一カ月近くfreqtradeを走らせてみて～結果報告～コピペ大歓迎

freqtradeを一カ月近く走らせました。Dryrun、すなわちデモ取引なのですが。なにかの参考になれば幸いです。戦略はフィボナッチ。先月デモ口座を走らせて、異常にロスが出たものは、取引しないような設定になっています。プロフィットファクターが3を超えたときもありましたが、BTCのETF承認後から雲行きが怪しくなり、どんどんロスが発生しています。

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement

# flake8: noqa: F401

# --- Do not remove these libs ---

import numpy as np # noqa

import pandas as pd # noqa

from pandas import DataFrame

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,

IStrategy, IntParameter)

# --------------------------------

# Add your lib to import here

import talib.abstract as ta

import pandas_ta as pta

import freqtrade.vendor.qtpylib.indicators as qtpylib

from technical.indicators import fibonacci_retracements

class Fibo_2(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'

# Minimal ROI designed for the strategy.

# This attribute will be overridden if the config file contains "minimal_roi".

minimal_roi = {

"1440": 0.0,

"360": 0.01,

"240": 0.02,

"120": 0.03,

"60": 0.04,

"0": 0.05

}

# Optimal stoploss designed for the strategy.

# This attribute will be overridden if the config file contains "stoploss".

stoploss = -0.10

# Trailing stoploss

trailing_stop = True

trailing_only_offset_is_reached = True

trailing_stop_positive = 0.10

trailing_stop_positive_offset = 0.11 # Disabled / not configured

# Run "populate_indicators()" only for new candle.

process_only_new_candles = False

# These values can be overridden in the "ask_strategy" section in the config.

use_sell_signal = True

sell_profit_only = False

ignore_roi_if_buy_signal = False

# Number of candles the strategy requires before producing valid signals

startup_candle_count: int = 30

# Strategy parameters

# Optional order type mapping.

order_types = {

'buy': 'limit',

'sell': 'limit',

'stoploss': 'market',

'stoploss_on_exchange': False

}

# Optional order time in force.

order_time_in_force = {

'buy': 'gtc',

'sell': 'gtc'

}

@property

def plot_config(self):

return {

'main_plot': {

'fibo': {},

'min_value': {

'plotly': {'name': 'L-0'},

# 'color': 'rgba(0,0,0,0)',

# 'fill_to': '1st_level',

# 'fill_color': 'rgba(51,51,255,0.2)', # blue

},

'1st_level': {

'plotly': {'name': 'L1-0.236 '},

# 'fill_to': '2nd_level',

# 'fill_color': 'rgba(204,204,255,0.5)', # purple

},

'2nd_level': {

'plotly': {'name': 'L2-0.382 '},

# 'fill_to': '3th_level',

# 'fill_color': 'rgba(102,255,255,0.5)', # cyan

},

'3th_level': {

'plotly': {'name': 'L3-0.5 '},

# 'fill_to': '4th_level',

# 'fill_color': 'rgba(255,204,204,0.5)', # orange

},

'4th_level': {

'plotly': {'name': 'L4-0.618 '},

# 'fill_to': 'max_value',

# 'fill_color': 'rgba(255,255,102,0.3)', # yellow

},

'max_value': {

'plotly': {'name': 'L-100'},

# 'color': 'rgba(0,0,0,0)',

},

},

}

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

# Fibonacci Levels ---------------------------------------------------------------------------------------- /

dataframe['max_value'] = dataframe['close'].max()

dataframe['min_value'] = dataframe['close'].min()

difference = dataframe['max_value'] - dataframe['min_value']

# Fibonacci Levels (UPTREND) - PKG ------------------------------------------------------------------------ /

fibo_df = fibonacci_retracements(dataframe)

dataframe['fibo'] = dataframe['min_value'] + difference * fibo_df

# Fibonacci Levels (UPTREND) - MANUAL ------------------------------------------------------------------------ /

dataframe['1st_level'] = dataframe['min_value'] + difference * 0.236

dataframe['2nd_level'] = dataframe['min_value'] + difference * 0.382

dataframe['3th_level'] = dataframe['min_value'] + difference * 0.5

dataframe['4th_level'] = dataframe['min_value'] + difference * 0.618

dataframe['max_value'] = dataframe['min_value'] + difference * 1.0

return dataframe

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

dataframe.loc[

(

(dataframe['close'] < dataframe['3th_level']) &

(dataframe['close'].shift(1) > dataframe['3th_level'].shift(1)) &

(dataframe['volume'] > 0) # Make sure Volume is not 0

),

'buy'] = 1

return dataframe

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

# dataframe.loc[:, 'sell'] = 0

dataframe.loc[

(

(dataframe['close'] > dataframe['max_value']) &

(dataframe['close'].shift(1) < dataframe['max_value'].shift(1)) &

(dataframe['volume'] > 0) # Make sure Volume is not 0

),

'sell'] = 1

return dataframe

環境としては、Dockerのうえで、VSCodeとTelegramを24時間動かしっぱなしにしました。

結果は以下の通り。

ROI: Closed trades
∙ -1360.87 USDT (-1.20%) (-9.07 Σ%)
∙ -1360.629 USD
ROI: All trades
∙ -1525.854 USDT (-1.29%) (-10.17 Σ%)
∙ -1525.584 USD
Total Trade Count: 118
Bot started: 2024-01-06 22:53:47
First Trade opened: 2 weeks ago (2024-01-07 02:10:11)
Latest Trade opened: 15 hours ago (2024-01-26 17:40:10)
Win / Loss: 88 / 25
Winrate: 77.88%
Expectancy (Ratio): -12.04 (-0.11)
Avg. Duration: 1 day, 7:19:38
Best Performing: ASTR/USDT: 13.98%
Trading volume: 230463.504 USDT
Profit factor: 0.50
Max Drawdown: 14.49% (2273.512 USDT)
from 2024-01-17 01:18:42 (686.703 USDT)
to 2024-01-23 14:11:52 (-1586.808 USDT)

あまり良くない。どんな戦略でもそうでしょうけれど、マクロとミクロがぴったり合わないと利益はでないですね。