From 81f328532e366eef171b71810b221a9294dda78f Mon Sep 17 00:00:00 2001
From: Administrator <admin@example.com>
Date: 星期四, 21 十二月 2023 14:31:58 +0800
Subject: [PATCH] 买入条件调整/L撤调整

---
 code_attribute/code_nature_analyse.py |   51 +++++++++++++++++++++++++++++++++++++++++++++------
 1 files changed, 45 insertions(+), 6 deletions(-)

diff --git a/code_attribute/code_nature_analyse.py b/code_attribute/code_nature_analyse.py
index 47081b6..f5c0d23 100644
--- a/code_attribute/code_nature_analyse.py
+++ b/code_attribute/code_nature_analyse.py
@@ -198,7 +198,7 @@
 
 
 # 鑾峰彇K绾垮舰鎬�
-# 杩斿洖 (15涓氦鏄撴棩娑ㄥ箙鏄惁澶т簬24.9%,鏄惁鐮村墠楂橈紝鏄惁瓒呰穼锛屾槸鍚︽帴杩戝墠楂橈紝鏄惁N,鏄惁V,鏄惁鏈夊舰鎬�,澶╅噺澶ч槼淇℃伅,鏄惁鍏锋湁杈ㄨ瘑搴�)
+# 杩斿洖 (15涓氦鏄撴棩娑ㄥ箙鏄惁澶т簬24.9%,鏄惁鐮村墠楂橈紝鏄惁瓒呰穼锛屾槸鍚︽帴杩戝墠楂橈紝鏄惁N,鏄惁V,鏄惁鏈夊舰鎬�,澶╅噺澶ч槼淇℃伅,鏄惁鍏锋湁杈ㄨ瘑搴�,杩�2澶╂湁10澶╁唴鏈�澶ч噺,涓婁釜浜ゆ槗鏃ユ槸鍚︾偢鏉�)
 def get_k_format(limit_up_price, record_datas):
     p1_data = get_lowest_price_rate(record_datas)
     p1 = p1_data[0] >= 0.249, p1_data[1]
@@ -218,13 +218,17 @@
     # 鏄惁鍏锋湁杈ㄨ瘑搴�
     p9 = is_special(record_datas)
     p10 = is_latest_10d_max_volume_at_latest_2d(record_datas)
+    # 鏈�杩�5澶╂槸鍚﹁穼鍋�/鐐告澘
+    p11 = __is_latest_open_limit_up_or_limit_down(record_datas, 5)
+    # 30澶╁唴鏄惁鏈夋定鍋�
+    p12 = __has_limit_up(record_datas, 30)
 
-    return p1, p2, p3, p4, p5, p6, p7, p8, p9, p10
+    return p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11, p12
 
 
 # 鏄惁鍏锋湁K绾垮舰鎬�
 def is_has_k_format(limit_up_price, record_datas):
-    is_too_high, is_new_top, is_lowest, is_near_new_top, is_n, is_v, has_format, volume_info, is_special, has_max_volume = get_k_format(
+    is_too_high, is_new_top, is_lowest, is_near_new_top, is_n, is_v, has_format, volume_info, is_special, has_max_volume, open_limit_up, is_limit_up_in_30days = get_k_format(
         float(limit_up_price), record_datas)
     if not has_format:
         return False, "涓嶆弧瓒矺绾垮舰鎬�"
@@ -349,7 +353,7 @@
 
 
 # 鍦ㄦ渶杩戝嚑澶╁唴鑲′环鏄惁闀垮緱澶珮
-def is_price_too_high_in_days(record_datas, limit_up_price, day_count=6):
+def is_price_too_high_in_days(record_datas, limit_up_price, day_count=5):
     datas = copy.deepcopy(record_datas)
     datas.sort(key=lambda x: x["bob"])
     datas = datas[0 - day_count:]
@@ -364,8 +368,8 @@
             min_price = d["low"]
         if max_price < d["high"]:
             max_price = d["high"]
-    if max_price > float(limit_up_price):
-        return False
+    # if max_price > float(limit_up_price):
+    #     return False
     rate = (float(limit_up_price) - min_price) / min_price
     # print(rate)
     if rate >= 0.28:
@@ -403,6 +407,10 @@
 
 def is_new_top(limit_up_price, datas):
     return __is_new_top(float(limit_up_price), datas)[0]
+
+
+def is_near_top(limit_up_price, datas):
+    return __is_near_new_top(float(limit_up_price), datas)[0]
 
 
 # 鎺ヨ繎鏂伴珮
@@ -469,6 +477,24 @@
     return False, ''
 
 
+# 鏈�杩戝嚑澶╂槸鍚︽湁鐐告澘鎴栬穼鍋�
+def __is_latest_open_limit_up_or_limit_down(datas, day_count):
+    datas = copy.deepcopy(datas)
+    datas.sort(key=lambda x: x["bob"])
+    items = datas[0-day_count]
+    for item in items:
+        limit_up_price = float(gpcode_manager.get_limit_up_price_by_preprice(item["pre_close"]))
+        if abs(limit_up_price - item["high"]) < 0.001 and abs(limit_up_price - item["close"]) > 0.001:
+            # 鐐告澘
+            return True
+        # 鏄惁鏈夎穼鍋�
+        limit_down_price = float(gpcode_manager.get_limit_down_price_by_preprice(item["pre_close"]))
+        if abs(limit_down_price - item["close"]) < 0.001:
+            # 璺屽仠
+            return True
+    return False
+
+
 # V瀛楀舰
 def __is_v_model(datas):
     datas = copy.deepcopy(datas)
@@ -520,6 +546,19 @@
     return abs(limit_up_price - data["high"]) < 0.001
 
 
+# 澶氬皯澶╁唴鏄惁鏈夋定鍋�/鏇炬定鍋�
+def __has_limit_up(datas, day_count):
+    datas = copy.deepcopy(datas)
+    datas.sort(key=lambda x: x["bob"])
+    datas = datas[0 - day_count:]
+    if len(datas) >= 1:
+        for i in range(0, len(datas)):
+            item = datas[i]
+            if __is_limit_up(item):
+                return True
+    return False
+
+
 # 棣栨澘娑ㄥ仠婧环鐜�
 def get_limit_up_premium_rate(datas):
     datas = copy.deepcopy(datas)

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