EMA Crossover Entries with a Higher-Timeframe SMA Filter
Summary
This proof-of-concept strategy uses an 8-period and a 21-period EMA on a four-hour chart. It enters long when the shorter EMA crosses above the longer one, provided price is above a 50-period SMA calculated on data resampled to a higher interval and volume is positive. It exits when the medium EMA crosses above the short EMA, again requiring positive volume. Bollinger Bands are calculated for chart display but do not affect the entry or exit rules.
The author characterizes the crossover idea as weak-performing and experimental, without supplying test results or a benchmark. Risk controls in the configuration include a fixed stop loss and a minimum ROI target, while trailing stops are disabled. The higher-timeframe price filter may help align trades with a broader trend, but the crossover can still lag or whipsaw, and no evidence is provided that the filter improves outcomes. It should be treated as an illustrative strategy example rather than validated performance guidance.
Key ideas
- A short EMA crossing above a medium EMA triggers a candidate long entry.
- The entry also requires price above a higher-timeframe 50-period SMA and positive volume.
- The exit signal occurs when the medium EMA crosses back above the short EMA.
- Bollinger Bands are calculated for display but do not drive the trade signals.
- The author describes the strategy as a proof of concept and provides no performance evidence.
Tags
Full text
# ReinforcedAverageStrategy
# ReinforcedAverageStrategy
author@: Gert Wohlgemuth
idea:
buys and sells on crossovers - doesn't really perfom that well and its just a proof of concept
###############################################################################
## 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, 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.