From c81110214aa7f33a5db0613e136a190b460ff1d7 Mon Sep 17 00:00:00 2001 From: lzybetter Date: Wed, 22 Jul 2026 19:48:32 +0800 Subject: [PATCH] =?UTF-8?q?=E5=B0=9D=E8=AF=95=E5=A2=9E=E5=8A=A0arbr?= =?UTF-8?q?=E6=8C=87=E6=A0=87?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- quant_strategy.py | 33 ++++++++++++++++++++++++++------- util.py | 42 ++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 66 insertions(+), 9 deletions(-) diff --git a/quant_strategy.py b/quant_strategy.py index b905c79..3b9b948 100644 --- a/quant_strategy.py +++ b/quant_strategy.py @@ -1,7 +1,7 @@ import numpy as np import pandas as pd import talib - +from util import calculate_brar # ===================================================================== # 1. 指标引擎：未来所有新发掘的指标，全写在这里 @@ -57,8 +57,14 @@ 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 = 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"] = ( @@ -90,6 +96,11 @@ class FeatureEngine: ) features["is_not_new_low_10d"] = latest["close"] > latest["low_min_10d"] + features["ar"] = latest["ar"] + features["prev_ar"] = prev["ar"] + features["br"] = latest["br"] + features["prev_br"] = prev["br"] + history_bw = self.df["bandwidth"].iloc[-250:] features["bw_quantile"] = (history_bw < latest["bandwidth"]).mean() @@ -113,13 +124,21 @@ 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 ( diff --git a/util.py b/util.py index 4192346..3e05431 100644 --- a/util.py +++ b/util.py @@ -19,6 +19,45 @@ CN_CODE_TYPE = { } +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 + def get_history_k(code, start_date='19700101', end_date=None): @@ -36,8 +75,7 @@ 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()