{"homepage":"https://gitcode.com/datavoid/quantcli","user-invocable":true}

多因子策略助手

引导您创建多因子选股策略并生成独立的YAML配置文件。

安装 quantcli

# Install from PyPI (recommended)
pip install quantcli

# Or install from source
git clone https://gitcode.com/datavoid/quantcli.git
cd quantcli
pip install -e .

验证安装：

quantcli --help

快速开始

一个完整的多因子选股策略YAML示例：

name: Value-Growth Hybrid Strategy
version: 1.0.0
description: ROE + Momentum factor stock selection

screening:
  fundamental_conditions:    # Stage 1: Financial condition screening
    - "roe > 0.10"           # ROE > 10%
    - "pe_ttm < 30"          # P/E < 30
    - "pe_ttm > 0"           # Exclude losses
  daily_conditions:          # Stage 2: Price condition screening
    - "close > ma10"         # Above 10-day MA
  limit: 100                 # Keep at most 100 stocks

# Factor configuration (supports two methods, factors at top level)
factors:
  # Method 1: Inline factor definition
  - name: ma10_deviation
    expr: "(close - ma(close, 10)) / ma(close, 10)"
    direction: negative
    description: "10-day MA deviation"

  # Method 2: External reference (reference factor files in factors/ directory, include .yaml suffix)
  - factors/alpha_001.yaml
  - factors/alpha_008.yaml

ranking:
  weights:                   # Weight fusion
    ma10_deviation: 0.20     # Inline factor
    factors/alpha_001.yaml: 0.40  # External reference factor
    factors/alpha_008.yaml: 0.40
  normalize: zscore          # Normalization method

output:
  limit: 30                  # Output top 30 stocks
  columns: [symbol, name, score, roe, pe_ttm, close, ma10_deviation]

因子配置方法

因子配置支持两种方法（可以混合使用）：

示例：混合用法

factors:
  # Inline: Custom factor
  - name: custom_momentum
    expr: "close / delay(close, 20) - 1"
    direction: positive

  # External: Alpha101 factor library (include .yaml suffix)
  - factors/alpha_001.yaml
  - factors/alpha_005.yaml
  - factors/alpha_009.yaml

ranking:
  weights:
    custom_momentum: 0.3
    factors/alpha_001.yaml: 0.3
    factors/alpha_005.yaml: 0.2
    factors/alpha_009.yaml: 0.2

运行策略：

quantcli filter run -f your_strategy.yaml

调用

/multi-factor-strategy

可用的表达式函数

数据处理函数

技术指标函数

排序与标准化函数

条件函数

基础字段

引导式工作流程

步骤1：策略目标定位

我将首先了解您的策略需求：

• 策略类型：价值型、成长型、动量型、波动性、混合型
• 选择数量：集中型（10-30只）、中等型（50-100只）、分散型（200只以上）
• 持有期限：日内、短期（周）、中期（月）、长期（季度）

步骤2：因子选择

根据您的策略目标，推荐合适的因子组合：

常见基本面因子：

常见技术因子：

Alpha101内置因子（可参考{baseDir}/alpha101/alpha_XXX）：

QuantCLI包含40个可直接引用的WorldQuant Alpha101因子：

查看所有内置因子：

quantcli factors list

使用示例：

factors:
  - alpha101/alpha_001   # Reversal factor
  - alpha101/alpha_008   # Capital inflow
  - alpha101/alpha_029   # 5-day momentum
ranking:
  weights:
    alpha101/alpha_001: 0.4
    alpha101/alpha_008: 0.3
    alpha101/alpha_029: 0.3

筛选条件示例：

screening:
  conditions:
    - "roe > 0.10"              # ROE > 10%
    - "netprofitmargin > 0.05"  # Net profit margin > 5%

步骤三：权重配置

根据因子重要性分配权重，0表示仅用于筛选，不参与评分：

ranking:
  weights:
    # Fundamental factors
    roe: 0.30
    pe: 0.20
    # Technical factors
    ma_deviation: 0.30
    momentum: 0.20
  normalize: zscore

步骤四：生成策略文件

我将为您生成一个完整的策略YAML文件：

name: Your Strategy Name
version: 1.0.0
description: Strategy description

# Stage 1: Fundamental screening
screening:
  conditions:
    - "roe > 0.10"
    - "pe < 30"
  limit: 200

# Stage 2: Technical ranking
ranking:
  weights:
    roe: 0.30
    pe: 0.20
    ma_deviation: 0.30
    momentum: 0.20
  normalize: zscore

output:
  columns: [symbol, score, rank, roe, pe, momentum]
  limit: 30

步骤五：运行与评估

运行策略：

quantcli filter run -f your_strategy.yaml --top 30

评估要点:

1. 入选股票数量: 检查筛选条件是否合理
2. 因子分布: 因子得分的分布情况
3. 行业分散度: 避免过度集中

常见问题

问：如何分配因子权重？答：核心因子0.3-0.4，辅助因子0.1-0.2，确保权重总和接近1

问：筛选条件过严导致结果为空？答：逐步放宽条件，先查看满足每个条件的股票数量

问：支持哪些表达式语法？答：支持40多个内置函数：ma()、ema()、delay()、rolling_sum()、rsi(),rank(),zscore(), 等等。