freqtrade-实战 11- 参数调优之使用 Tailing Stop 提高交易利润

一、追踪止损说明

二、实战代码

1、移动止损超参数优化命令：

# Trailing Stop Hyperopt Commands for Freqtrade

1. Hyperopt Command for Multiple Spaces
   Use this command to optimize parameters such as buy, sell, ROI, and stoploss:

   freqtrade hyperopt \
     --strategy AwesomeStrategy3 \
     --spaces buy sell roi stoploss \
     --hyperopt-loss SharpeHyperOptLossDaily \
     --config user_data/config_binance_spot.json \
     --timeframe 1h \
     --timeframe-detail 5m \
     -e 100 \
     --timerange 20240201-20240801 \
     --random-state 9319 \
     --min-trades 30

2. Hyperopt Command for Trailing Stop Loss
   Use this command to optimize only the trailing stop loss parameters:

   freqtrade hyperopt \
     --strategy AwesomeStrategy3 \
     --spaces trailing \
     --hyperopt-loss SharpeHyperOptLossDaily \
     --config user_data/config_binance_spot.json \
     --timeframe 1h \
     --timeframe-detail 5m \
     -e 100 \
     --timerange 20240201-20240801 \
     --random-state 9319 \
     --min-trades 30

3. Backtesting Command
   Use this command to perform backtesting and view monthly breakdowns:

   freqtrade backtesting \
     --strategy AwesomeStrategy3 \
     --timeframe 1h \
     --timerange 20240201-20240801 \
     --breakdown month \
     -c user_data/config_binance_spot.json \
     --timeframe-detail 5m

Documentation References:
- Trailing Stop Loss: https://www.freqtrade.io/en/stable/stoploss/#trailing-stop-loss
- Default Trailing Stop Search Space: https://www.freqtrade.io/en/stable/hyperopt/#default-trailing-stop-search-space

AwesomeStrategy3.py 策略代码：

# 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 pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union
from functools import reduce

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IntParameter, IStrategy, merge_informative_pair)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib

class AwesomeStrategy3(IStrategy):
    """
    This is a strategy template to get you started.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    # 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 = '5m'

    # Can this strategy go short?
    can_short: bool = False

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "60": 0.01,
        "30": 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

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 150

    # Strategy parameters
    buy_rsi = IntParameter(10, 40, default=30, space="buy")
    sell_rsi = IntParameter(60, 90, default=70, space="sell")

    buy_adx_enabled = BooleanParameter(default=True, space="buy")
    # buy_adx_enabled = CategoricalParameter([True, False], default=True, space="buy")

    buy_adx = DecimalParameter(20, 40, decimals=1, default=30.1, space="buy")

    buy_tema = IntParameter(5, 30, default=9, space="buy")

    # Optional order type mapping.
    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'entry': 'GTC',
        'exit': 'GTC'
    }

    @property
    def plot_config(self):

        plot_config = {
                "main_plot": {
                    f"tema_{self.buy_tema.value}": {
                        "color": "red",
                        "type": "line"
                    },
                    "bb_upperband": {
                        "color": "#008af4",
                        "type": "line",
                        "fill_to": "bb_lowerband"
                    },
                    "bb_middleband": {
                        "color": "#ffd700",
                        "type": "line"
                    },
                    "bb_lowerband": {
                        "color": "#008af4",
                        "type": "line"
                    }
                },
                "subplots": {
                    "RSI": {
                        "rsi": {
                            "color": "#ff8000",
                            "type": "line"
                        }
                    },
                    "ADX": {
                        "adx": {
                            "color": "#ff0000",
                            "type": "line"
                        }
                    }
                }
            }

        return plot_config

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        # ADX
        dataframe['adx'] = ta.ADX(dataframe)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe)
        dataframe['buy_rsi'] = self.buy_rsi.value
        dataframe['sell_rsi'] = self.sell_rsi.value

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe["bb_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"]) /
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
        )

        # TEMA - Triple Exponential Moving Average
        for val in self.buy_tema.range:
            dataframe[f'tema_{val}'] = ta.TEMA(dataframe, timeperiod=val)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []

        if self.buy_adx_enabled.value:
            conditions.append(dataframe['adx'] > self.buy_adx.value)

        conditions.append(
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) &  # Signal: RSI crosses above buy_rsi
                (dataframe[f'tema_{self.buy_tema.value}'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
                (dataframe[f'tema_{self.buy_tema.value}'] > dataframe[f'tema_{self.buy_tema.value}'].shift(1)) &  # Guard: tema is raising
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            )
        )

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'enter_long'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
                (dataframe[f'tema_{self.buy_tema.value}'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
                (dataframe[f'tema_{self.buy_tema.value}'] < dataframe[f'tema_{self.buy_tema.value}'].shift(1)) &  # Guard: tema is falling
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'exit_long'] = 1

        return dataframe

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