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44 lines
1.0 KiB
Python
44 lines
1.0 KiB
Python
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# Reference: https://github.com/ctxj/Time-Series-Transformer-Pytorch/tree/main
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import torch
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import torch.nn as nn
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import numpy as np
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import pandas as pd
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import copy
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import math
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import time
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import matplotlib.pyplot as plt
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from torchinfo import summary
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from torch.utils.data import Dataset, DataLoader
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from torch.nn import TransformerEncoder, TransformerEncoderLayer
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def log_features():
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df = pd.read_csv("./datasets/sh.600000.csv")
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close = df['close']
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log_prices = np.diff(np.log(close))
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log_prices_csum = log_prices.cumsum() # Cumulative sum of log prices
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print(log_prices_csum)
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print("------------")
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print(log_prices)
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return log_prices
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# draw
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fig1, ax1 = plt.subplots(2, 1)
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ax1[0].plot(close, color='red')
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ax1[0].set_title('Closed Price')
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ax1[0].set_xlabel('Time Steps')
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ax1[1].plot(log_prices_csum, color='blue')
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ax1[1].set_title('CSUM of Log Price')
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ax1[1].set_xlabel('Time Steps')
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fig1.tight_layout()
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log_features()
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