feat: 구조 리팩토링

src/ui/components/__init__.py

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+__all__ = ['charts']

src/ui/components/charts.py

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+"""
+Charting components for the Streamlit Quant application.
+"""
+import streamlit as st
+import pandas as pd
+import numpy as np
+import plotly.graph_objects as go
+import plotly.express as px
+from datetime import datetime, timedelta
+
+def plot_stock_price(price_data, ticker_column='종목코드', date_column='날짜', 
+                     price_column='종가', name_column='종목명'):
+    """
+    Plot stock price chart for selected stocks.
+    
+    Args:
+        price_data: DataFrame with price data
+        ticker_column: Column name for ticker code
+        date_column: Column name for date
+        price_column: Column name for price
+        name_column: Column name for stock name
+    """
+    if price_data.empty:
+        st.warning("가격 데이터가 없습니다.")
+        return
+    
+    # Get unique tickers
+    tickers = price_data[ticker_column].unique()
+    
+    if len(tickers) == 0:
+        st.warning("표시할 종목이 없습니다.")
+        return
+    
+    # Create figure
+    fig = go.Figure()
+    
+    # Add traces
+    for ticker in tickers:
+        ticker_data = price_data[price_data[ticker_column] == ticker]
+        
+        name = ticker
+        if name_column in ticker_data.columns:
+            name = ticker_data[name_column].iloc[0] if not ticker_data.empty else ticker
+            name = f"{name} ({ticker})"
+        
+        fig.add_trace(
+            go.Scatter(
+                x=ticker_data[date_column],
+                y=ticker_data[price_column],
+                mode='lines',
+                name=name
+            )
+        )
+    
+    # Update layout
+    fig.update_layout(
+        title='주가 차트',
+        xaxis_title='날짜',
+        yaxis_title='가격',
+        legend_title='종목',
+        hovermode='x unified'
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def plot_returns_comparison(returns_data, benchmark_data=None, 
+                           date_column='날짜', returns_column='누적수익률'):
+    """
+    Plot returns comparison chart for strategy vs benchmark.
+    
+    Args:
+        returns_data: DataFrame with strategy returns
+        benchmark_data: DataFrame with benchmark returns (optional)
+        date_column: Column name for date
+        returns_column: Column name for returns
+    """
+    if returns_data.empty:
+        st.warning("수익률 데이터가 없습니다.")
+        return
+    
+    # Create figure
+    fig = go.Figure()
+    
+    # Add strategy trace
+    fig.add_trace(
+        go.Scatter(
+            x=returns_data[date_column],
+            y=returns_data[returns_column],
+            mode='lines',
+            name='전략'
+        )
+    )
+    
+    # Add benchmark trace if provided
+    if benchmark_data is not None and not benchmark_data.empty:
+        fig.add_trace(
+            go.Scatter(
+                x=benchmark_data[date_column],
+                y=benchmark_data[returns_column],
+                mode='lines',
+                name='벤치마크'
+            )
+        )
+    
+    # Update layout
+    fig.update_layout(
+        title='수익률 비교',
+        xaxis_title='날짜',
+        yaxis_title='누적수익률 (%)',
+        legend_title='전략',
+        hovermode='x unified'
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def plot_factor_distribution(data, factor_column, title=None):
+    """
+    Plot histogram for factor distribution.
+    
+    Args:
+        data: DataFrame with factor data
+        factor_column: Column name for factor
+        title: Chart title (optional)
+    """
+    if data.empty:
+        st.warning("데이터가 없습니다.")
+        return
+    
+    if factor_column not in data.columns:
+        st.warning(f"{factor_column} 칼럼이, 데이터에 없습니다.")
+        return
+    
+    # Create histogram
+    fig = px.histogram(
+        data, 
+        x=factor_column,
+        nbins=30,
+        marginal="box",
+        title=title or f"{factor_column} 분포"
+    )
+    
+    # Update layout
+    fig.update_layout(
+        xaxis_title=factor_column,
+        yaxis_title='종목 수'
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def plot_scatter_matrix(data, columns, color_column=None, size_column=None, title=None):
+    """
+    Plot scatter matrix for multiple factors.
+    
+    Args:
+        data: DataFrame with factor data
+        columns: List of column names to plot
+        color_column: Column name for color (optional)
+        size_column: Column name for point size (optional)
+        title: Chart title (optional)
+    """
+    if data.empty:
+        st.warning("데이터가 없습니다.")
+        return
+    
+    # Validate columns
+    missing_cols = [col for col in columns if col not in data.columns]
+    if missing_cols:
+        st.warning(f"다음 칼럼이 데이터에 없습니다: {', '.join(missing_cols)}")
+        return
+    
+    # Create scatter matrix
+    fig = px.scatter_matrix(
+        data,
+        dimensions=columns,
+        color=color_column if color_column in data.columns else None,
+        size=size_column if size_column in data.columns else None,
+        title=title or "팩터 상관관계"
+    )
+    
+    # Update layout
+    fig.update_layout(
+        height=600,
+        width=800
+    )
+    
+    st.plotly_chart(fig, use_container_width=True)