12 KiB
Advanced Strategies
This page explains some advanced concepts available for strategies. If you're just getting started, please be familiar with the methods described in the Strategy Customization documentation and with the Freqtrade basics first.
Freqtrade basics describes in which sequence each method described below is called, which can be helpful to understand which method to use for your custom needs.
!!! Note All callback methods described below should only be implemented in a strategy if they are actually used.
Custom stoploss
A stoploss can only ever move upwards - so if you set it to an absolute profit of 2%, you can never move it below this price.
Also, the traditional stoploss value serves as an absolute lower level and will be instated as the initial stoploss.
The usage of the custom stoploss method must be enabled by setting custom_stoploss=True on the strategy object.
The method must return a stoploss value (float / number) with a relative ratio below the current price.
E.g. current_profit = 0.05 (5% profit) - stoploss returns 0.02 - then you "locked in" a profit of 3% (0.05 - 0.02 = 0.03).
custom_stoploss = True
def stoploss_value(self, pair: str, trade: Trade, current_rate: float, current_profit: float,
**kwargs) -> float:
# TODO: Add full docstring here
return 0.04
!!! Tip "Trailing stoploss"
It's recommended to disable trailing_stop when using custom stoploss values. Both can work in tandem, but you might encounter the trailing stop to move the price higher while your custom function would not want this, causing conflicting behavior.
Custom stoploss examples
Absolute stoploss
The below example sets absolute profit levels based on the current profit.
- Use the regular stoploss until 20% profit is reached
- Once profit is > 20% - stoploss will be set to 7%.s
- Once profit is > 25% - stoploss will be 15%.
- Once profit is > 40%, stoploss will be at 25%, locking in at least 25% of the profit.
custom_stoploss = True
def stoploss_value(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> float:
# TODO: Add full docstring here
# Calculate as `-desired_stop_from_open + current_profit` to get the distance between current_profit and initial price
if current_profit > 0.20:
return (-0.7 + current_profit)
if current_profit > 0.25:
return (-0.15 + current_profit)
if current_profit > 0.40:
return (-0.25 + current_profit)
return 1
Time based trailing stop
Use the initial stoploss for the first 60 minutes, after this change to 10% trailing stoploss, and after 2 hours (120 minutes) we use a 5% trailing stoploss.
custom_stoploss = True
def stoploss_value(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> float:
# TODO: Add full docstring here
if current_time - timedelta(minutes=60) > trade.open_time:
return -0.10
elif current_time - timedelta(minutes=120) > trade.open_time:
return -0.05
return 1
Custom order timeout rules
Simple, time-based order-timeouts can be configured either via strategy or in the configuration in the unfilledtimeout section.
However, freqtrade also offers a custom callback for both order types, which allows you to decide based on custom criteria if a order did time out or not.
!!! Note Unfilled order timeouts are not relevant during backtesting or hyperopt, and are only relevant during real (live) trading. Therefore these methods are only called in these circumstances.
Custom order timeout example
A simple example, which applies different unfilled-timeouts depending on the price of the asset can be seen below. It applies a tight timeout for higher priced assets, while allowing more time to fill on cheap coins.
The function must return either True (cancel order) or False (keep order alive).
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
unfilledtimeout = {
'buy': 60 * 25,
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
return True
return False
def check_sell_timeout(self, pair: str, trade: 'Trade', order: dict, **kwargs) -> bool:
if trade.open_rate > 100 and trade.open_date < datetime.utcnow() - timedelta(minutes=5):
return True
elif trade.open_rate > 10 and trade.open_date < datetime.utcnow() - timedelta(minutes=3):
return True
elif trade.open_rate < 1 and trade.open_date < datetime.utcnow() - timedelta(hours=24):
return True
return False
!!! Note
For the above example, unfilledtimeout must be set to something bigger than 24h, otherwise that type of timeout will apply first.
Custom order timeout example (using additional data)
from datetime import datetime
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
# Set unfilledtimeout to 25 hours, since our maximum timeout from below is 24 hours.
unfilledtimeout = {
'buy': 60 * 25,
'sell': 60 * 25
}
def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['bids'][0][0]
# Cancel buy order if price is more than 2% above the order.
if current_price > order['price'] * 1.02:
return True
return False
def check_sell_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
ob = self.dp.orderbook(pair, 1)
current_price = ob['asks'][0][0]
# Cancel sell order if price is more than 2% below the order.
if current_price < order['price'] * 0.98:
return True
return False
Bot loop start callback
A simple callback which is called once at the start of every bot throttling iteration. This can be used to perform calculations which are pair independent (apply to all pairs), loading of external data, etc.
import requests
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def bot_loop_start(self, **kwargs) -> None:
"""
Called at the start of the bot iteration (one loop).
Might be used to perform pair-independent tasks
(e.g. gather some remote resource for comparison)
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
"""
if self.config['runmode'].value in ('live', 'dry_run'):
# Assign this to the class by using self.*
# can then be used by populate_* methods
self.remote_data = requests.get('https://some_remote_source.example.com')
Bot order confirmation
Trade entry (buy order) confirmation
confirm_trade_entry() can be used to abort a trade entry at the latest second (maybe because the price is not what we expect).
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, **kwargs) -> bool:
"""
Called right before placing a buy order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be bought.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in target (quote) currency that's going to be traded.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the buy-order is placed on the exchange.
False aborts the process
"""
return True
Trade exit (sell order) confirmation
confirm_trade_exit() can be used to abort a trade exit (sell) at the latest second (maybe because the price is not what we expect).
from freqtrade.persistence import Trade
class AwesomeStrategy(IStrategy):
# ... populate_* methods
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
"""
Called right before placing a regular sell order.
Timing for this function is critical, so avoid doing heavy computations or
network requests in this method.
For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
When not implemented by a strategy, returns True (always confirming).
:param pair: Pair that's about to be sold.
:param order_type: Order type (as configured in order_types). usually limit or market.
:param amount: Amount in quote currency.
:param rate: Rate that's going to be used when using limit orders
:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
:param sell_reason: Sell reason.
Can be any of ['roi', 'stop_loss', 'stoploss_on_exchange', 'trailing_stop_loss',
'sell_signal', 'force_sell', 'emergency_sell']
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
:return bool: When True is returned, then the sell-order is placed on the exchange.
False aborts the process
"""
if sell_reason == 'force_sell' and trade.calc_profit_ratio(rate) < 0:
# Reject force-sells with negative profit
# This is just a sample, please adjust to your needs
# (this does not necessarily make sense, assuming you know when you're force-selling)
return False
return True
Derived strategies
The strategies can be derived from other strategies. This avoids duplication of your custom strategy code. You can use this technique to override small parts of your main strategy, leaving the rest untouched:
class MyAwesomeStrategy(IStrategy):
...
stoploss = 0.13
trailing_stop = False
# All other attributes and methods are here as they
# should be in any custom strategy...
...
class MyAwesomeStrategy2(MyAwesomeStrategy):
# Override something
stoploss = 0.08
trailing_stop = True
Both attributes and methods may be overridden, altering behavior of the original strategy in a way you need.