TIANHE
a51184497d
- 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
68 lines
2.4 KiB
Python
68 lines
2.4 KiB
Python
# ============================================================
|
||
# 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
|