Five-Minute Long Strategy Using ADX, Stochastic, and CCI
Summary
This Freqtrade strategy seeks long entries on a five-minute chart by combining trend strength, oversold conditions, and a stochastic crossover. It requires either a high short-period ADX or a higher slow ADX, a CCI reading below its oversold threshold, and both fast and slower stochastic readings to have been low on the prior candle. Entry occurs when the fast stochastic crosses above the slow line, with additional volume and price filters. Exit signals use a weakening slow ADX, elevated fast stochastic values, a prior bearish crossover condition, and price above a short EMA.
The configuration also specifies tiered profit targets by holding time, a fixed stop loss, and limit entries and exits. The document supplies rules and settings but no backtest results, market selection, or evidence that the thresholds are robust. Its rolling volume measure uses the prior twelve bars, and the code itself cautions that whole-dataframe volume averages can introduce lookahead if substituted during backtesting. Fees, slippage, and broader risk controls are not evaluated.
Key ideas
- The strategy combines ADX trend-strength filters with oversold CCI and stochastic conditions for long entries.
- A fast stochastic crossover is required after both fast and slow stochastic readings were low.
- The exit rule combines slow ADX weakness, elevated stochastic readings, and a price check against a short EMA.
- The configuration includes time-dependent ROI targets and a fixed stop loss.
- No performance evidence is supplied, and volume calculations must avoid lookahead in backtests.
Tags
Full text
# Strategy004.py
```py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
class Strategy004(IStrategy):
"""
Strategy 004
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy004
"""
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 = -0.10
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# run "populate_indicators" only for new candle
process_only_new_candles = True
# Experimental settings (configuration will overide these if set)
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# ADX
dataframe['adx'] = ta.ADX(dataframe)
dataframe['slowadx'] = ta.ADX(dataframe, 35)
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# Stoch
stoch = ta.STOCHF(dataframe, 5)
dataframe['fastd'] = stoch['fastd']
dataframe['fastk'] = stoch['fastk']
dataframe['fastk-previous'] = dataframe.fastk.shift(1)
dataframe['fastd-previous'] = dataframe.fastd.shift(1)
# Slow Stoch
slowstoch = ta.STOCHF(dataframe, 50)
dataframe['slowfastd'] = slowstoch['fastd']
dataframe['slowfastk'] = slowstoch['fastk']
dataframe['slowfastk-previous'] = dataframe.slowfastk.shift(1)
dataframe['slowfastd-previous'] = dataframe.slowfastd.shift(1)
# EMA - Exponential Moving Average
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# get the rolling volume mean for the last hour (12x5)
# Note: dataframe['volume'].mean() uses the whole dataframe in
# backtesting hence will have lookahead, but would be fine for dry/live use
dataframe['mean-volume'] = dataframe['volume'].rolling(12).mean()
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['adx'] > 50) |
(dataframe['slowadx'] > 26)
) &
(dataframe['cci'] < -100) &
(
(dataframe['fastk-previous'] < 20) &
(dataframe['fastd-previous'] < 20)
) &
(
(dataframe['slowfastk-previous'] < 30) &
(dataframe['slowfastd-previous'] < 30)
) &
(dataframe['fastk-previous'] < dataframe['fastd-previous']) &
(dataframe['fastk'] > dataframe['fastd']) &
(dataframe['mean-volume'] > 0.75) &
(dataframe['close'] > 0.00000100)
),
'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['slowadx'] < 25) &
((dataframe['fastk'] > 70) | (dataframe['fastd'] > 70)) &
(dataframe['fastk-previous'] < dataframe['fastd-previous']) &
(dataframe['close'] > dataframe['ema5'])
),
'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.