- The report discusses multiple quantitative models for A-share and Hong Kong stock markets, including short-term, medium-term, and long-term models. These models are constructed based on price-volume, momentum, acceleration, and trend perspectives, among others. The report emphasizes the importance of combining signals from different models and periods to achieve a balanced strategy[9][12][13] - For A-shares, the short-term models include the "Volume Model" (neutral for all broad-based indices), "Feature Institutional Model" (bearish), "Feature Volume Model" (bearish), and "Smart Algorithm Models" (neutral for CSI 300, bullish for CSI 500)[12][71] - Medium-term A-share models include the "Limit-Up and Limit-Down Model" (neutral), "Up-Down Return Difference Model" (bullish for all broad-based indices), and "Calendar Effect Model" (neutral)[13][72] - The long-term A-share model, "Long-Term Momentum Model," is bullish[14][73] - Comprehensive A-share models, such as "A-Share Comprehensive Weapon V3 Model" and "A-Share Comprehensive Guozheng 2000 Model," are bearish[15][74] - For Hong Kong stocks, the medium-term models include the "Turnover to Volatility Model" (bearish) and "Hang Seng Index Up-Down Return Difference Model" (neutral)[16][74] - The report highlights that the quantitative models are designed to provide market timing signals and are based on historical data, emphasizing simplicity and universality in their construction[9][12] - The backtesting results for the "Double Bottom Pattern" and "Cup and Handle Pattern" show that the double bottom pattern outperformed the Shanghai Composite Index by 1.93% this week, while the cup and handle pattern outperformed by 2.5%[44][50] - The cumulative performance of the double bottom pattern since December 31, 2020, is 13.99%, outperforming the Shanghai Composite Index by 2.02%. However, the cup and handle pattern underperformed the Shanghai Composite Index by -1.14% over the same period[44][50]

- The report includes multiple quantitative models for market timing, categorized into short-term, mid-term, and long-term models, such as the "Volume Model," "Feature Volume Model," "Smart Algorithm Model," "Up-Down Return Difference Model," "Calendar Effect Model," and "Long-Term Momentum Model" [1][11][12][13][63][64] - The "Volume Model" is constructed based on trading volume data to predict market trends, while the "Feature Volume Model" incorporates specific volume characteristics to enhance prediction accuracy [11][63] - The "Smart Algorithm Model" uses machine learning techniques to analyze historical data and generate market timing signals [11][63] - The "Up-Down Return Difference Model" calculates the difference between upward and downward returns to assess market sentiment [12][63] - The "Calendar Effect Model" leverages historical seasonal patterns to predict market movements [12][63] - The "Long-Term Momentum Model" focuses on long-term price trends to identify potential upward movements [13][64] - The report evaluates the models as effective tools for market timing, emphasizing their simplicity and universality, aligning with the principle of "大道至简" (great simplicity) [8][63] - The models are tested across various indices, including the Shanghai Composite Index, CSI 300, and CSI 500, with signals ranging from neutral to bearish for short-term models and bullish for long-term models [11][63][64] - The "Volume Model" shows neutral signals across all broad-based indices [11][63] - The "Feature Volume Model" indicates bearish signals for the CSI 500 and All-A indices [11][63] - The "Smart Algorithm Model" provides neutral signals for the CSI 300 and bearish signals for the CSI 500 [11][63] - The "Up-Down Return Difference Model" transitions from bullish to neutral for the CSI 2000 and All-A indices [12][63] - The "Long-Term Momentum Model" maintains bullish signals for long-term market trends [13][64]

Quantitative Models and Construction Methods - **Model Name**: Timing Radar Six-Dimensional Framework **Model Construction Idea**: The equity market is influenced by multiple dimensions of factors. This model selects 21 indicators from six dimensions: liquidity, economic fundamentals, valuation, capital flow, technicals, and crowding. These are summarized into four categories: "valuation cost-effectiveness," "macro fundamentals," "capital & trend," and "crowding & reversal," generating a composite timing score between [-1, 1][1][6][8] **Model Construction Process**: 1. Select 21 indicators across six dimensions 2. Group indicators into four categories: - Valuation cost-effectiveness - Macro fundamentals - Capital & trend - Crowding & reversal 3. Normalize the composite score to a range of [-1, 1] **Model Evaluation**: The model provides a comprehensive view of market conditions by integrating multiple dimensions, offering a balanced perspective on market timing[1][6][8] --- Quantitative Factors and Construction Methods Liquidity Factors 1. **Factor Name**: Monetary Direction Factor **Factor Construction Idea**: Measures the direction of monetary policy using central bank policy rates and short-term market rates[10] **Factor Construction Process**: - Calculate the average change in policy rates and short-term market rates over the past 90 days - If the factor > 0, monetary policy is considered expansionary; if < 0, it is contractionary **Factor Evaluation**: Effectively captures monetary policy direction[10] 2. **Factor Name**: Monetary Intensity Factor **Factor Construction Idea**: Based on the "interest rate corridor" concept, measures the deviation of short-term market rates from policy rates[12] **Factor Construction Process**: - Calculate deviation = DR007/7-year reverse repo rate - 1 - Smooth and z-score the deviation to form the factor - If the factor < -1.5 standard deviations, it indicates a loose environment; if > 1.5, it indicates a tight environment **Factor Evaluation**: Captures the relative intensity of monetary policy[12] 3. **Factor Name**: Credit Direction Factor **Factor Construction Idea**: Reflects the transmission of credit to the real economy using medium- and long-term loan data[15] **Factor Construction Process**: - Calculate the year-on-year growth of medium- and long-term loans over the past 12 months - If the factor rises compared to three months ago, it signals a positive trend; otherwise, negative **Factor Evaluation**: Provides insights into credit flow trends[15] 4. **Factor Name**: Credit Intensity Factor **Factor Construction Idea**: Measures whether credit data significantly exceeds or falls short of expectations[18] **Factor Construction Process**: - Calculate (new RMB loans - median forecast)/forecast standard deviation - If the factor > 1.5 standard deviations, it indicates a significantly positive credit environment; if < -1.5, it indicates a negative environment **Factor Evaluation**: Captures unexpected credit changes effectively[18] Economic Factors 1. **Factor Name**: Growth Direction Factor **Factor Construction Idea**: Based on PMI data, measures the direction of economic growth[20] **Factor Construction Process**: - Calculate the 12-month moving average of PMI and its year-on-year change - If the factor rises compared to three months ago, it signals a positive trend; otherwise, negative **Factor Evaluation**: Reflects economic growth trends accurately[20] 2. **Factor Name**: Growth Intensity Factor **Factor Construction Idea**: Measures whether economic growth data significantly exceeds or falls short of expectations[23] **Factor Construction Process**: - Calculate (PMI - median forecast)/forecast standard deviation - If the factor > 1.5 standard deviations, it indicates significantly positive growth; if < -1.5, it indicates negative growth **Factor Evaluation**: Captures unexpected economic growth changes effectively[23] 3. **Factor Name**: Inflation Direction Factor **Factor Construction Idea**: Measures the direction of inflation using CPI and PPI data[25] **Factor Construction Process**: - Calculate 0.5 × smoothed CPI year-on-year + 0.5 × raw PPI year-on-year - If the factor decreases compared to three months ago, it signals a deflationary environment; otherwise, inflationary **Factor Evaluation**: Reflects inflation trends effectively[25] 4. **Factor Name**: Inflation Intensity Factor **Factor Construction Idea**: Measures whether inflation data significantly exceeds or falls short of expectations[27] **Factor Construction Process**: - Calculate (CPI or PPI - median forecast)/forecast standard deviation - If the factor < -1.5, it indicates significantly lower-than-expected inflation; if > 1.5, it indicates higher-than-expected inflation **Factor Evaluation**: Captures unexpected inflation changes effectively[27] Valuation Factors 1. **Factor Name**: Shiller ERP **Factor Construction Idea**: Adjusts for economic cycles to measure equity risk premium[28] **Factor Construction Process**: - Calculate Shiller PE using 6-year inflation-adjusted average earnings - Compute ERP = 1/Shiller PE - 10-year government bond yield - Normalize using a 6-year z-score **Factor Evaluation**: Provides a cyclically adjusted view of equity valuation[28] 2. **Factor Name**: PB **Factor Construction Idea**: Measures valuation using price-to-book ratio[31] **Factor Construction Process**: - Multiply PB by -1 and normalize using a 6-year z-score - Standardize to ±1 after 1.5 standard deviation truncation **Factor Evaluation**: Offers a straightforward valuation metric[31] 3. **Factor Name**: AIAE **Factor Construction Idea**: Reflects market-wide equity allocation and risk appetite[33] **Factor Construction Process**: - Calculate AIAE = total market cap of CSI All Share/(total market cap + total corporate debt) - Multiply AIAE by -1 and normalize using a 6-year z-score **Factor Evaluation**: Captures market risk appetite effectively[33] Capital Flow Factors 1. **Factor Name**: Margin Trading Increment **Factor Construction Idea**: Measures market leverage through margin trading trends[36] **Factor Construction Process**: - Calculate the difference between financing and short-selling balances - Compare the 120-day average increment with the 240-day average increment - If the 120-day increment > 240-day increment, it signals a positive trend; otherwise, negative **Factor Evaluation**: Reflects market sentiment and leverage trends[36] 2. **Factor Name**: Turnover Trend **Factor Construction Idea**: Measures market activity through turnover trends[39] **Factor Construction Process**: - Calculate log turnover moving average distance = ma120/ma240 - 1 - If max(10, 30, 60-day moving averages) is positive, it signals a positive trend; otherwise, negative **Factor Evaluation**: Captures market activity effectively[39] 3. **Factor Name**: China Sovereign CDS Spread **Factor Construction Idea**: Reflects foreign investors' perception of China's economic and credit risk[43] **Factor Construction Process**: - Calculate the 20-day difference of smoothed CDS spreads - If the difference < 0, it signals a positive trend; otherwise, negative **Factor Evaluation**: Provides insights into foreign capital flow trends[43] 4. **Factor Name**: Overseas Risk Aversion Index **Factor Construction Idea**: Captures global market risk appetite using Citi RAI Index[45] **Factor Construction Process**: - Calculate the 20-day difference of smoothed RAI - If the difference < 0, it signals a positive trend; otherwise, negative **Factor Evaluation**: Reflects global risk sentiment effectively[45] Technical Factors 1. **Factor Name**: Price Trend **Factor Construction Idea**: Measures market trend direction and strength using moving averages[47] **Factor Construction Process**: - Calculate moving average distance = ma120/ma240 - 1 - Compute trend direction and strength scores, then average them **Factor Evaluation**: Captures market trend dynamics effectively[47] 2. **Factor Name**: New Highs and Lows **Factor Construction Idea**: Uses the difference between new highs and lows as a reversal signal[49] **Factor Construction Process**: - Calculate the 20-day moving average of new lows - new highs - If the value > 0, it signals a bottoming market; otherwise, a topping market **Factor Evaluation**: Provides reversal signals effectively[49] Crowding Factors 1. **Factor Name**: Implied Premium/Discount **Factor Construction Idea**: Derived from option pricing, reflects market sentiment[53] **Factor Construction Process**: - Use the put-call parity to calculate implied premium/discount

- The short-term trading volume model is neutral for A-shares[2][12] - The low volatility model is neutral for A-shares[2][12] - The characteristic institutional model is bearish for A-shares[2][12] - The characteristic trading volume model is bearish for A-shares[2][12] - The intelligent algorithm model for the CSI 300 is bearish for A-shares[2][12] - The intelligent algorithm model for the CSI 500 is bearish for A-shares[2][12] - The mid-term limit-up and limit-down model is neutral for A-shares[2][13] - The mid-term calendar effect model is neutral for A-shares[2][13] - The long-term momentum model is bullish for A-shares[2][14] - The comprehensive A-share model V3 is bearish[2][15] - The comprehensive A-share model for the CSI 2000 is bearish[2][15] - The mid-term trading volume to volatility model is bearish for Hong Kong stocks[2][16]

Core Viewpoint - The market is showing its first bearish signal in half a year, with a recommendation to reduce positions in the main board and small-cap sectors to low levels, indicating a potential shift in market dynamics [5]. Market Performance - Last week, the market recorded gains, with the CSI 300 index up 1.38%, the Shanghai Composite Index up 1.52%, and the CSI 500 index up 3.38%. Speculative funds became active again, pushing various sector indices to their highs before August [3]. Economic Indicators - The domestic economy is showing signs of weakening while inflation is rising. Recent import and export data showed significant weakness, which has hindered the enthusiasm of some institutional investors. Financial data released last Friday appeared stable but is actually weakening, with expectations of a slowdown in year-on-year growth in the coming months. Meanwhile, CPI and PPI data have shown a rebound, indicating a temporary stagflation cycle in the Chinese economy [3][4]. Technical Analysis - There is a deepening divergence in the funding landscape. While the market has been driven by funds since June, institutional funds have shown a more decisive reduction in positions, while speculative funds are attempting a final upward push. The strength of technical signals has weakened [4]. Sector Positioning - The main board's market-driving forces are becoming increasingly differentiated, shifting from fundamentals to funds, and then to speculative funds. This indicates that market volatility is likely to increase further. The recommendation is to reduce positions in the main board to low levels, marking the first sell signal in half a year. The small-cap sector also showed slight advantages due to speculative activity, but overall differentiation has increased, suggesting a balanced style for the time being [5]. Short-term Focus - The short-term momentum model suggests focusing on the communication industry [5].

Quantitative Models and Construction - **Model Name**: Volume Model **Construction Idea**: This model uses trading volume as a key indicator to predict market trends in the short term[12][65] **Construction Process**: The model evaluates the trading volume of broad-based indices to generate buy or sell signals. A higher trading volume relative to historical averages indicates a "bullish" signal, while lower volumes may indicate neutrality or bearishness[12][65] **Evaluation**: The model is effective in capturing short-term market momentum and is widely applicable across broad indices[12][65] - **Model Name**: Low Volatility Model **Construction Idea**: This model focuses on the volatility of indices to assess market stability and predict trends[12][65] **Construction Process**: The model calculates the historical volatility of indices over a defined period. If the volatility is low, the model remains neutral, indicating a stable market environment[12][65] **Evaluation**: The model is useful for identifying periods of market stability but may lack predictive power during high-volatility phases[12][65] - **Model Name**: Institutional Feature Model (Top Trader) **Construction Idea**: This model analyzes institutional trading patterns to predict market movements[12][65] **Construction Process**: The model tracks the trading activity of institutional investors, particularly their buying and selling patterns. A high level of institutional selling generates a "bearish" signal[12][65] **Evaluation**: The model provides insights into institutional sentiment but may be less effective in retail-dominated markets[12][65] - **Model Name**: Momentum Model **Construction Idea**: This model leverages price momentum to predict long-term market trends[14][67] **Construction Process**: The model calculates the rate of price change over a long-term horizon. Positive momentum generates a "bullish" signal, while negative momentum indicates bearishness[14][67] **Evaluation**: The model is effective in identifying long-term trends but may lag during sudden market reversals[14][67] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This is a composite model that integrates multiple signals across different time horizons[15][68] **Construction Process**: The model combines short-term, medium-term, and long-term signals from various sub-models (e.g., volume, momentum, institutional features) to generate an overall market outlook[15][68] **Evaluation**: The model balances short-term and long-term perspectives, making it robust for comprehensive market analysis[15][68] - **Model Name**: Hang Seng Turnover-to-Volatility Model **Construction Idea**: This model uses the ratio of turnover to volatility to predict medium-term trends in the Hong Kong market[16][69] **Construction Process**: The model calculates the turnover-to-volatility ratio for the Hang Seng Index. A higher ratio indicates a "bullish" signal, suggesting strong market participation relative to risk[16][69] **Evaluation**: The model is effective in capturing medium-term trends but may be less responsive to short-term fluctuations[16][69] Model Backtesting Results - **Volume Model**: All broad-based indices showed "bullish" signals in the short term[12][65] - **Low Volatility Model**: Neutral signals were observed, indicating stable market conditions[12][65] - **Institutional Feature Model**: Bearish signals were generated due to high institutional selling activity[12][65] - **Momentum Model**: Long-term "bullish" signals were observed, indicating positive price momentum[14][67] - **A-Share Comprehensive Weapon V3 Model**: Overall "bullish" signals were generated, reflecting a positive market outlook[15][68] - **Hang Seng Turnover-to-Volatility Model**: "Bullish" signals were observed, suggesting optimism in the Hong Kong market[16][69]

Quantitative Models and Construction Timing Radar Hexagon Model - **Model Name**: Timing Radar Hexagon Model - **Model Construction Idea**: The model evaluates equity market performance through a multi-dimensional framework, incorporating liquidity, economic fundamentals, valuation, capital flows, technical signals, and crowding indicators. These dimensions are aggregated into four categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal," generating a composite timing score within the range of [-1, 1][1][6][9] - **Model Construction Process**: 1. Select 21 indicators across six dimensions (liquidity, economic fundamentals, valuation, capital flows, technical signals, and crowding)[1][6] 2. Aggregate these indicators into four categories: "Valuation Cost-Effectiveness," "Macro Fundamentals," "Capital & Trend," and "Crowding & Reversal"[6] 3. Normalize the composite score to fall within the range of [-1, 1][6] - **Model Evaluation**: The model provides a comprehensive and systematic approach to market timing by integrating multiple dimensions of market dynamics[6] --- Quantitative Factors and Construction Liquidity Factors 1. **Factor Name**: Monetary Direction Factor - **Construction Idea**: Measures the direction of monetary policy based on changes in central bank policy rates and short-term market rates over the past 90 days[12] - **Construction Process**: - Calculate the average change in central bank policy rates and short-term market rates over the past 90 days - If the factor > 0, monetary policy is deemed accommodative; if < 0, it is deemed tight[12] - **Current View**: The factor is positive this week, signaling accommodative monetary policy, with a score of 1[12] 2. **Factor Name**: Monetary Strength Factor - **Construction Idea**: Captures the deviation of short-term market rates from policy rates using the "interest rate corridor" concept[15] - **Construction Process**: - Compute the deviation = DR007/7-year reverse repo rate - 1 - Smooth and z-score the deviation - If the factor < -1.5 standard deviations, it signals a loose environment (score = 1); if > 1.5 standard deviations, it signals a tight environment (score = -1)[15] - **Current View**: The factor signals a tight environment this week, with a score of -1[15] 3. **Factor Name**: Credit Direction Factor - **Construction Idea**: Reflects the transmission of credit to the real economy using medium- and long-term loan data[18] - **Construction Process**: - Calculate the year-over-year growth of the past 12 months' medium- and long-term loan increments - If the factor rises compared to three months ago, it signals a positive trend (score = 1); otherwise, it signals a negative trend (score = -1)[18] - **Current View**: The factor is in an upward trend this week, signaling a positive outlook, with a score of 1[19] 4. **Factor Name**: Credit Strength Factor - **Construction Idea**: Measures whether credit data significantly exceeds or falls short of expectations[21] - **Construction Process**: - Compute the credit strength factor = (new RMB loans - median forecast) / forecast standard deviation - If the factor > 1.5 standard deviations, it signals a significantly positive credit environment (score = 1); if < -1.5 standard deviations, it signals a negative environment (score = -1)[21] - **Current View**: The factor signals a negative environment this week, with a score of -1[21] --- Backtesting Results of Factors Liquidity Factors 1. **Monetary Direction Factor**: Current score = 1[12] 2. **Monetary Strength Factor**: Current score = -1[15] 3. **Credit Direction Factor**: Current score = 1[19] 4. **Credit Strength Factor**: Current score = -1[21]

- The short-term volume model indicates a bullish outlook for most broad-based indices[2][12][70] - The low volatility model is neutral in the short term[2][12][70] - The institutional model based on the characteristics of the Dragon and Tiger list is bearish in the short term[2][12][70] - The characteristic volume model is bullish in the short term[2][12][70] - The intelligent algorithm models for the CSI 300 and CSI 500 indices are bullish in the short term[2][12][70] - The mid-term limit-up and limit-down model is bullish[2][13][71] - The mid-term calendar effect model is neutral[2][13][71] - The long-term momentum model is bullish[2][14][72] - The comprehensive A-share models, including the A-share Comprehensive Weapon V3 model and the A-share Comprehensive Guozheng 2000 model, are bullish[2][15][73] - The mid-term turnover rate inverse volatility model for Hong Kong stocks is bullish[2][16][74]

Quantitative Models and Construction - **Model Name**: Volume Model **Construction Idea**: This model evaluates market trends based on trading volume changes over time [12][72] **Construction Process**: The model analyzes the trading volume of broad-based indices to determine short-term market sentiment. It transitions between "bullish," "neutral," and "bearish" signals based on volume dynamics [12][72] **Evaluation**: The model is effective in capturing short-term market sentiment but may require integration with other indicators for comprehensive analysis [12][72] - **Model Name**: Low Volatility Model **Construction Idea**: This model assesses market conditions by analyzing the volatility of indices [12][72] **Construction Process**: The model calculates the historical volatility of indices and assigns a "neutral" signal when volatility remains within a predefined range [12][72] **Evaluation**: The model provides a stable perspective on market conditions but may lag in highly volatile environments [12][72] - **Model Name**: Intelligent Algorithm Model (CSI 300 and CSI 500) **Construction Idea**: This model uses machine learning algorithms to predict market trends for specific indices [12][72] **Construction Process**: The model applies advanced algorithms to historical price and volume data, generating "bullish" signals for the CSI 300 and CSI 500 indices [12][72] **Evaluation**: The model demonstrates strong predictive capabilities for these indices, particularly in short-term scenarios [12][72] - **Model Name**: Limit-Up/Limit-Down Model **Construction Idea**: This model evaluates market sentiment based on the frequency of limit-up and limit-down events [13][73] **Construction Process**: The model tracks the number of stocks hitting daily price limits and assigns a "neutral" signal when no significant trend is observed [13][73] **Evaluation**: The model is useful for identifying extreme market conditions but may not capture subtle trends [13][73] - **Model Name**: Long-Term Momentum Model **Construction Idea**: This model identifies long-term trends by analyzing momentum indicators [14][74] **Construction Process**: The model calculates momentum metrics for indices like the SSE 50, which recently transitioned to a "bullish" signal [14][74] **Evaluation**: The model is effective for long-term trend analysis but may miss short-term fluctuations [14][74] - **Model Name**: A-Share Comprehensive Weapon V3 Model **Construction Idea**: This composite model integrates multiple signals to provide an overall market outlook [15][75] **Construction Process**: The model aggregates signals from various short-term, medium-term, and long-term models, currently indicating a "bearish" outlook [15][75] **Evaluation**: The model offers a holistic view but may dilute the impact of individual signals [15][75] - **Model Name**: HK Stock Turnover-to-Volatility Model **Construction Idea**: This model evaluates the Hong Kong market by analyzing turnover relative to volatility [16][76] **Construction Process**: The model calculates the ratio of turnover to volatility, currently signaling a "bullish" outlook for the Hang Seng Index [16][76] **Evaluation**: The model is effective for medium-term analysis but may require additional factors for short-term predictions [16][76] Model Backtesting Results - **Volume Model**: Short-term signal transitioned to "neutral" for most broad-based indices [12][72] - **Low Volatility Model**: Maintains a "neutral" signal [12][72] - **Intelligent Algorithm Model**: "Bullish" signals for CSI 300 and CSI 500 indices [12][72] - **Limit-Up/Limit-Down Model**: "Neutral" signal for medium-term analysis [13][73] - **Long-Term Momentum Model**: SSE 50 transitioned to "bullish" [14][74] - **A-Share Comprehensive Weapon V3 Model**: Overall "bearish" signal [15][75] - **HK Stock Turnover-to-Volatility Model**: "Bullish" signal for the Hang Seng Index [16][76]

- The report includes multiple quantitative models for A-share market timing, such as the "Volume Model," "Low Volatility Model," "Feature Institutional Model," "Feature Volume Model," "Smart Algorithm Model," and "Long-term Momentum Model" [12][13][14][76] - The "Volume Model" indicates a bullish signal for most broad-based indices in the short term [12][76] - The "Low Volatility Model" provides a neutral signal for the short term [12][76] - The "Feature Institutional Model" shows a bearish signal for the short term [12][76] - The "Feature Volume Model" indicates a bullish signal for the short term [12][76] - The "Smart Algorithm Model" shows bullish signals for the CSI 300 and CSI 500 indices in the short term [12][76] - The "Long-term Momentum Model" flips to bullish for the SSE 50 index in the long term [14][78] - The "Comprehensive Weapon V3 Model" and "Comprehensive Guozheng 2000 Model" indicate bullish signals for the A-share market [15][79] - For the Hong Kong market, the "Turnover-to-Volatility Model" provides a bullish signal for the mid-term [16][80] - Backtesting results for the "Double Bottom Pattern" show a weekly return of 1.73%, outperforming the SSE Composite Index by 0.05% [46][53] - Backtesting results for the "Cup-and-Handle Pattern" show a weekly return of 2.87%, outperforming the SSE Composite Index by 1.2% [46][47]