freqtrade_origin/freqtrade/data/converter/trade_converter_kraken.py

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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)
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from freqtrade.data.history.idatahandler import get_datahandler
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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':
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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
}
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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)
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# Load existing trades data
if not dfs:
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# edgecase, can only happen if the file was deleted between the above glob and here
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logger.info(f"No data found for pair {pair}")
continue
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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:
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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)