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bb_rsi_naive_strategy

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remiotore/ccxt-freqtrade/strategies/bb_rsi_naive_strategy_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 15m interface version: 3
Settings stoploss: -0.1 has minimal roi process only new candles: false startup candle count: 30
Indicators Bollinger_Bands RSI talib
15 related strategies (⧉ identical code, ≈ similar name)

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import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class bb_rsi_naive_strategy(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'60': 0.01, '30': 0.02, '0': 0.04}
    stoploss = -0.1
    trailing_stop = False
    timeframe = '15m'
    process_only_new_candles = False
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    startup_candle_count: int = 30
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    plot_config = {'main_plot': {'bb_upperband': {'color': 'green'}, 'bb_midband': {'color': 'orange'}, 'bb_lowerband': {'color': 'red'}}, 'subplots': {'RSI': {'rsi': {'color': 'yellow'}}}}

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_midband'] = bollinger['mid']
        dataframe['bb_lowerband'] = bollinger['lower']
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Signal: RSI is greater 25
        dataframe.loc[(dataframe['rsi'] > 25) & (dataframe['close'] < dataframe['bb_lowerband']), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:  # Signal: RSI is greater 70
        dataframe.loc[(dataframe['rsi'] > 70) & (dataframe['close'] > dataframe['bb_midband']), 'exit_long'] = 1
        return dataframe