Multi Factor Strategy

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

Multi-Factor Strategy Assistant

Guide you to create multi-factor stock selection strategies and generate independent YAML configuration files.

Install 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 .

Verify installation:

quantcli --help

Quick Start

A complete multi-factor stock selection strategy YAML example:

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]

Factor Configuration Methods

Factor configuration supports two methods (can be mixed):

Example: Mixed usage

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

Run strategy:

quantcli filter run -f your_strategy.yaml

Invocation

/multi-factor-strategy

Available Expression Functions

Data Processing Functions

Technical Indicator Functions

Ranking & Normalization Functions

Conditional Functions

Base Fields

Guided Workflow

Step 1: Strategy Goal定位

I will first understand your strategy needs:

• Strategy Type: Value, Growth, Momentum, Volatility, Hybrid
• Selection Count: Concentrated(10-30), Medium(50-100), Diversified(200+)
• Holding Period: Intraday, Short-term(week), Medium-term(month), Long-term(quarter)

Step 2: Factor Selection

Based on your strategy goals, recommend suitable factor combinations:

Common Fundamental Factors:

Common Technical Factors:

Alpha101 Built-in Factors (can reference {baseDir}/alpha101/alpha_XXX):

QuantCLI includes 40 WorldQuant Alpha101 factors that can be directly referenced:

View all built-in factors:

quantcli factors list

Usage Example:

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

Screening Conditions Example:

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

Step 3: Weight Configuration

Allocate weights based on factor importance, 0 means only for screening, not scoring:

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

Step 4: Generate Strategy File

I will generate a complete strategy YAML file for you:

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

Step 5: Run & Evaluate

Run strategy:

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

Evaluation points:

1. Selected stock count: Check if screening conditions are reasonable
2. Factor distribution: Distribution of factor scores
3. Industry diversification: Avoid over-concentration

FAQ

Q: How to allocate factor weights? A: Core factors 0.3-0.4, auxiliary factors 0.1-0.2, ensure weights sum close to 1

Q: Screening conditions too strict resulting in empty results? A: Gradually relax conditions, first see how many stocks meet each condition

Q: What expression syntax is supported? A: Supports 40+ built-in functions: ma(), ema(), delay(), rolling_sum(), rsi(), rank(), zscore(), etc.