import logging from pathlib import Path import pandas as pd from freqtrade.constants import DATETIME_PRINT_FORMAT, DEFAULT_TRADES_COLUMNS, Config from freqtrade.data.converter.trade_converter import (trades_convert_types, trades_df_remove_duplicates) from freqtrade.data.history.idatahandler import get_datahandler from freqtrade.exceptions import OperationalException from freqtrade.resolvers import ExchangeResolver logger = logging.getLogger(__name__) KRAKEN_CSV_TRADE_COLUMNS = ['timestamp', 'price', 'amount'] def import_kraken_trades_from_csv(config: Config, convert_to: str): """ Import kraken trades from csv """ if config['exchange']['name'] != 'kraken': raise OperationalException('This function is only for the kraken exchange.') datadir: Path = config['datadir'] data_handler = get_datahandler(datadir, data_format=convert_to) tradesdir: Path = config['datadir'] / 'trades_csv' exchange = ExchangeResolver.load_exchange(config, validate=False) # iterate through directories in this directory data_symbols = {p.stem for p in tradesdir.rglob('*.csv')} # create pair/filename mapping markets = { (m['symbol'], m['altname']) for m in exchange.markets.values() if m.get('altname') in data_symbols } logger.info(f"Found csv files for {', '.join(data_symbols)}.") for pair, name in markets: dfs = [] # Load and combine all csv files for this pair for f in tradesdir.rglob(f"{name}.csv"): df = pd.read_csv(f, names=KRAKEN_CSV_TRADE_COLUMNS) dfs.append(df) # Load existing trades data if not dfs: # edgecase, can only happen if the file was deleted between the above glob and here logger.info(f"No data found for pair {pair}") continue trades = pd.concat(dfs, ignore_index=True) trades.loc[:, 'timestamp'] = trades['timestamp'] * 1e3 trades.loc[:, 'cost'] = trades['price'] * trades['amount'] for col in DEFAULT_TRADES_COLUMNS: if col not in trades.columns: trades[col] = '' trades = trades[DEFAULT_TRADES_COLUMNS] trades = trades_convert_types(trades) trades_df = trades_df_remove_duplicates(trades) logger.info(f"{pair}: {len(trades_df)} trades, from " f"{trades_df['date'].min():{DATETIME_PRINT_FORMAT}} to " f"{trades_df['date'].max():{DATETIME_PRINT_FORMAT}}") data_handler.trades_store(pair, trades_df)