Report Summary 1. Report Industry Investment Rating No information about the industry investment rating is provided in the report. 2. Core View of the Report The report presents the market conditions of treasury bond futures on May 27, 2025, including price changes, trading volume, and related factors, and also mentions the situation of the equity market, money market, and macro - industry news, indicating that the expectations for treasury bond futures are limited and the market is in a state of waiting for a direction [1]. 3. Summary by Related Catalogs 3.1 Treasury Bond Futures Market Conditions - On May 27, treasury bond futures closed down across the board, with the 30 - year, 10 - year, 5 - year, and 2 - year main contracts down 0.26%, 0.11%, 0.03%, and 0.02% respectively [1]. - The treasury bond futures index was - 0.12. The volume - price factor was bullish, and the fundamental factor was bearish. Without leverage, the cumulative returns of the strategy in the past 20, 60, 120, and 240 days were 0.04%, - 0.53%, 0.14%, and 1.27% respectively [1]. - The trading volume of the 2 - year, 5 - year, 10 - year, and 30 - year main contracts was 32,028, 43,924, 58,575, and 62,401 respectively, and the open interest was 104,798, 128,934, 165,848, and 92,091 respectively [3]. - The IRR of the 2 - year, 5 - year, 10 - year, and 30 - year active CTD bonds was 1.95%, 2.07%, 1.88%, and 3.58% respectively, and the current R007 was about 1.6794% [3]. 3.2 Equity Market Conditions - On May 27, the equity market oscillated and adjusted throughout the day, with the ChiNext Index leading the decline. The Shanghai Composite Index fell 0.18%, the Shenzhen Component Index fell 0.61%, and the ChiNext Index fell 0.68%. The market hotspots were scattered, and the number of rising and falling stocks was basically the same [1]. 3.3 Money Market Conditions - On May 27, the overnight shibor was 1.4520%, down 5.4bp from the previous trading day; the 7 - day shibor was 1.5980%, up 1.9bp; the 14 - day shibor was 1.6670%, down 2.1bp; the 1 - month shibor was 1.6140%, up 0.2bp [2]. - The bank - to - bank pledged repurchase market traded 2.4 billion yuan, an increase of 1.62%. The overnight rate closed at 1.45%, up 1bp from the previous trading day; the 7 - day rate closed at 1.70%, up 19bp; the 14 - day rate closed at 1.65%, down 4bp; the 1 - month rate closed at 1.60%, down 6bp [4]. 3.4 Bond Yield Curve Conditions - The treasury bond yield curve rose by 0.29 - 1.10BP (the 2 - year yield rose 0.29BP to 1.47%; the 5 - year yield rose 0.78BP to 1.57%; the 10 - year yield rose 0.38BP to 1.72%; the 30 - year yield rose 1.10BP to 1.90%). The credit bond yield curve showed mixed changes [4]. 3.5 Net Long Position Changes by Institution Type - The daily net long position of private funds decreased by 3.27%, foreign capital decreased by 2.46%, and wealth management subsidiaries decreased by 2.4%. The weekly net long position of private funds decreased by 5.28%, foreign capital decreased by 4.11%, and wealth management subsidiaries decreased by 3.69% [6]. 3.6 Macro and Industry News - On May 27, the central bank conducted 448 billion yuan of 7 - day reverse repurchase operations at an operating rate of 1.40%, unchanged from before. There were 357 billion yuan of reverse repurchases due on the same day [8]. 3.7 Trend Intensity - The trend intensity of treasury bond futures was 0, indicating a neutral state [9].

Group 1: ETF Rotation Strategy Performance - The ETF rotation strategy, constructed using GBDT+NN machine learning factors, has shown excellent out-of-sample performance with an IC value of 44.48% and a long position excess return of 0.73% last week [3][14] - The annualized excess return of the strategy is 11.88%, with a maximum drawdown of 17.31% [17][18] - Recent performance includes an excess return of 0.20% last week, 1.64% for the month, and 0.35% year-to-date [18][20] Group 2: High-Frequency Factor Overview - Various high-frequency factors have demonstrated strong overall performance, with the price range factor showing a long position excess return of 4.93% year-to-date, while the regret avoidance factor has underperformed with a return of 0.27% [4][22] - The price range factor measures the activity level of stocks within different price ranges, indicating investor expectations for future price movements [5][25] - The regret avoidance factor reflects the impact of investor emotions on stock price expectations, showing stable out-of-sample excess returns [5][37] Group 3: High-Frequency and Fundamental Factor Combination - A combined strategy of high-frequency and fundamental factors has been developed, yielding an annualized excess return of 14.76% with a maximum drawdown of 4.52% [6][59] - The strategy has shown stable out-of-sample performance, with a year-to-date excess return of 3.74% [60] - The integration of fundamental factors with high-frequency factors has improved the performance metrics of the strategy [57][59]

Quantitative Models and Construction 1. Model Name: GRU - **Model Construction Idea**: The GRU model is used to predict future stock returns based on historical data and incorporates various technical and fundamental factors[3][4][5] - **Model Construction Process**: The GRU model is trained on historical data to predict future returns. It uses a recurrent neural network structure, specifically the Gated Recurrent Unit (GRU), to capture sequential dependencies in time-series data. The model is applied to different stock pools (e.g., CSI 300, CSI 500, CSI 1000) and is evaluated based on its long-short portfolio returns[3][4][5] - **Model Evaluation**: The GRU model demonstrates strong performance in predicting returns, with positive long-short portfolio returns in most cases. However, its performance varies across different stock pools and time horizons[3][4][5] 2. Model Name: Barra5d - **Model Construction Idea**: The Barra5d model predicts future returns by incorporating short-term technical factors and ensuring style neutrality[6][25] - **Model Construction Process**: The Barra5d model uses a combination of short-term technical indicators (e.g., 5-day momentum) and applies style-neutral constraints to ensure that the predictions are not biased by market-wide factors. The model is tested on various stock pools, including CSI 300, CSI 500, and CSI 1000[6][25] - **Model Evaluation**: The Barra5d model shows strong performance, particularly in the CSI 500 and CSI 1000 stock pools, with weekly long-short portfolio returns exceeding 3% in some cases[6][25] 3. Model Name: Open1d - **Model Construction Idea**: The Open1d model focuses on short-term price movements and is designed to capture immediate market reactions[19][21][23] - **Model Construction Process**: The Open1d model uses one-day price changes as its primary input and applies machine learning techniques to predict short-term returns. It is evaluated based on its ability to generate excess returns in long-short portfolios[19][21][23] - **Model Evaluation**: The Open1d model has shown strong performance year-to-date, with cumulative excess returns of 4.24% relative to the CSI 1000 index[19][21][23] --- Model Backtesting Results 1. GRU Model - Weekly long-short portfolio return: Positive in most cases, with variations across stock pools[3][4][5] - CSI 500 stock pool: Weekly long-short return > 3%[5] - CSI 1000 stock pool: Performance is mixed, with some models (e.g., Barra1d, Barra5d) performing well[6][25] 2. Barra5d Model - Weekly long-short portfolio return: > 3% in the CSI 500 stock pool[6][25] - Strong performance in the CSI 1000 stock pool, particularly in predicting style-neutral future returns[6][25] 3. Open1d Model - Year-to-date excess return: 4.24% relative to the CSI 1000 index[19][21][23] - Weekly long-short portfolio return: Mixed, with some weeks showing slight negative returns[19][21][23] --- Quantitative Factors and Construction 1. Factor Name: Beta - **Factor Construction Idea**: Measures the historical sensitivity of a stock's returns to market returns[15] - **Factor Construction Process**: Beta is calculated as the slope of the regression line between a stock's returns and market returns over a specified historical period[15] 2. Factor Name: Momentum - **Factor Construction Idea**: Captures the tendency of stocks with strong past performance to continue performing well[15] - **Factor Construction Process**: Momentum is calculated as the mean of historical excess returns over a specified period[15] 3. Factor Name: Volatility - **Factor Construction Idea**: Measures the variability of a stock's returns over time[15] - **Factor Construction Process**: $ \text{Volatility} = 0.74 \times \text{Historical Excess Return Volatility} + 0.16 \times \text{Cumulative Excess Return Deviation} + 0.1 \times \text{Residual Return Volatility} $ - Historical Excess Return Volatility: Standard deviation of excess returns - Cumulative Excess Return Deviation: Deviation of cumulative excess returns from the mean - Residual Return Volatility: Standard deviation of residual returns after removing market effects[15] 4. Factor Name: Liquidity - **Factor Construction Idea**: Measures the ease of trading a stock based on turnover rates[15] - **Factor Construction Process**: $ \text{Liquidity} = 0.35 \times \text{Monthly Turnover Rate} + 0.35 \times \text{Quarterly Turnover Rate} + 0.3 \times \text{Annual Turnover Rate} $ - Turnover Rate: Ratio of trading volume to total shares outstanding[15] --- Factor Backtesting Results 1. Beta Factor - Weekly long-short portfolio return: Strong performance in recent weeks[16] 2. Momentum Factor - Weekly long-short portfolio return: Positive for long-term momentum (e.g., 120-day), negative for short-term momentum (e.g., 20-day)[18][23][25] 3. Volatility Factor - Weekly long-short portfolio return: Positive for long-term volatility (e.g., 120-day), mixed for short-term volatility (e.g., 20-day)[18][23][25] 4. Liquidity Factor - Weekly long-short portfolio return: Strong performance, particularly in high-turnover stocks[16]

Group 1: Report Summary - The report is a weekly tracking of commodity quantitative CTA by Guotou Futures Research Institute's Financial Engineering Group [1][2] Group 2: Methanol Analysis - Last week, the supply factor of methanol increased by 0.08%, the demand factor strengthened by 0.09%, the inventory factor rose by 0.08%, and the synthetic factor decreased by 0.08%. This week's comprehensive signal is neutral [2][3] - The domestic methanol operating rate changed from an increase to a decrease, with the supply side being neutral; the MTO operating rate in the Jiangsu - Zhejiang region decreased last week, turning the demand side bearish; the inventory of production enterprises was low and continued to be destocked, with the inventory side remaining bullish; the methanol futures price and the Inner Mongolia - Shandong regional spread factor released a bearish signal, with the spread side being neutral to bearish [3] Group 3: Float Glass Analysis - Last week, the supply factor of float glass decreased by 0.20%, the inventory factor strengthened by 0.03%, the spread factor remained unchanged, and the synthetic factor decreased by 0.03%. This week's comprehensive signal is bearish [6] - The weekly output of float glass enterprises decreased slightly, with the supply side being neutral; the real - estate transaction volume in first - and second - tier cities decreased, but increased in third - tier cities, with the demand side being neutral; the inventory of float glass enterprises was at a high level and slightly increased, with the inventory side remaining bearish; the main continuous basis released a bearish signal, with the spread side being neutral to bearish; the pre - tax gross profit of pipeline - gas - made float glass turned from negative to positive, with the profit side being neutral to bullish [6] Group 4: Iron Ore Analysis - Last week, the supply factor of iron ore weakened by 0.23%, the demand factor weakened by 0.16%, the inventory factor weakened by 0.24%, the spread factor strengthened by 0.05%, and the synthetic factor weakened by 0.13%. This week's comprehensive signal remains bearish [7] - The SMM domestic lead concentrate price continued to rise, turning the supply side signal bullish; the export volume of Chinese lead alloys in April decreased compared to March, with the demand side signal remaining bearish; the LME lead registered warehouse receipts decreased, with the inventory side remaining neutral to bearish; the SMM domestic lead concentrate processing fee continued to decline, with the spread side signal remaining bearish [7] Group 5: Lead Analysis - Last week, the supply factor of lead weakened by 0.23%, the demand factor weakened by 0.16%, the inventory factor weakened by 0.24%, the spread factor strengthened by 0.05%, and the synthetic factor weakened by 0.13%. This week's comprehensive signal remains bearish [7] - The SMM domestic lead concentrate price continued to rise, turning the supply side signal bullish; the export volume of Chinese lead alloys in April decreased compared to March, with the demand side signal remaining bearish; the LME lead registered warehouse receipts decreased, with the inventory side remaining neutral to bearish; the SMM domestic lead concentrate processing fee continued to decline, with the spread side signal remaining bearish [7]

Quantitative Models and Construction 1. Model Name: GRU Model - **Model Construction Idea**: The GRU model is a machine learning-based quantitative model designed to capture complex patterns in stock price movements and factor relationships[3][19][23] - **Model Construction Process**: The GRU (Gated Recurrent Unit) model is trained on historical stock data, incorporating various financial and technical indicators as input features. The model uses a recurrent neural network structure to process sequential data, enabling it to predict stock price trends and factor performance. Specific details on the training parameters or architecture were not provided in the report[3][19][23] - **Model Evaluation**: The GRU model demonstrated strong performance in multi-factor strategies and outperformed other models in certain market conditions, particularly in the small-cap stock universe[7][29][33] 2. Model Name: Barra1d Model - **Model Construction Idea**: The Barra1d model is a factor-based quantitative model that utilizes the Barra risk model framework to analyze and predict stock returns[3][19][23] - **Model Construction Process**: The Barra1d model incorporates style factors such as size, value, momentum, and volatility, along with industry and country factors. It uses historical data to estimate factor exposures and risk premiums, which are then applied to construct long-short portfolios[3][19][23] - **Model Evaluation**: The Barra1d model experienced significant drawdowns in certain market conditions, particularly in the CSI 500 and CSI 1000 stock universes, indicating sensitivity to market volatility[5][26][29] 3. Model Name: Open1d Model - **Model Construction Idea**: The Open1d model focuses on short-term price movements and factor dynamics, leveraging daily open prices as a key input[3][19][29] - **Model Construction Process**: The Open1d model uses daily open prices and other technical indicators to construct long-short portfolios. It emphasizes short-term momentum and volatility factors to capture rapid market movements[3][19][29] - **Model Evaluation**: The Open1d model achieved strong performance, with its excess returns reaching new highs for the year, particularly in the CSI 1000 stock universe[6][29][33] 4. Model Name: Close1d Model - **Model Construction Idea**: The Close1d model is similar to the Open1d model but focuses on daily closing prices to capture end-of-day market dynamics[3][19][29] - **Model Construction Process**: The Close1d model uses daily closing prices and technical indicators to construct long-short portfolios. It emphasizes factors such as closing momentum and volatility to predict stock movements[3][19][29] - **Model Evaluation**: The Close1d model demonstrated strong performance, particularly in the CSI 1000 stock universe, with consistent positive excess returns[6][29][33] --- Model Backtesting Results 1. GRU Model - Weekly excess return: 0.46%-1.43% relative to the CSI 1000 index[7][33][34] - Year-to-date excess return: Not explicitly provided 2. Barra1d Model - Weekly excess return: 0.46%[33][34] - Year-to-date excess return: 2.10%[34] 3. Open1d Model - Weekly excess return: 1.43%[33][34] - Year-to-date excess return: 3.90%[34] 4. Close1d Model - Weekly excess return: 1.38%[33][34] - Year-to-date excess return: 1.87%[34] 5. Multi-Factor Strategy - Weekly excess return: 1.01%[33][34] - Year-to-date excess return: 2.15%[34] --- Quantitative Factors and Construction 1. Factor Name: Momentum - **Factor Construction Idea**: Momentum factors are designed to capture the tendency of stocks with strong past performance to continue performing well in the short term[15][16][19] - **Factor Construction Process**: - Historical excess return mean: $0.74 \times \text{volatility of excess returns} + 0.16 \times \text{cumulative excess return deviation} + 0.1 \times \text{residual return volatility}$[15] - **Factor Evaluation**: Momentum factors showed strong performance in the current market, particularly in high-volatility environments[3][19][23] 2. Factor Name: Valuation - **Factor Construction Idea**: Valuation factors aim to identify undervalued stocks based on financial ratios such as price-to-book (P/B) and price-to-earnings (P/E)[15][16][19] - **Factor Construction Process**: - Valuation factor: $1 / \text{P/B ratio}$[15] - **Factor Evaluation**: Valuation factors demonstrated strong performance in the small-cap stock universe, particularly in the CSI 1000 index[6][29][33] 3. Factor Name: Growth - **Factor Construction Idea**: Growth factors focus on identifying stocks with high earnings and revenue growth potential[15][16][19] - **Factor Construction Process**: - Growth factor: $0.24 \times \text{earnings growth rate} + 0.47 \times \text{revenue growth rate}$[15] - **Factor Evaluation**: Growth factors performed well across multiple stock universes, particularly in high-growth environments[3][19][23] 4. Factor Name: Volatility - **Factor Construction Idea**: Volatility factors measure the risk associated with stock price fluctuations, often used to identify low-risk investment opportunities[15][16][19] - **Factor Construction Process**: - Volatility factor: $0.74 \times \text{historical return volatility} + 0.16 \times \text{cumulative return deviation} + 0.1 \times \text{residual return volatility}$[15] - **Factor Evaluation**: Volatility factors showed mixed performance, with low-volatility stocks underperforming in certain market conditions[5][26][29] --- Factor Backtesting Results 1. Momentum Factor - Weekly excess return: 0.89%[17][19][23] - Year-to-date excess return: 42%[17][19][23] 2. Valuation Factor - Weekly excess return: 1.68%[17][19][23] - Year-to-date excess return: 1.14%[17][19][23] 3. Growth Factor - Weekly excess return: 1.20%[17][19][23] - Year-to-date excess return: 4.03%[17][19][23] 4. Volatility Factor - Weekly excess return: 0.16%[17][19][23] - Year-to-date excess return: 8.10%[17][19][23]

Group 1: ETF Rotation Strategy Tracking - The ETF rotation strategy, constructed using GBDT+NN machine learning factors, has shown strong performance in out-of-sample testing, with an annualized excess return of 11.90% and a maximum drawdown of 17.31% [2][12][17] - Recent performance indicates a weekly excess return of 0.77% and a monthly excess return of 1.10%, while the year-to-date excess return stands at -0.19% [20][24] - The strategy's information ratio is 0.68, reflecting its effectiveness in generating excess returns relative to risk [24] Group 2: High-Frequency Factor Overview - High-frequency factors have demonstrated overall strong performance, with the price range factor yielding a year-to-date excess return of 4.79% and the price-volume divergence factor achieving 10.08% [3][20] - The regret avoidance factor has underperformed with a year-to-date excess return of -0.56%, while the slope convexity factor has shown a year-to-date excess return of -3.64% [3][20] - The high-frequency "gold" combination strategy has an annualized excess return of 10.69% and a maximum drawdown of 6.04% [5][60] Group 3: High-Frequency Factor Performance Tracking - The price range factor measures the activity level of stocks within different price ranges, showing strong predictive power and stable performance this year [4][28] - The price-volume divergence factor assesses the correlation between stock price and trading volume, with recent performance indicating a mixed stability [4][39] - The regret avoidance factor reflects investor behavior, showing stable out-of-sample excess returns, while the slope convexity factor illustrates the impact of order book elasticity on expected returns [4][51] Group 4: Combined Strategies Performance - The high-frequency and fundamental resonance combination strategy has an annualized excess return of 14.98% and a maximum drawdown of 4.52% [5][64] - Recent performance for this combined strategy includes a weekly excess return of 0.63% and a monthly excess return of 2.00%, with a year-to-date excess return of 4.69% [67]