39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
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import pandas as pd
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import os as os
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from pandas_datareader import data
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from sklearn.model_selection import train_test_split
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import numpy as np
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dir_path = os.path.dirname(os.path.realpath(__file__))
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def load_financial_data(start_date, end_date, output_file):
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try:
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df = pd.read_pickle(output_file)
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print("File data found...reading GOOG data")
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except FileNotFoundError:
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print("File not found...downloading the GOOG data")
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df = data.DataReader('GOOG', 'yahoo', start_date, end_date)
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df.to_pickle(output_file)
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return df
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def create_classification_trading_condition(df):
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df['Open-Close'] = df.Open - df.Close
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df['High-Low'] = df.High - df.Low
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df = df.dropna()
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X = df[['Open-Close', 'High-Low']]
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Y = np.where(df['Close'].shift(-1) > df['Close'], 1, -1)
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return (X,Y)
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def create_regression_trading_condition(df):
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df['Open-Close'] = df.Open - df.Close
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df['High-Low'] = df.High - df.Low
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df = df.dropna()
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print(df)
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X = df[['Open-Close', 'High-Low']]
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Y = df['Close'].shift(-1) - df['Close']
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return(X,Y)
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def create_train_split_group(X, Y, split_ratio=0.8):
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# Split dataset into two groups
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return train_test_split(X, Y, shuffle=False, train_size=split_ratio)
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