Core Viewpoint - The current A-share market is experiencing a paradox of "index rising with shrinking volume," indicating potential risks of a false breakout, similar to historical patterns observed in March 2025 [1] Group 1: Volume-Price Divergence - The index has risen while trading volume has decreased to around 800 billion, close to the lower end of short-term liquidity [3] - Historical data suggests that a genuine breakout requires volume to increase by more than 300% and be sustained, whereas current volume is only one-third of peak levels, indicating weak buying interest from major funds [3] - A true breakout example occurred in February 2025 when a significant volume increase of 320% led to a doubling of stock prices within a month [3] Group 2: Fund Diversification - Despite a broad rise in individual stocks, the flow of funds reveals the intentions of major players, with significant concentration in a few leading stocks in the new energy sector, such as solid-state batteries and photovoltaic equipment [5] - Financial stocks, including banks and insurance, have shown a decline, yet their intraday movements have artificially boosted the index, creating a "false prosperity" scenario [5] - Today's net inflow of major funds was only 42.7 billion, a significant drop compared to previous inflows that often exceeded 100 billion, with funds highly concentrated in specific sectors [5] Group 3: Technical Divergence - Three major technical indicators signal potential risks: MACD divergence, where the index rises but MACD histogram shortens, and RSI shows overbought conditions [6] - The Shanghai Composite Index remains below the 5-day and 10-day moving averages, with the 20-day moving average acting as a critical support level [7] - A disorganized distribution of shares before the breakout, with some popular stocks showing over 30% turnover but stagnant prices, suggests potential selling by major players [7] Historical Context - A historical reference from August 2025 indicates that after a 26% volume drop, a false breakout occurred at 3700 points, leading to a subsequent retest of lower levels [8] - Investors are advised to use a "3-day confirmation" rule to assess the stability of key levels post-breakout, maintaining a conservative position [8] - A strategy of retaining leading stocks while liquidating those without performance support is recommended [8] Conclusion - The current market is in a "weak recovery" phase, characterized by a shrinking volume rise, which is more indicative of a bear market retreat rather than a strong bull market [9] - Investors should remain cautious, as 80% of breakouts are likely false, and only a combination of volume, funds, and technical analysis can help identify potential traps set by major players [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].

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]