- **Tracking indicators and calculation methods** The report uses two key metrics: the proportion of large buy order transaction amounts and the proportion of net active buy transaction amounts. The large buy order transaction amount proportion reflects the buying behavior of large funds. It is calculated by restoring tick-by-tick transaction data into buy and sell order data based on bid and ask sequence numbers, filtering for large orders by transaction volume, and computing the proportion of large buy order transaction amounts relative to the total daily transaction amount. The net active buy transaction amount proportion reflects investors' active buying behavior. It is calculated by identifying whether each transaction is an active buy or sell based on tick-by-tick transaction data, subtracting active sell transaction amounts from active buy transaction amounts, and computing the proportion of net active buy transaction amounts relative to the total daily transaction amount[7] - **Individual stock tracking** The report tracks individual stocks based on the two metrics mentioned above. For the past 5 trading days (20260323-20260327), the top 10 stocks with the highest average proportion of large buy order transaction amounts include New Energy Taishan (93.2%), Snow Wave Environment (85.7%), and Zhongli Group (85.4%). Similarly, the top 10 stocks with the highest average proportion of net active buy transaction amounts include Zhen De Medical (16.7%), China General Nuclear (15.9%), and Zhejiang Energy Power (12.6%)[9][10] - **Broad-based index tracking** The report applies the same metrics to major broad-based indices. For the past 5 trading days, the average proportion of large buy order transaction amounts for indices such as the Shanghai Composite Index, SSE 50, and CSI 300 ranged from 69.5% to 73.7%. The average proportion of net active buy transaction amounts for these indices ranged from 1.0% to 3.2%[12] - **Sector tracking** The report tracks the metrics across various sectors based on the CITIC primary industry classification. For the past 5 trading days, sectors such as coal (78.4%), steel (78.7%), and real estate (78.9%) had high proportions of large buy order transaction amounts. Sectors like medicine (12.3%), steel (10.8%), and food & beverage (10.6%) had high proportions of net active buy transaction amounts[13] - **ETF tracking** The report tracks ETFs using the same metrics. For the past 5 trading days, the top 10 ETFs with the highest average proportion of large buy order transaction amounts include Guotai CSI A500 ETF (92.4%), Huatai-PineBridge CSI A500 ETF (92.1%), and Penghua CSI Oil & Gas ETF (91.3%). The top 10 ETFs with the highest average proportion of net active buy transaction amounts include Haifutong SSE Urban Investment Bond ETF (24.4%), Fuguo ChiNext Artificial Intelligence ETF (19.4%), and Guotai SSE 10-Year Treasury Bond ETF (16.9%)[15][16]

- The report focuses on tracking large buy orders and net active buy orders using transaction detail data[1][2] - Two key indicators are used: the proportion of large buy order transaction amounts and the proportion of net active buy order amounts[7] - The proportion of large buy order transaction amounts reflects the buying behavior of large funds[7] - The proportion of net active buy order amounts reflects investors' active buying behavior[7] - The report provides rankings for stocks, industries, and ETFs based on these indicators over the past 5 trading days (20260309-20260313)[4][6] Quantitative Models and Construction Methods 1. **Model Name**: Large Buy Order Transaction Amount Proportion - **Construction Idea**: To track the buying behavior of large funds[7] - **Construction Process**: - Restore transaction data to buy and sell order data using the buy and sell sequence numbers in the transaction detail data - Filter out large orders based on transaction volume - Calculate the proportion of large buy order transaction amounts to the total transaction amount of the day[7] - **Evaluation**: This indicator effectively captures the buying behavior of large funds[7] 2. **Model Name**: Net Active Buy Order Amount Proportion - **Construction Idea**: To track investors' active buying behavior[7] - **Construction Process**: - Identify each transaction as either an active buy or an active sell using the buy and sell markers in the transaction detail data - Subtract the transaction amounts of active sells from active buys to get the net active buy amount - Calculate the proportion of net active buy amounts to the total transaction amount of the day[7] - **Evaluation**: This indicator effectively captures investors' active buying behavior[7] Model Backtest Results 1. **Large Buy Order Transaction Amount Proportion** - **Top 5 Stocks**: - Jiugang Hongxing: 87.2%, 90.5%[9] - Wentou Holdings: 86.6%, 97.1%[9] - Jinbin Development: 86.3%, 86.4%[9] - Ningbo Construction: 85.6%, 98.8%[9] - Xining Special Steel: 85.3%, 97.9%[9] - **Top 5 Industries**: - Banking: 81.3%, 61.3%[13] - Real Estate: 79.8%, 51.0%[13] - Construction: 78.5%, 88.9%[13] - Comprehensive: 77.9%, 46.1%[13] - Steel: 77.7%, 35.4%[13] - **Top 5 ETFs**: - Guotai SSE 10-Year Treasury Bond ETF: 95.4%, 99.6%[15] - Huatai-PineBridge MSCI China A50 Interconnection ETF: 94.0%, 93.4%[15] - Huatai-PineBridge CSI A500 ETF: 93.2%, 90.9%[15] - Guotai CSI A500 ETF: 92.5%, 53.9%[15] - Huaxia CSI A500 ETF: 92.0%, 97.5%[15] 2. **Net Active Buy Order Amount Proportion** - **Top 5 Stocks**: - Minsheng Bank: 22.2%, 98.8%[10] - SDIC Power: 21.8%, 97.1%[10] - Everbright Bank: 19.5%, 99.6%[10] - Zhejiang Bank: 19.2%, 96.3%[10] - Shangtai Technology: 18.9%, 100.0%[10] - **Top 5 Industries**: - Banking: 10.5%, 64.2%[13] - Food & Beverage: 4.7%, 56.0%[13] - Real Estate: 2.5%, 50.2%[13] - Construction: 0.4%, 72.4%[13] - Basic Chemicals: -0.9%, 75.7%[13] - **Top 5 ETFs**: - Harvest CSI Green Power ETF: 35.4%, 98.4%[16] - E Fund CSI Dividend Low Volatility ETF: 21.6%, 97.9%[16] - Huatai-PineBridge CSI All Index Power Utilities ETF: 18.7%, 97.9%[16] - Southern S&P China A-Share Large Cap Dividend Low Volatility 50 ETF: 15.7%, 96.7%[16] - GF GEM ETF: 13.8%, 90.9%[16]

Quantitative Factors and Construction Methods 1. Factor Name: Large Buy Order Transaction Amount Ratio - **Construction Idea**: This factor captures the buying behavior of large capital by measuring the proportion of large buy orders in the total transaction amount for a given day[7] - **Construction Process**: - Utilize tick-by-tick transaction data to identify buy and sell orders based on bid and ask sequence numbers - Filter transactions by order size to isolate large orders - Calculate the proportion of large buy order transaction amounts relative to the total transaction amount for the day - Formula: $ \text{Large Buy Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Amount}}{\text{Total Transaction Amount}} $ where "Large Buy Order Amount" represents the sum of transaction amounts for large buy orders, and "Total Transaction Amount" is the aggregate transaction amount for the day[7] - **Evaluation**: This factor effectively reflects the behavior of large-scale investors and their influence on market dynamics[7] 2. Factor Name: Net Active Buy Transaction Amount Ratio - **Construction Idea**: This factor measures the active buying behavior of investors by calculating the net active buy transaction amount as a proportion of the total transaction amount for a given day[7] - **Construction Process**: - Use tick-by-tick transaction data to classify each transaction as either an active buy or an active sell based on the buy/sell indicator - Compute the net active buy transaction amount by subtracting the active sell amount from the active buy amount - Calculate the proportion of the net active buy transaction amount relative to the total transaction amount - Formula: $ \text{Net Active Buy Transaction Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Transaction Amount}} $ where "Active Buy Amount" and "Active Sell Amount" represent the transaction amounts for active buy and sell orders, respectively, and "Total Transaction Amount" is the aggregate transaction amount for the day[7] - **Evaluation**: This factor provides insights into the level of active buying interest in the market, which can be indicative of investor sentiment[7] --- Factor Backtesting Results 1. Large Buy Order Transaction Amount Ratio - **Top 10 Stocks by 5-Day Average**: - Highest value: 89.4% (Zhouji Oil & Gas, 600759.SH)[9] - Lowest value in the top 10: 85.0% (Wentou Holdings, 600715.SH)[9] - Time-series percentile range: 81.1% to 100.0%[9] 2. Net Active Buy Transaction Amount Ratio - **Top 10 Stocks by 5-Day Average**: - Highest value: 14.2% (China Construction Bank, 601939.SH)[10] - Lowest value in the top 10: 11.5% (Zhende Medical, 603301.SH)[10] - Time-series percentile range: 93.8% to 100.0%[10] 3. Broad Market Indices - **Large Buy Order Transaction Amount Ratio (5-Day Average)**: - Highest: 73.7% (CSI 300 Index)[12] - Lowest: 70.4% (ChiNext Index)[12] - **Net Active Buy Transaction Amount Ratio (5-Day Average)**: - Highest: 2.0% (CSI 500 Index)[12] - Lowest: -0.4% (SSE 50 Index)[12] 4. Industry-Level Analysis - **Large Buy Order Transaction Amount Ratio (5-Day Average)**: - Highest: 79.2% (Non-Banking Financials)[13] - Lowest: 69.3% (Telecommunications)[13] - **Net Active Buy Transaction Amount Ratio (5-Day Average)**: - Highest: 12.8% (Non-Banking Financials)[13] - Lowest: -5.2% (Oil & Petrochemicals)[13] 5. ETFs - **Large Buy Order Transaction Amount Ratio (5-Day Average)**: - Highest: 94.3% (Huatai-PineBridge CSI A500 ETF, 563360.SH)[15] - Lowest in the top 10: 89.6% (Fuguo CSI Tourism Theme ETF, 159766.SZ)[15] - **Net Active Buy Transaction Amount Ratio (5-Day Average)**: - Highest: 33.1% (HFT SSE Urban Investment Bond ETF, 511220.SH)[16] - Lowest in the top 10: 9.8% (Harvest CSI Rare Earth Industry ETF, 516150.SH)[16]

Quantitative Factors and Construction Methods - **Factor Name**: Large Order Transaction Amount Ratio **Factor Construction Idea**: This factor captures the buying behavior of large capital by analyzing the proportion of large order transaction amounts in the total daily transaction amount[8] **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify buy and sell orders based on the sequence numbers of bids and asks 2. Filter transactions by order size to identify large orders 3. Calculate the proportion of large buy order transaction amounts in the total daily transaction amount **Formula**: $ \text{Large Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Transaction Amount}}{\text{Total Daily Transaction Amount}} $ **Evaluation**: This factor effectively reflects the buying behavior of large capital[8] - **Factor Name**: Net Active Buy Transaction Amount Ratio **Factor Construction Idea**: This factor measures the active buying behavior of investors by calculating the net active buy transaction amount as a proportion of the total daily transaction amount[8] **Factor Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on the buy/sell indicator 2. Subtract the active sell transaction amount from the active buy transaction amount to obtain the net active buy transaction amount 3. Calculate the proportion of the net active buy transaction amount in the total daily transaction amount **Formula**: $ \text{Net Active Buy Transaction Amount Ratio} = \frac{\text{Active Buy Transaction Amount} - \text{Active Sell Transaction Amount}}{\text{Total Daily Transaction Amount}} $ **Evaluation**: This factor provides insights into the active buying tendencies of investors[8] --- Factor Backtesting Results - **Large Order Transaction Amount Ratio**: - Top 10 stocks with the highest 5-day average values: 1. Hanjiang Heshan (603616.SH): 93.5%, 99.2% time-series percentile[10] 2. Minbao Optoelectronics (301362.SZ): 89.7%, 98.4% time-series percentile[10] 3. Hangdian Co., Ltd. (603618.SH): 86.1%, 99.6% time-series percentile[10] 4. Jinzhengda (002470.SZ): 85.2%, 83.9% time-series percentile[10] 5. Guofa Co., Ltd. (600538.SH): 84.9%, 92.4% time-series percentile[10] 6. Shunna Co., Ltd. (000533.SZ): 84.7%, 100.0% time-series percentile[10] 7. Quanzhu Co., Ltd. (603030.SH): 84.6%, 94.4% time-series percentile[10] 8. Beijing Investment Development (600683.SH): 84.5%, 96.0% time-series percentile[10] 9. Chongqing Steel (601005.SH): 84.3%, 52.2% time-series percentile[10] 10. Huadian International (600027.SH): 83.6%, 95.2% time-series percentile[10] - **Net Active Buy Transaction Amount Ratio**: - Top 10 stocks with the highest 5-day average values: 1. Minsheng Bank (600016.SH): 21.1%, 100.0% time-series percentile[11] 2. Kairuide (002072.SZ): 20.3%, 99.6% time-series percentile[11] 3. Intercontinental Oil & Gas (600759.SH): 19.5%, 100.0% time-series percentile[11] 4. Boen Group (001366.SZ): 17.8%, 100.0% time-series percentile[11] 5. Bowan Technology (600883.SH): 17.5%, 99.6% time-series percentile[11] 6. Xiangyou Technology (600476.SH): 16.8%, 99.2% time-series percentile[11] 7. Zhejiang Commercial Bank (601916.SH): 16.8%, 94.4% time-series percentile[11] 8. General Elevator (300931.SZ): 15.9%, 100.0% time-series percentile[11] 9. Weier Pharmaceutical (603351.SH): 15.3%, 94.0% time-series percentile[11] 10. Ruierte (002790.SZ): 15.2%, 98.4% time-series percentile[11]

Quantitative Models and Construction Methods 1. Model Name: Large Order Transaction Amount Ratio - **Model Construction Idea**: This model tracks the buying behavior of large funds by calculating the proportion of large order transaction amounts to the total daily transaction amount[7] - **Model Construction Process**: 1. Use tick-by-tick transaction data to identify buy and sell orders based on the sequence numbers of bids and asks 2. Filter transactions by order size to identify large orders 3. Calculate the proportion of large buy order transaction amounts to the total daily transaction amount - Formula: $ \text{Large Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Transaction Amount}}{\text{Total Daily Transaction Amount}} $ - **Model Evaluation**: This model effectively captures the buying behavior of large funds, providing insights into market dynamics[7] 2. Model Name: Net Active Buy Amount Ratio - **Model Construction Idea**: This model measures investors' active buying behavior by calculating the net active buy amount as a proportion of the total daily transaction amount[7] - **Model Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on the buy/sell flag 2. Calculate the net active buy amount by subtracting the active sell amount from the active buy amount 3. Compute the ratio of the net active buy amount to the total daily transaction amount - Formula: $ \text{Net Active Buy Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Daily Transaction Amount}} $ - **Model Evaluation**: This model provides a clear view of active investor sentiment and their willingness to buy[7] --- Model Backtesting Results 1. Large Order Transaction Amount Ratio - **Top 5 Stocks by 5-Day Average**: - Jianghua Microelectronics: 96.1% (98.8% time-series percentile)[9] - Fenglong Co.: 94.9% (92.1% time-series percentile)[9] - Ningbo Port: 86.4% (94.2% time-series percentile)[9] - Hongta Securities: 86.4% (100.0% time-series percentile)[9] - Chongqing Steel: 86.1% (77.0% time-series percentile)[9] 2. Net Active Buy Amount Ratio - **Top 5 Stocks by 5-Day Average**: - Liaogang Co.: 25.5% (98.8% time-series percentile)[10] - Rong'an Real Estate: 22.8% (99.6% time-series percentile)[10] - Beichen Industrial: 21.2% (100.0% time-series percentile)[10] - Angang Steel: 20.6% (100.0% time-series percentile)[10] - Anyang Steel: 20.3% (100.0% time-series percentile)[10] 3. Broad-Based Indices - **Large Order Transaction Amount Ratio (5-Day Average)**: - Shanghai Composite Index: 73.2% (47.7% percentile)[12] - CSI 300: 72.2% (28.0% percentile)[12] - CSI 500: 74.0% (97.9% percentile)[12] - **Net Active Buy Amount Ratio (5-Day Average)**: - Shanghai Composite Index: -0.6% (56.4% percentile)[12] - CSI 300: -7.4% (70.8% percentile)[12] - CSI 500: 4.9% (95.1% percentile)[12] 4. Industry-Level Results - **Top Industries by Large Order Transaction Amount Ratio (5-Day Average)**: - Steel: 78.6% (45.7% percentile)[13] - Coal: 77.7% (49.8% percentile)[13] - Media: 77.6% (70.4% percentile)[13] - **Top Industries by Net Active Buy Amount Ratio (5-Day Average)**: - Steel: 7.1% (19.3% percentile)[13] - Nonferrous Metals: 5.0% (9.1% percentile)[13] - Media: 3.2% (45.7% percentile)[13] 5. ETFs - **Top 5 ETFs by Large Order Transaction Amount Ratio (5-Day Average)**: - Huatai-PineBridge CSI A500 ETF: 93.0% (96.7% percentile)[15] - Harvest CSI 300 ETF: 92.3% (100.0% percentile)[15] - E Fund CSI 300 ETF: 92.1% (99.2% percentile)[15] - **Top 5 ETFs by Net Active Buy Amount Ratio (5-Day Average)**: - Penghua CSI Sub-Sector Chemical Industry ETF: 22.7% (92.2% percentile)[16] - Bosera CSI Sub-Sector Chemical Industry ETF: 18.2% (99.2% percentile)[16] - Huaxia GEM Artificial Intelligence ETF: 16.9% (96.6% percentile)[16]

Quantitative Factors and Construction Methods 1. Factor Name: Large Order Transaction Amount Ratio - **Factor Construction Idea**: This factor captures the buying behavior of large funds by analyzing the proportion of large order transaction amounts relative to the total daily transaction amount[7] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify buy and sell orders based on bid and ask sequence numbers 2. Filter transactions by order size to identify large orders 3. Calculate the proportion of large buy order transaction amounts to the total daily transaction amount Formula: $ \text{Large Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Transaction Amount}}{\text{Total Daily Transaction Amount}} $ - **Factor Evaluation**: This factor effectively reflects the buying behavior of large funds[7] 2. Factor Name: Net Active Buy Amount Ratio - **Factor Construction Idea**: This factor measures the active buying behavior of investors by calculating the net active buy amount as a proportion of the total daily transaction amount[7] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on trade direction 2. Subtract the active sell transaction amount from the active buy transaction amount to obtain the net active buy amount 3. Calculate the proportion of the net active buy amount to the total daily transaction amount Formula: $ \text{Net Active Buy Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Daily Transaction Amount}} $ - **Factor Evaluation**: This factor provides insights into the active buying behavior of investors[7] --- Factor Backtesting Results 1. Large Order Transaction Amount Ratio - **Top 5 Stocks by 5-Day Average**: 1. 惠博普 (92.6%, 99.6% percentile)[9] 2. 美年健康 (89.6%, 99.2% percentile)[9] 3. 志特新材 (89.2%, 99.2% percentile)[9] 4. 津滨发展 (88.4%, 99.6% percentile)[9] 5. 江南高纤 (87.7%, 98.8% percentile)[9] 2. Net Active Buy Amount Ratio - **Top 5 Stocks by 5-Day Average**: 1. 杭萧钢构 (16.7%, 99.8% percentile)[10] 2. 纬德信息 (15.4%, 100.0% percentile)[10] 3. 中科微至 (15.0%, 99.6% percentile)[10] 4. 新风光 (13.8%, 100.0% percentile)[10] 5. 联合水务 (13.3%, 97.5% percentile)[10] 3. Broad-Based Indices - **Large Order Transaction Amount Ratio (5-Day Average)**: - 上证指数: 73.8% (12.8% percentile)[12] - 上证50: 70.6% (64.2% percentile)[12] - 沪深300: 73.1% (64.2% percentile)[12] - 中证500: 73.0% (6.6% percentile)[12] - 创业板指: 71.6% (90.1% percentile)[12] - **Net Active Buy Amount Ratio (5-Day Average)**: - 上证指数: -5.8% (86.8% percentile)[12] - 上证50: -12.9% (90.5% percentile)[12] - 沪深300: -8.8% (89.3% percentile)[12] - 中证500: -3.4% (86.0% percentile)[12] - 创业板指: -4.4% (84.8% percentile)[12] 4. Industry-Level Results - **Top 5 Industries by Large Order Transaction Amount Ratio (5-Day Average)**: 1. 房地产: 79.8% (90.1% percentile)[13] 2. 煤炭: 78.5% (66.3% percentile)[13] 3. 钢铁: 78.2% (42.8% percentile)[13] 4. 建筑: 77.9% (24.3% percentile)[13] 5. 综合: 77.8% (50.6% percentile)[13] - **Top 5 Industries by Net Active Buy Amount Ratio (5-Day Average)**: 1. 房地产: -9.5% (95.1% percentile)[13] 2. 电子: 2.2% (78.6% percentile)[13] 3. 汽车: 0.9% (60.9% percentile)[13] 4. 家电: 0.1% (84.4% percentile)[13] 5. 通信: -4.7% (89.7% percentile)[13] 5. ETFs - **Top 5 ETFs by Large Order Transaction Amount Ratio (5-Day Average)**: 1. 华泰柏瑞中证A500ETF (92.9%, 96.3% percentile)[15] 2. 易方达中证A500ETF (91.6%, 100.0% percentile)[15] 3. 国泰中证A500ETF (91.5%, 15.6% percentile)[15] 4. 华泰柏瑞沪深300ETF (91.0%, 99.2% percentile)[15] 5. 易方达沪深300ETF (91.0%, 99.6% percentile)[15] - **Top 5 ETFs by Net Active Buy Amount Ratio (5-Day Average)**: 1. 东财上证科创板50成份ETF (23.4%, 100.0% percentile)[16] 2. 海富通上证城投债ETF (20.9%, 88.5% percentile)[16] 3. 国泰上证10年期国债ETF (15.6%, 61.3% percentile)[16] 4. 富国创业板人工智能ETF (14.3%, 65.9% percentile)[16] 5. 嘉实中证稀土产业ETF (14.1%, 92.6% percentile)[16]

Quantitative Models and Construction Methods 1. Model Name: Large Order Transaction Amount Ratio - **Model Construction Idea**: This model tracks the buying behavior of large funds by calculating the proportion of large order transaction amounts to the total daily transaction amount[7] - **Model Construction Process**: 1. Use tick-by-tick transaction data to identify buy and sell orders based on bid and ask sequence numbers 2. Filter transactions by order size to identify large orders 3. Calculate the ratio of large buy order transaction amounts to the total daily transaction amount - Formula: $ \text{Large Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Transaction Amount}}{\text{Total Daily Transaction Amount}} $ - **Model Evaluation**: This indicator effectively captures the buying behavior of large funds[7] 2. Model Name: Net Active Buy Amount Ratio - **Model Construction Idea**: This model measures the active buying behavior of investors by calculating the net active buy amount as a proportion of the total daily transaction amount[7] - **Model Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on the buy/sell flag 2. Calculate the net active buy amount by subtracting the active sell amount from the active buy amount 3. Compute the ratio of the net active buy amount to the total daily transaction amount - Formula: $ \text{Net Active Buy Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Daily Transaction Amount}} $ - **Model Evaluation**: This indicator effectively captures the active buying behavior of investors[7] --- Model Backtesting Results 1. Large Order Transaction Amount Ratio - **Top 10 Stocks by 5-Day Average**: - **East Securities (601198.SH)**: 88.1%, 99.6% time-series percentile[9] - **Shanghai Kaibao (300039.SZ)**: 86.3%, 100.0% time-series percentile[9] - **Sanxiang Impression (000863.SZ)**: 86.0%, 99.6% time-series percentile[9] - **Chongqing Steel (601005.SH)**: 86.0%, 78.7% time-series percentile[9] - **Jinzhengda (002470.SZ)**: 85.7%, 89.8% time-series percentile[9] - **Wanlong Optoelectronics (300710.SZ)**: 85.6%, 99.6% time-series percentile[9] - **Yasheng Group (600108.SH)**: 85.5%, 84.4% time-series percentile[9] - **Sinochem International (600500.SH)**: 85.5%, 90.4% time-series percentile[9] - **Chongqing Water (601158.SH)**: 85.2%, 96.7% time-series percentile[9] 2. Net Active Buy Amount Ratio - **Top 10 Stocks by 5-Day Average**: - **Jiuhua Tourism (603199.SH)**: 26.2%, 100.0% time-series percentile[10] - **Bailong Oriental (601339.SH)**: 22.9%, 100.0% time-series percentile[10] - **Zijin Bank (601860.SH)**: 20.2%, 100.0% time-series percentile[10] - **Bailong Chuangyuan (605016.SH)**: 19.5%, 100.0% time-series percentile[10] - **Hengshun Vinegar (600305.SH)**: 17.8%, 100.0% time-series percentile[10] - **Qingfangcheng (600790.SH)**: 17.7%, 99.6% time-series percentile[10] - **Shandong Steel (600022.SH)**: 17.7%, 99.6% time-series percentile[10] - **Shengda Forestry (002259.SZ)**: 17.5%, 100.0% time-series percentile[10] - **Taoli Bread (603866.SH)**: 17.2%, 100.0% time-series percentile[10] - **Jiangsu Sopo (600746.SH)**: 16.8%, 100.0% time-series percentile[10] 3. Broad-Based Indices - **5-Day Average Results**: - **Shanghai Composite Index**: Large Order Ratio 73.7% (82.0% percentile), Net Active Buy Ratio 2.2% (3.7% percentile)[12] - **SSE 50**: Large Order Ratio 71.7% (58.2% percentile), Net Active Buy Ratio 5.8% (92.6% percentile)[12] - **CSI 300**: Large Order Ratio 73.0% (41.0% percentile), Net Active Buy Ratio 2.9% (20.9% percentile)[12] - **CSI 500**: Large Order Ratio 73.8% (86.9% percentile), Net Active Buy Ratio 1.5% (3.3% percentile)[12] - **ChiNext Index**: Large Order Ratio 70.5% (6.1% percentile), Net Active Buy Ratio 0.1% (14.8% percentile)[12] 4. Industry-Level Analysis - **Top Industries by 5-Day Average**: - **Steel**: Large Order Ratio 79.0% (79.1% percentile), Net Active Buy Ratio 12.7% (0.8% percentile)[13] - **Agriculture, Forestry, Animal Husbandry, and Fishery**: Large Order Ratio 77.1% (87.7% percentile), Net Active Buy Ratio 10.8% (3.3% percentile)[13] - **Food and Beverage**: Large Order Ratio 71.5% (95.5% percentile), Net Active Buy Ratio 10.1% (32.8% percentile)[13] - **Real Estate**: Large Order Ratio 78.7% (70.9% percentile), Net Active Buy Ratio 8.8% (9.8% percentile)[13] - **Consumer Services**: Large Order Ratio 75.8% (32.4% percentile), Net Active Buy Ratio 8.9% (13.9% percentile)[13] 5. ETF Analysis - **Top 10 ETFs by Large Order Ratio**: - **Haifutong Shanghai Urban Investment Bond ETF (511220.SH)**: 93.4%, 63.5% percentile[15] - **Fortune Military Industry ETF (512710.SH)**: 92.1%, 100.0% percentile[15] - **Guotai CSI A500 ETF (159338.SZ)**: 91.5%, 19.7% percentile[15] - **Guotai 10-Year Treasury ETF (511260.SH)**: 91.5%, 91.8% percentile[15] - **Penghua National Defense ETF (512670.SH)**: 90.7%, 99.6% percentile[15] - **Top 10 ETFs by Net Active Buy Ratio**: - **Huaxia Food and Beverage ETF (515170.SH)**: 18.2%, 99.6% percentile[16] - **Yinhua 5G Communication ETF (159994.SZ)**: 16.7%, 100.0% percentile[16] - **E Fund CSI 300 Non-Bank ETF (512070.SH)**: 16.0%, 95.9% percentile[16] - **Huatai-PineBridge Dividend Low Volatility ETF (512890.SH)**: 15.7%, 94.3% percentile[16] - **Fortune Agriculture ETF (159825.SZ)**: 15.2%, 96.7% percentile[16]

Quantitative Factors and Construction Methods - **Factor Name**: Large Order Transaction Amount Ratio **Construction Idea**: This factor captures the buying behavior of large funds by analyzing the proportion of large order transaction amounts relative to the total daily transaction amount[7] **Construction Process**: 1. Use tick-by-tick transaction data to identify buy and sell orders based on the sequence numbers of bids and asks 2. Filter transactions by order size to identify large orders 3. Calculate the proportion of large buy order transaction amounts to the total daily transaction amount **Formula**: $ \text{Large Order Transaction Amount Ratio} = \frac{\text{Large Buy Order Transaction Amount}}{\text{Total Daily Transaction Amount}} $ **Evaluation**: This factor effectively reflects the buying behavior of large funds and provides insights into market dynamics[7] - **Factor Name**: Net Active Buy Amount Ratio **Construction Idea**: This factor measures the active buying behavior of investors by analyzing the net active buy amount as a proportion of the total daily transaction amount[7] **Construction Process**: 1. Use tick-by-tick transaction data to classify each transaction as either active buy or active sell based on the buy/sell indicator 2. Calculate the net active buy amount by subtracting the active sell amount from the active buy amount 3. Compute the proportion of the net active buy amount to the total daily transaction amount **Formula**: $ \text{Net Active Buy Amount Ratio} = \frac{\text{Active Buy Amount} - \text{Active Sell Amount}}{\text{Total Daily Transaction Amount}} $ **Evaluation**: This factor provides a clear representation of investors' active buying behavior and is useful for tracking market sentiment[7] --- Factor Backtesting Results - **Large Order Transaction Amount Ratio**: - Top 5 stocks with the highest 5-day average values: 1. *Zaisen Technology (603601.SH)*: 91.4%, time-series percentile: 99.6%[9] 2. *Annie Shares (002235.SZ)*: 91.2%, time-series percentile: 98.4%[9] 3. *Kangxin New Materials (600076.SH)*: 87.9%, time-series percentile: 99.6%[9] 4. *Guangtian Group (002482.SZ)*: 87.6%, time-series percentile: 100.0%[9] 5. *Zhongtai Chemical (002092.SZ)*: 87.5%, time-series percentile: 100.0%[9] - **Net Active Buy Amount Ratio**: - Top 5 stocks with the highest 5-day average values: 1. *Hot Scene Biology (688068.SH)*: 15.9%, time-series percentile: 100.0%[10] 2. *Lanxiao Technology (300487.SZ)*: 14.5%, time-series percentile: 100.0%[10] 3. *Yilian Technology (301631.SZ)*: 14.0%, time-series percentile: 100.0%[10] 4. *Xiamen Bank (601187.SH)*: 14.0%, time-series percentile: 99.2%[10] 5. *Huamao Technology (603306.SH)*: 13.1%, time-series percentile: 99.6%[10] --- Additional Factor Testing Results - **Large Order Transaction Amount Ratio for Broad-Based Indices**: - *Shanghai Composite Index*: 5-day average: 73.0%, percentile: 59.0%[12] - *CSI 300*: 5-day average: 72.0%, percentile: 33.6%[12] - *ChiNext Index*: 5-day average: 71.4%, percentile: 14.8%[12] - **Net Active Buy Amount Ratio for Broad-Based Indices**: - *Shanghai Composite Index*: 5-day average: 0.8%, percentile: 7.8%[12] - *CSI 300*: 5-day average: 2.6%, percentile: 4.9%[12] - *ChiNext Index*: 5-day average: 3.5%, percentile: 2.5%[12] - **Large Order Transaction Amount Ratio for Industries**: - *Non-Bank Financials*: 5-day average: 78.5%, percentile: 95.9%[13] - *Steel*: 5-day average: 78.2%, percentile: 43.9%[13] - *Electric Power and Utilities*: 5-day average: 77.6%, percentile: 13.9%[13] - **Net Active Buy Amount Ratio for Industries**: - *Non-Bank Financials*: 5-day average: 6.3%, percentile: 0.8%[13] - *Electric Power and Utilities*: 5-day average: 1.8%, percentile: 1.6%[13] - *Steel*: 5-day average: 1.4%, percentile: 9.4%[13] - **Large Order Transaction Amount Ratio for ETFs**: - Top ETF: *Guotai Zhongzheng A500 ETF (159338.SZ)*: 91.5%, percentile: 20.1%[15] - **Net Active Buy Amount Ratio for ETFs**: - Top ETF: *Guotai SSE 10-Year Treasury Bond ETF (511260.SH)*: 25.9%, percentile: 87.7%[16]

- The report introduces two key tracking indicators: "Large Order Transaction Amount Proportion" and "Net Active Buy Amount Proportion" [7] - The "Large Order Transaction Amount Proportion" is designed to capture the buying behavior of large funds. It is calculated by identifying large orders from transaction data and computing the proportion of large buy orders' transaction amounts relative to the total daily transaction amount [7] - The "Net Active Buy Amount Proportion" reflects investors' active buying behavior. It is derived by distinguishing active buy and sell transactions from transaction data, calculating the net active buy amount (active buy amount minus active sell amount), and expressing it as a proportion of the total daily transaction amount [7] Factor Backtesting Results - For individual stocks, the top 10 stocks with the highest 5-day average "Large Order Transaction Amount Proportion" include Xinhua Du (90.6%, 99.2% percentile), Beichen Industrial (89.1%, 98.8% percentile), and Zhongyou Engineering (88.8%, 100.0% percentile) [9] - For individual stocks, the top 10 stocks with the highest 5-day average "Net Active Buy Amount Proportion" include Senying Windows (22.3%, 100.0% percentile), Huitong Group (20.0%, 100.0% percentile), and Yuandong Biotech (19.6%, 100.0% percentile) [10] - For broad-based indices, the 5-day average "Large Order Transaction Amount Proportion" ranges from 71.7% (Shanghai 50 Index) to 74.3% (China Securities 500 Index), while the "Net Active Buy Amount Proportion" ranges from -5.2% (Shanghai 50 Index) to 1.9% (China Securities 500 Index) [12] - For industries, the 5-day average "Large Order Transaction Amount Proportion" is highest in the banking sector (80.6%, 86.5% percentile) and lowest in the electronics sector (70.8%, 16.4% percentile). The "Net Active Buy Amount Proportion" is highest in the steel sector (7.9%, 75.8% percentile) and lowest in the banking sector (-14.6%, 3.3% percentile) [13] - For ETFs, the top 10 ETFs with the highest 5-day average "Large Order Transaction Amount Proportion" include Guotai CSI A500 ETF (92.3%, 36.1% percentile) and Guotai SSE 10-Year Treasury Bond ETF (90.7%, 89.3% percentile) [15] - For ETFs, the top 10 ETFs with the highest 5-day average "Net Active Buy Amount Proportion" include Southern SSE STAR Chip ETF (27.5%, 100.0% percentile) and E Fund Hang Seng Dividend Low Volatility ETF (23.6%, 99.6% percentile) [16]

Group 1 - The report aims to track large purchases and net active purchases through transaction detail data, building relevant indicators [1] - The top five industries for large purchases in the last five trading days are: Banking, Real Estate, Steel, Comprehensive, and Textile & Apparel [2] - The top five industries for net active purchases in the last five trading days are: Banking, Transportation, Pharmaceuticals, Real Estate, and Oil & Petrochemicals [2] Group 2 - The top five ETFs for large purchases in the last five trading days are: Guotai CSI A500 ETF, Guotai SSE 10-Year Treasury ETF, Harvest S&P Oil & Gas Exploration and Production Selected Industry ETF, Southern Growth Enterprise Board AI ETF, and Hai Futong SSE Urban Investment Bond ETF [2] - The top five ETFs for net active purchases in the last five trading days are: Guotai SSE 10-Year Treasury ETF, E Fund CSI 300 Non-Bank ETF, Yinhua SSE Sci-Tech Innovation Board 100 ETF, Huabao CSI Nonferrous Metals ETF, and Penghua CSI Liquor ETF [2]