dienakdz
87f2845483
- Cleaned up whitespace and formatting in various files including http.py, language.py, logger.py, safe_exec.py, and SQL migration scripts. - Consolidated import statements and removed unnecessary blank lines. - Updated logging configuration for better clarity. - Enhanced the safe execution code with improved error handling and logging. - Removed commented-out code and unnecessary variables in backfill_zero_trades.py and other scripts. - Added a pyproject.toml for Ruff and Vulture configuration. - Introduced requirements-dev.txt for development dependencies. - Removed commented-out stock entries in init.sql for cleaner migration scripts.
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
# ============================================================
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# 截面策略指标示例 - 动量+RSI综合评分
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# Cross-Sectional Strategy Indicator Example
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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. 在交易助手中创建截面策略
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# 2. 选择此指标作为策略指标
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# 3. 配置标的列表、持仓大小、做多比例等参数
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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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# Iterate through all symbols
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for symbol, df in data.items():
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# Ensure we have enough data
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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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# === 系统自动处理逻辑 ===
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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. 自动生成买入/卖出/平仓信号
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