Datasets:
File size: 2,124 Bytes
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# data = data[['market', 'time', 'open', 'high', 'low', 'close', 'volume']]
# transform this dataset so to 'market', 'start', 'column', 'value1', 'value2', 'value3', 'value(n)'
import pandas as pd
import numpy as np
from tqdm import tqdm
columns = ['open','close','volume', 'rsi', 'sma']
window_size = 10
def create_sequences(df, columns=columns, window_size=window_size):
# group by market
grouped = df.groupby('market')
# create a list of dataframes
dfs = []
for name, group in tqdm(grouped):
# create a new dataframe
new_df = pd.DataFrame()
new_df['market'] = name
# create a list of lists
sequences = []
# only include the close column
# iterate over the rows of the dataframe
for i in range(len(group) - window_size):
# create a sequence
sequence = group.iloc[i:i+window_size][columns].values
# transpose the sequence so that it is a column
sequence = sequence.T
# create a dataframe from the sequence
sequence = pd.DataFrame(sequence)
# add the market, time, column_name to the sequence
sequence['market'] = name
sequence['time'] = group.iloc[i+window_size]['time']
sequence['column'] = columns
# set market, time as the first columns and index
sequence = sequence.set_index(['market', 'time', 'column'])
# add the sequence to the list of sequences
sequences.append(sequence)
if len(sequences) == 0:
continue
# create a dataframe from the list of lists
new_df = pd.concat(sequences)
# add the dataframe to the list of dataframes
dfs.append(new_df)
# concatenate the list of dataframes
final_df = pd.concat(dfs)
return final_df
df = pd.read_csv('indicators.csv')
# create the sequences
sequences = create_sequences(df, columns=columns, window_size=15)
# save the sequences to a new file
sequences.to_csv('sequences.csv') |