使用cci、kdj和站上5日线为买入标志

quant_strategy.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pandas as pd
 import talib
-from util import calculate_brar
+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
@@ -60,6 +60,14 @@ class FeatureEngine:
 
         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"]
@@ -75,6 +83,7 @@ class FeatureEngine:
 
         latest = self.df.iloc[-1]
         prev = self.df.iloc[-2]
+        prev_2 = self.df.iloc[-3]
 
         # 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）
@@ -96,11 +105,25 @@ 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"]
+
         history_bw = self.df["bandwidth"].iloc[-250:]
         features["bw_quantile"] = (history_bw < latest["bandwidth"]).mean()