"为什么我的投资判断的准确率很高，但是账户收益却不佳？" 这个问题，几乎大部分交易者都会遇到。很多人进入市场后，会先迷恋上"追求胜率"这件事。涨跌判断 准了，就觉得自己离盈利不远了。 在市场里待久了就会发现，胜率和赚钱，根本就是两件事。 高胜率为何换不来盈利?盈亏比很关键 我们通过AI分析来看一组真实的数据：某投资者统计了两个月的交易记录，方向判断正确率达到70%， 按理说这个成绩应该相当不错了。但打开账户一看，本金却从50万缩水到了46万。 想要改变这种局面，需要重新搭建自己的交易体系。这里有三条经过市场验证的参考思路： 第一，每笔交易开仓前必须同时设好止损和目标位。不要相信盘中临场发挥，因为情绪会扭曲判断。建 仓的同时，止损位和目标位就该同步设好。这个动作虽然看起来机械，但它能逼着你在最理性的时刻做 决策。 问题出在哪？仔细分析交易流水会发现：那些看对的交易行为，平均每笔赚800元；那些看错的单子， 平均每笔亏2400元。70%的胜率配上1：3的盈亏比，数学不会骗人——期望收益就是负的。 对此，AI有一个很形象的比喻： 你可以在大部分战斗中获胜，却在关键的几场战争里输得一败涂地。 为什么看对了却拿不住？这 ...

Core Viewpoint - The article emphasizes that high win rates do not necessarily lead to profitability, highlighting the importance of the risk-reward ratio in trading decisions [1][2][3]. Group 1: Win Rate vs. Profitability - A trader with a 70% win rate can still incur losses if the average loss per trade is significantly higher than the average gain, leading to a negative expected return [3]. - The article illustrates this with a case where a trader's account decreased from 500,000 to 460,000 despite a high win rate due to a poor risk-reward ratio of 1:3 [3]. Group 2: Human Psychology in Trading - Human emotions play a critical role in trading decisions, where traders often sell winning positions too early and hold onto losing positions for too long, leading to a detrimental trading pattern [5][6]. - This behavior contrasts with professional institutions that manage to maintain a better average profit and loss ratio, often earning 5% on winning trades and losing only 1.5% on losing trades [7]. Group 3: Building a Trading Framework - To improve trading outcomes, the article suggests establishing a structured trading framework that includes setting stop-loss and target levels before entering a trade [8]. - It advises against prematurely taking profits while allowing for earlier stop-loss adjustments if market conditions worsen [8]. - The focus should be on the overall capital curve rather than just the win rate, emphasizing the importance of maintaining a favorable risk-reward ratio [8]. Group 4: Different Investor Types - The article discusses different trading styles, noting that short-term traders may prioritize high win rates with smaller gains, while trend traders may accept lower win rates in exchange for larger profits [9][10]. - It highlights that no trading style is inherently superior; the choice should align with the investor's personality and risk tolerance [10]. Group 5: Long-Term Perspective - Understanding the impact of win rates and risk-reward ratios on investment returns is crucial, and a long-term perspective is necessary for success in trading [11]. - The market rewards those who not only make correct predictions but also execute their trades effectively, reinforcing the need to focus on both aspects [12].

Quantitative Models and Construction Methods 1. Model Name: Size Rotation Model Based on Odds and Win Rates - **Model Construction Idea**: The model integrates the concepts of odds and win rates to capture the rotation between large-cap and small-cap stocks. Odds are derived from valuation differences, while win rates are calculated using multiple indicators[4][30][40] - **Model Construction Process**: - **Odds Calculation**: - Define odds as the ratio of average positive returns to the absolute value of average negative returns - Formula: $ \mathbb{R}_{\mathbb{B}}^{\pm}\,\mathbb{R}=-\frac{\sum_{i=1}^{n}r e t u r n_{i}\,/n}{\sum_{j=1}^{m}r e t u r n_{j}\,/m} $ where $ \mathbb{R}_{\mathbb{B}}^{+} $ represents positive returns and $ \mathbb{R}_{\mathbb{B}}^{-} $ represents negative returns[30][31] - Use historical valuation differences between large-cap (CSI 300) and small-cap (CSI 2000) indices to estimate odds through linear regression[32][36] - **Win Rate Calculation**: - Combine multiple indicators (e.g., Shibor, short-term credit spread, market trend, market volatility, style momentum, style crowding, and calendar effects) to derive a composite win rate signal - Assign scores: 1 for large-cap signals, 0 for small-cap signals, and 0.5 for neutral signals. The average score represents the win rate[40][72] - **Kelly Formula for Allocation**: - Use the Kelly formula to calculate optimal allocation weights for large-cap and small-cap stocks based on odds and win rates - Formula: $ x = \frac{p*b - (1-p)}{b} $ where $ p $ is the win rate, $ b $ is the odds, and $ x $ is the allocation proportion[77] - Adjust weights to ensure they sum to 1 and avoid negative values, forming a complete rotation strategy[77][78] - **Model Evaluation**: The model effectively captures the rotation between large-cap and small-cap stocks, achieving significant excess returns and risk-adjusted performance[78] 2. Model Name: Weighted Size Rotation Strategy - **Model Construction Idea**: Adjust allocation weights between large-cap and small-cap stocks based on the difference in configuration scores derived from odds and win rates[82] - **Model Construction Process**: - Calculate the difference in configuration scores between large-cap and small-cap stocks - Standardize the score difference using a Z-score over the past 250 weeks - Map the standardized score to allocation weights using a predefined mapping table[83] - **Model Evaluation**: This strategy reduces maximum drawdown while maintaining a high level of excess returns and information ratio[84] 3. Model Name: Detailed Style Rotation Model - **Model Construction Idea**: Combine the size rotation model with a growth-value rotation model to form a detailed style rotation strategy, targeting large-cap growth, large-cap value, small-cap growth, and small-cap value[87] - **Model Construction Process**: - Use the size rotation model to determine the size preference (large-cap or small-cap) - Use the growth-value rotation model to determine the style preference (growth or value) - Combine the two signals to allocate to one of the four detailed styles[87] - **Model Evaluation**: The model demonstrates outstanding rotation effects, achieving the highest excess returns and information ratio among all strategies[90][92] --- Model Backtesting Results 1. Size Rotation Model Based on Odds and Win Rates - Total Return: 531.87% - Annualized Return: 23.70% - Annualized Volatility: 23.03% - Maximum Drawdown: 25.25% - Information Ratio (IR): 2.27 - Return-to-Drawdown Ratio: 2.79[79] 2. Weighted Size Rotation Strategy - Total Return: 204.13% - Annualized Return: 13.69% - Annualized Volatility: 22.02% - Maximum Drawdown: 29.17% - Information Ratio (IR): 2.47 - Return-to-Drawdown Ratio: 4.66[84] 3. Detailed Style Rotation Model - Total Return: 1329.51% - Annualized Return: 35.91% - Annualized Volatility: 23.97% - Maximum Drawdown: 23.37% - Information Ratio (IR): 3.11 - Return-to-Drawdown Ratio: 3.87[92] --- Quantitative Factors and Construction Methods 1. Factor Name: Shibor Signal - **Construction Idea**: Reflects the impact of liquidity conditions on small-cap and large-cap stocks[42] - **Construction Process**: - Calculate the historical percentile of the latest Shibor rate over the past year - Signal: If the percentile > 50%, favor large-cap; otherwise, favor small-cap[42] - **Backtesting Results**: - Annualized Excess Return: 11.46% - Information Ratio (IR): 1.23[43] 2. Factor Name: Short-Term Credit Spread - **Construction Idea**: Captures the impact of short-term credit market conditions on size rotation[47] - **Construction Process**: - Calculate the spread between 1-year and 7-day AAA+ short-term bond yields - Signal: If the 20-day average spread > 250-day average, favor large-cap; otherwise, favor small-cap[47] - **Backtesting Results**: - Annualized Excess Return: 7.41% - Information Ratio (IR): 0.79[48] 3. Factor Name: Market Trend - **Construction Idea**: Reflects the impact of market activity on size rotation[51] - **Construction Process**: - Compare the 5-day and 20-day moving averages of the CSI All Share Index - Signal: If the 5-day MA > 20-day MA and market volume is increasing, favor small-cap; otherwise, favor large-cap[51] - **Backtesting Results**: - Annualized Excess Return: 3.52% - Information Ratio (IR): 0.48[52] 4. Factor Name: Market Volatility - **Construction Idea**: Reflects the impact of market stability on size rotation[54] - **Construction Process**: - Compare the 20-day market volatility with its 3-year average - Signal: If volatility < average, favor large-cap; otherwise, favor small-cap[54] - **Backtesting Results**: - Annualized Excess Return: 13.18% - Information Ratio (IR): 1.42[55] 5. Factor Name: Style Momentum - **Construction Idea**: Captures the momentum effect in size rotation[57] - **Construction Process**: - Compare the past 4-week returns of CSI 300 and CSI 2000 indices - Signal: If CSI 300 return > CSI 2000 return, favor large-cap; otherwise, favor small-cap[57] - **Backtesting Results**: - Annualized Excess Return: 8.16% - Information Ratio (IR): 0.87[58] 6. Factor Name: Style Crowding - **Construction Idea**: Reflects the risk of style overcrowding and potential reversals[60] - **Construction Process**: - Calculate the historical percentile of the 20-day trading volume of the largest 20% and smallest 20% stocks - Signal: If large-cap volume > 75th percentile, favor small-cap; if small-cap volume > 75th percentile, favor large-cap[60] - **Backtesting Results**: - Annualized Excess Return: 6.63% - Information Ratio (IR): 0.93[61] 7. Factor Name: Calendar Effect - **Construction Idea**: Reflects the impact of periodic events on size rotation[63] - **Construction Process**: - Calculate the historical win rate of large-cap over small-cap for each calendar month - Signal: If the win rate > 50%, favor large-cap; otherwise, favor small-cap[66] - **Backtesting Results**: - Annualized Excess Return: 4.73% - Information Ratio (IR): 0.50[67] 8. Factor Name: Composite Win Rate Signal - **Construction Idea**: Combines all individual factors into a single composite signal[72] - **Construction Process**: - Average the scores of all individual factors to derive the composite win rate - Signal: If the composite score > 0.5, favor large-cap; otherwise, favor small-cap[72] - **Backtesting Results**: - Annualized Excess Return: 19.72% - Information Ratio (IR): 2.17[73]

Group 1 - The core concept of investment is defined by two main metrics: win rate and odds, where win rate represents the probability of making a profit and odds indicate the profit-loss ratio [1] - An example illustrates that if an investor buys 10 funds and 7 are profitable while 3 incur losses, the win rate is 70% [1] Group 2 - Emphasizing the importance of foundational knowledge in investment, particularly in selecting the right funds, is crucial for successful investing [3]