sess.run(pred,feed_dict=feed_dict_train) + rs=sess.run(merged,feed_dict=feed_dict_train) + writer.add_summary(summary=rs,global_step=i) + saver.save(sess=sess,save_path="bp_stock_model/bp_stock.model",global_step=i) + try: + ax.lines.remove(lines[0]) + except: + pass + lines=ax.plot(range(66),y_pred[0:66],'r--') + path="./picture/"+str(i)+'.jpg' + plt.savefig(path) + if i==(n-1): + y_pred=sess.run(pred,feed_dict={xs:X_test,ys:y_test,keep_prob_s:keep_prob}) + try: + ax.lines.remove(lines[0]) + except: + pass + ax.plot(range(40), y_test, 'yellow') + lines=ax.plot(range(40),y_pred[0:40],'r--') + path = "./picture/test.jpg" + plt.savefig(path)''' + + if i%100==0: + value=[] + value.append(i) + + print("epoch:%d\tloss:%.5f" %(i,_loss)) + y_pred=sess.run(pred,feed_dict={xs:X_train,ys:y_train,keep_prob_s:1}) + rs=sess.run(merged,feed_dict=feed_dict_train) + writer.add_summary(summary=rs,global_step=i) + + #保存模型 + #saver.save(sess=sess,save_path="bp_stock_model/bp_stock.model",global_step=i) + + try: + ax.lines.remove(lines[0]) + except: + pass + lines=ax.plot(range(100),y_pred[0:100],'g--',lw=2,label='train_predict_price') + plt.legend() + plt.pause(1) + + + y_pred = sess.run(pred, feed_dict={xs: X_test, ys: y_test, keep_prob_s: 1}) + # 计算准确率 + count = 0 #用来记录预测涨跌正确的股票数 + index = [] + for j in range(x_test.shape[0]): + if (y_test[j:j + 1] - x_test[j:j + 1]) * (y_pred[j:j + 1] - x_test[j:j + 1]) >= 0: + count += 1.0 + index.append(j) + accuracy=count / x_test.shape[0] + print('准确率：',accuracy) + + #保存模型 + if (count / x_test.shape[0])>0.6: + saver.save(sess=sess, save_path="bp_stock_model/bp_stock.model", global_step=i) + + #计算收益 + money=0 + predict_zhang_and_correct=0 + predict_zhang=0 + predict_die=0 + predict_die_and_correct=0 + for j in range(x_test.shape[0]): + if y_pred[j:j+1]>=x_test[j:j+1]: + predict_zhang+=1 + money += 10000 * (y_test[j:j + 1] - x_test[j:j + 1]) + if y_pred[j:j + 1] < x_test[j:j + 1]: + predict_die += 1 + if (y_pred[j:j+1]>=x_test[j:j+1]) and ((y_test[j:j + 1] - x_test[j:j + 1]) * (y_pred[j:j + 1] - x_test[j:j + 1]) >= 0): + predict_zhang_and_correct+=1 + if (y_pred[j:j+1]= 0): + predict_die_and_correct+=1 + print("投资总额：",predict_zhang*10000," 预期收益：",money,"预测涨：",predict_zhang," 预测要涨且正确：",predict_zhang_and_correct) + print("预测跌：",predict_die,"预测跌且正确：",predict_die_and_correct,'\n') + + value.append(predict_zhang) + value.append(predict_zhang_and_correct) + value.append(predict_die) + value.append(predict_die_and_correct) + value.append(accuracy) + print(value) + ws.append(value) + wb.save(path) + + if i==(n-1): + y_pred=sess.run(pred,feed_dict={xs:X_test,ys:y_test,keep_prob_s:1}) + try: + ax.lines.remove(lines[0]) + except: + pass + ax.lines.remove(l1[0]) + ax.plot(range(x_test.shape[0]),x_test,'black',lw=2,label='test_present_price') + ax.plot(range(x_test.shape[0]), y_test, 'yellow',lw=3,label='test_real_price') + lines=ax.plot(range(x_test.shape[0]),y_pred[0:x_test.shape[0]],'r--',label='test_predict_price') + plt.legend() + + #计算准确率 + count=0 + index=[] + for j in range(x_test.shape[0]): + if (y_test[j:j+1]-x_test[j:j + 1])*(y_pred[j:j+1]-x_test[j:j + 1])>=0: + count+=1.0 + index.append(j) + print(count/x_test.shape[0]) + print(index) + plt.pause(100) + + + wb.save(path) + + #saver.save(sess=sess, save_path="nn_boston_model/nn_boston.model", global_step=n) # 保存模型 + +fit(X=X_train,y=y_train,n=ITER,keep_prob=keep_prob,ax=ax) + + +#setting->Tools->Python Scientific 关闭Show plots in tool window才能用matplotlib画出动态更新的图 + +#cd C:\Users\music\PycharmProjects\stockPricePredict +#tensorboard --logdir "bp_stock_log" \ No newline at end of file diff --git a/stockPricePredict/model_CNN.py b/stockPricePredict/model_CNN.py new file mode 100644 index 0000000..d3905aa --- /dev/null +++ b/stockPricePredict/model_CNN.py @@ -0,0 +1,205 @@ +import tensorflow as tf +import numpy as np +import matplotlib.pyplot as plt +from sklearn.preprocessing import scale +from sklearn.model_selection import train_test_split + +from sklearn import preprocessing #数据预处理 + + +import pandas as pd +import pandas as pd +data2015=pd.read_excel("./data_set/data_set_2015.xlsx") +data2016=pd.read_excel("./data_set/data_set_2016.xlsx") +data2017=pd.read_excel("./data_set/data_set_2017.xlsx") +data2018=pd.read_excel("./data_set/data_set_2018.xlsx") +data=data2015.append(data2016) +data=data.append(data2017) +data=data.append(data2018) + + +#剔除银行 +ex_list = list(data.销售毛利率) +#print(ex_list) +tt=ex_list[0] +for i in range(90): + ex_list.remove(tt) +data=data[data.销售毛利率.isin(ex_list)] +print(data.shape) + + + +data1=data.iloc[:,3:29] #26个特征输入 +data2=data.iloc[:,29] #1个输出结果（所要预测的股价） +X_train,X_test,y_train,y_test=train_test_split(data1,data2,test_size=0.1,random_state=0) +x_test=X_test.iloc[:,-1] +x_test=np.array(x_test).reshape(-1,1) +print(x_test.shape[0]) +#print(x_test[0,0]) + +X_train=X_train.iloc[:,0:25] +X_test=X_test.iloc[:,0:25] + + +X_train=scale(X_train) +X_test=scale(X_test) +y_train=np.array([y_train]).reshape(-1,1) +y_test=np.array([y_test]).reshape(-1,1) +print(y_test.shape) + +#变厚矩阵 +def weight_variable(shape): + initial = tf.truncated_normal(shape,stddev=0.1) ################# + return tf.Variable(initial) + +#偏置 +def bias_variable(shape): + initial = tf.constant(0.1,shape=shape) + return tf.Variable(initial) + +#卷积处理 变厚过程 +def conv2d(x,W): + return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') + +#pool 长宽缩小一倍 +def max_poll_2x2(x): + return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') + + +#define placeholder for inputs to network +xs=tf.placeholder(tf.float32,[None,25]) #原始数据的维度：25 +ys=tf.placeholder(tf.float32,[None,1]) #输出数据维度：1 + +keep_prob=tf.placeholder(tf.float32) #dropout的比例 + +x_image=tf.reshape(xs,[-1,5,5,1]) #原始数据16列变成二维图片5*5 + + +#conv1 layer 第一卷积层 +W_conv1 = weight_variable([2,2,1,32]) +b_conv1= bias_variable([32]) +h_conv1=tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1) + + +#conv2 layer 第二卷积层 +W_conv2 = weight_variable([2,2,32,64]) +b_conv2= bias_variable([64]) +h_conv2=tf.nn.relu(conv2d(h_conv1,W_conv2)+b_conv2) + +#fcl layer 全连接层 +W_fcl=weight_variable([5*5*64,512]) +b_fcl=bias_variable([512]) + +h_pool2_flat=tf.reshape(h_conv2,[-1,5*5*64]) +h_fcl=tf.nn.relu(tf.matmul(h_pool2_flat,W_fcl)+b_fcl) +h_fcl_drop=tf.nn.dropout(h_fcl,keep_prob) + +W_fc2=weight_variable([512,1]) +b_fc2=bias_variable([1]) +prediction=tf.matmul(h_fcl_drop,W_fc2)+b_fc2 + +cross_entropy=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1])) +train_step=tf.train.AdamOptimizer(0.01).minimize(cross_entropy) + + +ITER=3000 + +#draw pics +fig=plt.figure() +ax=fig.add_subplot(1,1,1) +l1=ax.plot(range(66),y_train[0:66],'b',label='train_real_price') +ax.set_ylim([0,50]) +plt.ion() +plt.show() + +def fit(X,y,ax,n,keep_prob): + init=tf.global_variables_initializer() + feed_dict_train={ys:y,xs:X,keep_prob:1} ################################## + feed_dict_test={ys:y_test,xs:X_test,keep_prob:1} + with tf.Session() as sess: + saver=tf.train.Saver(tf.global_variables(),max_to_keep=15) + merged=tf.summary.merge_all() + writer=tf.summary.FileWriter(logdir="bp_stock_log",graph=sess.graph) + sess.run(init) + for i in range(n): + _loss,_=sess.run([cross_entropy,train_step],feed_dict=feed_dict_train) + '''if i%100==0: + print("epoch:%d\tloss:%.5f" %(i,_loss)) + y_pred=sess.run(pred,feed_dict=feed_dict_train) + rs=sess.run(merged,feed_dict=feed_dict_train) + writer.add_summary(summary=rs,global_step=i) + saver.save(sess=sess,save_path="bp_stock_model/bp_stock.model",global_step=i) + try: + ax.lines.remove(lines[0]) + except: + pass + lines=ax.plot(range(66),y_pred[0:66],'r--') + path="./picture/"+str(i)+'.jpg' + plt.savefig(path) + if i==(n-1): + y_pred=sess.run(pred,feed_dict={xs:X_test,ys:y_test,keep_prob_s:keep_prob}) + try: + ax.lines.remove(lines[0]) + except: + pass + ax.plot(range(40), y_test, 'yellow') + lines=ax.plot(range(40),y_pred[0:40],'r--') + path = "./picture/test.jpg" + plt.savefig(path)''' + + if i%100==0: + print("epoch:%d\tloss:%.5f" %(i,_loss)) + y_pred=sess.run(prediction,feed_dict=feed_dict_train) + # rs=sess.run(merged,feed_dict=feed_dict_test) + # writer.add_summary(summary=rs,global_step=i) + # saver.save(sess=sess,save_path="bp_stock_model/bp_stock.model",global_step=i) + try: + ax.lines.remove(lines[0]) + except: + pass + lines=ax.plot(range(66),y_pred[0:66],'g--',lw=2,label='train_predict_price') + plt.legend() + plt.pause(1) + + + y_pred = sess.run(prediction, feed_dict=feed_dict_test) + # 计算准确率 + count = 0 + index = [] + for j in range(x_test.shape[0]): + if (y_test[j:j + 1] - x_test[j:j + 1]) * (y_pred[j:j + 1] - x_test[j:j + 1]) >= 0: + count += 1.0 + index.append(j) + print('准确率：',count / x_test.shape[0]) + + + if i==(n-1): + y_pred=sess.run(prediction,feed_dict=feed_dict_test) + try: + ax.lines.remove(lines[0]) + except: + pass + ax.lines.remove(l1[0]) + ax.plot(range(x_test.shape[0]),x_test,'black',lw=2,label='test_present_price') + ax.plot(range(x_test.shape[0]), y_test, 'yellow',lw=3,label='test_real_price') + lines=ax.plot(range(x_test.shape[0]),y_pred[0:x_test.shape[0]],'r--',label='test_predict_price') + plt.legend() + + #计算准确率 + count=0 + index=[] + for j in range(x_test.shape[0]): + print(j,y_test[j:j+1]) + print(j,y_pred[j:j + 1]) + print(j,x_test[j:j + 1]) + + if (y_test[j:j+1]-x_test[j:j + 1])*(y_pred[j:j+1]-x_test[j:j + 1])>=0: + count+=1.0 + index.append(j) + print(count/x_test.shape[0]) + print(index) + plt.pause(100) + + #saver.save(sess=sess, save_path="nn_boston_model/nn_boston.model", global_step=n) # 保存模型 + +fit(X=X_train,y=y_train,n=ITER,keep_prob=keep_prob,ax=ax) \ No newline at end of file diff --git a/stockPricePredict/model_LSTM.py b/stockPricePredict/model_LSTM.py new file mode 100644 index 0000000..1e8961d --- /dev/null +++ b/stockPricePredict/model_LSTM.py @@ -0,0 +1 @@ +import tensorflow as tf \ No newline at end of file diff --git a/stockPricePredict/modify_data_set.py b/stockPricePredict/modify_data_set.py new file mode 100644 index 0000000..0b1d919 --- /dev/null +++ b/stockPricePredict/modify_data_set.py @@ -0,0 +1,26 @@ +import pandas as pd +data2015=pd.read_excel("./data_set/data_set_2015.xlsx") +data2016=pd.read_excel("./data_set/data_set_2016.xlsx") +data2017=pd.read_excel("./data_set/data_set_2017.xlsx") +data2018=pd.read_excel("./data_set/data_set_2018.xlsx") + +# for i in range(data2015.shape[0]): +# data2015.ix[i,'股东权益比率']=100-data2015.ix[i,'资产负债率'] +# +# pf=pd.DataFrame(data2015) +# pf.to_excel('./aa.xlsx') + +for i in range(data2016.shape[0]): + data2016.ix[i,'股东权益比率']=100-data2016.ix[i,'资产负债率'] +pf=pd.DataFrame(data2016) +pf.to_excel('./bb.xlsx') + +for i in range(data2017.shape[0]): + data2017.ix[i,'股东权益比率']=100-data2017.ix[i,'资产负债率'] +pf=pd.DataFrame(data2017) +pf.to_excel('./cc.xlsx') + +for i in range(data2018.shape[0]): + data2018.ix[i,'股东权益比率']=100-data2018.ix[i,'资产负债率'] +pf=pd.DataFrame(data2018) +pf.to_excel('./dd.xlsx') \ No newline at end of file diff --git a/stockPricePredict/picture/0.jpg b/stockPricePredict/picture/0.jpg new file mode 100644 index 0000000..014ed1c Binary files /dev/null and b/stockPricePredict/picture/0.jpg differ diff --git a/stockPricePredict/picture/100.jpg b/stockPricePredict/picture/100.jpg new file mode 100644 index 0000000..a19347d Binary files /dev/null and b/stockPricePredict/picture/100.jpg differ diff --git a/stockPricePredict/picture/1000.jpg b/stockPricePredict/picture/1000.jpg new file mode 100644 index 0000000..a96d02e Binary files /dev/null and b/stockPricePredict/picture/1000.jpg 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0000000..46ea9bb Binary files /dev/null and b/stockPricePredict/stock_price/stock_price_2016.xlsx differ diff --git a/stockPricePredict/stock_price/stock_price_2017.xlsx b/stockPricePredict/stock_price/stock_price_2017.xlsx new file mode 100644 index 0000000..a83b3ed Binary files /dev/null and b/stockPricePredict/stock_price/stock_price_2017.xlsx differ diff --git a/stockPricePredict/stock_price/stock_price_2018.xlsx b/stockPricePredict/stock_price/stock_price_2018.xlsx new file mode 100644 index 0000000..eeaa2cb Binary files /dev/null and b/stockPricePredict/stock_price/stock_price_2018.xlsx differ diff --git a/stockPricePredict/test.py b/stockPricePredict/test.py new file mode 100644 index 0000000..dc9d5c3 --- /dev/null +++ b/stockPricePredict/test.py @@ -0,0 +1,62 @@ +# import matplotlib.pyplot as plt +# fig=plt.figure() +# ax=fig.add_subplot(221) +# line=ax.plot([0,1],[0,1],'b') +# ax.set_ylim([0,1]) +# ax.set_xlim([0,1]) +# +# plt.ion() +# plt.show() +# for i in range(5): +# try: +# ax.lines.remove(lines[0]) +# except: +# pass +# lines = ax.plot([0,1],[0,i/5.0], 'r--') +# plt.pause(1) + +# import tensorflow as tf +# import matplotlib.pyplot as plt +# plt.figure(1) # 第一张图 +# plt.subplot(211) # 第一张图中的第一张子图 +# plt.plot([1,2,3]) +# plt.subplot(212) # 第一张图中的第二张子图 +# plt.plot([4,5,6]) +# plt.figure(2) # 第二张图 +# plt.plot([4,5,6]) # 默认创建子图subplot(111) +# plt.figure(1) # 切换到figure 1 ; 子图subplot(212)仍旧是当前图 +# plt.subplot(211) # 令子图subplot(211)成为figure1的当前图 +# plt.title('Easy as 1,2,3') # 添加subplot 211 的标题 +# plt.show() + +# import tensorflow as tf +# import matplotlib.pyplot as plt +# plt.figure(1) # 第一张图 +# plt.subplot(211) # 第一张图中的第一张子图 +# plt.plot([1,2,3]) +# plt.subplot(212) # 第一张图中的第二张子图 +# plt.plot([4,5,6]) +# plt.figure(2) # 第二张图 +# plt.plot([4,5,6]) # 默认创建子图subplot(111) +# plt.figure(1) # 切换到figure 1 ; 子图subplot(212)仍旧是当前图 +# plt.subplot(211) # 令子图subplot(211)成为figure1的当前图 +# plt.title('Easy as 1,2,3') # 添加subplot 211 的标题 +# plt.show() + +import numpy as np +import matplotlib.pyplot as plt + +plt.axis([0, 100, 0, 1]) +plt.ion() + +xs = [0, 0] +ys = [1, 1] + +for i in range(10): + y = np.random.random() + xs[0] = xs[1] + ys[0] = ys[1] + xs[1] = i + ys[1] = y + plt.plot(xs, ys) + plt.pause(0.1) diff --git a/stockPricePredict/test2.py b/stockPricePredict/test2.py new file mode 100644 index 0000000..aaddce6 --- /dev/null +++ b/stockPricePredict/test2.py @@ -0,0 +1,12 @@ +import openpyxl +path='./accuracy_log.xlsx' +wb=openpyxl.load_workbook(path) +ws=wb['Sheet1'] +value=['当前训练轮次','预测股价上涨','预测上涨且正确','预测股票下跌','预测下跌且正确','准确率'] +ws.append(value) +value=[] +for i in range(6): + value.append(i) +ws.append(value) +print(value) +wb.save(path) \ No newline at end of file diff --git a/test/justfortest.txt b/test/justfortest.txt deleted file mode 100644 index e69de29..0000000 diff --git "a/\350\265\265\344\270\200\351\270\277/Original_last_key.csv" "b/\350\265\265\344\270\200\351\270\277/Original_last_key.csv" new file mode 100644 index 0000000..11ad905 --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/Original_last_key.csv" @@ -0,0 +1,31 @@ +12.854,1,15.315168,1,13.484 +3.998,0,3.2655754,0,3.89 +4.204,0,3.4300113,0,4.066 +54.808,1,56.857784,1,58.67 +2.528,0,0.5582583,0,1.638 +4.198,0,2.6771631,0,3.278 +5.91,0,5.1022906,0,5.886 +5.478,0,3.72656,0,4.67 +5.124,0,3.5964203,0,4.31 +8.738,1,8.040789,0,9.472 +10.196,0,9.123481,0,9.716 +34.354,1,40.46222,1,35.232 +3.202,1,2.5267227,0,3.96 +10.602,1,12.411624,1,11.328 +14.046,1,17.448948,1,14.674 +11.304,1,10.807307,0,11.582 +9.834,1,11.332066,1,9.838 +16.966,1,18.40277,1,17.2 +4.93,1,4.3266063,0,5.126 +8.21,1,8.103687,0,8.582 +8.056,0,7.678814,0,7.74 +13.108,0,12.082008,0,10.732 +9.13,0,8.838147,0,9.094 +19.82,0,19.570087,0,18.388 +8.27,0,6.798372,0,7.492 +14.172,1,19.519445,1,15.792 +3.936,0,3.5111508,0,3.894 +8.086,0,6.1954904,0,7.8 +3.896,0,3.693924,0,3.58 +7.65,0,7.290596,0,6.454 +10.346,1,12.17953,1,11.114 diff --git "a/\350\265\265\344\270\200\351\270\277/README.md" "b/\350\265\265\344\270\200\351\270\277/README.md" new file mode 100644 index 0000000..8b13789 --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/README.md" @@ -0,0 +1 @@ + diff --git "a/\350\265\265\344\270\200\351\270\277/Standard_Test_data.csv" "b/\350\265\265\344\270\200\351\270\277/Standard_Test_data.csv" new file mode 100644 index 0000000..32342b5 --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/Standard_Test_data.csv" @@ -0,0 +1,31 @@ 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"a/\350\265\265\344\270\200\351\270\277/index_num.pkl" "b/\350\265\265\344\270\200\351\270\277/index_num.pkl" new file mode 100644 index 0000000..fd6f268 Binary files /dev/null and "b/\350\265\265\344\270\200\351\270\277/index_num.pkl" differ diff --git "a/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/0pytdx.txt" "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/0pytdx.txt" new file mode 100644 index 0000000..3d402f8 --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/0pytdx.txt" @@ -0,0 +1,75 @@ +DIR D:\new_tdx\T0002\export /b >D:\name.csv + +hqreader -d hf -o D:/TDX/13.csv D:/new_tdx/vipdoc/cw/gpcw20071231.dat +hqreader -d hf -o D:/TDX/17.csv D:/new_tdx/vipdoc/cw/gpcw20081231.dat +hqreader -d hf -o D:/TDX/21.csv D:/new_tdx/vipdoc/cw/gpcw20091231.dat +hqreader -d hf -o D:/TDX/25.csv D:/new_tdx/vipdoc/cw/gpcw20101231.dat +hqreader -d hf -o D:/TDX/29.csv D:/new_tdx/vipdoc/cw/gpcw20111231.dat +hqreader -d hf -o D:/TDX/33.csv D:/new_tdx/vipdoc/cw/gpcw20121231.dat +hqreader -d hf -o D:/TDX/37.csv D:/new_tdx/vipdoc/cw/gpcw20131231.dat +hqreader -d hf -o D:/TDX/41.csv D:/new_tdx/vipdoc/cw/gpcw20141231.dat +hqreader -d hf -o D:/TDX/45.csv D:/new_tdx/vipdoc/cw/gpcw20151231.dat +hqreader -d hf -o D:/TDX/49.csv D:/new_tdx/vipdoc/cw/gpcw20161231.dat +hqreader -d hf -o D:/TDX/53.csv D:/new_tdx/vipdoc/cw/gpcw20171231.dat + + +hqreader -d hf -o D:/TDX/10.csv D:/new_tdx/vipdoc/cw/gpcw20070331.dat +hqreader -d hf -o D:/TDX/11.csv D:/new_tdx/vipdoc/cw/gpcw20070630.dat +hqreader -d hf -o D:/TDX/12.csv D:/new_tdx/vipdoc/cw/gpcw20070930.dat +hqreader -d hf -o D:/TDX/13.csv D:/new_tdx/vipdoc/cw/gpcw20071231.dat + +hqreader -d hf -o D:/TDX/14.csv D:/new_tdx/vipdoc/cw/gpcw20080331.dat +hqreader -d hf -o D:/TDX/15.csv D:/new_tdx/vipdoc/cw/gpcw20080630.dat +hqreader -d hf -o D:/TDX/16.csv D:/new_tdx/vipdoc/cw/gpcw20080930.dat +hqreader -d hf -o D:/TDX/17.csv D:/new_tdx/vipdoc/cw/gpcw20081231.dat + +hqreader -d hf -o D:/TDX/18.csv D:/new_tdx/vipdoc/cw/gpcw20090331.dat +hqreader -d hf -o D:/TDX/19.csv D:/new_tdx/vipdoc/cw/gpcw20090630.dat +hqreader -d hf -o D:/TDX/20.csv D:/new_tdx/vipdoc/cw/gpcw20090930.dat +hqreader -d hf -o D:/TDX/21.csv D:/new_tdx/vipdoc/cw/gpcw20091231.dat + +hqreader -d hf -o D:/TDX/22.csv D:/new_tdx/vipdoc/cw/gpcw20100331.dat +hqreader -d hf -o D:/TDX/23.csv D:/new_tdx/vipdoc/cw/gpcw20100630.dat +hqreader -d hf -o D:/TDX/24.csv D:/new_tdx/vipdoc/cw/gpcw20100930.dat +hqreader -d hf -o D:/TDX/25.csv D:/new_tdx/vipdoc/cw/gpcw20101231.dat + +hqreader -d hf -o D:/TDX/26.csv D:/new_tdx/vipdoc/cw/gpcw20110331.dat +hqreader -d hf -o D:/TDX/27.csv D:/new_tdx/vipdoc/cw/gpcw20110630.dat +hqreader -d hf -o D:/TDX/28.csv D:/new_tdx/vipdoc/cw/gpcw20110930.dat +hqreader -d hf -o D:/TDX/29.csv D:/new_tdx/vipdoc/cw/gpcw20111231.dat + +hqreader -d hf -o D:/TDX/30.csv D:/new_tdx/vipdoc/cw/gpcw20120331.dat +hqreader -d hf -o D:/TDX/31.csv D:/new_tdx/vipdoc/cw/gpcw20120630.dat +hqreader -d hf -o D:/TDX/32.csv D:/new_tdx/vipdoc/cw/gpcw20120930.dat +hqreader -d hf -o D:/TDX/33.csv D:/new_tdx/vipdoc/cw/gpcw20121231.dat + +hqreader -d hf -o D:/TDX/34.csv D:/new_tdx/vipdoc/cw/gpcw20130331.dat +hqreader -d hf -o D:/TDX/35.csv D:/new_tdx/vipdoc/cw/gpcw20130630.dat +hqreader -d hf -o D:/TDX/36.csv D:/new_tdx/vipdoc/cw/gpcw20130930.dat +hqreader -d hf -o D:/TDX/37.csv D:/new_tdx/vipdoc/cw/gpcw20131231.dat + +hqreader -d hf -o D:/TDX/38.csv D:/new_tdx/vipdoc/cw/gpcw20140331.dat +hqreader -d hf -o D:/TDX/39.csv D:/new_tdx/vipdoc/cw/gpcw20140630.dat +hqreader -d hf -o D:/TDX/40.csv D:/new_tdx/vipdoc/cw/gpcw20140930.dat +hqreader -d hf -o D:/TDX/41.csv D:/new_tdx/vipdoc/cw/gpcw20141231.dat + +hqreader -d hf -o D:/TDX/42.csv D:/new_tdx/vipdoc/cw/gpcw20150331.dat +hqreader -d hf -o D:/TDX/43.csv D:/new_tdx/vipdoc/cw/gpcw20150630.dat +hqreader -d hf -o D:/TDX/44.csv D:/new_tdx/vipdoc/cw/gpcw20150930.dat +hqreader -d hf -o D:/TDX/45.csv D:/new_tdx/vipdoc/cw/gpcw20151231.dat + +hqreader -d hf -o D:/TDX/46.csv D:/new_tdx/vipdoc/cw/gpcw20160331.dat +hqreader -d hf -o D:/TDX/47.csv D:/new_tdx/vipdoc/cw/gpcw20160630.dat +hqreader -d hf -o D:/TDX/48.csv D:/new_tdx/vipdoc/cw/gpcw20160930.dat +hqreader -d hf -o D:/TDX/49.csv D:/new_tdx/vipdoc/cw/gpcw20161231.dat + +hqreader -d hf -o D:/TDX/50.csv D:/new_tdx/vipdoc/cw/gpcw20170331.dat +hqreader -d hf -o D:/TDX/51.csv D:/new_tdx/vipdoc/cw/gpcw20170630.dat +hqreader -d hf -o D:/TDX/52.csv D:/new_tdx/vipdoc/cw/gpcw20170930.dat +hqreader -d hf -o D:/TDX/53.csv D:/new_tdx/vipdoc/cw/gpcw20171231.dat + +hqreader -d hf -o D:/TDX/54.csv D:/new_tdx/vipdoc/cw/gpcw20180331.dat + + + +006 199 202 095 001 074 238 \ No newline at end of file diff --git "a/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/1publish_date.txt" "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/1publish_date.txt" new file mode 100644 index 0000000..1e3318b --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/1publish_date.txt" @@ -0,0 +1,41 @@ +import baostock as bs +import pandas as pd +data = pd.read_csv('D:\\name.csv',header=None) +data = data.values +p=data.shape[0]# +puda = pd.DataFrame() + +# ½ϵͳ +lg = bs.login() +# ʾ½Ϣ +print('login respond error_code:'+lg.error_code) +print('login respond error_msg:'+lg.error_msg) + +# ѯƵֵָӯ +for i in range(0,p): + for j in range(2007, 2018): + for k in range(4, 5): + if j==2018 and k==2: + break + temp=[] + profit_tem = [j,k,data[i, 0]] + profit_list=[] + rs_profit = bs.query_balance_data(code=data[i, 0], year=j, quarter=k) + while (rs_profit.error_code == '0') & rs_profit.next(): + temp=rs_profit.get_row_data() + profit_tem.append(temp[1]) + print(profit_tem) + + if len(profit_tem)!=4: + profit_tem.append('') + profit_list.append(profit_tem) + puda_profit1 = pd.DataFrame(profit_list, columns=None) + puda=puda.append(puda_profit1,ignore_index=True,sort=False) + +date=pd.to_datetime(puda.iloc[:,3],format='%Y/%m/%d') +date=date.dt.strftime('%Y%m%d') +puda.iloc[:,3]=date +puda.to_csv("D:\\publish_date.csv", encoding="gbk", index=0) +# dzϵͳ +bs.logout() + diff --git "a/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/2balance_sheet.txt" "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/2balance_sheet.txt" new file mode 100644 index 0000000..5853a41 --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/2balance_sheet.txt" @@ -0,0 +1,86 @@ + +import pandas as pd +import glob,os,sys +import numpy as np +from sklearn.preprocessing import MinMaxScaler + +name = pd.read_csv('D:\\name1.csv',header=None) +dd = pd.read_csv('D:\\publish_date.csv',header=0) +name = name.values +result = pd.DataFrame() +all_files=glob.glob('D:/TDX/*.csv') +all_files.sort() +data_all = [] +print(all_files) + +for file in all_files: + data_temp = pd.read_csv(file, header=0, index_col=None) + data_temp.drop_duplicates(['code'], inplace=True, keep='first') + data_temp.set_index('code', inplace=True) + data_all.append(data_temp) + +for z in range(0,name.shape[0]): + i = 0 + for j in range(2007, 2018): + for k in range(4, 5): + if j == 2018 and k == 2: + break + tem = [j, k, name[z, 0]] + list = [] + data = data_all[i] + i = i + 1 + + try: + data = data.loc[name[z, 0]] + except: + for u in range(0, 13): + tem.append('') + list.append(tem) + result_profit1 = pd.DataFrame(list, columns=None) + result = result.append(result_profit1, ignore_index=True, sort=False) + else: + + tem.append('') + tem.append(data[0]) + tem.append(data['col159']) + tem.append(data['col160']) + tem.append(data['col172']) + tem.append(data['col173']) + tem.append(data['col174']) + tem.append(data['col72']) + tem.append(data['col73']) + tem.append(data['col96']) + tem.append(data['col150']) + tem.append(data['col158']) + tem.append(data['col238']) + + list.append(tem) + result_profit1 = pd.DataFrame(list, columns=None) + result = result.append(result_profit1, ignore_index=True, sort=False) +result.iloc[:,3]=dd.iloc[:,3] + +for i in range(0,result.shape[0]): + for j in range(5,result.shape[1]): + if result.iloc[i,j]=='': + result.iloc[i, j]=np.nan + elif result.iloc[i,j]<-10000000000000: + result.iloc[i, j]=np.nan + + +result=result.dropna() + +for i in range(0,result.shape[0]): + result.iloc[i,10]=result.iloc[i,10]/result.iloc[i,15] + result.iloc[i, 11] = result.iloc[i, 11] / result.iloc[i, 15] + result.iloc[i, 12] = result.iloc[i, 12] / result.iloc[i, 15] + result.iloc[i, 13] = result.iloc[i, 13] / result.iloc[i, 15] + result.iloc[i, 14] = result.iloc[i, 14] / result.iloc[i, 15] +result=result.iloc[:,0:15] +for i in range(5, 15): + temp = result.iloc[:, i].values.reshape(-1, 1) + scaler = MinMaxScaler().fit(temp) + temp = scaler.transform(temp) + result.iloc[:, i] = temp +result.to_csv("D:\\balance_sheet.csv", encoding="gbk", index=False) + + diff --git "a/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/\346\227\245\346\234\237\347\274\226\345\217\267\345\272\217\345\210\227\345\214\226.txt" "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/\346\227\245\346\234\237\347\274\226\345\217\267\345\272\217\345\210\227\345\214\226.txt" new file mode 100644 index 0000000..6b202b6 --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/\346\227\245\346\234\237\347\274\226\345\217\267\345\272\217\345\210\227\345\214\226.txt" @@ -0,0 +1,21 @@ +import pandas as pd +import pickle as pickle +data = pd.read_csv('D:\\000001.csv', header=None) +data = data.values +header=data[:,0] +val=[[i for i in range(0,4034)]] +re = pd.DataFrame(val,columns=header) +header=[i for i in range(0,4034)] +val=[data[:,0]] +re1 = pd.DataFrame(val,columns=header) + + +with open('index_date.pkl','wb+') as f: + pickle.dump(re, f, 2) +with open('index_num.pkl','wb+') as f: + pickle.dump(re1, f, 2) + +pkl_file = open('index_date.pkl', 'rb') + +data1 = pickle.load(pkl_file) +print(data1) \ No newline at end of file diff --git "a/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/\346\227\245\347\272\277\345\272\217\345\210\227\345\214\226.txt" "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/\346\227\245\347\272\277\345\272\217\345\210\227\345\214\226.txt" new file mode 100644 index 0000000..a62dd33 --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/\346\225\260\346\215\256\345\244\204\347\220\206\344\273\243\347\240\201/\346\227\245\347\272\277\345\272\217\345\210\227\345\214\226.txt" @@ -0,0 +1,69 @@ +import pandas as pd +import pickle as pickle + +name = pd.read_csv('D:\\name.csv',header=None) +name = name.values +pkl_file = open('index_date.pkl', 'rb') +arr = pickle.load(pkl_file) +pkl1_file = open('index_num.pkl', 'rb') +arr1 = pickle.load(pkl1_file) +#һյdataframe + +for k in range(0,name.shape[0]): + result = pd.DataFrame() + data = pd.read_csv('D:\\new_tdx\\T0002\\export\\' + name[k, 0] + '.txt', header=None,encoding='gb2312') + data.drop([data.shape[0]-1],inplace=True) + data.to_csv('D:\\SH\\' +name[k, 0] + '.csv',encoding = "utf-8",index=False,header=False) + data = pd.read_csv('D:\\SH\\' + name[k, 0] + '.csv', header=None) + data1 = data.values + p = 20020104.0 + + if data1[0, 0] != p: + list = [] + tem = [] + list.append('') + list.append('') + list.append(p) + tem.append(list) + result = result.append(pd.DataFrame(tem, columns=['close', 'volume', 'index']), ignore_index=True, sort=False) + list = [] + tem = [] + + for i in range(0, data1.shape[0]): + a = arr.loc[:, data1[i, 0]] + b = arr.loc[:, p] + list = [] + tem = [] + for j in range(b[0] + 1, a[0]): + list.append('') + list.append('') + list.append(arr1.loc[0, j]) + if len(list) != 0: + tem.append(list) + result = result.append(pd.DataFrame(tem, columns=['close', 'volume', 'index']), ignore_index=True, + sort=False) + list = [] + tem = [] + + p = data1[i, 0] + list.append(data.iloc[i, 4]) + list.append(data.iloc[i, 5]) + list.append(p) + + tem.append(list) + result = result.append(pd.DataFrame(tem, columns=['close', 'volume', 'index']), ignore_index=True, sort=False) + result = result.set_index('index') + + with open(name[k, 0] + '.pkl', 'wb+') as f: + pickle.dump(result, f, 2) + result.to_csv(name[k, 0] + '.csv',encoding = "utf-8") + + + + + + + + + + diff --git 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+SH.900937 +SH.900938 +SH.900939 +SH.900940 +SH.900941 +SH.900942 +SH.900943 +SH.900945 +SH.900946 +SH.900947 +SH.900948 +SH.900951 +SH.900952 +SH.900953 +SH.900955 +SH.900956 +SH.900957 diff --git "a/\350\265\265\344\270\200\351\270\277/\351\242\204\346\265\213.py" "b/\350\265\265\344\270\200\351\270\277/\351\242\204\346\265\213.py" new file mode 100644 index 0000000..0c02413 --- /dev/null +++ "b/\350\265\265\344\270\200\351\270\277/\351\242\204\346\265\213.py" @@ -0,0 +1,372 @@ +from sklearn.preprocessing import MinMaxScaler +import pickle as pickle +import pandas as pd +import numpy as np +import tensorflow as tf +import math +from copy import deepcopy + +OUTPUT_NODE = 1 +BATCH_SIZE = 50 +LEARNING_RATE_BASE = 1.0 +LEARNING_RATE_DECAY = 0.99 +REGULARIZATION_RATE = 0.00001 +MOVING_AVERAGE_DECAY = 0.99 + + +def get_weight(shape, lambda1): + weight_initializer = tf.variance_scaling_initializer(mode="fan_avg", distribution="uniform", scale=1) + var = tf.Variable(weight_initializer(shape)) + tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(lambda1)(var)) + return var + + +def train(length,traning_steps): + global INPUT_NODE + global LAYER1_NODE + global LAYER2_NODE + global LAYER3_NODE + INPUT_NODE=result1.shape[1] + LAYER1_NODE = 7 + LAYER2_NODE = 7 + LAYER3_NODE = 7 + LAYER4_NODE = 7 + + x = tf.placeholder(tf.float32,shape=[None,INPUT_NODE],name='x-input') + y_ = tf.placeholder(tf.float32, shape=[None,1],name='y-input') + + weight_initializer = tf.variance_scaling_initializer(mode="fan_avg", distribution="uniform", scale=1) + bias_initializer = tf.zeros_initializer() + W_hidden_1 = tf.Variable(weight_initializer([INPUT_NODE, LAYER1_NODE])) + tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)(W_hidden_1)) + bias_hidden_1 = tf.Variable(bias_initializer([LAYER1_NODE])) + W_hidden_2 = tf.Variable(weight_initializer([LAYER1_NODE, LAYER2_NODE])) + tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)(W_hidden_2)) + bias_hidden_2 = tf.Variable(bias_initializer([LAYER2_NODE])) + W_hidden_3 = tf.Variable(weight_initializer([LAYER2_NODE, LAYER3_NODE])) + tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)(W_hidden_3)) + bias_hidden_3 = tf.Variable(bias_initializer([LAYER3_NODE])) + + W_hidden_4 = tf.Variable(weight_initializer([LAYER3_NODE, LAYER4_NODE])) + tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)(W_hidden_4)) + bias_hidden_4 = tf.Variable(bias_initializer([LAYER4_NODE])) + + W_out = tf.Variable(weight_initializer([LAYER4_NODE, 1])) + tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(REGULARIZATION_RATE)(W_out)) + bias_out = tf.Variable(bias_initializer([1])) + + hidden_1 = tf.nn.relu(tf.add(tf.matmul(x, W_hidden_1), bias_hidden_1)) + hidden_2 = tf.nn.relu(tf.add(tf.matmul(hidden_1, W_hidden_2), bias_hidden_2)) + hidden_3 = tf.nn.relu(tf.add(tf.matmul(hidden_2, W_hidden_3), bias_hidden_3)) + hidden_4 = tf.nn.relu(tf.add(tf.matmul(hidden_3, W_hidden_4), bias_hidden_4)) + + out =tf.add(tf.matmul(hidden_4, W_out), bias_out) + + global_step = tf.Variable(0, trainable=False) + variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) + variables_averages_op = variable_averages.apply(tf.trainable_variables()) + + mse = tf.reduce_mean(tf.square(y_ - out)) + tf.add_to_collection('losses', mse) + loss = tf.add_n(tf.get_collection('losses')) + #learning_rate = tf.train.exponential_decay(LEARNING_RATE_BASE,global_step,500, LEARNING_RATE_DECAY,staircase=False) + train_step = tf.train.AdamOptimizer(0.00001).minimize(loss) + #train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) + with tf.control_dependencies([train_step, variables_averages_op]): + train_op = tf.no_op(name='train') + + with tf.Session() as sess: + tf.global_variables_initializer().run() + for i in range(0,traning_steps): + start = (i * BATCH_SIZE) % size + end = min(start + BATCH_SIZE, size) + + sess.run(train_step, feed_dict={x: result1[start:end], y_: result2[start:end]}) + + if i % 1000 == 0: + pre=sess.run(out, feed_dict={x: result3}) + print(scaler2.inverse_transform(pre)-1) + #print(sess.run(learning_rate)) + total_mse = sess.run(loss, feed_dict={x: result1, y_: result2}) + print("After %d training step(s), loss on training batch is %g." % (i, math.sqrt(total_mse))) + total_mse = sess.run(loss, feed_dict={x: result3, y_: result4}) + print("After %d training step(s), loss on testing batch is %g, average is %g.\nStandard average is %g\n" % (i, math.sqrt(total_mse),np.mean(scaler2.inverse_transform(pre))-1,average4)) + + + +def find_valid(seq,begin,data1): + i=0 + temp=begin + if seq==1: + for i in range(begin, 4034): + if data1.iloc[i,0] != '' : + return data1.index[i] + if i == 4034: + for i in range(temp, -1, -1): + if data1.iloc[i, 0] != '': + return data1.index[i] + return 0 + elif seq==-1: + for i in range(begin, -1, -1): + if data1.iloc[i, 0] != '': + return data1.index[i] + if i == 0: + for i in range(temp, 4034): + if data1.iloc[i,0] != '' : + return data1.index[i] + return 0 + + +def pretreatment(target_date, begin_date,length,interval,num,year,data): + global result1 + global result2 + global result3 + global result4 + global size + global scaler2 + global average4 + result1=pd.DataFrame() + result2 = pd.DataFrame() + result3 = pd.DataFrame() + result4 =pd.DataFrame() + tem5 = [] + name = pd.read_csv('D:\\name.csv', header=None) + name = name.values + + for k in range(0, num): + print("Gathering %s"%(name[k,0])) + retr = balance[balance.iloc[:, 2] == name[k, 0]] + if retr.empty: + continue + pkl_file = open('STOCK/'+name[k,0]+'.pkl', 'rb') + data1 = pickle.load(pkl_file) + + beginindex = data.loc[:, [begin_date]].values[0, 0] + terminal = data.loc[:, [target_date]].values[0, 0] + target_date = find_valid(1, terminal, data1) + + if target_date == 0: + continue + terminal = data.loc[:, [target_date]].values[0, 0] + ll=deepcopy(terminal) + + begin_date = find_valid(-1, beginindex, data1) + if begin_date == 0: + continue + beginindex = data.loc[:, [begin_date]].values[0, 0] + if (beginindex+length+2*interval)>=terminal: + continue + data2=deepcopy(data1) + data1 = data1[~data1.isin([''])] + data1 = data1.dropna(axis=0, how='any') + beginindex = data1.index.get_loc(begin_date) + terminal = data1.index.get_loc(target_date) + endindex = terminal - length - 2*interval + flag=0 + + loc=[] + + for i in range(0,year): + ll=ll-252 + if ll<0: + continue + for z in range(0,20): + if data2.iloc[ll+z, 0] != '': + loc.append(data2.index[ll+z]) + print(loc) + for i in range(len(loc)-1,-1,-1): + flag=0 + list3 = [] + tem3 = [] + list4 = [] + tem4 = [] + ter = data1.index.get_loc(loc[i]) + endate=data1.index[ter-interval] + #list3.append(name[k, 0]) + #list3.append(data1.index[ter - interval]) + #list4.append(name[k, 0]) + #list4.append(data1.index[ter - interval]) + for z in range(0,length): + list3.append(data1.iloc[ter - interval - length + z, 0]) + list4.append(data1.iloc[ter - interval - length + z, 1]) + + for g in range(0,retr.shape[0]): + if endate