Core Insights - The report emphasizes the importance of monitoring volume and price indicators to respond to unpredictable major events like the US-China tariff negotiations, focusing on market expectations and risk-reward balance [2][5] - Key volume-price factors that signaled market movements before critical dates include KUP1, cors, HIGH0, and KSFT1 [5][6] Phase 1: Accumulation of Bullish Forces Before April 3 - The market from late March to early April 2025 can be divided into "consolidation" and "breakout" phases, with bullish sentiment gradually building up [6][7] - On March 28 to April 2, the market showed signs of bullish testing, with T2506 fluctuating between 107.3 and 108.0, indicating tentative bullish entry without significant volume increase [6][7] - On April 3, the announcement of a dual tariff system by the US led to a bullish breakout, with T2506 opening significantly higher and surpassing previous highs [6][8] Phase 2: Marginal Exit of Speculative Funds Before May 12 - In early May 2025, the bond futures market exhibited a "price increase with volume decrease" characteristic, indicating cautious market sentiment ahead of tariff negotiation results [11][12] - On May 7, indicators KUP1, HIGH0, and KSFT1 signaled a reduction in bullish momentum, reflecting a shift in market dynamics [11][12] - By May 9, the cors indicator confirmed the marginal exit of bullish forces, suggesting a potential increase in downside risk [12][13] Current Indicator Performance - As of late May, the US tariff policy remains uncertain, and the domestic bond market is in a narrow fluctuation pattern, highlighting the need for timely market observation [15] - The KSFT1 indicator has issued a bullish signal, indicating a release of bearish sentiment, while other indicators have yet to confirm further changes in market dynamics [15]

Core Viewpoint - The article emphasizes the importance of monitoring market expectations and institutional movements in response to unpredictable major events like the US-China tariff disputes, suggesting that strategies should be adjusted flexibly to balance risk and return [1]. Summary by Sections Phase 1: Accumulation of Bullish Forces Before April 3 Tariff Implementation - The market from late March to early April 2025 can be divided into "consolidation" and "breakout" phases, where indicators such as KUP1, cors, HIGH0, and KSFT1 effectively reflected the accumulation of bullish forces, signaling an impending market turning point [2]. - HIGH0 was the first indicator to issue a bullish signal [2]. - The weak fluctuation in treasury futures alongside rising volume-price correlation indicated a weakening of bearish energy, with cors subsequently issuing a bullish signal [2]. - KUP1 and KSFT1 indicators confirmed the bullish turning point simultaneously [2]. Phase 2: Marginal Exit of Speculative Funds Before May 12 Negotiation Results - In early May 2025, the treasury futures market exhibited characteristics of "price increase with volume decrease," reflecting the marginal withdrawal of speculative funds [2]. - On May 7, KUP1, HIGH0, and KSFT1 simultaneously indicated a contraction in bullish momentum [2]. - The contraction of bullish forces was further confirmed by cors on May 9, with a release of profit-taking demand leading to a subsequent reduction in trading volume [2]. Current Situation and Recommendations - As of late May, the US tariff policy remains volatile, and the intensification of the Russia-Ukraine conflict alongside Middle Eastern risks creates a turbulent overseas environment; however, the domestic bond market remains in a narrow fluctuation pattern [3]. - The KSFT1 indicator has issued a mildly bullish signal, suggesting a release of bearish sentiment, while other indicators have yet to confirm further changes in bullish and bearish forces [3]. - Historical backtesting shows that KSFT1 broke below the threshold on May 28 and May 30, indicating that bearish sentiment may have been sufficiently released, issuing a mildly bullish signal [3]. - Continuous monitoring of the aforementioned volume-price indicators is recommended to capture the movements of major institutional investors [3].

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].

Quantitative Models and Construction Methods 1. Model Name: Volume-Price Industry Rotation Strategy - **Model Construction Idea**: The strategy is based on six dimensions of volume-price factors, including momentum, trading volatility, turnover rate, long-short comparison, volume-price divergence, and volume-amplitude alignment. These factors are tested on a single-factor basis at the monthly frequency for the CSI Level-1 industries, resulting in 11 effective and logically strong industry factors[6] - **Model Construction Process**: 1. Construct 11 volume-price factors based on the six dimensions mentioned above 2. At the end of each month, select the top five industries with the highest composite factor scores from the CSI Level-1 industries (excluding "Comprehensive" and "Comprehensive Finance") 3. Apply equal weighting within factors and equal weighting across industries to form the final strategy[7] - **Model Evaluation**: The model demonstrates strong logical consistency and effectiveness in identifying outperforming industries[6] --- Quantitative Factors and Construction Methods 1. Factor Name: Second-Order Momentum - **Factor Construction Idea**: Measures the exponential weighted moving average (EWMA) of the closing price relative to its historical mean[7] - **Factor Construction Process**: $ \text{Second-Order Momentum} = \text{Close}_t \cdot \text{EWMA}(\text{Close}_{t-\text{window1}:t}) - \text{mean}(\text{Close}_{t-\text{window1}:t}) $ - Parameters: "Close" represents the closing price, "window1" defines the lookback period[7] 2. Factor Name: Momentum Term Spread - **Factor Construction Idea**: Captures the difference in momentum over two different time windows[7] - **Factor Construction Process**: $ \text{Momentum Term Spread} = \frac{\text{Close}_t - \text{Close}_{t-\text{window1}}}{\text{Close}_{t-\text{window1}}} - \frac{\text{Close}_t - \text{Close}_{t-\text{window2}}}{\text{Close}_{t-\text{window2}}} $ - Parameters: "window1" and "window2" represent two different lookback periods[7] 3. Factor Name: Trading Amount Volatility - **Factor Construction Idea**: Measures the standard deviation of trading amounts over a specific window[7] - **Factor Construction Process**: $ \text{Trading Amount Volatility} = -\text{STD}(\text{Amount}) $ - Parameters: "Amount" refers to the trading amount, and "STD" is the standard deviation operator[7] 4. Factor Name: Volume-Price Divergence Covariance - **Factor Construction Idea**: Measures the covariance between ranked closing prices and ranked volumes over a specific window[7] - **Factor Construction Process**: $ \text{Volume-Price Divergence Covariance} = \text{rank}(\text{covariance}[\text{rank}(\text{Close}), \text{rank}(\text{Volume}), \text{window}]) $ - Parameters: "Close" represents the closing price, "Volume" represents the trading volume, and "window" defines the lookback period[7] 5. Factor Name: Volume-Amplitude Alignment - **Factor Construction Idea**: Measures the correlation between ranked volumes and ranked price ranges over a specific window[7] - **Factor Construction Process**: $ \text{Volume-Amplitude Alignment} = \text{correlation}[\text{rank}(\text{Volume}_{i-1}), \text{rank}(\text{High}_i - \text{Low}_i), \text{window}] $ - Parameters: "High" and "Low" represent the highest and lowest prices, respectively, and "window" defines the lookback period[7] --- Backtesting Results of the Model 1. Volume-Price Industry Rotation Strategy - **Cumulative Return (2010-2025)**: 694.50%[9] - **Cumulative Excess Return over Equal-Weighted Industry Portfolio**: 605.20%[9] - **April 2025 Monthly Return**: -1.59%[9] - **April 2025 Excess Return over Equal-Weighted Industry Portfolio**: 0.81%[9]

Report Industry Investment Rating - No relevant content provided Core Viewpoints of the Report - The styrene industry chain starts from crude oil, goes through the production of benzene and ethylene, then to the production of styrene and its derivatives, and is finally applied in multiple fields such as packaging, automotive, electronics, and construction. It is an important part of the petrochemical industry, with characteristics of high dependence on crude oil, a long chain, and wide - ranging demand. The factors affecting styrene futures prices are complex. Fundamental quantitative factors cover 9 aspects, and volume - price factors include 7 aspects. By back - testing and screening, setting parameters such as back - testing time, handling fees, and leverage, and combining factors in a simple equal - weight addition way, a trend strength signal can be output [3]. - The fundamental multi - factor portfolio has an annualized return of 50.7% and a Sharpe ratio of 2.85 since 2019. The volume - price multi - factor portfolio has an annualized return of 35.3% and a Sharpe ratio of 2.14 since 2019. In the comprehensive model, all single factors are equally weighted, with an annualized return of 32.2% and a Sharpe ratio of 1.86 since 2019. Fundamental factors and volume - price factors have a low correlation. Investors can adjust the proportion of fundamental and volume - price factors in the comprehensive model according to their target returns and risk requirements [4]. Summary According to the Directory 1. Styrene Single - Commodity Timing Factor Framework - Styrene is an important organic chemical raw material with a clear upstream - downstream industrial chain. The model divides factors into two categories: fundamental quantitative factors and volume - price factors. Fundamental factors are constructed from dimensions such as inventory, basis, upstream inventory, profit, and overseas prices. Volume - price factors are constructed from dimensions such as momentum, moving averages, and technical indicators based on daily - frequency market data. As of the writing of the report, the model includes 9 fundamental quantitative factors and 7 volume - price factors [8][10]. - When back - testing and screening factors, the back - testing time for most fundamental factors and volume - price factors starts from October 2019, with the out - of - sample back - testing starting from January 2023 and ending in December 2024. The handling fee is set at a bilateral rate of 0.03%, and the leverage is 1x. Other settings such as cumulative return calculation, factor value mapping, and signal update rules are also specified [11][12][13] 2. Introduction and Back - Testing Results of Styrene Fundamental Quantitative Factors 2.1 Styrene Weekly Shipment Volume - A significant increase in styrene weekly shipment volume may lead to an oversupply situation if downstream demand does not increase synchronously, causing price decline. The data used is from the East China region, Jiangsu Province, China, and is published every Monday. Since 2019, its back - testing performance shows an annualized return of 30.3%, a Sharpe ratio of 1.68, a Calmar ratio of 1.23, a win rate of 51.0%, an average holding period of 13.7 days, and a maximum drawdown of 24.6% [19]. 2.2 Styrene Overseas Price - An increase in overseas styrene prices may push up domestic prices, while a decrease may suppress domestic prices. This factor mainly considers prices in the US Gulf, Rotterdam, and South Korea. The data is published with a one - day lag. Since 2016, its back - testing performance shows an annualized return of 19.6%, a Sharpe ratio of 0.99, a Calmar ratio of 0.73, a win rate of 52.5%, an average holding period of 19.1 days, and a maximum drawdown of 27% [21]. 2.3 Styrene Basis - When the market supply is tight, the basis widens; when the supply is excessive, the basis narrows. The data is from the Guojun Futures database and is published daily. Since 2019, its back - testing performance shows an annualized return of 27.7%, a Sharpe ratio of 1.12, a Calmar ratio of 0.71, a win rate of 51.9%, an average holding period of 2.6 days, and a maximum drawdown of 39.1% [23]. 2.4 Pure Benzene: Port Inventory - A low level of pure benzene port inventory may increase the production cost of styrene. The data is from the East China region and is published every Friday. Since 2019, its back - testing performance shows an annualized return of 15.8%, a Sharpe ratio of 0.67, a Calmar ratio of 0.42, a win rate of 50.8%, an average holding period of 38.2 days, and a maximum drawdown of 37.3% [25]. 2.5 Styrene: Non - Integrated Plant: Production Gross Margin - A high production gross margin of non - integrated styrene plants may encourage enterprises to increase production, leading to an increase in market supply. The data is from the Steel Union and is published after the market closes. Since 2019, its back - testing performance shows an annualized return of 12.5%, a Sharpe ratio of 0.46, a Calmar ratio of 0.3, a win rate of 50.4%, an average holding period of 11.3 days, and a maximum drawdown of 34.4% [27]. 2.6 Styrene Capacity Utilization Rate - An increase in styrene capacity utilization rate may lead to an oversupply situation and price decline. The data is from the Steel Union and is published weekly. Since 2019, its back - testing performance shows an annualized return of 16.5%, a Sharpe ratio of 0.91, a Calmar ratio of 0.85, a win rate of 50%, an average holding period of 28.6 days, and a maximum drawdown of 19.4% [27]. 2.7 Styrene Warehouse Receipts - An increase in warehouse receipts indicates sufficient market supply, while a decrease indicates tight supply. The data is from Flush and is published after the market closes. Since 2020, its back - testing performance shows an annualized return of 22.6%, a Sharpe ratio of 1.34, a Calmar ratio of 1.04, a win rate of 50.2%, an average holding period of 9.6 days, and a maximum drawdown of 21.7% [30]. 2.8 Styrene Arbitrage Spread - The internal - external spread has a mean - reversion characteristic. This factor considers styrene prices in Europe, Asia, and the Americas. The data is from the Steel Union and is updated with a one - day lag. Since 2019, its back - testing performance shows an annualized return of 33.8%, a Sharpe ratio of 1.68, a Calmar ratio of 0.93, a win rate of 53.2%, an average holding period of 12.5 days, and a maximum drawdown of 36.4% [32]. 2.9 Styrene: Spot Inventory - High inventory usually means sufficient or excessive market supply, while low inventory may indicate tight supply. The data is from the Steel Union and is updated every Monday. Since 2019, its back - testing performance shows an annualized return of 25.9%, a Sharpe ratio of 1.45, a Calmar ratio of 1.52, a win rate of 52.3%, an average holding period of 114.6 days, and a maximum drawdown of 17.1% [35]. 2.10 Fundamental Multi - Factor - By equally weighting the above 9 fundamental single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 50.7%, a Sharpe ratio of 2.85, a Calmar ratio of 2.08, a win rate of 52.6%, an average holding period of 6 days, and a maximum drawdown of 24.4% [37]. 3. Introduction and Back - Testing Results of Styrene Volume - Price Factors 3.1 Intraday Momentum - Intraday momentum is defined as the average of the daily high and low prices divided by the opening price. A larger value indicates a faster price increase. Since 2020, its back - testing performance shows an annualized return of 27.6%, a Sharpe ratio of 1.51, a Calmar ratio of 1.7, a win rate of 47.2%, an average holding period of 3.7 days, and a maximum drawdown of 16.2% [40]. 3.2 Median Double Moving Averages - Similar to double moving averages, but the price for calculating the moving average is the median of the daily high and low prices. Since 2019, its back - testing performance shows an annualized return of 18%, a Sharpe ratio of 0.81, a Calmar ratio of 0.56, a win rate of 51.6%, an average holding period of 8.5 days, and a maximum drawdown of 32.4% [42]. 3.3 Kaufman Adaptive Moving Average (KAMA) - Calculated through steps such as efficiency coefficient (ER) and smoothing constant (SC). Since 2019, its back - testing performance shows an annualized return of 21.1%, a Sharpe ratio of 1.23, a Calmar ratio of 1.19, a win rate of 48.8%, an average holding period of 30.6 days, and a maximum drawdown of 17.8% [45]. 3.4 On - Balance Volume (OBV) - Calculated based on the relationship between daily closing prices and trading volumes, and a long - short double moving average strategy is constructed. Since 2020, its back - testing performance shows an annualized return of 21.2%, a Sharpe ratio of 1.17, a Calmar ratio of 1.28, a win rate of 50.4%, an average holding period of 72.4 days, and a maximum drawdown of 16.6% [49]. 3.5 Commodity Channel Index (CCI) - When CCI breaks through + 100, it is a potential selling signal; when it breaks through - 100, it is a potential buying signal. Since 2019, its back - testing performance shows an annualized return of 28.9%, a Sharpe ratio of 1.72, a Calmar ratio of 1.98, a win rate of 51.0%, an average holding period of 29.9 days, and a maximum drawdown of 12.0% [53]. 3.6 TRIX - Defined through exponential moving averages and a long - short double moving average strategy is constructed based on its daily change rate. Since 2019, its back - testing performance shows an annualized return of 28.9%, a Sharpe ratio of 1.72, a Calmar ratio of 1.98, a win rate of 51.0%, an average holding period of 29.9 days, and a maximum drawdown of 14.6% [55]. 3.7 MESA Adaptive Moving Average - Hilbert transform is used to process price data. MAMA and FAMA lines are calculated, and a double moving average strategy is constructed for timing. Since 2019, its back - testing performance shows an annualized return of 20.5%, a Sharpe ratio of 1.11, a Calmar ratio of 1.11, a win rate of 49.8%, an average holding period of 29.3 days, and a maximum drawdown of 18.5% [55]. 3.8 Volume - Price Multi - Factor - By equally weighting the above 7 volume - price single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 35.3%, a Sharpe ratio of 2.14, a Calmar ratio of 2.41, a win rate of 51.5%, an average holding period of 10.3 days, and a maximum drawdown of 14.7% [59]. 4. Fundamental Quantitative and Volume - Price Multi - Factor Comprehensive Model 4.1 All - Factor Portfolio Long - Short Model - By equally weighting all 16 single factors to form a long - short timing model, since 2019, the back - testing shows an annualized return of 32.2%, a Sharpe ratio of 1.86, a Calmar ratio of 2.07, a win rate of 46.6%, an average holding period of 5.1 days, and a maximum drawdown of 15.6% [61]. 4.2 Only - Long Model - Fundamental only - long model: By equally weighting the first 9 single factors, when a short - selling signal is generated, it is regarded as closing the existing long position or staying in cash; when a long - buying signal is triggered, open a long position or hold the existing long contract. Since 2019, the back - testing shows an annualized return of 29.6%, a Sharpe ratio of 1.89, a Calmar ratio of 1.31, an average holding period of 6.7 days, and a maximum drawdown of 22.6%. - Volume - price only - long model: By equally weighting the latter 7 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 22.1%, a Sharpe ratio of 1.57, a Calmar ratio of 1.68, an average holding period of 10.6 days, and a maximum drawdown of 13.1%. - All - factor comprehensive only - long model: By equally weighting all 16 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 20.0%, a Sharpe ratio of 1.32, a Calmar ratio of 1.27, an average holding period of 7.6 days, and a maximum drawdown of 15.8% [64][67][69]. 4.3 Only - Short Model - Fundamental only - short model: By equally weighting the first 9 single factors, when a long - buying signal is generated, it is regarded as closing the existing short position or staying in cash; when a short - selling signal is triggered, open a short position or hold the existing short contract. Since 2019, the back - testing shows an annualized return of 20.0%, a Sharpe ratio of 1.48, a Calmar ratio of 1.28, an average holding period of 6.3 days, and a maximum drawdown of 15.7%. - Volume - price only - short model: By equally weighting the latter 7 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 12.5%, a Sharpe ratio of 0.87, a Calmar ratio of 0.9, an average holding period of 16.7 days, and a maximum drawdown of 13.9%. - All - factor comprehensive only - short model: By equally weighting all 16 single factors, with similar signal - handling rules. Since 2019, the back - testing shows an annualized return of 11.8%, a Sharpe ratio of 0.87, a Calmar ratio of 0.82, an average holding period of 9 days, and a maximum drawdown of 14.4% [72][75][76].