feat: Add cross-sectional strategy support

- Add cross-sectional strategy type (single vs cross-sectional)
- Support multi-symbol portfolio management with automatic ranking
- Add portfolio size, long ratio, and rebalance frequency configuration
- Implement parallel order execution for cross-sectional strategies
- Add frontend UI for strategy type selection and configuration
- Add i18n support (Chinese and English) for cross-sectional features
- Fix decimal precision issues in exchange order quantities
- Add last_rebalance_at field to database schema
- Add comprehensive documentation and examples

Database migration required: Add last_rebalance_at column to qd_strategies_trading table

docs/examples/cross_sectional_momentum_rsi.py

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+# ============================================================
+# 截面策略指标示例 - 动量+RSI综合评分
+# Cross-Sectional Strategy Indicator Example
+# Momentum + RSI Composite Score
+# ============================================================
+# 
+# 使用方法:
+# 1. 在交易助手中创建截面策略
+# 2. 选择此指标作为策略指标
+# 3. 配置标的列表、持仓大小、做多比例等参数
+# 
+# 评分逻辑:
+# - 动量因子 (20周期): 价格变化率，越高越好
+# - RSI指标 (14周期): 反转RSI值，越低越好（100 - RSI）
+# - 综合评分: 70% 动量 + 30% RSI反转值
+# 
+# ============================================================
+
+# 截面策略指标
+# 输入: data = {symbol1: df1, symbol2: df2, ...}
+# 输出: scores = {symbol1: score1, symbol2: score2, ...}
+
+scores = {}
+
+# Iterate through all symbols
+for symbol, df in data.items():
+    # Ensure we have enough data
+    if len(df) < 20:
+        scores[symbol] = 0
+        continue
+    
+    # === 1. 计算动量因子 (20周期) ===
+    # 动量 = (当前价格 / 20周期前价格 - 1) * 100
+    momentum = (df['close'].iloc[-1] / df['close'].iloc[-20] - 1) * 100
+    
+    # === 2. 计算RSI指标 (14周期) ===
+    def calculate_rsi(prices, period=14):
+        """计算RSI指标"""
+        delta = prices.diff()
+        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
+        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
+        rs = gain / loss
+        rsi = 100 - (100 / (1 + rs))
+        return rsi.iloc[-1]
+    
+    rsi_value = calculate_rsi(df['close'], 14)
+    
+    # === 3. 综合评分 ===
+    # 动量越高 = 评分越高
+    # RSI越低（超卖）= 评分越高（100 - RSI）
+    # 权重: 70% 动量 + 30% RSI反转值
+    momentum_score = momentum
+    rsi_score = 100 - rsi_value  # 反转RSI（RSI越低，评分越高）
+    
+    composite_score = momentum_score * 0.7 + rsi_score * 0.3
+    
+    scores[symbol] = composite_score
+
+# === 可选: 手动指定排序 ===
+# 如果不提供，系统会根据scores自动排序
+# rankings = sorted(scores.keys(), key=lambda x: scores[x], reverse=True)
+
+# === 系统自动处理逻辑 ===
+# 1. 根据评分对所有标的进行排序（从高到低）
+# 2. 选择排名靠前的N个标的做多（基于 portfolio_size * long_ratio）
+# 3. 选择排名靠后的N个标的做空（基于 portfolio_size * (1 - long_ratio)）
+# 4. 自动生成买入/卖出/平仓信号