{"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):

Method	Type	Example	Description
Inline	`dict`	`{name: xxx, expr: "..."}`	Define expression directly in YAML
External	`str`	`factors/alpha_001.yaml`	Load factor file from `factors/` directory

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

Function	Usage	Description
delay	`delay(x, n)`	Lag n periods
ma	`ma(x, n)`	Simple moving average
ema	`ema(x, n)`	Exponential moving average
rolling_sum	`rolling_sum(x, n)`	Rolling sum
rolling_std	`rolling_std(x, n)`	Rolling standard deviation

Technical Indicator Functions

Function	Usage	Description
rsi	`rsi(x, n=14)`	Relative strength index
correlation	`correlation(x, y, n)`	Correlation coefficient
cross_up	`cross_up(a, b)`	Golden cross (a crosses above b)
cross_down	`cross_down(a, b)`	Death cross (a crosses below b)

Ranking & Normalization Functions

Function	Usage	Description
rank	`rank(x)`	Cross-sectional ranking (0-1)
zscore	`zscore(x)`	Standardization
sign	`sign(x)`	Sign function
clamp	`clamp(x, min, max)`	Clipping function

Conditional Functions

Function	Usage	Description
where	`where(cond, t, f)`	Conditional selection
if	`if(cond, t, f)`	Conditional selection (alias)

Base Fields

Field	Description
open, high, low, close	OHLC prices
volume	Trading volume
pe, pb	P/E ratio, P/B ratio
roe	Return on equity
netprofitmargin	Net profit margin

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:

Factor	Expression	Direction	Description
roe	`roe`	positive	Return on equity
pe	`pe`	negative	Lower P/E is better
pb	`pb`	negative	Price-to-book ratio
netprofitmargin	`netprofitmargin`	positive	Net profit margin
revenue_growth	`revenue_yoy`	positive	Revenue growth rate

Common Technical Factors:

Factor	Expression	Direction	Description
momentum	`(close/delay(close,20))-1`	positive	N-day momentum
ma_deviation	`(close-ma(close,10))/ma(close,10)`	negative	MA deviation
ma_slope	`(ma(close,10)-delay(ma(close,10),5))/delay(ma(close,10),5)`	positive	MA slope
volume_ratio	`volume/ma(volume,5)`	negative	Volume ratio

Alpha101 Built-in Factors (can reference

{baseDir}/alpha101/alpha_XXX

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

Factor	Category	Description
`alpha101/alpha_001`	Reversal	20-day new high then decline
`alpha101/alpha_002`	Reversal	Down volume bottom
`alpha101/alpha_003`	Volatility	Low volatility stability
`alpha101/alpha_004`	Capital Flow	Net capital inflow
`alpha101/alpha_005`	Trend	Uptrend
`alpha101/alpha_008`	Capital Flow	Capital inflow
`alpha101/alpha_009`	Momentum	Long-term momentum
`alpha101/alpha_010`	Reversal	MA deviation reversal
`alpha101/alpha_011` ~ `alpha_020`	Extended	Volatility, momentum, price-volume factors
`alpha101/alpha_021` ~ `alpha_030`	Extended	Price-volume, trend, strength factors
`alpha101/alpha_031` ~ `alpha_040`	Extended	Position, volatility, capital factors

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:

Selected stock count: Check if screening conditions are reasonable
Factor distribution: Distribution of factor scores
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.

multi-factor-strategy

AI Skill Market Insights

Be Part of the 0+ Developer Community