A 5-Minute Crypto Strategy Combining Volume Spikes and Oscillators
Article Strategy library · Author: freqtrade
Summary
This Freqtrade strategy describes long entries on a five-minute chart using a combination of unusually high volume, price below a 40-period simple moving average, and momentum indicators. Entry conditions also require the fast stochastic D line to exceed its K line and threshold, RSI to be above a configurable floor, and normalized inverse Fisher RSI to be below a configurable ceiling. Several thresholds are tunable, and the document supplies example parameter values.
The strategy offers two alternative exit rules: an RSI crossover above a threshold while MACD remains negative and minus directional movement is strong, or a bearish parabolic SAR position together with a Fisher RSI threshold condition. A staged minimal-return schedule, a 10% stop loss, and limit entry and exit orders are specified. The document gives implementation logic and settings, but no backtest results, market-specific evaluation, or evidence of profitability. Its indicator combinations and parameters should therefore be treated as a strategy specification to test, with transaction costs and market conditions considered.
Key ideas
- Long entries require a volume surge relative to a rolling average and price below a moving average.
- The entry signal combines stochastic, RSI, and normalized inverse Fisher RSI conditions.
- The strategy switches between two distinct indicator-based exit rules.
- The specification includes staged return targets, a fixed stop loss, and limit orders, but provides no performance evidence.
Tags
Full text
# Strategy005
# Strategy005
Strategy 005
author@: Gerald Lonlas
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy005
## Source (GPL-3.0)
```python
# --- 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.