Quantitative Models and Construction Methods 1. Model Name: A-Share Technical Scoring Model - **Model Construction Idea**: The model aims to fully explore technical information to depict market conditions, breaking down the abstract concept of "market state" into five dimensions: price, volume, volatility, trend, and crowding. It generates a comprehensive score ranging from -1 to +1 based on equal-weighted voting of signals from 10 selected indicators across these dimensions[9][14] - **Model Construction Process**: 1. Select 10 effective market observation indicators across the five dimensions[14] 2. Generate long/short timing signals for each indicator individually 3. Aggregate the signals through equal-weighted voting to form a comprehensive score between -1 and +1[9] - **Model Evaluation**: The model provides a straightforward and timely way for investors to observe and understand the market[9] 2. Model Name: Style Timing Model (Small-Cap Crowding) - **Model Construction Idea**: The model uses a crowding-based trend approach to time large-cap and small-cap styles. Crowding is measured by the difference in momentum and trading volume ratios between small-cap and large-cap indices[3][20] - **Model Construction Process**: 1. Calculate the momentum difference between the Wind Micro-Cap Index and the CSI 300 Index across 10/20/30/40/50/60-day windows 2. Compute the trading volume ratio between the two indices over the same windows 3. Derive crowding scores for small-cap and large-cap styles by averaging the highest and lowest quantiles of the above metrics, respectively 4. Combine the momentum and volume scores to obtain the final crowding score. A score above 90% indicates high small-cap crowding, while below 10% indicates high large-cap crowding[25] - **Model Evaluation**: The model effectively captures the dynamics of style crowding and provides actionable insights for timing decisions[20][25] 3. Model Name: Industry Rotation Model (Genetic Programming) - **Model Construction Idea**: The model applies genetic programming to directly extract factors from industry indices' price, volume, and valuation data, without relying on predefined scoring rules. It uses a dual-objective approach to optimize factor monotonicity and top-group performance[28][32][33] - **Model Construction Process**: 1. Use NSGA-II algorithm to optimize two objectives: |IC| (information coefficient) and NDCG@5 (normalized discounted cumulative gain for top 5 groups) 2. Combine weakly collinear factors using a greedy strategy and variance inflation factor to form industry scores 3. Select the top 5 industries with the highest multi-factor scores for equal-weight allocation, rebalancing weekly[32][34] - **Model Evaluation**: The dual-objective genetic programming approach enhances factor diversity and reduces overfitting risks, making it a robust tool for industry rotation[32][34] 4. Model Name: China Domestic All-Weather Enhanced Portfolio - **Model Construction Idea**: The model adopts a macro-factor risk parity framework, emphasizing risk diversification across underlying macro risk sources rather than asset classes. It actively overweights favorable quadrants based on macro momentum[39][42] - **Model Construction Process**: 1. Divide macro risks into four quadrants based on growth and inflation expectations: growth above/below expectations and inflation above/below expectations 2. Construct sub-portfolios within each quadrant using equal-weighted assets, focusing on downside risk 3. Adjust quadrant risk budgets monthly based on macro momentum indicators, which combine buy-side momentum from asset prices and sell-side momentum from economic forecast surprises[42] - **Model Evaluation**: The strategy effectively integrates macroeconomic insights into portfolio construction, achieving enhanced performance through active allocation adjustments[39][42] --- Model Backtesting Results 1. A-Share Technical Scoring Model - Annualized Return: 20.78% - Annualized Volatility: 17.32% - Maximum Drawdown: -23.74% - Sharpe Ratio: 1.20 - Calmar Ratio: 0.88[15] 2. Style Timing Model (Small-Cap Crowding) - Annualized Return: 28.46% - Maximum Drawdown: -32.05% - Sharpe Ratio: 1.19 - Calmar Ratio: 0.89 - YTD Return: 11.85% - Weekly Return: 5.25%[26] 3. Industry Rotation Model (Genetic Programming) - Annualized Return: 32.92% - Annualized Volatility: 17.43% - Maximum Drawdown: -19.63% - Sharpe Ratio: 1.89 - Calmar Ratio: 1.68 - YTD Return: 6.80% - Weekly Return: 3.37%[31] 4. China Domestic All-Weather Enhanced Portfolio - Annualized Return: 11.93% - Annualized Volatility: 6.20% - Maximum Drawdown: -6.30% - Sharpe Ratio: 1.92 - Calmar Ratio: 1.89 - YTD Return: 3.59% - Weekly Return: 1.54%[43] --- Quantitative Factors and Construction Methods 1. Factor Name: Small-Cap Crowding Factor - **Factor Construction Idea**: Measures the crowding level of small-cap style based on momentum and trading volume differences between small-cap and large-cap indices[20][25] - **Factor Construction Process**: 1. Calculate momentum differences and trading volume ratios for multiple time windows 2. Derive crowding scores by averaging the highest and lowest quantiles of these metrics 3. Combine momentum and volume scores to obtain the final crowding score[25] 2. Factor Name: Industry Rotation Factor (Genetic Programming) - **Factor Construction Idea**: Extracts factors from industry indices using genetic programming, optimizing for monotonicity and top-group performance[32][34] - **Factor Construction Process**: 1. Perform cross-sectional regression of standardized daily trading volume against daily price gaps to obtain residuals (Variable A) 2. Identify the trading day with the highest standardized volume in the past 9 days (Variable B) 3. Conduct time-series regression of Variables A and B over the past 50 days to obtain intercepts (Variable C) 4. Compute the covariance of Variable C and standardized monthly opening prices over the past 45 days[38] --- Factor Backtesting Results 1. Small-Cap Crowding Factor - YTD Return: 11.85% - Weekly Return: 5.25%[26] 2. Industry Rotation Factor (Genetic Programming) - Training Set IC: 0.340 - Factor Weight: 18.7% - YTD Return: 6.80% - Weekly Return: 3.37%[31][38]

1. Report Industry Investment Rating No relevant content provided. 2. Core Viewpoints of the Report - The commodity market rebounded after a correction last week, with an overall increase of 0.43%. Precious metals led the gains at 4.48%, followed by non - ferrous metals at 0.73%. Energy, chemicals, agricultural products, and black commodities declined by 0.06%, 1.23%, and 1.95% respectively. [2][7] - Due to uncertainties in the Fed's interest - rate cut path and the non - realization of expected domestic interest - rate cut policies, short - term liquidity is disrupted, and the commodity market may fluctuate. [2] - Different sectors have different short - term trends: precious metals may fluctuate; non - ferrous metals may remain stable; black commodities may fluctuate weakly; energy may fluctuate; chemical products face pressure; and agricultural products and oilseeds may fluctuate. [3][4] 3. Summary by Relevant Catalogs 3.1 Market Review - **Overall Performance**: The commodity market rose 0.43% last week. Precious metals led with a 4.48% increase, non - ferrous metals rose 0.73%, while energy, chemicals, agricultural products, and black commodities declined. [2][7] - **Top Gainers and Losers**: Silver, fuel oil, and copper had the highest increases at 6.63%, 4.36%, and 3.28% respectively. Rapeseed meal, coking coal, and coke had the largest declines at 4.64%, 2.88%, and 2.65% respectively. [2][7] - **Volatility**: The 20 - day average volatility of the commodity market continued to rise, especially for oilseeds. [2][7] - **Funds**: The overall market scale increased slightly, with net inflows in non - ferrous and precious metal sectors. [2][7] 3.2 Outlook - **Precious Metals**: PCE data met expectations, reducing pressure on the Fed's interest - rate cut rhythm. Uncertainties in interest - rate cut expectations may lead to short - term fluctuations. [3] - **Non - Ferrous Metals**: The stronger US dollar after the interest - rate meeting suppresses the sector, but domestic demand expectations and pre - holiday restocking support prices. The Grasberg copper mine accident affects supply and copper prices. The sector may remain stable in the short term. [3] - **Black Commodities**: Rebar demand improved, production stabilized, and inventory decreased. Steel mills have thin profits, and raw material supply is stable. The sector may fluctuate weakly in the short term. [3] - **Energy**: US inventory declines and geopolitical risks support oil prices. Geopolitical risks may rise around the National Day, but the rebound space is limited. The sector may fluctuate in the short term. [4] - **Chemical Products**: Polyester sales increased, reducing inventory pressure, but inventory accumulation and low profits continue to pressure the industry. [4] - **Agricultural Products**: Argentina's agricultural policy changes and China's increased soybean purchases reduce the supply gap risk next year. Palm oil is in a production - reduction cycle, and the oilseed sector may fluctuate in the short term. [4] 3.3 Commodity Fund Overview - **Gold ETFs**: Most gold ETFs had positive returns, with a combined scale increase of 1.83% and a combined trading volume increase of 4.52%. [39] - **Other ETFs**: The energy - chemical ETF had a 0.63% return, the soybean meal ETF had a - 1.81% return, the non - ferrous metal ETF had a 1.82% return, and the silver futures fund had a 5.72% return. [39]

Core Viewpoint - The Dachen Nonferrous ETF (159980.SZ) is experiencing upward momentum, supported by positive trends in the underlying nonferrous metal index and significant capital inflows, indicating a bullish outlook for the sector [1]. Group 1: Market Performance - As of August 12, 11:15, the Dachen Nonferrous ETF (159980.SZ) increased by 0.18% [1]. - The underlying index, the Nonferrous Metal Index (IMCI.SHF), has established a bullish trend on both daily and weekly levels [1]. - The ETF's latest scale reached 1.207 billion yuan, marking a three-month high [1]. Group 2: Capital Inflows - As of August 11, the Dachen Nonferrous ETF (159980.SZ) saw net capital inflows in 4 out of the last 5 trading days, totaling 79.6212 million yuan [1]. Group 3: Economic Context - Citic Construction Investment Securities noted that poor economic and employment data from the U.S., along with the nomination of a new Federal Reserve governor by Trump, has strengthened market expectations for a rate cut in September [1]. - The ongoing domestic "anti-involution" policy aims to optimize production factors, enhancing profitability across various sectors and improving market expectations, which is favorable for the transmission of rising metal prices to downstream industries [1]. - The valuation of the industrial metals sector is currently low, suggesting potential for upward correction [1].

Group 1 - The Dachen Nonferrous ETF (159980.SZ) has shown a bullish signal after three months, entering a relatively certain bullish cycle, with continuous fund inflows [1] - The ETF's underlying index includes six components: copper (approximately 50%), aluminum (approximately 16%), nickel (approximately 11%), tin (approximately 8%), lead (approximately 8%), and zinc (approximately 8%), all maintaining bullish signals at the daily level [1] - As of July 1, the Dachen Nonferrous ETF has seen a net inflow of 81.7191 million yuan over the past five days, reaching a new high in scale at 966 million yuan and a new high in shares at 565 million [1] Group 2 - Citic Securities analysis indicates that the refined copper market remains in a tight balance due to limited production guidance and declining TC/RC fees, with support for copper prices from China's economic stability and a soft landing in the U.S. economy [2] - Current market prices for copper are considered reasonable, with potential for further upward movement contingent on domestic macro policies and overseas economic recovery [2] - Expectations of rising inflation, interest rate cuts, and a slight decline in the U.S. dollar index may support copper prices in maintaining a strong oscillating trend [2]