├── China Merchants Bank.csv ├── LICENSE ├── README.md ├── dataprocess.py ├── pics ├── history.jpg ├── pred_stock.jpg └── price.jpg └── stock_pre.py /China Merchants Bank.csv: -------------------------------------------------------------------------------- 1 | Date,Close,Chg,Net Capital Inflow,Extra Large Single Net Inflow,Large Order Net Inflow,Net inflow of intermediate orders,Net inflow of small orders 2 | 2021-09-17,47.99,0.0116,71383500.0,44908800.0,26474700.0,78791500.0,-150000000.0 3 | 2021-09-22,46.15,-0.0383,-779000000.0,-388000000.0,-391000000.0,131000000.0,648000000.0 4 | 2021-09-23,45.58,-0.0124,-146000000.0,-281000000.0,135000000.0,-91392000.0,237000000.0 5 | 2021-09-24,45.51,-0.0015,53415000.0,5624200.0,47790800.0,-33374800.0,-20040200.0 6 | 2021-09-27,45.83,0.007,25565800.0,-8766500.0,34332200.0,-81307600.0,55741900.0 7 | 2021-09-28,47.16,0.029,394000000.0,421000000.0,-26993700.0,-108000000.0,-286000000.0 8 | 2021-09-29,48.30,0.0242,301000000.0,71063800.0,230000000.0,-43703800.0,-257000000.0 9 | 2021-09-30,47.19,-0.023,-115000000.0,-82451600.0,-32580400.0,81191200.0,33840800.0 10 | 2021-10-08,47.67,0.0102,30735400.0,52469600.0,-21734300.0,16078500.0,-46813800.0 11 | 2021-10-11,49.74,0.0434,375000000.0,209000000.0,166000000.0,-334000000.0,-40726700.0 12 | 2021-10-12,49.83,0.0018,361000000.0,177000000.0,185000000.0,-207000000.0,-155000000.0 13 | 2021-10-13,49.77,-0.0012,107000000.0,-31128800.0,138000000.0,-215000000.0,108000000.0 14 | 2021-10-14,49.10,-0.0135,-138000000.0,-120000000.0,-17825300.0,57252800.0,81044600.0 15 | 2021-10-15,50.05,0.0193,292000000.0,132000000.0,159000000.0,-125000000.0,-167000000.0 16 | 2021-10-18,48.60,-0.029,-91497000.0,-180000000.0,88614700.0,-88259700.0,180000000.0 17 | 2021-10-19,49.62,0.021,122000000.0,32206500.0,89827200.0,-55826300.0,-66207400.0 18 | 2021-10-20,49.14,-0.0097,33739500.0,-42861800.0,76601300.0,-39288800.0,5549300.0 19 | 2021-10-21,50.49,0.0275,360000000.0,152000000.0,208000000.0,-141000000.0,-219000000.0 20 | 2021-10-22,51.03,0.0107,98902200.0,47686300.0,51216000.0,95486500.0,-194000000.0 21 | 2021-10-25,51.33,0.0059,140000000.0,-77337000.0,218000000.0,-4905500.0,-135000000.0 22 | 2021-10-26,52.04,0.0138,81299900.0,26788700.0,54511200.0,129000000.0,-210000000.0 23 | 2021-10-27,51.26,-0.015,-374000000.0,-307000000.0,-66558700.0,163000000.0,210000000.0 24 | 2021-10-28,50.96,-0.0059,-140000000.0,-117000000.0,-22267000.0,125000000.0,14655800.0 25 | 2021-10-29,50.71,-0.0049,-58797800.0,30281300.0,-89079100.0,65588300.0,-6790400.0 26 | 2021-11-01,50.86,0.003,52327600.0,72166000.0,-19838400.0,17263500.0,-69591100.0 27 | 2021-11-02,48.99,-0.0368,-441000000.0,-232000000.0,-209000000.0,270000000.0,170000000.0 28 | 2021-11-03,48.44,-0.0112,-15507400.0,-142000000.0,126000000.0,57175500.0,-41668100.0 29 | 2021-11-04,48.49,0.001,-199000000.0,-36150400.0,-162000000.0,138000000.0,60932000.0 30 | 2021-11-05,48.30,-0.0039,-39189500.0,-170000000.0,130000000.0,94536700.0,-55347200.0 31 | 2021-11-08,48.04,-0.0054,-366000000.0,-286000000.0,-80080300.0,269000000.0,96998300.0 32 | 2021-11-09,47.80,-0.005,-246000000.0,-204000000.0,-41604200.0,174000000.0,71143800.0 33 | 2021-11-10,47.37,-0.009,-360000000.0,-87682700.0,-272000000.0,82368600.0,277000000.0 34 | 2021-11-11,49.56,0.0462,801000000.0,674000000.0,127000000.0,-353000000.0,-448000000.0 35 | 2021-11-12,49.14,-0.0085,15010000.0,-90572600.0,106000000.0,3348700.0,-18358800.0 36 | 2021-11-15,49.67,0.0108,83653600.0,59210400.0,24443200.0,533300.0,-84186800.0 37 | 2021-11-16,49.24,-0.0087,-129000000.0,-62585400.0,-66339500.0,50823500.0,78101500.0 38 | 2021-11-17,48.58,-0.0134,-232000000.0,-47318400.0,-185000000.0,65227400.0,167000000.0 39 | 2021-11-18,48.15,-0.0089,-90381200.0,7103600.0,-97484900.0,33597500.0,56783700.0 40 | 2021-11-19,49.07,0.0191,270000000.0,174000000.0,96575400.0,-38784500.0,-231000000.0 41 | 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使用LSTM进行多变量时间序列预测股价——学习记录 2 | ## 背景 3 | 作为一个资深的A股韭菜，一切有利于解套的手段都值得尝试一下。\ 4 | 偶然看到了一篇机器学习的博文，主要介绍利用深度学习方法(LSTM)进行多元时间序列预测。跟股价的模型有点相似，尝试一下，说干就干！ 5 | ## 目录 6 | ### 基本概念 7 | * 什么是时间序列分析？ 8 | * 什么是 LSTM？ 9 | 10 | **时间序列分析**：时间序列表示基于时间顺序的一系列数据。它可以是秒、分钟、小时、天、周、月、年。未来的数据将取决于它以前的值。主要有两种类型的时间序列分析—— 11 | * 单变量时间序列 12 | * 多元时间序列 13 | 14 | 在多元时间序列数据的情况下，将有不同类型的特征值并且目标数据将依赖于这些特征。 15 | ![](/pics/price.jpg) 16 | 17 | 例如在上图中看到的，在多元变量中将有多个列来对目标值进行预测，Close列为收盘价格，不仅取决于它以前的值，还取决于其他特征。因此，要预测即将到来的Close值，我们必须考虑包括目标列在内的所有列来对目标值进行预测。 18 | 19 | 在训练时，如果我们使用 7 列 [target,feature1, feature2, feature3, feature4, feature5, feature6] 来训练模型，我们需要为即将到来的预测日提供 6 列 [feature1, feature2, feature3, feature4, feature5, feature6] 20 | 21 | **LSTM**:是一个循环神经网络，能够处理长期依赖关系，不做过多的介绍，可以参考 [LSTM 长短期记忆人工神经网络](https://baike.baidu.com/item/%E9%95%BF%E7%9F%AD%E6%9C%9F%E8%AE%B0%E5%BF%86%E4%BA%BA%E5%B7%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C/17541107?fr=ge_ala) 22 | 23 | 24 | ### 预测招商银行股价实现思路 25 | ## 训练数据获取 26 | 从百度股市通中抓取招商银行的历史资金流向，保存文件 zhaoshang ![招商银行资金流向](/pics/history.jpg) \ 27 | 使用脚本 dataprocess.py进行数据格式化处理，处理后的数据参见 \ 28 | [股价数据预处理脚本 dataprocess.py](/dataprocess.py) \ 29 | [股价数据预处理结果 China Merchants Bank.csv](/China%20Merchants%20Bank.csv) 30 | 31 | ## 预测模型实现 32 | 这里我选取了 Close 列，收盘价作为将要预测的值 33 | ### 训练数据拆分 34 | ``` 35 | df=pd.read_csv("China Merchants Bank.csv",parse_dates=["Date"],index_col=[0]) 36 | df.head() 37 | print(df.head()) 38 | # print(df.tail()) 39 | # print(df.shape) 40 | 41 | test_split=round(len(df)*0.20) 42 | 43 | # print(test_split) 44 | 45 | df_for_training=df[:-test_split] 46 | df_for_testing=df[-test_split:] 47 | ``` 48 | 49 | ### 数据缩放 50 | 可以注意到数据范围非常大，并且它们没有在相同的范围内缩放，因此为了避免预测错误，让我们先使用MinMaxScaler缩放数据。 51 | 52 | ``` 53 | scaler=MinMaxScaler(feature_range=(0,1)) 54 | df_for_training_scaled = scaler.fit_transform(df_for_training) 55 | df_for_testing_scaled = scaler.fit_transform(df_for_testing) 56 | ``` 57 | 58 | ### 数据拆分 59 | 将数据拆分为X和Y，这是最重要的部分 60 | 61 | ``` 62 | def createXY(dataset,n_past): 63 | dataX = [] 64 | dataY = [] 65 | for i in range(n_past, len(dataset)): 66 | dataX.append(dataset[i - n_past:i, 0:dataset.shape[1]]) 67 | dataY.append(dataset[i,0]) 68 | return np.array(dataX),np.array(dataY) 69 | 70 | trainX,trainY=createXY(df_for_training_scaled,30) 71 | testX,testY=createXY(df_for_testing_scaled,30) 72 | ``` 73 | 74 | 解释 \ 75 | n_past是我们在预测下一个目标值时将在过去查看的步骤数。这里使用30，意味着将使用过去的30个值(包括目标列在内的所有特性)来预测第31个目标值。因此，在trainX中我们会有所有的特征值，而在trainY中我们只有目标值。 76 | 77 | for循环分解：对于训练，dataset = df_for_training_scaled, n_past=30 78 | * 当i=30时:data_X.addend (df_for_training_scaled[i - n_past:i, 0:df_for_training.shape[1]])， 79 | 从n_past开始的范围是30，所以第一次数据范围将是 [30 - 30,30,0:6] 相当于 [0:30,0:7] 80 | * 因此在dataX列表中，df_for_training_scaled[0:30,0:7]数组将第一次出现。 81 | * dataY.append(df_for_training_scaled[i,0])，i = 30，所以它将只取第30行开始的Close(因为在预测中，我们只需要Close列，所以列范围仅为0，表示Close列)。第一次在dataY列表中存储df_for_training_scaled[30,0]值。 82 | 83 | 所以包含7列的前30行存储在dataX中，只有Close列的第31行存储在dataY中。然后我们将dataX和dataY列表转换为数组，它们以数组格式在LSTM中进行训练 。 84 | 85 | ### 训练模型 86 | 87 | ``` 88 | def build_model(optimizer): 89 | grid_model = Sequential() 90 | grid_model.add(LSTM(50,return_sequences=True,input_shape=(30,7))) 91 | grid_model.add(LSTM(50)) 92 | grid_model.add(Dropout(0.2)) 93 | grid_model.add(Dense(1)) 94 | 95 | grid_model.compile(loss = 'mse',optimizer = optimizer) 96 | return grid_model 97 | 98 | grid_model = KerasRegressor(build_fn=build_model,verbose=1,validation_data=(testX,testY)) 99 | parameters = {'batch_size' : [16,20], 100 | 'epochs' : [8,10], 101 | 'optimizer' : ['adam','Adadelta'] } 102 | 103 | grid_search = GridSearchCV(estimator = grid_model, 104 | param_grid = parameters, 105 | cv = 2) 106 | 107 | grid_search = grid_search.fit(trainX,trainY) 108 | ``` 109 | 110 | ### 测试模型 111 | 利用测试数据集测试模型 112 | ``` 113 | my_model=grid_search.best_estimator_.model 114 | prediction=my_model.predict(testX) 115 | ``` 116 | 117 | ### 图形化预测结果和真实数据 118 | 119 | ``` 120 | plt.plot(original, color = 'red', label = 'Real Stock Price') 121 | plt.plot(pred, color = 'blue', label = 'Predicted Stock Price') 122 | plt.title('Stock Price Prediction') 123 | plt.xlabel('Time') 124 | plt.ylabel('China Merchants Bank Stock Price') 125 | plt.legend() 126 | plt.show() 127 | 128 | ``` 129 | 130 | **真实股价与预测股价对比** 131 | ![预测结果](/pics/pred_stock.jpg) 132 | 133 | ### 未解决的问题&一些思考 134 | 正如上文介绍，利用一些多元的参数，实现了对股价的“预测”，预测的趋势折现图与实际折线图趋势基本吻合，仔细的读者会发现，“预测” 是加了引号的，也就是说，当前只实现了基于历史多元参数的历史趋势的“预测”，而历史的数据是已经发生过的，实际值真实存在。并没有真正实现对未来数据的预测。 \ 135 | 而互联网上的大多数资料均出自以下参考链接中的中文翻译版本，甚至连代码和测试数据集都没有调整，并没有解决实际问题。 136 | 137 | 这里就出现了一个悖论，要对未来的股价进行预测，当前选取的多元变量也是还未发生，且未来不确定值的变量，这些数据如何产生？当前我还没搞懂，以下是几个猜测的方向，将继续探索。 138 | 139 | * 多元变量选取的错误，不应该选取未来不确定的参数作为多元变量？ 140 | * 预测模型的入参需要基础数据，预测未来的数据，入参从哪来？入参如果也是未来的数据怎么办？ 141 | 142 | 机器学习理论近几年发展较为迅速，但成功的应用于实际业务助力业务发展的案例却较少，可能受限于自己的知识范围，还没有接触到有比较成功的将机器学习应用于证券交易的案例，希望能发现学习一下 143 | 144 | ### 参考 145 | [Stock Price Prediction – Machine Learning Project in Python](https://data-flair.training/blogs/stock-price-prediction-machine-learning-project-in-python/) 146 | 147 | -------------------------------------------------------------------------------- /dataprocess.py: -------------------------------------------------------------------------------- 1 | import os 2 | 3 | with open("zhaoshang", "r") as file: 4 | lines = file.readlines() 5 | i = 0 6 | newLines = [] 7 | newLine = '' 8 | 9 | for line in lines: 10 | line = line.strip("\n") 11 | 12 | if line.find("/") != -1: 13 | line = line.replace("/","-") 14 | if line.find("+") != -1: 15 | line = line.replace("+","") 16 | 17 | if line.find("万") != -1: 18 | line = line.replace("万","") 19 | line = str(round(float(line) * 10000, 1)) 20 | elif line.find("亿") != -1: 21 | line = line.replace("亿","") 22 | line = str(round(float(line) * 100000000, 1)) 23 | elif line.find("%") != -1: 24 | line = line.replace("%","") 25 | line = str(round(float(line) / 100, 4)) 26 | 27 | if i % 8 == 7: 28 | newLine = newLine + line + "\n" 29 | newLines.append(newLine) 30 | newLine = '' 31 | elif i % 8 < 7: 32 | line = line + "," 33 | newLine = newLine + line 34 | i = i + 1 35 | # print(line) 36 | # print(newLines) 37 | 38 | # print(newLines[-1:-len(newLines):-1]) 39 | 40 | sortedLines = newLines[-1:-len(newLines):-1] 41 | 42 | # print(newLines[0]) 43 | 44 | sortedLines.insert(0, newLines[0]) 45 | 46 | print(sortedLines) 47 | 48 | 49 | with open("China Merchants Bank.csv", "w") as file: 50 | file.writelines(sortedLines) 51 | -------------------------------------------------------------------------------- /pics/history.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/githubguoheng/Stock-Price-Prediction-Project-Code/cc5554ca75d1baa1c99ced43cd8f92a4445522f0/pics/history.jpg -------------------------------------------------------------------------------- /pics/pred_stock.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/githubguoheng/Stock-Price-Prediction-Project-Code/cc5554ca75d1baa1c99ced43cd8f92a4445522f0/pics/pred_stock.jpg -------------------------------------------------------------------------------- /pics/price.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/githubguoheng/Stock-Price-Prediction-Project-Code/cc5554ca75d1baa1c99ced43cd8f92a4445522f0/pics/price.jpg -------------------------------------------------------------------------------- /stock_pre.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | import numpy as np 3 | 4 | import matplotlib.pyplot as plt 5 | 6 | from matplotlib.pylab import rcParams 7 | rcParams['figure.figsize']=20,10 8 | 9 | from sklearn.preprocessing import MinMaxScaler 10 | 11 | from keras.models import Sequential 12 | from keras.layers import LSTM,Dropout,Dense 13 | 14 | from keras.wrappers.scikit_learn import KerasRegressor 15 | from sklearn.model_selection import GridSearchCV 16 | 17 | def createXY(dataset,n_past): 18 | dataX = [] 19 | dataY = [] 20 | for i in range(n_past, len(dataset)): 21 | dataX.append(dataset[i - n_past:i, 0:dataset.shape[1]]) 22 | dataY.append(dataset[i,0]) 23 | return np.array(dataX),np.array(dataY) 24 | 25 | def build_model(optimizer): 26 | grid_model = Sequential() 27 | grid_model.add(LSTM(50,return_sequences=True,input_shape=(30,7))) 28 | grid_model.add(LSTM(50)) 29 | grid_model.add(Dropout(0.2)) 30 | grid_model.add(Dense(1)) 31 | 32 | grid_model.compile(loss = 'mse',optimizer = optimizer) 33 | return grid_model 34 | 35 | 36 | df=pd.read_csv("China Merchants Bank.csv",parse_dates=["Date"],index_col=[0]) 37 | df.head() 38 | print(df.head()) 39 | # print(df.tail()) 40 | # print(df.shape) 41 | 42 | test_split=round(len(df)*0.20) 43 | 44 | # print(test_split) 45 | 46 | df_for_training=df[:-test_split] 47 | df_for_testing=df[-test_split:] 48 | 49 | # print(df_for_training.shape) 50 | # print(df_for_testing.shape) 51 | 52 | scaler=MinMaxScaler(feature_range=(0,1)) 53 | df_for_training_scaled = scaler.fit_transform(df_for_training) 54 | df_for_testing_scaled = scaler.fit_transform(df_for_testing) 55 | 56 | trainX,trainY=createXY(df_for_training_scaled,30) 57 | testX,testY=createXY(df_for_testing_scaled,30) 58 | 59 | print("trainX Shape-- ", trainX.shape) 60 | print("trainY Shape-- ",trainY.shape) 61 | 62 | print("testX Shape-- ",testX.shape) 63 | print("testY Shape-- ",testY.shape) 64 | 65 | 66 | print("trainX[0].shape-- \n",trainX[0].shape) 67 | # print("trainY[0]-- ",trainY[0]) 68 | 69 | grid_model = KerasRegressor(build_fn=build_model,verbose=1,validation_data=(testX,testY)) 70 | parameters = {'batch_size' : [16,20], 71 | 'epochs' : [8,10], 72 | 'optimizer' : ['adam','Adadelta'] } 73 | 74 | grid_search = GridSearchCV(estimator = grid_model, 75 | param_grid = parameters, 76 | cv = 2) 77 | 78 | grid_search = grid_search.fit(trainX,trainY) 79 | 80 | print(grid_search.best_params_) 81 | 82 | my_model=grid_search.best_estimator_.model 83 | 84 | prediction=my_model.predict(testX) 85 | # print("prediction\n", prediction) 86 | # print("\nPrediction Shape-",prediction.shape) 87 | 88 | prediction_copies_array = np.repeat(prediction,7, axis=-1) 89 | 90 | pred=scaler.inverse_transform(np.reshape(prediction_copies_array,(len(prediction),7)))[:,0] 91 | 92 | original_copies_array = np.repeat(testY,7, axis=-1) 93 | original=scaler.inverse_transform(np.reshape(original_copies_array,(len(testY),7)))[:,0] 94 | 95 | print("Pred Values-- " ,pred) 96 | print("\nOriginal Values-- " ,original) 97 | 98 | plt.plot(original, color = 'red', label = 'Real Stock Price') 99 | plt.plot(pred, color = 'blue', label = 'Predicted Stock Price') 100 | plt.title('Stock Price Prediction') 101 | plt.xlabel('Time') 102 | plt.ylabel('China Merchants Bank Stock Price') 103 | plt.legend() 104 | plt.show() --------------------------------------------------------------------------------