oficcejo
485 lines
17 KiB
Python
485 lines
17 KiB
Python
"""
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智能盯盘 - K线图绘制模块
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支持AI决策标注、实时更新
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"""
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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import pandas as pd
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from datetime import datetime, timedelta
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from typing import Dict, List, Optional
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import logging
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class SmartMonitorKline:
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"""智能盯盘K线图"""
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def __init__(self):
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"""初始化K线图"""
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self.logger = logging.getLogger(__name__)
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def create_kline_with_decisions(
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self,
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stock_code: str,
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stock_name: str,
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kline_data: pd.DataFrame,
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ai_decisions: List[Dict],
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show_volume: bool = True,
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show_ma: bool = True,
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height: int = 600
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) -> go.Figure:
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"""
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创建带AI决策标注的K线图
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Args:
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stock_code: 股票代码
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stock_name: 股票名称
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kline_data: K线数据（DataFrame）
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ai_decisions: AI决策列表
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show_volume: 是否显示成交量
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show_ma: 是否显示均线
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height: 图表高度
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Returns:
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plotly Figure对象
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"""
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try:
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# 确保数据不为空
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if kline_data is None or kline_data.empty:
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self.logger.warning(f"K线数据为空 {stock_code}")
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return self._create_empty_figure(stock_code, stock_name, height)
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# 确保必需的列存在
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required_cols = ['日期', '开盘', '收盘', '最高', '最低']
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if not all(col in kline_data.columns for col in required_cols):
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self.logger.error(f"K线数据缺少必需列 {stock_code}")
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return self._create_empty_figure(stock_code, stock_name, height)
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# 创建子图
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if show_volume:
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fig = make_subplots(
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rows=2, cols=1,
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shared_xaxes=True,
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vertical_spacing=0.03,
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row_heights=[0.7, 0.3],
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subplot_titles=(f'{stock_code} {stock_name}', '成交量')
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)
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else:
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fig = make_subplots(
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rows=1, cols=1,
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subplot_titles=(f'{stock_code} {stock_name}',)
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)
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# 1. 添加K线图
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fig.add_trace(
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go.Candlestick(
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x=kline_data['日期'],
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open=kline_data['开盘'],
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high=kline_data['最高'],
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low=kline_data['最低'],
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close=kline_data['收盘'],
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name='K线',
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increasing_line_color='#ef5350', # 红色（涨）
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decreasing_line_color='#26a69a' # 绿色（跌）
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),
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row=1, col=1
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)
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# 2. 添加均线（如果需要）
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if show_ma:
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self._add_moving_averages(fig, kline_data, row=1, col=1)
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# 3. 添加AI决策标注
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if ai_decisions:
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self._add_ai_decision_markers(fig, kline_data, ai_decisions, row=1, col=1)
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# 4. 添加成交量（如果需要）
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if show_volume and '成交量' in kline_data.columns:
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self._add_volume(fig, kline_data, row=2, col=1)
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# 5. 更新布局
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fig.update_layout(
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height=height,
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xaxis_rangeslider_visible=False,
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showlegend=True,
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hovermode='x unified',
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template='plotly_white',
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margin=dict(l=50, r=50, t=50, b=50),
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)
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# 更新x轴
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fig.update_xaxes(
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title_text="日期",
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row=2 if show_volume else 1,
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col=1
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)
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# 更新y轴
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fig.update_yaxes(title_text="价格(元)", row=1, col=1)
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if show_volume:
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fig.update_yaxes(title_text="成交量", row=2, col=1)
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return fig
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except Exception as e:
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self.logger.error(f"创建K线图失败 {stock_code}: {e}")
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import traceback
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self.logger.debug(traceback.format_exc())
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return self._create_empty_figure(stock_code, stock_name, height)
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def _add_moving_averages(self, fig, kline_data: pd.DataFrame, row: int, col: int):
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"""添加均线"""
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try:
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# 计算均线
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ma_periods = [5, 10, 20, 60]
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ma_colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A']
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for period, color in zip(ma_periods, ma_colors):
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if len(kline_data) >= period:
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ma = kline_data['收盘'].rolling(window=period).mean()
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fig.add_trace(
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go.Scatter(
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x=kline_data['日期'],
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y=ma,
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name=f'MA{period}',
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line=dict(color=color, width=1),
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opacity=0.7
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),
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row=row, col=col
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)
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except Exception as e:
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self.logger.warning(f"添加均线失败: {e}")
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def _add_ai_decision_markers(
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self,
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fig,
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kline_data: pd.DataFrame,
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ai_decisions: List[Dict],
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row: int,
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col: int
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):
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"""在K线图上添加AI决策标注"""
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try:
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# 决策类型映射
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action_config = {
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'buy': {
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'symbol': 'triangle-up',
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'color': '#ef5350',
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'text': '买入',
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'size': 15
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},
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'sell': {
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'symbol': 'triangle-down',
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'color': '#26a69a',
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'text': '卖出',
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'size': 15
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},
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'add_position': {
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'symbol': 'triangle-up',
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'color': '#ff9800',
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'text': '加仓',
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'size': 12
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},
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'reduce_position': {
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'symbol': 'triangle-down',
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'color': '#9c27b0',
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'text': '减仓',
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'size': 12
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},
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'hold': {
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'symbol': 'circle',
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'color': '#607d8b',
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'text': '持有',
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'size': 8
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}
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}
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# 将K线数据日期转换为字符串，便于匹配
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kline_data['日期_str'] = pd.to_datetime(kline_data['日期']).dt.strftime('%Y-%m-%d')
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# 按决策类型分组
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for action_type, config in action_config.items():
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decisions_of_type = [d for d in ai_decisions if d.get('action') == action_type]
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if not decisions_of_type:
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continue
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# 提取决策的日期和价格
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decision_dates = []
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decision_prices = []
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decision_texts = []
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for decision in decisions_of_type:
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decision_date = decision.get('decision_time', '').split()[0] # 只取日期部分
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# 在K线数据中查找对应日期的收盘价
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matching_rows = kline_data[kline_data['日期_str'] == decision_date]
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if not matching_rows.empty:
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price = matching_rows.iloc[0]['收盘']
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decision_dates.append(decision_date)
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decision_prices.append(price)
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# 构建hover文本
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confidence = decision.get('confidence', 0)
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reasoning = decision.get('reasoning', '无')[:50] # 截断过长的推理
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hover_text = (
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f"<b>{config['text']}</b><br>"
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f"日期: {decision_date}<br>"
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f"价格: ¥{price:.2f}<br>"
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f"置信度: {confidence}%<br>"
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f"推理: {reasoning}..."
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)
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decision_texts.append(hover_text)
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# 添加标注
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if decision_dates:
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fig.add_trace(
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go.Scatter(
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x=decision_dates,
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y=decision_prices,
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mode='markers+text',
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name=config['text'],
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marker=dict(
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symbol=config['symbol'],
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size=config['size'],
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color=config['color'],
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line=dict(color='white', width=1)
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),
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text=[config['text']] * len(decision_dates),
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textposition='top center',
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textfont=dict(size=10, color=config['color']),
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hovertext=decision_texts,
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hoverinfo='text',
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showlegend=True
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),
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row=row, col=col
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)
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except Exception as e:
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self.logger.error(f"添加AI决策标注失败: {e}")
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import traceback
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self.logger.debug(traceback.format_exc())
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def _add_volume(self, fig, kline_data: pd.DataFrame, row: int, col: int):
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"""添加成交量柱状图"""
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try:
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# 计算颜色（红涨绿跌）
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colors = []
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for i in range(len(kline_data)):
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if i == 0:
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colors.append('#ef5350')
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else:
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if kline_data.iloc[i]['收盘'] >= kline_data.iloc[i-1]['收盘']:
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colors.append('#ef5350') # 红色（涨）
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else:
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colors.append('#26a69a') # 绿色（跌）
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fig.add_trace(
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go.Bar(
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x=kline_data['日期'],
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y=kline_data['成交量'],
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name='成交量',
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marker_color=colors,
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showlegend=False
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),
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row=row, col=col
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)
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except Exception as e:
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self.logger.warning(f"添加成交量失败: {e}")
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def _create_empty_figure(self, stock_code: str, stock_name: str, height: int) -> go.Figure:
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"""创建空图表"""
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fig = go.Figure()
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fig.add_annotation(
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text=f"暂无 {stock_code} {stock_name} 的K线数据",
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xref="paper",
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yref="paper",
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x=0.5,
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y=0.5,
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showarrow=False,
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font=dict(size=20, color="gray")
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)
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fig.update_layout(
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height=height,
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xaxis=dict(visible=False),
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yaxis=dict(visible=False),
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template='plotly_white'
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)
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return fig
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def get_kline_data(self, stock_code: str, days: int = 60, data_fetcher=None) -> Optional[pd.DataFrame]:
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"""
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获取K线数据（带Tushare降级机制）
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Args:
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stock_code: 股票代码
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days: 获取天数
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data_fetcher: 数据获取器实例
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Returns:
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K线数据DataFrame
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"""
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try:
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if data_fetcher is None:
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from smart_monitor_data import SmartMonitorDataFetcher
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data_fetcher = SmartMonitorDataFetcher()
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# 计算日期范围
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end_date = datetime.now().strftime('%Y%m%d')
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start_date = (datetime.now() - timedelta(days=days + 30)).strftime('%Y%m%d') # 多取30天以确保足够数据
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# 方法1: 尝试使用AKShare获取（只尝试1次，避免IP封禁）
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try:
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import akshare as ak
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df = ak.stock_zh_a_hist(
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symbol=stock_code,
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period='daily',
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start_date=start_date,
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end_date=end_date,
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adjust='qfq'
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)
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if df is not None and not df.empty:
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# 只保留最近days天的数据
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df = df.tail(days)
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self.logger.info(f"✅ AKShare获取K线数据成功 {stock_code}，共{len(df)}条")
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return df
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else:
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self.logger.warning(f"AKShare未返回K线数据 {stock_code}，尝试降级到Tushare")
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except Exception as e:
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self.logger.warning(f"AKShare获取K线数据失败 {stock_code}: {type(e).__name__}, 尝试降级到Tushare")
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# 方法2: 降级到Tushare
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if data_fetcher and data_fetcher.ts_pro:
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self.logger.info(f"降级使用Tushare获取K线数据 {stock_code}")
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df = self._get_kline_from_tushare(stock_code, days, data_fetcher.ts_pro)
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if df is not None and not df.empty:
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self.logger.info(f"✅ Tushare获取K线数据成功 {stock_code}，共{len(df)}条")
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return df
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self.logger.error(f"所有数据源都无法获取K线数据 {stock_code}")
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return None
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except Exception as e:
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self.logger.error(f"获取K线数据失败 {stock_code}: {e}")
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import traceback
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self.logger.debug(traceback.format_exc())
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return None
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def _get_kline_from_tushare(self, stock_code: str, days: int, ts_pro) -> Optional[pd.DataFrame]:
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"""
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从Tushare获取K线数据
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Args:
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stock_code: 股票代码
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days: 获取天数
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ts_pro: Tushare API实例
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Returns:
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K线数据DataFrame
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"""
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try:
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# 转换股票代码格式
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if stock_code.startswith('6'):
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ts_code = f"{stock_code}.SH"
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elif stock_code.startswith(('0', '3')):
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ts_code = f"{stock_code}.SZ"
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else:
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ts_code = stock_code
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# 计算日期范围（多取一些确保足够）
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end_date = datetime.now().strftime('%Y%m%d')
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start_date = (datetime.now() - timedelta(days=days + 60)).strftime('%Y%m%d')
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# 获取日K线数据（前复权）
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df = ts_pro.daily(
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ts_code=ts_code,
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start_date=start_date,
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end_date=end_date,
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adj='qfq'
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)
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if df is None or df.empty:
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self.logger.error(f"Tushare未返回K线数据 {stock_code}")
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return None
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# Tushare数据是从新到旧，需要反转
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df = df.sort_values('trade_date', ascending=True).reset_index(drop=True)
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# 统一列名为AKShare格式
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df = df.rename(columns={
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'trade_date': '日期',
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'open': '开盘',
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'high': '最高',
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'low': '最低',
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'close': '收盘',
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'vol': '成交量',
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'amount': '成交额'
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})
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# 转换日期格式（Tushare: 20240115 -> 2024-01-15）
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df['日期'] = pd.to_datetime(df['日期'])
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# 只保留最近days天的数据
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df = df.tail(days)
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return df
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except Exception as e:
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self.logger.error(f"Tushare获取K线数据失败 {stock_code}: {type(e).__name__}: {str(e)}")
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return None
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if __name__ == '__main__':
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# 测试代码
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logging.basicConfig(level=logging.INFO)
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kline = SmartMonitorKline()
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# 测试获取K线数据
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df = kline.get_kline_data('600519', days=60)
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if df is not None:
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print(f"获取到 {len(df)} 条K线数据")
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print(df.head())
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# 模拟AI决策
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ai_decisions = [
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{
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'decision_time': '2024-01-15 10:00:00',
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'action': 'buy',
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'confidence': 85,
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'reasoning': '技术指标良好，MACD金叉'
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},
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{
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'decision_time': '2024-01-20 14:30:00',
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'action': 'add_position',
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'confidence': 75,
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'reasoning': '突破关键压力位'
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},
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{
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'decision_time': '2024-01-25 11:00:00',
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'action': 'sell',
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'confidence': 80,
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'reasoning': 'RSI超买，建议止盈'
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}
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]
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# 创建K线图
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fig = kline.create_kline_with_decisions(
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stock_code='600519',
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stock_name='贵州茅台',
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kline_data=df,
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ai_decisions=ai_decisions,
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show_volume=True,
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show_ma=True
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)
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# 保存为HTML
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fig.write_html('test_kline.html')
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print("K线图已保存到 test_kline.html")
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else:
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print("获取K线数据失败")
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|