BinHV27 Long Strategy Using Trend, Directional Movement, and RSI
Code Freqtrade
Summary
This five-minute Freqtrade strategy combines moving averages, directional movement, ADX, and RSI to generate long entries and exits. It compares 120-period and 240-period simple moving averages to classify the broader trend, while exponential averages of price and indicator values help gauge shorter-term conditions. Entry rules require price below selected averages, rising RSI, and specific combinations of trend direction, ADX strength, and smoothed RSI levels.
Exit rules use conditions involving price crossing averages, changes in trend strength, directional indicators, and high smoothed RSI readings. The strategy sets a 50% stop loss and a minimal ROI threshold of 1, but the document supplies no backtest results, market selection, or evaluation of trading costs. Its many conditional combinations may be sensitive to parameter choices, and the code alone does not establish that the signals are profitable or robust across assets and market regimes.
Key ideas
- The strategy uses five-minute candles and combines trend filters with short-term momentum indicators.
- A comparison of 120-period and 240-period simple moving averages classifies the broader trend.
- Long entries require price and indicator conditions that vary with trend direction and ADX strength.
- Exit signals combine moving-average crossings, trend changes, directional movement, and elevated smoothed RSI.
- The document provides rules and parameters but no performance evidence or robustness analysis.
Tags
Full text
# BinHV27.py
```py
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 BinHV27(IStrategy):
"""
strategy sponsored by user BinH from slack
"""
INTERFACE_VERSION: int = 3
minimal_roi = {
"0": 1
}
stoploss = -0.50
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
dataframe['emarsi'] = numpy.nan_to_num(ta.EMA(rsiframe, timeperiod=5))
dataframe['adx'] = numpy.nan_to_num(ta.ADX(dataframe))
dataframe['minusdi'] = numpy.nan_to_num(ta.MINUS_DI(dataframe))
minusdiframe = DataFrame(dataframe['minusdi']).rename(columns={'minusdi': 'close'})
dataframe['minusdiema'] = numpy.nan_to_num(ta.EMA(minusdiframe, timeperiod=25))
dataframe['plusdi'] = numpy.nan_to_num(ta.PLUS_DI(dataframe))
plusdiframe = DataFrame(dataframe['plusdi']).rename(columns={'plusdi': 'close'})
dataframe['plusdiema'] = numpy.nan_to_num(ta.EMA(plusdiframe, timeperiod=5))
dataframe['lowsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=60))
dataframe['highsma'] = numpy.nan_to_num(ta.EMA(dataframe, timeperiod=120))
dataframe['fastsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=120))
dataframe['slowsma'] = numpy.nan_to_num(ta.SMA(dataframe, timeperiod=240))
dataframe['bigup'] = dataframe['fastsma'].gt(dataframe['slowsma']) & ((dataframe['fastsma'] - dataframe['slowsma']) > dataframe['close'] / 300)
dataframe['bigdown'] = ~dataframe['bigup']
dataframe['trend'] = dataframe['fastsma'] - dataframe['slowsma']
dataframe['preparechangetrend'] = dataframe['trend'].gt(dataframe['trend'].shift())
dataframe['preparechangetrendconfirm'] = dataframe['preparechangetrend'] & dataframe['trend'].shift().gt(dataframe['trend'].shift(2))
dataframe['continueup'] = dataframe['slowsma'].gt(dataframe['slowsma'].shift()) & dataframe['slowsma'].shift().gt(dataframe['slowsma'].shift(2))
dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
dataframe['slowsma'].gt(0) &
dataframe['close'].lt(dataframe['highsma']) &
dataframe['close'].lt(dataframe['lowsma']) &
dataframe['minusdi'].gt(dataframe['minusdiema']) &
dataframe['rsi'].ge(dataframe['rsi'].shift()) &
(
(
~dataframe['preparechangetrend'] &
~dataframe['continueup'] &
dataframe['adx'].gt(25) &
dataframe['bigdown'] &
dataframe['emarsi'].le(20)
) |
(
~dataframe['preparechangetrend'] &
dataframe['continueup'] &
dataframe['adx'].gt(30) &
dataframe['bigdown'] &
dataframe['emarsi'].le(20)
) |
(
~dataframe['continueup'] &
dataframe['adx'].gt(35) &
dataframe['bigup'] &
dataframe['emarsi'].le(20)
) |
(
dataframe['continueup'] &
dataframe['adx'].gt(30) &
dataframe['bigup'] &
dataframe['emarsi'].le(25)
)
),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
~dataframe['preparechangetrendconfirm'] &
~dataframe['continueup'] &
(dataframe['close'].gt(dataframe['lowsma']) | dataframe['close'].gt(dataframe['highsma'])) &
dataframe['highsma'].gt(0) &
dataframe['bigdown']
) |
(
~dataframe['preparechangetrendconfirm'] &
~dataframe['continueup'] &
dataframe['close'].gt(dataframe['highsma']) &
dataframe['highsma'].gt(0) &
(dataframe['emarsi'].ge(75) | dataframe['close'].gt(dataframe['slowsma'])) &
dataframe['bigdown']
) |
(
~dataframe['preparechangetrendconfirm'] &
dataframe['close'].gt(dataframe['highsma']) &
dataframe['highsma'].gt(0) &
dataframe['adx'].gt(30) &
dataframe['emarsi'].ge(80) &
dataframe['bigup']
) |
(
dataframe['preparechangetrendconfirm'] &
~dataframe['continueup'] &
dataframe['slowingdown'] &
dataframe['emarsi'].ge(75) &
dataframe['slowsma'].gt(0)
) |
(
dataframe['preparechangetrendconfirm'] &
dataframe['minusdi'].lt(dataframe['plusdi']) &
dataframe['close'].gt(dataframe['lowsma']) &
dataframe['slowsma'].gt(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.