Core Viewpoint - The article discusses the relationship between trading volume and price movements, emphasizing that volume can be a leading indicator for price changes, and introduces a volume-weighted moving average (VWMA) as a more responsive alternative to traditional moving averages [1][2]. Group 1: Volume and Price Relationship - The concept of volume leading price movements is explored, suggesting that if volume is a leading variable, it should have a cumulative effect on price movements [1]. - A method is proposed to analyze this relationship by applying a volume-weighted approach to price movements, comparing it to traditional moving averages [1]. Group 2: VWMA vs. EMA - The VWMA formula is presented as VWMA: SUM(V*C, N) / SUM(V, N), where V is volume and C is price, with a common period of 20 days used for comparison [2]. - The VWMA is shown to react more quickly to price changes compared to the Exponential Moving Average (EMA), indicating its effectiveness in reflecting investor participation in price movements [2][4]. Group 3: Indicator Analysis - The article references the book "Trend is Gold," which provides a comprehensive analysis of the reliability and backtesting results of various indicators, including VWMA, demonstrating its superiority over EMA [4]. - The emphasis on volume in the VWMA approach is highlighted as a way to capture the significance of trading days with higher volume, making it more relevant to market dynamics [4].

裸K分析可能是最早最原始的技术分析方法，有个正式的名称叫：价格行为分析。这一派的理论基石是：价格包含了一切信息。因此，基于这一理 论，纯粹的价格行为分析是不看成交量的，以价格变化为唯一指标。 现在比较被接受的价格行为分析一般要结合成交量，即大家常说的量价分析，除了量价，其它什么都不看。 欧马体系中，量价分析是一个重要组成部分，对图形应该说是相当重视的，但并不把量价作为唯二要素。我总结CANSLIM和SEPA两个体系，共同 的进化是在量价的基础上增加了：势和锚两个要素，也即更加注重对市场局面、产业内涵的审视和联系。 这种要素叠加，有利于缩小选股范围，而缩小范围是符合市场幂律原理的。通俗一点讲，就是要定位精准，要有特点和亮点，要做利基市场，只取 一瓢饮。 价格行为分析好比风清扬的独孤九剑，只讲招式无需其它，只求一个快字，所谓天下武功唯快不破。但正因为此，对招式就必须更加讲究，对应到 股票的走势结构，就是形态要求极致完美，但凡拖泥带水之形皆不可用，如此才能极致收敛选股范围，到达幂律之巅。 在实战中，我收集和运用过一些极致形态，比欧奈尔和马克米讷维尼书中讲的更加严格。窃以为如果只凭量价分析的话，只有类这样的形态才可以 ...

Core Viewpoint - The article emphasizes the importance of focusing on "quantity" over "price" in the current economic environment, highlighting that the constraints on price are increasing while the clarity of quantity as a mainline is evident [4][25][26]. Group 1: Asset Perspectives - Equity investment should focus on identifying certainty from "quantity," with a low volatility environment expected to persist, and an upward movement in the market is still pending verification [16][18]. - The bond market is expected to see a defined interest rate range influenced by central bank policies, with a focus on long-term bond positioning [19][20]. - The currency exchange rate is anticipated to seek stability, with the RMB/USD exchange rate expected to remain within a narrow range due to policy interventions [20][21]. - Gold is viewed as a long-term strategic investment, with expectations of price increases driven by global order restructuring [21]. Group 2: Economic Analysis - The article discusses the relationship between exports and employment, indicating that a 1% shock in exports could impact approximately 1.053 million jobs, emphasizing the importance of stabilizing employment in the current economic climate [7][28]. - The analysis of external demand highlights the need for a balanced approach to internal and external economic pressures, with a focus on increasing domestic demand to counteract potential declines in trade surplus [40][41]. - The article outlines potential growth areas for exports, including new energy, metal products, and machinery, with a significant increase in exports to countries involved in the Belt and Road Initiative [12][55]. Group 3: Investment Opportunities - Investment strategies are shifting from construction-focused to equipment acquisition, driven by technological innovation and urban renewal projects, with significant government support for high-end equipment purchases [62]. - The article identifies key sectors for investment growth, including technology innovation, urban infrastructure updates, and industrial backup, with specific emphasis on the demand for advanced equipment in sectors like robotics and data processing [62].

Summary of Conference Call Notes Industry Overview - The report focuses on the A-share market and its valuation dynamics, indicating that since mid-April, the market has been primarily driven by valuation increases, reaching a neutral position [1][3]. Core Insights and Arguments - **Market Outlook**: The A-share market is expected to have upward potential in the second half of the year, although fluctuations are anticipated due to policy catalysts [1][3]. - **Capital Inflows**: Indicators such as institutional net buying and large financing balances show that capital is still flowing into the market, supporting current valuations [1][4]. - **Earnings Structure**: The earnings center of the A-share market has shifted to a neutral to slightly positive position, suggesting that long-term investments should wait for short-term or earnings lows to enhance safety margins [1][6]. - **Small vs. Large Cap Stocks**: Small-cap stocks are currently experiencing low trading volumes compared to large-cap stocks, which may lead to short-term outperformance of small caps. However, large caps are expected to maintain an advantage post fundamental recovery [1][7]. - **Sector Rotation**: The market is currently in a neutral to slightly positive earnings position, which may accelerate sector rotation. It is advised to invest in undervalued sectors with low earnings, such as electronics, semiconductor technology, and lithium batteries, while being cautious of crowded high-valuation sectors like pharmaceuticals [1][8]. Important but Overlooked Content - **Risk Factors**: There are risks associated with potential tariff changes or policy shifts that could lead to reduced trading volumes and adjustments in market dynamics [1][10][11]. - **Profitability Indicators**: The divergence between the support of the profit center and the profitability of early investors indicates a weakening overall trading profit effect, necessitating attention to capital flow and volume changes [2][12]. - **Market Sentiment**: Recent trends show that while institutional net buying and financing balances have surged, there is a need to monitor for potential cooling effects from policy changes that could lead to volume adjustments [10][11][13]. This summary encapsulates the key points from the conference call, providing insights into the current state and future outlook of the A-share market, along with associated risks and sector-specific recommendations.

Quantitative Models and Construction Methods GRU Model - **Model Name**: GRU - **Model Construction Idea**: The GRU model is used to mine volume and price information, and this report explores its ability to incorporate financial information[2][14]. - **Model Construction Process**: - **Data Range**: 20130101-20250430, all market stocks (excluding Beijing Stock Exchange)[16] - **Input**: Each stock has one sample at the end of each month, containing volume and price information for the past 240 trading days, including 7 fields: opening price, highest price, lowest price, closing price, trading volume, trading amount, and turnover rate. Each field is standardized using z-score for 240 values[16]. - **Prediction Target**: Next month's return rate standardized by cross-section (opening price at the beginning of the month to closing price at the end of the month)[16]. - **Training Set**: Samples from the past 6 years, divided into training and validation sets in a 4:1 ratio according to time sequence[16]. - **Training Method**: Rolling training every month, early stopping if the loss function does not decrease for 10 consecutive rounds[16]. - **Model Evaluation**: The GRU model can simultaneously mine volume and price information and financial information. The high-frequency processing of financial information improves the model results to some extent[2][18]. - **Model Testing Results**: - **Annualized Excess Return**: 8.75% - **IR**: 2.25 - **Maximum Drawdown**: 4.71%[3][19][23] GRU Model with Financial Information - **Model Name**: GRU with Financial Information - **Model Construction Idea**: Incorporating financial information into the GRU model to improve its performance[4][24]. - **Model Construction Process**: - **Simple Splicing of Financial Information**: Financial data is calculated as TTM value according to the latest available quarterly report for each trading day, then spliced into new columns. The matrix containing volume and price information and fundamental information is standardized and input into the GRU network[25]. - **Adjusted Financial Information**: Assuming the TTM value of financial indicators grows steadily at the quarterly growth rate, the daily adjustment formula for TTM values is: $$ \mathrm{DFTTM}_{\mathrm{q1}}={\frac{\mathrm{FactorTTM}_{\mathrm{q1}}-\mathrm{FactorTTM}_{\mathrm{q0}}}{a b s\big(\mathrm{FactorTTM}_{\mathrm{q0}}\big)}} $$ $$ \mathrm{Factort} = \mathrm{FactorTTMq} + \mathrm{abs(FactorTTMq)} \times \left(\frac{90}{1}\right) $$ where t is the trading day, q is the financial report period (March 31, June 30, September 30, December 31)[36][38]. - **Model Evaluation**: Incorporating financial information improves the overall performance of the baseline model, especially before 2022. However, after 2023, the improvement is weaker or even negative[4][35][42]. - **Model Testing Results**: - **Annualized Excess Return**: 7.76% - **IR**: 1.65 - **Maximum Drawdown**: 5.40%[41][44] GRU Model with Simplified Financial Information - **Model Name**: GRU with Simplified Financial Information - **Model Construction Idea**: Simplifying the financial indicators to only include important ones like net profit TTM and market value[45]. - **Model Construction Process**: - **Simplified Financial Information**: Only retaining important indicators like net profit TTM and market value, and incorporating them into the GRU model[45]. - **Model Evaluation**: Simplifying the financial indicators improves the overall performance of the model, especially before 2022. After 2023, the improvement is weaker but still positive[45][55]. - **Model Testing Results**: - **Annualized Excess Return**: 9.97% - **IR**: 1.93 - **Maximum Drawdown**: 5.70%[51][52] Mixed Frequency Model - **Model Name**: Mixed Frequency Model (barra5d + daily GRU) - **Model Construction Idea**: Combining long-term and short-term prediction capabilities by integrating barra5d and daily GRU models[56][65]. - **Model Construction Process**: - **Input**: Combining the daily GRU model with the barra5d model, which is trained on 240-minute intraday data to predict the next 1-5 days' returns[56][65]. - **Model Evaluation**: The mixed frequency model significantly improves the performance of the barra5d model, especially after October 2024. Adding fundamental information further stabilizes the annual excess performance[65][72][80]. - **Model Testing Results**: - **Annualized Excess Return**: 11.82% - **IR**: 2.39 - **Maximum Drawdown**: 5.70%[77][78] Model Backtesting Results GRU Model - **Annualized Excess Return**: 8.75% - **IR**: 2.25 - **Maximum Drawdown**: 4.71%[3][19][23] GRU Model with Financial Information - **Annualized Excess Return**: 7.76% - **IR**: 1.65 - **Maximum Drawdown**: 5.40%[41][44] GRU Model with Simplified Financial Information - **Annualized Excess Return**: 9.97% - **IR**: 1.93 - **Maximum Drawdown**: 5.70%[51][52] Mixed Frequency Model (barra5d + daily GRU) - **Annualized Excess Return**: 11.82% - **IR**: 2.39 - **Maximum Drawdown**: 5.70%[77][78]

Quantitative Models and Construction GRU Model - **Model Name**: GRU baseline model [2][3][14] - **Model Construction Idea**: The GRU model is designed to extract information from historical price and volume data to predict future returns. It serves as a baseline to evaluate the impact of adding financial data [14][15]. - **Model Construction Process**: - **Data Range**: All A-share stocks (excluding Beijing Stock Exchange) from 2013-01-01 to 2025-04-30 [16]. - **Input Features**: Past 240 trading days' price and volume data, including open price, high price, low price, close price, trading volume, turnover, and turnover rate. Each feature is standardized using z-score [16]. - **Prediction Target**: Next month's standardized return (from the opening price at the beginning of the month to the closing price at the end of the month) [16]. - **Training**: Rolling training with a 4:1 split between training and validation sets over the past six years. Early stopping is applied if the loss function does not decrease for 10 consecutive iterations [16]. - **Portfolio Construction**: Enhanced portfolio based on the CSI 1000 index, with constraints on stock weight deviation (1%), style deviation (within 0.1 standard deviation), and industry deviation (1%). Monthly rebalancing with a turnover rate of 50% per side [18]. - **Model Evaluation**: The GRU model demonstrates stable performance in extracting price-volume information, achieving consistent excess returns across years [19]. GRU Model with Financial Data - **Model Name**: GRU with financial data [4][24][25] - **Model Construction Idea**: Incorporates financial data into the GRU model to enhance its ability to predict future returns by combining price-volume and fundamental information [14][24]. - **Model Construction Process**: - **Financial Data**: Includes 20 fields from income statements, such as revenue, cost of goods sold, management expenses, R&D costs, and net profit. Data is converted to TTM (trailing twelve months) values [24][25]. - **Integration**: Financial data is appended to the price-volume matrix, standardized, and input into the GRU model [25]. - **Adjustment**: To address frequency mismatches, financial data is adjusted daily based on the assumption of stable TTM growth rates. The adjustment formula is: $$ \text{Factor}_{t} = \text{Factor}_{\text{TTM}_{q}} + \text{abs}(\text{Factor}_{\text{TTM}_{q}}) \cdot \frac{90}{\text{days in quarter}} $$ where \( t \) is the trading day and \( q \) is the financial reporting quarter [36][38]. - **Model Evaluation**: Adding financial data improves performance before 2023 but weakens it afterward. Adjusting financial data enhances overall performance, especially in earlier years [42][45]. Mixed-Frequency GRU Model - **Model Name**: Mixed-frequency GRU model (barra5d + daily GRU) [5][56][65] - **Model Construction Idea**: Combines long-term and short-term prediction capabilities by integrating daily and intraday GRU models [56][65]. - **Model Construction Process**: - **Daily GRU**: Trained on 240 trading days of daily data to predict monthly returns [16]. - **Intraday GRU (barra5d)**: Trained on 240 minutes of intraday data to predict 5-day returns, neutralized for Barra style factors [56]. - **Integration**: The two models are combined to leverage their complementary strengths [65]. - **Model Evaluation**: The mixed-frequency model significantly improves stability and excess returns, addressing weaknesses in individual models [67][68]. Mixed-Frequency GRU with Financial Data - **Model Name**: Mixed-frequency GRU with financial data (barra5d + daily GRU + financial data) [5][73][74] - **Model Construction Idea**: Enhances the mixed-frequency model by incorporating selected financial data to improve stability and performance across years [73][74]. - **Model Construction Process**: - **Financial Data Selection**: Only key financial indicators, such as net profit TTM and market capitalization, are retained to avoid redundancy [45]. - **Integration**: Financial data is appended to the mixed-frequency model, following the same adjustment process as the GRU with financial data model [36][38]. - **Model Evaluation**: The addition of financial data further stabilizes annual excess returns and improves overall performance metrics [77][80]. --- Model Backtesting Results GRU Baseline Model - **Excess Annualized Return**: 8.75% [19][23] - **IR**: 2.25 [19][23] - **Maximum Drawdown**: 4.71% [19][23] GRU with Financial Data - **Excess Annualized Return**: 6.86% [32][33] - **IR**: 1.46 [32][34] - **Maximum Drawdown**: 6.14% [32][34] GRU with Adjusted Financial Data - **Excess Annualized Return**: 7.76% [41][44] - **IR**: 1.65 [41][44] - **Maximum Drawdown**: 5.40% [41][44] GRU with Selected Financial Data - **Excess Annualized Return**: 9.97% [51][52] - **IR**: 1.93 [51][52] - **Maximum Drawdown**: 5.70% [51][52] Mixed-Frequency GRU Model - **Excess Annualized Return**: 11.32% [68][69] - **IR**: 2.42 [68][69] - **Maximum Drawdown**: 8.19% [68][69] Mixed-Frequency GRU with Financial Data - **Excess Annualized Return**: 11.82% [77][78] - **IR**: 2.39 [77][78] - **Maximum Drawdown**: 5.70% [77][78]

翠华珠宝后起之秀，锋芒不让 菜百股份稳扎稳打步步为营，堪为教科书案例 多研究领涨股，以及它们的行业归属和联动性。 潮宏基的早期领涨，奠定首饰分支的主锚位置，完美的猛兽轮廓侧写 如上图，首饰行业指数的价格、成交量、动量VAD都是一致峰峰攀高，整个市场如此完美的结构独此一份。 这就是本ID在量价系列文章中探讨过的宏观舒展结构，有此结构不涨都难。 整个蛰伏过程，单从价格形态看，看不出任何端倪的，但是价、量、动量结合起来评估，就能体会其中之妙。 一阶段和二阶段的转折点，RSR恰逢其时地翻红，伴随二次超量突破后，开启单边扬升。 与此配套的是首饰类别中多只个股一季报报喜，而当前涨得最好的就是它们。 曼卡龙的完美吸筹过程和攻击韵律 ...

我看评论区有人留言，需要对机器人、军工、游戏这些方向的评估。可以说一下。不过，您为什么要指定用量价方法呢？我的核心方法可是动量分析哦， 单从量价是很难推导走势的。 机器人板块，我一直有重点跟踪，之前的文章中经常会提及，最近总锚之一北特科技有走强迹象，应该是个好的征兆，但是大多数个股仍然在阴跌之中， 我们来看一下图： 记得我在讲3C突破的时候说过，当时的量价关系是局部多头舒展的，右侧后量大于前量，有利于继续上行，但同时也提醒过，50日均线已经凸形反转， 很可能反弹高度在凸形区受阻，事实后来就是在这个位置上不去了。那么受阻位置形成了一个成交量的峰值，这个峰值比3月份大跌前的峰值低，这样就 构成宏观结构上的憋屈，意味着大趋势可能转向，即便后续有大行情，必定也是要经过长时间的磨砺。 现在就是在不断缩量阴跌，量能已经接近前底量，价格落在3C突破那天的位置，整体看还算有秩序，不算溃不成军，此处发生反弹是有可能的，但不要 期望过高，除非50日均线像北特科技那样走平且反转，才可以多一些乐观。 军工板块，我跟踪比较少，就整体看一下国防军工这个大类吧： 5.12日发生过一次超量冲高，也就是印巴打仗那件事情催化了一下，随后就是快速 ...