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Tencent SkillHub ยท Content Creation
multi-factor-strategy
Guide users to create multi-factor stock selection strategies and generate independent YAML configuration files
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Guide users to create multi-factor stock selection strategies and generate independent YAML configuration files
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Documentation
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): MethodTypeExampleDescriptionInlinedict{name: xxx, expr: "..."}Define expression directly in YAMLExternalstrfactors/alpha_001.yamlLoad 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
Data Processing Functions
FunctionUsageDescriptiondelaydelay(x, n)Lag n periodsmama(x, n)Simple moving averageemaema(x, n)Exponential moving averagerolling_sumrolling_sum(x, n)Rolling sumrolling_stdrolling_std(x, n)Rolling standard deviation
Technical Indicator Functions
FunctionUsageDescriptionrsirsi(x, n=14)Relative strength indexcorrelationcorrelation(x, y, n)Correlation coefficientcross_upcross_up(a, b)Golden cross (a crosses above b)cross_downcross_down(a, b)Death cross (a crosses below b)
Ranking & Normalization Functions
FunctionUsageDescriptionrankrank(x)Cross-sectional ranking (0-1)zscorezscore(x)Standardizationsignsign(x)Sign functionclampclamp(x, min, max)Clipping function
Conditional Functions
FunctionUsageDescriptionwherewhere(cond, t, f)Conditional selectionifif(cond, t, f)Conditional selection (alias)
Base Fields
FieldDescriptionopen, high, low, closeOHLC pricesvolumeTrading volumepe, pbP/E ratio, P/B ratioroeReturn on equitynetprofitmarginNet profit margin
Step 1: Strategy Goalๅฎไฝ
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: FactorExpressionDirectionDescriptionroeroepositiveReturn on equitypepenegativeLower P/E is betterpbpbnegativePrice-to-book rationetprofitmarginnetprofitmarginpositiveNet profit marginrevenue_growthrevenue_yoypositiveRevenue growth rate Common Technical Factors: FactorExpressionDirectionDescriptionmomentum(close/delay(close,20))-1positiveN-day momentumma_deviation(close-ma(close,10))/ma(close,10)negativeMA deviationma_slope(ma(close,10)-delay(ma(close,10),5))/delay(ma(close,10),5)positiveMA slopevolume_ratiovolume/ma(volume,5)negativeVolume ratio Alpha101 Built-in Factors (can reference {baseDir}/alpha101/alpha_XXX): QuantCLI includes 40 WorldQuant Alpha101 factors that can be directly referenced: FactorCategoryDescriptionalpha101/alpha_001Reversal20-day new high then declinealpha101/alpha_002ReversalDown volume bottomalpha101/alpha_003VolatilityLow volatility stabilityalpha101/alpha_004Capital FlowNet capital inflowalpha101/alpha_005TrendUptrendalpha101/alpha_008Capital FlowCapital inflowalpha101/alpha_009MomentumLong-term momentumalpha101/alpha_010ReversalMA deviation reversalalpha101/alpha_011 ~ alpha_020ExtendedVolatility, momentum, price-volume factorsalpha101/alpha_021 ~ alpha_030ExtendedPrice-volume, trend, strength factorsalpha101/alpha_031 ~ alpha_040ExtendedPosition, 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.
Category context
Writing, remixing, publishing, visual generation, and marketing content production.
Source: Tencent SkillHub
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Package contents
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1 Docs
- skill.md