FSD – Technical libraries

DISCLAIMER
The codes in this post is just for educational purpose, and not to be used on live trading

There are some technical libraries available for use, such as ta-lib, pandas-ta and finta. They have implemented most of publicly-known indicators so you don’t have to code them yourself. These libraries are installed in Freqtrade by default

• ta-lib
• pandas-ta
• finta (no more development)
• ta

One thing you must know is each of them has different way of input and output format, even within the same library. Let’s take ta-lib for example. If you see previous example, I calculated rsi as ta.RSI(dataframe, 14). If you look at ta-lib’s documentation, I should have give it the close column instead of the whole dataframe, but it has been coded internally to deal with it. But then if I’m going to calculate cci for example, I can’t just write ta.CCI(dataframe, 14). I have to write it the proper way as ta.CCI(dataframe['high'], dataframe['low'], dataframe['close'], 14).

So just for the sake of clarity and following the proper format, the code from previous post would look like this

from freqtrade.strategy import IStrategy, informative
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
import talib.abstract as ta
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from typing import Optional, Union

class strat_template (IStrategy):

    def version(self) -> str:
        return "template-v1"

    INTERFACE_VERSION = 3

    minimal_roi = {
        "0": 0.05
    }

    stoploss = -0.05

    timeframe = '15m'

    process_only_new_candles = True
    startup_candle_count = 999

    use_custom_stoploss = True

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        
        sl_new = 1

        if (current_time - timedelta(minutes=15) >= trade.open_date_utc):

            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            current_candle = dataframe.iloc[-1].squeeze()
            current_profit = trade.calc_profit_ratio(current_candle['close'])

            if (current_profit >= 0.03):
                sl_new = 0.01

        return sl_new

    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]:

        if ((current_time - timedelta(minutes=15)) >= trade.open_date_utc):

            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            current_candle = dataframe.iloc[-1].squeeze()
            current_profit = trade.calc_profit_ratio(current_candle['close'])
            
            if (current_profit >= 0):
                if (current_candle['rsi'] >= 70):
                    return "rsi_overbought"

    @informative('30m')
    def populate_indicators_inf1(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        dataframe['rsi'] = ta.RSI(dataframe['close'], 14)

        return dataframe

    @informative('1h')
    def populate_indicators_inf2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        
        dataframe['rsi'] = ta.RSI(dataframe['close'], 14)

        return dataframe

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

        dataframe['ema_9'] = ta.EMA(dataframe['close'], 9)
        dataframe['ema_20'] = ta.EMA(dataframe['close'], 20)
        dataframe['rsi'] = ta.RSI(dataframe['close'], 14)

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

        dataframe.loc[
            qtpylib.crossed_above(dataframe['ema_9'], dataframe['ema_20'])
            &
            (dataframe['rsi_30m'] < 50)
            &
            (dataframe['rsi_1h'] < 30)
            &
            (dataframe['volume'] > 0)
            , ['enter_long', 'enter_tag']
        ] = (1, 'golden cross')

        return dataframe

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

        dataframe.loc[
            qtpylib.crossed_below(dataframe['ema_9'], dataframe['ema_20'])
            &
            (dataframe['volume'] > 0)
            , ['exit_long', 'exit_tag']
        ] = (1, 'death cross')

        return dataframe