EMA Crossover Entries Filtered by a Higher-Timeframe Average
Summary
This strategy combines a short and medium exponential moving average crossover with a trend filter from a longer resampled timeframe. It enters long when the short average crosses above the medium average, provided the close is above the longer-timeframe simple moving average and volume is positive. It exits when the medium average crosses above the short average, with positive volume as a further condition.
The configuration uses a four-hour chart, an eight-period and a 21-period EMA, and a 50-period SMA on data resampled to twelve times the chart interval. It also defines a profit target and stop loss, while disabling trailing stops. The author characterizes the crossover idea as a proof of concept that does not perform especially well. No backtest results, market, or evaluation period are supplied, so the settings should not be treated as evidence of an effective strategy.
Key ideas
- Long entries require the short EMA to cross above the medium EMA.
- The entry is filtered by price above a resampled higher-timeframe SMA and positive volume.
- An opposite EMA crossover triggers the long exit when volume is positive.
- The strategy includes fixed profit and stop settings but reports no performance evidence.
- The author describes the approach as a weak-performing proof of concept.
Tags
Full text
# ReinforcedAverageStrategy.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, merge, DatetimeIndex
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge
from freqtrade.exchange import timeframe_to_minutes
class ReinforcedAverageStrategy(IStrategy):
"""
author@: Gert Wohlgemuth
idea:
buys and sells on crossovers - doesn't really perfom that well and its just a proof of concept
"""
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": 0.5
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.2
# Optimal timeframe for the strategy
timeframe = '4h'
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
trailing_only_offset_is_reached = False
# 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 = False
ignore_roi_if_entry_signal = False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
dataframe['maMedium'] = ta.EMA(dataframe, timeperiod=21)
##################################################################################
# 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']
self.resample_interval = timeframe_to_minutes(self.timeframe) * 12
dataframe_long = resample_to_interval(dataframe, self.resample_interval)
dataframe_long['sma'] = ta.SMA(dataframe_long, timeperiod=50, price='close')
dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)
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[
(
qtpylib.crossed_above(dataframe['maShort'], dataframe['maMedium']) &
(dataframe['close'] > dataframe[f'resample_{self.resample_interval}_sma']) &
(dataframe['volume'] > 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[
(
qtpylib.crossed_above(dataframe['maMedium'], dataframe['maShort']) &
(dataframe['volume'] > 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.