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
Freqtrade allows storing/retrieving user custom information associated with a specific trade in the database.
Using a trade object, information can be stored using trade.set_custom_data(key='my_key', value=my_value) and retrieved using trade.get_custom_data(key='my_key'). Each data entry is associated with a trade and a user supplied key (of type string). This means that this can only be used in callbacks that also provide a trade object.
For the data to be able to be stored within the database, freqtrade must serialized the data. This is done by converting the data to a JSON formatted string. Freqtrade will attempt to reverse this action on retrieval, so from a strategy perspective, this should not be relevant.
from freqtrade.persistence import Trade
from datetime import timedelta
class AwesomeStrategy(IStrategy):
def bot_loop_start(self, **kwargs) -> None:
for trade in Trade.get_open_order_trades():
fills = trade.select_filled_orders(trade.entry_side)
if trade.pair == 'ETH/USDT':
trade_entry_type = trade.get_custom_data(key='entry_type')
if trade_entry_type is None:
trade_entry_type = 'breakout' if 'entry_1' in trade.enter_tag else 'dip'
elif fills > 1:
trade_entry_type = 'buy_up'
trade.set_custom_data(key='entry_type', value=trade_entry_type)
return super().bot_loop_start(**kwargs)
def adjust_entry_price(self, trade: Trade, order: Optional[Order], pair: str,
current_time: datetime, proposed_rate: float, current_order_rate: float,
entry_tag: Optional[str], side: str, **kwargs) -> float:
# Limit orders to use and follow SMA200 as price target for the first 10 minutes since entry trigger for BTC/USDT pair.
if (
pair == 'BTC/USDT'
and entry_tag == 'long_sma200'
and side == 'long'
and (current_time - timedelta(minutes=10)) > trade.open_date_utc
and order.filled == 0.0
):
dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
current_candle = dataframe.iloc[-1].squeeze()
# store information about entry adjustment
existing_count = trade.get_custom_data('num_entry_adjustments', default=0)
if not existing_count:
existing_count = 1
else:
existing_count += 1
trade.set_custom_data(key='num_entry_adjustments', value=existing_count)
# adjust order price
return current_candle['sma_200']
# default: maintain existing order
return current_order_rate
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs):
entry_adjustment_count = trade.get_custom_data(key='num_entry_adjustments')
trade_entry_type = trade.get_custom_data(key='entry_type')
if entry_adjustment_count is None:
if current_profit > 0.01 and (current_time - timedelta(minutes=100) > trade.open_date_utc):
return True, 'exit_1'
else
if entry_adjustment_count > 0 and if current_profit > 0.05:
return True, 'exit_2'
if trade_entry_type == 'breakout' and current_profit > 0.1:
return True, 'exit_3
return False, NoneThe above is a simple example - there are simpler ways to retrieve trade data like entry-adjustments.
!!! Note
It is recommended that simple data types are used [bool, int, float, str] to ensure no issues when serializing the data that needs to be stored.
Storing big junks of data may lead to unintended side-effects, like a database becoming big (and as a consequence, also slow).
!!! Warning "Non-serializable data"
If supplied data cannot be serialized a warning is logged and the entry for the specified key will contain None as data.
??? Note "All attributes"
custom-data has the following accessors through the Trade object (assumed as trade below):
* `trade.get_custom_data(key='something', default=0)` - Returns the actual value given in the type provided.
* `trade.get_custom_data_entry(key='something')` - Returns the entry - including metadata. The value is accessible via `.value` property.
* `trade.set_custom_data(key='something', value={'some': 'value'})` - set or update the corresponding key for this trade. Value must be serializable - and we recommend to keep the stored data relatively small.
"value" can be any type (both in setting and receiving) - but must be json serializable.
!!! Warning "Deprecated"
This method of storing information is deprecated and we do advise against using non-persistent storage.
Please use Persistent Storage instead.
It's content has therefore been collapsed.
??? Abstract "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.
```python
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.
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()
# <...>!!! Warning "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.
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.
Similar to Entry Tagging, you can also specify an exit 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 dataframeThe 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.
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.
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 = TrueBoth 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.
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.
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!
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-existent.