CCI Oversold Entries with Money-Flow and Resampled Trend Filters
Summary
This one-minute Freqtrade strategy combines two Commodity Channel Index readings with Chaikin Money Flow and Money Flow Index. A long entry requires both CCI values to be below -100, money flow to be negative beyond a stated threshold, and MFI to be below 25. Additional conditions compare moving averages calculated on five-minute resampled data with the current close, adding a trend-context filter.
The exit rule waits for both CCI readings to exceed 100 and Chaikin Money Flow to rise above its exit threshold, while requiring an ordering between the resampled moving averages. RSI and Bollinger Bands are calculated but do not appear in the signal conditions. The code also defines a 10% ROI target and a 2% stop loss. No market, backtest period, or performance results are supplied, so the example explains indicator logic without demonstrating its effectiveness. Its unusually strict, multi-indicator conditions and resampling implementation warrant validation for signal frequency and timing.
Key ideas
- Long entries require two oversold CCI readings, weak money flow, low MFI, and resampled moving-average filters.
- Exit signals require elevated CCI readings, stronger Chaikin Money Flow, and a specified ordering of resampled averages.
- The example sets a 10% ROI target and a 2% stop loss.
- RSI and Bollinger Bands are calculated but are not used in entry or exit conditions.
- No backtest results or evidence of profitability are provided.
Tags
Full text
# CCIStrategy
# CCIStrategy
## Source (GPL-3.0)
```python
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, Series, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class CCIStrategy(IStrategy):
INTERFACE_VERSION: int = 3
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"0": 0.1
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.02
# Optimal timeframe for the strategy
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.timeframe, 5)
dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170)
dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34)
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['cmf'] = self.chaikin_mf(dataframe)
# required for graphing
bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
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['cci_one'] < -100)
& (dataframe['cci_two'] < -100)
& (dataframe['cmf'] < -0.1)
& (dataframe['mfi'] < 25)
# insurance
& (dataframe['resample_medium'] > dataframe['resample_short'])
& (dataframe['resample_long'] < dataframe['close'])
),
'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['cci_one'] > 100)
& (dataframe['cci_two'] > 100)
& (dataframe['cmf'] > 0.3)
& (dataframe['resample_sma'] < dataframe['resample_medium'])
& (dataframe['resample_medium'] < dataframe['resample_short'])
),
'exit_long'] = 1
return dataframe
def chaikin_mf(self, df, periods=20):
close = df['close']
low = df['low']
high = df['high']
volume = df['volume']
mfv = ((close - low) - (high - close)) / (high - low)
mfv = mfv.fillna(0.0) # float division by zero
mfv *= volume
cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum()
return Series(cmf, name='cmf')
def resample(self, dataframe, interval, factor):
# defines the reinforcement logic
# resampled dataframe to establish if we are in an uptrend, downtrend or sideways trend
df = dataframe.copy()
df = df.set_index(DatetimeIndex(df['date']))
ohlc_dict = {
'open': 'first',
'high': 'max',
'low': 'min',
'close': 'last'
}
df = df.resample(str(int(interval[:-1]) * factor) + 'min', label="right").agg(ohlc_dict)
df['resample_sma'] = ta.SMA(df, timeperiod=100, price='close')
df['resample_medium'] = ta.SMA(df, timeperiod=50, price='close')
df['resample_short'] = ta.SMA(df, timeperiod=25, price='close')
df['resample_long'] = ta.SMA(df, timeperiod=200, price='close')
df = df.drop(columns=['open', 'high', 'low', 'close'])
df = df.resample(interval[:-1] + 'min')
df = df.interpolate(method='time')
df['date'] = df.index
df.index = range(len(df))
dataframe = merge(dataframe, df, on='date', how='left')
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.