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Bot Optimization

This page explains where to customize your strategies, and add new indicators.

Install a custom strategy file
Customize your strategy
Add more Indicator
Where is the default strategy

Since the version 0.16.0 the bot allows using custom strategy file.

Install a custom strategy file

This is very simple. Copy paste your strategy file into the folder user_data/strategies.

Let assume you have a class called AwesomeStrategy in the file awesome-strategy.py:

Move your file into user_data/strategies (you should have user_data/strategies/awesome-strategy.py
Start the bot with the param --strategy AwesomeStrategy (the parameter is the class name)

python3 ./freqtrade/main.py --strategy AwesomeStrategy

Change your strategy

The bot includes a default strategy file. However, we recommend you to use your own file to not have to lose your parameters every time the default strategy file will be updated on Github. Put your custom strategy file into the folder user_data/strategies.

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

Buy strategy rules
Sell strategy rules
Minimal ROI recommended
Stoploss recommended

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

python3 ./freqtrade/main.py --strategy AwesomeStrategy

Specify custom strategy location

If you want to use a strategy from a different folder you can pass --strategy-path

python3 ./freqtrade/main.py --strategy AwesomeStrategy --strategy-path /some/folder

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

Buy strategy

Edit the method populate_buy_trend() into your strategy file to update your buy strategy.

Sample from user_data/strategies/test_strategy.py:

def populate_buy_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
    :param metadata: Additional information, like the currently traded pair
    :return: DataFrame with buy column
    """
    dataframe.loc[
        (
            (dataframe['adx'] > 30) &
            (dataframe['tema'] <= dataframe['bb_middleband']) &
            (dataframe['tema'] > dataframe['tema'].shift(1))
        ),
        'buy'] = 1

    return dataframe

Sell strategy

Edit the method populate_sell_trend() into your strategy file to update your sell strategy. Please note that the sell-signal is only used if use_sell_signal is set to true in the configuration.

Sample from user_data/strategies/test_strategy.py:

def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    """
    Based on TA indicators, populates the sell signal for the given dataframe
    :param dataframe: DataFrame populated with indicators
    :param metadata: Additional information, like the currently traded pair
    :return: DataFrame with buy column
    """
    dataframe.loc[
        (
            (dataframe['adx'] > 70) &
            (dataframe['tema'] > dataframe['bb_middleband']) &
            (dataframe['tema'] < dataframe['tema'].shift(1))
        ),
        'sell'] = 1
    return dataframe

Add more Indicators

As you have seen, buy and sell strategies 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_buy_trend(), populate_sell_trend(), or to populate another indicator, otherwise performance may suffer.

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: Raw data from the exchange and parsed by parse_ticker_dataframe()
    :param metadata: Additional information, like the currently traded pair
    :return: a Dataframe with all mandatory indicators for the strategies
    """
    dataframe['sar'] = ta.SAR(dataframe)
    dataframe['adx'] = ta.ADX(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['htleadsine'] = hilbert['leadsine']
    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

Metadata dict

The metadata-dict (available for populate_buy_trend, populate_sell_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.

Want more indicator examples

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

Where is the default strategy?

The default buy strategy is located in the file freqtrade/default_strategy.py.

Further strategy ideas

To get additional Ideas for strategies, head over to our 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.

We also got a strategy-sharing channel in our Slack community which is a great place to get and/or share ideas.

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.

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