A Five-Minute Volume Spike Strategy with RSI and MACD Exits
Code Freqtrade
Summary
This Freqtrade strategy trades five-minute candles using a volume surge and several momentum and price filters. It enters long when volume exceeds four times its rolling average, price is below a 40-period simple moving average, and fast stochastic, RSI, and normalized Fisher RSI conditions are met. The thresholds are exposed as parameters for optimization, with example buy settings included.
Exits can use either an RSI crossing combined with negative MACD and elevated minus directional movement, or a Parabolic SAR and Fisher RSI combination. The configuration also specifies a stop loss, time-based profit targets, limit entry and exit orders, and no trailing stop. The document provides implementation details but no backtest results, market selection, or evidence that the parameter values are robust. Its unusually selective entry conditions and optimized thresholds should therefore be evaluated against suitable data and trading costs before practical use.
Key ideas
- The strategy evaluates five-minute candles and requires a large volume increase relative to a rolling average.
- Long entries combine price below a 40-period average with stochastic, RSI, and Fisher RSI filters.
- Exit signals can be selected from two alternative combinations of momentum and trend indicators.
- The strategy includes configurable profit targets and a fixed stop loss, but gives no performance evidence.
Tags
Full text
# Strategy005.py
```py
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, IntParameter
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class Strategy005(IStrategy):
"""
Strategy 005
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy005
"""
INTERFACE_VERSION = 3
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
"1440": 0.01,
"80": 0.02,
"40": 0.03,
"20": 0.04,
"0": 0.05
}
# Optimal stoploss designed for the strategy
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.02
# run "populate_indicators" only for new candle
process_only_new_candles = True
# Experimental settings (configuration will overide these if set)
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
buy_volumeAVG = IntParameter(low=50, high=300, default=70, space='buy', optimize=True)
buy_rsi = IntParameter(low=1, high=100, default=30, space='buy', optimize=True)
buy_fastd = IntParameter(low=1, high=100, default=30, space='buy', optimize=True)
buy_fishRsiNorma = IntParameter(low=1, high=100, default=30, space='buy', optimize=True)
sell_rsi = IntParameter(low=1, high=100, default=70, space='sell', optimize=True)
sell_minusDI = IntParameter(low=1, high=100, default=50, space='sell', optimize=True)
sell_fishRsiNorma = IntParameter(low=1, high=100, default=50, space='sell', optimize=True)
sell_trigger = CategoricalParameter(["rsi-macd-minusdi", "sar-fisherRsi"],
default=30, space='sell', optimize=True)
# Buy hyperspace params:
buy_params = {
"buy_fastd": 1,
"buy_fishRsiNorma": 5,
"buy_rsi": 26,
"buy_volumeAVG": 150,
}
# Sell hyperspace params:
sell_params = {
"sell_fishRsiNorma": 30,
"sell_minusDI": 4,
"sell_rsi": 74,
"sell_trigger": "rsi-macd-minusdi",
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# Minus Directional Indicator / Movement
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Overlap Studies
# ------------------------------------
# SAR Parabol
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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[
# Prod
(
(dataframe['close'] > 0.00000200) &
(dataframe['volume'] > dataframe['volume'].rolling(self.buy_volumeAVG.value).mean() * 4) &
(dataframe['close'] < dataframe['sma']) &
(dataframe['fastd'] > dataframe['fastk']) &
(dataframe['rsi'] > self.buy_rsi.value) &
(dataframe['fastd'] > self.buy_fastd.value) &
(dataframe['fisher_rsi_norma'] < self.buy_fishRsiNorma.value)
),
'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
"""
conditions = []
if self.sell_trigger.value == 'rsi-macd-minusdi':
conditions.append(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value))
conditions.append(dataframe['macd'] < 0)
conditions.append(dataframe['minus_di'] > self.sell_minusDI.value)
if self.sell_trigger.value == 'sar-fisherRsi':
conditions.append(dataframe['sar'] > dataframe['close'])
conditions.append(dataframe['fisher_rsi'] > self.sell_fishRsiNorma.value)
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.