Core Insights - The article emphasizes the importance of a systematic approach to trading, focusing on multi-timeframe analysis and trend-following strategies to enhance trading success [6][10][15] Group 1: Trading Strategies - Multi-timeframe resonance strategy increases win rates from 50% to 70% by confirming market direction across different timeframes [6][8] - The strategy involves three layers: long-term trend identification (daily), directional confirmation (60-minute), and precise entry points (5-minute) [7] - The article illustrates a successful trade in lithium carbonate, highlighting a significant profit with a risk-reward ratio of 1:12 [7][12] Group 2: Risk Management - Emphasizes the importance of trend-following and avoiding counter-trend trades, which account for 90% of losses [9][10] - Advocates for small stop-losses (not exceeding 0.5% of total capital) and allowing profits to run without predetermined exit points [11][12] - The article stresses that small stop-losses serve as a safety net while larger take-profits act as a profit engine [13] Group 3: Selection of Trading Instruments - Highlights the significance of choosing the right instruments, suggesting that traders should select the strongest instruments for long positions and the weakest for short positions [14] - Provides an example of iron ore outperforming other commodities during a market downturn, reinforcing the principle of "the strong get stronger, and the weak get weaker" [14] Group 4: High Probability Trading - Defines high probability trading not as prediction but as adherence to established rules [15] - The article encourages traders to build their own high-probability rules based on multi-timeframe analysis and trend confirmation [15]

Quantitative Models and Construction Methods - **Model Name**: Multi-cycle timing model for domestic interest rate price-volume trends **Model Construction Idea**: The model uses kernel regression algorithms to capture interest rate trend patterns, identifying support and resistance lines based on the shape of interest rate movements across different investment cycles [10][24] **Model Construction Process**: 1. **Data Input**: Utilize 5-year, 10-year, and 30-year government bond YTM data as the basis for analysis [10][24] 2. **Cycle Classification**: Divide the investment horizon into long-term (monthly frequency), medium-term (bi-weekly frequency), and short-term (weekly frequency) cycles [10][24] 3. **Signal Identification**: Detect upward or downward breakthroughs of support and resistance lines for each cycle [10][24] 4. **Composite Scoring**: Aggregate signals across cycles, assigning scores based on the number of consistent breakthroughs (e.g., 2/3 consistent signals lead to a "buy" or "sell" recommendation) [10][24] **Model Evaluation**: The model effectively captures multi-cycle resonance in interest rate trends, providing actionable timing signals for bond trading strategies [10][24] - **Model Name**: Multi-cycle timing model for U.S. interest rate price-volume trends **Model Construction Idea**: Apply the domestic interest rate price-volume timing model to the U.S. Treasury market [21] **Model Construction Process**: 1. **Data Input**: Use 10-year U.S. Treasury YTM data for analysis [21] 2. **Cycle Classification**: Similar to the domestic model, divide the investment horizon into long-term, medium-term, and short-term cycles [21] 3. **Signal Identification**: Detect upward or downward breakthroughs of support and resistance lines for each cycle [21] 4. **Composite Scoring**: Aggregate signals across cycles, assigning scores based on the number of consistent breakthroughs [21] **Model Evaluation**: The model provides a neutral-to-bullish outlook for U.S. Treasury yields, indicating its adaptability to international markets [21] Model Backtesting Results - **Domestic Multi-cycle Timing Model**: - **5-year YTM**: - Long-term annualized return: 5.5% - Maximum drawdown: 2.88% - Return-to-drawdown ratio: 1.91 - Short-term annualized return (since 2024): 1.86% - Maximum drawdown: 0.59% - Return-to-drawdown ratio: 3.16 - Long-term excess return: 1.07% - Short-term excess return: 0.85% [25][27] - **10-year YTM**: - Long-term annualized return: 6.09% - Maximum drawdown: 2.74% - Return-to-drawdown ratio: 2.22 - Short-term annualized return (since 2024): 2.42% - Maximum drawdown: 0.58% - Return-to-drawdown ratio: 4.19 - Long-term excess return: 1.66% - Short-term excess return: 1.55% [28][32] - **30-year YTM**: - Long-term annualized return: 7.38% - Maximum drawdown: 4.27% - Return-to-drawdown ratio: 1.73 - Short-term annualized return (since 2024): 3.11% - Maximum drawdown: 0.92% - Return-to-drawdown ratio: 3.39 - Long-term excess return: 2.42% - Short-term excess return: 2.87% [33][35] - **U.S. Multi-cycle Timing Model**: - **10-year YTM**: - Current signal: Neutral-to-bullish - Long-term annualized return: Not provided - Maximum drawdown: Not provided - Return-to-drawdown ratio: Not provided [21][23] Quantitative Factors and Construction Methods - **Factor Name**: Interest rate structure indicators (level, term, convexity) **Factor Construction Idea**: Transform YTM data into structural indicators to analyze the interest rate market from a mean-reversion perspective [7] **Factor Construction Process**: 1. **Level Structure**: Calculate the average YTM across maturities (1-10 years) 2. **Term Structure**: Measure the slope between short-term and long-term YTM 3. **Convexity Structure**: Assess the curvature of the yield curve [7] **Factor Evaluation**: The indicators effectively capture the current state of the interest rate market, highlighting deviations from historical averages [7] Factor Backtesting Results - **Interest Rate Structure Indicators**: - **Level Structure**: Current reading: 1.64%, historical 10-year percentile: 7% - **Term Structure**: Current reading: 0.38%, historical 10-year percentile: 16% - **Convexity Structure**: Current reading: -0.09%, historical 10-year percentile: 1% [7]