我不止一次讲过，对于普通初学者，枢轴点是最好的顺势择时信号，但真能听得进去用得好 的，估计仍是凤毛麟角。 大多数人在交易中都有极度的不安全感，什么都想尝试，但总是遇到反例，然后渐渐就不再 相信任何交易信号。心里总希望有一种精准的择时方法，而且最好还能每次涨个翻倍，但实 战中依然屡遭挫折，无法接受过程的不完美。 事实上，凡是顺势择时系统，不加运气成分，以及正确的策略体系和主观经验引导的话，无 差别选择股票交易，胜率一般都低于45%。这是天定的，没法改变。所以，从一开始你就要 有接受不完美的心理准备。 客观高胜率的择时系统都是短期逆向的，就是埃尔德的三重滤网思维，追求高胜率的人就应 该好好研究《以交易为生》的滤网系统，其中的短期逆向指标就是埃尔德首创的强力指标。 也可以用OVS指标，因为OVS就是在强力指标基础上改进的。 追求客观高胜率，必定会牺牲赔率，这也是天定的。 枢轴点是波段低位顺势择时点。在后台我看到有个新来的老铁在和AI商量，如何提高枢轴 点的确定性，探讨过程十分有趣，也很有借鉴意义。 主要思路是加入VAD>0条件的限制、RSL1和SSV1大于均值条件的限制，我回测了一下，还 是有些效果的，噪音信号会减 ...

Group 1 - The article emphasizes the importance of distinguishing between different time cycles in momentum analysis, highlighting that many individuals overlook this aspect and focus solely on the visual conformity of charts [1] - It explains the relationships between various momentum indicators, such as RSL (144 days), VAD (21 days), and OVS (2-4 days), indicating that RSL represents long-term momentum, VAD represents medium-term momentum, and OVS represents short-term momentum [1] - The article notes that the hierarchical relationship between long, medium, and short-term indicators is approximately 5-7 times, which is rooted in the principles of Chan theory, where a coherent segment requires at least 5 candlesticks [1] Group 2 - The article uses the example of Zhongji Xuchuang to illustrate the differences in momentum divergence observed in long and medium-term indicators, indicating that RSL shows a clear downward momentum while remaining above the zero axis, suggesting a secondary level of momentum divergence [2][3] - It clarifies that for VAD to be considered a divergence at its level, it must show a peak decline and subsequently form a new peak, which is not the case in certain scenarios discussed [3][4] - The article stresses that the nature and severity of pullbacks caused by divergences differ across time frames, with a layered time-space model reflecting human behavioral patterns influenced by various time habits [5]

Group 1 - The article discusses the characteristics of the "游资生态" (speculative capital ecosystem), highlighting its features such as high turnover and poor performance, which are typical in small-cap stocks [1] - It emphasizes the distinction between the speculative capital ecosystem and the institutional ecosystem, noting that they are almost completely isolated from each other [1] - The author suggests that the current market is still undergoing adjustments and that it is premature to conclude which new main lines will emerge, as significant rebounds often occur after major market drops [1] Group 2 - The article advises a slow thinking approach when analyzing institutional information, referencing Daniel Kahneman's book "Thinking, Fast and Slow" to illustrate the common psychological traps investors fall into [2] - It points out that the market is currently experiencing a serious adjustment, indicating that it has not yet exited the adjustment framework [2] - The author describes the market's behavior as a "danger signal," with a continuous decrease in volume during convex reversals, suggesting a potential risk for investors [2]

Core Viewpoint - The article aims to enhance understanding of the Beast Momentum System for stock selection, providing updated methodologies and formulas for users to apply in their investment strategies [1]. Group 1: Basic Setup - Users are advised to set up SSV and RSL indicators, with specific formulas provided for both mobile and computer users to facilitate stock selection [1][2]. - A pre-selection stock pool can be created based on fundamental requirements, with a formula shared for identifying stocks with cumulative growth in the third quarter [2]. Group 2: Leading Stock Pool - After setting up the basic indicators, users can identify strong stocks using specific criteria, resulting in a selection of over 100 stocks that are currently performing well [5]. Group 3: Short-term Momentum Conditions - To further narrow down the stock list, short-term momentum conditions can be applied, focusing on either breakout or pullback strategies [6]. - Two short-term momentum indicators, OVS and RS_D, are highlighted for use in identifying suitable stocks for low-risk entry points [6]. Group 4: Breakout and Pivot Point Stock Pools - Breakout strategies are simplified through specific criteria that can be applied to identify stocks poised for upward movement [8]. - The article also discusses the use of pivot point indicators to capture significant entry opportunities, with recommendations to incorporate industry strength values for better selection [10]. Group 5: Final Strategy - Understanding the differences between long-term and short-term strength indicators simplifies the stock selection process, allowing for adjustments in strength thresholds to target desired stock ranges [11].

Group 1 - The article emphasizes the importance of setting up SSV and RSL indicators for stock selection, providing a comprehensive guide for both mobile and computer users [1][2][3] - It suggests creating a pre-selection stock pool based on fundamental requirements, which can be skipped if there are no fundamental criteria [2] - The article provides specific formulas for calculating RSR and RSL, indicating that the results may vary slightly but are generally similar [3][4] Group 2 - To narrow down the stock selection further, the article introduces short-term momentum conditions, highlighting two approaches: focusing on breakthroughs or on low volatility [5] - It mentions the use of short-term momentum indicators like OVS and RS_D to identify stocks suitable for low absorption strategies [5][6] - The breakthrough stock pool is defined by specific conditions that ensure stocks are in a strong momentum phase [6][7] Group 3 - The article discusses the use of pivot point indicators for stock selection, recommending this method to avoid missing important entry points [8] - It suggests incorporating industry strength values to refine the stock selection process further [8][9] - The final strategy involves understanding the differences between long-term and short-term strength, allowing for flexible stock selection based on desired thresholds [9]

Core Viewpoint - The article emphasizes the importance of momentum indicators in identifying market trends and potential investment opportunities, particularly focusing on the VAD and OVS indicators for different time frames [1][2]. Group 1: Medium-Term Momentum Indicators - The selection of medium-term momentum indicators should prioritize their divergence characteristics to effectively determine wave turning points [1]. - The VAD indicator, derived from Williams' accumulation/distribution line, integrates both price and volume factors, showcasing the relationship between price momentum divergence and volume-price divergence [1][2]. Group 2: Short-Term Momentum Indicators - A more reasonable short-term momentum indicator should consider the combination of volume and price, as trend traders emphasize the matching relationship between the two [2]. - The OVS indicator is defined as the product of price change and transaction amount, reflecting the monetary effect during trading [2]. - In the "Beast Momentum System," VAD, OVS, RSR, and RSLine are equally important, each representing core logic for different stages and cycles of momentum [2]. Group 3: Indicator Formulas - The OVS indicator formula includes components such as price efficiency ratio and daily price change, which are essential for calculating the indicator [2]. - The VAD indicator formula calculates the cumulative effect of price changes relative to the previous closing price, adjusted for transaction volume [5][6].

Core Viewpoint - The article emphasizes the characteristics of the "stacking volume" model in stock trading, highlighting specific stocks that exhibit strong performance and potential for further gains [1][4]. Group 1: Characteristics of Stacking Volume Stocks - The first characteristic is a strong initial breakout, which can be described using the OVS indicator, focusing on discovering initial volatility and significant price movements [4]. - The second characteristic involves maintaining the TR level during consolidation, with RSR values remaining high, ideally above 92 [4]. - The third characteristic is a rapid convergence of the RS_D indicator during horizontal trading, indicating reduced volatility, with DR absolute values ideally below 10 for accurate entry timing [4]. Group 2: Quantitative Model Development - A simple quantitative model is proposed, consisting of pre-OVS, horizontal RSR, stable TR, and converging DR values, which can be used for stock selection [4]. - The underlying principle of this model is similar to the blue-green diamond model but represents a more intense breakout from consolidation, suggesting significant potential gains [4].