RSI Reversal Entries with TEMA and Bollinger Band Filters
Article Strategy library · Author: freqtrade
Summary
This sample strategy calculates several indicators, including RSI, TEMA, Bollinger Bands, MACD, ADX, stochastic, and MFI. Its actual entry rules use RSI crossings together with TEMA’s position and direction relative to the Bollinger middle band. A long entry follows an RSI move above 30 when TEMA is at or below the band midpoint and rising; a short entry follows an RSI move above 70 when TEMA is above the midpoint and falling. Nonzero volume is also required.
The exit rules reuse those same short and long conditions, respectively, so an exit can be signaled by a condition that also defines the opposite entry. The strategy is configured for hourly candles, allows shorting, sets a five percent stop loss, and disables trailing stops. The file is an example rather than a tested performance report: it gives no market, backtest results, transaction-cost assumptions, or evidence that the indicator combination is profitable. Several calculated indicators are not used in the trading rules.
Key ideas
- The long setup pairs an RSI move above 30 with rising TEMA below or at the Bollinger midpoint.
- The short setup pairs an RSI move above 70 with falling TEMA above the midpoint.
- Entry and exit conditions reuse the same indicator patterns for opposite trade states.
- The strategy calculates several indicators that do not affect its entry or exit signals.
- The sample configuration includes a fixed stop loss and no trailing stop, but provides no performance evidence.
Tags
Full text
# FSampleStrategy
# FSampleStrategy
## Source (GPL-3.0)
```python
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import (
BooleanParameter,
CategoricalParameter,
DecimalParameter,
IStrategy,
IntParameter,
)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.
class FSampleStrategy(IStrategy):
INTERFACE_VERSION = 3
timeframe = "1h"
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
# minimal_roi = {"60": 0.1, "30": 0.2, "0": 0.2}
minimal_roi = {"0": 1}
stoploss = -0.05
can_short = True
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["adx"] = ta.ADX(dataframe)
# RSI
dataframe["rsi"] = ta.RSI(dataframe)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe["fastd"] = stoch_fast["fastd"]
dataframe["fastk"] = stoch_fast["fastk"]
# MACD
macd = ta.MACD(dataframe)
dataframe["macd"] = macd["macd"]
dataframe["macdsignal"] = macd["macdsignal"]
dataframe["macdhist"] = macd["macdhist"]
# MFI
dataframe["mfi"] = ta.MFI(dataframe)
# Bollinger Bands
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["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
dataframe["bb_upperband"] - dataframe["bb_lowerband"]
)
dataframe["bb_width"] = (
dataframe["bb_upperband"] - dataframe["bb_lowerband"]
) / dataframe["bb_middleband"]
# Parabolic SAR
dataframe["sar"] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe["htsine"] = hilbert["sine"]
dataframe["htleadsine"] = hilbert["leadsine"]
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe["rsi"], 30))
& (dataframe["tema"] <= dataframe["bb_middleband"])
& ( # Guard: tema below BB middle
dataframe["tema"] > dataframe["tema"].shift(1)
)
& ( # Guard: tema is raising
dataframe["volume"] > 0
) # Make sure Volume is not 0
),
"enter_long",
] = 1
dataframe.loc[
(
# Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe["rsi"], 70))
& (dataframe["tema"] > dataframe["bb_middleband"])
& ( # Guard: tema above BB middle
dataframe["tema"] < dataframe["tema"].shift(1)
)
& ( # Guard: tema is falling
dataframe["volume"] > 0
) # Make sure Volume is not 0
),
"enter_short",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe["rsi"], 70))
& (dataframe["tema"] > dataframe["bb_middleband"])
& ( # Guard: tema above BB middle
dataframe["tema"] < dataframe["tema"].shift(1)
)
& ( # Guard: tema is falling
dataframe["volume"] > 0
) # Make sure Volume is not 0
),
"exit_long",
] = 1
dataframe.loc[
(
# Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe["rsi"], 30))
&
# Guard: tema below BB middle
(dataframe["tema"] <= dataframe["bb_middleband"])
& (dataframe["tema"] > dataframe["tema"].shift(1))
& ( # Guard: tema is raising
dataframe["volume"] > 0
) # Make sure Volume is not 0
),
"exit_short",
] = 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.