68 lines
3.8 KiB
Diff
68 lines
3.8 KiB
Diff
--- original_strategy.py
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+++ modified_strategy.py
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@@ -28,8 +28,8 @@ class FreqaiPrimer(IStrategy):
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TRAILING_STOP_DISTANCE = DecimalParameter(0.005, 0.02, default=0.01, space='sell', optimize=True)
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- BUY_THRESHOLD_MIN = DecimalParameter(-0.1, -0.01, default=-0.05, space='buy', optimize=True)
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- BUY_THRESHOLD_MAX = DecimalParameter(-0.02, -0.001, default=-0.005, space='buy', optimize=True)
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+ BUY_THRESHOLD_MIN = DecimalParameter(-0.15, -0.03, default=-0.07, space='buy', optimize=True)
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+ BUY_THRESHOLD_MAX = DecimalParameter(-0.04, -0.005, default=-0.01, space='buy', optimize=True)
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SELL_THRESHOLD_MIN = DecimalParameter(0.001, 0.02, default=0.005, space='sell', optimize=True)
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SELL_THRESHOLD_MAX = DecimalParameter(0.02, 0.1, default=0.05, space='sell', optimize=True)
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@@ -356,10 +356,10 @@ class FreqaiPrimer(IStrategy):
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volume_z_score_min = 0.5
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volume_z_score_max = 1.5
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volume_z_score_threshold = self.linear_map(trend_score, 0, 100, volume_z_score_max, volume_z_score_min)
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- rsi_min = 40
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- rsi_max = 60
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+ rsi_min = 35
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+ rsi_max = 55
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rsi_threshold = self.linear_map(trend_score, 0, 100, rsi_max, rsi_min)
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- stochrsi_min = 30
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- stochrsi_max = 50
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+ stochrsi_min = 25
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+ stochrsi_max = 45
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stochrsi_threshold = self.linear_map(trend_score, 0, 100, stochrsi_max, stochrsi_min)
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if "&-price_value_divergence" in dataframe.columns:
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# 计算 STOCHRSI
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stochrsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3)
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dataframe["stochrsi_k"] = stochrsi["fastk"]
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+ # 添加 15m 时间框架的 EMA200
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+ dataframe_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m")
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+ dataframe_15m["ema200_15m"] = ta.EMA(dataframe_15m, timeperiod=200)
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+ dataframe["ema200_15m"] = dataframe_15m["ema200_15m"].reindex(dataframe.index, method="ffill")
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cond1 = (dataframe["&-price_value_divergence"] < self.buy_threshold)
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cond2 = (dataframe["volume_z_score"] > volume_z_score_threshold)
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cond3 = (dataframe["rsi"] < rsi_threshold)
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cond4 = (dataframe["close"] <= dataframe["bb_lowerband"])
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- cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
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- buy_condition = cond1 & cond2 & cond3 & cond4 & cond5
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+ cond5 = (dataframe["stochrsi_k"] < stochrsi_threshold)
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+ cond6 = (dataframe["close"] < dataframe["ema200_15m"]) # 价格低于 15m EMA200
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+ buy_condition = cond1 & cond2 & cond3 & cond4 & cond5 & cond6
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conditions.append(buy_condition)
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@@ -534,7 +534,7 @@ class FreqaiPrimer(IStrategy):
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def get_market_trend(self, dataframe: DataFrame = None, metadata: dict = None) -> int:
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try:
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timeframes = ["3m", "15m", "1h"]
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- weights = {"3m": 0.5, "15m": 0.3, "1h": 0.2}
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+ weights = {"3m": 0.3, "15m": 0.35, "1h": 0.35}
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trend_scores = {}
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pair = metadata.get('pair', 'Unknown') if metadata else 'Unknown'
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@@ -565,7 +565,7 @@ class FreqaiPrimer(IStrategy):
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pair_df["ema_short_slope"] = (pair_df["ema_short"] - pair_df["ema_short"].shift(10)) / pair_df["ema_short"].shift(10)
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# 动态调整权重
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- if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20:
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- weights = {"3m": 0.3, "15m": 0.3, "1h": 0.4}
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+ if trend_scores.get("1h", 50) - trend_scores.get("3m", 50) > 20 or trend_scores.get("15m", 50) - trend_scores.get("3m", 50) > 20:
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+ weights = {"3m": 0.2, "15m": 0.35, "1h": 0.45}
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logger.debug(f"[{pair}] 1h 趋势得分（{trend_scores.get('1h', 50)}）显著高于 3m（{trend_scores.get('3m', 50)}），调整权重为 {weights}")
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