Report Overview - The report is a weekly quantitative strategy report on stock index futures, Treasury bond futures, and commodity CTA strategies, covering market reviews, strategy performance tracking, and future strategy recommendations [4][56][71] 1. Stock Index Futures 1.1 Market Review - The market declined significantly last week. Electronics and power equipment contributed to the main decline in each index, while banks contributed to the main increase. The trading volume of each contract increased month - on - month, and the basis of each variety weakened significantly [4] 1.2 Basis Strategy - The basis weakened due to market sentiment. IH remained at a premium, IF at a shallow discount, and IC and IM at a deep discount. The current hedging demand in stock index futures is still mainly short - side. It is expected that the deep discount pattern of IC and IM will continue. It is recommended to pay attention to the opportunity to build a long - short spread arbitrage position when the discount narrows driven by market sentiment. The roll - over strategy recommends going long on the near - term contract and short on the far - term contract [4] 1.3 Arbitrage Strategy - **Inter - period Arbitrage**: Last week, the performance of each strategy was differentiated. The annualized basis rate factor lost 0.6%, the long - short spread strategy gained 0.4%, and the momentum factor gained 0.8% (6 - times leverage). The annualized basis rate factor turned to a long - short spread signal [5] - **Inter - variety Arbitrage**: The market style shifted to large - cap stocks. The net value of the inter - variety time - series synthetic strategy lost 0.6% last week. The latest signal of the inter - variety strategy recommends holding an empty position in IC/IF and a 50% long - IM and short - IC position [5] 1.4 Timing Strategy - The daily timing strategy was generally profitable last week. The Shanghai Composite 50, CSI 300, CSI 500, and CSI 1000 gained 2.2%, lost 1.0%, gained 0.4%, and gained 2.0% respectively. The latest signal of the timing model is bullish on each index [6] 2. Treasury Bond Futures 2.1 This Week's Strategy Focus - **Basis and Inter - period**: The IRR of Treasury bond futures declined this week, and the inter - period spread fluctuated strongly. Since the continuous decline of IRR has realized the long - short spread profit to a certain extent, the subsequent long - short spread space is relatively limited, and it is expected to maintain a volatile operation [56] - **Interest Rate Timing and Hedging Signal**: The interest rate timing signal predicts a decline in interest rates, with strong bearish signals from the macro, production, inventory, and price factors. It is recommended to choose high - duration varieties for hedging [56] - **Futures Timing Strategy**: The multi - factor timing strategy signal is neutral. The main bullish factors are the basis factor and the high - frequency factor, while the main bearish factors are the spread factor and the volume - price factor [56] - **Futures Inter - variety Arbitrage Strategy**: The latest signal of the Treasury bond futures inter - variety arbitrage strategy TS - T is neutral, and the T - TL signal is also neutral [56] 3. Commodity CTA 3.1 Commodity Factor Performance - Most commodity varieties in the market declined last week, with only a few varieties such as gold, silver, and polysilicon rising. Among the commodity factors, the volume - price trend factors and value factors performed prominently, while the spot - futures basis factors and warehouse receipt factors declined by more than 0.5%. The overall commodity trend may still be highly volatile due to external macro - factor disturbances. Compared with short - cycle strategies, medium - and long - cycle trend - following CTA strategies may face certain risks [71] 3.2 Tracking Strategy Performance - Different strategies have different performance indicators such as annualized return, Sharpe ratio, Calmar ratio, and maximum drawdown. For example, the CWFT strategy has an annualized return of 9.3%, a Sharpe ratio of 1.58, and a maximum drawdown of - 8.81% [71]

Core Viewpoint - The recent rise of A-shares above 3800 points presents an opportunity for investors to reassess their portfolios, emphasizing the importance of a diversified asset allocation strategy to navigate market volatility and potential downturns [4][6]. Group 1: Market Sentiment and Fund Performance - Despite the current bullish market sentiment and many funds reaching historical highs, a significant number of investors redeemed their holdings before the market's upswing, leading to missed opportunities [6]. - Data indicates that from 2022 to 2024, the net subscription scale of equity funds has continuously shrunk, with net redemptions peaking in the first quarter and fourth quarter of 2024 [6]. Group 2: Volatility and Historical Performance - The Shanghai Composite Index has a compound annual growth rate of 11.6% since its inception in 1990, but it also has an annualized volatility of 43.71%, which is significantly higher than many global indices [10][11]. - Since 2014, the annual maximum drawdown for the CSI 300 and equity fund indices has exceeded 15% in about 60% of the years, highlighting the challenges of long-term holding for domestic investors [12]. Group 3: Timing Strategies and Their Challenges - The desire to time the market is common among investors, but the reality often leads to missed opportunities, as evidenced by the significant drop in annualized returns when missing the best-performing days [16][18]. - From 2014 to the present, maintaining a position in equity funds yields an average annual return of around 15%, but missing the top-performing days drastically reduces this return [16][18]. Group 4: Asset Allocation Strategies - Given the high volatility of the A-share market, a diversified asset allocation strategy is recommended to mitigate risks and enhance returns [22]. - Different asset classes exhibit varying risk-return characteristics, and combining low or negatively correlated assets can help reduce overall portfolio volatility [22][24]. Group 5: Simulation of Asset Allocation - Simulations show that adjusting the asset allocation to include dividend stocks and global indices can lead to smoother net value curves and reduced drawdowns during market downturns [30][32]. - Incorporating bonds into the asset mix further stabilizes the portfolio, increasing the likelihood of maintaining positions during market fluctuations [35][37]. Group 6: Importance of Diversification - Diversification in asset allocation is emphasized as a crucial strategy for investors, with notable figures in finance advocating for a mix of uncorrelated return streams to enhance portfolio performance [38].

Quantitative Models and Construction Methods - **Model Name**: Volume Model **Construction Idea**: This model evaluates market trends based on trading volume dynamics[1][11][62] **Construction Process**: The model uses trading volume data across broad-based indices to generate neutral signals for short-term market timing[11][62] - **Model Name**: Low Volatility Model **Construction Idea**: This model assesses market trends by analyzing low-volatility characteristics[1][11][62] **Construction Process**: The model evaluates the volatility of broad-based indices and generates neutral signals for short-term market timing[11][62] - **Model Name**: Institutional Feature Model (LHB) **Construction Idea**: This model leverages institutional trading data from the "Dragon and Tiger List" to predict market trends[1][11][62] **Construction Process**: The model analyzes institutional trading patterns and generates bullish signals for short-term market timing[11][62] - **Model Name**: Feature Volume Model **Construction Idea**: This model uses specific volume characteristics to predict market trends[1][11][62] **Construction Process**: The model evaluates unique volume features and generates bearish signals for short-term market timing[11][62] - **Model Name**: Intelligent Algorithm Model (CSI 300 and CSI 500) **Construction Idea**: This model applies machine learning algorithms to predict market trends for specific indices[1][11][62] **Construction Process**: The model generates bearish signals for both CSI 300 and CSI 500 indices based on algorithmic predictions[11][62] - **Model Name**: Limit-Up/Down Model **Construction Idea**: This model evaluates market trends by analyzing the frequency of limit-up and limit-down events[1][12][63] **Construction Process**: The model generates neutral signals for mid-term market timing based on historical limit-up/down data[12][63] - **Model Name**: Calendar Effect Model **Construction Idea**: This model incorporates seasonal and calendar-based effects to predict market trends[1][12][63] **Construction Process**: The model generates neutral signals for mid-term market timing based on calendar patterns[12][63] - **Model Name**: Long-Term Momentum Model **Construction Idea**: This model evaluates long-term market trends using momentum indicators[1][13][64] **Construction Process**: The model generates bullish signals for long-term market timing based on momentum analysis[13][64] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This composite model integrates multiple signals to provide a comprehensive market outlook[1][14][65] **Construction Process**: The model generates bearish signals for A-shares by combining various short, mid, and long-term indicators[14][65] - **Model Name**: A-Share Comprehensive Guozheng 2000 Model **Construction Idea**: This composite model focuses on the Guozheng 2000 index using integrated signals[1][14][65] **Construction Process**: The model generates bearish signals for the Guozheng 2000 index by combining multiple indicators[14][65] - **Model Name**: Turnover-to-Amplitude Model (Hong Kong Market) **Construction Idea**: This model evaluates the Hong Kong market by analyzing turnover relative to price amplitude[1][15][66] **Construction Process**: The model generates bullish signals for mid-term market timing in the Hong Kong market[15][66] Model Backtesting Results - **Volume Model**: Neutral signals for all broad-based indices[11][62] - **Low Volatility Model**: Neutral signals for all broad-based indices[11][62] - **Institutional Feature Model (LHB)**: Bullish signals for short-term market timing[11][62] - **Feature Volume Model**: Bearish signals for short-term market timing[11][62] - **Intelligent Algorithm Model (CSI 300 and CSI 500)**: Bearish signals for both indices[11][62] - **Limit-Up/Down Model**: Neutral signals for mid-term market timing[12][63] - **Calendar Effect Model**: Neutral signals for mid-term market timing[12][63] - **Long-Term Momentum Model**: Bullish signals for long-term market timing[13][64] - **A-Share Comprehensive Weapon V3 Model**: Bearish signals for A-shares[14][65] - **A-Share Comprehensive Guozheng 2000 Model**: Bearish signals for the Guozheng 2000 index[14][65] - **Turnover-to-Amplitude Model (Hong Kong Market)**: Bullish signals for mid-term market timing[15][66]

Core Viewpoint - The article emphasizes the importance of constructing a diversified asset allocation strategy to navigate the volatility of the A-share market, especially as it recently surpassed the 3800-point mark. Investors should prepare for both gains and losses, as the market does not guarantee a one-way upward journey [2]. Group 1: Market Sentiment and Investor Behavior - Despite the current bullish sentiment and many funds reaching historical highs, numerous investors redeemed their holdings before the market's rise. Data shows that from 2022 to 2024, the net subscription scale of equity funds continuously shrank, with significant net redemptions in the first and fourth quarters of 2024 [3][4]. - Many investors did not endure the market downturn alongside their funds, resulting in missed opportunities for recent gains [3]. Group 2: Market Volatility and Challenges - The Shanghai Composite Index has a compound annual growth rate of 11.6% since its inception in 1990, but it also has an annualized volatility of 43.71%, which is significantly higher than many other global indices [7]. - Since 2014, the annual maximum drawdown for the CSI 300 and equity fund indices has exceeded 15% in about 60% of the years, with the ChiNext Index experiencing over 15% drawdowns every year since 2014 [8][9]. Group 3: Timing Strategies and Their Limitations - Investors often wish to time the market to buy low and sell high, but this is frequently counterproductive. Missing just a few of the best-performing days can drastically reduce annualized returns [11][14]. - From 2014 to the present, holding equity funds consistently yields an average annual return of around 15%, but missing the top-performing days can lead to significantly lower or even negative returns [14][16]. Group 4: Asset Allocation Strategies - The article suggests that different asset types have varying risk-return characteristics, and a reasonable asset allocation can help reduce portfolio volatility and alleviate the need for timing the market [20]. - Simulations show that adjusting asset allocations, such as incorporating dividend assets and global indices, can lead to smoother net value curves and reduced drawdowns during market declines [24][26][30]. - A final portfolio that includes a mix of equity funds, dividend indices, global indices, gold, and bonds demonstrates significantly improved performance and reduced volatility compared to a portfolio solely invested in equity funds [33][35]. Group 5: Importance of Diversification - The article highlights that diversification in asset allocation is crucial for investors, as it provides the necessary resilience to endure market downturns and ultimately benefit from long-term gains [36]. - The concept of diversification as a "free lunch" in investing is supported by notable figures in finance, emphasizing the need for a well-rounded investment approach [37].

Core Viewpoint - The article discusses the increasing interest in mixed equity and fixed income products, particularly the "fixed income +" products, as investors seek absolute returns while benefiting from equity markets. The focus is on the performance of the Huaxia Hope Bond managed by Wu Fan, which has seen significant institutional investment and strong returns while maintaining low volatility [1][2]. Group 1: Product Performance - The Huaxia Hope Bond received 4.1 billion yuan in institutional investment in 2024, ranking among the top three fixed income + products for institutional investors [1]. - The product achieved a maximum drawdown of less than 1% and an absolute return of 6.43% [1][22]. - Wu Fan's management strategy includes maintaining a low equity position (within 10%) to control volatility while achieving good returns [1][2]. Group 2: Investment Strategy - Wu Fan's investment approach is characterized by a top-down timing strategy, which has been a significant source of excess returns [1][9]. - The strategy includes avoiding high-valuation assets and focusing on low-valuation convertible bonds and stocks [2][4]. - The investment framework combines macroeconomic analysis with individual security selection, allowing for effective market timing and asset allocation [5][11]. Group 3: Market Insights - The article highlights the differences in volatility between domestic stocks and bonds, noting that domestic stocks exhibit higher volatility due to a larger proportion of retail investors [6][26]. - Wu Fan emphasizes the importance of dynamic asset allocation in response to changing market conditions, moving beyond a simple stock-bond mix to a more nuanced strategy [7][20]. - The current investment environment is described as a transition from a basic stock-bond mix to a more sophisticated strategy that includes low, medium, and high volatility products [7][20]. Group 4: Future Outlook - Wu Fan anticipates that the market will remain in a state of fluctuation, with macroeconomic fundamentals stabilizing but not yet entering a strong recovery phase [25][26]. - The focus on dividend-paying assets is expected to grow, particularly in a low-interest-rate environment, with an emphasis on selecting specific markets and sectors for investment [27][20]. - The article concludes with a recognition of the challenges posed by increasing institutionalization of the market and the need for active management to achieve excess returns [29].