Research strategy: BTC short trend-following on controlled Donchian breaks.
Timeframe
15m
Direction
Long & Short
Stoploss
-6.0%
Trailing Stop
Yes
ROI
0m: 6.5%, 240m: 3.2%, 720m: 0.0%
Interface Version
3
Startup Candles
420
Indicators
5
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
from datetime import datetime
from pandas import DataFrame
import talib.abstract as ta
from freqtrade.strategy import DecimalParameter, IStrategy, IntParameter, informative
class BtcDonchianBreakoutV1(IStrategy):
"""Research strategy: BTC short trend-following on controlled Donchian breaks."""
INTERFACE_VERSION = 3
can_short = True
timeframe = "15m"
startup_candle_count = 420
process_only_new_candles = True
position_adjustment_enable = False
max_entry_position_adjustment = 0
minimal_roi = {
"0": 0.065,
"240": 0.032,
"720": 0.0,
}
stoploss = -0.060
trailing_stop = True
trailing_stop_positive = 0.016
trailing_stop_positive_offset = 0.040
trailing_only_offset_is_reached = True
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
adx_min = IntParameter(16, 34, default=22, space="buy", optimize=False)
rsi_min = IntParameter(24, 40, default=30, space="buy", optimize=False)
rsi_max = IntParameter(42, 58, default=51, space="buy", optimize=False)
volume_factor = DecimalParameter(0.7, 2.0, default=1.05, decimals=2, space="buy", optimize=False)
max_break_extension_atr = DecimalParameter(0.3, 1.5, default=0.8, decimals=1, space="buy", optimize=False)
max_candle_range_atr = DecimalParameter(1.5, 4.0, default=2.6, decimals=1, space="buy", optimize=False)
exit_ema_buffer = DecimalParameter(0.000, 0.012, default=0.003, decimals=3, space="sell", optimize=False)
profit_take_rsi = IntParameter(16, 32, default=24, space="sell", optimize=False)
@property
def protections(self) -> list[dict]:
return [
{"method": "CooldownPeriod", "stop_duration_candles": 4},
{
"method": "StoplossGuard",
"lookback_period_candles": 96,
"trade_limit": 2,
"stop_duration_candles": 24,
"required_profit": 0.0,
"only_per_pair": False,
"only_per_side": True,
},
{
"method": "MaxDrawdown",
"calculation_mode": "equity",
"lookback_period_candles": 192,
"trade_limit": 8,
"stop_duration_candles": 32,
"max_allowed_drawdown": 0.08,
},
]
@staticmethod
def _is_btc_pair(pair: str) -> bool:
return pair.upper().startswith("BTC/")
@informative("1h")
def populate_indicators_1h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14)
dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14)
dataframe["ema_50_slope"] = dataframe["ema_50"] - dataframe["ema_50"].shift(6)
dataframe["donchian_low_48"] = dataframe["low"].rolling(48, min_periods=48).min().shift(1)
return dataframe
@informative("4h")
def populate_indicators_4h(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14)
dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14)
dataframe["ema_50_slope"] = dataframe["ema_50"] - dataframe["ema_50"].shift(4)
return dataframe
@informative("1d")
def populate_indicators_1d(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14)
dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14)
dataframe["ema_50_slope"] = dataframe["ema_50"] - dataframe["ema_50"].shift(5)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20)
dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
dataframe["plus_di"] = ta.PLUS_DI(dataframe, timeperiod=14)
dataframe["minus_di"] = ta.MINUS_DI(dataframe, timeperiod=14)
dataframe["volume_mean_20"] = dataframe["volume"].rolling(20, min_periods=20).mean()
dataframe["donchian_low_96"] = dataframe["low"].rolling(96, min_periods=96).min().shift(1)
dataframe["donchian_high_96"] = dataframe["high"].rolling(96, min_periods=96).max().shift(1)
dataframe["range_atr"] = (dataframe["high"] - dataframe["low"]) / dataframe["atr"]
dataframe["break_extension_atr"] = (dataframe["donchian_low_96"] - dataframe["close"]) / dataframe["atr"]
dataframe["ema_50_slope"] = dataframe["ema_50"] - dataframe["ema_50"].shift(6)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["enter_long"] = 0
dataframe["enter_short"] = 0
if not self._is_btc_pair(metadata["pair"]):
return dataframe
volume_ok = (
(dataframe["volume"] > 0)
& (dataframe["volume_mean_20"] > 0)
& (dataframe["volume"] >= dataframe["volume_mean_20"] * self.volume_factor.value)
)
higher_tf_bear = (
(dataframe["close_1d"] < dataframe["ema_50_1d"])
& (dataframe["ema_50_slope_1d"] < 0)
& (dataframe["minus_di_1d"] > dataframe["plus_di_1d"])
& (dataframe["close_4h"] < dataframe["ema_200_4h"])
& (dataframe["ema_50_slope_4h"] < 0)
& (dataframe["minus_di_4h"] > dataframe["plus_di_4h"])
& (dataframe["close_1h"] < dataframe["ema_200_1h"])
& (dataframe["ema_50_slope_1h"] < 0)
)
controlled_break = (
(dataframe["close"] < dataframe["donchian_low_96"])
& (dataframe["close_1h"] < dataframe["donchian_low_48_1h"])
& (dataframe["close"] < dataframe["open"])
& (dataframe["break_extension_atr"] > 0)
& (dataframe["break_extension_atr"] < self.max_break_extension_atr.value)
& (dataframe["range_atr"] < self.max_candle_range_atr.value)
)
short_conditions = (
volume_ok
& higher_tf_bear
& controlled_break
& (dataframe["close"] < dataframe["ema_200"])
& (dataframe["ema_50"] < dataframe["ema_200"])
& (dataframe["adx"] > self.adx_min.value)
& (dataframe["adx_1h"] > self.adx_min.value)
& (dataframe["minus_di"] > dataframe["plus_di"])
& (dataframe["rsi"] > self.rsi_min.value)
& (dataframe["rsi"] < self.rsi_max.value)
)
dataframe.loc[short_conditions, ["enter_short", "enter_tag"]] = (1, "btc_donchian_breakout")
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["exit_long"] = 0
dataframe["exit_short"] = 0
return dataframe
def custom_exit(
self,
pair: str,
trade,
current_time: datetime,
current_rate: float,
current_profit: float,
**kwargs,
) -> str | bool | None:
if not self.dp:
return None
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if dataframe.empty or not trade.is_short:
return None
candle = dataframe.iloc[-1]
if current_profit > 0.025 and candle["rsi"] < self.profit_take_rsi.value:
return "donchian_rsi_profit_take"
if current_profit > 0.012 and current_rate > candle["ema_20"]:
return "donchian_profit_ema20_reclaim"
if current_rate > candle["ema_50"] * (1 + self.exit_ema_buffer.value):
return "donchian_ema50_reclaim"
if candle["close_1h"] > candle["ema_50_1h"] and current_profit < 0.01:
return "donchian_1h_reclaim"
return None
def leverage(
self,
pair: str,
current_time: datetime,
current_rate: float,
proposed_leverage: float,
max_leverage: float,
entry_tag: str | None,
side: str,
**kwargs,
) -> float:
return 1.0