优化退出条件, 试图提升获利空间 https://x.com/i/grok?conversation=1940703718133125312

freqtrade/templates/df.diff

@@ -0,0 +1,64 @@
+--- original_strategy.py
++++ modified_strategy.py
+@@ -297,6 +297,9 @@ class FreqaiPrimer(IStrategy):
+         dataframe["bb_lowerband"] = lowerband
+         dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
+         dataframe["volume_mean_20"] = dataframe["volume"].rolling(20).mean()
+         dataframe["volume_std_20"] = dataframe["volume"].rolling(20).std()
+         dataframe["volume_z_score"] = (dataframe["volume"] - dataframe["volume_mean_20"]) / dataframe["volume_std_20"]
++        # 计算15分钟ATR和3分钟EMA10、EMA20
++        dataframe_15m = self.dp.get_pair_dataframe(pair=pair, timeframe="15m")
++ cuyuan
++        dataframe_15m['atr15m'] = ta.ATR(dataframe_15m, timeperiod=14)
++        dataframe['atr15m'] = dataframe_15m['atr15m'].reindex(dataframe.index, method="ffill")
++        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
++        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
+
+@@ -390,8 +390,11 @@ class FreqaiPrimer(IStrategy):
+         conditions = []
+
+         if "&-price_value_divergence" in dataframe.columns:
+             cond1 = (dataframe["&-price_value_divergence"] > self.sell_threshold * 1.025)
+-            cond2 = (dataframe["rsi"] > 75)
++            rsi_threshold = 70 if trend_score > 50 else 65  # 动态RSI阈值
++            cond2 = (dataframe["rsi"] > rsi_threshold)
++            cond3 = (dataframe['ema10'] < dataframe['ema20'])  # 短期均线交叉
+             sell_condition = cond1 | cond2
+             conditions.append(sell_condition)
+
+@@ -442,6 +442,13 @@ class FreqaiPrimer(IStrategy):
+         initial_stake_amount = trade.stake_amount / 3
+         logger.debug(f"{pair} 首次入场金额: {initial_stake_amount:.2f}, 当前持仓金额: {trade.stake_amount:.2f}, "
+                     f"加仓次数: {trade.nr_of_successful_entries - 1}")
+
++        # 分批出场：短期涨幅触发
++        short_term_return = (current_rate - dataframe['close'].shift(3).iloc[-1]) / dataframe['close'].shift(3).iloc[-1]
++        if short_term_return >= 0.03:  # 3%涨幅
++            reduce_amount = -0.5 * trade.stake_amount  # 卖出50%仓位
++            logger.info(f"{pair} 短期涨幅 {short_term_return*100:.2f}%，卖出50%仓位 {abs(reduce_amount):.2f}")
++            return (reduce_amount, f"Short-term gain {short_term_return*100:.2f}%")
++
+         # 加仓逻辑
+         max_entry_adjustments = self.MAX_ENTRY_POSITION_ADJUSTMENT.value
+@@ -484,12 +484,11 @@ class FreqaiPrimer(IStrategy):
+         # 追踪止损逻辑
+         trailing_stop_start = self.TRAILING_STOP_START.value
+         trailing_stop_distance = self.TRAILING_STOP_DISTANCE.value
+-        # 使用 Sigmoid 映射调整追踪止损参数
+-        sigmoid = 1 / (1 + np.exp(-0.1 * (trend_score - 50)))
+-        trailing_factor = 0.8 + (1.2 - 0.8) * sigmoid  # 牛市(100) -> 1.2, 熊市(0) -> 0.8
+-        distance_factor = 0.7 + (1.5 - 0.7) * sigmoid  # 牛市(100) -> 1.5, 熊市(0) -> 0.7
+-        trailing_stop_start *= trailing_factor
+-        trailing_stop_distance *= distance_factor
++        # 使用15分钟ATR动态调整止损距离
++        atr15m = dataframe['atr15m'].iloc[-1]
++        trailing_stop_distance = min(max(2 * atr15m / current_rate, 0.01), 0.05)  # 2倍ATR，限制在0.01-0.05
++        if trend_score > 50:  # 强趋势时放宽止损
++            trailing_stop_distance *= 1.2
++        trailing_stop_price = max_rate * (1 - trailing_stop_distance)
+
+         if profit_ratio >= trailing_stop_start and not self.trailing_stop_enabled:
+             self.trailing_stop_enabled = True
+             trade.adjust_min_max_rates(current_rate, current_rate)
+             logger.info(f"{pair} 价格上涨超过 {trailing_stop_start*100:.1f}%，启动追踪止损")
+             return None

freqtrade/templates/freqaiprimer.json.orig

@@ -0,0 +1,35 @@
+{
+  "strategy_name": "FreqaiPrimer",
+  "params": {
+    "roi": {},
+    "stoploss": {
+      "stoploss": -0.05
+    },
+    "max_open_trades": {
+      "max_open_trades": 5
+    },
+    "buy": {
+      "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": 0.472,
+      "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": 0.106,
+      "trailing_stop_positive_offset": 0.196,
+      "trailing_only_offset_is_reached": false
+    }
+  },
+  "ft_stratparam_v": 1,
+  "export_time": "2025-07-01 14:51:29.420394+00:00"
+}

freqtrade/templates/freqaiprimer.py

@@ -352,20 +352,67 @@ class FreqaiPrimer(IStrategy):
         conditions = []
 
         if "&-price_value_divergence" in dataframe.columns:
-            cond1 = (dataframe["&-price_value_divergence"] > self.sell_threshold * 1.025)
-            cond2 = (dataframe["rsi"] > 75)
-            sell_condition = cond1 | cond2
+            # 计算额外指标：StochRSI、ADX 和短期价格变化
+            stochrsi = ta.STOCHRSI(dataframe, timeperiod=14, fastk_period=3, fastd_period=3)
+            dataframe["stochrsi_k"] = stochrsi["fastk"]
+            dataframe["adx"] = ta.ADX(dataframe, timeperiod=14)
+            # 计算短期价格涨幅（最近 5 根 K 线，约 15 分钟）
+            dataframe["short_term_return"] = dataframe["close"].pct_change(5) * 100  # 百分比回报
+
+            # 获取市场趋势得分
+            trend_score = self.get_market_trend(dataframe=dataframe, metadata={'pair': pair})
+
+            # 条件 1：高阈值 &-price_value_divergence
+            cond1 = (
+                (dataframe["&-price_value_divergence"] > self.sell_threshold * 1.06) &  # 提高到 1.06
+                (dataframe["adx"] > 20)  # 趋势强度过滤
+            )
+
+            # 条件 2：超买信号
+            cond2 = (
+                (dataframe["rsi"] > 65) &
+                (dataframe["stochrsi_k"] > 70) &  # StochRSI 超买
+                (dataframe["adx"] > 25)  # 趋势强度
+            )
+
+            # 条件 3：快速拉升退出
+            # 检测最近 5 根 K 线（约 15 分钟）涨幅超过 3%，且有最低 2% 利润，结合 StochRSI 超买
+            min_profit = 0.02  # 最低利润 2%
+            rapid_rise_threshold = self.linear_map(trend_score, 0, 100, 4.0, 2.5)  # 熊市 4%，牛市 2.5%
+            cond3 = (
+                (dataframe["short_term_return"] > rapid_rise_threshold) &  # 短期快速拉升
+                (dataframe["close"] / dataframe["close"].shift(5) - 1 > min_profit) &  # 确保最低利润
+                (dataframe["stochrsi_k"] > 80) &  # 超买确认
+                (dataframe["adx"] > 20)  # 趋势强度
+            )
+
+            # 综合卖出条件：根据 trend_score 调整逻辑
+            if trend_score > 85:
+                sell_condition = (cond1 & cond2) | (cond1 & cond3) | (cond2 & cond3)  # 中等趋势，至少两个条件满足
+                logger.debug(f"[{pair}] 趋势得分 {trend_score:.2f} > 55，需满足至少两个条件")
+            else:
+                sell_condition = cond1 | cond2 | cond3  # 弱势趋势，任一条件满足
+                logger.debug(f"[{pair}] 趋势得分 {trend_score:.2f} <= 55，任一条件满足")
+
             conditions.append(sell_condition)
 
+            # 调试日志
+            divergence_value = dataframe["&-price_value_divergence"].iloc[-1] if not dataframe["&-price_value_divergence"].isna().all() else np.nan
+            rsi_value = dataframe["rsi"].iloc[-1] if not dataframe["rsi"].isna().all() else np.nan
+            stochrsi_value = dataframe["stochrsi_k"].iloc[-1] if not dataframe["stochrsi_k"].isna().all() else np.nan
+            adx_value = dataframe["adx"].iloc[-1] if not dataframe["adx"].isna().all() else np.nan
+            short_term_return = dataframe["short_term_return"].iloc[-1] if not dataframe["short_term_return"].isna().all() else np.nan
             logger.debug(f"[{pair}] 卖出条件检查 - "
-                         f"&-price_value_divergence > {self.sell_threshold:.6f}: {cond1.iloc[-1]}, "
-                         f"rsi > 75: {cond2.iloc[-1]}")
+                        f"&-price_value_divergence={divergence_value:.6f} > {self.sell_threshold * 1.06:.6f}: {cond1.iloc[-1]}, "
+                        f"rsi={rsi_value:.2f} > 75 & stochrsi_k={stochrsi_value:.2f} > 80: {cond2.iloc[-1]}, "
+                        f"short_term_return={short_term_return:.2f}% > {rapid_rise_threshold:.2f}% & profit > {min_profit*100:.2f}%: {cond3.iloc[-1]}, "
+                        f"adx={adx_value:.2f}, trend_score={trend_score:.2f}")
         else:
             logger.warning(f"[{pair}] ⚠️ &-price_value_divergence 列缺失，跳过该条件")
 
         if len(conditions) > 0:
             dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
-            logger.debug(f"[{pair}] 出场信号触发，条件满足")
+            logger.info(f"[{pair}] 出场信号触发，条件满足，趋势得分：{trend_score:.2f}")
         else:
             logger.debug(f"[{pair}] 无有效卖出条件")
 

tools/exchanges.py

@@ -0,0 +1,45 @@
+import pandas as pd
+
+# 加载回测结果
+df = pd.read_csv('../result/backtest_trades.csv')
+
+# 转换时间为 datetime 类型
+df['open_date'] = pd.to_datetime(df['open_date'])
+df['close_date'] = pd.to_datetime(df['close_date'])
+
+# 按币种分组
+grouped = df.groupby('pair')
+
+# 存储最终结果
+results = []
+
+for pair, group in grouped:
+    # 按照开仓时间排序
+    group = group.sort_values(by='open_date')
+
+    for _, trade in group.iterrows():
+        entry_time = trade['open_date']
+        exit_time = trade['close_date']
+        entry_price = trade['open_rate']
+        exit_price = trade['close_rate']
+        profit_abs = trade['profit_abs']
+        profit_ratio = trade['profit_ratio']
+
+        results.append({
+            '币种': pair,
+            '入场时间': entry_time,
+            '入场价格': entry_price,
+            '出场时间': exit_time,
+            '出场价格': exit_price,
+            '盈利金额': profit_abs,
+            '盈利比例': f"{profit_ratio * 100:.2f}%"
+        })
+
+# 输出为 DataFrame 并保存到 CSV
+trade_log = pd.DataFrame(results)
+trade_log.to_csv('../result/trade_log_detailed.csv', index=False)
+
+print("✅ 已生成详细交易日志：../result/trade_log_detailed.csv")
+print(trade_log)
+
+

tools/exchanges.sh

@@ -0,0 +1,4 @@
+cd ../
+source .venv/bin/activate
+cd -
+python exchanges.py