Bollinger Band and RSI Oversold Entry Strategy
Summary
This Freqtrade strategy combines a 14-period Relative Strength Index with 20-period Bollinger Bands calculated from typical price using two standard deviations. It enters a long position when RSI is below 30 and the close is below the lower band, treating agreement between an oversold reading and a downside band breach as an entry signal. It exits when RSI rises above 70.
The example also sets a one-hour timeframe, a minimal return-on-investment target, and a stop loss. These are configuration choices, not evidence that the approach is profitable. The document provides implementation logic but no backtest, market selection, trading costs, or performance analysis. A band breach can accompany a sustained decline, and an RSI threshold alone does not establish a reversal, so the rules require independent testing and risk controls before practical use.
Key ideas
- The strategy enters long when RSI is below 30 and price closes below the lower Bollinger Band.
- The Bollinger Bands use a 20-period window and two standard deviations.
- The strategy exits when RSI rises above 70.
- The example specifies a one-hour timeframe, a return target, and a stop loss but gives no performance evidence.
Tags
Full text
# BbandRsi.py
```py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# --------------------------------
class BbandRsi(IStrategy):
"""
author@: Gert Wohlgemuth
converted from:
https://github.com/sthewissen/Mynt/blob/master/src/Mynt.Core/Strategies/BbandRsi.cs
"""
INTERFACE_VERSION: int = 3
# Minimal ROI designed for the strategy.
# adjust based on market conditions. We would recommend to keep it low for quick turn arounds
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.1
}
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
# Bollinger bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < 30) &
(dataframe['close'] < dataframe['bb_lowerband'])
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] > 70)
),
'exit_long'] = 1
return dataframe
```Shown in full with attribution under the source's licence. Licence: GPL-3.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.