This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/
Timeframe
5m
Direction
Long Only
Stoploss
-3.7%
Trailing Stop
No
ROI
0m: 20.8%, 90m: 15.4%, 251m: 6.1%, 606m: 0.0%
Interface Version
3
Startup Candles
N/A
Indicators
6
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# 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 emaCrossStrategy(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://www.freqtrade.io/en/latest/strategy-customization/
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3
# Can this strategy go short?
can_short: bool = False
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.208,
"90": 0.154,
"251": 0.061,
"606": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.037
# Trailing stoploss
trailing_stop = False
trailing_stop_positive = None
trailing_stop_positive_offset = 0.0
trailing_only_offset_is_reached = False
# Optimal timeframe for the strategy.
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
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:
# RSI
dataframe['rsi'] = ta.RSI(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']
# # EMA - Exponential Moving Average
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
((dataframe['rsi'] > 29) & # When the RSI goes above 29, then there is less risk for the buy condition
(dataframe['close'] < dataframe['bb_lowerband'])) | # When the close of the candle is less than that of the lower Bollinger Band, then this is seen as a bargain as the currency pair is oversold
((dataframe['ema5'] > dataframe['ema21']) & # When the EMA5 crosses above the EMA21, this is called a "Golden Cross" and is a bullish indicator
(dataframe['ema5'].shift(1) <= dataframe['ema21'])) # Because the EMA21 was previously greater than or equal to the EMA5 of the previous candle (previous candle is denoted by .shift(1))
),
'buy'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
((dataframe['rsi'] > 71) & # When the RSI goes above 71, then the currency pair is overbought and it is a good time to sell
(dataframe['close'] > dataframe['bb_middleband'])) | # When the close of the candle is greater than that of the middle Bollinger Band, then the price is up so sell
((dataframe['ema5'] < dataframe['ema21']) & # When the EMA21 crosses above the EMA5, this is called a "Death Cross" and is a bearish indicator
(dataframe['ema5'].shift(1) > dataframe['ema21'])) # Since the EMA5 was previously greater than or equal to the EMA21 of the previous candle
),
'sell'] = 1
return dataframe