Coverage for LSTMPredictStock\__init__.py : + 100% +
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+ # this lstm core module implementation provides an implementation +# of time series prediction using a lstm approach. It is provided +# as is with no warranties or support. ++ + + + + + + + + |
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+ + + + + + """A class for loading and transforming data for the lstm model""" ++ + ''' +filename:数据所在文件名， '.csv'格式文件 +split:训练与测试数据分割变量 +cols:选择data的一列或者多列进行分析，如 Close 和 Volume +''' + + + + + + + ++ + + ''' +Create x, y test data windows +Warning: batch method, not generative, make sure you have enough memory to +load data, otherwise reduce size of the training split. +''' + + + ++ + + + + + + + + + ''' +Create x, y train data windows +Warning: batch method, not generative, make sure you have enough memory to +load data, otherwise use generate_training_window() method. +''' +data_x = [] +data_y = [] +for i in range(self.len_train - seq_len): +x, y = self._next_window(i, seq_len, normalise) +data_x.append(x) +data_y.append(y) ++ return np.array(data_x), np.array(data_y) ++ + '''Yield a generator of training data from filename on given list of cols split for train/test''' + + + + + + +# stop-condition for a smaller final batch if data doesn't divide evenly + + + + + + ++ + '''Generates the next data window from the given index location i''' + + + + +# 只返回该元素的值[]和()用于索引都是切片操作，所以这里的y即label是 第一列Close列 + ++ + '''Normalise window with a base value of zero''' + + + + + + + + + + + + |
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+ # 获取国内股票历史数据 + + + + ++ + # 从网易接口获取数据 + + ++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + # start_date = '2010-06-21' # 只能按整年获取至今日数据 + + + + ++ + + + + # start_date = '2010-06-21' # 只能按整年获取至今日数据 + + + + ++ + + + + get_all_last_data("2010-01-01") + + |
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+ import json +import pandas_datareader.data as pdr +import fix_yahoo_finance as fix +import time +fix.pdr_override() ++ def get_stock_data(ticker, start_date, end_date): +""" +Gets historical stock data of given tickers between dates +:param ticker: company, or companies whose data is to fetched +:type ticker: string or list of strings +:param start_date: starting date for stock prices +:type start_date: string of date "YYYY-mm-dd" +:param end_date: end date for stock prices +:type end_date: string of date "YYYY-mm-dd" +:return: stock_data.csv +""" +i = 1 +try: +all_data = pdr.get_data_yahoo(ticker, start_date, end_date) #数据列包括：Date Open High Low Close Adj Close Volume +except ValueError: +print("ValueError, trying again") +i += 1 +if i < 5: +time.sleep(10) +get_stock_data(ticker, start_date, end_date) +else: +print("Tried 5 times, Yahoo error. Trying after 2 minutes") +time.sleep(120) +get_stock_data(ticker, start_date, end_date) +# print("date:%s - %s" % (start_date,end_date)) +# print("all_data:\n",all_data) +# all_data.to_csv("stock_all_prices.csv") +# stock_data = all_data["Adj Close"] +stock_data = all_data +filename = "..\\data\\"+ticker.lower()+"_stock.csv" +stock_data.to_csv(filename) +# insert_date_close_col(filename) ++ def insert_date_close_col(filename): +with open(filename,"r+") as f: # r只读，r+读写，不创建。文件不存在则报错 +old = f.read() # 读取原来所有内容 +f.seek(0) # 将文件索引重定位到文件头 +f.write("Date,Close\n") +f.write(old) ++ + if __name__ == "__main__": +start_date = "2014-04-25" +end_date = "2018-05-25" +config = json.load(open("..\\config.json","r")) +companies_name = config["companies"]["name"] +print(companies_name) +for name in companies_name: +get_stock_data(name,start_date,end_date) +print("finish!\n") + + |
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+ + + """A class for an building and inferencing an lstm model""" ++ + + + + ''' +从本地保存的模型参数来加载模型 +filepath: .h5 格式文件 +''' + + ++ + """ +新建一个模型 +configs:配置文件 +""" + + ++ + + + + + + + + + + + + + + + + + + + + + timer = Timer() +timer.start() +print('[Model] Training Started') +print('[Model] %s epochs, %s batch size' % (epochs, batch_size)) ++ save_fname = os.path.join(save_dir, '%s-e%s.h5' % (dt.datetime.now().strftime('%d%m%Y-%H%M%S'), str(epochs))) +callbacks = [ +EarlyStopping(monitor='val_loss', patience=2), +ModelCheckpoint(filepath=save_fname, monitor='val_loss', save_best_only=True) +] +self.model.fit( +x, +y, +epochs=epochs, +batch_size=batch_size, +callbacks=callbacks +) +self.model.save(save_fname) #保存训练好的模型 ++ print('[Model] Training Completed. Model saved as %s' % save_fname) +timer.stop() #输出训练耗时 ++ + ''' +由data_gen数据产生器来，逐步产生训练数据，而不是一次性将数据读入到内存 +''' + + + + ++ # save_fname = os.path.join(save_dir, '%s-e%s.h5' % (dt.datetime.now().strftime('%d%m%Y-%H%M%S'), str(epochs))) + + +ModelCheckpoint(filepath=save_fname, monitor='loss', save_best_only=True) +] + +data_gen, +steps_per_epoch=steps_per_epoch, +epochs=epochs, +callbacks=callbacks, +workers=1 +) ++ + + + # data必须是三维数据，即shape:(a,b,c) + +#Predict each timestep given the last sequence of true data, in effect only predicting 1 step ahead each time +predicted = self.model.predict(data) # data有多少行就输出多少个预测值,返回的预测值是一个2维数组:(a,1) a:为data的行数 +predicted = np.reshape(predicted, (predicted.size,)) # 这里将二维数组，变成一维数组 +return predicted # 返回一维数组，元素个数与data的a相同 ++ # 对data进行多段预测，每段预测基于一个窗口大小（window_size）的数据，然后输出prediction_len长的预测值（一维数组） +# 再从上一个窗口移动prediction_len的长度，得到下一个窗口的数据，并基于该数据再预测prediction_len长的预测值 +# 所以prediction_len决定了窗口的移位步数，每次的窗口大小是一样的，所以最后预测的段数 = 窗口个数/预测长度 +# 相当于多次调用predict_1_win_sequence方法 + +#Predict sequence of 50 steps before shifting prediction run forward by 50 steps +prediction_seqs = [] +for i in range(int(len(data)/prediction_len)): +curr_frame = data[i*prediction_len] +predicted = [] +for j in range(prediction_len): +predicted.append(self.model.predict(curr_frame[newaxis,:,:])[0,0]) # newaxis：增加新轴，使得curr_frame变成(1,x,x)三维数据 +curr_frame = curr_frame[1:] +curr_frame = np.insert(curr_frame, [window_size-2], predicted[-1], axis=0) +prediction_seqs.append(predicted) +return prediction_seqs ++ # 输入一个窗口的数据，指定预测的长度，data:依旧是三维数组(1,win_len,fea_len) +# 返回预测数组 + +#Shift the window by 1 new prediction each time, re-run predictions on new window +curr_frame = data[0] # 基于data[0]一个窗口的数据，来预测len(data)个输出 +predicted = [] +for i in range(len(data)): +predicted.append(self.model.predict(curr_frame[newaxis,:,:])[0,0]) # append了一个预测值（标量） +curr_frame = curr_frame[1:] +curr_frame = np.insert(curr_frame, [window_size-2], predicted[-1], axis=0) # 插入位置[window_size-2]:curr_frame的末尾，predicted[-1]：插入值 +return predicted ++ # 输入一个窗口的数据，指定预测的长度，data:依旧是三维数组(1,win_len,fea_len) +# 返回预测数组 + +#Shift the window by 1 new prediction each time, re-run predictions on new window + + + + + + + + + |
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+ + + + + + + + + + + + + + + + + + + + + fig = plt.figure(facecolor='white') +ax = fig.add_subplot(111) +ax.plot(true_data, label='True Data') +plt.plot(predicted_data, label='Prediction') +plt.legend() +plt.show() ++ + # predicted_data每个元素的长度必须为prediction_len + +fig = plt.figure(facecolor='white') +ax = fig.add_subplot(111) +ax.plot(true_data, label='True Data') +# Pad the list of predictions to shift it in the graph to it's correct start +for i, data in enumerate(predicted_data): # data为一维数组，长度为prediction_len。predicted_data：二维数组，每个元素为list +padding = [None for p in range(i * prediction_len)] +plt.plot(padding + data, label='Prediction') # padding + data：list拼接操作 +plt.legend() +plt.show() ++ + # 只用于训练模型，但同时可根据参数进行模型的评估 + +''' +训练并保存模型，同时根据测试数据对模型进行评估（绘图方式） +''' ++ + + os.makedirs(os.path.join(get_parent_dir(),configs['model']['save_dir'])) # 创建保存模型的目录 ++ + + + + + os.path.join(get_parent_dir(),os.path.join('data', stock_code + ".csv")), # configs['data']['filename'] +split, +configs['data']['columns'] # 选择某些列的数据进行训练 +) ++ + + + ''' +# in-memory training +model.train( +x, +y, +epochs = configs['training']['epochs'], +batch_size = configs['training']['batch_size'], +save_dir = configs['model']['save_dir'] +) +''' +# 训练模型： +# out-of memory generative training + +(data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size']) + +data_gen=data.generate_train_batch( +seq_len=configs['data']['sequence_length'], +batch_size=configs['training']['batch_size'], +normalise=configs['data']['normalise'] +), +epochs=configs['training']['epochs'], +batch_size=configs['training']['batch_size'], +steps_per_epoch=steps_per_epoch, +save_dir=os.path.join(get_parent_dir(),configs['model']['save_dir']), +save_name=stock_code +) ++ # 预测 + +x_test, y_test = data.get_test_data( +seq_len=configs['data']['sequence_length'], +normalise=configs['data']['normalise'] +) ++ predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], +configs['data']['sequence_length']) +print("训练：\n", predictions) +# plot_results_multiple(predictions, y_test, configs['data']['sequence_length']) ++ + # 对指定公司的股票进行预测 + +''' +使用保存的模型，对输入数据进行预测 +''' + + + +os.path.join(get_data_path(), stock_code + ".csv"), # configs['data']['filename'] +configs['data']['train_test_split'], +configs['data']['columns'] +) ++ + + + + + # predict_length = configs['data']['sequence_length'] # 预测长度 + + + +else: # 用指定位置的一个窗口数据进行预测，有对比真实数据（用于绘图对比） +win_position = -configs['data']['sequence_length'] ++ + seq_len=configs['data']['sequence_length'], +normalise=False +) ++ + + + y_test_real = y_test[win_position:win_position + predict_length] ++ + + + + seq_len=configs['data']['sequence_length'], +normalise=configs['data']['normalise'] +) + + ++ # predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], +# predict_length) ++ + # 反归一化 + + + ++ # print("预测数据:\n", predictions) + +print("真实数据：\n", y_test_real) ++ # plot_results_multiple(predictions, y_test, predict_length) + +if real: +plot_results(predictions, []) +else: +plot_results(predictions, y_test_real) ++ + + + + + + + + + + + + + + + + + + + ''' +def main(stock_code, train=False, predict=False): +configs = json.load(open(get_config_path(), 'r')) +companies = configs['companies'] ++ if stock_code not in companies.keys(): +print("该公司不在指定范围内") +return -1 ++ if train: +train_model(stock_code) +return 0 # 训练完成 ++ if predict: +# for root, dirs, files in os.walk('saved_models'): +# root:当前目录路径 dirs: 当前路径下所有子目录 files:当前路径下所有非目录子文件 +if stock_code + ".h5" in os.listdir("saved_models"): # os.listdir:获得当前目录下的所有文件名。不包括子目录 +return prediction(stock_code=stock_code, real=True, pre_len=20) +else: +return -2 # 该公司还没有训练模型 +''' +# 二维数组：[[data,value],[...]] + + + + + + + + ++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + # if __name__ == '__main__': +# # get_all_last_data("2010-01-01") # 先获得最新数据 +# train_all_stock() +# # predict_all_stock() + + |
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+ """ +Django settings for WebStockPredict project. ++ Generated by 'django-admin startproject' using Django 2.1.4. ++ For more information on this file, see +https://docs.djangoproject.com/en/2.1/topics/settings/ ++ For the full list of settings and their values, see +https://docs.djangoproject.com/en/2.1/ref/settings/ +""" ++ + + # Build paths inside the project like this: os.path.join(BASE_DIR, ...) + ++ + # Quick-start development settings - unsuitable for production +# See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ ++ # SECURITY WARNING: keep the secret key used in production secret! + ++ # SECURITY WARNING: don't run with debug turned on in production! + ++ + + + # Application definition ++ + 'django.contrib.admin', +'django.contrib.auth', +'django.contrib.contenttypes', +'django.contrib.sessions', +'django.contrib.messages', +'django.contrib.staticfiles', +'stock_predict.apps.StockPredictConfig' +] ++ + 'django.middleware.security.SecurityMiddleware', +'django.contrib.sessions.middleware.SessionMiddleware', +'django.middleware.common.CommonMiddleware', +'django.middleware.csrf.CsrfViewMiddleware', +'django.contrib.auth.middleware.AuthenticationMiddleware', +'django.contrib.messages.middleware.MessageMiddleware', +'django.middleware.clickjacking.XFrameOptionsMiddleware', +] ++ + + + { +'BACKEND': 'django.template.backends.django.DjangoTemplates', +'DIRS': [], +'APP_DIRS': True, +'OPTIONS': { +'context_processors': [ +'django.template.context_processors.debug', +'django.template.context_processors.request', +'django.contrib.auth.context_processors.auth', +'django.contrib.messages.context_processors.messages', +], +}, +}, +] ++ + + + # Database +# https://docs.djangoproject.com/en/2.1/ref/settings/#databases ++ + 'default': { +'ENGINE': 'django.db.backends.sqlite3', +'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), +} +} ++ + # Password validation +# https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators ++ + { +'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', +}, +{ +'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', +}, +{ +'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', +}, +{ +'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', +}, +] ++ + # Internationalization +# https://docs.djangoproject.com/en/2.1/topics/i18n/ ++ + + + + + + + + + + + # Static files (CSS, JavaScript, Images) +# https://docs.djangoproject.com/en/2.1/howto/static-files/ ++ + + |
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+ """WebStockPredict URL Configuration ++ The `urlpatterns` list routes URLs to views. For more information please see: +https://docs.djangoproject.com/en/2.1/topics/http/urls/ +Examples: +Function views +1. Add an import: from my_app import views +2. Add a URL to urlpatterns: path('', views.home, name='home') +Class-based views +1. Add an import: from other_app.views import Home +2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') +Including another URLconf +1. Import the include() function: from django.urls import include, path +2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) +""" + + ++ + path('admin/', admin.site.urls), +path("stock_predict/",include('stock_predict.urls')) +] + + |
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+ """ +WSGI config for WebStockPredict project. ++ It exposes the WSGI callable as a module-level variable named ``application``. ++ For more information on this file, see +https://docs.djangoproject.com/en/2.1/howto/deployment/wsgi/ +""" ++ import os ++ from django.core.wsgi import get_wsgi_application ++ os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'WebStockPredict.settings') ++ application = get_wsgi_application() + + |
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+| Module | +statements | +missing | +excluded | + +coverage | +
|---|---|---|---|---|
| Total | +698 | +71 | +0 | + +90% | +
| LSTMPredictStock\__init__.py | +0 | +0 | +0 | + +100% | +
| LSTMPredictStock\core\__init__.py | +6 | +0 | +0 | + +100% | +
| LSTMPredictStock\core\data_processor.py | +58 | +7 | +0 | + +88% | +
| LSTMPredictStock\core\get_domestic_hist_stock.py | +45 | +1 | +0 | + +98% | +
| LSTMPredictStock\core\model.py | +86 | +30 | +0 | + +65% | +
| LSTMPredictStock\core\utils.py | +9 | +0 | +0 | + +100% | +
| LSTMPredictStock\run.py | +126 | +24 | +0 | + +81% | +
| WebStockPredict\__init__.py | +0 | +0 | +0 | + +100% | +
| WebStockPredict\settings.py | +18 | +0 | +0 | + +100% | +
| WebStockPredict\urls.py | +3 | +0 | +0 | + +100% | +
| WebStockPredict\wsgi.py | +4 | +4 | +0 | + +0% | +
| manage.py | +9 | +2 | +0 | + +78% | +
| stock_predict\__init__.py | +0 | +0 | +0 | + +100% | +
| stock_predict\add_companies_to_db.py | +8 | +0 | +0 | + +100% | +
| stock_predict\admin.py | +5 | +0 | +0 | + +100% | +
| stock_predict\apps.py | +3 | +0 | +0 | + +100% | +
| stock_predict\migrations\0001_initial.py | +6 | +0 | +0 | + +100% | +
| stock_predict\migrations\0002_auto_20181212_1138.py | +4 | +0 | +0 | + +100% | +
| stock_predict\migrations\0003_auto_20181212_1206.py | +4 | +0 | +0 | + +100% | +
| stock_predict\migrations\0004_auto_20181213_1455.py | +4 | +0 | +0 | + +100% | +
| stock_predict\migrations\0005_stockindex.py | +5 | +0 | +0 | + +100% | +
| stock_predict\migrations\0006_stockindex_zong_he.py | +4 | +0 | +0 | + +100% | +
| stock_predict\migrations\__init__.py | +0 | +0 | +0 | + +100% | +
| stock_predict\models.py | +43 | +0 | +0 | + +100% | +
| stock_predict\tests.py | +170 | +2 | +0 | + +99% | +
| stock_predict\urls.py | +5 | +0 | +0 | + +100% | +
| stock_predict\views.py | +73 | +1 | +0 | + +99% | +
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+ #!/usr/bin/env python + + ++ + + + + except ImportError as exc: +raise ImportError( +"Couldn't import Django. Are you sure it's installed and " +"available on your PYTHONPATH environment variable? Did you " +"forget to activate a virtual environment?" +) from exc + + + |
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+ + + + "000066": "中国长城","601766":"中国中车","601390":"中国中铁","000768":"中航飞机","000063":"中兴通讯"} ++ + + + + + + + |
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+| + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + | +
+ # Generated by Django 2.1.4 on 2018-12-12 03:15 ++ + + + + + + + + + ] ++ + migrations.CreateModel( +name='Company', +fields=[ +('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), +('stock_code', models.CharField(max_length=20)), +('name', models.CharField(max_length=100)), +], +), +migrations.CreateModel( +name='HistoryData', +fields=[ +('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), +('data', models.TextField()), +('company', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='stock_predict.Company')), +], +), +migrations.CreateModel( +name='PredictData', +fields=[ +('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), +('data', models.TextField()), +('company', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='stock_predict.Company')), +], +), +] + + |
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+ # Generated by Django 2.1.4 on 2018-12-12 03:38 ++ + + + + + + ('stock_predict', '0001_initial'), +] ++ + migrations.AddField( +model_name='historydata', +name='start_date', +field=models.DateTimeField(null=True), +), +migrations.AddField( +model_name='predictdata', +name='start_date', +field=models.DateTimeField(null=True), +), +] + + |
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+ # Generated by Django 2.1.4 on 2018-12-12 04:06 ++ + + + + + + ('stock_predict', '0002_auto_20181212_1138'), +] ++ + migrations.AlterField( +model_name='historydata', +name='start_date', +field=models.DateField(), +), +migrations.AlterField( +model_name='predictdata', +name='start_date', +field=models.DateField(), +), +] + + |
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+ # Generated by Django 2.1.4 on 2018-12-13 06:55 ++ + + + + + + ('stock_predict', '0003_auto_20181212_1206'), +] ++ + migrations.AlterField( +model_name='historydata', +name='start_date', +field=models.CharField(max_length=30), +), +migrations.AlterField( +model_name='predictdata', +name='start_date', +field=models.CharField(max_length=30), +), +] + + |
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+ # Generated by Django 2.1.4 on 2018-12-22 01:36 ++ + + + + + + + ('stock_predict', '0004_auto_20181213_1455'), +] ++ + migrations.CreateModel( +name='StockIndex', +fields=[ +('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), +('ri_qi', models.CharField(max_length=30)), +('zi_jin', models.IntegerField(default=0)), +('qiang_du', models.IntegerField(default=0)), +('feng_xian', models.IntegerField(default=0)), +('zhuan_qiang', models.IntegerField(default=0)), +('chang_yu', models.IntegerField(default=0)), +('jin_zi', models.IntegerField(default=0)), +('company', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='stock_predict.Company')), +], +), +] + + |
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+ # Generated by Django 2.1.4 on 2018-12-22 02:03 ++ + + + + + + ('stock_predict', '0005_stockindex'), +] ++ + migrations.AddField( +model_name='stockindex', +name='zong_he', +field=models.IntegerField(default=0), +), +] + + |
+
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+
+ + + + + # Create your models here. + + + ++ + + + + + + + + + + + + + + + + + + + + + + + else: + + ++ + + + + + + + + + + + + + + else: + + ++ + + + |
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+| + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + | +
+
+
+ + + + + + + + + + + + + + """ +测试使用非list类型传入set_data，应该抛出异常 +""" + +# 测试函数hd.set_data（list_data=data）Raises Exception，并且包含指定报错msg + + + + + + + + ++ + + """ +测试正确调用set_data方法，验证HistoryData对象的属性data与start_date被修改 +""" + + + + + ++ + """ +测试test_get_data返回 list对象 +""" + + + + ++ + + """ +测试使用非list类型传入set_data，应该抛出异常 +""" + +# 测试函数hd.set_data（list_data=data）Raises Exception，并且包含指定报错msg + + + + + + + + ++ + + """ +测试正确调用set_data方法，验证HistoryData对象的属性data与start_date被正确修改 +""" + + + + + ++ + """ +测试test_get_data返回的是list对象 +""" + + + + ++ + + + + + """ +测试 get_hist_predict_data 方法 +""" + +""" +传入未知的stock_code， 返回404错误 +""" + + + + + ++ + """ +当数据库存在数据且为最新数据时，返回数据库中的数据 +""" + + +# 构造最新历史、预测数据 + + + ++ + + + + + + + + """ +测试当指定股票代码的历史数据和预测数据，在数据库中没有记录时，则从API获取历史数据，使用模型预测数据，然后保存到数据库 +""" + + + + + + ++ + + + elif now.isoweekday() == 7: +now = now + timedelta(days=1) ++ + + + + """ +测试当数据库所存数据不是最新时，应该将数据更新 +""" + +# 构造过时历史、预测数据 + + ++ + + + + + + + + + + + + + """ +测试调用该方法，数据库成功添加了股票公司数据 +""" + + + + + + ++ # 为公司创造指标数据 + + +'jin_zi': 7, +'zong_he': 9} + +'jin_zi': 6, +'zong_he': 7} + +'jin_zi': 5, +'zong_he': 8} + +feng_xian=row['feng_xian'], +zhuan_qiang=row['zhuan_qiang'], chang_yu=row['chang_yu'], jin_zi=row['jin_zi'], +zong_he=row['zong_he']) + +feng_xian=row2['feng_xian'], +zhuan_qiang=row2['zhuan_qiang'], chang_yu=row2['chang_yu'], jin_zi=row2['jin_zi'], +zong_he=row2['zong_he']) + +feng_xian=row3['feng_xian'], +zhuan_qiang=row3['zhuan_qiang'], chang_yu=row3['chang_yu'], jin_zi=row3['jin_zi'], +zong_he=row3['zong_he']) + ++ # 为股票创造最新的历史与预测数据 + + + + ++ + + + + + + """ +测试访问主页时，返回股票代码为：600718的数据，包括：历史、预测和指标数据 +""" +# 构造最新历史、预测数据 + + + ++ + + + + + + + + + + + + + + """ +预测不存在的股票代码，返回404 +""" + + + ++ + """ +测试输入正确的股票代码时，应该返回该股票的历史、预测、指标数据 +""" + + + ++ + + + + + + + + + + + + """ +测试方法 get_crawl_save_data +""" + +""" +测试调用该方法，数据库增加了 StockIndex 数据 +""" + +"000066": "中国长城","601766":"中国中车","601390":"中国中铁","000768":"中航飞机","000063":"中兴通讯"} + + + + + ++ + + """ +测试训练所有的模型 +""" + ++ + + """ +测试预测所有数据的方法 +""" + ++ # sched = Scheduler() ++ + + + + |
+
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+
+
+ + + + + + path("home/",views.home,name='home'), +path("predict/",views.predict_stock_action,name='predict'), +] + + |
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+
+
+ + + + + + + + + + + + # LOCAL = False ++ + + # company = models.Company.objects.get(stock_code=stock_code) + ++ + + + + + + else: + + + + + + + + ++ + + + + + + + + + else: + + + + + + + ++ + + + + + + """ +将10个公司的指标数据爬取并保存到数据库 +""" +# 此处应是从网上爬取数据，并保存为csv文件 + + + + + + + + + +zhuan_qiang=row['zhuan_qiang'],chang_yu=row['chang_yu'],jin_zi=row['jin_zi'],zong_he=row['zong_he']) ++ + """ +获取股票的各项指标数据 +""" + + +# 将爬取的数据存入数据库 +get_crawl_save_data() +# 从数据库获取近三天的数据 + + ++ + + + + + + + + + # print("stock_code:\n",stock_code) + + + + ++ + sched = Scheduler() +# 定时任务 +# @sched.interval_schedule(seconds=2) # 每2s执行一次 +@sched.cron_schedule(hour=0,minute=0) # 每日凌晨调度一次 +def train_models(): +run.train_all_stock() ++ sched.start() +''' + + |
+