国投期货 商品量化CTA周度跟踪 国投期货研究院 金融工程组 2025/11/25 | 黑色动周期动量回升 | | --- | 商品本周维持空头信号主导,主要表现 为能化板块因子强度下降,黑色和农产 品板块有所回升。目前,截面偏强的板 块是有色,截面偏弱的是能源。具体来 看,黄金时序动量下降,白银的持仓量 变化不大,截面两端分化收窄。有色板 量 (1) 块持仓量因子边际上升,截面动量分化 收窄,截面上锡偏强,氧化铝偏弱。黑 CTA 色板块,焦煤焦炭持仓量维持低位,但 是短周期动量时序回升。能源板块短周 期动量因子回落,化工板块处于截面偏 强端。农产品方面,油粕截面分化扩收 窄,豆油长周期动量低于豆粕。 | | 上周收益(%) | 当月收益(%) | | --- | --- | --- | | 供給 | -0.02 | 1.86 | | 需求 | 0.03 | -1.00 | | 库存 | 0.38 | 0.27 | | 价差 | 0.00 | 0.00 | | 大类累加 | 0.26 | 0.15 | 用 醇 策略净值方面,上周供给因子走弱0. 02%,需求因子走高0.03%,库存因子走 强0.38%, 合成 ...

国技期货 商品量化CTA周度跟踪 国投期货研究院 金融工程组 2025/11/18 有色板块短周期动量下降 商品本周空头占比有所回升,主要表现 为贵金属和有色板块因子强度下降,黑 色板块有所回升。目前,截面偏强的板 块是黑色,截面偏弱的是有色和农产品 。具体来看,黄金时序动量下降,白银 的持仓量降幅较大,截面两端分化扩大 。有色板块持仓量因子边际下降,截面 动量分化收窄,截面上铅偏弱。黑色板 块,铁矿螺纹持仓量小幅下降,但是短 周期动量时序回升,螺纹截面偏强。能 源板块短周期动量因子回落,化工板块 处于截面偏强端。农产品方面,油粕截 面分化扩收窄,整体长周期动量小幅企 稳。 | | 上周收益(%) | 当月收益(%) | | --- | --- | --- | | सिंह | 0.57 | 2.43 | | 需求 | -0.40 | -0.40 | | 库存 | 0.58 | -0.32 | | 价差 | 0.00 | 0.00 | | 大类累加 | 0.45 | 0.55 | 用 策略净值方面,上周供给因子走高0. 57%,需求因子下行0.40%,库存因子走 强0.58%,合成因子上行0.45%,本周综 ...

- The report tracks high-frequency stock selection factors, including Price Range Factor, Price-Volume Divergence Factor, Regret Avoidance Factor, and Slope Convexity Factor, with their respective excess returns detailed for different periods [2][3][13] - Price Range Factor measures the activity level of stocks in different intraday price ranges, reflecting investor expectations for future stock trends. It shows strong predictive performance and stable results this year [3][11][17] - Price-Volume Divergence Factor evaluates the correlation between stock price and trading volume. Lower correlation indicates higher potential for future stock price increases. However, its performance has been unstable in recent years [3][22][24] - Regret Avoidance Factor examines the proportion and degree of stock rebound after being sold by investors, leveraging behavioral finance theories. It demonstrates stable excess returns out-of-sample, indicating significant influence of regret avoidance sentiment on stock price expectations [3][25][34] - Slope Convexity Factor is constructed using high-frequency order book data, analyzing the slope and convexity of order books to assess the impact of investor patience and supply-demand elasticity on expected returns. It includes High-Level Slope Factor and High-Level Convexity Factor [3][36][39] - A high-frequency "Gold" portfolio strategy was created by equally combining the three high-frequency factors, achieving an annualized excess return of 10.09% and an IR of 2.36 [3][43][46] - A combined high-frequency and fundamental factor strategy was developed, integrating high-frequency factors with fundamental factors like consensus expectations, growth, and technical factors. This strategy achieved an annualized excess return of 14.28% and an IR of 3.41 [3][47][50]

Core Insights - The participant "Riyue" achieved the eighth place in the quantitative group of a trading competition, with a net profit of 26.463 million yuan, marking a significant accomplishment in his trading journey [1] - "Riyue" emphasizes a focus on stable growth rather than high-risk, high-reward strategies, indicating a preference for risk management and steady returns [3][4] Group 1: Trading Strategy - "Riyue" primarily employs arbitrage and intraday trading strategies in the competition [2] - His trading approach integrates fundamental factors and unique indicators, enhancing adaptability and competitiveness in volatile market conditions [3] Group 2: Personal Development and Philosophy - The trading journey of "Riyue" faced challenges, particularly in 2018 when a singular strategy led to significant drawdowns, prompting a shift towards diversified investment and risk management [3] - "Riyue" believes that discipline is more important than intelligence in trading, advocating for a solid understanding of programming and market mechanisms for aspiring quantitative traders [4] Group 3: Future Outlook - "Riyue" sees potential in medium to low-frequency strategies that incorporate fundamental factors, expressing optimism about the future performance of CTA strategies [4]

Report Summary 1. Report Industry Investment Rating No relevant information provided. 2. Core Viewpoints - This week, the proportion of short positions in commodities has rebounded, mainly due to the decline in the factor strength of the black sector and the recovery in the agricultural products sector. Currently, the relatively strong sectors in the cross - section are non - ferrous metals and agricultural products, while the relatively weak ones are black metals and energy [3]. - The comprehensive signal for methanol this week is short, while for iron ore, it has turned to long, and for lead, it remains short, and for glass, it is long [4][13][15]. 3. Summary by Related Content Commodity Sector Analysis - **Black Sector**: The short - cycle momentum has declined. The positions of iron ore and rebar have decreased, indicating a cautious sentiment after the positive news is realized. Coking coal is relatively strong in the cross - section [3]. - **Non - ferrous Sector**: The position factor has marginally recovered, the long - cycle momentum continues to rise. Copper is relatively strong and alumina is relatively weak in the cross - section [3]. - **Energy and Chemical Sector**: The short - cycle momentum cross - section differentiation has expanded, and the chemical sector is at the short end of the cross - section [3]. - **Agricultural Products Sector**: There is a reversal in the cross - section. The short - cycle momentum of soybean oil has marginally decreased, while that of soybean meal has increased, and soybean meal is relatively strong in the short - term cross - section [3]. - **Precious Metals Sector**: The marginal time - series momentum of gold has recovered, the decline in the position of silver is small, and the differentiation at both ends of the cross - section has narrowed [3]. Strategy Net Value and Factor Analysis - **Methanol**: Last week, the supply factor increased by 0.98%, the demand factor decreased by 0.64%, the inventory factor decreased by 0.48%, and the synthetic factor weakened by 0.62%. The comprehensive signal this week is short. In terms of fundamental factors, the supply side is more bearish, the demand side is neutral to bearish, the inventory side is neutral, and the spread side is neutral [3][4]. - **Iron Ore**: Last week, the supply factor increased by 0.49%, the demand factor strengthened by 0.47%, the spread factor decreased by 0.09%, and the synthetic factor strengthened by 0.2%. The comprehensive signal this week is long. The supply side's bullish feedback has weakened, the demand side has turned to bullish feedback, the inventory side has turned to bullish feedback, and the spread side's bullish feedback has weakened [13]. - **Lead**: Last week, the supply factor increased by 0.49%, the demand factor strengthened by 0.47%, the spread factor decreased by 0.09%, and the synthetic factor strengthened by 0.2%. The comprehensive signal this week remains short. The supply side signal remains bearish, the inventory side signal remains neutral, and the spread side signal turns bearish [13]. - **Glass**: Last week, the inventory factor decreased by 0.05%, the spread factor weakened by 0.05%, and the synthetic factor decreased by 0.04%. The comprehensive signal this week is long. The supply side is neutral to bearish, the demand side is bullish, the inventory side remains bearish, and the spread side is bullish [15].

- The report tracks the performance of style factors, including liquidity, volatility, and nonlinear market capitalization, which showed strong long positions, while valuation, profitability, and leverage factors exhibited strong short positions [2][16] - Barra style factors are constructed using various financial and technical metrics, such as historical beta, logarithm of total market capitalization, historical excess return momentum, and volatility calculated as a weighted combination of historical excess return volatility, cumulative excess return deviation, and residual return volatility [14][15] - Liquidity factor is calculated as a weighted combination of monthly turnover rate (35%), quarterly turnover rate (35%), and annual turnover rate (30%) [15] - Profitability factor is constructed using a weighted combination of analyst forecast earnings-to-price ratio (68%), inverse cash flow ratio (21%), inverse PE ratio (11%), forecast long-term earnings growth rate (18%), and forecast short-term earnings growth rate (11%) [15] - Growth factor is calculated using a weighted combination of earnings growth rate (24%) and revenue growth rate (47%) [15] - Leverage factor is constructed using market leverage ratio (38%), book leverage (35%), and asset-liability ratio (27%) [15] - GRU models, including open1d, close1d, barra1d, and barra5d, are tracked for their multi-factor performance across different stock pools, showing varied results in terms of long-short returns [3][4][5][6] - GRU models demonstrated strong performance in certain configurations, such as close1d and barra5d, while open1d and barra1d showed weaker returns in specific periods [31][33] - Multi-factor portfolios underperformed this week, with relative excess returns against the CSI 1000 index showing a decline of 0.95% [33][34] - Barra5d model exhibited strong year-to-date performance, achieving an excess return of 5.81% against the CSI 1000 index [33][34] - Technical factors, including short-term and long-term momentum and volatility metrics, showed mixed results across different stock pools, with short-term metrics generally outperforming [19][21][24][26] - Basic financial factors, such as static financial metrics and growth-related metrics, generally showed negative long-short returns, with low-growth stocks outperforming [19][21][24][26] - GRU models' long-short returns varied across stock pools, with close1d and barra5d models showing strong positive returns, while open1d and barra1d models experienced slight pullbacks [31][33] - The liquidity factor achieved a weekly return of 1.39%, while the volatility factor returned 0.92% over the same period [17] - Profitability factor showed a weekly return of -1.31%, and valuation factor returned -1.53% [17] - Growth factor achieved a weekly return of 0.21%, while leverage factor returned -0.83% [17] - GRU models' weekly returns included -0.82% for open1d, 2.88% for close1d, -0.45% for barra1d, and 1.23% for barra5d [31] - Multi-factor portfolio weekly return was -0.95% relative to the CSI 1000 index [34]

- **Barra style factors**: The report tracks several style factors including Beta, Market Cap, Momentum, Volatility, Non-linear Market Cap, Valuation, Liquidity, Profitability, Growth, and Leverage. These factors are constructed using historical data and financial metrics such as turnover rates, earnings growth rates, and market leverage ratios. For example, the Beta factor represents historical beta, while the Valuation factor is calculated as the inverse of the price-to-book ratio. The formulas for constructing these factors include weighted combinations of metrics like turnover rates and earnings ratios [14][15][16] - **Factor performance tracking**: The report evaluates the recent performance of style factors across the market. Beta, Liquidity, and Momentum factors showed strong long positions, while Market Cap, Non-linear Market Cap, and Valuation factors performed better in short positions. The tracking methodology involves selecting stocks from the Wind All A pool, excluding ST stocks, suspended stocks, and newly listed stocks under 120 days. Long positions are taken in the top 10% of stocks with the highest factor values, and short positions in the bottom 10%, with equal weight allocation [16][19][20] - **Factor backtesting results**: The report provides detailed backtesting results for style factors. For example, Beta achieved a weekly return of 4.58%, while Market Cap showed a negative weekly return of -3.55%. Other factors like Momentum and Liquidity also demonstrated varied performance across different time horizons, such as one week, one month, and year-to-date. The report highlights the annualized returns for three-year and five-year periods for each factor [17][18][19] - **GRU factor performance**: GRU factors showed weaker performance overall, with only the barra1d model achieving positive returns. Other GRU models experienced drawdowns in their long-short portfolios. This indicates potential challenges in the effectiveness of GRU factors under current market conditions [20][25][29] - **Technical factors**: Technical factors such as 20-day Momentum, 60-day Momentum, and various volatility measures (e.g., 120-day Volatility) were tracked. These factors generally showed positive returns in long positions, particularly in high-volatility and high-momentum stocks. For example, 120-day Volatility achieved a weekly return of 5.92% in the CSI 300 stock pool [24][27][31] - **Fundamental factors**: Fundamental factors like ROA growth, ROC growth, and Net Profit growth were analyzed. In the CSI 300 stock pool, Net Profit growth achieved a weekly return of 2.51%, while ROA growth showed a return of 1.19%. These factors generally favored stocks with stable and strong growth metrics [23][25][30] - **Multi-factor portfolio performance**: The report evaluates the performance of multi-factor portfolios. The barra5d model outperformed the CSI 1000 index by 0.27% this week and achieved a year-to-date excess return of 5.91%. Other models showed mixed results, with some experiencing slight drawdowns. The multi-factor portfolio achieved a weekly excess return of 0.04% relative to the CSI 1000 index [8][33][34]

- The report tracks high-frequency stock selection factors, including price range factor, price-volume divergence factor, regret avoidance factor, and slope convexity factor, with their out-of-sample performance being generally strong[2][3][11] - **Price Range Factor**: Measures the activity of stock transactions within different intraday price ranges, reflecting investors' expectations of future stock trends. High price range transaction volume and transaction count factors are negatively correlated with future stock returns, while low price range average transaction volume factor is positively correlated with future stock returns. The factor is constructed by combining three sub-factors: high price 80% range transaction volume factor (VH80TAW), high price 80% range transaction count factor (MIH80TAW), and low price 10% range average transaction volume factor (VPML10TAW). These sub-factors are weighted at 25%, 25%, and 50%, respectively, and are industry market value neutralized[12][14][17] - **Price-Volume Divergence Factor**: Measures the correlation between stock price and trading volume. When price and volume diverge, the likelihood of future price increases is higher, while convergence indicates a higher likelihood of price decreases. The factor is constructed using high-frequency snapshot data to calculate the correlation between snapshot transaction price and snapshot trading volume, as well as snapshot transaction price and transaction count. Two sub-factors are used: price and transaction count correlation factor (CorrPM) and price and trading volume correlation factor (CorrPV). These sub-factors are equally weighted and industry market value neutralized[22][23][25] - **Regret Avoidance Factor**: Based on behavioral finance theory, this factor utilizes investors' regret avoidance emotions to construct effective stock selection factors. It examines the proportion and degree of stock price rebound after being sold by investors. The factor is constructed using transaction data to identify active buy/sell directions, with additional restrictions on small orders and closing trades to enhance performance. Two sub-factors are used: sell rebound proportion factor (LCVOLESW) and sell rebound deviation factor (LCPESW). These sub-factors are equally weighted and industry market value neutralized[26][32][35] - **Slope Convexity Factor**: Derived from the elasticity of supply and demand, this factor uses high-frequency snapshot data from limit order books to calculate the slope and convexity of buy and sell orders. The factor is constructed by aggregating order volume data by level and calculating the slope of buy and sell order books. Two sub-factors are used: low-level slope factor (Slope_abl) and high-level seller convexity factor (Slope_alh). These sub-factors are equally weighted and industry market value neutralized[36][41][43] - **High-frequency "Gold" Portfolio Strategy**: Combines the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with equal weights to construct an enhanced strategy for the CSI 1000 Index. The strategy includes mechanisms to reduce transaction costs, such as weekly rebalancing and turnover rate buffering. The strategy's annualized excess return is 10.20%, with an IR of 2.38 and maximum excess drawdown of 6.04%[44][46][47] - **High-frequency & Fundamental Resonance Portfolio Strategy**: Combines high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to construct an enhanced strategy for the CSI 1000 Index. The strategy's annualized excess return is 14.49%, with an IR of 3.46 and maximum excess drawdown of 4.52%[48][50][52]

Core Insights - The article provides a comprehensive analysis of market factors and their recent performance, highlighting the positive returns from liquidity and leverage factors, while noting negative returns from beta and growth factors [4][5]. Factor Performance - In the last two weeks, the liquidity factor and leverage factor yielded positive returns of 0.36% and 0.34% respectively, while the profitability factor achieved a positive return of 0.27%. Other factors like valuation and market capitalization also showed positive returns, albeit lower [4]. - For the CSI 300 stock pool, the best-performing factors included quarterly operating profit growth rate (2.54%) and quarterly net profit growth rate (2.36%), while total asset growth rate showed a negative return of -1.94% [5]. - In the CSI 500 stock pool, the top factors were the inverse of price-to-sales ratio (1.90%) and net profit gap (1.55%), with the worst performers being quarterly total asset gross margin (-2.12%) [5]. - The liquidity 1500 stock pool saw strong performance from the price-to-earnings ratio (2.19%) and inverse price-to-earnings ratio (2.09%), while total asset gross margin factors performed poorly [5]. Industry Factor Performance - Recent weeks showed a divergence in fundamental factors across industries, with net asset growth rate and net profit growth rate performing well in textiles, non-bank financials, and leisure services [6][7]. - Valuation factors, particularly the BP factor, achieved positive returns across multiple industries, while liquidity factors showed significant positive returns in the beauty and personal care sector [7]. Combination Tracking - The PB-ROE-50 combination achieved positive excess returns in the CSI 800 and overall market stock pools, with a notable excess return of 1.45% in the CSI 800 pool [8]. - Public and private fund research strategies yielded positive excess returns, with public research strategies outperforming the CSI 800 by 1.03% and private strategies by 1.89% [9]. Block Trade and Directed Issuance Tracking - The block trade combination underperformed relative to the CSI All Index, with an excess return of -0.57% [10]. - Similarly, the directed issuance combination also showed negative excess returns of -1.13% compared to the CSI All Index [11].

Report Summary 1. Report Industry Investment Rating - Not provided in the given content 2. Core Viewpoints - The proportion of short positions in commodities increased slightly this week, with the intensity of black and energy - chemical factors declining and the differentiation between non - ferrous and black sectors expanding. The cross - sectionally strong sectors are precious metals and non - ferrous metals, while the weak sectors are energy and black sectors [2]. - The comprehensive signals of strategies for methanol, float glass, iron ore, lead, and aluminum are neutral this week, except for iron ore which is bearish [3][6][9]. 3. Summary by Related Catalogs Commodity Market Overview - The proportion of short positions in commodities increased slightly this week, with the intensity of black and energy - chemical factors falling and the differentiation between non - ferrous and black sectors widening. Precious metals and non - ferrous metals are strong, while energy and black sectors are weak. Gold's time - series momentum rebounded significantly, but the internal difference between gold and silver continued to expand. The position factor of the non - ferrous sector increased marginally, with copper being strong. In the black sector, the momentum factor increased marginally, and iron ore was stronger than rebar in the term structure. In the energy - chemical sector, cross - sectional momentum was differentiated, with chemicals weaker than energy, and soda ash being weak. In the agricultural products sector, the positions of soybean oil and palm oil decreased, while that of soybean meal increased, and one can short the oil - meal ratio [2]. Methanol - Last week, the supply factor of the strategy net value weakened by 0.09%, the demand factor strengthened by 0.11%, the spread factor decreased by 0.09%, and the synthetic factor decreased by 0.07%. This week, the comprehensive signal is neutral. Fundamentally, the capacity utilization rate of domestic methanol decreased (bullish on the supply side); the average start - up of traditional downstream industries continued to decline, but the start - up of the olefin industry rebounded (neutral on the demand side); ports continued to accumulate inventory significantly (bearish on the inventory side); overseas methanol spot market prices and import profits released bearish signals, and the bullish strength of the spread side weakened and turned neutral [3]. Float Glass - Last week, the returns of major category factors were flat month - on - month, and this week, the comprehensive signal remains neutral. Fundamentally, the start - up load of float glass was flat compared with last week (neutral on the supply side); the transaction area of commercial housing in 30 large - and medium - sized Chinese cities decreased slightly (neutral on the demand side); the inventory of float glass enterprises decreased (slightly bullish on the inventory side); the profit of pipeline - gas - made float glass declined, and the bullish strength of the profit side weakened and remained neutral; the spread factor in the Shenyang - Shahe area released a bearish signal (slightly bearish on the spread side) [6]. Iron Ore - Last week, the supply factor of the strategy net value weakened by 0.21%, the spread factor decreased by 0.25%, and the synthetic factor decreased by 0.16%. This week, the comprehensive signal remains bearish. Fundamentally, the import volume in August increased, and the shipment volume from Brazil rose (bearish on the supply side); the consumption of sintering ore powder by steel mills increased, and the bullish feedback on the demand side strengthened, but the signal remained neutral; the inventory of major port iron ore continued to accumulate, and the bearish feedback on the inventory side strengthened, with the signal remaining neutral; the freight rate decreased, but the spot price increased, and the bearish feedback on the spread side weakened, with the signal remaining bearish [9]. Lead - Last week, the supply factor of the strategy net value weakened by 0.27%, the inventory factor increased by 0.04%, the spread factor decreased by 0.03%, and the synthetic factor decreased by 0.07%. This week, the comprehensive signal turned neutral. Fundamentally, the profit of SMM recycled lead was repaired, and the supply - side signal turned from bearish to neutral; LME lead registered warehouses and inventory continued to reduce, and the inventory - side signal remained neutral; the LME near - far - month spread widened, and the spread - side signal turned from neutral to bullish [9]. Aluminum - Last week, the supply factor of the strategy net value weakened, and the spread factor decreased by 0.03%, and the synthetic factor decreased by 0.07%. This week, the comprehensive signal is neutral. Fundamentally, the recovery speed of the supply side slowed down, and the supply - side signal turned from bearish to neutral [9].