mirror of
https://github.com/asavinov/intelligent-trading-bot.git
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111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
import sys
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from pathlib import Path
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from datetime import datetime, timezone, timedelta
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from typing import Union
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import json
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import pickle
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import click
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import numpy as np
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import pandas as pd
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from service.App import *
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from common.feature_generation import *
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from common.label_generation import *
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#
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# Parameters
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#
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class P:
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feature_sets = ["kline", ] # "futur"
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in_nrows = 10_000_000
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@click.command()
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@click.option('--config_file', '-c', type=click.Path(), default='', help='Configuration file name')
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def main(config_file):
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load_config(config_file)
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freq = "1m"
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symbol = App.config["symbol"]
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data_path = Path(App.config["data_folder"])
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if not data_path.is_dir():
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print(f"Data folder does not exist: {data_path}")
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return
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start_dt = datetime.now()
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#
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# Load historic data
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#
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in_path = (data_path / f"{symbol}-{freq}.csv").resolve()
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print(f"Loading data from source file {str(in_path)}...")
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in_df = pd.read_csv(in_path, parse_dates=['timestamp'], nrows=P.in_nrows)
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print(f"Finished loading {len(in_df)} records with {len(in_df.columns)} columns.")
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#
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# Generate derived features
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#
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if "kline" in P.feature_sets:
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print(f"Generating klines features...")
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k_features = generate_features(
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in_df, use_differences=False,
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base_window=App.config["base_window_kline"], windows=App.config["windows_kline"],
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area_windows=App.config["area_windows_kline"]
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)
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print(f"Finished generating {len(k_features)} kline features")
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else:
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k_features = []
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if "futur" in P.feature_sets:
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print(f"Generating futur features...")
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f_features = generate_features_futur(in_df)
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print(f"Finished generating {len(f_features)} futur features")
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else:
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f_features = []
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if "depth" in P.feature_sets:
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print(f"Generating depth features...")
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d_features = generate_features_depth(in_df)
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print(f"Finished generating {len(f_features)} depth features")
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else:
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d_features = []
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#
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# Generate labels (always the same, currently based on kline data which must be therefore present)
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#
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print(f"Generating labels...")
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labels = []
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# Binary labels whether max has exceeded a threshold or not
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labels += generate_labels_thresholds(in_df, horizon=App.config["label_horizon"])
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# Numeric label which is a ratio between areas over and under the latest price
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labels += add_area_ratio(in_df, is_future=True, column_name="close", windows=[60, 120, 180, 300], suffix = "_area_future")
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print(f"Finished generating {len(labels)} labels")
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#
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# Store feature matrix in output file
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#
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out_file_name = f"{symbol}-{freq}-features.csv"
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out_file = (data_path / out_file_name).resolve()
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print(f"Storing feature matrix with {len(in_df)} records and {len(in_df.columns)} columns in output file...")
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in_df.to_csv(out_file, index=False, float_format="%.4f")
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#in_df.to_parquet(out_path.with_suffix('.parquet'), engine='auto', compression=None, index=None, partition_cols=None)
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elapsed = datetime.now() - start_dt
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print(f"Finished feature generation in {int(elapsed.total_seconds())} seconds")
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print(f"Output file location: {out_file}")
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if __name__ == '__main__':
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main()
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