Reinforced Quickie: Pullback Entries in a Higher-Timeframe Uptrend
Article Strategy library · Author: berlinguyinca
Summary
ReinforcedQuickie is a five-minute long-only strategy designed to buy pullbacks while a slower resampled trend filter points upward. Its entry logic accepts either a short-term low near the lower Bollinger Band, with price below short and medium EMAs, or a declining-then-rising sequence of average prices accompanied by weak CCI, RSI, and MFI readings. Both entry patterns require price above the resampled simple moving average, that average to be rising, and volume to remain below a specified spike threshold.
For exits, the strategy looks for price strength near recent highs and the upper Bollinger Band with elevated MFI, or a run of eight rising candles with RSI above 70. The configuration specifies a 1% minimal ROI and a 5% stop loss. These rules describe a pullback-and-recovery approach, but the document supplies no backtest evidence. Its indicators and thresholds may behave differently across assets and market regimes; the code also depends on resampling and interpolation, whose alignment should be checked when evaluating signals.
Key ideas
- The strategy seeks long entries on a five-minute chart only when a slower resampled average is rising and price is above it.
- Entries use either a lower-band and local-low condition or a reversal pattern accompanied by weak momentum readings.
- Volume is filtered to exclude unusually large spikes relative to its recent average.
- Exits use overbought strength near recent highs or a sequence of eight rising candles with high RSI.
- The stated ROI and stop-loss settings are not supported by reported backtest results.
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Full text
# ReinforcedQuickie
# ReinforcedQuickie
author@: Gert Wohlgemuth
works on new objectify branch!
idea:
only buy on an upward tending market
############################################################################### ###############################################################################
## 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
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class ReinforcedQuickie(IStrategy):
"""
author@: Gert Wohlgemuth
works on new objectify branch!
idea:
only buy on an upward tending market
"""
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": 0.01
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.05
# Optimal timeframe for the strategy
timeframe = '5m'
# resample factor to establish our general trend. Basically don't buy if a trend is not given
resample_factor = 12
EMA_SHORT_TERM = 5
EMA_MEDIUM_TERM = 12
EMA_LONG_TERM = 21
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.timeframe, self.resample_factor)
##################################################################################
# buy and sell indicators
dataframe['ema_{}'.format(self.EMA_SHORT_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_SHORT_TERM
)
dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_MEDIUM_TERM
)
dataframe['ema_{}'.format(self.EMA_LONG_TERM)] = ta.EMA(
dataframe, timeperiod=self.EMA_LONG_TERM
)
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=2
)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['min'] = ta.MIN(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['max'] = ta.MAX(dataframe, timeperiod=self.EMA_MEDIUM_TERM)
dataframe['cci'] = ta.CCI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
##################################################################################
# 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']
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
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['close'] < dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
(dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
(dataframe['close'] == dataframe['min']) &
(dataframe['close'] <= dataframe['bb_lowerband'])
)
|
# simple v bottom shape (lopsided to the left to increase reactivity)
# which has to be below a very slow average
# this pattern only catches a few, but normally very good buy points
(
(dataframe['average'].shift(5) > dataframe['average'].shift(4))
& (dataframe['average'].shift(4) > dataframe['average'].shift(3))
& (dataframe['average'].shift(3) > dataframe['average'].shift(2))
& (dataframe['average'].shift(2) > dataframe['average'].shift(1))
& (dataframe['average'].shift(1) < dataframe['average'].shift(0))
& (dataframe['low'].shift(1) < dataframe['bb_middleband'])
& (dataframe['cci'].shift(1) < -100)
& (dataframe['rsi'].shift(1) < 30)
& (dataframe['mfi'].shift(1) < 30)
)
)
# safeguard against down trending markets and a pump and dump
&
(
(dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) &
(dataframe['resample_sma'] < dataframe['close']) &
(dataframe['resample_sma'].shift(1) < dataframe['resample_sma'])
)
)
,
'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['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
(dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
(dataframe['close'] >= dataframe['max']) &
(dataframe['close'] >= dataframe['bb_upperband']) &
(dataframe['mfi'] > 80)
) |
# always sell on eight green candles
# with a high rsi
(
(dataframe['open'] < dataframe['close']) &
(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
(dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
(dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
(dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
(dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
(dataframe['open'].shift(7) < dataframe['close'].shift(7)) &
(dataframe['rsi'] > 70)
)
,
'exit_long'
] = 1
return dataframe
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).dropna(how='any')
df['resample_sma'] = ta.SMA(df, timeperiod=25, 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.