Timeframe
15m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 4.0%, 120m: 1.5%, 360m: 0.0%
Interface Version
N/A
Startup Candles
100
Indicators
2
freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
freqtrade/freqtrade-strategies
This strategy uses custom_stoploss() to enforce a fixed risk/reward ratio by first calculating a dynamic initial stoploss via ATR - last negative peak
freqtrade/freqtrade-strategies
# Klineo Backtesting Strategy: Donchian + ATR Breakout
# Timeframe: 15m | Entry: close > donch_high.shift(1) | Exit: close < donch_low.shift(1)
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta
class KlineoDonchianAtrBreakout(IStrategy):
timeframe = "15m"
startup_candle_count = 100
minimal_roi = {"0": 0.04, "120": 0.015, "360": 0.0}
stoploss = -0.10
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["donch_high"] = dataframe["high"].rolling(window=20).max()
dataframe["donch_low"] = dataframe["low"].rolling(window=20).min()
dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Entry: close > previous candle's donchian high
prev_donch_high = dataframe["donch_high"].shift(1)
dataframe.loc[
dataframe["close"] > prev_donch_high,
"enter_long",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Exit: close < previous candle's donchian low
prev_donch_low = dataframe["donch_low"].shift(1)
dataframe.loc[
dataframe["close"] < prev_donch_low,
"exit_long",
] = 1
return dataframe