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.

Tip

You can get a strategy template containing all below 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 cust_ 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.

Storing custom information using DatetimeIndex from `dataframe`¶

Imagine you need to store an indicator like ATR or RSI into custom_info. To use this in a meaningful way, you will not only need the raw data of the indicator, but probably also need to keep the right timestamps.

```python import talib.abstract as ta class AwesomeStrategy(IStrategy): # Create custom dictionary custom_info = {}

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    # using "ATR" here as example
    dataframe['atr'] = ta.ATR(dataframe)
    if self.dp.runmode.value in ('backtest', 'hyperopt'):
      # add indicator mapped to correct DatetimeIndex to custom_info
      self.custom_info[metadata['pair']] = dataframe[['date', 'atr']].copy().set_index('date')
    return dataframe

```

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.

See custom_stoploss examples below on how to access the saved dataframe columns

Custom stoploss¶

The stoploss price can only ever move upwards - if the stoploss value returned from custom_stoploss would result in a lower stoploss price than was previously set, it will be ignored. 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 use_custom_stoploss=True on the strategy object. The method must return a stoploss value (float / number) as a percentage of the current price. E.g. If the current_rate is 200 USD, then returning 0.02 will set the stoploss price 2% lower, at 196 USD.

The absolute value of the return value is used (the sign is ignored), so returning 0.05 or -0.05 have the same result, a stoploss 5% below the current price.

To simulate a regular trailing stoploss of 4% (trailing 4% behind the maximum reached price) you would use the following very simple method:

``` python

additional imports required¶

from datetime import datetime from freqtrade.persistence import Trade

class AwesomeStrategy(IStrategy):

# ... populate_* methods

use_custom_stoploss = True

def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> float:
    """
    Custom stoploss logic, returning the new distance relative to current_rate (as ratio).
    e.g. returning -0.05 would create a stoploss 5% below current_rate.
    The custom stoploss can never be below self.stoploss, which serves as a hard maximum loss.

    For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/

    When not implemented by a strategy, returns the initial stoploss value
    Only called when use_custom_stoploss is set to True.

    :param pair: Pair that's currently analyzed
    :param trade: trade object.
    :param current_time: datetime object, containing the current datetime
    :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
    :param current_profit: Current profit (as ratio), calculated based on current_rate.
    :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
    :return float: New stoploss value, relative to the currentrate
    """
    return -0.04

```

Stoploss on exchange works similar to trailing_stop, and the stoploss on exchange is updated as configured in stoploss_on_exchange_interval (More details about stoploss on exchange).

Use of dates

All time-based calculations should be done based on current_time - using datetime.now() or datetime.utcnow() is discouraged, as this will break backtesting support.

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¶

The next section will show some examples on what's possible with the custom stoploss function. Of course, many more things are possible, and all examples can be combined at will.

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.

``` python from datetime import datetime, timedelta from freqtrade.persistence import Trade

class AwesomeStrategy(IStrategy):

# ... populate_* methods

use_custom_stoploss = True

def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> float:

    # Make sure you have the longest interval first - these conditions are evaluated from top to bottom.
    if current_time - timedelta(minutes=120) > trade.open_date_utc:
        return -0.05
    elif current_time - timedelta(minutes=60) > trade.open_date_utc:
        return -0.10
    return 1

```

Different stoploss per pair¶

Use a different stoploss depending on the pair. In this example, we'll trail the highest price with 10% trailing stoploss for ETH/BTC and XRP/BTC, with 5% trailing stoploss for LTC/BTC and with 15% for all other pairs.

``` python from datetime import datetime from freqtrade.persistence import Trade

class AwesomeStrategy(IStrategy):

# ... populate_* methods

use_custom_stoploss = True

def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> float:

    if pair in ('ETH/BTC', 'XRP/BTC'):
        return -0.10
    elif pair in ('LTC/BTC'):
        return -0.05
    return -0.15

```

Trailing stoploss with positive offset¶

Use the initial stoploss until the profit is above 4%, then use a trailing stoploss of 50% of the current profit with a minimum of 2.5% and a maximum of 5%.

Please note that the stoploss can only increase, values lower than the current stoploss are ignored.

``` python from datetime import datetime, timedelta from freqtrade.persistence import Trade

class AwesomeStrategy(IStrategy):

# ... populate_* methods

use_custom_stoploss = True

def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> float:

    if current_profit < 0.04:
        return -1 # return a value bigger than the inital stoploss to keep using the inital stoploss

    # After reaching the desired offset, allow the stoploss to trail by half the profit
    desired_stoploss = current_profit / 2

    # Use a minimum of 2.5% and a maximum of 5%
    return max(min(desired_stoploss, 0.05), 0.025)

```

Calculating stoploss relative to open price¶

Stoploss values returned from custom_stoploss() always specify a percentage relative to current_rate. In order to set a stoploss relative to the open price, we need to use current_profit to calculate what percentage relative to the current_rate will give you the same result as if the percentage was specified from the open price.

The helper function stoploss_from_open() can be used to convert from an open price relative stop, to a current price relative stop which can be returned from custom_stoploss().

Stepped stoploss¶

Instead of continuously trailing behind the current price, this example sets fixed stoploss price levels based on the current profit.

Use the regular stoploss until 20% profit is reached
Once profit is > 20% - set stoploss to 7% above open price.
Once profit is > 25% - set stoploss to 15% above open price.
Once profit is > 40% - set stoploss to 25% above open price.

``` python from datetime import datetime from freqtrade.persistence import Trade from freqtrade.strategy import stoploss_from_open

class AwesomeStrategy(IStrategy):

# ... populate_* methods

use_custom_stoploss = True

def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> float:

    # evaluate highest to lowest, so that highest possible stop is used
    if current_profit > 0.40:
        return stoploss_from_open(0.25, current_profit)
    elif current_profit > 0.25:
        return stoploss_from_open(0.15, current_profit)
    elif current_profit > 0.20:
        return stoploss_from_open(0.07, current_profit)

    # return maximum stoploss value, keeping current stoploss price unchanged
    return 1

```

Custom stoploss using an indicator from dataframe example¶

Imagine you want to use custom_stoploss() to use a trailing indicator like e.g. "ATR"

See: "Storing custom information using DatetimeIndex from dataframe" example above) on how to store the indicator into custom_info

Warning

only use .iat[-1] in live mode, not in backtesting/hyperopt otherwise you will look into the future see Common mistakes when developing strategies for more info.

``` python from freqtrade.persistence import Trade from freqtrade.state import RunMode

class AwesomeStrategy(IStrategy):

# ... populate_* methods

use_custom_stoploss = True

def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                    current_rate: float, current_profit: float, **kwargs) -> float:

    result = 1
    if self.custom_info and pair in self.custom_info and trade:
        # using current_time directly (like below) will only work in backtesting.
        # so check "runmode" to make sure that it's only used in backtesting/hyperopt
        if self.dp and self.dp.runmode.value in ('backtest', 'hyperopt'):
          relative_sl = self.custom_info[pair].loc[current_time]['atr']
        # in live / dry-run, it'll be really the current time
        else:
          # but we can just use the last entry from an already analyzed dataframe instead
          dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
                                                                   timeframe=self.timeframe)
          # WARNING
          # only use .iat[-1] in live mode, not in backtesting/hyperopt
          # otherwise you will look into the future
          # see: https://www.freqtrade.io/en/latest/strategy-customization/#common-mistakes-when-developing-strategies
          relative_sl = dataframe['atr'].iat[-1]

        if (relative_sl is not None):
            # new stoploss relative to current_rate
            new_stoploss = (current_rate-relative_sl)/current_rate
            # turn into relative negative offset required by `custom_stoploss` return implementation
            result = new_stoploss - 1

    return result

```

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).

``` python from datetime import datetime, timedelta, timezone 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_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
        return True
    elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
        return True
    elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - 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_utc < datetime.now(timezone.utc) - timedelta(minutes=5):
        return True
    elif trade.open_rate > 10 and trade.open_date_utc < datetime.now(timezone.utc) - timedelta(minutes=3):
        return True
    elif trade.open_rate < 1 and trade.open_date_utc < datetime.now(timezone.utc) - 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)¶

``` python 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.

``` python 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).

``` python 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).

``` python 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:

``` python 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.

Parent-strategy in different files

If you have the parent-strategy in a different file, you'll need to add the following to the top of your "child"-file to ensure proper loading, otherwise freqtrade may not be able to load the parent strategy correctly.

``` python import sys from pathlib import Path sys.path.append(str(Path(file).parent))

from myawesomestrategy import MyAwesomeStrategy ```

Embedding Strategies¶

Freqtrade provides you with 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

```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

json "strategy": "NameOfStrategy:BASE64String"

Please ensure that 'NameOfStrategy' is identical to the strategy name!

Advanced Strategies¶

Storing information¶

Storing custom information using DatetimeIndex from dataframe¶

Custom stoploss¶

additional imports required¶

Custom stoploss examples¶

Time based trailing stop¶

Different stoploss per pair¶

Trailing stoploss with positive offset¶

Calculating stoploss relative to open price¶

Stepped stoploss¶

Custom stoploss using an indicator from dataframe example¶

Custom order timeout rules¶

Custom order timeout example¶

Custom order timeout example (using additional data)¶

Bot loop start callback¶

Bot order confirmation¶

Trade entry (buy order) confirmation¶

Trade exit (sell order) confirmation¶

Derived strategies¶

Embedding Strategies¶

Encoding a string as BASE64¶

Storing custom information using DatetimeIndex from `dataframe`¶