Datasets:
| # 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') |