upd
This commit is contained in:
aaron
parent
9236b54c87
commit
db5ec7a6be
@ -59,6 +59,7 @@ strategy:
|
||||
timeframes: ["1h", "daily", "weekly"] # 支持的时间周期
|
||||
scan_stocks_count: 5000 # 扫描股票数量限制
|
||||
analysis_days: 60 # 分析的历史天数
|
||||
follow_up_days: 30 # 后续强势验证天数（形态完成后N天内不回踩阴线高点且不跌破EMA20）
|
||||
|
||||
# 回踩监控配置
|
||||
pullback_tolerance: 0.02 # 回踩容忍度（2%），价格接近阴线最高点的阈值
|
||||
|
||||
@ -74,6 +74,7 @@ strategy:
|
||||
timeframes: ["1h", "daily", "weekly"] # 支持的时间周期
|
||||
scan_stocks_count: 100 # Docker环境默认扫描股票数量
|
||||
analysis_days: 60 # 分析的历史天数
|
||||
follow_up_days: 30 # 后续强势验证天数（形态完成后N天内不回踩阴线高点且不跌破EMA20）
|
||||
|
||||
# 回踩监控配置
|
||||
pullback_tolerance: 0.02 # 回踩容忍度（2%），价格接近阴线最高点的阈值
|
||||
|
||||
@ -110,6 +110,48 @@ class KLinePatternStrategy:
|
||||
|
||||
return df
|
||||
|
||||
def check_follow_up_strength(self, df: pd.DataFrame, pattern_index: int, yin_high: float, ema20_price: float) -> bool:
|
||||
"""
|
||||
检查形态发生后的后续强势条件
|
||||
|
||||
Args:
|
||||
df: K线数据DataFrame
|
||||
pattern_index: 形态完成的索引位置（第4根阳线的位置）
|
||||
yin_high: 阴线的最高价
|
||||
ema20_price: 形态完成时的EMA20价格
|
||||
|
||||
Returns:
|
||||
bool: 是否满足后续强势条件
|
||||
"""
|
||||
# 获取配置的后续验证天数，默认为3天
|
||||
follow_up_days = self.config.get('follow_up_days', 3)
|
||||
|
||||
# 检查是否有足够的后续数据
|
||||
if pattern_index + follow_up_days >= len(df):
|
||||
# 如果没有足够的后续数据，说明是最新的形态，暂时通过验证
|
||||
return True
|
||||
|
||||
# 检查后续N天的K线
|
||||
for j in range(1, follow_up_days + 1):
|
||||
if pattern_index + j >= len(df):
|
||||
break
|
||||
|
||||
next_kline = df.iloc[pattern_index + j]
|
||||
|
||||
# 检查1: 最低价不能回踩阴线最高价
|
||||
if next_kline['low'] <= yin_high:
|
||||
logger.debug(f"后续第{j}天最低价{next_kline['low']:.2f}回踩阴线最高价{yin_high:.2f}，不符合强势条件")
|
||||
return False
|
||||
|
||||
# 检查2: 收盘价不能跌破EMA20
|
||||
next_ema20 = next_kline.get('ema20', ema20_price)
|
||||
if next_kline['close'] <= next_ema20:
|
||||
logger.debug(f"后续第{j}天收盘价{next_kline['close']:.2f}跌破EMA20价格{next_ema20:.2f}，不符合强势条件")
|
||||
return False
|
||||
|
||||
logger.debug(f"后续{follow_up_days}天强势验证通过")
|
||||
return True
|
||||
|
||||
def detect_pattern(self, df: pd.DataFrame) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
检测"两阳线+阴线+阳线"形态
|
||||
@ -172,6 +214,12 @@ class KLinePatternStrategy:
|
||||
if not turnover_valid:
|
||||
continue
|
||||
|
||||
# 检查后续强势条件（如果有后续K线数据的话）
|
||||
follow_up_strength_valid = self.check_follow_up_strength(df, i, k3['high'], k4.get('ema20', 0))
|
||||
|
||||
if not follow_up_strength_valid:
|
||||
continue
|
||||
|
||||
# 构建信号
|
||||
signal = {
|
||||
'index': i,
|
||||
@ -190,7 +238,9 @@ class KLinePatternStrategy:
|
||||
'breakout_pct': (k4['close'] - k3['high']) / k3['high'] * 100 if k3['high'] > 0 else 0,
|
||||
'ema20_price': k4.get('ema20', 0),
|
||||
'above_ema20': k4.get('above_ema20', False),
|
||||
'turnover_ratio': turnover_ratio
|
||||
'turnover_ratio': turnover_ratio,
|
||||
'follow_up_strength': follow_up_strength_valid,
|
||||
'follow_up_days': self.config.get('follow_up_days', 3)
|
||||
}
|
||||
|
||||
signals.append(signal)
|
||||
@ -204,6 +254,7 @@ class KLinePatternStrategy:
|
||||
logger.info(f"💥 突破幅度: {signal['breakout_pct']:.2f}% (突破阴线最高价{signal['yin_high']:.2f}元)")
|
||||
logger.info(f"📈 EMA20: {signal['ema20_price']:.2f}元 ({'✅上方' if signal['above_ema20'] else '❌下方'})")
|
||||
logger.info(f"🔄 换手率: {signal['turnover_ratio']:.2f}% ({'✅合规' if signal['turnover_ratio'] <= self.max_turnover_ratio else '❌过高'})")
|
||||
logger.info(f"💪 后续强势: {'✅通过' if signal['follow_up_strength'] else '❌未通过'}({signal['follow_up_days']}天验证)")
|
||||
logger.info("🎯" + "="*60)
|
||||
|
||||
return signals
|
||||
|
||||
52
web/app.py
52
web/app.py
@ -7,7 +7,7 @@ AI 智能选股大师 Web 展示界面
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from flask import Flask, render_template, jsonify, request
|
||||
from datetime import datetime, date, timedelta
|
||||
from datetime import datetime, date, timedelta, timezone
|
||||
import pandas as pd
|
||||
|
||||
# 添加项目根目录到路径
|
||||
@ -57,20 +57,32 @@ def signals():
|
||||
timeframe=timeframe if timeframe else None
|
||||
)
|
||||
|
||||
# 按扫描日期分组，每组内按信号日期排序
|
||||
# 按扫描时间（精确到小时）分组，每组内按信号日期排序
|
||||
signals_grouped = {}
|
||||
if not signals_df.empty:
|
||||
# 确保scan_time是datetime类型
|
||||
signals_df['scan_time'] = pd.to_datetime(signals_df['scan_time'])
|
||||
signals_df['scan_date'] = signals_df['scan_time'].dt.date
|
||||
|
||||
# 按扫描日期分组
|
||||
for scan_date, group in signals_df.groupby('scan_date'):
|
||||
# 转换为东八区时间
|
||||
china_tz = timezone(timedelta(hours=8))
|
||||
|
||||
# 如果是naive datetime，假设是UTC时间
|
||||
if signals_df['scan_time'].dt.tz is None:
|
||||
signals_df['scan_time'] = signals_df['scan_time'].dt.tz_localize('UTC')
|
||||
|
||||
# 转换为东八区时间
|
||||
signals_df['scan_time_china'] = signals_df['scan_time'].dt.tz_convert(china_tz)
|
||||
|
||||
# 创建小时级别的分组键（基于东八区时间）
|
||||
signals_df['scan_hour'] = signals_df['scan_time_china'].dt.floor('h')
|
||||
|
||||
# 按扫描小时分组
|
||||
for scan_hour, group in signals_df.groupby('scan_hour'):
|
||||
# 每组内按信号日期排序（降序）
|
||||
group_sorted = group.sort_values('signal_date', ascending=False)
|
||||
signals_grouped[scan_date] = group_sorted
|
||||
signals_grouped[scan_hour] = group_sorted
|
||||
|
||||
# 按扫描日期排序（最新的在前）
|
||||
# 按扫描小时排序（最新的在前）
|
||||
signals_grouped = dict(sorted(signals_grouped.items(), key=lambda x: x[0], reverse=True))
|
||||
|
||||
# 分页处理
|
||||
@ -80,18 +92,27 @@ def signals():
|
||||
|
||||
# 将分组数据展平用于分页
|
||||
flattened_signals = []
|
||||
for scan_date, group in signals_grouped.items():
|
||||
for scan_hour, group in signals_grouped.items():
|
||||
flattened_signals.extend(group.to_dict('records'))
|
||||
|
||||
paginated_signals = flattened_signals[start_idx:end_idx]
|
||||
|
||||
# 重新按扫描日期分组分页后的数据
|
||||
# 重新按扫描小时分组分页后的数据
|
||||
paginated_grouped = {}
|
||||
for signal in paginated_signals:
|
||||
scan_date = pd.to_datetime(signal['scan_time']).date()
|
||||
if scan_date not in paginated_grouped:
|
||||
paginated_grouped[scan_date] = []
|
||||
paginated_grouped[scan_date].append(signal)
|
||||
# 转换为东八区时间进行分组
|
||||
china_tz = timezone(timedelta(hours=8))
|
||||
|
||||
scan_time = pd.to_datetime(signal['scan_time'])
|
||||
# 如果是naive datetime，假设是UTC时间
|
||||
if scan_time.tz is None:
|
||||
scan_time = scan_time.tz_localize('UTC')
|
||||
# 转换为东八区时间并按小时分组
|
||||
scan_hour = scan_time.tz_convert(china_tz).floor('h')
|
||||
|
||||
if scan_hour not in paginated_grouped:
|
||||
paginated_grouped[scan_hour] = []
|
||||
paginated_grouped[scan_hour].append(signal)
|
||||
|
||||
# 计算分页信息
|
||||
total_pages = (total_records + per_page - 1) // per_page
|
||||
@ -188,8 +209,7 @@ def datetime_format(value, format='%Y-%m-%d %H:%M'):
|
||||
if value is None:
|
||||
return ''
|
||||
|
||||
# 导入时区支持
|
||||
from datetime import timezone, timedelta
|
||||
# 使用已导入的时区支持
|
||||
|
||||
if isinstance(value, str):
|
||||
try:
|
||||
@ -244,4 +264,4 @@ if __name__ == '__main__':
|
||||
print("📊 访问地址: http://localhost:8080")
|
||||
print("=" * 60)
|
||||
|
||||
app.run(host='0.0.0.0', port=8080, debug=True)
|
||||
app.run(host='0.0.0.0', port=8081, debug=True)
|
||||
@ -55,16 +55,16 @@
|
||||
<div class="card-body p-0">
|
||||
{% if signals_grouped %}
|
||||
<div class="table-container">
|
||||
{% for scan_date, signals in signals_grouped.items() %}
|
||||
<!-- 扫描日期分组标题 -->
|
||||
{% for scan_hour, signals in signals_grouped.items() %}
|
||||
<!-- 扫描时间分组标题 -->
|
||||
<div class="scan-date-header bg-light px-3 py-2">
|
||||
<h6 class="mb-0 text-primary fw-bold">
|
||||
<i class="fas fa-calendar-day me-2"></i>扫描日期: {{ scan_date }}
|
||||
<i class="fas fa-clock me-2"></i>扫描时间: {{ scan_hour.strftime('%Y-%m-%d %H:%M') }}
|
||||
<span class="badge bg-primary ms-2">{{ signals|length }} 条信号</span>
|
||||
</h6>
|
||||
</div>
|
||||
|
||||
<!-- 该扫描日期下的信号表格 -->
|
||||
<!-- 该扫描时间下的信号表格 -->
|
||||
<div class="table-responsive">
|
||||
<table class="table table-hover mb-0">
|
||||
<thead class="table-light">
|
||||
|
||||
Loading…
Reference in New Issue
Block a user