jack/Chan
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

add fx strentth

Browse Source
This commit is contained in:
jackyu66git
2025-05-23 21:19:50 +08:00
parent a1e033432b
commit a9d24233b0
14 changed files with 2067 additions and 399 deletions
Download Patch File Download Diff File Expand all files Collapse all files
strategies/Chan_SOL_2.py
+283 -268
View File
@@ -1,9 +1,10 @@
# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from typing import Dict, List
from typing import Dict, List, Tuple, Optional
from functools import reduce
from pandas import DataFrame, pandas
from pandas import DataFrame
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas as pd
# --------------------------------
from technical.util import resample_to_interval, resampled_merge
@@ -12,329 +13,343 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
from datetime import datetime, timedelta, timezone
from freqtrade.persistence import Trade, Order
from typing import Optional
import numpy as np
import logging
logger = logging.getLogger(__name__)
### Now you can use logger.info('asfd') to log
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy Chan_SOL_2 --strategy-path ./user_data/Chan/strategies --timerange=20240801-20241201
# freqtrade trade -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL_2 --strategy-path ./user_data/Chan/strategies --timerange=20250309-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_SOL.json -t 1m --pairs SOL/USDT:USDT --timerange=20250501-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_SOL_2 --strategy-path ./user_data/strategies -c ./user_data/ChanLun_SOL.json -e 200 --timerange=20250101-20250215
# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies --timerange=20250101-
# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan.json --pairs SOL/USDT:USDT -t 1m --timerange 20240101-
# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan.json --strategy Chan_SOL_2 --strategy-path ./user_data/strategies
class ChanLun_SOL_2(IStrategy):
class Chan_SOL_2(IStrategy):
"""
稳定盈利交易策略 - 基于多重技术分析
结合趋势跟踪、动量指标和风险管理
"""
INTERFACE_VERSION: int = 3
# 优化的ROI设置 - 更快速获利
# 优化的ROI设置 - 阶梯式获利了结
minimal_roi = {
"0": 0.012, # 立即获利1.2%
"5": 0.01, # 5分钟后获利1%
"15": 0.007, # 15分钟后获利0.7%
"30": 0.005 # 30分钟后获利0.5%
"0": 0.15, # 15%快速获利
"30": 0.08, # 30分钟后8%
"60": 0.05, # 1小时后5%
"120": 0.03, # 2小时后3%
"240": 0.02, # 4小时后2%
"480": 0.015, # 8小时后1.5%
"960": 0.01 # 16小时后1%
}
can_short = True
stoploss = -0.007 # 降低止损为0.7%
stoploss = -0.08 # 8%止损
# 追踪止损设置 - 更积极的追踪止损
# 动态追踪止损
trailing_stop = True
trailing_stop_positive = 0.003 # 0.3%
trailing_stop_positive_offset = 0.005 # 0.5%
trailing_stop_positive = 0.015 # 1.5%开始追踪
trailing_stop_positive_offset = 0.025 # 2.5%偏移
trailing_only_offset_is_reached = True
# 时间周期
# 仓位管理
position_adjustment_enable = True
max_entry_position_adjustment = 2
max_dca_multiplier = 3.0
timeframe = '5m'
informative_timeframe = '1h'
startup_candle_count = 200
startup_candle_count = 200
# 只做空头策略
only_short = True
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
return informative_pairs
# 自定义参数
buy_volume_threshold = 1.5
sell_volume_threshold = 1.2
rsi_oversold = 25
rsi_overbought = 75
adx_trend_threshold = 25
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 获取更高时间周期的数据
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
"""
添加技术指标 - 多维度分析
"""
# === 趋势指标 ===
# 多周期移动平均线
dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
dataframe['ema_21'] = ta.EMA(dataframe, timeperiod=21)
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
# === 高时间周期指标 ===
# 三均线系统
informative['ema50'] = ta.EMA(informative, timeperiod=50)
informative['ema100'] = ta.EMA(informative, timeperiod=100)
informative['ema200'] = ta.SMA(informative, timeperiod=200) # 使用SMA作为长期趋势
# === 动量指标 ===
# RSI - 超买超卖
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=9)
dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=21)
# 趋势方向
informative['uptrend'] = (
(informative['ema50'] > informative['ema100']) &
(informative['ema100'] > informative['ema200']) &
(informative['close'] > informative['ema50'])
).astype(int)
# MACD - 趋势动量
macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
informative['downtrend'] = (
(informative['ema50'] < informative['ema100']) &
(informative['ema100'] < informative['ema200']) &
(informative['close'] < informative['ema50'])
).astype(int)
# === 波动率指标 ===
# ATR - 真实波动幅度
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
# 强下降趋势
informative['strong_downtrend'] = (
(informative['ema50'] < informative['ema100']) &
(informative['ema100'] < informative['ema200']) &
(informative['close'] < informative['ema50']) &
(informative['ema50'].shift(3) < informative['ema50']) # 确认EMA50下降
).astype(int)
# 添加高时间周期的ADX指标
informative['adx'] = ta.ADX(informative, timeperiod=14)
# 添加高时间周期的波动率
informative['atr'] = ta.ATR(informative, timeperiod=14)
informative['atr_percent'] = (informative['atr'] / informative['close']) * 100
# 高时间周期RSI
informative['rsi'] = ta.RSI(informative, timeperiod=14)
# 将informative数据帧中的列重命名，以便在合并后区分
for col in informative.columns:
if col not in ['date', 'open', 'high', 'low', 'close', 'volume']:
informative[f"{col}_{self.informative_timeframe}"] = informative[col]
# 删除原始列，只保留重命名后的列和必要的日期、OHLCV列
for col in list(informative.columns):
if col not in ['date', 'open', 'high', 'low', 'close', 'volume'] and not col.endswith(f"_{self.informative_timeframe}"):
del informative[col]
# 打印列名以便调试
logger.info(f"Informative columns after renaming: {informative.columns.tolist()}")
# 合并数据 - 使用正确的参数
dataframe = resampled_merge(dataframe, informative, self.informative_timeframe)
# 打印合并后的列名以便调试
logger.info(f"Dataframe columns after merge: {dataframe.columns.tolist()}")
# === 主时间周期指标 ===
# 布林带
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_width'] = ((bollinger['upper'] - bollinger['lower']) / bollinger['mid'])
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']
# 动量指标
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# 均线
dataframe['ema9'] = ta.EMA(dataframe, timeperiod=9)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# 成交量
dataframe['volume_mean'] = dataframe['volume'].rolling(window=20).mean()
dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_mean']
# 波动率
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
# ADX - 趋势强度指标
# === 趋势强度指标 ===
# ADX - 趋势强度
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
dataframe['plus_di'] = ta.PLUS_DI(dataframe, timeperiod=14)
dataframe['minus_di'] = ta.MINUS_DI(dataframe, timeperiod=14)
# 价格突破
dataframe['upper_break'] = (
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift() <= dataframe['bb_upperband'].shift())
).astype(int)
# === 成交量指标 ===
# 成交量移动平均
dataframe['volume_sma_20'] = dataframe['volume'].rolling(window=20).mean()
dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma_20']
dataframe['lower_break'] = (
(dataframe['close'] < dataframe['bb_lowerband']) &
(dataframe['close'].shift() >= dataframe['bb_lowerband'].shift())
).astype(int)
# OBV - 能量潮
dataframe['obv'] = ta.OBV(dataframe)
dataframe['obv_ema'] = ta.EMA(dataframe['obv'], timeperiod=20)
# 均线交叉
dataframe['ema_cross_up'] = (
(dataframe['ema9'] > dataframe['ema21']) &
(dataframe['ema9'].shift() <= dataframe['ema21'].shift())
).astype(int)
# === 价格行为指标 ===
# 价格变化率
dataframe['price_change'] = dataframe['close'].pct_change()
dataframe['price_change_5'] = dataframe['close'].pct_change(periods=5)
dataframe['ema_cross_down'] = (
(dataframe['ema9'] < dataframe['ema21']) &
(dataframe['ema9'].shift() >= dataframe['ema21'].shift())
).astype(int)
# 高低点分析
dataframe['high_20'] = dataframe['high'].rolling(window=20).max()
dataframe['low_20'] = dataframe['low'].rolling(window=20).min()
# 超买超卖区域
dataframe['rsi_oversold'] = (dataframe['rsi'] < 30).astype(int)
dataframe['rsi_overbought'] = (dataframe['rsi'] > 70).astype(int)
# === 自定义复合指标 ===
# 趋势确认信号
dataframe['trend_up'] = (
(dataframe['ema_8'] > dataframe['ema_21']) &
(dataframe['ema_21'] > dataframe['ema_50']) &
(dataframe['close'] > dataframe['ema_8'])
)
# 价格与均线的关系
dataframe['price_above_ema50'] = (dataframe['close'] > dataframe['ema50']).astype(int)
dataframe['price_below_ema50'] = (dataframe['close'] < dataframe['ema50']).astype(int)
dataframe['trend_down'] = (
(dataframe['ema_8'] < dataframe['ema_21']) &
(dataframe['ema_21'] < dataframe['ema_50']) &
(dataframe['close'] < dataframe['ema_8'])
)
# 趋势强度
dataframe['strong_trend'] = (dataframe['adx'] > 25).astype(int)
# 动量强度评分
dataframe['momentum_score'] = (
((dataframe['rsi'] > 50).astype(int) * 1) +
((dataframe['macd'] > dataframe['macdsignal']).astype(int) * 1) +
((dataframe['adx'] > self.adx_trend_threshold).astype(int) * 1) +
((dataframe['volume_ratio'] > 1.0).astype(int) * 1)
)
# 添加蜡烛图形态识别
dataframe['doji'] = ta.CDLDOJI(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['engulfing'] = ta.CDLENGULFING(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['hammer'] = ta.CDLHAMMER(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
dataframe['shooting_star'] = ta.CDLSHOOTINGSTAR(dataframe['open'], dataframe['high'], dataframe['low'], dataframe['close'])
# 价格动量
dataframe['momentum'] = dataframe['close'] - dataframe['close'].shift(5)
# 波动率适应性指标
dataframe['volatility_high'] = dataframe['atr'] > dataframe['atr'].rolling(window=20).mean() * 1.5
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 检查列名是否存在
downtrend_col = 'resample_60_downtrend_1h'
strong_downtrend_col = 'resample_60_strong_downtrend_1h'
adx_col = 'resample_60_adx_1h'
rsi_col = 'resample_60_rsi_1h'
"""
入场信号 - 多条件确认系统
"""
# === 多头入场条件 ===
# 如果列名不存在，使用替代方案
for col, default_value in [
(downtrend_col, 0),
(strong_downtrend_col, 0),
(adx_col, 25),
(rsi_col, 50)
]:
if col not in dataframe.columns:
logger.warning(f"Column {col} not found in dataframe. Creating with default value {default_value}.")
dataframe[col] = default_value
# 条件1: 强势突破入场
dataframe.loc[
(
# 趋势确认
(dataframe['trend_up']) &
(dataframe['close'] > dataframe['ema_21']) &
# 动量确认
(dataframe['rsi'] > 45) & (dataframe['rsi'] < 75) &
(dataframe['macd'] > dataframe['macdsignal']) &
(dataframe['macdhist'] > dataframe['macdhist'].shift(1)) &
# 成交量确认
(dataframe['volume_ratio'] > self.buy_volume_threshold) &
(dataframe['obv'] > dataframe['obv_ema']) &
# 价格行为确认
(dataframe['close'] > dataframe['bb_middleband']) &
(dataframe['bb_percent'] > 0.2) & (dataframe['bb_percent'] < 0.8) &
# 趋势强度确认
(dataframe['adx'] > self.adx_trend_threshold) &
(dataframe['plus_di'] > dataframe['minus_di'])
),
['enter_long', 'enter_tag']] = (1, 'breakout_long')
# 禁用多头入场
dataframe['enter_long'] = 0
# 条件2: 超卖反弹入场
dataframe.loc[
(
# 超卖反弹
(dataframe['rsi'] < self.rsi_oversold + 10) &
(dataframe['rsi'] > dataframe['rsi'].shift(1)) &
(dataframe['bb_percent'] < 0.2) &
# 趋势不能太差
(dataframe['ema_8'] >= dataframe['ema_50']) &
(dataframe['close'] > dataframe['low_20'] * 1.02) &
# 成交量支持
(dataframe['volume_ratio'] > 1.2) &
# MACD底背离迹象
(dataframe['macdhist'] > dataframe['macdhist'].shift(1)) &
# 不与第一个条件重复
(~dataframe['enter_long'].astype(bool))
),
['enter_long', 'enter_tag']] = (1, 'oversold_long')
# 空头入场条件 - 专注于空头策略
short_conditions = (
# 高时间周期处于下降趋势
(dataframe[downtrend_col] > 0) &
# 趋势强度确认
(dataframe[adx_col] > 25) &
# 条件1: 价格突破上轨后回落 + 成交量确认
(
(dataframe['upper_break'].rolling(window=5).sum() > 0) & # 最近5根K线内有突破上轨
(dataframe['close'] < dataframe['close'].shift(2)) & # 价格开始下跌
(dataframe['close'] < dataframe['ema9']) & # 价格在短期均线下方
(dataframe['volume_ratio'] > 1.3) & # 成交量放大
(dataframe['rsi'] < 70) & # RSI不在极度超买区
(dataframe['rsi'] > 40) & # RSI不在超卖区
(dataframe[rsi_col] < 60) # 高时间周期RSI不过高
) |
# 条件2: 均线死叉 + RSI超买回落 + 趋势确认
(
(dataframe['ema_cross_down'] > 0) & # 均线死叉
(dataframe['rsi'] > 55) & # RSI相对较高
(dataframe['rsi'] < dataframe['rsi'].shift(3)) & # RSI下降
(dataframe['volume_ratio'] > 1.2) & # 成交量放大
(dataframe['adx'] > 20) & # ADX显示有一定趋势强度
((dataframe['shooting_star'] > 0) | (dataframe['engulfing'] < 0)) # 流星线或看跌吞没形态
) |
# 条件3: 价格在高点回落 + 强趋势
(
(dataframe['close'] < dataframe['high'].shift()) &
(dataframe['high'].shift() > dataframe['high'].shift(2)) &
(dataframe['close'] < dataframe['ema21']) &
(dataframe['adx'] > 30) &
(dataframe['rsi'] < dataframe['rsi'].shift()) &
(dataframe['rsi'].shift() > 65) &
(dataframe['volume_ratio'] > 1.0)
) |
# 条件4: 强下降趋势确认
(
(dataframe[strong_downtrend_col] > 0) &
(dataframe['close'] < dataframe['ema21']) &
(dataframe['close'] < dataframe['close'].shift(3)) &
(dataframe['momentum'] < 0) &
(dataframe['volume_ratio'] > 1.1) &
(dataframe['adx'] > 25)
)
)
# === 空头入场条件 ===
dataframe.loc[short_conditions, 'enter_short'] = 1
dataframe.loc[short_conditions, 'enter_tag'] = 'chan_sol_short'
# 条件1: 强势下跌入场
dataframe.loc[
(
# 趋势确认
(dataframe['trend_down']) &
(dataframe['close'] < dataframe['ema_21']) &
# 动量确认
(dataframe['rsi'] < 55) & (dataframe['rsi'] > 25) &
(dataframe['macd'] < dataframe['macdsignal']) &
(dataframe['macdhist'] < dataframe['macdhist'].shift(1)) &
# 成交量确认
(dataframe['volume_ratio'] > self.sell_volume_threshold) &
(dataframe['obv'] < dataframe['obv_ema']) &
# 价格行为确认
(dataframe['close'] < dataframe['bb_middleband']) &
(dataframe['bb_percent'] > 0.2) & (dataframe['bb_percent'] < 0.8) &
# 趋势强度确认
(dataframe['adx'] > self.adx_trend_threshold) &
(dataframe['minus_di'] > dataframe['plus_di'])
),
['enter_short', 'enter_tag']] = (1, 'breakdown_short')
# 条件2: 超买回调入场
dataframe.loc[
(
# 超买回调
(dataframe['rsi'] > self.rsi_overbought - 10) &
(dataframe['rsi'] < dataframe['rsi'].shift(1)) &
(dataframe['bb_percent'] > 0.8) &
# 趋势不能太好
(dataframe['ema_8'] <= dataframe['ema_50']) &
(dataframe['close'] < dataframe['high_20'] * 0.98) &
# 成交量支持
(dataframe['volume_ratio'] > 1.2) &
# MACD顶背离迹象
(dataframe['macdhist'] < dataframe['macdhist'].shift(1)) &
# 不与第一个条件重复
(~dataframe['enter_short'].astype(bool))
),
['enter_short', 'enter_tag']] = (1, 'overbought_short')
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 禁用多头出场
dataframe['exit_long'] = 0
"""
出场信号 - 及时止盈止损
"""
# === 多头出场条件 ===
# 空头出场条件 - 更精确的出场
short_exit_conditions = (
# 条件1: 趋势反转信号
# 条件1: 趋势转弱
dataframe.loc[
(
(dataframe['ema_cross_up'] > 0) & # 均线金叉
(dataframe['volume_ratio'] > 1.0) # 成交量确认
) |
# 条件2: 价格突破中期均线
(
(dataframe['close'] > dataframe['ema21']) &
(dataframe['close'].shift() < dataframe['ema21'].shift()) & # 确认是刚刚突破
(dataframe['volume_ratio'] > 1.2) # 成交量确认
) |
# 条件3: 超卖信号
(
(dataframe['rsi'] < 30) & # RSI超卖
(dataframe['close'] < dataframe['bb_lowerband']) # 价格突破下轨
) |
# 条件4: 动量减弱
(
(dataframe['rsi'] < 35) &
(dataframe['rsi'] > dataframe['rsi'].shift()) &
(dataframe['rsi'].shift() > dataframe['rsi'].shift(2)) & # RSI连续两根K线上升
(dataframe['momentum'] > 0) # 价格动量转为正
) |
# 条件5: 锤子线形态 (潜在反转信号)
(
(dataframe['hammer'] > 0) &
(dataframe['volume_ratio'] > 1.3)
)
)
(
(dataframe['rsi'] > self.rsi_overbought) |
(dataframe['macd'] < dataframe['macdsignal']) |
(dataframe['close'] < dataframe['ema_8']) |
(dataframe['bb_percent'] > 0.95) |
(dataframe['adx'] < 20)
) &
(dataframe['volume_ratio'] > 1.0)
),
['exit_long', 'exit_tag']] = (1, 'trend_weak_long')
dataframe.loc[short_exit_conditions, 'exit_short'] = 1
dataframe.loc[short_exit_conditions, 'exit_tag'] = 'chan_sol_short_exit'
# === 空头出场条件 ===
# 条件1: 趋势转强
dataframe.loc[
(
(
(dataframe['rsi'] < self.rsi_oversold) |
(dataframe['macd'] > dataframe['macdsignal']) |
(dataframe['close'] > dataframe['ema_8']) |
(dataframe['bb_percent'] < 0.05) |
(dataframe['adx'] < 20)
) &
(dataframe['volume_ratio'] > 1.0)
),
['exit_short', 'exit_tag']] = (1, 'trend_strong_short')
return dataframe
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
time_in_force: str, current_time: datetime, entry_tag: Optional[str],
side: str, **kwargs) -> bool:
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
"""
在进入交易前进行额外的确认
动态止损策略
"""
# 只做空头交易
if side == "sell" and entry_tag == "chan_sol_short":
return True
return False
# 基础止损
if current_profit < -0.05: # 如果亏损超过5%，严格止损
return -0.08
# 盈利后的动态止损
if current_profit > 0.02: # 盈利超过2%后，调整止损至成本价附近
return 0.005
elif current_profit > 0.05: # 盈利超过5%后，保证1%利润
return -current_profit + 0.01
elif current_profit > 0.10: # 盈利超过10%后，保证5%利润
return -current_profit + 0.05
return self.stoploss
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float,
min_stake: float, max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs) -> Optional[float]:
"""
仓位调整策略 - 金字塔加仓
"""
# 如果亏损超过3%，不加仓
if current_profit < -0.03:
return None
# 如果盈利超过2%且趋势持续，可以加仓
if current_profit > 0.02 and len(trade.select_filled_orders(trade.entry_side)) < self.max_entry_position_adjustment:
# 获取当前数据进行趋势确认
try:
# 简单的趋势确认逻辑
if trade.is_short:
return max_stake * 0.5 # 空头加仓
else:
return max_stake * 0.5 # 多头加仓
except:
pass
return None
def leverage(self, pair: str, current_time: datetime, current_rate: float,
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
**kwargs) -> float:
return 1.0
def get_ticker_indicator(self):
return int(self.timeframe[:-1])
"""
杠杆设置 - 保守策略
"""
# 根据入场类型调整杠杆
if entry_tag and 'breakout' in entry_tag:
return min(2.0, max_leverage) # 突破信号使用较高杠杆
elif entry_tag and ('oversold' in entry_tag or 'overbought' in entry_tag):
return min(1.5, max_leverage) # 超买超卖信号使用中等杠杆
else:
return 1.0 # 默认无杠杆