Quantitative Models and Construction Methods - **Model Name**: Monte Carlo Backtesting **Model Construction Idea**: Shift from historical path fitting to future robustness testing by generating multiple random paths to evaluate strategy performance across diverse scenarios [1][10] **Model Construction Process**: 1. Generate thousands of random price paths that follow historical statistical characteristics (e.g., return distribution, volatility, correlation) but differ from the original historical path [10] 2. Perform stress tests on strategies across these simulated paths to observe performance under various market conditions [10] 3. Calculate risk metrics such as Sharpe ratio, maximum drawdown, and value-at-risk (VaR) based on the distribution of strategy returns [10] **Model Evaluation**: Effectively reduces overfitting to specific historical paths and provides a more comprehensive robustness assessment [10][46] - **Model Name**: Non-Parametric Monte Carlo Simulation **Model Construction Idea**: Use historical data directly without assuming any parametric distribution, preserving cross-sectional correlation [2][13] **Model Construction Process**: 1. **Method 1**: Multi-Asset Time-Series Return Joint Rearrangement - Extract daily returns of all assets as a "data block" - Randomly sample and sequentially concatenate these blocks to form simulated paths [18] 2. **Method 2**: Multi-Asset Time-Series Return Block Bootstrap - Divide historical returns into fixed-length overlapping/non-overlapping blocks - Randomly sample blocks and concatenate them to form simulated paths [19] **Model Evaluation**: Preserves cross-sectional correlation but disrupts time-series structures like volatility clustering and autocorrelation [14][20] - **Model Name**: Residual Bootstrap (Factor Model-Based) **Model Construction Idea**: Separate systematic risk and idiosyncratic risk using factor models, then randomize residuals for simulation [2][23] **Model Construction Process**: 1. Construct risk factors (e.g., market, size, value, momentum) and calculate historical daily returns [23] 2. Perform cross-sectional regression to estimate factor exposures (β) and extract residual returns [23] 3. Randomly shuffle residuals while preserving cross-sectional correlation [23] 4. Reconstruct paths using historical factor returns and randomized residuals [23] **Model Evaluation**: Useful for analyzing alpha and risk exposure but limited by the explanatory power of the factor model [24][25] - **Model Name**: Geometric Brownian Motion (GBM) Simulation **Model Construction Idea**: Assume asset returns follow a normal distribution and simulate paths using drift and volatility parameters [2][28] **Model Construction Process**: $$d S_{i}(t)=\mu_{i}S_{i}(t)d t+\sigma_{i}S_{i}(t)d W_{i}(t),i=1,\ldots,n$$ - \( \mu_{i} \): Drift rate (expected return) - \( \sigma_{i} \): Volatility - \( W_{i}(t) \): Standard Brownian motion Discretized path: $$S_{i}^{(j)}(t_{k})=X_{i}(0)\,e x p[(\,k\Delta t+\sum_{l=1}^{k}\sum_{p=1}^{n}L_{i p}Z_{l,p}^{(j)}\,]$$ - \( L \): Cholesky decomposition of covariance matrix - \( Z_{l,p}^{(j)} \): Independent standard normal random variables [28] **Model Evaluation**: Accurately replicates volatility and correlation but fails to capture tail risks and price jumps [28][47] Model Backtesting Results - **Monte Carlo Backtesting**: - Historical price path Sharpe ratio: 0.96 (25-day window) - Simulated path Sharpe ratio: 0.19 (25-day window, GBM method) [45][46] - **Non-Parametric Monte Carlo Simulation**: - Historical price path Sharpe ratio: 0.96 (25-day window) - Simulated path Sharpe ratio: 0.22 (15-day window, joint rearrangement method) [45][46] - **Residual Bootstrap**: - Historical price path Sharpe ratio: 0.96 (25-day window) - Simulated path Sharpe ratio: 0.19 (25-day window) [45][46] - **Geometric Brownian Motion (GBM)**: - Historical price path Sharpe ratio: 0.96 (25-day window) - Simulated path Sharpe ratio: 0.19 (25-day window) [45][46] Quantitative Factors and Construction Methods - **Factor Name**: Momentum and Volatility Dual Factor **Factor Construction Idea**: Combine momentum and volatility factors using Z-score normalization and equal weighting [35] **Factor Construction Process**: $$S c o r e_{i}=0.5*Z S c o r e_{i,m o m}+0.5*Z S c o r e_{i,v o l}$$ - Momentum and volatility calculated over different window lengths (N ∈ [15, 20, 40]) [35] **Factor Evaluation**: Provides a balanced scoring mechanism for style rotation strategies [35][37] Factor Backtesting Results - **Momentum and Volatility Dual Factor**: - Historical price path cumulative return: 535% (25-day window) - Simulated path cumulative return: 62.25% (15-day window, GBM method) [38][42]

Core Viewpoint - The article discusses the development of a self-researched backtesting engine and its future direction, focusing on two main areas: insights and strategies [1][3]. Insights - The insights section will include AI-based financial report analysis, customizable financial calendars, and real-time interpretations based on economic principles and market sentiment [3]. - A live streaming system is being developed to provide real-time analysis of earnings calls with a delay of under five minutes, allowing for contextual understanding of market reactions [3][8]. Strategy - The strategy section will focus on systematic and quantitative trading, discussing classic trading strategies and their effectiveness based on academic papers [3][5]. - The website will feature over 40 classic strategy papers and a search engine for 5,000 G14 category economic papers, with plans for continuous optimization and public verification of the backtesting system [5][6]. Features - Users can interact with detailed backtesting charts and access structured trading checklists, enhancing the personalization of their dashboards [8]. - The backtesting results presented in the article are theoretical and do not account for real-world factors such as risk-free rates, transaction costs, and slippage, which could significantly reduce actual returns [8].