备注：本文是重新编辑后免费开放，正好 OVS 指标最近作了更新，老用户注意更新一下， VAD 是不用更新的。原文因为标题写得不够明显，导致不好找， 现在不存在问题了。 OVS是我标配的短期动量指标，它的基本定义是：涨幅*成交金额，必要时也考虑再乘上一个价格多空效率比。这个指标的原型出自埃尔德所创 的强力指标（《以交易为生》一书有讲），我只不过把其中的量柱定义V改成了成交金额AMO，这样做的好处是准确反映了交易过程中发生的金 额效应。 在猛兽动量体系中，VAD、OVS和RSR、RSLine同等重要，分别表达了不同阶段不同周期动量的核心逻辑。长、中、短周期指标相互配合，灵活 运用，可以高效发现强势板块和个股，并为评估买卖点提供重要参考依据。具体的使用技巧，散见号内其它文章，多补课就是。 以下是OVS指标和VAD指标源码： OVS指标： YC:=REF(C,1);HI:=MAX(H,YC);LW:=MIN(L,YC);BSR:=ABS(C-YC)/(HI-LW);{流通效率比}ZF:=(C-YC)/YC;{日间涨跌幅} YAMO:=REF(AMO,1);PV2:SUM(BSR*ZF*AMO/MA(AMO,N1),N ...

然后再来看结构，从缠论的结构上来说，目前依然属于是日线级别上涨段落的一个延续，而当下的防守 线就是这个位置，也就是说这个位不破反弹还可以去延续，若跌破它宣告这一轮的上涨结束，然后有一 波回踩，可是这个位置有点远。其实对于新手来说，今天我给一个建议，因为面对于这样的宽幅震荡整 理，对于新手来说非常的不友好，而且对风险来说又是指数表现很好，对于个股来说不断的一个轮动。 所以说对于新手来说，不是很友好的一个行情。那没有耐心的交易者，特别是想做短线交易者，接下来 的行情要注意休息了，而那些大涨过的个股沪指是一样的，也可以选择进行减仓做防守了。但是如果说 你是做大波段的，你是做长线的那这个位置不用去理会它，为什么？因为对于整体来说，咱们两个月之 前咱就聊过，这一轮的上涨是周线级别的多头，周线级别远远没有结束，前方高点还没给突破，他是要 给通过的。所以说这一次我们要防的是什么？是周线级别回落一笔去出现。所以说对于长线和大波段的 交易者不用去理会，而对于那些想做短线交易者，而对于没有耐心的交易者，这个位置今年开始做个小 防守，然后呢对于右侧交易者，咱们提出这个点位3547点，如果不破这个位置，也不用去担心。可 市场连续三根 ...

Core Insights - The report focuses on adjusting index positions based on technical indicators to achieve excess returns, utilizing a variety of indicators derived from volume and price data [3][8] - A total of 27 technical indicators were constructed and tested across three major indices: CSI 300, CSI 500, and CSI 1000, as well as 31 industry indices, with an average excess annualized return of 3.75% achieved through a specific technical indicator based on volume-price divergence [3][8] - The report highlights two main strategies: the "Rolling Steady Strategy" suitable for low-risk investors, and the "Rolling Momentum Strategy" for high-risk investors, with the latter showing stronger momentum capabilities [3][8] Latest Index Positioning - As of early August 2025, the CSI 300 has 16 indicators signaling bullish trends and 8 indicating a reduction in positions, while the CSI 500 has 15 bullish and 8 bearish signals. The CSI 1000 also shows 15 bullish signals and 8 bearish signals, with the optimal single indicator maintaining a bullish signal across all indices [2][16][18] Performance Statistics - The Rolling Momentum Strategy recorded an excess return of -0.32% for the CSI 300 and 0.00% for the CSI 1000 in July 2025, indicating varied performance across different indices [9][12] - The report provides detailed performance statistics for various sectors, with notable excess returns and losses across different strategies, highlighting the performance of the Rolling Steady and Rolling Momentum strategies [10][12][13] Signal Analysis - The report includes a comprehensive analysis of the signals for various sectors, indicating the number of bullish and bearish indicators, which can guide investment decisions [18][20][22] - For instance, the Basic Chemical sector has 18 bullish signals and 6 bearish signals, while the Public Utilities sector shows 5 bullish and 19 bearish signals, reflecting differing market sentiments across sectors [18][20]

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 - The article introduces a new reversal indicator for trading, specifically designed for short-term traders [1] - The VIP service offers a discount of 470 yuan, promoting the exclusive algorithm for identifying divergence signals [1] - The indicator is positioned as an essential tool for traders looking to make informed decisions based on price and volume divergence [1]

Core Insights - Bitcoin (BTC) price has recently surpassed a key resistance level, reaching a new three-month high of $108,652, despite a decrease in trading volume [1] - XBIT decentralized exchange platform is highlighted as a tool for investors to capture market opportunities, leveraging deep liquidity pools and precise strategy tools [1] Price Dynamics - BTC's recent price action shows a "big bullish candle" pattern, closing above the $108,000 mark, but there are conflicting signals in the technical analysis [1] - Key observations include a price increase with a 12% drop in trading volume, indicating potential exhaustion of upward momentum [1] - The MACD indicator shows a bullish crossover, suggesting a quiet resurgence of buying power, while the KDJ indicator indicates a neutral signal, suggesting a critical point for market direction [1][2] XBIT's Trading Tools - XBIT offers innovative tools to enhance trading certainty, including: - Smart grid trading bots that allow users to set buy/sell ranges and automatically adjust positions based on market movements [4][5] - A volume divergence alert system that monitors liquidity across exchanges and warns users of potential momentum decay [5] - A KDJ neutral zone arbitrage model that helps users manage positions based on KDJ signals [5] Security Measures - XBIT emphasizes security with three main protective features: - On-chain settlement mechanisms that execute orders via smart contracts, ensuring funds are stored in user-controlled wallets [7] - Anti-witch attack networks that utilize zero-knowledge proof technology to protect transaction privacy [7] - An insurance fund that allocates 20% of platform fees to a risk reserve for user compensation during extreme market conditions [7] Liquidity Solutions - XBIT addresses liquidity challenges with innovative solutions: - Cross-chain liquidity aggregation that connects to multiple blockchains for optimal price matching [9] - Institutional-grade dark pools for large trades to minimize market impact [9] - Liquidity mining rewards that incentivize users to provide liquidity, with annual returns up to 18% [9] Market Predictions - Analysts predict three potential scenarios for BTC's price movement: - Optimistic: If BTC closes above $108,309, it could target $120,000 [11] - Neutral: Trading within the $103,249 to $108,309 range, suitable for high-low trading strategies [11] - Pessimistic: A drop below $103,249 could signal a double-top formation [11] - XBIT is preparing to launch new products and features to adapt to market conditions, including leveraged ETF products and integration with Bitcoin Layer 2 networks [11]

Core Viewpoint - The article discusses the MACD (Moving Average Convergence Divergence) indicator, describing it as a "trend detector" in the stock market that helps investors understand price movements and market sentiment. Group 1: MACD Overview - MACD consists of two lines (DIFF and DEA) and a histogram (energy bars), which visually represent market trends and momentum [3]. - The white line (DIFF) is the fast line, while the yellow line (DEA) is the slow line, with red and green bars indicating bullish and bearish momentum respectively [3]. Group 2: Practical Techniques - **Golden Cross and Death Cross**: A golden cross occurs when the DIFF line crosses above the DEA line, signaling a potential buying opportunity, while a death cross indicates a selling signal when the DIFF crosses below the DEA [4]. - **Energy Bars**: The appearance of red bars indicates bullish strength, while green bars suggest bearish pressure. The length of these bars reflects the intensity of the market movement [4]. - **Divergence**: A top divergence occurs when the stock price reaches a new high but the MACD does not, indicating potential weakness. Conversely, a bottom divergence suggests a possible reversal when the stock price hits a new low but the MACD does not [4]. Group 3: Practical Considerations - MACD is best suited for analyzing medium-term trends, such as 30-minute or daily charts, while it may be too sensitive for short-term analysis [5]. - In a volatile market, relying solely on MACD can be misleading; it is advisable to combine it with other indicators like moving averages for better accuracy [5]. - It is crucial to consider volume alongside MACD signals to avoid false indicators, as a lack of volume during a golden cross may suggest a weak signal [5].

Market Overview - The major A-share indices in Shanghai and Shenzhen experienced a decline, which was largely anticipated. Most stocks fell, with 20 hitting the daily limit down, indicating low market sentiment [1] - The Shanghai Composite Index showed a significant drop, forming a bearish candlestick pattern with a large body, suggesting a high probability of further adjustments in the coming week [3] Index Analysis - The Shanghai Composite Index's recent performance indicates a potential double top formation, with today's bearish candlestick breaking the neckline, confirming a phase of adjustment ahead [3] - The hourly chart reveals that the last two hours of trading saw a drop, breaking a significant double top formation, indicating a confirmed phase top [3] Sci-Tech 50 Index - The Sci-Tech 50 Index experienced a rebound during the day, reaching its target at the ten-day moving average before retreating, which is considered a normal market behavior [6] - The K-line for the Sci-Tech 50 Index showed a large body and long upper shadow, signaling an adjustment ahead [6] Trading Strategy - The current A-share market suggests that as long as there is no significant decline in the indices, structural opportunities for individual stocks will continue to emerge. However, a correct trend is essential to avoid prolonged losses [6] - The analysis of K-lines, patterns, and central structures can help accurately grasp price fluctuations. Breakouts followed by pullbacks serve as entry points for phased investments [6]

Group 1 - The core viewpoint indicates a significant contraction in the supply of land in Shanghai, with a total supply area of only 658,952 square meters in April 2025, representing a month-on-month decrease of 48.99% and a year-on-year decrease of 10.73% [1] - Despite the reduction in supply area, the starting total price for land has surged to 982,536 million yuan, reflecting a month-on-month increase of 397.32%, although it still shows a year-on-year decline of 20.32% [1] - The increase in starting prices is attributed to changes in land supply structure, with a higher proportion of scarce core area plots and high-priced suburban commercial and residential land [1] Group 2 - In April 2024, the Shanghai land market exhibited a "volume-price divergence," with a total of 11 commercial land transactions covering an area of 1,542,232 square meters, marking a month-on-month increase of 145.27% and a year-on-year increase of 84.85% [5] - The total transaction amount for the month was 224,193 million yuan, showing a significant month-on-month decline of 86.66% and a year-on-year decline of 83.44% [5] - The land transactions were concentrated in six administrative districts, with Jinshan leading in both transaction volume and amount, totaling 117.93 million square meters and 1.41 billion yuan respectively [5] Group 3 - The month saw only one office land transaction, with a transaction area of over 17,440 square meters (1.13% of total) and a transaction amount of nearly 43.6 million yuan (19.45% of total), reflecting month-on-month declines of 88.29% and 73.97% respectively [5] - The land market is characterized by a focus on low-priced industrial and research land, with high-premium commercial and residential land being scarce [12] - The government is compensating for price declines by increasing the supply of low-cost land in suburban areas to meet annual supply plans and alleviate pressure on developers [12]