CCI and RSI Threshold Strategy

Code Freqtrade

Summary

This Freqtrade strategy generates long entries when both the Commodity Channel Index and Relative Strength Index fall below configurable thresholds. It exits when both indicators rise above separate exit thresholds. Indicator periods and thresholds are exposed as optimizable parameters, with example parameter values supplied for the strategy. The implementation uses 15-minute candles and specifies stop-loss and time-dependent profit-taking settings. It contains no backtest results, market selection details, or explanation of how the parameter values were chosen. The thresholds may therefore require careful testing for the intended asset and data period; the code alone does not establish that the strategy is profitable or robust.

Key ideas

Long entries require both CCI and RSI to be below their configured thresholds.
Long exits require both indicators to exceed their separate configured thresholds.
The indicator periods and thresholds can be optimized as strategy parameters.
The strategy specifies a 15-minute timeframe, stop loss, and staged return targets, but provides no performance evidence.

Tags

Technical indicators Mean reversion Backtesting Risk management

Full text

# SwingHighToSky.py


```py
"""
author      = "Kevin Ossenbrück"
copyright   = "Free For Use"
credits     = ["Bloom Trading, Mohsen Hassan"]
license     = "MIT"
version     = "1.0"
maintainer  = "Kevin Ossenbrück"
email       = "kevin.ossenbrueck@pm.de"
status      = "Live"
"""

from freqtrade.strategy import IStrategy
from freqtrade.strategy import IntParameter
from functools import reduce
from pandas import DataFrame

import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy



# CCI timerperiods and values
cciBuyTP = 72
cciBuyVal = -175
cciSellTP = 66
cciSellVal = -106

# RSI timeperiods and values
rsiBuyTP = 36
rsiBuyVal = 90
rsiSellTP = 45
rsiSellVal = 88


class SwingHighToSky(IStrategy):
    INTERFACE_VERSION = 3

    timeframe = '15m'

    stoploss = -0.34338

    minimal_roi = {"0": 0.27058, "33": 0.0853, "64": 0.04093, "244": 0}

    buy_cci = IntParameter(low=-200, high=200, default=100, space='buy', optimize=True)
    buy_cciTime = IntParameter(low=10, high=80, default=20, space='buy', optimize=True)
    buy_rsi = IntParameter(low=10, high=90, default=30, space='buy', optimize=True)
    buy_rsiTime = IntParameter(low=10, high=80, default=26, space='buy', optimize=True)

    sell_cci = IntParameter(low=-200, high=200, default=100, space='sell', optimize=True)
    sell_cciTime = IntParameter(low=10, high=80, default=20, space='sell', optimize=True)
    sell_rsi = IntParameter(low=10, high=90, default=30, space='sell', optimize=True)
    sell_rsiTime = IntParameter(low=10, high=80, default=26, space='sell', optimize=True)

    # Buy hyperspace params:
    buy_params = {
        "buy_cci": -175,
        "buy_cciTime": 72,
        "buy_rsi": 90,
        "buy_rsiTime": 36,
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_cci": -106,
        "sell_cciTime": 66,
        "sell_rsi": 88,
        "sell_rsiTime": 45,
    }

    def informative_pairs(self):
        return []

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

        for val in self.buy_cciTime.range:
            dataframe[f'cci-{val}'] = ta.CCI(dataframe, timeperiod=val)

        for val in self.sell_cciTime.range:
            dataframe[f'cci-sell-{val}'] = ta.CCI(dataframe, timeperiod=val)

        for val in self.buy_rsiTime.range:
            dataframe[f'rsi-{val}'] = ta.RSI(dataframe, timeperiod=val)

        for val in self.sell_rsiTime.range:
            dataframe[f'rsi-sell-{val}'] = ta.RSI(dataframe, timeperiod=val)

        return dataframe

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

        dataframe.loc[
            (
                (dataframe[f'cci-{self.buy_cciTime.value}'] < self.buy_cci.value) &
                (dataframe[f'rsi-{self.buy_rsiTime.value}'] < self.buy_rsi.value)
            ),
            'enter_long'] = 1

        return dataframe

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

        dataframe.loc[
            (
                (dataframe[f'cci-sell-{self.sell_cciTime.value}'] > self.sell_cci.value) &
                (dataframe[f'rsi-sell-{self.sell_rsiTime.value}'] > self.sell_rsi.value)
            ),
            '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.

CCI and RSI Threshold Strategy for 15-Minute Trading

Summary

Key ideas

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