195 lines
7.8 KiB
Python
195 lines
7.8 KiB
Python
""" Kraken exchange subclass """
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import logging
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from datetime import datetime
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from typing import Any, Dict, List, Optional, Tuple
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import ccxt
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from pandas import DataFrame
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from freqtrade.enums import Collateral, TradingMode
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from freqtrade.exceptions import (DDosProtection, InsufficientFundsError, InvalidOrderException,
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OperationalException, TemporaryError)
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from freqtrade.exchange import Exchange
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from freqtrade.exchange.common import retrier
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logger = logging.getLogger(__name__)
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class Kraken(Exchange):
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_params: Dict = {"trading_agreement": "agree"}
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_ft_has: Dict = {
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"stoploss_on_exchange": True,
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"ohlcv_candle_limit": 720,
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"trades_pagination": "id",
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"trades_pagination_arg": "since",
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"mark_ohlcv_timeframe": "4h",
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}
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_supported_trading_mode_collateral_pairs: List[Tuple[TradingMode, Collateral]] = [
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# TradingMode.SPOT always supported and not required in this list
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# (TradingMode.MARGIN, Collateral.CROSS),
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# (TradingMode.FUTURES, Collateral.CROSS)
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]
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def market_is_tradable(self, market: Dict[str, Any]) -> bool:
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"""
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Check if the market symbol is tradable by Freqtrade.
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Default checks + check if pair is darkpool pair.
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"""
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parent_check = super().market_is_tradable(market)
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return (parent_check and
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market.get('darkpool', False) is False)
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@retrier
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def get_balances(self) -> dict:
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if self._config['dry_run']:
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return {}
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try:
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balances = self._api.fetch_balance()
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# Remove additional info from ccxt results
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balances.pop("info", None)
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balances.pop("free", None)
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balances.pop("total", None)
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balances.pop("used", None)
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orders = self._api.fetch_open_orders()
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order_list = [(x["symbol"].split("/")[0 if x["side"] == "sell" else 1],
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x["remaining"] if x["side"] == "sell" else x["remaining"] * x["price"],
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# Don't remove the below comment, this can be important for debugging
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# x["side"], x["amount"],
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) for x in orders]
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for bal in balances:
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if not isinstance(balances[bal], dict):
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continue
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balances[bal]['used'] = sum(order[1] for order in order_list if order[0] == bal)
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balances[bal]['free'] = balances[bal]['total'] - balances[bal]['used']
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return balances
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except ccxt.DDoSProtection as e:
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raise DDosProtection(e) from e
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except (ccxt.NetworkError, ccxt.ExchangeError) as e:
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raise TemporaryError(
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f'Could not get balance due to {e.__class__.__name__}. Message: {e}') from e
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except ccxt.BaseError as e:
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raise OperationalException(e) from e
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def stoploss_adjust(self, stop_loss: float, order: Dict, side: str) -> bool:
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"""
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Verify stop_loss against stoploss-order value (limit or price)
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Returns True if adjustment is necessary.
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"""
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return (order['type'] in ('stop-loss', 'stop-loss-limit') and (
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(side == "sell" and stop_loss > float(order['price'])) or
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(side == "buy" and stop_loss < float(order['price']))
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))
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@retrier(retries=0)
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def stoploss(self, pair: str, amount: float, stop_price: float,
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order_types: Dict, side: str, leverage: float) -> Dict:
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"""
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Creates a stoploss market order.
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Stoploss market orders is the only stoploss type supported by kraken.
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"""
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params = self._params.copy()
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if order_types.get('stoploss', 'market') == 'limit':
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ordertype = "stop-loss-limit"
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limit_price_pct = order_types.get('stoploss_on_exchange_limit_ratio', 0.99)
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if side == "sell":
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limit_rate = stop_price * limit_price_pct
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else:
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limit_rate = stop_price * (2 - limit_price_pct)
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params['price2'] = self.price_to_precision(pair, limit_rate)
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else:
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ordertype = "stop-loss"
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stop_price = self.price_to_precision(pair, stop_price)
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if self._config['dry_run']:
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dry_order = self.create_dry_run_order(
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pair, ordertype, side, amount, stop_price, leverage)
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return dry_order
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try:
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amount = self.amount_to_precision(pair, amount)
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order = self._api.create_order(symbol=pair, type=ordertype, side=side,
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amount=amount, price=stop_price, params=params)
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self._log_exchange_response('create_stoploss_order', order)
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logger.info('stoploss order added for %s. '
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'stop price: %s.', pair, stop_price)
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return order
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except ccxt.InsufficientFunds as e:
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raise InsufficientFundsError(
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f'Insufficient funds to create {ordertype} {side} order on market {pair}. '
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f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
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f'Message: {e}') from e
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except ccxt.InvalidOrder as e:
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raise InvalidOrderException(
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f'Could not create {ordertype} {side} order on market {pair}. '
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f'Tried to create stoploss with amount {amount} at stoploss {stop_price}. '
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f'Message: {e}') from e
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except ccxt.DDoSProtection as e:
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raise DDosProtection(e) from e
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except (ccxt.NetworkError, ccxt.ExchangeError) as e:
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raise TemporaryError(
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f'Could not place {side} order due to {e.__class__.__name__}. Message: {e}') from e
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except ccxt.BaseError as e:
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raise OperationalException(e) from e
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def _set_leverage(
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self,
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leverage: float,
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pair: Optional[str] = None,
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trading_mode: Optional[TradingMode] = None
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):
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"""
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Kraken set's the leverage as an option in the order object, so we need to
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add it to params
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"""
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return
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def _get_params(self, ordertype: str, leverage: float, time_in_force: str = 'gtc') -> Dict:
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params = super()._get_params(ordertype, leverage, time_in_force)
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if leverage > 1.0:
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params['leverage'] = leverage
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return params
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def calculate_funding_fees(
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self,
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df: DataFrame,
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amount: float,
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is_short: bool,
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open_date: datetime,
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close_date: Optional[datetime] = None,
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time_in_ratio: Optional[float] = None
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) -> float:
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"""
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# ! This method will always error when run by Freqtrade because time_in_ratio is never
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# ! passed to _get_funding_fee. For kraken futures to work in dry run and backtesting
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# ! functionality must be added that passes the parameter time_in_ratio to
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# ! _get_funding_fee when using Kraken
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calculates the sum of all funding fees that occurred for a pair during a futures trade
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:param df: Dataframe containing combined funding and mark rates
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as `open_fund` and `open_mark`.
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:param amount: The quantity of the trade
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:param is_short: trade direction
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:param open_date: The date and time that the trade started
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:param close_date: The date and time that the trade ended
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:param time_in_ratio: Not used by most exchange classes
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"""
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if not time_in_ratio:
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raise OperationalException(
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f"time_in_ratio is required for {self.name}._get_funding_fee")
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fees: float = 0
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if not df.empty:
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df = df[(df['date'] >= open_date) & (df['date'] <= close_date)]
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fees = sum(df['open_fund'] * df['open_mark'] * amount * time_in_ratio)
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return fees if is_short else -fees
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