Core Insights - The article discusses the common misconception among traders that high win rates directly correlate with profitability, emphasizing that win rates and actual earnings are two distinct concepts [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 significantly outweighs the average gain, leading to a negative expected return [3][6]. - 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][6]. Group 2: Psychological Factors in Trading - Human psychology plays a crucial role in trading decisions, where traders often sell early to secure small profits but hold onto larger losses, creating a detrimental trading pattern [5][6]. - The article highlights the difference in behavior between retail investors and professional institutions, where institutions manage to earn more during winning trades and lose less during losing trades [6]. 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 trades [7]. - It emphasizes the importance of not prematurely taking profits while being flexible with stop-loss adjustments based on market conditions [7]. Group 4: Different Trading Styles - The article notes that different trading styles, such as short-term versus swing trading, require distinct approaches to profit-taking and loss management [8]. - Short-term traders may prioritize high win rates with smaller gains, while swing traders may accept lower win rates but aim for larger profits, highlighting the need for strategies that align with individual risk tolerance and personality [8][9]. Group 5: Long-Term Perspective - The article concludes that the market rewards those who not only predict correctly but also execute effectively, urging traders to focus on the ratio of average profits to average losses rather than just win rates [10]. - It stresses that successful investing is a long-term endeavor, where the key is to ensure that profits from correct predictions outweigh losses from incorrect ones [10].

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]