BinHV27: RSI and Moving Average Conditions for Long Entries
Article Strategy library · Author: berlinguyinca
Summary
This five-minute long-only strategy combines RSI, directional indicators, ADX, and moving averages to identify entry and exit conditions. Entries require price below two exponential averages, rising RSI, and minus directional movement above its smoothed value. Additional conditions vary with the relationship between 120- and 240-period simple averages and the strength of ADX; they also use smoothed RSI thresholds. The rules aim to enter during weak price action under several trend configurations.
Exits use price moving above selected averages, changes in trend measures, directional indicator relationships, and smoothed RSI thresholds. The strategy specifies a 50% stop loss and a minimal return-on-investment target of 1, but the document gives no backtest results, market-specific performance evidence, or rationale for these settings. Its layered conditions may make behavior difficult to interpret and validate. The strategy is long-only, so it does not provide short-entry rules, and practical results would depend on market, execution, and parameter choices.
Key ideas
- The strategy evaluates entries on five-minute candles.
- Long entries combine price location, RSI movement, directional indicators, ADX, and moving average relationships.
- Entry thresholds differ according to trend configuration and ADX strength.
- Exit rules use price crossing averages, directional movement, trend changes, and smoothed RSI.
- The document provides no performance evidence to establish the strategy's effectiveness.
Tags
Full text
# BinHV27
# BinHV27
strategy sponsored by user BinH from slack
## Source (GPL-3.0)
```python
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.