调整一下买入和卖出的指标

main.py

@@ -71,9 +71,37 @@ class TelegramQuantStateMachine(QuantStateMachine):
         if self.current_state in ["STATE_1_OSCILLATION", "STATE_1_OSCILLATION_SQUEEZE"]:
             if self.current_state == "STATE_1_OSCILLATION_SQUEEZE":
                 msg += "⚠️ *[变盘警告]*：弹簧已压紧，随时大突破，做T手速要快！\n"
-            if f['percent_b'] >= 1.0 or f['min_bias'] > 0.035:
+
+            ## 盘整阶段，三个指标会触发卖出提醒
+            ## kdj死叉，且cci在下降中，且收盘价在5日均线下
+            ## cci顶背离，且k线在d线之下，且收盘价在5日均线下
+            ## cci从100以上下降到100以下，且k线在d线之下，且收盘价在5日均线下
+
+            if ((f['K'] < f['D'] and f['prev_K'] > f['prev_D']) ## kdj死叉
+                and (f['cci'] < f['prev_cci']) ## cci在下降中
+                and (f['close'] < f['ma5'])): ## 收盘价在5日均线下
                 msg += f"🟢 *【建议高抛】*：当前处于震荡高位(价格:{f['close']})，建议尾盘或明日开盘*手动卖出网格仓*！"
-            elif f['percent_b'] <= 0.0 or f['min_bias'] < -0.035:
+
+            elif ((f['close'] > f['prev_close'] and f['cci'] < f['prev_cci']) ## cci顶背离
+                and (f['K'] < f['D']) ## k线在d线之下
+                and (f['close'] < f['ma5'])): ## 收盘价在5日均线下
+                msg += f"🟢 *【建议高抛】*：当前处于震荡高位(价格:{f['close']})，建议尾盘或明日开盘*手动卖出网格仓*！"
+
+            elif ((f['cci'] < 100 and f['prev_cci'] > 100) ## cci从100跌到100以下
+                and (f['K'] < f['D']) ## k线在d线之下
+                and (f['close'] < f['ma5'])): ## 收盘价在5日均线下
+                msg += f"🟢 *【建议高抛】*：当前处于震荡高位(价格:{f['close']})，建议尾盘或明日开盘*手动卖出网格仓*！"
+
+            ## 盘整阶段，两个指标会触发买入提醒
+            ## kdj金叉，且cci连续两天上涨，且收盘价在5日均线上
+            ## cci连续两天上涨，且k线在d线上，且收盘价在5日均线上
+            elif ((f['cci'] > f['prev_cci'] and f['prev_cci'] > f['prev_cci_2'])  ## cci连续两天上涨
+                and (f['K'] > f['D'] and f['prev_K'] <= f['prev_D'])  ## kdj金叉
+                and (f['close'] > f['ma5'])): ## 收盘价站上五日线
+                msg += f"🔴 *【建议低吸】*：当前处于震荡超跌区(价格:{f['close']})，建议尾盘或明日开盘*手动买回筹码*！"
+            elif ((f['cci'] > f['prev_cci'] and f['prev_cci'] > f['prev_cci_2'] and f['prev_cci_2'] < f['prev_cci_3'])
+                and (f['K'] >= f['D'])
+                and (f['close'] > f['ma5'])):
                 msg += f"🔴 *【建议低吸】*：当前处于震荡超跌区(价格:{f['close']})，建议尾盘或明日开盘*手动买回筹码*！"
             else:
                 msg += "⚪ *【建议观望】*：处于安全中枢内，未触及边界，明天*不要乱动*。"

quant_strategy.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 import talib
-
+from util import calculate_brar, calc_kdj_tdx
 
 # =====================================================================
 # 1. 指标引擎：未来所有新发掘的指标，全写在这里
@@ -57,8 +57,22 @@ class FeatureEngine:
         up, mid, low = talib.BBANDS(
             self.close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0
         )
+
         self.df["bb_up"], self.df["bb_mid"], self.df["bb_low"] = up, mid, low
 
+        df_tmp = calc_kdj_tdx(self.df)
+        self.df["K"] = df_tmp['K']
+        self.df["D"] = df_tmp['D']
+        self.df["J"] = df_tmp['J']
+
+        cci = talib.CCI(self.df['high'], self.df['low'], self.df['close'], timeperiod=14)
+        self.df["cci"] = cci
+
+        df_tmp = calculate_brar(self.df)
+
+        self.df["ar"] = df_tmp["ar"]
+        self.df['br'] = df_tmp["br"]
+
         self.df["percent_b"] = (self.df["close"] - low) / (up - low)
         self.df["bandwidth"] = (up - low) / mid
         self.df["amount_avg_20d"] = (
@@ -69,6 +83,8 @@ class FeatureEngine:
 
         latest = self.df.iloc[-1]
         prev = self.df.iloc[-2]
+        prev_2 = self.df.iloc[-3]
+        prev_3 = self.df.iloc[-4]
 
         # 如果 5分钟线数据拿到了，现价以 5分钟最新收盘价为准（更接近 14:48 真实盘面）
         # 如果没拿到，退化使用日线昨日收盘（做测试用）
@@ -90,6 +106,26 @@ class FeatureEngine:
         )
         features["is_not_new_low_10d"] = latest["close"] > latest["low_min_10d"]
 
+        features["prev_ma5"] = prev["ma5"] if "ma5" in prev else prev["close"]
+
+        features["K"] = latest["K"]
+        features["D"] = latest["D"]
+        features["J"] = latest["J"]
+
+        features["prev_K"] = prev["K"]
+        features["prev_D"] = prev["D"]
+        features["prev_J"] = prev["J"]
+
+        features["ar"] = latest["ar"]
+        features["prev_ar"] = prev["ar"]
+        features["br"] = latest["br"]
+        features["prev_br"] = prev["br"]
+
+        features["cci"] = latest["cci"]
+        features["prev_cci"] = prev["cci"]
+        features["prev_cci_2"] = prev_2["cci"]
+        features["prev_cci_3"] = prev_3["cci"]
+
         history_bw = self.df["bandwidth"].iloc[-250:]
         features["bw_quantile"] = (history_bw < latest["bandwidth"]).mean()
 
@@ -113,13 +149,21 @@ class StateRuleBook:
         # 【判断是否从主升浪跌破】
         if (
             current_state == "STATE_3_MAIN_WAVE"
-            and f["close"] < f["ma10"]
-            and f["prev_close"] > f["prev_ma10"]
+            and ((f["close"] < f["ma10"]
+            and f["prev_close"] > f["prev_ma10"])
+            or ((f["prev_ar"] >= 150 and f["ar"] < 150)
+            and (f["prev_br"] >= 300 and f["br"] < 300)))
         ):
-            return (
-                "STATE_4_WAVE_END",
-                "⚠️ 主升浪确认结束！清空做T仓，准备重新激活做T。",
-            )
+            if (f["close"] < f["ma10"] and f["prev_close"] > f["prev_ma10"]):
+                return (
+                    "STATE_4_WAVE_END",
+                    "⚠️ 主升浪确认结束！清空做T仓，准备重新激活做T。(股价跌破10日均线)",
+                )
+            elif ((f["prev_ar"] >= 150 and f["ar"] < 150) and (f["prev_br"] >= 300 and f["br"] < 300)):
+                return (
+                    "STATE_4_WAVE_END",
+                    "⚠️ 主升浪确认结束！清空做T仓，准备重新激活做T。(ARBR跌破警戒线)",
+                )
 
         # 【判断是否爆发主升浪】
         if (

util.py

@@ -1,5 +1,6 @@
 import requests
 import pandas as pd
+import numpy as np
 
 CN_CODE_TYPE = {
     "600":'sh',
@@ -18,8 +19,77 @@ CN_CODE_TYPE = {
     "18":"sz"
     }
 
+## arbr指标
+def calculate_brar(df: pd.DataFrame, N: int = 26) -> pd.DataFrame:
+    """
+    计算AR和BR情绪指标
 
+    参数:
+        df (pd.DataFrame): 包含 'high', 'low', 'open', 'close' 列的DataFrame
+        N (int): 计算周期，默认通常为26
+    返回:
+        pd.DataFrame: 包含原始数据及计算出的 'ar' 和 'br' 列的DataFrame
+    """
+    # 创建副本，避免修改原始数据
+    data = df.copy()
 
+    # --- 计算 AR (人气指标) ---
+    # 公式: AR = (N日内 (最高价 - 开盘价) 之和) / (N日内 (开盘价 - 最低价) 之和) * 100
+    high_open = data['high'] - data['open']
+    open_low = data['open'] - data['low']
+
+    data['ar'] = (high_open.rolling(window=N, min_periods=1).sum() /
+                  open_low.rolling(window=N, min_periods=1).sum()) * 100
+
+    # --- 计算 BR (意愿指标) ---
+    # 公式: BR = (N日内 MAX(0, 最高价 - 前一日收盘价) 之和) /
+    #           (N日内 MAX(0, 前一日收盘价 - 最低价) 之和) * 100
+    prev_close = data['close'].shift(1)
+
+    # 计算多头意愿：当日最高价 - 前一日收盘价，若小于0则记为0
+    high_prev_close = data['high'] - prev_close
+    high_prev_close = high_prev_close.where(high_prev_close > 0, 0)
+
+    # 计算空头意愿：前一日收盘价 - 当日最低价，若小于0则记为0
+    prev_close_low = prev_close - data['low']
+    prev_close_low = prev_close_low.where(prev_close_low > 0, 0)
+
+    data['br'] = (high_prev_close.rolling(window=N, min_periods=1).sum() /
+                  prev_close_low.rolling(window=N, min_periods=1).sum()) * 100
+
+    return data
+
+## kdj计算
+def calc_kdj_tdx(df, n=9, m1=3, m2=3):
+    # 1. 计算 RSV
+    low_min = df['low'].rolling(n).min()
+    high_max = df['high'].rolling(n).max()
+    rsv = (df['close'] - low_min) / (high_max - low_min) * 100
+    rsv = rsv.fillna(0)  # 或者处理 NaN
+
+    # 2. 自定义 SMA 函数（通达信风格）
+    def sma_tdx(series, period, weight):
+        # period=N, weight=M。公式：Y = (X*M + PREV_Y*(N-M))/N
+        result = np.zeros(len(series))
+        # 初始化第一个有效值。通常，如果数据足够，初始值为 series[0]
+        result[0] = series[0] if not np.isnan(series[0]) else 0
+        for i in range(1, len(series)):
+            # 如果 series[i] 是 NaN（例如，前 n 天），则保持 0 或向前填充，但通常 RSV 已填充。
+            val = series[i] if not np.isnan(series[i]) else 0
+            result[i] = (val * weight + result[i - 1] * (period - weight)) / period
+        return result
+
+    # 3. 计算 K 和 D
+    k_series = sma_tdx(rsv.values, m1, 1)  # K = SMA(RSV, 3, 1)
+    d_series = sma_tdx(k_series, m2, 1)  # D = SMA(K, 3, 1)
+
+    df['K'] = k_series
+    df['D'] = d_series
+    df['J'] = 3 * df['K'] - 2 * df['D']
+
+    return df
+
+## 获取历史k线
 def get_history_k(code, start_date='19700101', end_date=None):
 
     if '.' in code:
@@ -36,8 +106,7 @@ def get_history_k(code, start_date='19700101', end_date=None):
         url = "https://api.zhituapi.com/hs/history/%s/d/f?token=6E0E86BC-15AD-4275-8A95-B02D168D63C1&st=%s"%(stock_code, start_date)
 
         if end_date is not None:
-            url += "&end_date=%s"%(end_date)
-
+            url += "&et=%s"%(end_date)
         response = requests.get(url)
 
         data = response.json()
@@ -55,6 +124,7 @@ def get_history_k(code, start_date='19700101', end_date=None):
     return df
 
 
+## 获取当日5分钟的k线
 def get_5min_k(code, start_date='19700101', end_date=None):
     stock_code = ''