期货交易实战中开平仓、加减仓有什么技巧？如何更好地做好仓位管理？点击限时免费领取《期货交易 实战课程》，学习技术实战思路，还有导师1V1答疑，仅限前50名 相关链接 0元领期货交易实战课 ...

- The report defines a market timing system using the distance between the long-term moving average (120 days) and the short-term moving average (20 days) of the Wind All A Index to distinguish the overall market environment[2][8][13] - The distance between the 20-day moving average and the 120-day moving average has narrowed from -2.80% to -1.33%, indicating the market is in a volatile state[2][8][13] - The industry allocation model recommends sectors such as Hang Seng Medical, Hong Kong automotive, and new consumption industries from a mid-term perspective[2][3][9] - The TWO BETA model continues to recommend the technology sector, focusing on information innovation and communication[2][3][9] - The Wind All A Index's overall PE is around the 60th percentile, indicating a medium level, while the PB is around the 10th percentile, indicating a relatively low level[3][9] - The position management model suggests an absolute return product with Wind All A as the main stock allocation should have a 50% position[3][9] - The market is expected to continue to decline in trading volume, with a potential rebound when the volume shrinks to around 900 billion[2][3][9] Model Backtest Results - The distance between the 20-day and 120-day moving averages is -1.33%[2][8][13] - The Wind All A Index's PE is at the 60th percentile[3][9] - The Wind All A Index's PB is at the 10th percentile[3][9] - The recommended position for absolute return products is 50%[3][9]

Quantitative Models and Construction Methods - **Model Name**: Industry Allocation Model **Model Construction Idea**: This model aims to recommend industry sectors based on medium-term perspectives, focusing on sectors with potential for recovery or growth trends[2][3][10] **Model Construction Process**: The model identifies sectors with recovery potential ("困境反转型板块") and growth opportunities. It recommends sectors such as healthcare (恒生医疗), export-related consumer sectors (e.g., light industry and home appliances), and technology sectors (信创, communication, solid-state batteries). Additionally, it highlights sectors with ongoing upward trends, such as banking and gold[2][3][10] **Model Evaluation**: The model provides actionable insights for medium-term industry allocation, emphasizing sectors with recovery potential and growth trends[2][3][10] - **Model Name**: TWO BETA Model **Model Construction Idea**: This model focuses on identifying technology-related sectors with growth potential[2][3][10] **Model Construction Process**: The TWO BETA model recommends technology sectors, including 信创, communication, and solid-state batteries, based on their growth potential and market trends[2][3][10] **Model Evaluation**: The model effectively identifies technology sectors with strong growth potential, aligning with market trends[2][3][10] - **Model Name**: Timing System Model **Model Construction Idea**: This model evaluates market conditions by analyzing the distance between short-term and long-term moving averages to determine market trends[2][9][14] **Model Construction Process**: 1. Define the short-term moving average (20-day) and long-term moving average (120-day) for the Wind All A Index 2. Calculate the difference between the two moving averages: $ \text{Difference} = \text{20-day MA} - \text{120-day MA} $ - Latest values: 20-day MA = 4946, 120-day MA = 5088 - Difference = -2.80% (previous week: -3.63%) 3. Monitor the absolute value of the difference; when it falls below 3%, the market is considered to be in a consolidation phase[2][9][14] **Model Evaluation**: The model provides a clear signal for market consolidation, aiding in timing decisions[2][9][14] - **Model Name**: Position Management Model **Model Construction Idea**: This model determines the recommended equity allocation based on valuation levels and short-term market trends[3][10] **Model Construction Process**: 1. Assess valuation levels of the Wind All A Index: - PE ratio: 50th percentile (medium level) - PB ratio: 10th percentile (low level) 2. Combine valuation levels with short-term market trends to recommend a 60% equity allocation for absolute return products[3][10] **Model Evaluation**: The model provides a systematic approach to position management, balancing valuation and market trends[3][10] Backtesting Results of Models - **Industry Allocation Model**: No specific numerical backtesting results provided[2][3][10] - **TWO BETA Model**: No specific numerical backtesting results provided[2][3][10] - **Timing System Model**: - Latest moving average difference: -2.80% - Previous week difference: -3.63% - Absolute difference < 3%, indicating a consolidation phase[2][9][14] - **Position Management Model**: - Recommended equity allocation: 60%[3][10]

以下文章来源于范范爱养基 ，作者范范 范范爱养基 . 专注基金投资分享，说人话，不拽词！不保证说的都对，但都是当下我最最真实的想法。（雪球号同名） 长按即可参与 风险提示：本文所提到的观点仅代表个人的意见，所涉及标的不作推荐，据此买卖，风险自负。 作者： 范范爱养基 来源：雪球 "止损"的关键不在这个步骤本身，而在于其前置步骤 ——如果你在最初买入时就做好了充分的价值判断、仓位管理、风险评估，那么，几乎就不 会存在止损操作！ 3类不同标的，处理亏损方式不同 最近看到一个观点，我觉得还挺有道理。 止损是什么？ 止损不是一个投资行为，而是一个仓位管理行为，仅仅是对之前仓位管理没做好的修正和弥补罢。 换言之，如果你的投入比例是合理分散的，为什么需要止损？ 想想看也是~~ 如果你是All In了某个资产，那就非常需要设置止损点。俗话说"留得青山在"，万一你买的资产最后真"归零"了，那就完全失去了东山再起的机 会。 但如果你提前先做好了仓位管理，假设你有100W本金，其中20%在港股市场，甚至，其中某一只港股基金占比只有5%，也就是5万，那么即便这 只基金亏了50%，也不过是浮亏2.5W，占总仓位2.5%。 类似 AL ...

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Core Viewpoint - The article analyzes the performance of global macro assets (stocks, commodities, bonds, and currencies) during extreme events such as the 2015 stock market crash, the 2018 US-China trade war, and the 2020 pandemic, providing insights on trading strategies to maximize returns while managing risks [1][4][20]. Group 1: Asset Performance During Extreme Events - In the 2015 stock market crash, the Shanghai Composite Index fell from 5178 to 3373, a decline of 35%, with high-leverage funds causing liquidity issues [5] - During the 2018 US-China trade war, the S&P 500 experienced a maximum drawdown of 20%, while agricultural products like US soybeans plummeted due to tariffs, and gold rose by 10% due to safe-haven demand [5] - The 2020 pandemic led to a global market decline, with the S&P 500 dropping 34%, while technology stocks benefitted from increased demand for home services [5] Group 2: Trading Strategies in Response to Events - In the initial phase of extreme events, risk assets typically experience panic selling, while safe-haven assets like government bonds and gold rise [4][8] - Strategies include shorting volatility by selling put options when implied volatility is high, and hedging risks by buying government bond futures or gold ETFs [6][9] - During the policy response phase, investors should focus on oversold growth stocks and consider commodity arbitrage opportunities [9][10] Group 3: Economic Recovery Phase - In the recovery phase, equity assets show differentiation, with technology and consumer sectors leading, while industrial metals like copper rise due to increased demand [10] - Strategies include increasing positions in cyclical stocks and high-yield bonds when their yields exceed 15% and collateral is sufficient [11][12] - An exit mechanism involves gradually reducing commodity positions when PMI rises above the neutral line [13] Group 4: Risk Management Principles - Position management should limit single asset exposure to 15% and total leverage to 1.5 times [14] - Hedging tools like options should be used to mitigate tail risks, with premiums kept within 2% of the position [14] - Monitoring macro indicators such as PMI and unemployment rates, as well as market sentiment indicators like the VIX index, is crucial for effective risk management [15][16] Group 5: Historical Insights - Common patterns indicate that safe-haven assets perform well in the early stages of extreme events, followed by a rebound in risk assets post-policy intervention, and a need for portfolio adjustment based on fundamental differentiation during recovery [17][20] - Specific responses to events include focusing on tariff lists during trade wars and liquidity recovery during pandemics [18][19]