71 lines
2.7 KiB
Python
71 lines
2.7 KiB
Python
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# ============================================================
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# 截面策略指标示例（研究参考版）
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# Cross-Sectional Strategy Indicator Example (Research Reference)
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# Momentum + RSI Composite Score
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# ============================================================
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#
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# 使用方法:
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# 1. 作为截面研究思路示例阅读
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# 2. 用于理解“对多个标的打分再排序”的基本写法
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#
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# 当前限制:
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# - 策略快照回测（cross_sectional）暂不支持；实盘指标截面策略可在「交易助手」创建
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# - 创建时选择「截面策略」并插入模板，或在本指标中实现 scores 字典
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#
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# 评分逻辑:
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# - 动量因子（20周期）：价格变化率，越高越好
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# - RSI 指标（14周期）：反转 RSI 值，越低越好（100 - RSI）
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# - 综合评分：70% 动量 + 30% RSI 反转值
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#
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# ============================================================
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# 截面策略指标
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# 输入: data = {symbol1: df1, symbol2: df2, ...}
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# 输出: scores = {symbol1: score1, symbol2: score2, ...}
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scores = {}
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# 遍历全部标的
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for symbol, df in data.items():
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# 确保数据长度足够
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if len(df) < 20:
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scores[symbol] = 0
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continue
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# === 1. 计算动量因子 (20周期) ===
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# 动量 = (当前价格 / 20周期前价格 - 1) * 100
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momentum = (df['close'].iloc[-1] / df['close'].iloc[-20] - 1) * 100
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# === 2. 计算RSI指标 (14周期) ===
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def calculate_rsi(prices, period=14):
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"""计算 RSI 指标"""
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delta = prices.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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rsi = 100 - (100 / (1 + rs))
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return rsi.iloc[-1]
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rsi_value = calculate_rsi(df['close'], 14)
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# === 3. 综合评分 ===
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# 动量越高 = 评分越高
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# RSI越低（超卖）= 评分越高（100 - RSI）
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# 权重: 70% 动量 + 30% RSI反转值
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momentum_score = momentum
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rsi_score = 100 - rsi_value # 反转 RSI（RSI 越低，评分越高）
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composite_score = momentum_score * 0.7 + rsi_score * 0.3
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scores[symbol] = composite_score
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# === 可选：手动指定排序 ===
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# 如果不提供，系统会根据 scores 自动排序
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# rankings = sorted(scores.keys(), key=lambda x: scores[x], reverse=True)
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# === 研究语义说明 ===
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# 1. 根据评分对所有标的进行排序（从高到低）
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# 2. 选择排名靠前的 N 个标的做多（基于 portfolio_size * long_ratio）
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# 3. 选择排名靠后的 N 个标的做空（基于 portfolio_size * (1 - long_ratio)）
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# 4. 在未来平台链路完善后，可由系统统一生成买入 / 卖出 / 平仓动作
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