商品量化CTA周度跟踪

Quantitative Models and Construction Methods 1. Model Name: Composite Signal Model - Model Construction Idea: The composite signal model integrates multiple factors, including supply, demand, inventory, and price spread, to generate a comprehensive signal for market positioning [3][7] - Model Construction Process: - The model aggregates signals from individual factors such as supply, demand, inventory, and price spread - Each factor contributes to the overall signal based on its respective weight and directional strength - The final composite signal is determined by summing the weighted contributions of these factors [3][7] - Model Evaluation: The model provides a balanced view by incorporating multiple dimensions of market dynamics, but its effectiveness depends on the relative strength and alignment of individual factors [3][7] 2. Model Name: Momentum Model - Model Construction Idea: The momentum model captures price trends over time (time-series momentum) and across assets (cross-sectional momentum) to identify potential trading opportunities [3][4] - Model Construction Process: - Time-Series Momentum: Measures the directional strength of price movements within a single asset over a specific period - Cross-Sectional Momentum: Compares the relative performance of multiple assets to identify outperformers and underperformers - The model assigns scores to assets based on their momentum strength and aggregates these scores for sector-level analysis [3][4] - Model Evaluation: The model effectively identifies trend-following opportunities but may underperform in mean-reverting or range-bound markets [3][4] --- Backtesting Results of Models 1. Composite Signal Model - Weekly Return: 1.33% [7] - Monthly Return: 2.04% [7] 2. Momentum Model - Sector-Level Performance: - Black Metals: Time-Series Momentum (0), Cross-Sectional Momentum (0.09) [4] - Non-Ferrous Metals: Time-Series Momentum (0.05), Cross-Sectional Momentum (-0.21) [4] - Energy and Chemicals: Time-Series Momentum (-0.02), Cross-Sectional Momentum (0.18) [4] - Agricultural Products: Time-Series Momentum (0.13), Cross-Sectional Momentum (0.35) [4] - Stock Indices: Time-Series Momentum (-0.71), Cross-Sectional Momentum (0.46) [4] - Precious Metals: Time-Series Momentum (0.12), Cross-Sectional Momentum (N/A) [4] --- Quantitative Factors and Construction Methods 1. Factor Name: Supply Factor - Factor Construction Idea: Measures the supply-side dynamics of commodities, including production levels and inventory changes [3][7] - Factor Construction Process: - Tracks production data, such as operating rates and output levels - Incorporates inventory trends to assess supply-side pressure - Aggregates these metrics into a single supply signal [3][7] - Factor Evaluation: The factor effectively captures supply-side influences but may lag in responding to sudden disruptions [3][7] 2. Factor Name: Demand Factor - Factor Construction Idea: Evaluates demand-side conditions using consumption data and downstream activity levels [3][7] - Factor Construction Process: - Monitors downstream consumption metrics, such as procurement volumes and production activity - Aggregates these indicators to generate a demand signal [3][7] - Factor Evaluation: The factor provides insights into consumption trends but may be influenced by seasonal variations [3][7] 3. Factor Name: Inventory Factor - Factor Construction Idea: Tracks inventory levels to assess market balance and potential price pressure [3][7] - Factor Construction Process: - Collects inventory data from key regions and aggregates it into a composite signal - Differentiates between regional inventory trends to identify localized imbalances [3][7] - Factor Evaluation: The factor is useful for identifying supply-demand mismatches but may not fully capture speculative inventory behavior [3][7] 4. Factor Name: Price Spread Factor - Factor Construction Idea: Analyzes price differentials across contracts or regions to identify arbitrage opportunities [3][7] - Factor Construction Process: - Calculates the spread between near-month and far-month contracts or between regional prices - Normalizes the spread to account for seasonal and structural differences - Aggregates the spread data into a directional signal [3][7] - Factor Evaluation: The factor is effective in identifying relative value opportunities but may be sensitive to short-term noise [3][7] 5. Factor Name: Profit Factor - Factor Construction Idea: Measures profitability dynamics in production and processing activities [7] - Factor Construction Process: - Tracks input costs and output prices to calculate profit margins - Aggregates margin data across regions and production methods to generate a profit signal [7] - Factor Evaluation: The factor captures economic incentives but may lag in responding to rapid cost or price changes [7] --- Backtesting Results of Factors 1. Supply Factor - Weekly Return: 1.92% [7] - Monthly Return: 2.04% [7] 2. Demand Factor - Weekly Return: -1.28% [7] - Monthly Return: 1.14% [7] 3. Inventory Factor - Weekly Return: 0.00% [7] - Monthly Return: 2.77% [7] 4. Price Spread Factor - Weekly Return: 0.00% [7] - Monthly Return: 1.50% [7] 5. Profit Factor - Weekly Return: 1.89% [7] - Monthly Return: 0.26% [7]