MACD Trend Alignment with CCI Thresholds for Entries and Exits
Summary
This five-minute strategy uses MACD direction to define trend alignment and the Commodity Channel Index (CCI) to set entry and exit thresholds. It enters long when MACD is above its signal line, CCI is at or below a configurable buy threshold, and volume is nonzero. It exits the long when MACD falls below its signal line and CCI reaches or exceeds a configurable sell threshold. The documented implementation does not include short entries.
The proposal is to optimize the buy and sell CCI thresholds independently, with search ranges from negative values to zero for buys and zero to positive values for exits. The code also specifies return-on-investment targets and a stop loss, but provides no backtest settings, measured outcomes, or evidence that the selected parameter values generalize. Threshold optimization can fit historical data, so any use would require out-of-sample evaluation and realistic trading costs.
Key ideas
- The strategy enters long when MACD is above its signal and CCI is below a configurable threshold.
- It exits when MACD turns below its signal and CCI exceeds a separate threshold.
- A nonzero-volume condition prevents signals on bars without reported volume.
- The proposed optimization focuses on buy and sell CCI thresholds.
- No performance evidence or out-of-sample validation is provided.
Tags
Full text
# MACDStrategy
# MACDStrategy
author@: Gert Wohlgemuth
idea:
uptrend definition:
MACD above MACD signal
and CCI < -50
downtrend definition:
MACD below MACD signal
and CCI > 100
freqtrade hyperopt --strategy MACDStrategy --hyperopt-loss <someLossFunction> --spaces buy sell
The idea is to optimize only the CCI value.
- Buy side: CCI between -700 and 0
- Sell side: CCI between 0 and 700
## Source (GPL-3.0)
```python
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
class MACDStrategy(IStrategy):
"""
author@: Gert Wohlgemuth
idea:
uptrend definition:
MACD above MACD signal
and CCI < -50
downtrend definition:
MACD below MACD signal
and CCI > 100
freqtrade hyperopt --strategy MACDStrategy --hyperopt-loss <someLossFunction> --spaces buy sell
The idea is to optimize only the CCI value.
- Buy side: CCI between -700 and 0
- Sell side: CCI between 0 and 700
"""
INTERFACE_VERSION: int = 3
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"60": 0.01,
"30": 0.03,
"20": 0.04,
"0": 0.05
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3
# Optimal timeframe for the strategy
timeframe = '5m'
buy_cci = IntParameter(low=-700, high=0, default=-50, space='buy', optimize=True)
sell_cci = IntParameter(low=0, high=700, default=100, space='sell', optimize=True)
# Buy hyperspace params:
buy_params = {
"buy_cci": -48,
}
# Sell hyperspace params:
sell_params = {
"sell_cci": 687,
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['macd'] > dataframe['macdsignal']) &
(dataframe['cci'] <= self.buy_cci.value) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['macd'] < dataframe['macdsignal']) &
(dataframe['cci'] >= self.sell_cci.value) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'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.