Timeframe
1d
Direction
Long Only
Stoploss
-15.0%
Trailing Stop
No
ROI
0m: 10000.0%
Interface Version
3
Startup Candles
N/A
Indicators
1
freqtrade/freqtrade-strategies
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
from datetime import datetime
import pandas as pd
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, stoploss_from_open
from pandas import DataFrame
class Nr7VolatilityContractionBreakoutStrategy(IStrategy):
INTERFACE_VERSION = 3
timeframe = "1d"
can_short: bool = False
minimal_roi = {
"0": 100,
}
stoploss = -0.15
trailing_stop = False
use_custom_stoploss = False
process_only_new_candles = True
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
startup_candle_count: int = 240
nr_window = IntParameter(3, 20, default=7, space="buy")
atr_multiplier = DecimalParameter(1.5, 3.0, default=2.0, space="sell")
trail_atr_multiplier = DecimalParameter(1.0, 2.0, default=1.5, space="sell")
order_types = {
"entry": "limit",
"exit": "limit",
"stoploss": "market",
"stoploss_on_exchange": False,
}
order_time_in_force = {
"entry": "GTC",
"exit": "GTC",
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
n = int(self.nr_window.value)
dataframe["range"] = dataframe["high"] - dataframe["low"]
dataframe["nr_lowest_range"] = dataframe["range"].rolling(n).min()
dataframe["is_nr"] = (dataframe["range"] == dataframe["nr_lowest_range"]).astype(int)
nr_high = dataframe["high"].where(dataframe["is_nr"] == 1).ffill()
nr_low = dataframe["low"].where(dataframe["is_nr"] == 1).ffill()
dataframe["nr_high"] = nr_high
dataframe["nr_low"] = nr_low
dataframe["nr_high_trigger"] = nr_high.shift(1)
dataframe["nr_low_trigger"] = nr_low.shift(1)
dataframe["atr"] = ta.ATR(dataframe, timeperiod=14)
dataframe["volume_mean"] = dataframe["volume"].rolling(window=20).mean()
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["enter_long"] = 0
dataframe["enter_short"] = 0
dataframe["enter_tag"] = ""
vol_ok = dataframe["volume"] > 0
dataframe.loc[(dataframe["close"] > dataframe["nr_high_trigger"]) & vol_ok, ["enter_long", "enter_tag"]] = [
1,
"nr7_breakout_up",
]
dataframe.loc[(dataframe["close"] < dataframe["nr_low_trigger"]) & vol_ok, ["enter_short", "enter_tag"]] = [
1,
"nr7_breakout_down",
]
conflict = (dataframe["enter_long"] == 1) & (dataframe["enter_short"] == 1)
dataframe.loc[conflict, ["enter_long", "enter_short", "enter_tag"]] = [0, 0, ""]
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["exit_long"] = 0
dataframe["exit_short"] = 0
vol_ok = dataframe["volume"] > 0
dataframe.loc[(dataframe["close"] < dataframe["nr_low_trigger"]) & vol_ok, "exit_long"] = 1
dataframe.loc[(dataframe["close"] > dataframe["nr_high_trigger"]) & vol_ok, "exit_short"] = 1
return dataframe
def custom_stoploss(
self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs
) -> float:
try:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
if dataframe is None or len(dataframe) < 20:
return 1.0
if "date" in dataframe.columns:
df = dataframe
if not pd.api.types.is_datetime64_any_dtype(df["date"]):
df = df.copy()
df["date"] = pd.to_datetime(df["date"], utc=True, errors="coerce")
df = df.dropna(subset=["date"]).sort_values("date")
else:
df = dataframe.copy()
df["date"] = pd.to_datetime(df.index, utc=True, errors="coerce")
df = df.dropna(subset=["date"]).sort_values("date")
entry_date = pd.Timestamp(trade.open_date_utc)
if entry_date.tzinfo is None:
entry_date = entry_date.tz_localize("UTC")
else:
entry_date = entry_date.tz_convert("UTC")
hist = df.loc[df["date"] <= entry_date]
if hist.empty:
return 1.0
entry_row = hist.iloc[-1]
atr_at_entry = float(entry_row.get("atr", 0.0) or 0.0)
if atr_at_entry <= 0.0 or pd.isna(atr_at_entry):
return 1.0
current_atr = float(df["atr"].iloc[-1] or 0.0)
if current_atr <= 0.0 or pd.isna(current_atr):
return 1.0
initial_mult = float(self.atr_multiplier.value)
trail_mult = float(self.trail_atr_multiplier.value)
atr_pct_entry = atr_at_entry / float(trade.open_rate)
open_relative_stop = -atr_pct_entry * initial_mult
if current_profit > 1.5 * atr_pct_entry:
trail_distance = current_atr * trail_mult
if bool(getattr(trade, "is_short", False)):
stop_price = float(current_rate) + float(trail_distance)
if stop_price > float(trade.open_rate):
stop_price = float(trade.open_rate)
open_relative_stop = max(
open_relative_stop, (float(trade.open_rate) - stop_price) / float(trade.open_rate)
)
else:
stop_price = float(current_rate) - float(trail_distance)
if stop_price < float(trade.open_rate):
stop_price = float(trade.open_rate)
open_relative_stop = max(open_relative_stop, (stop_price / float(trade.open_rate)) - 1.0)
sl = float(stoploss_from_open(open_relative_stop, current_profit))
if sl <= 0.0:
return 1.0
return sl
except Exception as e:
if hasattr(self, "log"):
self.log.error(f"Custom stoploss error for {pair}: {e!s}")
return 1.0
def custom_exit(
self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs
) -> str | None:
if current_profit <= 0:
return None
max_profit = trade.max_profit if hasattr(trade, "max_profit") else current_profit
# 只在高盈利位置回撤时才出,给趋势足够空间
if max_profit > 0.40 and (max_profit - current_profit) > 0.15:
return "trailing_profit_15pct"
if max_profit > 0.20 and (max_profit - current_profit) > 0.10:
return "trailing_profit_10pct"
return None
@property
def plot_config(self):
return {
"main_plot": {
"close": {"color": "black"},
"nr_high_trigger": {"color": "orange"},
"nr_low_trigger": {"color": "orange"},
},
"subplots": {
"ATR": {"atr": {"color": "white"}},
"Range": {"range": {"color": "gray"}},
"Volume": {"volume": {"color": "gray", "type": "bar"}, "volume_mean": {"color": "blue"}},
},
}