Optimization¶

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

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.

Best copy the test-strategy and modify this copy to avoid having bot-updates override your changes. cp user_data/strategies/test_strategy.py user_data/strategies/awesome-strategy.py

Anatomy of a strategy¶

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

Indicators
Buy strategy rules
Sell strategy rules
Minimal ROI recommended
Stoploss strongly 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

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

!!! Note: Strategies and Backtesting To avoid problems and unexpected differences between Backtesting and dry/live modes, please be aware that during backtesting the full time-interval 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.

Customize Indicators¶

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.

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: 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

Want more indicator examples?

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

Buy signal rules¶

Edit the method populate_buy_trend() in your strategy file to update your buy 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 will method will also define a new column, "buy", which needs to contain 1 for buys, and 0 for "no action".

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 signal rules¶

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.

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 will method will also define a new column, "sell", which needs to contain 1 for sells, and 0 for "no action".

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

Minimal ROI¶

This dict defines the minimal Return On Investment (ROI) a trade should reach before selling, independent from the sell 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:

Sell whenever 4% profit was reached
Sell when 2% profit was reached (in effect after 20 minutes)
Sell when 1% profit was reached (in effect after 30 minutes)
Sell 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 sell once the trade reaches 10000% Profit.

Stoploss¶

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

Sample:

stoploss = -0.10

This would signify a stoploss of -10%. If your exchange supports it, it's recommended to also set "stoploss_on_exchange" in the order dict, so your stoploss is on the exchange and cannot be missed for network-problems (or other problems).

For more information on order_types please look here.

Ticker interval¶

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 buy/sell signals may work with one interval, but not the other. This setting is accessible within the strategy by using self.ticker_interval.

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.

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

Storing information¶

Storing information can be accomplished by crating a new dictionary within the strategy class.

The name of the variable can be choosen at will, but should be prefixed with cust_ to avoid naming collisions with predefined strategy variables.

class Awesomestrategy(IStrategy):
    # Create custom dictionary
    cust_info = {}
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Check if the entry already exists
        if "crosstime" in self.cust_info[metadata["pair"]:
            self.cust_info[metadata["pair"]["crosstime"] += 1
        else:
            self.cust_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.

Additional data (DataProvider)¶

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

!!!Note: The DataProvier is currently not available during backtesting / hyperopt, but this is planned for the future.

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

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

Possible options for DataProvider¶

available_pairs - Property with tuples listing cached pairs with their intervals. (pair, interval)
ohlcv(pair, ticker_interval) - Currently cached ticker data for all pairs in the whitelist, returns DataFrame or empty DataFrame
historic_ohlcv(pair, ticker_interval) - Data stored on disk
runmode - Property containing the current runmode.

ohlcv / historic_ohlcv¶

if self.dp:
    if dp.runmode == 'live':
        if ('ETH/BTC', ticker_interval) in self.dp.available_pairs:
            data_eth = self.dp.ohlcv(pair='ETH/BTC',
                                     ticker_interval=ticker_interval)
    else:
        # Get historic ohlcv data (cached on disk).
        history_eth = self.dp.historic_ohlcv(pair='ETH/BTC',
                                             ticker_interval='1h')

!!! Warning: Warning about backtesting Be carefull when using dataprovider in backtesting. historic_ohlcv() provides the full time-range in one go, so please be aware of it and make sure to not "look into the future" to avoid surprises when running in dry/live mode).

Available Pairs¶

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

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 above). 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", "interval"), with pair as the first and time interval as the second argument.

Sample:

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

!!! Warning: As these pairs will be refreshed as part of the regular whitelist refresh, it's best to keep this list short. All intervals 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 time-intervals when possible to avoid hammering the exchange with too many requests and risk beeing blocked.

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

Where is the default strategy?¶

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

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

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.