Band-Based Entries with RSI, MFI, EMA Filters, and Trailing Exits
Article Strategy library · Author: Robert Roman
Summary
Bandtastic is a 15-minute long-entry strategy that buys when price closes below a selected Bollinger lower band. It calculates four band widths, using one through four standard deviations around typical price, and lets the user choose one as the trigger. Optional guards require oversold RSI or MFI readings, or a fast EMA above a slow EMA. Exits use a selected upper band, with optional RSI, MFI, or EMA conditions; the published defaults include an MFI exit guard. ROI targets, a wide stop loss, and a trailing stop provide additional exit rules.
The source reports optimization against one year of data and lists trade counts and profit figures, but does not provide the market sample, validation procedure, or out-of-sample results. Those figures are not evidence of future performance and may reflect parameter selection on the same data. The large parameter search and many configurable filters make overfitting a concern; trading costs and market-specific robustness are not discussed.
Key ideas
- The entry trigger is a close below one of four Bollinger lower bands.
- RSI, MFI, and EMA comparisons can be enabled as optional entry filters.
- Exits can use an upper Bollinger band, optional indicator guards, ROI targets, and trailing stops.
- The reported optimization figures lack out-of-sample validation details and do not establish robustness.
Tags
Full text
# Bandtastic
# Bandtastic
## Source (GPL-3.0)
```python
import talib.abstract as ta
import numpy as np # noqa
import pandas as pd
from functools import reduce
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import IStrategy, CategoricalParameter, DecimalParameter, IntParameter, RealParameter
__author__ = "Robert Roman"
__copyright__ = "Free For Use"
__license__ = "MIT"
__version__ = "1.0"
__maintainer__ = "Robert Roman"
__email__ = "robertroman7@gmail.com"
__BTC_donation__ = "3FgFaG15yntZYSUzfEpxr5mDt1RArvcQrK"
# Optimized With Sharpe Ratio and 1 year data
# 199/40000: 30918 trades. 18982/3408/8528 Wins/Draws/Losses. Avg profit 0.39%. Median profit 0.65%. Total profit 119934.26007495 USDT ( 119.93%). Avg duration 8:12:00 min. Objective: -127.60220
class Bandtastic(IStrategy):
INTERFACE_VERSION = 3
timeframe = '15m'
# ROI table:
minimal_roi = {
"0": 0.162,
"69": 0.097,
"229": 0.061,
"566": 0
}
# Stoploss:
stoploss = -0.345
startup_candle_count = 999
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.058
trailing_only_offset_is_reached = False
# Hyperopt Buy Parameters
buy_fastema = IntParameter(low=1, high=236, default=211, space='buy', optimize=True, load=True)
buy_slowema = IntParameter(low=1, high=250, default=250, space='buy', optimize=True, load=True)
buy_rsi = IntParameter(low=15, high=70, default=52, space='buy', optimize=True, load=True)
buy_mfi = IntParameter(low=15, high=70, default=30, space='buy', optimize=True, load=True)
buy_rsi_enabled = CategoricalParameter([True, False], space='buy', optimize=True, default=False)
buy_mfi_enabled = CategoricalParameter([True, False], space='buy', optimize=True, default=False)
buy_ema_enabled = CategoricalParameter([True, False], space='buy', optimize=True, default=False)
buy_trigger = CategoricalParameter(["bb_lower1", "bb_lower2", "bb_lower3", "bb_lower4"], default="bb_lower1", space="buy")
# Hyperopt Sell Parameters
sell_fastema = IntParameter(low=1, high=365, default=7, space='sell', optimize=True, load=True)
sell_slowema = IntParameter(low=1, high=365, default=6, space='sell', optimize=True, load=True)
sell_rsi = IntParameter(low=30, high=100, default=57, space='sell', optimize=True, load=True)
sell_mfi = IntParameter(low=30, high=100, default=46, space='sell', optimize=True, load=True)
sell_rsi_enabled = CategoricalParameter([True, False], space='sell', optimize=True, default=False)
sell_mfi_enabled = CategoricalParameter([True, False], space='sell', optimize=True, default=True)
sell_ema_enabled = CategoricalParameter([True, False], space='sell', optimize=True, default=False)
sell_trigger = CategoricalParameter(["sell-bb_upper1", "sell-bb_upper2", "sell-bb_upper3", "sell-bb_upper4"], default="sell-bb_upper2", space="sell")
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['mfi'] = ta.MFI(dataframe)
# Bollinger Bands 1,2,3 and 4
bollinger1 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=1)
dataframe['bb_lowerband1'] = bollinger1['lower']
dataframe['bb_middleband1'] = bollinger1['mid']
dataframe['bb_upperband1'] = bollinger1['upper']
bollinger2 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband2'] = bollinger2['lower']
dataframe['bb_middleband2'] = bollinger2['mid']
dataframe['bb_upperband2'] = bollinger2['upper']
bollinger3 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=3)
dataframe['bb_lowerband3'] = bollinger3['lower']
dataframe['bb_middleband3'] = bollinger3['mid']
dataframe['bb_upperband3'] = bollinger3['upper']
bollinger4 = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=4)
dataframe['bb_lowerband4'] = bollinger4['lower']
dataframe['bb_middleband4'] = bollinger4['mid']
dataframe['bb_upperband4'] = bollinger4['upper']
# Build EMA rows - combine all ranges to a single set to avoid duplicate calculations.
for period in set(
list(self.buy_fastema.range)
+ list(self.buy_slowema.range)
+ list(self.sell_fastema.range)
+ list(self.sell_slowema.range)
):
dataframe[f'EMA_{period}'] = ta.EMA(dataframe, timeperiod=period)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# GUARDS
if self.buy_rsi_enabled.value:
conditions.append(dataframe['rsi'] < self.buy_rsi.value)
if self.buy_mfi_enabled.value:
conditions.append(dataframe['mfi'] < self.buy_mfi.value)
if self.buy_ema_enabled.value:
conditions.append(dataframe[f'EMA_{self.buy_fastema.value}'] > dataframe[f'EMA_{self.buy_slowema.value}'])
# TRIGGERS
if self.buy_trigger.value == 'bb_lower1':
conditions.append(dataframe["close"] < dataframe['bb_lowerband1'])
if self.buy_trigger.value == 'bb_lower2':
conditions.append(dataframe["close"] < dataframe['bb_lowerband2'])
if self.buy_trigger.value == 'bb_lower3':
conditions.append(dataframe["close"] < dataframe['bb_lowerband3'])
if self.buy_trigger.value == 'bb_lower4':
conditions.append(dataframe["close"] < dataframe['bb_lowerband4'])
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# GUARDS
if self.sell_rsi_enabled.value:
conditions.append(dataframe['rsi'] > self.sell_rsi.value)
if self.sell_mfi_enabled.value:
conditions.append(dataframe['mfi'] > self.sell_mfi.value)
if self.sell_ema_enabled.value:
conditions.append(dataframe[f'EMA_{self.sell_fastema.value}'] < dataframe[f'EMA_{self.sell_slowema.value}'])
# TRIGGERS
if self.sell_trigger.value == 'sell-bb_upper1':
conditions.append(dataframe["close"] > dataframe['bb_upperband1'])
if self.sell_trigger.value == 'sell-bb_upper2':
conditions.append(dataframe["close"] > dataframe['bb_upperband2'])
if self.sell_trigger.value == 'sell-bb_upper3':
conditions.append(dataframe["close"] > dataframe['bb_upperband3'])
if self.sell_trigger.value == 'sell-bb_upper4':
conditions.append(dataframe["close"] > dataframe['bb_upperband4'])
# Check that volume is not 0
conditions.append(dataframe['volume'] > 0)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'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.