Multi-Timeframe RSI Strategy

Code Freqtrade

Summary

This Freqtrade example calculates a five-period and a 200-period simple moving average alongside relative strength index values on the base timeframe and on two resampled, longer intervals. It enters long when the base-timeframe RSI falls at least 20 points below the longest-interval RSI, subject to a moving-average condition. It exits when the base RSI rises above both resampled RSI values. The strategy also sets a five-minute timeframe, a fixed stop loss, and a small return-on-investment target.

The example provides indicator and signal rules, but no backtest results, transaction-cost analysis, or rationale for the thresholds. Its entry comment calls the setup bearish, while the stated condition has the short moving average at or above the long moving average; that mismatch makes the intended trend filter unclear. The code alone does not show whether resampling avoids look-ahead bias or whether the strategy is profitable across assets and market regimes.

Key ideas

The strategy compares RSI on the base interval with RSI from two resampled intervals.
A long entry requires the base RSI to fall well below the longest-interval RSI.
The entry also applies a short-to-long moving-average condition that conflicts with its bearish comment.
A long exit occurs when the base RSI exceeds both resampled RSI values.
No backtest or robustness evidence is included.

Tags

Technical indicators Mean reversion Backtesting Risk management

Full text

# MultiRSI.py


```py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
from technical.util import resample_to_interval, resampled_merge


class MultiRSI(IStrategy):
    """

    author@: Gert Wohlgemuth

    based on work from Creslin

    """
    INTERFACE_VERSION: int = 3
    minimal_roi = {
        "0": 0.01
    }

    # Optimal stoploss designed for the strategy
    stoploss = -0.05

    # Optimal timeframe for the strategy
    timeframe = '5m'

    def get_ticker_indicator(self):
        return int(self.timeframe[:-1])

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

        dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)

        # resample our dataframes
        dataframe_short = resample_to_interval(dataframe, self.get_ticker_indicator() * 2)
        dataframe_long = resample_to_interval(dataframe, self.get_ticker_indicator() * 8)

        # compute our RSI's
        dataframe_short['rsi'] = ta.RSI(dataframe_short, timeperiod=14)
        dataframe_long['rsi'] = ta.RSI(dataframe_long, timeperiod=14)

        # merge dataframe back together
        dataframe = resampled_merge(dataframe, dataframe_short)
        dataframe = resampled_merge(dataframe, dataframe_long)

        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

        dataframe.ffill(inplace=True)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                # must be bearish
                (dataframe['sma5'] >= dataframe['sma200']) &
                (dataframe['rsi'] < (dataframe['resample_{}_rsi'.format(self.get_ticker_indicator() * 8)] - 20))
            ),
            'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['rsi'] > dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*2)]) &
                (dataframe['rsi'] > dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*8)])
            ),
            '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.

A Multi-Timeframe RSI Strategy with Moving-Average Entries

Summary

Key ideas

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