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import numpy as np | |
import pandas as pd | |
import scipy.stats as ss | |
def create_combined_vector(assessment_file): | |
comb_df = pd.read_csv(assessment_file) | |
# Seperate out vectors | |
days = [0 for i in range(len(comb_df.values))] | |
trend = comb_df['trend'].values | |
close = comb_df['adjusted_close'].values | |
# Resize and normalize | |
days = days[1:] | |
trend = ss.zscore(trend[1:]) | |
close = ss.zscore(np.diff(close)) | |
return (trend, close, days) | |
# Generate combined vecotr ready for ingestion by the | |
# Granger Causality function (days used for later graph) | |
(trend, close, days) = create_combined_vector(filename) | |
combined_vector = [] | |
for i in range(len(trend)): | |
combined_vector.append((trend[i], close[i])) |
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