# Strategy Customization¶

Please familiarize yourself with Freqtrade basics first, which provides overall info on how the bot operates.

## Develop your own strategy¶

The bot includes a default strategy file. Also, several other strategies are available in the strategy repository.

You will however most likely have your own idea for a strategy. This document intends to help you convert your strategy idea into your own strategy.

To get started, use freqtrade new-strategy --strategy AwesomeStrategy (you can obviously use your own naming for your strategy). This will create a new strategy file from a template, which will be located under user_data/strategies/AwesomeStrategy.py.

Note

This is just a template file, which will most likely not be profitable out of the box.

Different template levels

freqtrade new-strategy has an additional parameter, --template, which controls the amount of pre-build information you get in the created strategy. Use --template minimal to get an empty strategy without any indicator examples, or --template advanced to get a template with most callbacks defined.

### Anatomy of a strategy¶

A strategy file contains all the information needed to build a good strategy:

• Indicators
• Entry strategy rules
• Exit strategy rules
• Minimal ROI recommended
• Stoploss strongly recommended

The bot also include a sample strategy called SampleStrategy you can update: user_data/strategies/sample_strategy.py. You can test it with the parameter: --strategy SampleStrategy

Additionally, there is an attribute called INTERFACE_VERSION, which defines the version of the strategy interface the bot should use. The current version is 3 - which is also the default when it's not set explicitly in the strategy.

Future versions will require this to be set.

freqtrade trade --strategy AwesomeStrategy


For the following section we will use the user_data/strategies/sample_strategy.py file as reference.

Strategies and Backtesting

To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware that during backtesting the full time range is passed to the populate_*() methods at once. It is therefore best to use vectorized operations (across the whole dataframe, not loops) and avoid index referencing (df.iloc[-1]), but instead use df.shift() to get to the previous candle.

Warning: Using future data

Since backtesting passes the full time range to the populate_*() methods, the strategy author needs to take care to avoid having the strategy utilize data from the future. Some common patterns for this are listed in the Common Mistakes section of this document.

### Dataframe¶

Freqtrade uses pandas to store/provide the candlestick (OHLCV) data. Pandas is a great library developed for processing large amounts of data.

Each row in a dataframe corresponds to one candle on a chart, with the latest candle always being the last in the dataframe (sorted by date).

> dataframe.head()
date      open      high       low     close     volume
0 2021-11-09 23:25:00+00:00  67279.67  67321.84  67255.01  67300.97   44.62253
1 2021-11-09 23:30:00+00:00  67300.97  67301.34  67183.03  67187.01   61.38076
2 2021-11-09 23:35:00+00:00  67187.02  67187.02  67031.93  67123.81  113.42728
3 2021-11-09 23:40:00+00:00  67123.80  67222.40  67080.33  67160.48   78.96008
4 2021-11-09 23:45:00+00:00  67160.48  67160.48  66901.26  66943.37  111.39292


Pandas provides fast ways to calculate metrics. To benefit from this speed, it's advised to not use loops, but use vectorized methods instead.

Vectorized operations perform calculations across the whole range of data and are therefore, compared to looping through each row, a lot faster when calculating indicators.

As a dataframe is a table, simple python comparisons like the following will not work

    if dataframe['rsi'] > 30:
dataframe['enter_long'] = 1


The above section will fail with The truth value of a Series is ambiguous. [...].

This must instead be written in a pandas-compatible way, so the operation is performed across the whole dataframe.

    dataframe.loc[
(dataframe['rsi'] > 30)
, 'enter_long'] = 1


With this section, you have a new column in your dataframe, which has 1 assigned whenever RSI is above 30.

### Customize Indicators¶

Buy and sell signals need indicators. You can add more indicators by extending the list contained in the method populate_indicators() from your strategy file.

You should only add the indicators used in either populate_entry_trend(), populate_exit_trend(), or to populate another indicator, otherwise performance may suffer.

It's important to always return the dataframe without removing/modifying the columns "open", "high", "low", "close", "volume", otherwise these fields would contain something unexpected.

Sample:

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame

Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Dataframe with data from the exchange
:return: a Dataframe with all mandatory indicators for the strategies
"""
dataframe['sar'] = ta.SAR(dataframe)
stoch = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ao'] = awesome_oscillator(dataframe)
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
return dataframe


Want more indicator examples?

Look into the user_data/strategies/sample_strategy.py. Then uncomment indicators you need.

#### Indicator libraries¶

Out of the box, freqtrade installs the following technical libraries:

Additional technical libraries can be installed as necessary, or custom indicators may be written / invented by the strategy author.

### Strategy startup period¶

Most indicators have an instable startup period, in which they are either not available (NaN), or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be. To account for this, the strategy can be assigned the startup_candle_count attribute. This should be set to the maximum number of candles that the strategy requires to calculate stable indicators. In the case where a user includes higher timeframes with informative pairs, the startup_candle_count does not necessarily change. The value is the maximum period (in candles) that any of the informatives timeframes need to compute stable indicators.

In this example strategy, this should be set to 100 (startup_candle_count = 100), since the longest needed history is 100 candles.

    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)


By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.

Using x calls to get OHLCV

If you receive a warning like WARNING - Using 3 calls to get OHLCV. This can result in slower operations for the bot. Please check if you really need 1500 candles for your strategy - you should consider if you really need this much historic data for your signals. Having this will cause Freqtrade to make multiple calls for the same pair, which will obviously be slower than one network request. As a consequence, Freqtrade will take longer to refresh candles - and should therefore be avoided if possible. This is capped to 5 total calls to avoid overloading the exchange, or make freqtrade too slow.

Warning

startup_candle_count should be below ohlcv_candle_limit * 5 (which is 500 * 5 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.

#### Example¶

Let's try to backtest 1 month (January 2019) of 5m candles using an example strategy with EMA100, as above.

freqtrade backtesting --timerange 20190101-20190201 --timeframe 5m


Assuming startup_candle_count is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from 20190101 - (100 * 5m) - which is ~2018-12-31 15:30:00. If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.

Note

If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.

### Entry signal rules¶

Edit the method populate_entry_trend() in your strategy file to update your entry strategy.

It's important to always return the dataframe without removing/modifying the columns "open", "high", "low", "close", "volume", otherwise these fields would contain something unexpected.

This method will also define a new column, "enter_long" ("enter_short" for shorts), which needs to contain 1 for entries, and 0 for "no action". enter_long is a mandatory column that must be set even if the strategy is shorting only.

Sample from user_data/strategies/sample_strategy.py:

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
(dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
(dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
(dataframe['volume'] > 0)  # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (1, 'rsi_cross')

return dataframe


Short-entries can be created by setting enter_short (corresponds to enter_long for long trades). The enter_tag column remains identical. Short-trades need to be supported by your exchange and market configuration! Please make sure to set can_short appropriately on your strategy if you intend to short.

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
(dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard
(dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
(dataframe['volume'] > 0)  # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (1, 'rsi_cross')

dataframe.loc[
(
(qtpylib.crossed_below(dataframe['rsi'], 70)) &  # Signal: RSI crosses below 70
(dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
(dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
(dataframe['volume'] > 0)  # Make sure Volume is not 0
),
['enter_short', 'enter_tag']] = (1, 'rsi_cross')

return dataframe


Note

Buying requires sellers to buy from - therefore volume needs to be > 0 (dataframe['volume'] > 0) to make sure that the bot does not buy/sell in no-activity periods.

### Exit signal rules¶

Edit the method populate_exit_trend() into your strategy file to update your exit strategy. The exit-signal is only used for exits if use_exit_signal is set to true in the configuration. use_exit_signal will not influence signal collision rules - which will still apply and can prevent entries.

It's important to always return the dataframe without removing/modifying the columns "open", "high", "low", "close", "volume", otherwise these fields would contain something unexpected.

This method will also define a new column, "exit_long" ("exit_short" for shorts), which needs to contain 1 for exits, and 0 for "no action".

Sample from user_data/strategies/sample_strategy.py:

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
(dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
(dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
(dataframe['volume'] > 0)  # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
return dataframe


Short-exits can be created by setting exit_short (corresponds to exit_long). The exit_tag column remains identical. Short-trades need to be supported by your exchange and market configuration!

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
(dataframe['tema'] > dataframe['bb_middleband']) &  # Guard
(dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard
(dataframe['volume'] > 0)  # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (1, 'rsi_too_high')
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['rsi'], 30)) &  # Signal: RSI crosses below 30
(dataframe['tema'] < dataframe['bb_middleband']) &  # Guard
(dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard
(dataframe['volume'] > 0)  # Make sure Volume is not 0
),
['exit_short', 'exit_tag']] = (1, 'rsi_too_low')
return dataframe


### Minimal ROI¶

This dict defines the minimal Return On Investment (ROI) a trade should reach before exiting, independent from the exit signal.

It is of the following format, with the dict key (left side of the colon) being the minutes passed since the trade opened, and the value (right side of the colon) being the percentage.

minimal_roi = {
"40": 0.0,
"30": 0.01,
"20": 0.02,
"0": 0.04
}


The above configuration would therefore mean:

• Exit whenever 4% profit was reached
• Exit when 2% profit was reached (in effect after 20 minutes)
• Exit when 1% profit was reached (in effect after 30 minutes)
• Exit when trade is non-loosing (in effect after 40 minutes)

The calculation does include fees.

To disable ROI completely, set it to an insanely high number:

minimal_roi = {
"0": 100
}


While technically not completely disabled, this would exit once the trade reaches 10000% Profit.

To use times based on candle duration (timeframe), the following snippet can be handy. This will allow you to change the timeframe for the strategy, and ROI times will still be set as candles (e.g. after 3 candles ...)

from freqtrade.exchange import timeframe_to_minutes

class AwesomeStrategy(IStrategy):

timeframe = "1d"
timeframe_mins = timeframe_to_minutes(timeframe)
minimal_roi = {
"0": 0.05,                             # 5% for the first 3 candles
str(timeframe_mins * 3)): 0.02,  # 2% after 3 candles
str(timeframe_mins * 6)): 0.01,  # 1% After 6 candles
}


### Stoploss¶

Setting a stoploss is highly recommended to protect your capital from strong moves against you.

Sample of setting a 10% stoploss:

stoploss = -0.10


For the full documentation on stoploss features, look at the dedicated stoploss page.

### Timeframe¶

This is the set of candles the bot should download and use for the analysis. Common values are "1m", "5m", "15m", "1h", however all values supported by your exchange should work.

Please note that the same entry/exit signals may work well with one timeframe, but not with the others.

This setting is accessible within the strategy methods as the self.timeframe attribute.

### Can short¶

To use short signals in futures markets, you will have to let us know to do so by setting can_short=True. Strategies which enable this will fail to load on spot markets. Disabling of this will have short signals ignored (also in futures markets).

The metadata-dict (available for populate_entry_trend, populate_exit_trend, populate_indicators) contains additional information. Currently this is pair, which can be accessed using metadata['pair'] - and will return a pair in the format XRP/BTC.

The Metadata-dict should not be modified and does not persist information across multiple calls. Instead, have a look at the Storing information section.

By default, freqtrade will attempt to load strategies from all .py files within user_data/strategies.

Assuming your strategy is called AwesomeStrategy, stored in the file user_data/strategies/AwesomeStrategy.py, then you can start freqtrade with freqtrade trade --strategy AwesomeStrategy. Note that we're using the class-name, not the file name.

You can use freqtrade list-strategies to see a list of all strategies Freqtrade is able to load (all strategies in the correct folder). It will also include a "status" field, highlighting potential problems.

Customize strategy directory

You can use a different directory by using --strategy-path user_data/otherPath. This parameter is available to all commands that require a strategy.

## Informative Pairs¶

### Get data for non-tradeable pairs¶

Data for additional, informative pairs (reference pairs) can be beneficial for some strategies. OHLCV data for these pairs will be downloaded as part of the regular whitelist refresh process and is available via DataProvider just as other pairs (see below). These parts will not be traded unless they are also specified in the pair whitelist, or have been selected by Dynamic Whitelisting.

The pairs need to be specified as tuples in the format ("pair", "timeframe"), with pair as the first and timeframe as the second argument.

Sample:

def informative_pairs(self):
return [("ETH/USDT", "5m"),
("BTC/TUSD", "15m"),
]


A full sample can be found in the DataProvider section.

Warning

As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short. All timeframes and all pairs can be specified as long as they are available (and active) on the used exchange. It is however better to use resampling to longer timeframes whenever possible to avoid hammering the exchange with too many requests and risk being blocked.

Alternative candle types

Informative_pairs can also provide a 3rd tuple element defining the candle type explicitly. Availability of alternative candle-types will depend on the trading-mode and the exchange. In general, spot pairs cannot be used in futures markets, and futures candles can't be used as informative pairs for spot bots. Details about this may vary, if they do, this can be found in the exchange documentation.

def informative_pairs(self):
return [
("ETH/USDT", "5m", ""),   # Uses default candletype, depends on trading_mode (recommended)
("ETH/USDT", "5m", "spot"),   # Forces usage of spot candles (only valid for bots running on spot markets).
("BTC/TUSD", "15m", "futures"),  # Uses futures candles (only bots with trading_mode=futures)
("BTC/TUSD", "15m", "mark"),  # Uses mark candles (only bots with trading_mode=futures)
]


### Informative pairs decorator (@informative())¶

In most common case it is possible to easily define informative pairs by using a decorator. All decorated populate_indicators_* methods run in isolation, not having access to data from other informative pairs, in the end all informative dataframes are merged and passed to main populate_indicators() method. When hyperopting, use of hyperoptable parameter .value attribute is not supported. Please use .range attribute. See optimizing an indicator parameter for more information.

Full documentation
def informative(timeframe: str, asset: str = '',
fmt: Optional[Union[str, Callable[[KwArg(str)], str]]] = None,
*,
candle_type: Optional[CandleType] = None,
ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
"""
A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to
define informative indicators.

Example usage:

@informative('1h')
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe

:param timeframe: Informative timeframe. Must always be equal or higher than strategy timeframe.
:param asset: Informative asset, for example BTC, BTC/USDT, ETH/BTC. Do not specify to use
current pair.
:param fmt: Column format (str) or column formatter (callable(name, asset, timeframe)). When not
specified, defaults to:
* {base}_{quote}_{column}_{timeframe} if asset is specified.
* {column}_{timeframe} if asset is not specified.
Format string supports these format variables:
* {asset} - full name of the asset, for example 'BTC/USDT'.
* {base} - base currency in lower case, for example 'eth'.
* {BASE} - same as {base}, except in upper case.
* {quote} - quote currency in lower case, for example 'usdt'.
* {QUOTE} - same as {quote}, except in upper case.
* {column} - name of dataframe column.
* {timeframe} - timeframe of informative dataframe.
:param ffill: ffill dataframe after merging informative pair.
:param candle_type: '', mark, index, premiumIndex, or funding_rate
"""

Fast and easy way to define informative pairs

Most of the time we do not need power and flexibility offered by merge_informative_pair(), therefore we can use a decorator to quickly define informative pairs.

from datetime import datetime
from freqtrade.strategy import IStrategy, informative

class AwesomeStrategy(IStrategy):

# This method is not required.
# def informative_pairs(self): ...

# Define informative upper timeframe for each pair. Decorators can be stacked on same
# method. Available in populate_indicators as 'rsi_30m' and 'rsi_1h'.
@informative('30m')
@informative('1h')
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe

# Define BTC/STAKE informative pair. Available in populate_indicators and other methods as
# 'btc_rsi_1h'. Current stake currency should be specified as {stake} format variable
# instead of hard-coding actual stake currency. Available in populate_indicators and other
# methods as 'btc_usdt_rsi_1h' (when stake currency is USDT).
@informative('1h', 'BTC/{stake}')
def populate_indicators_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe

# Define BTC/ETH informative pair. You must specify quote currency if it is different from
# stake currency. Available in populate_indicators and other methods as 'eth_btc_rsi_1h'.
@informative('1h', 'ETH/BTC')
def populate_indicators_eth_btc_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe

# Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
# column names. A callable fmt(**kwargs) -> str may be specified, to implement custom
# formatting. Available in populate_indicators and other methods as 'rsi_upper'.
@informative('1h', 'BTC/{stake}', '{column}')
def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14)
return dataframe

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Strategy timeframe indicators for current pair.
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Informative pairs are available in this method.
dataframe['rsi_less'] = dataframe['rsi'] < dataframe['rsi_1h']
return dataframe


Note

Do not use @informative decorator if you need to use data of one informative pair when generating another informative pair. Instead, define informative pairs manually as described in the DataProvider section.

Note

Use string formatting when accessing informative dataframes of other pairs. This will allow easily changing stake currency in config without having to adjust strategy code.

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
stake = self.config['stake_currency']
dataframe.loc[
(
(dataframe[f'btc_{stake}_rsi_1h'] < 35)
&
(dataframe['volume'] > 0)
),
['enter_long', 'enter_tag']] = (1, 'buy_signal_rsi')

return dataframe


Alternatively column renaming may be used to remove stake currency from column names: @informative('1h', 'BTC/{stake}', fmt='{base}_{column}_{timeframe}').

Duplicate method names

Methods tagged with @informative() decorator must always have unique names! Re-using same name (for example when copy-pasting already defined informative method) will overwrite previously defined method and not produce any errors due to limitations of Python programming language. In such cases you will find that indicators created in earlier-defined methods are not available in the dataframe. Carefully review method names and make sure they are unique!

## Additional data (DataProvider)¶

The strategy provides access to the DataProvider. This allows you to get additional data to use in your strategy.

All methods return None in case of failure (do not raise an exception).

Please always check the mode of operation to select the correct method to get data (samples see below).

Hyperopt

Dataprovider is available during hyperopt, however it can only be used in populate_indicators() within a strategy. It is not available in populate_buy() and populate_sell() methods, nor in populate_indicators(), if this method located in the hyperopt file.

### Possible options for DataProvider¶

• available_pairs - Property with tuples listing cached pairs with their timeframe (pair, timeframe).
• current_whitelist() - Returns a current list of whitelisted pairs. Useful for accessing dynamic whitelists (i.e. VolumePairlist)
• get_pair_dataframe(pair, timeframe) - This is a universal method, which returns either historical data (for backtesting) or cached live data (for the Dry-Run and Live-Run modes).
• get_analyzed_dataframe(pair, timeframe) - Returns the analyzed dataframe (after calling populate_indicators(), populate_buy(), populate_sell()) and the time of the latest analysis.
• historic_ohlcv(pair, timeframe) - Returns historical data stored on disk.
• market(pair) - Returns market data for the pair: fees, limits, precisions, activity flag, etc. See ccxt documentation for more details on the Market data structure.
• ohlcv(pair, timeframe) - Currently cached candle (OHLCV) data for the pair, returns DataFrame or empty DataFrame.
• orderbook(pair, maximum) - Returns latest orderbook data for the pair, a dict with bids/asks with a total of maximum entries.
• ticker(pair) - Returns current ticker data for the pair. See ccxt documentation for more details on the Ticker data structure.
• runmode - Property containing the current runmode.

### available_pairs¶

for pair, timeframe in self.dp.available_pairs:
print(f"available {pair}, {timeframe}")


### current_whitelist()¶

Imagine you've developed a strategy that trades the 5m timeframe using signals generated from a 1d timeframe on the top 10 volume pairs by volume.

The strategy might look something like this:

Scan through the top 10 pairs by volume using the VolumePairList every 5 minutes and use a 14 day RSI to buy and sell.

Due to the limited available data, it's very difficult to resample 5m candles into daily candles for use in a 14 day RSI. Most exchanges limit us to just 500-1000 candles which effectively gives us around 1.74 daily candles. We need 14 days at least!

Since we can't resample the data we will have to use an informative pair; and since the whitelist will be dynamic we don't know which pair(s) to use.

This is where calling self.dp.current_whitelist() comes in handy.

    def informative_pairs(self):

# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
return informative_pairs

Plotting with current_whitelist

Current whitelist is not supported for plot-dataframe, as this command is usually used by providing an explicit pairlist - and would therefore make the return values of this method misleading.

### get_pair_dataframe(pair, timeframe)¶

# fetch live / historical candle (OHLCV) data for the first informative pair
inf_pair, inf_timeframe = self.informative_pairs()[0]
informative = self.dp.get_pair_dataframe(pair=inf_pair,
timeframe=inf_timeframe)


In backtesting, dp.get_pair_dataframe() behavior differs depending on where it's called. Within populate_*() methods, dp.get_pair_dataframe() returns the full timerange. Please make sure to not "look into the future" to avoid surprises when running in dry/live mode. Within callbacks, you'll get the full timerange up to the current (simulated) candle.

### get_analyzed_dataframe(pair, timeframe)¶

This method is used by freqtrade internally to determine the last signal. It can also be used in specific callbacks to get the signal that caused the action (see Advanced Strategy Documentation for more details on available callbacks).

# fetch current dataframe
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=metadata['pair'],
timeframe=self.timeframe)


No data available

Returns an empty dataframe if the requested pair was not cached. You can check for this with if dataframe.empty: and handle this case accordingly. This should not happen when using whitelisted pairs.

### orderbook(pair, maximum)¶

if self.dp.runmode.value in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]


The orderbook structure is aligned with the order structure from ccxt, so the result will look as follows:

{
'bids': [
[ price, amount ], // [ float, float ]
[ price, amount ],
...
],
[ price, amount ],
[ price, amount ],
//...
],
//...
}


Therefore, using ob['bids'][0][0] as demonstrated above will result in using the best bid price. ob['bids'][0][1] would look at the amount at this orderbook position.

The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return uptodate values.

### ticker(pair)¶

if self.dp.runmode.value in ('live', 'dry_run'):
dataframe['last_price'] = ticker['last']
dataframe['volume24h'] = ticker['quoteVolume']
dataframe['vwap'] = ticker['vwap']


Warning

Although the ticker data structure is a part of the ccxt Unified Interface, the values returned by this method can vary for different exchanges. For instance, many exchanges do not return vwap values, some exchanges does not always fills in the last field (so it can be None), etc. So you need to carefully verify the ticker data returned from the exchange and add appropriate error handling / defaults.

This method will always return up-to-date values - so usage during backtesting / hyperopt without runmode checks will lead to wrong results.

The dataprovider .send_msg() function allows you to send custom notifications from your strategy. Identical notifications will only be sent once per candle, unless the 2nd argument (always_send) is set to True.

    self.dp.send_msg(f"{metadata['pair']} just got hot!")

# Force send this notification, avoid caching (Please read warning below!)
self.dp.send_msg(f"{metadata['pair']} just got hot!", always_send=True)


Notifications will only be sent in trading modes (Live/Dry-run) - so this method can be called without conditions for backtesting.

Spamming

You can spam yourself pretty good by setting always_send=True in this method. Use this with great care and only in conditions you know will not happen throughout a candle to avoid a message every 5 seconds.

### Complete Data-provider sample¶

from freqtrade.strategy import IStrategy, merge_informative_pair
from pandas import DataFrame

class SampleStrategy(IStrategy):
# strategy init stuff...

timeframe = '5m'

# more strategy init stuff..

def informative_pairs(self):

# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1d') for pair in pairs]
informative_pairs += [("ETH/USDT", "5m"),
("BTC/TUSD", "15m"),
]
return informative_pairs

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if not self.dp:
# Don't do anything if DataProvider is not available.
return dataframe

inf_tf = '1d'
# Get the informative pair
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
# Get the 14 day rsi
informative['rsi'] = ta.RSI(informative, timeperiod=14)

# Use the helper function merge_informative_pair to safely merge the pair
# Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
# use ffill to have the 1d value available in every row throughout the day.
# Without this, comparisons between columns of the original and the informative pair would only work once per day.
# Full documentation of this method, see below
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)

# Calculate rsi of the original dataframe (5m timeframe)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

# Do other stuff
# ...

return dataframe

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
(dataframe['rsi_1d'] < 30) &                     # Ensure daily RSI is < 30
(dataframe['volume'] > 0)                        # Ensure this candle had volume (important for backtesting)
),
['enter_long', 'enter_tag']] = (1, 'rsi_cross')


## Helper functions¶

### merge_informative_pair()¶

This method helps you merge an informative pair to a regular dataframe without lookahead bias. It's there to help you merge the dataframe in a safe and consistent way.

Options:

• Rename the columns for you to create unique columns
• Merge the dataframe without lookahead bias
• Forward-fill (optional)

For a full sample, please refer to the complete data provider example below.

All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:

Column renaming

Assuming inf_tf = '1d' the resulting columns will be:

'date', 'open', 'high', 'low', 'close', 'rsi'                     # from the original dataframe
'date_1d', 'open_1d', 'high_1d', 'low_1d', 'close_1d', 'rsi_1d'   # from the informative dataframe

Column renaming - 1h

Assuming inf_tf = '1h' the resulting columns will be:

'date', 'open', 'high', 'low', 'close', 'rsi'                     # from the original dataframe
'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h'   # from the informative dataframe

Custom implementation

A custom implementation for this is possible, and can be done as follows:

# Shift date by 1 candle
# This is necessary since the data is always the "open date"
# and a 15m candle starting at 12:15 should not know the close of the 1h candle from 12:00 to 13:00
minutes = timeframe_to_minutes(inf_tf)
# Only do this if the timeframes are different:
informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')

# Rename columns to be unique
informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
# Assuming inf_tf = '1d' - then the columns will now be:
# date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d

# Combine the 2 dataframes
# all indicators on the informative sample MUST be calculated before this point
dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
# FFill to have the 1d value available in every row throughout the day.
# Without this, comparisons would only work once per day.
dataframe = dataframe.ffill()


Informative timeframe < timeframe

Using informative timeframes smaller than the dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide. To use the more detailed information properly, more advanced methods should be applied (which are out of scope for freqtrade documentation, as it'll depend on the respective need).

### stoploss_from_open()¶

Stoploss values returned from custom_stoploss must specify a percentage relative to current_rate, but sometimes you may want to specify a stoploss relative to the open price instead. stoploss_from_open() is a helper function to calculate a stoploss value that can be returned from custom_stoploss which will be equivalent to the desired percentage above the open price.

Returning a stoploss relative to the open price from the custom stoploss function

Say the open price was $100, and current_price is$121 (current_profit will be 0.21).

If we want a stop price at 7% above the open price we can call stoploss_from_open(0.07, current_profit, False) which will return 0.1157024793. 11.57% below $121 is$107, which is the same as 7% above \$100.

from datetime import datetime
from freqtrade.strategy import IStrategy, 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:

# once the profit has risen above 10%, keep the stoploss at 7% above the open price
if current_profit > 0.10:
return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)

return 1


Full examples can be found in the Custom stoploss section of the Documentation.

Note

Providing invalid input to stoploss_from_open() may produce "CustomStoploss function did not return valid stoploss" warnings. This may happen if current_profit parameter is below specified open_relative_stop. Such situations may arise when closing trade is blocked by confirm_trade_exit() method. Warnings can be solved by never blocking stop loss sells by checking exit_reason in confirm_trade_exit(), or by using return stoploss_from_open(...) or 1 idiom, which will request to not change stop loss when current_profit < open_relative_stop.

### stoploss_from_absolute()¶

In some situations it may be confusing to deal with stops relative to current rate. Instead, you may define a stoploss level using an absolute price.

Returning a stoploss using absolute price from the custom stoploss function

If we want to trail a stop price at 2xATR below current price we can call stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short).

from datetime import datetime
from freqtrade.strategy import IStrategy, stoploss_from_absolute

class AwesomeStrategy(IStrategy):

use_custom_stoploss = True

def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
return dataframe

def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
candle = dataframe.iloc[-1].squeeze()
return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)


## Additional data (Wallets)¶

The strategy provides access to the Wallets object. This contains the current balances on the exchange.

Note

Wallets is not available during backtesting / hyperopt.

Please always check if Wallets is available to avoid failures during backtesting.

if self.wallets:
free_eth = self.wallets.get_free('ETH')
used_eth = self.wallets.get_used('ETH')
total_eth = self.wallets.get_total('ETH')


### Possible options for Wallets¶

• get_free(asset) - currently available balance to trade
• get_used(asset) - currently tied up balance (open orders)
• get_total(asset) - total available balance - sum of the 2 above

A history of Trades can be retrieved in the strategy by querying the database.

At the top of the file, import Trade.

from freqtrade.persistence import Trade


The following example queries for the current pair and trades from today, however other filters can easily be added.

if self.config['runmode'].value in ('live', 'dry_run'):
Trade.open_date > datetime.utcnow() - timedelta(days=1),
# Summarize profit for this pair.


Get amount of stake_currency currently invested in Trades:

if self.config['runmode'].value in ('live', 'dry_run'):


Retrieve performance per pair. Returns a List of dicts per pair.

if self.config['runmode'].value in ('live', 'dry_run'):


Sample return value: ETH/BTC had 5 trades, with a total profit of 1.5% (ratio of 0.015).

{"pair": "ETH/BTC", "profit": 0.015, "count": 5}


Warning

Trade history is not available during backtesting or hyperopt.

## Prevent trades from happening for a specific pair¶

Freqtrade locks pairs automatically for the current candle (until that candle is over) when a pair is sold, preventing an immediate re-buy of that pair.

Locked pairs will show the message Pair <pair> is currently locked..

### Locking pairs from within the strategy¶

Sometimes it may be desired to lock a pair after certain events happen (e.g. multiple losing trades in a row).

Freqtrade has an easy method to do this from within the strategy, by calling self.lock_pair(pair, until, [reason]). until must be a datetime object in the future, after which trading will be re-enabled for that pair, while reason is an optional string detailing why the pair was locked.

Locks can also be lifted manually, by calling self.unlock_pair(pair) or self.unlock_reason(<reason>) - providing reason the pair was locked with. self.unlock_reason(<reason>) will unlock all pairs currently locked with the provided reason.

To verify if a pair is currently locked, use self.is_pair_locked(pair).

Note

Locked pairs will always be rounded up to the next candle. So assuming a 5m timeframe, a lock with until set to 10:18 will lock the pair until the candle from 10:15-10:20 will be finished.

Warning

Manually locking pairs is not available during backtesting, only locks via Protections are allowed.

#### Pair locking example¶

from freqtrade.persistence import Trade
from datetime import timedelta, datetime, timezone
# Put the above lines a the top of the strategy file, next to all the other imports
# --------

# Within populate indicators (or populate_buy):
if self.config['runmode'].value in ('live', 'dry_run'):
# fetch closed trades for the last 2 days
Trade.open_date > datetime.utcnow() - timedelta(days=2),
]).all()
# Analyze the conditions you'd like to lock the pair .... will probably be different for every strategy
if sumprofit < 0:
# Lock pair for 12 hours
self.lock_pair(metadata['pair'], until=datetime.now(timezone.utc) + timedelta(hours=12))


To inspect the created dataframe, you can issue a print-statement in either populate_entry_trend() or populate_exit_trend(). You may also want to print the pair so it's clear what data is currently shown.

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
#>> whatever condition<<<
),
['enter_long', 'enter_tag']] = (1, 'somestring')

# Print the Analyzed pair

# Inspect the last 5 rows
print(dataframe.tail())

return dataframe


Printing more than a few rows is also possible (simply use print(dataframe) instead of print(dataframe.tail())), however not recommended, as that will be very verbose (~500 lines per pair every 5 seconds).

## Common mistakes when developing strategies¶

### Peeking into the future while backtesting¶

Backtesting analyzes the whole time-range at once for performance reasons. Because of this, strategy authors need to make sure that strategies do not look-ahead into the future. This is a common pain-point, which can cause huge differences between backtesting and dry/live run methods, since they all use data which is not available during dry/live runs, so these strategies will perform well during backtesting, but will fail / perform badly in real conditions.

The following lists some common patterns which should be avoided to prevent frustration:

• don't use shift(-1). This uses data from the future, which is not available.
• don't use .iloc[-1] or any other absolute position in the dataframe, this will be different between dry-run and backtesting.
• don't use dataframe['volume'].mean(). This uses the full DataFrame for backtesting, including data from the future. Use dataframe['volume'].rolling(<window>).mean() instead
• don't use .resample('1h'). This uses the left border of the interval, so moves data from an hour to the start of the hour. Use .resample('1h', label='right') instead.

### Colliding signals¶

When conflicting signals collide (e.g. both 'enter_long' and 'exit_long' are 1), freqtrade will do nothing and ignore the entry signal. This will avoid trades that enter, and exit immediately. Obviously, this can potentially lead to missed entries.

The following rules apply, and entry signals will be ignored if more than one of the 3 signals is set:

• enter_long -> exit_long, enter_short
• enter_short -> exit_short, enter_long

## Further strategy ideas¶

To get additional Ideas for strategies, head over to the strategy repository. Feel free to use them as they are - but results will depend on the current market situation, pairs used etc. - therefore please backtest the strategy for your exchange/desired pairs first, evaluate carefully, use at your own risk. Feel free to use any of them as inspiration for your own strategies. We're happy to accept Pull Requests containing new Strategies to that repo.

## Next step¶

Now you have a perfect strategy you probably want to backtest it. Your next step is to learn How to use the Backtesting.