Advanced Strategies¶
This page explains some advanced concepts available for strategies. If you're just getting started, please familiarize yourself with the Freqtrade basics and methods described in Strategy Customization first.
The call sequence of the methods described here is covered under bot execution logic. Those docs are also helpful in deciding which method is most suitable for your customisation needs.
Note
Callback methods should only be implemented if a strategy uses them.
Tip
Start off with a strategy template containing all available callback methods by running freqtrade new-strategy --strategy MyAwesomeStrategy --template advanced
Storing information¶
Storing information can be accomplished by creating a new dictionary within the strategy class.
The name of the variable can be chosen at will, but should be prefixed with custom_ to avoid naming collisions with predefined strategy variables.
class AwesomeStrategy(IStrategy):
# Create custom dictionary
custom_info = {}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Check if the entry already exists
if not metadata["pair"] in self.custom_info:
# Create empty entry for this pair
self.custom_info[metadata["pair"]] = {}
if "crosstime" in self.custom_info[metadata["pair"]]:
self.custom_info[metadata["pair"]]["crosstime"] += 1
else:
self.custom_info[metadata["pair"]]["crosstime"] = 1
Warning
The data is not persisted after a bot-restart (or config-reload). Also, the amount of data should be kept smallish (no DataFrames and such), otherwise the bot will start to consume a lot of memory and eventually run out of memory and crash.
Note
If the data is pair-specific, make sure to use pair as one of the keys in the dictionary.
Dataframe access¶
You may access dataframe in various strategy functions by querying it from dataprovider.
from freqtrade.exchange import timeframe_to_prev_date
class AwesomeStrategy(IStrategy):
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
rate: float, time_in_force: str, exit_reason: str,
current_time: 'datetime', **kwargs) -> bool:
# Obtain pair dataframe.
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
# Obtain last available candle. Do not use current_time to look up latest candle, because
# current_time points to current incomplete candle whose data is not available.
last_candle = dataframe.iloc[-1].squeeze()
# <...>
# In dry/live runs trade open date will not match candle open date therefore it must be
# rounded.
trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
# Look up trade candle.
trade_candle = dataframe.loc[dataframe['date'] == trade_date]
# trade_candle may be empty for trades that just opened as it is still incomplete.
if not trade_candle.empty:
trade_candle = trade_candle.squeeze()
# <...>
Using .iloc[-1]
You can use .iloc[-1] here because get_analyzed_dataframe() only returns candles that backtesting is allowed to see.
This will not work in populate_* methods, so make sure to not use .iloc[] in that area.
Also, this will only work starting with version 2021.5.
Enter Tag¶
When your strategy has multiple buy signals, you can name the signal that triggered.
Then you can access your buy signal on custom_exit
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < 35) &
(dataframe['volume'] > 0)
),
['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')
return dataframe
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
if trade.enter_tag == 'buy_signal_rsi' and last_candle['rsi'] > 80:
return 'sell_signal_rsi'
return None
Note
enter_tag is limited to 100 characters, remaining data will be truncated.
Warning
There is only one enter_tag column, which is used for both long and short trades.
As a consequence, this column must be treated as "last write wins" (it's just a dataframe column after all).
In fancy situations, where multiple signals collide (or if signals are deactivated again based on different conditions), this can lead to odd results with the wrong tag applied to an entry signal.
These results are a consequence of the strategy overwriting prior tags - where the last tag will "stick" and will be the one freqtrade will use.
Exit tag¶
Similar to Buy Tagging, you can also specify a sell tag.
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] > 70) &
(dataframe['volume'] > 0)
),
['exit_long', 'exit_tag']] = (1, 'exit_rsi')
return dataframe
The provided exit-tag is then used as sell-reason - and shown as such in backtest results.
Note
exit_reason is limited to 100 characters, remaining data will be truncated.
Strategy version¶
You can implement custom strategy versioning by using the "version" method, and returning the version you would like this strategy to have.
def version(self) -> str:
"""
Returns version of the strategy.
"""
return "1.1"
Note
You should make sure to implement proper version control (like a git repository) alongside this, as freqtrade will not keep historic versions of your strategy, so it's up to the user to be able to eventually roll back to a prior version of the strategy.
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...
...
from myawesomestrategy import MyAwesomeStrategy
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.
While keeping the subclass in the same file is technically possible, it can lead to some problems with hyperopt parameter files, we therefore recommend to use separate strategy files, and import the parent strategy as shown above.
Embedding Strategies¶
Freqtrade provides you with an easy way to embed the strategy into your configuration file. This is done by utilizing BASE64 encoding and providing this string at the strategy configuration field, in your chosen config file.
Encoding a string as BASE64¶
This is a quick example, how to generate the BASE64 string in python
from base64 import urlsafe_b64encode
with open(file, 'r') as f:
content = f.read()
content = urlsafe_b64encode(content.encode('utf-8'))
The variable 'content', will contain the strategy file in a BASE64 encoded form. Which can now be set in your configurations file as following
"strategy": "NameOfStrategy:BASE64String"
Please ensure that 'NameOfStrategy' is identical to the strategy name!
Performance warning¶
When executing a strategy, one can sometimes be greeted by the following in the logs
PerformanceWarning: DataFrame is highly fragmented.
This is a warning from pandas and as the warning continues to say:
use pd.concat(axis=1).
This can have slight performance implications, which are usually only visible during hyperopt (when optimizing an indicator).
For example:
for val in self.buy_ema_short.range:
dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)
should be rewritten to
frames = [dataframe]
for val in self.buy_ema_short.range:
frames.append(DataFrame({
f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
}))
# Combine all dataframes, and reassign the original dataframe column
dataframe = pd.concat(frames, axis=1)
Freqtrade does however also counter this by running dataframe.copy() on the dataframe right after the populate_indicators() method - so performance implications of this should be low to non-existant.