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custom roi https://x.com/i/grok?conversation=1939978729234866501

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Ubuntu 2025-07-01 23:16:47 +08:00
parent 84b30833dc
commit b5e5c52390
3 changed files with 75 additions and 21 deletions
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2
config_examples/freqaiprimer.json
View File

@ -11,7 +11,7 @@
"timeframe": "3m",
"dry_run_wallet": 2000,
"cancel_open_orders_on_exit": true,
"stoploss": -0.05,
"stoploss": -0.14,
"max_entry_position_adjustment": 3,
"position_adjustment_enable": true,
"amount_reserve_percent": 0.05,

26
freqtrade/templates/freqaiprimer.json
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@ -9,27 +9,27 @@
"max_open_trades": 5
},
"buy": {
"ADD_POSITION_THRESHOLD": -0.013,
"BUY_THRESHOLD_MAX": -0.007,
"BUY_THRESHOLD_MIN": -0.066,
"COOLDOWN_PERIOD_MINUTES": 10,
"MAX_ENTRY_POSITION_ADJUSTMENT": 1
"ADD_POSITION_THRESHOLD": -0.021,
"BUY_THRESHOLD_MAX": -0.001,
"BUY_THRESHOLD_MIN": -0.035,
"COOLDOWN_PERIOD_MINUTES": 9,
"MAX_ENTRY_POSITION_ADJUSTMENT": 3
},
"sell": {
"EXIT_POSITION_RATIO": 0.207,
"SELL_THRESHOLD_MAX": 0.029,
"SELL_THRESHOLD_MIN": 0.011,
"TRAILING_STOP_DISTANCE": 0.019,
"TRAILING_STOP_START": 0.03
"EXIT_POSITION_RATIO": 0.472,
"SELL_THRESHOLD_MAX": 0.065,
"SELL_THRESHOLD_MIN": 0.002,
"TRAILING_STOP_DISTANCE": 0.015,
"TRAILING_STOP_START": 0.016
},
"protection": {},
"trailing": {
"trailing_stop": true,
"trailing_stop_positive": 0.126,
"trailing_stop_positive_offset": 0.197,
"trailing_stop_positive": 0.106,
"trailing_stop_positive_offset": 0.196,
"trailing_only_offset_is_reached": false
}
},
"ft_stratparam_v": 1,
"export_time": "2025-07-01 03:49:57.502762+00:00"
"export_time": "2025-07-01 14:51:29.420394+00:00"
}

68
freqtrade/templates/freqaiprimer.py
View File

@ -15,7 +15,7 @@ logger = logging.getLogger(__name__)
class FreqaiPrimer(IStrategy):
"""
基于 FreqAI 的动态阈值交易策略，集成动态加仓和减仓逻辑，兼容最新 Freqtrade 版本
基于 FreqAI 的动态阈值交易策略，集成动态加仓、减仓和自定义 ROI 逻辑，兼容最新 Freqtrade 版本
"""
# --- 🧪 Hyperopt Parameters ---
@ -34,11 +34,18 @@ class FreqaiPrimer(IStrategy):
MAX_ENTRY_POSITION_ADJUSTMENT = IntParameter(1, 3, default=2, space='buy', optimize=True)
# --- 🛠️ 固定配置参数 ---
stoploss = -0.015
stoploss = -0.15
timeframe = "3m"
use_custom_stoploss = True
position_adjustment_enable = True # 启用动态仓位调整
minimal_roi = {
"0": 0.08, # 30分钟（0-30分钟）内，8% 盈利退出
"15": 0.04, # 2小时（30-120分钟）内，4% 盈利退出
"60": 0.02, # 4小时（120-240分钟）内，2% 盈利退出
"120": 0.0 # 8小时（240-480分钟）内，0% 盈利退出
}
plot_config = {
"main_plot": {
"ema200": {"color": "blue"},
@ -270,7 +277,6 @@ class FreqaiPrimer(IStrategy):
]
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
pair = metadata.get('pair', 'Unknown')
conditions = []
@ -329,6 +335,7 @@ class FreqaiPrimer(IStrategy):
logger.debug(f"[{pair}] 无有效买入条件")
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
pair = metadata.get('pair', 'Unknown')
conditions = []
@ -509,6 +516,54 @@ class FreqaiPrimer(IStrategy):
f"调整后卖出价：{adjusted_rate:.6f}")
return True
def custom_roi(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int,
current_rate: float = None, min_stake: float | None = None, max_stake: float | None = None) -> dict:
"""
动态调整 ROI 表格，基于 FreqAI 预测的 &-price_value_divergence 和 RSI。
- 负的 divergence（预测上涨）或低 RSI 时提高 ROI。
- 正的 divergence（预测下跌）或高 RSI 时降低 ROI。
- 长时间持仓降低 ROI 目标。
"""
pair = trade.pair
logger.debug(f"[{pair}] 计算自定义 ROI，当前盈利: {current_profit:.2%}, 持仓时间: {trade_dur} 分钟")
# 获取最新数据
dataframe = self.dp.get_pair_dataframe(pair=pair, timeframe=self.timeframe)
dataframe = self.populate_indicators(dataframe, {'pair': pair}) # 计算指标
# 获取 FreqAI 预测和 RSI
divergence = dataframe["&-price_value_divergence"].iloc[-1] if "&-price_value_divergence" in dataframe else 0
rsi = dataframe["rsi"].iloc[-1] if "rsi" in dataframe else 50
# 计算调整系数
# 1. Divergence 调整：负值（预测上涨）-> 提高 ROI，正值（预测下跌）-> 降低 ROI
divergence_factor = self.linear_map(divergence, -0.1, 0.1, 1.2, 0.8)
# 2. RSI 调整：低 RSI（超卖）-> 提高 ROI，高 RSI（超买）-> 降低 ROI
rsi_factor = self.linear_map(rsi, 30, 70, 1.2, 0.8)
# 3. 时间调整：持仓时间越长，ROI 目标降低
time_factor = self.linear_map(trade_dur, 0, 240, 1.0, 0.7) # 4小时后 ROI 降低到 70%
# 综合调整系数
roi_factor = divergence_factor * rsi_factor * time_factor
# 默认 ROI 表格
base_roi = {
0: 0.08,
15: 0.04,
60: 0.02,
120: 0.0
}
# 动态调整 ROI，限制在 0% 到 20% 之间
dynamic_roi = {time: min(max(roi * roi_factor, 0.0), 0.2) for time, roi in base_roi.items()}
logger.debug(f"[{pair}] Divergence: {divergence:.4f}, RSI: {rsi:.2f}, 持仓时间: {trade_dur} 分钟, "
f"调整系数: divergence={divergence_factor:.2f}, rsi={rsi_factor:.2f}, time={time_factor:.2f}, "
f"总系数={roi_factor:.2f}, 动态 ROI 表格: {dynamic_roi}")
return dynamic_roi
def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
entry_tag: str | None, side: str, **kwargs) -> float:
adjusted_rate = proposed_rate * (1 - 0.005)
@ -522,7 +577,6 @@ class FreqaiPrimer(IStrategy):
logger.debug(f"[{pair}] 自定义卖出价：{adjusted_rate:.6f}（原价：{proposed_rate:.6f}）")
return adjusted_rate
def get_market_trend(self, dataframe: DataFrame = None, metadata: dict = None) -> int:
try:
timeframes = ["3m", "15m", "1h"]
@ -646,10 +700,10 @@ class FreqaiPrimer(IStrategy):
final_score = max(0, min(100, final_score))
logger.info(f"[{pair}] 最终趋势得分：{final_score}, "
f"3m得分：{trend_scores.get('3m', 50)}, 15m得分：{trend_scores.get('15m', 50)}, "
f"1h得分：{trend_scores.get('1h', 50)}")
f"3m得分：{trend_scores.get('3m', 50)}, 15m得分：{trend_scores.get('15m', 50)}, "
f"1h得分：{trend_scores.get('1h', 50)}")
return final_score
except Exception as e:
logger.error(f"[{pair}] 获取市场趋势失败：{e}", exc_info=True)
return 50
return 50