mirror of
https://github.com/dupenf/stock-lstm.git
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68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
# import requirement libraries and tools
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import torch
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import torch.optim as optim
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import yfinance as yf
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import torch.nn as nn
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import torch.functional as F
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import plotly.graph_objects as go
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from tqdm.notebook import tqdm
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from torchsummary import summary
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from sklearn.preprocessing import MinMaxScaler
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from torch.utils.data import TensorDataset, DataLoader
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df = pd.read_csv("./datasets/sh.600000.csv")
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# Move column 'Close' to the first position
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col_close = df.pop('close')
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df.insert(0, 'close', col_close)
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df.head()
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df.tail()
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df.shape
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df.info()
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df.describe().T
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df.duplicated().sum()
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df.plot(subplots=True, figsize=(15, 15))
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plt.suptitle('stock attributes from 2016 to 2023', y=0.91)
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plt.show()
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df.asfreq('w', method='ffill').plot(subplots=True, figsize=(15,15), style='-')
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plt.suptitle('Stock attributes over time(Weekly frequency)', y=0.91)
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plt.show()
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df.asfreq('m', method='ffill').plot(subplots=True, figsize=(15,15), style='-')
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plt.suptitle('Stock attributes over time(Monthly frequency)', y=0.91)
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plt.show()
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df[['close']]
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# computing moving average(ma)
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ma_day = [10, 20, 50]
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for ma in ma_day:
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col_name = f'MA for {ma} days'
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df[col_name] = df['close'].rolling(ma).mean()
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df[['close', 'MA for 10 days', 'MA for 20 days', 'MA for 50 days']].plot(figsize=(15,5))
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plt.title('Comparision some MA and Close of Google stock')
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plt.show()
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# use pct_change to find the percent change for each day
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df['Daily_Return'] = df['close'].pct_change()
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# plot the daily return percentage
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df.Daily_Return.plot(legend=True, figsize=(15,5))
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plt.title('Daily return percentage of stock')
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plt.show()
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