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 2025/12/30 有色板块截面动量反转 商品本周多空占比变化不大,主要表现 有色和贵金属板块因子强度小幅回落, 农产品板块小幅上升。目前,截面偏强 的板块是有色和黑色,截面偏弱的是农 产品。具体来看,黄金时序动量小幅上 升,尽管持续处于偏强区间,但白银的 持仓量出现边际下降。有色板块短周期 动量继续上升,期限结构存在一定反 转,前期强势品种下降,截面上铜和氧 CTA 化铝均偏弱。黑色板块时序动量显示边 际回升,截面分化收窄,焦煤焦炭持仓 量仍然位于高位。能化板块短周期动量 因子回升,纯碱处于截面偏空端。农产 品方面,油粕截面分化收窄,整体动量 时序有所回升,但持仓量并没有显著变 代。 | | 上周收益(%) | 当月收益(%) | | --- | --- | --- | | 供給 | 0.02 | 0.02 | | 語求 | -0.03 | -0.31 | | 库存 | 0.05 | 0.51 | | 价差 | 0.00 | 0.00 | | 大类累加 | 0.04 | 0.05 | 用 策略净值方面,上周供给因子走强 0.02%,需求因子走弱 ...

Quantitative Models and Construction Methods 1. Model Name: High-frequency "Gold" Combination CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This model combines three types of high-frequency factors (price range, price-volume divergence, and regret avoidance) with equal weights to enhance the CSI 1000 Index. It aims to leverage the predictive power of high-frequency factors for stock selection[3][62][66] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with weights of 25%, 25%, and 50%, respectively[36][42][51] 2. Neutralize the combined factor by industry market capitalization[36][42][51] 3. Implement weekly rebalancing with a turnover buffer mechanism to reduce transaction costs[62][66] - **Model Evaluation**: The model demonstrates strong excess return performance both in-sample and out-of-sample, with a stable upward trend in the net value curve[39][66] 2. Model Name: High-frequency & Fundamental Resonance Combination CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This model integrates high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to improve the performance of multi-factor investment portfolios[67][69] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with fundamental factors (consensus expectations, growth, and technical factors) using equal weights[67][69] 2. Neutralize the combined factor by industry market capitalization[67][69] 3. Implement weekly rebalancing with a turnover buffer mechanism to reduce transaction costs[67][69] - **Model Evaluation**: The model shows improved performance metrics compared to the high-frequency-only strategy, with higher annualized returns and Sharpe ratios[69][71] --- Model Backtesting Results 1. High-frequency "Gold" Combination CSI 1000 Index Enhanced Strategy - Annualized Return: 9.63% - Annualized Volatility: 23.82% - Sharpe Ratio: 0.40 - Maximum Drawdown: 47.77% - Annualized Excess Return: 9.85% - Tracking Error: 4.32% - IR: 2.28 - Maximum Excess Drawdown: 6.04%[63][66] 2. High-frequency & Fundamental Resonance Combination CSI 1000 Index Enhanced Strategy - Annualized Return: 13.80% - Annualized Volatility: 23.44% - Sharpe Ratio: 0.59 - Maximum Drawdown: 39.60% - Annualized Excess Return: 13.93% - Tracking Error: 4.20% - IR: 3.31 - Maximum Excess Drawdown: 4.52%[69][71] --- Quantitative Factors and Construction Methods 1. Factor Name: Price Range Factor - **Factor Construction Idea**: Measures the activity of stock transactions in different price ranges during the day, reflecting investors' expectations of future stock trends[3][33] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate transaction volume and number of transactions in high (80%) and low (10%) price ranges[33][36] 2. Combine sub-factors with weights of 25%, 25%, and 50%[36] 3. Neutralize the combined factor by industry market capitalization[36] - **Factor Evaluation**: The factor shows strong predictive power and stable performance, with a steadily upward excess net value curve[39] 2. Factor Name: Price-Volume Divergence Factor - **Factor Construction Idea**: Measures the correlation between stock price and trading volume. Lower correlation indicates a higher probability of future price increases[3][40] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate the correlation between price and trading volume, as well as price and transaction count[40][42] 2. Combine sub-factors with equal weights[42] 3. Neutralize the combined factor by industry market capitalization[42] - **Factor Evaluation**: The factor's performance has been relatively flat in recent years but has shown good excess return this year[44] 3. Factor Name: Regret Avoidance Factor - **Factor Construction Idea**: Based on behavioral finance, this factor captures investors' regret avoidance emotions, such as the impact of selling stocks that later rebound[3][46] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify active buy/sell directions[46] 2. Construct sub-factors like sell rebound ratio and sell rebound deviation, and apply restrictions on small orders and closing trades[46] 3. Combine sub-factors with equal weights and neutralize by industry market capitalization[46][51] - **Factor Evaluation**: The factor shows stable upward performance and strong excess return levels out-of-sample[53] 4. Factor Name: Slope Convexity Factor - **Factor Construction Idea**: Captures the impact of order book slope and convexity on expected returns, reflecting investor patience and supply-demand elasticity[3][54] - **Factor Construction Process**: 1. Use order book data to calculate the slope of buy and sell orders at different levels[54] 2. Construct sub-factors for low-level slope and high-level convexity, and combine them[54][58] 3. Neutralize the combined factor by industry market capitalization[58] - **Factor Evaluation**: The factor has shown stable performance since 2016, with relatively flat out-of-sample results[61] --- Factor Backtesting Results 1. Price Range Factor - Annualized Excess Return: 4.90% - IR: 1.13 - Maximum Excess Drawdown: 1.89%[36][39] 2. Price-Volume Divergence Factor - Annualized Excess Return: 5.59% - IR: 1.29 - Maximum Excess Drawdown: 2.13%[42][44] 3. Regret Avoidance Factor - Annualized Excess Return: -2.62% - IR: -0.61 - Maximum Excess Drawdown: 1.69%[46][53] 4. Slope Convexity Factor - Annualized Excess Return: -10.40% - IR: -2.35 - Maximum Excess Drawdown: 2.42%[58][61]

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 2025/12/23 有色截面动量分化 商品本周多头占比小幅上升,主要表现 贵金属因子强度维持高位,农产品板块 小幅下降。目前,截面偏强的板块是贵 金属和有色,黑色和能源也位于中性以 上区间,截面偏弱的是农产品。具体来 看,黄金时序动量小幅上升,白银的持 仓量边际上升幅度更大,持续处于偏强 区间,截面两端分化扩大。有色板块短 周期动量回升,期限结构分化收窄,截 面上铜和锡均偏强。黑色板块,时序动 量显示边际回落,铁矿和螺纹持仓量维 持中性,焦煤焦炭持仓量仍然位于高位 。能化板块短周期动量因子回升,纯碱 处于截面偏空端。农产品方面,油粕截 面分化收窄,虽然时序动量层面下行趋 势边际减弱,但持仓量处于近期低位。 玻 寶 策略净值方面,上周供给因子上行 1.51%,需求因子走强1.62%,库存因子 走弱0.13%,价差因子走高0.29%,利润 因子走强0.21%，合成因子上行1.38%， 本周综合信号空头。基本面因子上,浮 法玻璃企业开工环比持平,供给端中 性;二线城市商品房成交数量增多,需 求端中性偏多;河北、湖北浮法玻璃企 业小幅累库,库存端多头强度 ...

国投期货 策略净值方面,需求因子走弱0.07%,库 存因子走高0.12%, 合成因子走强 0.05%,本周综合信号多头。基本面因子 上,进口甲醇到港量减少,供给端中性 偏多;甲醇制烯烃企业产能利用率下 降,酷酸装置开工有所提升,需求端中 性;上周甲醇港口以及内地均呈现去 库,库存端转为多头;甲醇区域价差因 子多头强度走弱,价差端中性。 | | | 动量时序 动量截面 期限结构 持仓量 | | | | --- | --- | --- | --- | --- | | 黑色板块 | 0 | 0. 09 | 0 | -0.08 | | 有色板駅 | 0. 05 | -0. 21 | 0. 52 | 1.13 | | 能化板块 | -0. 02 | 0.18 | 0. 37 | 0. 69 | | 农产品板块 | 0.13 | 0. 35 | 0. 41 | -0. 19 | | 股指板块 | -0. 71 | 0. 46 | -0. 63 | 1. 06 | | 员金屋板块 | 0.12 - | | | 0. 88 | 商品量化CTA周度跟踪 国投期货研究院 金融工程组 2025/12/16 农产品板块内部分化扩大 ...

- The report tracks the performance of high-frequency stock selection factors, including Price Range Factor, Price-Volume Divergence Factor, Regret Avoidance Factor, and Slope Convexity Factor. These factors are evaluated based on their excess returns and predictive capabilities[2][3][11] - **Price Range Factor**: This factor measures the activity of stock transactions in different price ranges during the day, reflecting investors' expectations for future stock trends. It includes sub-factors such as high-price range transaction volume (VH80TAW), high-price range transaction count (MIH80TAW), and low-price range average transaction volume (VPML10TAW). The factor shows a strong predictive effect and stable performance this year[3][12][14] - **Price-Volume Divergence Factor**: This factor evaluates the correlation between stock prices and trading volumes. A lower correlation indicates a higher likelihood of future price increases. Sub-factors include price-to-transaction count correlation (CorrPM) and price-to-volume correlation (CorrPV). The factor has shown relatively stable performance this year, despite a declining trend since 2020[3][20][22] - **Regret Avoidance Factor**: Based on behavioral finance, this factor examines the proportion and degree of stock price rebounds after being sold by investors. Sub-factors include sell-rebound proportion (LCVOLESW) and sell-rebound deviation (LCPESW). The factor demonstrates stable out-of-sample excess returns, indicating that regret avoidance sentiment significantly impacts stock price expectations[3][23][31] - **Slope Convexity Factor**: Derived from the elasticity of supply and demand, this factor uses order book data to calculate the slope and convexity of buy and sell orders. Sub-factors include low-level slope (Slope_abl) and high-level convexity (Slope_alh). The factor's performance has been relatively flat in recent years, with some fluctuations in recent weeks[3][32][35] - The report constructs two enhanced strategies: the "High-Frequency Gold" portfolio and the "High-Frequency & Fundamental Resonance" portfolio. The "High-Frequency Gold" portfolio combines the three high-frequency factors with equal weights, achieving an annualized excess return of 10.11% and an IR of 2.36. The "High-Frequency & Fundamental Resonance" portfolio integrates high-frequency factors with fundamental factors (e.g., consensus expectations, growth, and technical factors), achieving an annualized excess return of 14.21% and an IR of 3.39[3][39][44] - **Performance Metrics for High-Frequency Gold Portfolio**: Annualized return: 9.49%, Annualized volatility: 23.87%, Sharpe ratio: 0.40, Maximum drawdown: 47.77%, Annualized excess return: 10.11%, IR: 2.36, Maximum excess drawdown: 6.04%[40][43] - **Performance Metrics for High-Frequency & Fundamental Resonance Portfolio**: Annualized return: 13.66%, Annualized volatility: 23.49%, Sharpe ratio: 0.58, Maximum drawdown: 39.60%, Annualized excess return: 14.21%, IR: 3.39, Maximum excess drawdown: 4.52%[47][48]

1. Report Industry Investment Rating - Not provided in the content 2. Core Viewpoints of the Report - This week, the proportion of long positions in commodities decreased slightly. The factor intensity of the black and chemical sectors declined, while the non - ferrous sector increased slightly. The cross - sectionally stronger sector is non - ferrous, and the weaker ones are chemicals and agricultural products. The overall signal for commodities this week is a combination of long and short positions in different sectors and factors [3]. 3. Summary by Related Catalogs Commodity Overall Situation - The proportion of long positions in commodities decreased slightly this week. The factor intensity of the black and chemical sectors declined, and the non - ferrous sector increased slightly. The cross - sectionally stronger sector is non - ferrous, and the weaker ones are chemicals and agricultural products [3]. Specific Sector Analysis Non - ferrous Metals - Gold's time - series momentum declined, silver's trading volume remained at a high level, and the cross - sectional divergence widened. The short - term momentum of the non - ferrous sector increased marginally, the cross - sectional momentum divergence narrowed, and copper and zinc were strong while tin was weak in the cross - section [3]. Black Metals - The term structure showed narrowing divergence. The trading volumes of coking coal and coke remained at low levels, and the short - term momentum of iron ore reversed and declined [3]. Energy and Chemicals - The long - term momentum factor declined, and ethylene glycol was at the short end of the cross - section [3]. Agricultural Products - The cross - sectional divergence of oilseeds and meals narrowed, and the trading volume of soybean oil decreased marginally [3]. Strategy Net Worth and Fundamental Factors Commodities - The demand factor weakened by 0.01%, the inventory factor increased by 0.36%, the synthetic factor strengthened by 0.01%, and the comprehensive signal this week was short. For methanol, the domestic production capacity utilization rate was flat, the demand side's long - position strength weakened to neutral, the inventory side signaled short - positions, and the spread side was slightly long [3]. Float Glass - Last week, the inventory factor decreased by 0.39%, the profit factor strengthened by 0.74%, and this week's comprehensive signal was long. The supply, demand, and inventory sides signaled long - positions, the spread side was neutral, and the profit side continued to signal short - positions [9]. Iron Ore - Last week, the supply factor decreased by 0.22%, the comprehensive factor weakened by 0.04%, and this week's comprehensive signal changed from long to short. The supply, demand, inventory, and spread sides all had changes in their signals, with the overall trend turning bearish [11]. Lead - Last week, the supply factor increased by 0.02%, the demand factor weakened by 0.46%, the inventory factor decreased by 0.5%, the spread factor weakened by 0.33%, the synthetic factor decreased by 0.32%, and this week's comprehensive signal remained short. The supply side's long - position feedback further weakened, the inventory side's short - position feedback weakened, and the spread side's short - position signal intensity increased [11].

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 ...

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 precious metals and non - ferrous sectors, while the black sector has recovered. The black sector is relatively strong in cross - section, while the non - ferrous and agricultural sectors are relatively weak [3]. - The comprehensive signals of methanol, float glass, iron ore, and lead have different trends this week, with methanol and float glass showing long signals, iron ore showing a short signal, and lead maintaining a short signal [5][8][11]. 3. Summary by Related Content Commodity Market Overview - In the precious metals sector, the time - series momentum of gold has declined, and the trading volume of silver has decreased significantly, with an expanding divergence at both ends of the cross - section. In the non - ferrous sector, the position factor has decreased marginally, the cross - section momentum divergence has narrowed, and lead is relatively weak in the cross - section. In the black sector, the positions of iron ore and rebar have decreased slightly, but the short - term momentum time - series has recovered, and rebar is relatively strong in the cross - section. In the energy sector, the short - term momentum factor has declined, and the chemical sector is at the relatively strong end of the cross - section. In the agricultural products sector, the cross - section divergence of oil and meal has narrowed, and the overall long - term momentum has stabilized slightly [3]. Strategy Net Value and Fundamental Factors - **Methanol**: Last week, the supply factor increased by 0.57%, the demand factor decreased by 0.40%, the inventory factor strengthened by 0.58%, and the synthetic factor increased by 0.45%. This week, the comprehensive signal has turned long. In terms of fundamental factors, the supply side has turned neutral, the demand side has weakened from a long signal to neutral, the inventory side is long, and the spread side is slightly bearish [5]. - **Float Glass**: Last week, the profit factor increased by 0.05%, the spread factor weakened by 0.36%, and the synthetic factor decreased by 0.26%. This week, the comprehensive signal is long. The supply side is neutral, the demand side is slightly bearish, the inventory side is long, and the spread side has weakened significantly from a long signal to neutral [8]. - **Iron Ore**: Last week, the supply factor decreased by 0.2%, the inventory factor strengthened by 0.3%, and the comprehensive factor increased by 0.06%. This week, the comprehensive signal has turned short. The supply side remains bearish, the demand side has turned bearish, the inventory side has turned neutral, and the spread side remains neutral [11]. - **Lead**: Last week, the supply factor decreased by 0.18%, the demand factor weakened by 0.17%, the inventory factor decreased by 0.16%, the spread factor weakened by 0.07%, and the synthetic factor decreased by 0.14%. This week, the comprehensive signal remains short. The supply side has turned neutral, the inventory side remains bearish, and the spread side remains bearish [11].

- 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]