def eval_and_plot(self):
x, label = self.env.get_stock_test_data()
y = self.predict(x)
with open(self.save_path + '_y.json', mode='w') as fp:
json.dump(y.tolist(), fp, indent=True)
with open(self.save_path + '_label.json', mode='w') as fp:
json.dump(label.tolist(), fp, indent=True)
data_ploter.plot_stock_series(self.env.codes, y, label, self.save_path)
def eval_and_plot(self):
x, label = self.env.get_test_data() # x的形状为(561,5,25), label的形状为(561,5) ; 分别返回整个测试集的所有输入序列 和 整个测试集的输出序列,对应着每个输入序列后一天的close
y = self.predict(x) #形状为(561,5) , 0.2*总数据=561
with open(self.save_path + '_y.json', mode='w') as fp:
json.dump(y.tolist(), fp, indent=True) #保存为model_y.json 在checkpoints\SL\stock\model_y.json 保存着预测值y
with open(self.save_path + '_label.json', mode='w') as fp:
json.dump(label.tolist(), fp, indent=True) # #保存为model_label.json 在checkpoints\SL\stock\model_label.json 保存着测试集的真实的标签值
data_ploter.plot_stock_series(self.env.codes, # env.codes == <class 'list'>: ['600036', '601328', '601998', '601398']
y, # 形状为(561,5)
label, # label的形状为(561,5)
self.save_path) # 保存着和单独运行plot_prices.py一样的结果的图片
def eval_and_plot(self):
def _to_price(list):
### return to price
func = np.vectorize(lambda x: x / 100 + 1)
### return to daily change
list = func(list)
label1 = [
reduce(lambda x, y: x * y, list[:i + 1]).tolist()
for i in range(len(list))
]
price_list = np.array([[i[0] * first_close] for i in label1])
return price_list
x, label = self.env.get_test_data()
# price = self.env.original_frame
# add new_code, scale the label and predict back
first_index = self.env.e_data_indices[0] - 1
first_close = self.env.origin_frames['600036']['close'].iloc[
first_index]
# mean = self.env.scaler[0].mean_[-1]
# std = math.sqrt(self.env.scaler[0].var_[-1])
label_price = _to_price(label)
y = self.predict(x)
y_price = _to_price(y)
with open(self.save_path + '_label_price.json', mode='w') as fp:
json.dump(label_price.tolist(), fp, indent=True)
with open(self.save_path + '_label_daily_change.json', mode='w') as fp:
json.dump(label.tolist(), fp, indent=True)
with open(self.save_path + '_y_price.json', mode='w') as fp:
json.dump(y_price.tolist(), fp, indent=True)
with open(self.save_path + '_y_daily_change.json', mode='w') as fp:
json.dump(y.tolist(), fp, indent=True)
data_ploter.plot_stock_series(self.env.codes, y_price, label_price,
self.save_path + '_price')
data_ploter.plot_stock_series(self.env.codes, y, label,
self.save_path + '_daily_change')
def eval_and_plot_backtest(self, code, model_name):
x, label = self.env.get_test_data()
y = self.predict(x)
with open(self.save_path + '_y.json', mode='w') as fp:
json.dump(y.tolist(), fp, indent=True)
with open(self.save_path + '_label.json', mode='w') as fp:
json.dump(label.tolist(), fp, indent=True)
data_ploter.plot_stock_series(self.env.codes, y, label, self.save_path)
# reformat the data and transformed the data to back testing data
date_index = self.env.dates[self.env.e_data_indices[0] +
self.env.seq_length + 1:]
dataframe_backtest = pd.DataFrame(
{
'label': label.flatten(),
'y': y.flatten()
}, index=date_index)
original_frames = self.env.origin_frames[
self.env.codes[0]][self.env.e_data_indices[0] +
self.env.seq_length:]
dataframe_backtest = pd.concat([dataframe_backtest, original_frames],
axis=1)
dataframe_backtest['date'] = dataframe_backtest.index
dataframe_backtest['date'] = dataframe_backtest['date'].apply(
lambda x: pd.to_datetime(x).strftime("%Y-%m-%d %H:%M:%S"))
dataframe_backtest.dropna(how="any", inplace=True)
dataframe_backtest.set_index(['date'], inplace=True)
dataframe_backtest['openinterest'] = 0
col_order = [
'open', 'high', 'low', 'close', 'volume', 'openinterest', 'label',
'y'
]
dataframe_backtest = dataframe_backtest[col_order]
# dataframe_backtest = dataframe_backtest.round(2)
dataframe_backtest.to_csv(
"../../back_testing/data/{}_{}_for_backtest.csv".format(
model_name, code),
date_format='%Y-%m-%d %H:%M:%S')
print('dataframe is updated')
def eval_and_plot(self):
x, label = self.env.get_test_data()
y = self.predict(x)
with open(self.save_path + '_y.json', mode='w') as fp:
json.dump(y.tolist(), fp, indent=True)
with open(self.save_path + '_label.json', mode='w') as fp:
json.dump(label.tolist(), fp, indent=True)
data_ploter.plot_stock_series(self.env.codes, y, label, self.save_path)
date_index = self.env.dates[self.env.e_data_indices[0] +
self.env.seq_length + 1:]
dataframe_backtest = pd.DataFrame(
{
'label': label.flatten(),
'y': y.flatten()
}, index=date_index)
original_frames = self.env.origin_frames[
self.env.codes[0]][self.env.e_data_indices[0] +
self.env.seq_length:]
dataframe_backtest = pd.concat([dataframe_backtest, original_frames],
axis=1)
return dataframe_backtest
def eval_and_plot(self):
x, label = self.env.get_stock_test_data()
y = self.predict(x)
data_ploter.plot_stock_series(self.env.codes, y, label, self.save_path)