249 lines
7.4 KiB
Markdown
249 lines
7.4 KiB
Markdown
-------
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- Datetime: 2025-05-05 17:27:07
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- Model: _qwen3/qwen-plus-latest
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- Selected files:
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- chat/report.md
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- chat/report2.md
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- freqtrade/templates/OKXRegressionStrategy.py
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> 请把两个md文档的内容,结合当前策略文件的现状,进行总结,合并成一个md文档
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Filepath: chat/combined_report.md
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Replace lines: 1-0
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```markdown
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# 📈 Freqtrade AI 策略优化报告：OKXRegressionStrategy
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## 📅 日期
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- Datetime: 2025-05-05 10:06:53
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- Model: _qwen3/qwen-plus-latest
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---
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## 📁 分析文件
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### ✅ 已选文件
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- `result/backtest-result-2025-05-04_18-41-05.json`
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- `result/backtest-result-2025-05-04_18-41-05.meta.json`
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- `result/backtest-result-2025-05-04_18-41-05_OKXRegressionStrategy.py`
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- `result/backtest-result-2025-05-04_18-41-05_config.json`
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- `result/output_filted.log`
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### 📄 当前策略文件
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- `freqtrade/templates/OKXRegressionStrategy.py`
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---
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## 🧠 策略表现分析
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### 📊 回测结果摘要（OKB/USDT）
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| 指标 | 数值 | 分析 |
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|------|------|------|
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| 总交易数 | 38 笔 | 交易频率适中 |
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| 胜率 (Win Rate) | 50%（19 胜 / 19 败） | 虽然胜率较高，但总体仍为小幅亏损 |
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| 平均盈利 | -0.16% | 即使在赢的情况下，盈利幅度也小于亏损 |
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| 总亏损 | -1.225 USDT | 表明盈亏比失衡 |
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| 最大回撤 (Max Drawdown) | -2.52 USDT（占账户的 0.25%） | 回撤过大说明模型对趋势反转反应迟钝 |
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### 🔍 关键问题总结
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| 问题 | 影响 | 建议 |
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|------|------|--------|
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| 胜率虽为 50%，但总体仍亏损 | 盈亏比失衡 | 引入动态 ROI 或强化止盈逻辑 |
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| 最大回撤过高 | 资金曲线不稳定 | 加强回调止损逻辑（如 Chandelier Exit） |
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| 单一退出方式（exit_signal） | 无法及时止损 | 恢复并优化 trailing stop loss |
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| 固定 ATR multiplier | 不适应不同波动率 | 使用波动率百分位动态调整 |
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| 无交叉验证 | 模型稳定性未知 | 加入 CV 验证防止过拟合 |
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---
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## 🛠️ 策略优化建议与实现
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### ✅ 1. 动态 ATR Multiplier（Stop Loss / Take Profit）
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#### 💡 实现思路：
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根据历史 ATR 百分位动态调整止损倍数，避免固定值带来的滞后或过早平仓。
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```python
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, profit_percent: float,
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after_fill: bool, **kwargs) -> Optional[float]:
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if trade.enter_tag != 'long':
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return None
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dataframe = self.dp.get_pair_dataframe(pair, timeframe=self.timeframe)
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atr_col = 'ATR_14'
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if atr_col not in dataframe.columns:
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dataframe[atr_col] = ta.ATR(dataframe['high'], dataframe['low'], dataframe['close'], timeperiod=14)
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latest_row = dataframe.iloc[-1]
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historical_atr = dataframe[atr_col].rolling(window=20).mean().dropna().values
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if len(historical_atr) < 20:
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return None
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current_atr = latest_row[atr_col]
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percentile = (np.sum(historical_atr < current_atr) / len(historical_atr)) * 100
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# 动态 ATR 乘数
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if percentile > 80:
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atr_multiplier = 1.5 # 高波动 → 更紧止损
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elif percentile < 20:
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atr_multiplier = 2.5 # 低波动 → 更松止损
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else:
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atr_multiplier = 2.0
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trailing_stop_price = current_rate - latest_row[atr_col] * atr_multiplier
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min_profit_ratio = 0.005 # 至少盈利 0.5%
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final_stop_price = max(trailing_stop_price, trade.open_rate * (1 + min_profit_ratio))
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return final_stop_price / current_rate - 1
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```
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---
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### ✅ 2. 市场状态识别（Market State Detection）
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#### 💡 实现思路：
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通过 ADX 判断市场处于趋势还是震荡状态，并据此调整 exit_signal 条件。
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```python
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# 在 feature_engineering_expand_all 中添加 ADX
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dataframe[f"%-%-adx-{period}"] = ta.ADX(dataframe["high"], dataframe["low"], dataframe["close"], timeperiod=period)
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# 在 set_freqai_targets 中设置 market_state
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dataframe["market_state"] = np.where(
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dataframe["adx"] > 25, "trend",
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np.where(dataframe["adx"] < 20, "oscillation", "neutral")
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)
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```
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#### 🛠️ populate_exit_trend 改进：
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```python
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def populate_exit_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
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pair = metadata['pair']
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dataframe['exit_signal'] = 0
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# 根据市场状态调整 exit 阈值
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trend_condition = (dataframe["&-s_close"] < 0.005) & (dataframe["%-%-rsi-14"] > 70)
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oscillation_condition = (dataframe["&-s_close"] < 0.01) & (dataframe["%-%-rsi-14"] > 65)
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dataframe.loc[
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((dataframe["market_state"] == "trend") & trend_condition) |
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((dataframe["market_state"] == "oscillation") & oscillation_condition),
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'exit_signal'
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] = 1
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dataframe.loc[dataframe['exit_signal'] == 1, 'exit_long'] = 1
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return dataframe
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```
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---
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### ✅ 3. 增强特征工程（Feature Engineering）
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#### 💡 实现建议：
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启用 PCA 并加入更多高阶特征（如布林带宽度、成交量偏移、趋势线差值等）。
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```python
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# 在 freqai_config 中启用 PCA
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"feature_parameters": {
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"include_timeframes": ["5m", "15m", "1h"],
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"include_corr_pairlist": ["BTC/USDT", "ETH/USDT"],
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"label_period_candles": 12,
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"include_shifted_candles": 2,
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"principal_component_analysis": True,
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"DI_threshold": 0.5,
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"weight_factor": 0.9
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}
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```
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#### 新增特征示例：
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```python
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def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs) -> DataFrame:
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# 布林带宽度
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upper, middle, lower = ta.BBANDS(dataframe["close"], timeperiod=period)
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dataframe[f"%-%-bb_width-{period}"] = (upper - lower) / middle
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# 成交量偏移
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dataframe[f"%-%-volume_change_{period}"] = dataframe["volume"].pct_change(period)
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# 趋势线差值
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dataframe[f"%-%-trend_diff_{period}"] = dataframe["close"] - ta.SMA(dataframe["close"], timeperiod=period)
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return dataframe
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```
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---
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### ✅ 4. 启用 Time Series Cross Validation 提升泛化能力
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#### 💡 实现建议：
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使用 `time_series_split` 替代简单划分，提升模型在时间序列上的稳定性。
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```json
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{
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"data_split_parameters": {
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"test_size": 0.2,
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"shuffle": false,
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"stratify": false,
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"time_series_split": true,
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"n_splits": 5
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}
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}
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```
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---
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### ✅ 5. 加入模型预测置信度过滤
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#### 💡 实现建议：
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利用模型输出的 `pred_upper` 和 `pred_lower` 过滤低置信度信号。
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```python
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def populate_entry_trend(self, dataframe: DataFrame, metadata: Dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe["&-s_close"] > 0.01) &
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(dataframe["do_predict"] == 1) &
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(dataframe["%-%-rsi-14"] < dataframe["&-buy_rsi_pred"]) &
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(dataframe["pred_upper"] - dataframe["pred_lower"] > 0.02) # 置信区间大于 2%
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),
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"enter_long"
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] = 1
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return dataframe
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```
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---
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## 📦 推荐修改文件结构
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```bash
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freqtrade/
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├── templates/
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│ └── OKXRegressionStrategy.py # 主策略文件
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├── user_data/
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│ └── models/
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│ └── test175/ # 存放模型缓存
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└── config_examples/
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└── config_freqai.okx.json # 启用 PCA、TS CV 等配置
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```
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---
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## 📝 总结
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| 问题 | 解决方案 |
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|------|----------|
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| Exit Signal 不稳定 | 引入 Market State + 分级条件 |
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| 模型泛化能力弱 | 启用 PCA + TimeSeriesSplit |
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| 特征维度低 | 添加 BB Width、Volume Change、Trend Diff |
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| 固定止损不灵活 | 动态 ATR Multiplier |
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| 低质量信号多 | 加入 pred_upper/pred_lower 区间过滤 |
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