I'll search for specific information about Timothy Masters' recommendations on permutation tests for trading and newer alternatives in 2025.Based on my research, here's a comprehensive overview of **Timothy Masters' recommendations and newer alternatives for permutation and randomization tests in trading (2025)**:

## Timothy Masters' Permutation Test Recommendations

Timothy Masters, in his book "Permutation and Randomization Tests for Trading System Development," advocates for several key permutation tests:

### 1. **Core Permutation Methods**
The book covers: Testing for overfitting at the earliest possible stage, Evaluating the luckiness-versus-skill of a fully developed system before deploying it, Testing the effectiveness and reliability of a trading system factory, Removing selection bias when screening a large number of indicators

### 2. **Monte Carlo Permutation Test**
The Monte Carlo permutation test of Timothy Masters involves simulating multiple iterations of the strategy using randomly permuted versions of historical data. The process:
- Transforms absolute prices into log-returns for better statistical properties
- Creates 500 or 1000 permuted versions resulting in worse performance for >>90% if the strategy picks up a real "signal"

### 3. **Key Applications**
One usage of the Monte Carlo Permutation test allows us to discover the degree of data overfitting, without using the out-of-sample data
- Tests whether trading performance beats 99% of what could be expected by luck
- Preserves the single-usage principle of out-of-sample data
- If our trading system beats 99% of what can be expected by good luck, we can conclude that our indicator, trading system or trading system development method is actually worthwhile

### 4. **Selection Bias Testing**
On March 30, 2020, I released a third edition of the book which includes an important new section that introduces an alternative permutation test for selection bias that is substantially superior to the traditional test

## Newer Alternatives and Advances in 2025

### 1. **System Parameter Permutation (SPP) and Randomization (SPR)**
System Parameter Randomization (SPR) is introduced as a close cousin of SPP to address computational limitations and provide a better alternative

**Advantages over Masters' approach:**
- The idea behind System Parameter Permutation is that we must test ALL the possible parameter combinations and only from the data of all optimization runs we can make some assumptions about the realistic strategy performance
- Computes median values for all performance statistics (Net profit, Drawdown, Sharpe ratio, etc.)
- More computationally feasible for complex trading systems

### 2. **Advanced Bootstrap Methods**

**Block Bootstrap Variants:**
Advanced block bootstrap methods are specifically tailored for dependent time series data, including stationary bootstrap, circular bootstrap, and tapered block bootstrap
- **Stationary Bootstrap**: Uses random block lengths from geometric distribution
- **Circular Bootstrap**: Attempts to solve edge effects by wrapping the series
- **Tapered Block Bootstrap**: Offers smoother transitions at block boundaries

### 3. **White's Reality Check and Hansen's SPA Test**
White's Reality Check bootstrap methodology evaluates simple technical trading rules while quantifying the data-snooping bias

**Superior Predictive Ability (SPA) Test:**
- The stepwise SPA test is more powerful than the stepwise Reality Check test of Romano and Wolf
- Addresses data snooping bias more effectively
- Can identify predictive models without potential bias

### 4. **Conformal Prediction for Trading**

This is one of the most promising newer alternatives:

**Key Features:**
- Distribution-free nature - unlike Bayesian approaches and other statistical methods that require specific assumptions about the data distribution, conformal prediction makes no such assumptions
- Provides valid prediction intervals with guaranteed coverage
- The EnbPI (Ensemble batch Prediction Intervals) method removes the data exchangeability requirement and can thus be applied in time series forecasting

**Applications in Trading:**
- Uncertainty quantification for price predictions
- Risk-adjusted position sizing based on prediction intervals
- Model confidence assessment without distributional assumptions

### 5. **Synthetic Data Generation Methods**

Deep generative models produce synthetic time-series data, enhancing the amount of data available for training predictive models

**Benefits for Trading Strategy Validation:**
- Creates realistic market scenarios while preserving statistical properties
- Synthetic data obtained this way replicates the distribution properties of real historical data, leads to better performance, and enables thorough validation of predictive models
- Addresses data scarcity issues in certain market conditions
- In the finance sector, synthetic data is used for fraud detection, risk assessment, and algorithmic trading

### 6. **Hybrid Approaches**

**Combining Multiple Methods:**
The variance test takes the historical trades and creates a probable distribution to resample, selecting random trades from this distribution until it reaches the backtest's total trade count

**Modern Robustness Tests:**
- Noise testing, Vs Shifted, Variance testing or Monte Carlo Permutation test multiple price paths
- Address limitations of traditional walk-forward analysis
- Four-step approach: in-sample excellence, Monte Carlo permutation testing, walk-forward testing, and walk-forward Monte Carlo testing

## Key Advantages of Newer Methods

1. **Computational Efficiency**: Modern methods like SPR and conformal prediction are more computationally efficient than exhaustive permutation testing

2. **Better Coverage**: CopulaCPTS produces significantly sharper and more calibrated uncertainty estimates than state-of-the-art baselines

3. **No Distributional Assumptions**: Conformal prediction and synthetic data methods work without assuming specific data distributions

4. **Adaptive Capabilities**: Methods like EnbPI update intervals as new data arrives, maintaining adaptiveness

5. **Multi-Path Testing**: Unlike single-path methods, newer approaches test multiple possible market scenarios simultaneously

## Recommendations for 2025

For modern trading system development, consider:

1. **Start with Masters' Monte Carlo Permutation** for basic validation
2. **Implement SPP/SPR** for parameter robustness testing
3. **Use Conformal Prediction** for uncertainty quantification and risk management
4. **Apply Synthetic Data Generation** for stress testing under various market conditions
5. **Combine multiple methods** for comprehensive validation

The field has evolved significantly, with newer methods offering better computational efficiency, fewer assumptions, and more robust validation while maintaining the core principles that Masters established.