86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
|
|
# --- Do not remove these libs ---
|
|
from freqtrade.strategy.interface import IStrategy
|
|
from pandas import DataFrame
|
|
# --------------------------------
|
|
|
|
# Add your lib to import here
|
|
import talib.abstract as ta
|
|
|
|
|
|
# This class is a sample. Feel free to customize it.
|
|
class TestStrategyLegacy(IStrategy):
|
|
"""
|
|
This is a test strategy using the legacy function headers, which will be
|
|
removed in a future update.
|
|
Please do not use this as a template, but refer to user_data/strategy/sample_strategy.py
|
|
for a uptodate version of this template.
|
|
"""
|
|
|
|
# Minimal ROI designed for the strategy.
|
|
# This attribute will be overridden if the config file contains "minimal_roi"
|
|
minimal_roi = {
|
|
"40": 0.0,
|
|
"30": 0.01,
|
|
"20": 0.02,
|
|
"0": 0.04
|
|
}
|
|
|
|
# Optimal stoploss designed for the strategy
|
|
# This attribute will be overridden if the config file contains "stoploss"
|
|
stoploss = -0.10
|
|
|
|
# Optimal ticker interval for the strategy
|
|
ticker_interval = '5m'
|
|
|
|
def populate_indicators(self, dataframe: DataFrame) -> 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.
|
|
"""
|
|
|
|
# Momentum Indicator
|
|
# ------------------------------------
|
|
|
|
# ADX
|
|
dataframe['adx'] = ta.ADX(dataframe)
|
|
|
|
# TEMA - Triple Exponential Moving Average
|
|
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
|
|
|
return dataframe
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the buy signal for the given dataframe
|
|
:param dataframe: DataFrame
|
|
:return: DataFrame with buy column
|
|
"""
|
|
dataframe.loc[
|
|
(
|
|
(dataframe['adx'] > 30) &
|
|
(dataframe['tema'] > dataframe['tema'].shift(1)) &
|
|
(dataframe['volume'] > 0)
|
|
),
|
|
'buy'] = 1
|
|
|
|
return dataframe
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
|
|
"""
|
|
Based on TA indicators, populates the sell signal for the given dataframe
|
|
:param dataframe: DataFrame
|
|
:return: DataFrame with buy column
|
|
"""
|
|
dataframe.loc[
|
|
(
|
|
(dataframe['adx'] > 70) &
|
|
(dataframe['tema'] < dataframe['tema'].shift(1)) &
|
|
(dataframe['volume'] > 0)
|
|
),
|
|
'sell'] = 1
|
|
return dataframe
|