346 lines
16 KiB
Python
346 lines
16 KiB
Python
import logging
|
||
from freqtrade.strategy import IStrategy, IntParameter, DecimalParameter
|
||
from pandas import DataFrame
|
||
import pandas_ta as ta
|
||
from freqtrade.persistence import Trade
|
||
import numpy as np
|
||
import datetime
|
||
import pandas as pd
|
||
import math
|
||
|
||
# 设置pandas选项以解决FutureWarning警告
|
||
pd.set_option('future.no_silent_downcasting', True)
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
class MartinGale(IStrategy):
|
||
# 策略参数
|
||
loglevel = "warning"
|
||
minimal_roi = {}
|
||
|
||
# 启用自定义ROI回调函数
|
||
use_custom_roi = True
|
||
|
||
# 固定止损设置为与OKX App一致
|
||
stoploss = -0.12 # 对应OKX App中的12.00%止损
|
||
trailing_stop = True
|
||
trailing_stop_positive_offset = 0.005 # 跟踪止损偏移量 0.5%
|
||
|
||
# 用于跟踪市场状态的数据框缓存
|
||
_dataframe_cache = None
|
||
|
||
def __init__(self, config=None):
|
||
"""初始化策略参数"""
|
||
super().__init__(config)
|
||
self._trailing_stop_positive_default = 0.004 # 降低默认值以更容易触发跟踪止盈
|
||
|
||
@property
|
||
def protections(self):
|
||
"""
|
||
保护机制配置
|
||
基于最新Freqtrade规范，保护机制应定义在策略文件中而非配置文件
|
||
"""
|
||
return [
|
||
{
|
||
"method": "StoplossGuard",
|
||
"lookback_period_candles": 60, # 3小时回看期（60根3分钟K线）
|
||
"trade_limit": 2, # 最多2笔止损交易
|
||
"stop_duration_candles": 60, # 暂停180分钟（60根3分钟K线）
|
||
"only_per_pair": False # 仅针对单个币对
|
||
},
|
||
{
|
||
"method": "CooldownPeriod",
|
||
"stop_duration_candles": 2 # 6分钟冷却期（2根3分钟K线）
|
||
},
|
||
{
|
||
"method": "MaxDrawdown",
|
||
"lookback_period_candles": 48, # 2.4小时回看期
|
||
"trade_limit": 4, # 4笔交易限制
|
||
"stop_duration_candles": 24, # 72分钟暂停（24根3分钟K线）
|
||
"max_allowed_drawdown": 0.20 # 20%最大回撤容忍度
|
||
}
|
||
]
|
||
|
||
@property
|
||
def trailing_stop_positive(self):
|
||
"""根据市场状态动态调整跟踪止盈参数"""
|
||
# 获取当前市场状态
|
||
if self._dataframe_cache is not None and len(self._dataframe_cache) > 0:
|
||
current_state = self._dataframe_cache['market_state'].iloc[-1]
|
||
if current_state == 'strong_bull':
|
||
return 0.007 # 强劲牛市中降低跟踪止盈，让利润奔跑
|
||
elif current_state == 'weak_bull':
|
||
return 0.005 # 弱势牛市中保持较低的跟踪止盈
|
||
return self._trailing_stop_positive_default # 返回默认值
|
||
|
||
@trailing_stop_positive.setter
|
||
def trailing_stop_positive(self, value):
|
||
"""设置trailing_stop_positive的默认值"""
|
||
self._trailing_stop_positive_default = value
|
||
|
||
# 时间框架设置 - 主时间框架为3分钟，与OKX App一致
|
||
timeframe = "3m" # 主时间框架为 3 分钟
|
||
can_short = False # 禁用做空
|
||
|
||
# 自定义指标参数 - 设置为可通过Hyperopt优化
|
||
# 从OKX App中获取的参数
|
||
rsi_length = IntParameter(7, 21, default=14, optimize=True, load=True, space='buy') # RSI周期为14，可优化范围7-21
|
||
rsi_oversold = IntParameter(20, 40, default=30, optimize=True, load=True, space='buy') # RSI触发阈值为30，可优化范围20-40
|
||
|
||
# 马丁格尔策略参数 - 基于OKX App界面设置
|
||
max_entry_adjustments = IntParameter(5, 15, default=10, optimize=True, load=True, space='buy') # 最大加仓次数10次，可优化范围5-15
|
||
add_position_callback = DecimalParameter(0.005, 0.015, decimals=3, default=0.0066, optimize=True, load=True, space='buy') # 跌幅加仓阈值0.66%，可优化范围0.5%-1.5%
|
||
|
||
# 马丁格尔加仓比例参数
|
||
step_coefficient = DecimalParameter(1.0, 1.1, decimals=2, default=1.05, optimize=True, load=True, space='buy') # 加仓比例1.05倍，可优化范围1.0-1.1
|
||
stake_divisor = DecimalParameter(0.9, 1.1, decimals=2, default=1.0, optimize=True, load=True, space='buy') # 初始比例，可优化范围0.9-1.1
|
||
|
||
# 止盈目标参数 - 对应OKX App中的单周期止盈目标2.50%
|
||
take_profit_target = DecimalParameter(0.02, 0.03, decimals=3, default=0.025, optimize=True, load=True, space='sell') # 止盈目标2.5%，可优化范围2.0%-3.0%
|
||
|
||
def informative_pairs(self):
|
||
"""定义辅助时间框架"""
|
||
pairs = self.dp.current_whitelist()
|
||
# 添加15m和1h作为辅助时间框架
|
||
return [(pair, '15m') for pair in pairs] + [(pair, '1h') for pair in pairs]
|
||
|
||
def custom_stake_amount(self, pair: str, current_time: pd.Timestamp,
|
||
current_rate: float,
|
||
proposed_stake: float,
|
||
min_stake: float,
|
||
max_stake: float,
|
||
**kwargs) -> float:
|
||
"""自定义下单金额"""
|
||
# 初始资金设置
|
||
initial_balance = self.config.get('dry_run_wallet', 10000)
|
||
# 固定使用初始资金的5%作为初次下单金额（在OKX App范围内）
|
||
desired_stake = initial_balance * 0.05
|
||
desired_stake = math.floor(desired_stake) # 取整
|
||
# 确保在OKX App设定的范围内（2-50,000 USDT）
|
||
return max(min(desired_stake, 50000, max_stake), 2, min_stake)
|
||
|
||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
"""计算各种技术指标"""
|
||
# 计算3m周期的指标
|
||
rsi_length_value = self.rsi_length.value
|
||
|
||
# 计算3m RSI指标
|
||
dataframe['rsi_3m'] = ta.rsi(dataframe['close'], length=rsi_length_value)
|
||
|
||
# 计算3m时间框架的EMA50和EMA200
|
||
dataframe['ema_50_3m'] = ta.ema(dataframe['close'], length=50)
|
||
dataframe['ema_200_3m'] = ta.ema(dataframe['close'], length=200)
|
||
|
||
# 成交量过滤
|
||
dataframe['volume_ma'] = dataframe['volume'].rolling(20).mean()
|
||
|
||
# 计算ATR用于动态止损
|
||
dataframe['atr'] = ta.atr(dataframe['high'], dataframe['low'], dataframe['close'], length=14)
|
||
|
||
# 获取15m数据
|
||
df_15m = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='15m')
|
||
df_15m['rsi_15m'] = ta.rsi(df_15m['close'], length=rsi_length_value)
|
||
# 计算15m时间框架的EMA50和EMA200
|
||
df_15m['ema_50_15m'] = ta.ema(df_15m['close'], length=50)
|
||
df_15m['ema_200_15m'] = ta.ema(df_15m['close'], length=200)
|
||
|
||
# 将15m数据重新索引到主时间框架(3m)
|
||
df_15m = df_15m.set_index('date').reindex(dataframe['date']).reset_index()
|
||
df_15m = df_15m.rename(columns={'index': 'date'})
|
||
df_15m = df_15m[['date', 'rsi_15m', 'ema_50_15m', 'ema_200_15m']].ffill()
|
||
|
||
# 合并15m数据
|
||
dataframe = dataframe.merge(df_15m, how='left', on='date')
|
||
|
||
# 获取1h数据
|
||
df_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1h')
|
||
|
||
# 计算1h RSI和EMA
|
||
df_1h['rsi_1h'] = ta.rsi(df_1h['close'], length=rsi_length_value)
|
||
df_1h['ema_50_1h'] = ta.ema(df_1h['close'], length=50) # 1h 50周期EMA
|
||
df_1h['ema_200_1h'] = ta.ema(df_1h['close'], length=200) # 1h 200周期EMA
|
||
|
||
# 将1h数据重新索引到主时间框架(3m)，并填充缺失值
|
||
df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().bfill().reset_index()
|
||
|
||
# 计算1h上涨趋势，确保不包含None值
|
||
df_1h['trend_1h'] = df_1h['close'] > df_1h['ema_50_1h'] # 1h上涨趋势
|
||
df_1h = df_1h.rename(columns={'index': 'date'})
|
||
df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h']].ffill()
|
||
|
||
# 合并1h数据
|
||
dataframe = dataframe.merge(df_1h, how='left', on='date').ffill()
|
||
|
||
# 计算各时间框架的趋势状态（牛/熊）
|
||
# 3m时间框架：ema50下穿ema200为熊，上穿为牛
|
||
dataframe['trend_3m'] = np.where(dataframe['ema_50_3m'] > dataframe['ema_200_3m'], 1, 0)
|
||
|
||
# 15m时间框架：ema50下穿ema200为熊，上穿为牛
|
||
dataframe['trend_15m'] = np.where(dataframe['ema_50_15m'] > dataframe['ema_200_15m'], 1, 0)
|
||
|
||
# 1h时间框架：ema50下穿ema200为熊，上穿为牛
|
||
dataframe['trend_1h_ema'] = np.where(dataframe['ema_50_1h'] > dataframe['ema_200_1h'], 1, 0)
|
||
|
||
# 计算熊牛得分（0-100）
|
||
# 权重：3m熊牛权重10，15m熊牛权重35，1h熊牛权重65
|
||
dataframe['market_score'] = (
|
||
dataframe['trend_3m'] * 10 +
|
||
dataframe['trend_15m'] * 35 +
|
||
dataframe['trend_1h_ema'] * 65
|
||
)
|
||
|
||
# 确保得分在0-100范围内
|
||
dataframe['market_score'] = dataframe['market_score'].clip(lower=0, upper=100)
|
||
|
||
# 根据得分分类市场状态
|
||
dataframe['market_state'] = 'neutral'
|
||
dataframe.loc[dataframe['market_score'] > 70, 'market_state'] = 'strong_bull'
|
||
dataframe.loc[(dataframe['market_score'] > 50) & (dataframe['market_score'] <= 70), 'market_state'] = 'weak_bull'
|
||
dataframe.loc[(dataframe['market_score'] >= 30) & (dataframe['market_score'] <= 50), 'market_state'] = 'neutral'
|
||
dataframe.loc[(dataframe['market_score'] > 10) & (dataframe['market_score'] < 30), 'market_state'] = 'weak_bear'
|
||
dataframe.loc[dataframe['market_score'] <= 10, 'market_state'] = 'strong_bear'
|
||
|
||
# 创建一个使用前一行市场状态的列
|
||
dataframe['prev_market_state'] = dataframe['market_state'].shift(1)
|
||
# 为第一行设置默认值
|
||
dataframe['prev_market_state'] = dataframe['prev_market_state'].fillna('neutral')
|
||
|
||
# 保存数据框缓存用于trailing_stop_positive计算
|
||
self._dataframe_cache = dataframe
|
||
|
||
return dataframe
|
||
|
||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
"""设置出场条件"""
|
||
# 基于固定止盈目标（2.50%）
|
||
# 注意：实际止盈将在custom_exit中实现
|
||
|
||
# 当RSI进入超买区域时考虑出场
|
||
rsi_overbought = dataframe['rsi_1h'] > 70
|
||
|
||
# 设置出场信号
|
||
dataframe.loc[rsi_overbought, 'exit_long'] = 1
|
||
|
||
return dataframe
|
||
|
||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
"""设置入场条件"""
|
||
# 确保prev_market_state列存在
|
||
if 'prev_market_state' not in dataframe.columns:
|
||
dataframe['prev_market_state'] = 'neutral'
|
||
|
||
# 基于OKX App的RSI条件：RSI-14向下穿过30阈值
|
||
# 条件1: RSI处于超卖区域（根据市场状态动态调整）
|
||
rsi_condition = dataframe.apply(lambda row:
|
||
row['rsi_3m'] < self.rsi_oversold.value if row['prev_market_state'] in ['strong_bull', 'weak_bull'] else row['rsi_3m'] < self.rsi_oversold.value,
|
||
axis=1)
|
||
|
||
# 条件2: RSI向下穿过阈值（交叉检测）
|
||
rsi_cross_down = (dataframe['rsi_3m'] < self.rsi_oversold.value) & (dataframe['rsi_3m'].shift(1) >= self.rsi_oversold.value)
|
||
|
||
# 条件3: 成交量放大确认信号
|
||
volume_spike = dataframe['volume'] > dataframe['volume_ma'] * 1.2
|
||
|
||
# 条件4: 至少有一个时间框架的趋势确认
|
||
trend_confirmation = (dataframe['trend_3m'] == 1) | (dataframe['trend_15m'] == 1) | (dataframe['trend_1h_ema'] == 1)
|
||
|
||
# 合并所有条件
|
||
final_condition = rsi_cross_down & volume_spike & trend_confirmation
|
||
|
||
# 设置入场信号
|
||
dataframe.loc[final_condition, 'enter_long'] = 1
|
||
|
||
# 日志记录
|
||
# if dataframe['enter_long'].sum() > 0:
|
||
# logger.info(f"[{metadata['pair']}] 发现入场信号数量: {dataframe['enter_long'].sum()}")
|
||
|
||
return dataframe
|
||
|
||
def custom_stoploss(self, pair: str, trade: 'Trade', current_time, current_rate: float,
|
||
current_profit: float, **kwargs) -> float:
|
||
"""自定义动态止损"""
|
||
# 动态止损基于ATR
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
last_candle = dataframe.iloc[-1]
|
||
atr = last_candle['atr']
|
||
|
||
# 获取当前市场状态
|
||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'unknown'
|
||
|
||
# 更激进的渐进式止损策略
|
||
if current_profit > 0.05: # 利润超过5%时
|
||
return -3.0 * atr / current_rate # 更大幅扩大止损范围，让利润奔跑
|
||
elif current_profit > 0.03: # 利润超过3%时
|
||
return -2.5 * atr / current_rate # 更中等扩大止损范围
|
||
elif current_profit > 0.01: # 利润超过1%时
|
||
return -2.0 * atr / current_rate # 更轻微扩大止损范围
|
||
|
||
# 在强劲牛市中，即使小亏损也可以容忍更大回调
|
||
if current_state == 'strong_bull' and current_profit > -0.01:
|
||
return -1.8 * atr / current_rate
|
||
|
||
if atr > 0:
|
||
return -1.2 * atr / current_rate # 基础1.2倍ATR止损
|
||
return self.stoploss
|
||
|
||
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
|
||
current_profit: float, **kwargs) -> float:
|
||
"""自定义出场逻辑，实现固定止盈目标"""
|
||
if trade.is_short:
|
||
return 0.0
|
||
|
||
# 基于OKX App的单周期止盈目标2.50%
|
||
if current_profit >= self.take_profit_target.value:
|
||
return 1.0 # 全额出场
|
||
|
||
# 未达到止盈目标，不出场
|
||
return 0.0
|
||
|
||
def adjust_trade_position(self, trade: 'Trade', current_time, current_rate: float,
|
||
current_profit: float, min_stake: float, max_stake: float, **kwargs) -> float:
|
||
"""
|
||
实现马丁格尔加仓逻辑
|
||
- 基于OKX App的参数：跌幅0.66%加仓，最大10次加仓，1.05倍加仓比例
|
||
"""
|
||
# 获取当前交易对
|
||
pair = trade.pair
|
||
|
||
# 获取当前交易的加仓次数
|
||
entry_count = len(trade.orders) # 获取所有入场订单数量
|
||
|
||
# 如果已经达到最大加仓次数，则不再加仓
|
||
if entry_count - 1 >= self.max_entry_adjustments.value:
|
||
logger.info(f"[{pair}] 已达到最大加仓次数 {self.max_entry_adjustments.value}，停止加仓")
|
||
return 0.0
|
||
|
||
# 获取初始入场价格和当前价格的差值百分比
|
||
initial_price = trade.open_rate
|
||
if initial_price == 0:
|
||
return 0.0
|
||
price_diff_pct = (current_rate - initial_price) / initial_price
|
||
|
||
# 检查价格回调是否达到加仓间隔（OKX App中的0.66%跌幅）
|
||
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
||
current_state = dataframe['market_state'].iloc[-1] if 'market_state' in dataframe.columns else 'neutral'
|
||
|
||
if price_diff_pct <= -self.add_position_callback.value and current_state not in ['bear', 'weak_bear']:
|
||
# 计算初始入场金额
|
||
initial_stake = trade.orders[0].cost # 第一笔订单的成本
|
||
|
||
# 计算加仓次数（从1开始计数）
|
||
adjustment_count = entry_count - 1 # 已加仓次数
|
||
|
||
# 计算加仓金额: (initial_stake * step_coefficient) ^ (adjustment_count + 1) / stake_divisor
|
||
# 实现OKX App中的1.05倍加仓比例
|
||
additional_stake = (self.step_coefficient.value * initial_stake / self.stake_divisor.value) ** (adjustment_count + 1)
|
||
|
||
# 确保加仓金额在允许的范围内（≥2 USDT，与OKX App一致）
|
||
additional_stake = max(min_stake, min(additional_stake, max_stake - trade.stake_amount))
|
||
|
||
logger.info(f"[{pair}] 触发加仓: 第{adjustment_count + 1}次加仓, 初始金额{initial_stake:.2f}, \
|
||
加仓金额{additional_stake:.2f}, 价格差{price_diff_pct:.2%}, 当前利润{current_profit:.2%}")
|
||
|
||
return additional_stake
|
||
|
||
# 不符合加仓条件，返回0
|
||
return 0.0 |