Timeframe
15m
Direction
Long Only
Stoploss
-100.0%
Trailing Stop
No
ROI
N/A
Interface Version
3
Startup Candles
100
Indicators
2
freqtrade/freqtrade-strategies
this is an example class, implementing a PSAR based trailing stop loss you are supposed to take the `custom_stoploss()` and `populate_indicators()` parts and adapt it to your own strategy
freqtrade/freqtrade-strategies
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
import ast
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union
from joblib import load
# additional imports required
from datetime import datetime
from freqtrade.persistence import Trade
from functools import reduce
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import IStrategy, stoploss_from_absolute
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib
from camel_relaxed import fibo_retracement, find_entry_from
import logging
logger = logging.getLogger(__name__)
class Camel_opt(IStrategy):
# 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
# Optimal timeframe for the strategy.
timeframe = '15m'
# Indicators startup period
startup_candle_count = 100
# Can this strategy go short?
can_short: bool = False
# Minimal ROI designed for the strategy.
# Establecer el stop loss absoluto
stoploss = -1
trailing_stop = False
use_custom_stoploss = False
trailing_stop_positive: None
trailing_stop_positive_offset: 0.0
trailing_only_offset_is_reached: False
# These values can be overridden in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Define sell parameters search space
SL = CategoricalParameter(["out", "optimal", "aggressive"], default="optimal", space="sell", optimize=True)
TP = DecimalParameter(2.0, 3.0, default=2.6, space="sell", decimals=1, optimize=True)
rebounce_level = DecimalParameter(0.48, 0.53, default=0.5, space="buy", decimals=2, optimize=True)
fail_level = DecimalParameter(0.78, 0.855, default=0.85, space="buy", decimals=2, optimize=True)
# Optional order time in force.
order_time_in_force = {
'entry': 'GTC',
'exit': 'GTC'
}
@property
def plot_config(self):
return {
# Main plot indicators (Moving averages, ...)
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
}
}
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Add fractals indicators
for i in range(2, len(dataframe) - 2):
sides = [i-2,i-1,i+1,i+2]
# Fractal Up (Bearish)
dataframe.loc[i, 'fractal_up'] = 1 if all(dataframe.loc[i, 'high'] > dataframe.loc[j, 'high'] for j in sides) else 0
# Fractal Down (Bullish)
dataframe.loc[i, 'fractal_down'] = 1 if all(dataframe.loc[i, 'low'] < dataframe.loc[j, 'low'] for j in sides) else 0
dataframe = dataframe.fillna(0)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['enter_long'] = 0
entries = []
self.SL.value = 'optimal'
self.TP.value = 2.75
self.fail_level.value = 0.85
self.rebounce_level.value = 0.5
for i in range(len(dataframe)):
if dataframe.loc[i, 'fractal_down']:
entry_points = find_entry_from(dataframe, i, rebounce_level=self.rebounce_level.value, fail_level=self.fail_level.value)
if len(entry_points) > 0: entries.append(entry_points)
entries = list(set(tuple(lst) for lst in entries))
entries.sort()
for i in range(len(entries)):
A, C, E = dataframe.loc[entries[i][0], 'low'], dataframe.loc[entries[i][2], 'low'], dataframe.loc[entries[i][4], 'low']
B, D = dataframe.loc[entries[i][1], 'high'], dataframe.loc[entries[i][3], 'high']
SL = -1
if self.SL.value == 'out':
SL = fibo_retracement(A, B, 1.272)
elif self.SL.value == 'optimal':
SL = fibo_retracement(A, B, 0.855)
elif self.SL.value == 'aggressive':
SL = fibo_retracement(C, D, 0.855)
TP = fibo_retracement(D, E, self.TP.value)
difference = TP/SL
if difference > 1:
dataframe.loc[entries[i][-1], ['enter_long', 'enter_tag']] = (1, str([int(TP), int(SL)]))
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs):
vals = ast.literal_eval(trade.enter_tag)
TP, SL = vals
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
#if last_candle['close'] > dataframe.loc[F, 'close']*1.3: return 'TP'
if last_candle['high'] > TP: return 'TP'
if last_candle['low'] < SL: return 'SL'