数据速看： 1.主力资金： 近两周主力资金合计净流出-2170.43亿元，有两个行业实现主力资金净流入，分别为银行和综合，主力资金净流出额前三的行业为机械设 备、医药生物和国防军工。 2.融资融券数据： 当前市场融资融券余额为21467.95亿元，较上期上升10.24%，其中融资余额21319.52亿元，融券余额148.43亿元。本期两融日均交易额 为2432.61亿元，较上期上升33.96%，其中融资日均净买入2426.33亿元，较两周前上升34.04%，融券日均净卖出6.28亿元，较上期上升8.35%。近两周融资 净买入前三的行业分别为电子、计算机和通信；融券净卖出前三的行业分别为非银金融、电子和计算机。 3.涨跌情况： 近两周全市场上涨家数高于下跌家数，近两周涨幅前三的行业为通信、电子和计算机，跌幅前二的行业为银行和钢铁。 4.强弱分析： 近两周全部A股强弱分析得分为6.55，沪深300强弱分析得分6.57，创业板强弱分析得分6.62，科创板强弱分析得分6.76，处于中性偏强。 以下文章来源于申万宏源证券上海分公司 ，作者李金玲 申万宏源证券上海分公司 . 申万宏源证券上海分公司官微，能为您提供账户开立 ...

股市资金流向图：今日沪深两市主力资金净流入56.44亿元，占比0.31%；大单资金净流出94.74亿元，占比0.51%；小单资金净流 入133.96亿元，占比0.73%。 ...

Report Information - Report Title: Stock Index Futures Panoramic Daily Report 2025/7/22 [1] - Researcher: Liao Hongbin [3] - Futures Practitioner Qualification Number: F30825507 [3] - Futures Investment Consulting Practitioner Certificate Number: Z0020723 [3] Investment Rating - Not provided Core Viewpoints - A total of 1,540 A-share listed companies disclosed their semi-annual performance forecasts for 2025 as of July 18, 2025, with 674 companies expecting good news, a pre - happy ratio of about 43.77% [2] - On July 21, the 1 - year and 5 - year - plus loan prime rates (LPR) remained unchanged from the previous month [2] - A - share major indices rose collectively, with the Shanghai Composite Index up 0.62%, the Shenzhen Component Index up 0.84%, and the ChiNext Index up 0.61%. The trading volume of the two markets increased for four consecutive trading days [2] - The real estate market still drags down fixed - asset investment growth, and the support of trade - in for social retail sales has weakened, but the loose monetary policy has shown results in financial data, which may be reflected in subsequent economic indicators [2] - As the Politburo meeting at the end of July approaches, market bulls may make early arrangements, and stock indices still have long - term upward potential. It is recommended to buy on dips with a light position [2] Summary by Relevant Catalogs Futures Contract Data - IF, IH, IC, and IM contracts' main and sub - main contracts all showed upward trends in price changes compared to the previous period [2] - The spreads between different contracts such as IC - IF, IF - IH, etc. also had corresponding changes, with some increasing and some decreasing [2] - The net positions of the top 20 in futures contracts mostly decreased, such as IH with a decrease of 1,666.0 and IF with a decrease of 518.0 [2] Basis and Market Sentiment Data - The basis of the main contracts of IF, IH, IC, and IM all increased compared to the previous period [2] - The margin trading balance increased by 1,338.16 billion yuan, and the A - share trading volume increased by 155.82 billion yuan [2] - The reverse repurchase operation volume increased by 140.75 billion yuan, and the north - bound trading volume increased by 2,148.0 [2] - The MLF net injection decreased by 465.57 billion yuan [2] Option and Volatility Data - The closing price of the IO at - the - money call option (2508) increased by 18.40, and its implied volatility increased by 0.26% [2] - The closing price of the IO at - the - money put option (2508) decreased by 19.20, and its implied volatility increased by 0.26% [2] - The 20 - day volatility of the CSI 300 index decreased by 0.42%, and the trading volume PCR increased by 1.67% [2] - The position PCR increased by 5.72% [2] Technical and Market Analysis Data - The Wind market strength of all A - shares decreased by 1.50, and the technical aspect decreased by 2.60 [2] - The capital aspect decreased by 0.30 [2] Key Events to Watch - On July 24, 15:15 - 16:30, the preliminary SPGI manufacturing PMI values for France, Germany, the Eurozone, and the UK in July will be released [3] - On July 24, 20:15, the European Central Bank will announce its interest rate decision [3] - On July 24, 20:30, the number of initial jobless claims in the US for the week ending July 19 will be released, and at 21:45, the preliminary SPGI manufacturing PMI value for the US in July will be released [3] - On July 27, 9:30, China's industrial enterprise profits above designated size for June will be released [3]

股市资金流向图：今日沪深两市主力资金净流入67.40亿元，占比0.44%；大单资金净流出78.49亿元，占比0.51%；小单资金净流 入60.46亿元，占比0.39%。 ...

以下文章来源于申万宏源证券上海分公司 ，作者李金玲 申万宏源证券上海分公司 . 2.融资融券数据： 当前市场融资融券余额为18737.13亿元，较上期上升1.92%，其中融资余 额18605.04亿元，融券余额132.09亿元。本期两融日均交易额为1338.13亿元，较上期上升 12.86%，其中融资日均净买入1332.50亿元，较两周前上升12.86%，融券日均净卖出5.63 亿元，较上期上升11.65%。近两周融资净买入前三的行业分别为有色金属、非银金融和电力 设备；融券净卖出前三的行业分别为电子、基础化工和电力设备。 3.涨跌情况： 近两周全市场上涨家数高于下跌家数，近两周涨幅前三的行业为钢铁、建筑材 料和综合，下跌的行业为汽车。 4.强弱分析： 近两周全部A股强弱分析得分为6.19，沪深300强弱分析得分6.29，创业板强 弱分析得分5.97，科创板强弱分析得分5.91，处于中性区间。 测量结果：健康 诊断说明： 本周上证综指冲上3500点，7月11日两市成交额也创4个月新高，虽然后市面临 关键节点，但积极因素仍在累积。后续继续看好科技及港股机会。 申万宏源证券上海分公司官微，能为您提供账户开立、软件 ...

Group 1 - The core point of the article indicates that the net inflow of main funds in the Shanghai and Shenzhen stock markets today is 11.398 billion, accounting for 0.78% [1] - Large single fund outflow is 3.670 billion, representing 0.25% [1] - Small single fund inflow is 3.385 billion, which makes up 0.23% [1]

Group 1: Main Points of the Article - The article emphasizes the importance of intraday trading behaviors of major funds, particularly during specific market moments characterized by significant price drops, low stock prices, and high trading volumes [1][3][4] - A standardized average transaction amount factor (SATD) is introduced to capture the trading behavior of major funds, which is derived from the average transaction amount during special moments divided by the average transaction amount for the entire day [1][17][18] Group 2: Trading Behavior Based on Price Movements - The SATD factor shows a strong predictive ability for future stock returns, especially during moments of price decline, with a higher performance observed as the decline deepens [1][54] - The construction of the SATD factor is improved by incorporating tick-by-tick transaction data, allowing for a distinction between "main buy" and "main sell" transactions [1][59][62] Group 3: Trading Behavior Based on Price Levels - The SATD factor constructed during the lowest price moments demonstrates a strong predictive capability for future returns, outperforming factors constructed during the highest price moments [1][82][88] - The performance of the SATD factor improves as the threshold for defining low price moments becomes stricter [1][82] Group 4: Trading Behavior Based on Trading Volume - The SATD factor derived from the highest trading volume moments also exhibits strong predictive power, with a RankIC mean of 10.69% and a monthly win rate of 86% [1][3] - The article highlights the effectiveness of the composite factor constructed from various SATD factors across different market conditions and stock pools [1][3][4] Group 5: Composite Factor Performance - The composite factor, which combines various SATD factors, achieves a monthly RankIC mean of 10.33% and an annualized RankICIR of 4.32, indicating robust stock selection effectiveness across different indices and styles [1][3][4] - The composite factor maintains strong predictive capabilities even after traditional factors are stripped away, demonstrating its reliability in forecasting future stock returns [1][3][4]

Quantitative Models and Factor Construction Quantitative Models and Construction Process - **Model Name**: Standardized Average Transaction Amount Factor (SATD) **Construction Idea**: This factor captures the trading behavior of major funds by normalizing the average transaction amount during specific time periods against the daily average transaction amount[1][25][26] **Construction Process**: 1. Calculate the average transaction amount for specific time periods: $ ATD_{P} = \frac{\sum_{t \in P} Amt_{t}}{\sum_{t \in P} DealNum_{t}} $ Here, $ ATD_{P} $ represents the average transaction amount for the specific time period $ P $, $ Amt_{t} $ is the transaction amount at time $ t $, and $ DealNum_{t} $ is the number of transactions at time $ t $[26][27] 2. Calculate the daily average transaction amount: $ ATD_{T} = \frac{\sum_{t \in T} Amt_{t}}{\sum_{t \in T} DealNum_{t}} $ Here, $ ATD_{T} $ represents the daily average transaction amount[27] 3. Normalize the specific time period's average transaction amount: $ SATD_{P} = \frac{ATD_{P}}{ATD_{T}} $ Here, $ SATD_{P} $ is the standardized average transaction amount factor for the specific time period $ P $[28][29] Quantitative Factors and Construction Process - **Factor Name**: Downward Price Movement Factor **Construction Idea**: This factor identifies the predictive power of major fund activity during periods of price decline[39][40] **Construction Process**: 1. Classify minute-level price movements into upward, downward, and flat periods using the following formulas: $ UpFlag_{t} = \begin{cases} 1, & if\ ret_{i} > 0 \\ 0, & if\ ret_{i} \leq 0 \end{cases} $ $ DownFlag_{t} = \begin{cases} 0, & if\ ret_{i} \geq 0 \\ 1, & if\ ret_{i} < 0 \end{cases} $ $ ZeroFlag_{t} = \begin{cases} 0, & if\ ret_{i} \neq 0 \\ 1, & if\ ret_{i} = 0 \end{cases} $ Here, $ ret_{i} $ represents the minute-level return[39][40] 2. Calculate the average transaction amount for downward periods and normalize it against the daily average transaction amount: $ SATDDown = \frac{ATD_{DownFlag}}{ATD_{T}} $[43][44] - **Factor Name**: Maximum Downward Price Movement Factor **Construction Idea**: This factor focuses on the periods with the largest price declines, hypothesizing that major funds are more active during these times[59][60] **Construction Process**: 1. Rank minute-level price movements by their magnitude of decline 2. Select the top N% of minutes with the largest price declines 3. Calculate the average transaction amount for these periods and normalize it against the daily average transaction amount[59][60] - **Factor Name**: Lowest Price Factor **Construction Idea**: This factor identifies periods when the stock price is at its lowest, hypothesizing that major funds are more active during these times[87][89] **Construction Process**: 1. Rank minute-level prices from lowest to highest 2. Select the bottom N% of minutes with the lowest prices 3. Calculate the average transaction amount for these periods and normalize it against the daily average transaction amount[89][91] - **Factor Name**: Highest Volume Factor **Construction Idea**: This factor identifies periods with the highest trading volume, hypothesizing that these periods contain more significant information[109][110] **Construction Process**: 1. Rank minute-level trading volumes from highest to lowest 2. Select the top N% of minutes with the highest volumes 3. Calculate the average transaction amount for these periods and normalize it against the daily average transaction amount[109][110] - **Factor Name**: Volume-Price Divergence Factor **Construction Idea**: This factor identifies periods where trading volume and price movements are negatively correlated, hypothesizing that these periods contain more significant information[128][129] **Construction Process**: 1. Calculate the correlation coefficient between transaction prices and volumes for each minute 2. Rank minutes by their correlation coefficients 3. Select the bottom N% of minutes with the lowest correlation coefficients 4. Calculate the average transaction amount for these periods and normalize it against the daily average transaction amount[129][134] - **Factor Name**: Composite Factor **Construction Idea**: This factor combines the most effective factors (e.g., maximum downward price movement, lowest price, and highest volume factors) to enhance predictive power[160][161] **Construction Process**: 1. Combine the selected factors using equal weighting: $ CompositeFactor = DownwardFactor + LowestPriceFactor + HighestVolumeFactor $[160][161] Backtesting Results for Factors - **Downward Price Movement Factor**: RankIC Mean = 6.84%, Annualized RankICIR = 3.23, Monthly Win Rate = 83.93%[46][48] - **Maximum Downward Price Movement Factor**: RankIC Mean = 7.31%, Annualized RankICIR = 4.04, Monthly Win Rate = 86.49%[60][61] - **Lowest Price Factor**: RankIC Mean = 7.21%, Annualized RankICIR = 4.52, Monthly Win Rate = 91.96%[91][92] - **Highest Volume Factor**: RankIC Mean = 9.70%, Annualized RankICIR = 3.67, Monthly Win Rate = 83.04%[110][113] - **Volume-Price Divergence Factor**: RankIC Mean = 5.41%, Annualized RankICIR = 3.20, Monthly Win Rate = 81.25%[134][135] - **Composite Factor**: RankIC Mean = 10.33%, Annualized RankICIR = 4.32, Monthly Win Rate = 90.18%[160][161]

Group 1 - Main capital outflow in the last two weeks totaled 138.204 billion, with net inflows in the home appliance and coal industries, while the top three industries for net outflows were computer, basic chemicals, and national defense [2] - Current margin trading balance is 1,838.493 billion, up 0.94% from the previous period, with financing balance at 1,826.553 billion and securities lending balance at 11.940 billion [2] - In the last two weeks, the number of rising stocks exceeded the number of falling stocks, with the top three industries in terms of growth being communication, computer, and electronics, while the top three industries with declines were beauty care, oil and petrochemicals, and pharmaceuticals [2] Group 2 - The overall A-share strength analysis score is 5.97, with the CSI 300 score at 5.83, the ChiNext score at 6.28, and the STAR Market score at 6.40, indicating a neutral range [2] - The Shanghai Composite Index has broken through the March high, but there is still a gap to a bull market initiation, with short-term indices possibly reaching higher levels but unlikely to achieve significant long-term increases [3] - Future focus on opportunities in robotics and national defense industries, with a recommendation to pay attention to Hong Kong stock opportunities [3]

Group 1 - The net inflow of main funds in the Shanghai and Shenzhen stock markets today is 393 million, accounting for 0.03% [1] - The net outflow of large single funds is 5.657 billion, accounting for 0.44% [1] - The net inflow of small single funds is 7.990 billion, accounting for 0.62% [1]