CCI and RSI Threshold Signals

Article Strategy library · Author: Kevin Ossenbrück

Summary

This Freqtrade strategy uses CCI and RSI thresholds to generate long entries and exits on a 15-minute timeframe. It calculates separate indicator periods for each side. An entry is signaled when both the selected CCI and RSI values fall below their configured thresholds; an exit requires both to rise above their respective thresholds. The document lists the chosen periods and levels, alongside a stop-loss and a stepped return-on-investment schedule.

The source provides implementation details and adjustable parameters, but no backtest results or evidence that the settings are profitable. Its RSI entry threshold is unusually high while the CCI threshold is negative, so the combined rule should be understood as written rather than assumed to represent a conventional oversold signal. The strategy defines no informative timeframes or additional market filters, and the document does not explain how its parameters were selected or which assets were evaluated.

Key ideas

Entries require both CCI and RSI to be below their configured thresholds.
Exits require both indicators to be above their separate configured thresholds.
The strategy is configured for 15-minute candles and includes a stop-loss and stepped return targets.
No performance results or asset-specific validation are reported.

Tags

Technical indicators Mean reversion Risk management

Full text

# SwingHighToSky


# SwingHighToSky









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"

## Source (GPL-3.0)

```python
"""
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 Signals for Long Entries and Exits

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