引入freqai优化
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zhangkun9038@dingtalk.com
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104bb08f99
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00f2e91599
@ -34,7 +34,28 @@ class FreqaiPrimer(IStrategy):
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super().__init__(config) # 调用父类的初始化方法并传递config
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# 存储从配置文件加载的默认值
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self._trailing_stop_positive_default = 0.004 # 降低默认值以更容易触发跟踪止盈
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# FreqAI配置
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freqai_info = {
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"model": "LightGBMRegressor",
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"feature_parameters": {
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"include_timeframes": ["3m", "15m", "1h"],
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"include_corr_pairlist": [],
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"label_period_candles": 12,
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"include_shifted_candles": 3,
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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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},
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"model_training_parameters": {
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"n_estimators": 200,
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"learning_rate": 0.05,
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"num_leaves": 31,
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"verbose": -1,
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},
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}
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@property
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def protections(self):
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"""
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@ -107,6 +128,35 @@ class FreqaiPrimer(IStrategy):
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if missing_columns:
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logger.warning(f"[{metadata['pair']}] 数据框中缺少以下列: {missing_columns}")
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def set_freqai_targets(self, dataframe: DataFrame, metadata: dict, **kwargs) -> DataFrame:
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logger.info(f"设置 FreqAI 目标，交易对：{metadata['pair']}")
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if "close" not in dataframe.columns:
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logger.error("数据框缺少必要的 'close' 列")
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raise ValueError("数据框缺少必要的 'close' 列")
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try:
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label_period = self.freqai_info["feature_parameters"]["label_period_candles"]
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# 创建我们想要预测的目标：动态RSI入场阈值
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dataframe["&-rsi_entry_threshold"] = np.where(
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dataframe['market_state'].isin(['strong_bull', 'weak_bull']), 50, 45
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)
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# 计算未来收益作为额外目标，帮助模型学习
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dataframe["&-future_return"] = dataframe["close"].pct_change(label_period).shift(-label_period)
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# 处理可能的NaN值
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for col in ["&-rsi_entry_threshold", "&-future_return"]:
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dataframe[col] = dataframe[col].replace([np.inf, -np.inf], 0)
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dataframe[col] = dataframe[col].ffill()
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dataframe[col] = dataframe[col].fillna(0)
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except Exception as e:
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logger.error(f"创建 FreqAI 目标失败：{str(e)}")
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raise
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logger.info(f"目标列预览：\n{dataframe[["&-rsi_entry_threshold"]].head().to_string()}")
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# 计算 3m 周期的指标
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bb_3m = ta.bbands(dataframe['close'], length=self.bb_length, std=self.bb_std)
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@ -198,10 +248,11 @@ class FreqaiPrimer(IStrategy):
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# 确保重新索引时不引入未来数据
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df_1h = df_1h.set_index('date').reindex(dataframe['date']).ffill().fillna(method='bfill').reset_index()
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df_1h = df_1h.rename(columns={'index': 'date'})
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# Include macd_1h and macd_signal_1h in the column selection
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# Include macd_1h and macd_signal_1h in the column selection
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df_1h = df_1h[['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h', 'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h', 'macd_1h', 'macd_signal_1h']].ffill()
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# Validate that all required columns are present
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# Validate that all required columns are present
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required_columns = ['date', 'rsi_1h', 'trend_1h', 'ema_50_1h', 'ema_200_1h',
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'bb_lower_1h', 'bb_upper_1h', 'stochrsi_k_1h', 'stochrsi_d_1h',
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'macd_1h', 'macd_signal_1h']
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@ -362,9 +413,27 @@ class FreqaiPrimer(IStrategy):
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else:
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logger.info(f"[{metadata['pair']}] 数据修复完成，所有行数据均有效")
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# 调用FreqAI进行预测
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try:
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dataframe = self.freqai.start(dataframe, metadata, self)
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logger.info(f"[{metadata['pair']}] FreqAI处理完成，输出列: {list(dataframe.columns)}")
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except Exception as e:
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logger.error(f"[{metadata['pair']}] FreqAI处理失败: {str(e)}")
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# 如果FreqAI失败，继续使用默认参数
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# 动态计算每行的市场状态，避免未来数据泄漏
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dataframe['current_score'] = dataframe['market_score']
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dataframe['current_state'] = dataframe['market_state']
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# 如果FreqAI提供了预测的RSI入场阈值，使用它；否则使用默认值
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if '&-rsi_entry_threshold' in dataframe.columns:
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dataframe['rsi_entry_threshold'] = dataframe['&-rsi_entry_threshold']
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# 确保阈值在合理范围内
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dataframe['rsi_entry_threshold'] = dataframe['rsi_entry_threshold'].clip(30, 60)
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else:
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# 回退到基于市场状态的静态阈值
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dataframe['rsi_entry_threshold'] = np.where(
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dataframe['market_state'].isin(['strong_bull', 'weak_bull']), 50, 45
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)
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# 记录最后一条记录的市场状态
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if len(dataframe) > 0:
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@ -418,75 +487,56 @@ class FreqaiPrimer(IStrategy):
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return dataframe
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def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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pair = metadata['pair']
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current_time = datetime.datetime.now()
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# 入场条件
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# 基本条件：价格接近布林带下轨，RSI超卖，StochRSI超卖，MACD上升趋势
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# 使用FreqAI优化的RSI入场阈值
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if 'rsi_entry_threshold' in dataframe.columns:
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# 使用FreqAI优化的动态阈值
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logger.info(f"[{metadata['pair']}] 使用FreqAI优化的RSI入场阈值")
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rsi_condition = dataframe['rsi_3m'] < dataframe['rsi_entry_threshold']
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else:
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# 回退到基于市场状态的静态阈值
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logger.info(f"[{metadata['pair']}] 使用基于市场状态的RSI入场阈值")
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rsi_condition = np.where(
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dataframe['market_state'].isin(['strong_bull', 'weak_bull']),
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dataframe['rsi_3m'] < 50, # 牛市中超卖阈值提高
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dataframe['rsi_3m'] < 45 # 非牛市中超卖阈值降低
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)
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# 检测剧烈拉升
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is_unstable_region, unstable_time = self.detect_h1_rapid_rise(pair, dataframe, metadata)
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# 其他入场条件保持不变
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bb_condition = dataframe['close'] < dataframe['bb_lower_3m'] # 价格在布林带下轨下方
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stochrsi_condition = dataframe['stochrsi_k_3m'] < 20 # StochRSI超卖
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macd_condition = dataframe['macd_3m'] > dataframe['macd_signal_3m'] # MACD金叉
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volume_condition = dataframe['volume'] > dataframe['volume_ma'] * 1.5 # 成交量放大
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bb_width_condition = (dataframe['bb_upper_3m'] - dataframe['bb_lower_3m']) > dataframe['bb_lower_3m'] * 0.02 # 布林带宽度足够
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trend_condition = dataframe['close'] > dataframe['ema_50_3m'] # 短期趋势向上
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bullish_candle_condition = dataframe['close'] > dataframe['open'] # 收阳
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# 如果检测到剧烈拉升且未过回看期，则阻止入场信号
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if is_unstable_region:
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dataframe['enter_long'] = 0
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logger.info(f"[{pair}] 处于不稳固区域，阻止入场信号")
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return dataframe
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# 市场状态已在 populate_indicators 中计算，无需重复获取
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# 组合条件：至少满足3个基本条件
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# 计算满足的条件数量
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dataframe['satisfied_conditions'] = 0
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for condition in [rsi_condition, bb_condition, stochrsi_condition, macd_condition,
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volume_condition, trend_condition]:
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dataframe['satisfied_conditions'] += condition.astype(int)
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# 条件1: 价格接近布林带下轨（允许一定偏差）
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close_to_bb_lower_1h = (dataframe['close'] <= dataframe['bb_lower_1h'] * 1.03) # 放宽到3%偏差
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# 条件2: RSI 不高于阈值（根据市场状态动态调整）
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rsi_threshold = np.where(
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dataframe['market_state'].isin(['strong_bull', 'weak_bull']), 50, 45
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# 入场信号
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dataframe['enter_long'] = (
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(dataframe['satisfied_conditions'] >= 3) & # 至少满足3个条件
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~self.detect_h1_rapid_rise(dataframe, metadata) # 非剧烈拉升后的回调
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)
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rsi_condition_1h = dataframe['rsi_1h'] < rsi_threshold
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# 条件3: StochRSI 处于超卖区域（根据市场状态动态调整）
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stochrsi_threshold = np.where(
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dataframe['market_state'].isin(['strong_bull', 'weak_bull']), 35, 25
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)
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stochrsi_condition_1h = (dataframe['stochrsi_k_1h'] < stochrsi_threshold) & (dataframe['stochrsi_d_1h'] < stochrsi_threshold)
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# 条件4: MACD 上升趋势
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macd_condition_1h = dataframe['macd_1h'] > dataframe['macd_signal_1h']
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# 条件5: 成交量显著放大（可选条件）
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volume_spike = dataframe['volume'] > dataframe['volume_ma'] * 1.5
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# 条件6: 布林带宽度过滤（避免窄幅震荡）
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bb_width = (dataframe['bb_upper_1h'] - dataframe['bb_lower_1h']) / dataframe['close']
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bb_width_condition = bb_width > 0.02 # 布林带宽度大于2%
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# 辅助条件: 3m 和 15m 趋势确认（允许部分时间框架不一致）
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trend_confirmation = (dataframe['trend_3m'] == 1) | (dataframe['trend_15m'] == 1)
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# 合并所有条件（减少强制性条件）
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# 至少满足5个条件中的3个
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condition_count = (
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close_to_bb_lower_1h.astype(int) +
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rsi_condition_1h.astype(int) +
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stochrsi_condition_1h.astype(int) +
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macd_condition_1h.astype(int) +
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(volume_spike | bb_width_condition).astype(int) + # 成交量或布林带宽度满足其一即可
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trend_confirmation.astype(int)
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)
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final_condition = condition_count >= 3
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# 设置入场信号
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dataframe.loc[final_condition, 'enter_long'] = 1
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# 增强调试信息
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logger.info(f"[{metadata['pair']}] 入场条件检查:")
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logger.info(f" - 价格接近布林带下轨: {close_to_bb_lower_1h.sum()} 次")
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logger.info(f" - RSI 超卖: {rsi_condition_1h.sum()} 次")
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logger.info(f" - StochRSI 超卖: {stochrsi_condition_1h.sum()} 次")
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logger.info(f" - MACD 上升趋势: {macd_condition_1h.sum()} 次")
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logger.info(f" - 成交量或布林带宽度: {(volume_spike | bb_width_condition).sum()} 次")
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logger.info(f" - 趋势确认: {trend_confirmation.sum()} 次")
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logger.info(f" - 最终条件: {final_condition.sum()} 次")
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# 日志记录
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if dataframe['enter_long'].sum() > 0:
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logger.info(f"[{metadata['pair']}] 发现入场信号数量: {dataframe['enter_long'].sum()}")
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# 在入场点添加入场标签，用于可视化和调试
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if 'enter_long' in dataframe.columns:
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entry_points = dataframe[dataframe['enter_long']].index
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if len(entry_points) > 0:
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logger.info(f"[{metadata['pair']}] 检测到 {len(entry_points)} 个入场信号")
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# 记录每个入场信号的详细信息
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for i in entry_points[-3:]: # 只记录最近3个信号
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row = dataframe.iloc[i]
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logger.debug(f"[{metadata['pair']}] 入场信号 - 时间: {row.name}, \
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价格: {row['close']:.4f}, \
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RSI: {row['rsi_3m']:.2f}, \
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满足条件数: {row['satisfied_conditions']}")
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return dataframe
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