get_market_trend加入了长周期
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zhangkun9038@dingtalk.com
parent
eb1c40cbf5
commit
af12fe6b25
@ -8,33 +8,33 @@
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"max_open_trades": 5
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},
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"buy": {
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"ADD_POSITION_THRESHOLD": -0.01,
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"BUY_THRESHOLD_MAX": -0.011,
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"BUY_THRESHOLD_MIN": -0.079,
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"COOLDOWN_PERIOD_MINUTES": 8,
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"MAX_ENTRY_POSITION_ADJUSTMENT": 1
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"ADD_POSITION_THRESHOLD": -0.028,
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"BUY_THRESHOLD_MAX": -0.003,
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"BUY_THRESHOLD_MIN": -0.028,
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"COOLDOWN_PERIOD_MINUTES": 4,
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"MAX_ENTRY_POSITION_ADJUSTMENT": 2
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},
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"sell": {
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"EXIT_POSITION_RATIO": 0.341,
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"SELL_THRESHOLD_MAX": 0.082,
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"SELL_THRESHOLD_MIN": 0.012,
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"TRAILING_STOP_DISTANCE": 0.016,
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"TRAILING_STOP_START": 0.026
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"EXIT_POSITION_RATIO": 0.335,
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"SELL_THRESHOLD_MAX": 0.054,
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"SELL_THRESHOLD_MIN": 0.001,
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"TRAILING_STOP_DISTANCE": 0.005,
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"TRAILING_STOP_START": 0.048
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},
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"protection": {},
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"roi": {
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"0": 0.057,
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"15": 0.025,
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"22": 0.013,
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"39": 0
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"0": 0.156,
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"20": 0.057999999999999996,
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"50": 0.018,
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"113": 0
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},
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"trailing": {
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"trailing_stop": true,
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"trailing_stop_positive": 0.139,
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"trailing_stop_positive_offset": 0.23700000000000002,
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"trailing_stop_positive": 0.172,
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"trailing_stop_positive_offset": 0.23199999999999998,
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"trailing_only_offset_is_reached": false
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}
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},
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"ft_stratparam_v": 1,
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"export_time": "2025-06-22 18:22:25.969981+00:00"
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"export_time": "2025-06-23 00:37:56.498155+00:00"
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}
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@ -537,131 +537,139 @@ class FreqaiPrimer(IStrategy):
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return adjusted_rate
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def get_market_trend(self, dataframe: DataFrame = None, metadata: dict = None) -> int:
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"""
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计算市场趋势得分，0 表示强熊，100 表示强牛，50 表示中性。
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使用对数函数映射，非线性增强两端趋势。
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综合价格趋势、K 线形态、StochRSI 和量价关系。
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综合 3m、15m 和 1h 时间框架的趋势，使用对数函数映射，非线性增强两端趋势。
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融合价格趋势、K 线形态、StochRSI 和量价关系。
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参数：
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dataframe: 可选，外部传入的 BTC/USDT 数据，默认为 None（自动获取）。
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dataframe: 可选，外部传入的 BTC/USDT 数据（3m 时间框架），默认为 None（自动获取）。
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metadata: 可选，包含币种信息。
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返回值：
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int: 0-100 的趋势得分。
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"""
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try:
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# 获取 BTC/USDT 数据
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if dataframe is None:
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btc_df = self.dp.get_pair_dataframe("BTC/USDT", self.timeframe)
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else:
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btc_df = dataframe
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# 初始化得分和权重
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timeframes = ["3m", "15m", "1h"]
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weights = {"3m": 0.5, "15m": 0.3, "1h": 0.2} # 短期框架权重更高
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trend_scores = {}
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if len(btc_df) < 200:
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logger.warning("BTC 数据不足，返回默认趋势得分：50")
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return 50
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# 示例：使用 metadata 记录币种信息
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pair = metadata.get('pair', 'Unknown') if metadata else 'Unknown'
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logger.debug(f"[{pair}] 正在计算市场趋势得分")
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# 获取 BTC/USDT 数据
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btc_df = dataframe if dataframe is not None else self.dp.get_pair_dataframe("BTC/USDT", self.timeframe)
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if len(btc_df) < 200:
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logger.warning("BTC 数据不足，返回默认趋势得分：50")
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return 50
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logger.debug(f"[{pair}] 正在计算多时间框架市场趋势得分")
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# --- 价格趋势 ---
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btc_df["ema50"] = ta.EMA(btc_df, timeperiod=50)
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btc_df["ema200"] = ta.EMA(btc_df, timeperiod=200)
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btc_df["ema50_slope"] = (btc_df["ema50"] - btc_df["ema50"].shift(10)) / btc_df["ema50"].shift(10)
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price_above_ema = btc_df["close"].iloc[-1] > btc_df["ema200"].iloc[-1]
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ema50_above_ema200 = btc_df["ema50"].iloc[-1] > btc_df["ema200"].iloc[-1]
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ema50_slope = btc_df["ema50_slope"].iloc[-1]
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price_score = 0
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if price_above_ema:
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price_score += 20
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if ema50_above_ema200:
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price_score += 20
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if ema50_slope > 0.005:
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price_score += 15
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elif ema50_slope < -0.005:
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price_score -= 15
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for tf in timeframes:
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# 获取对应时间框架的数据
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if tf == "3m" and dataframe is not None:
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btc_df = dataframe
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else:
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btc_df = self.dp.get_pair_dataframe("BTC/USDT", tf)
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# --- K 线形态 ---
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btc_df["bullish_engulfing"] = (
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(btc_df["close"].shift(1) < btc_df["open"].shift(1)) & # 前一天阴线
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(btc_df["close"] > btc_df["open"]) & # 当天阳线
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(btc_df["close"] > btc_df["open"].shift(1)) & # 吞没前一天开盘价
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(btc_df["open"] < btc_df["close"].shift(1)) # 吞没前一天收盘价
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)
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btc_df["bearish_engulfing"] = (
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(btc_df["close"].shift(1) > btc_df["open"].shift(1)) & # 前一天阳线
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(btc_df["close"] < btc_df["open"]) & # 当天阴线
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(btc_df["close"] < btc_df["open"].shift(1)) & # 吞没前一天开盘价
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(btc_df["open"] > btc_df["close"].shift(1)) # 吞没前一天收盘价
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)
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kline_score = 0
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if btc_df["bullish_engulfing"].iloc[-1]:
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kline_score += 15
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elif btc_df["bearish_engulfing"].iloc[-1]:
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kline_score -= 15
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# 近期波动性
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volatility = btc_df["close"].pct_change(10).std() * 100
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if volatility > 0.5:
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kline_score += 10 if price_score > 0 else -10
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# 检查数据量
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min_candles = 200 if tf == "3m" else 100 if tf == "15m" else 50 # 不同时间框架所需最小数据量
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if len(btc_df) < min_candles:
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logger.warning(f"BTC 数据不足（{tf}，{len(btc_df)} 根K线），使用默认得分：50")
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trend_scores[tf] = 50
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continue
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# --- StochRSI ---
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stochrsi = ta.STOCHRSI(btc_df, timeperiod=14, fastk_period=3, fastd_period=3)
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btc_df["stochrsi_k"] = stochrsi["fastk"]
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btc_df["stochrsi_d"] = stochrsi["fastd"]
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stochrsi_score = 0
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stochrsi_k = btc_df["stochrsi_k"].iloc[-1]
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stochrsi_d = btc_df["stochrsi_d"].iloc[-1]
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if stochrsi_k > 80 and stochrsi_k < stochrsi_d:
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stochrsi_score -= 15 # 超买且 K 下穿 D
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elif stochrsi_k < 20 and stochrsi_k > stochrsi_d:
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stochrsi_score += 15 # 超卖且 K 上穿 D
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elif stochrsi_k > 50:
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stochrsi_score += 5
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elif stochrsi_k < 50:
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stochrsi_score -= 5
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# --- 价格趋势 ---
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ema_short_period = 50 if tf == "3m" else 20 if tf == "15m" else 12 # 调整周期以适应时间框架
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ema_long_period = 200 if tf == "3m" else 80 if tf == "15m" else 50
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btc_df["ema_short"] = ta.EMA(btc_df, timeperiod=ema_short_period)
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btc_df["ema_long"] = ta.EMA(btc_df, timeperiod=ema_long_period)
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btc_df["ema_short_slope"] = (btc_df["ema_short"] - btc_df["ema_short"].shift(10)) / btc_df["ema_short"].shift(10)
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# --- 量价关系 ---
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btc_df["volume_mean_20"] = btc_df["volume"].rolling(20).mean()
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btc_df["volume_std_20"] = btc_df["volume"].rolling(20).std()
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btc_df["volume_z_score"] = (btc_df["volume"] - btc_df["volume_mean_20"]) / btc_df["volume_std_20"]
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btc_df["adx"] = ta.ADX(btc_df, timeperiod=14)
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volume_score = 0
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if btc_df["volume_z_score"].iloc[-1] > 1.5:
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volume_score += 10 if price_score > 0 else -10
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if btc_df["adx"].iloc[-1] > 25:
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volume_score += 10 if price_score > 0 else -10
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price_above_ema = btc_df["close"].iloc[-1] > btc_df["ema_long"].iloc[-1]
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ema_short_above_ema_long = btc_df["ema_short"].iloc[-1] > btc_df["ema_long"].iloc[-1]
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ema_short_slope = btc_df["ema_short_slope"].iloc[-1]
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# --- 综合得分 ---
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raw_score = price_score + kline_score + stochrsi_score + volume_score
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# 归一化到 [-50, 50]
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raw_score = max(min(raw_score, 50), -50)
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# 对数映射到 [0, 100]，50 为中性
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if raw_score >= 0:
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# 正向：50 到 100，log(x + 1) 确保非线性
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mapped_score = 50 + 50 * (np.log1p(raw_score / 50) / np.log1p(1))
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else:
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# 负向：0 到 50，log(1 - x)
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mapped_score = 50 * (np.log1p(-raw_score / 50) / np.log1p(1))
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final_score = int(round(mapped_score))
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price_score = 0
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if price_above_ema:
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price_score += 20
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if ema_short_above_ema_long:
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price_score += 20
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if ema_short_slope > 0.005:
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price_score += 15
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elif ema_short_slope < -0.005:
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price_score -= 15
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# --- K 线形态 ---
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btc_df["bullish_engulfing"] = (
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(btc_df["close"].shift(1) < btc_df["open"].shift(1)) &
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(btc_df["close"] > btc_df["open"]) &
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(btc_df["close"] > btc_df["open"].shift(1)) &
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(btc_df["open"] < btc_df["close"].shift(1))
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)
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btc_df["bearish_engulfing"] = (
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(btc_df["close"].shift(1) > btc_df["open"].shift(1)) &
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(btc_df["close"] < btc_df["open"]) &
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(btc_df["close"] < btc_df["open"].shift(1)) &
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(btc_df["open"] > btc_df["close"].shift(1))
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)
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kline_score = 0
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if btc_df["bullish_engulfing"].iloc[-1]:
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kline_score += 15
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elif btc_df["bearish_engulfing"].iloc[-1]:
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kline_score -= 15
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volatility = btc_df["close"].pct_change(10).std() * 100
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if volatility > 0.5:
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kline_score += 10 if price_score > 0 else -10
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# --- StochRSI ---
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stochrsi = ta.STOCHRSI(btc_df, timeperiod=14, fastk_period=3, fastd_period=3)
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btc_df["stochrsi_k"] = stochrsi["fastk"]
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btc_df["stochrsi_d"] = stochrsi["fastd"]
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stochrsi_score = 0
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stochrsi_k = btc_df["stochrsi_k"].iloc[-1]
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stochrsi_d = btc_df["stochrsi_d"].iloc[-1]
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if stochrsi_k > 80 and stochrsi_k < stochrsi_d:
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stochrsi_score -= 15
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elif stochrsi_k < 20 and stochrsi_k > stochrsi_d:
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stochrsi_score += 15
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elif stochrsi_k > 50:
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stochrsi_score += 5
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elif stochrsi_k < 50:
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stochrsi_score -= 5
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# --- 量价关系 ---
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btc_df["volume_mean_20"] = btc_df["volume"].rolling(20).mean()
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btc_df["volume_std_20"] = btc_df["volume"].rolling(20).std()
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btc_df["volume_z_score"] = (btc_df["volume"] - btc_df["volume_mean_20"]) / btc_df["volume_std_20"]
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btc_df["adx"] = ta.ADX(btc_df, timeperiod=14)
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volume_score = 0
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if btc_df["volume_z_score"].iloc[-1] > 1.5:
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volume_score += 10 if price_score > 0 else -10
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if btc_df["adx"].iloc[-1] > 25:
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volume_score += 10 if price_score > 0 else -10
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# --- 综合得分 ---
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raw_score = price_score + kline_score + stochrsi_score + volume_score
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raw_score = max(min(raw_score, 50), -50)
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# 对数映射到 [0, 100]
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if raw_score >= 0:
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mapped_score = 50 + 50 * (np.log1p(raw_score / 50) / np.log1p(1))
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else:
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mapped_score = 50 * (np.log1p(-raw_score / 50) / np.log1p(1))
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trend_scores[tf] = max(0, min(100, int(round(mapped_score))))
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logger.debug(f"[{pair}] {tf} 趋势得分：{trend_scores[tf]}, 原始得分：{raw_score}, "
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f"价格得分：{price_score}, K线得分：{kline_score}, "
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f"StochRSI得分：{stochrsi_score}, 量价得分：{volume_score}")
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# 加权融合多时间框架得分
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final_score = sum(trend_scores[tf] * weights[tf] for tf in timeframes)
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final_score = int(round(final_score))
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final_score = max(0, min(100, final_score))
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logger.debug(f"BTC 趋势得分：{final_score}, 原始得分：{raw_score}, "
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f"价格得分：{price_score}, K线得分：{kline_score}, "
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f"StochRSI得分：{stochrsi_score}, 量价得分：{volume_score}")
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logger.info(f"[{pair}] 最终趋势得分：{final_score}, "
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f"3m得分：{trend_scores.get('3m', 50)}, 15m得分：{trend_scores.get('15m', 50)}, "
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f"1h得分：{trend_scores.get('1h', 50)}")
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return final_score
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except Exception as e:
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logger.error(f"获取市场趋势失败：{e}", exc_info=True)
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logger.error(f"[{pair}] 获取市场趋势失败：{e}", exc_info=True)
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return 50
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