Core Viewpoint - Intelligent investment is an advanced version of regular investment, focusing on automatic adjustment of investment amounts based on market conditions, rather than fixed amounts [3][4]. Group 1: Definition and Strategies of Intelligent Investment - Intelligent investment adjusts the investment amount according to market fluctuations, increasing the amount when the fund price drops and decreasing it when the price rises [3][4]. - Two main strategies of intelligent investment are: 1. Valuation Strategy: Invests only when the market is undervalued, avoiding investments when the valuation is not favorable [5]. 2. Moving Average Strategy: Increases investment when the price is below the moving average and decreases it when above, with a variable investment rate between 60% and 120% of the base amount [5][6]. Group 2: Comparison with Regular Investment - The key differences between intelligent and regular investment lie in: 1. Investment Method: Regular investment has a fixed amount, while intelligent investment adjusts based on market conditions [6][7]. 2. Profit Logic: Regular investment relies on passive averaging of costs, while intelligent investment actively optimizes buying at lower prices [8]. 3. Funding Requirements: Regular investment has a low threshold, while intelligent investment, especially with the moving average strategy, requires sufficient reserve funds to avoid failed transactions [9]. Group 3: Practical Considerations - There are two important rules for modifying intelligent investment agreements: 1. For the moving average strategy, modifications cannot be made from 15:00 the day before the deduction until 15:00 on the deduction day [10]. 2. For the valuation strategy, modifications are not allowed from midnight to 15:00 on the deduction day [10]. Group 4: Conclusion - While intelligent investment offers more sophisticated strategies than regular investment, consistent adherence to the investment plan is crucial for achieving cost-averaging benefits [11][12].

Core Viewpoint - The establishment of the China Bond-Gold Backed Credit Bond Risk Parity Index and the China Bond-Gold Backed CDB Bond Risk Parity Index aims to meet market demand for "fixed income plus" investment strategies, utilizing a risk parity model to dynamically adjust the allocation between gold and bonds [1][2]. Group 1: Index Details - The China Bond-Gold Backed Credit Bond Risk Parity Index includes short-duration credit bonds and gold ETF funds, while the China Bond-Gold Backed CDB Bond Risk Parity Index comprises short-duration CDB bonds and gold ETF funds [1]. - Both indices use a risk parity model and moving average strategy to adjust the allocation of gold and bonds, serving as performance benchmarks for these asset combinations [1]. Group 2: Performance Metrics - As of November 28, 2025, the annualized return for the China Bond-Gold Backed Credit Bond Risk Parity Index over the past five years is 5.14%, with an annualized volatility of 1.27% [2]. - The China Bond-Gold Backed CDB Bond Risk Parity Index has an annualized return of 4.46% and an annualized volatility of 1.23% [2]. - The Sharpe ratio for the China Bond-Gold Backed Credit Bond Risk Parity Index is 2.56, while the Kappa ratio is 3.12, with a maximum drawdown of 1.65% [2]. - For the China Bond-Gold Backed CDB Bond Risk Parity Index, the Sharpe ratio is 2.11, the Kappa ratio is 5.89, and the maximum drawdown is 0.76% [2].

Group 1 - The report emphasizes the importance of identifying trend strength through moving average strategies, which can help in recognizing market trends effectively [3][4][9] - It introduces three key moving average indicators: moving average arrangement score, moving average dispersion distance, and moving average time series change, which collectively help in assessing market trends [8][10][13] - The report suggests that a higher composite score indicates a stronger upward trend, while a lower score suggests a stronger downward trend [13][21] Group 2 - The report outlines a funding flow strategy that aggregates various funding flow indicators to identify market consensus, focusing on both institutional and retail investor behaviors [26][28] - It highlights the significance of funding flow volatility over mere funding direction, suggesting that stable funding behavior can indicate potential market reversals [31][41] - The report proposes a combined approach of trend strength and funding consensus to select indices with clear trends and stable funding, enhancing investment decision-making [42][39] Group 3 - The report presents historical performance data for industry rotation and ETF rotation strategies, showing annual returns and excess returns compared to equal-weighted benchmarks [50][52] - It indicates that the industry rotation strategy has shown significant excess returns in certain years, particularly in 2020 with a return of 76.84% [50] - The ETF rotation strategy also demonstrated strong performance in 2019 and 2020, with excess returns of 30.33% and 26.62% respectively [52]

Core Insights - The essence of moving average lines is to eliminate random price fluctuations and seek price trends [3] - The effectiveness of moving averages is closely related to the selected parameter N [3] - Different types of moving averages can be categorized based on their calculation methods [4][5] Single Moving Average Strategy - The single moving average strategy involves selecting a significant moving average as a reference. If the closing price is above the moving average, it indicates a bullish trend, while a closing price below suggests a bearish trend [8][9] - The strategy includes entry and exit filters, requiring the moving average to be rising for long positions and falling for short positions [9][11][12] - Performance evaluation shows a net value of 14.6155 and an annual compound return of 14.3512% from 2005 to 2024 [14][16] Double Moving Average Strategy - The double moving average strategy uses the relationship between short-term and long-term moving averages to determine price trends. A short-term average above a long-term average indicates an upward trend, while the opposite suggests a downward trend [21][22] - The strategy's effectiveness is influenced by the selected short-term and long-term parameters [23] - Performance evaluation indicates a net value of 19.5463 and an annual compound return of 16.0254% from 2005 to 2024 [25][27] Triple Moving Average Strategy - The triple moving average strategy builds on the double moving average strategy by adding a longer-term moving average as a filter. Long positions are only taken when the short-term average is above the longest average, and short positions are taken when the short-term average is below the longest average [31][32] - Performance evaluation shows a net value of 9.9713 and an annual compound return of 12.1815% from 2005 to 2024 [36] Moving Average Divergence Strategy - The moving average divergence strategy is derived from the single moving average strategy, incorporating divergence rates to assess trend strength. It uses short-term, medium-term, and long-term moving averages to calculate a weighted divergence rate [42] - Performance evaluation indicates a net value of 23.1849 and an annual compound return of 17.02% from 2005 to 2024 [45] Moving Average Convergence Strategy - The moving average convergence strategy measures the degree of divergence between moving averages to assess trends. It calculates divergence values between short-term, medium-term, and long-term moving averages [51] - Performance evaluation shows a net value of 15.0084 and an annual compound return of 14.503% from 2005 to 2024 [55] Strategy Application Considerations - The report presents five moving average strategies, each with distinct approaches and performance characteristics, making direct comparisons challenging [61] - The performance of these strategies is based on specific parameters, and investors are encouraged to explore various parameter combinations [62] - The strategies exhibit significant drawdown levels, necessitating careful consideration of capital allocation and psychological resilience [63]