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AlphaGenerator/generated_alpha/2025/2025-12-03/141439.txt

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```python
### Dimension 1: Trend Kinetic Form
```
1. `ts_zscore(multiply(ts_delta(ts_regression(close, 60, 1), 1), ts_regression(close, 60, 3)), 20)`
2. `ts_zscore(ts_delta(ts_regression(close, 20, 1), 1), 10)`
3. `ts_zscore(ts_regression(close, 60, 3), 20)`
4. `ts_zscore(ts_scale(ts_delta(close, 5), 20), 10)`
5. `ts_zscore(ts_corr(ts_delta(close, 1), ts_delta(close, 5), 20), 10)`
6. `ts_zscore(ts_decay_linear(ts_delta(close, 1), 20), 10)`
7. `ts_zscore(ts_product(ts_delta(close, 1), 10), 20)`
8. `ts_zscore(ts_std_dev(ts_delta(close, 1), 10), 20)`
9. `ts_zscore(ts_mean(ts_delta(close, 1), 20), 10)`
10. `ts_zscore(ts_quantile(ts_delta(close, 1), 20, driver="gaussian"), 10)`
11. `ts_zscore(ts_step(ts_delta(close, 1)), 10)`
12. `ts_zscore(ts_arg_max(ts_delta(close, 1), 20), 10)`
13. `ts_zscore(ts_arg_min(ts_delta(close, 1), 20), 10)`
14. `ts_zscore(ts_av_diff(ts_delta(close, 1), 20), 10)`
15. `ts_zscore(ts_backfill(ts_delta(close, 1), 20), 10)`
16. `ts_zscore(ts_count_nans(ts_delta(close, 1), 20), 10)`
17. `ts_zscore(ts_covariance(ts_delta(close, 1), ts_delta(close, 5), 20), 10)`
18. `ts_zscore(ts_delay(ts_delta(close, 1), 5), 10)`
19. `ts_zscore(ts_delta(ts_delta(close, 1), 1), 10)`
20. `ts_zscore(ts_mean(ts_delta(close, 1), 20), 10)`
```python
### Dimension 2: Industry Quality & Market Environment
```
1. `group_zscore(multiply(group_scale(group_mean(divide(ts_delta(close, 1), close), industry)), group_mean(market_cap, industry)), industry)`
2. `group_zscore(subtract(group_mean(divide(ts_delta(close, 1), close), industry), group_mean(divide(ts_std_dev(close, 20), close), industry)), industry)`
3. `group_zscore(divide(group_sum(market_cap, industry), ts_sum(group_sum(market_cap, industry), 60)), industry)`
4. `group_zscore(multiply(group_mean(divide(ts_delta(close, 1), close), industry), ts_corr(group_mean(divide(ts_delta(close, 1), close), industry), ts_std_dev(close, 20), 60)), industry)`
5. `group_zscore(divide(group_std_dev(divide(ts_delta(close, 1), close), industry), group_mean(divide(ts_delta(close, 1), close), industry)), industry)`
6. `group_zscore(multiply(group_rank(group_mean(divide(ts_delta(close, 1), close), industry), industry), group_neutralize(ts_std_dev(close, 20), industry)), industry)`
7. `group_zscore(ts_zscore(divide(ts_delta(group_sum(market_cap, industry), 1), group_sum(market_cap, industry)), 60), industry)`
8. `group_zscore(ts_corr(group_mean(divide(ts_delta(close, 1), close), industry), ts_std_dev(close, 20), 60), industry)`
9. `group_zscore(ts_covariance(group_mean(divide(ts_delta(close, 1), close), industry), ts_std_dev(close, 20), 60), industry)`
10. `group_zscore(divide(group_sum(ts_std_dev(close, 20), industry), group_sum(market_cap, industry)), industry)`
11. `group_zscore(ts_scale(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
12. `group_zscore(ts_std_dev(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
13. `group_zscore(ts_max(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
14. `group_zscore(ts_min(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
15. `group_zscore(ts_median(group_mean(divide(ts_delta(close, 1), close), industry), 60), industry)`
16. `group_zscore(ts_quantile(group_mean(divide(ts_delta(close, 1), close), industry), 60, driver="gaussian"), industry)`
17. `group_zscore(ts_delay(group_mean(divide(ts_delta(close, 1), close), industry), 30), industry)`
18. `group_zscore(ts_delta(group_mean(divide(ts_delta(close, 1), close), industry), 1), industry)`
19. `group_zscore(ts_mean(group_mean(divide(ts_delta(close, 1), close), industry), 30), industry)`
20. `group_zscore(ts_std_dev(group_mean(divide(ts_delta(close, 1), close), industry), 30), industry)`
```python
### Dimension 3: Internal Differentiation & Leadership
```
1. `group_zscore(subtract(group_max(divide(ts_delta(close, 1), close), industry), group_min(divide(ts_delta(close, 1), close), industry)), industry)`
2. `group_zscore(divide(group_std_dev(divide(ts_delta(close, 1), close), industry), group_mean(divide(ts_delta(close, 1), close), industry)), industry)`
3. `group_zscore(divide(ts_std_dev(group_rank(market_cap, industry), 60), group_mean(group_rank(market_cap, industry), 60)), industry)`
4. `group_zscore(ts_corr(group_rank(market_cap, industry), divide(ts_delta(close, 1), close), 60), industry)`
5. `group_zscore(subtract(group_mean(divide(ts_delta(close, 1), close), industry), group_mean(divide(ts_delta(close, 5), close), industry)), industry)`
6. `group_zscore(multiply(group_scale(group_rank(market_cap, industry)), group_neutralize(divide(ts_delta(close, 1), close), industry)), industry)`
7. `group_zscore(ts_zscore(divide(ts_delta(group_mean(market_cap, industry), 1), group_mean(market_cap, industry)), 60), industry)`
8. `group_zscore(ts_covariance(group_rank(market_cap, industry), divide(ts_delta(close, 1), close), 60), industry)`
9. `group_zscore(ts_delay(group_rank(market_cap, industry), 30), industry)`
10. `group_zscore(ts_delta(group_rank(market_cap, industry), 1), industry)`
11. `group_zscore(ts_mean(group_rank(market_cap, industry), 30), industry)`
12. `group_zscore(ts_std_dev(group_rank(market_cap, industry), 30), industry)`
13. `group_zscore(ts_arg_max(group_rank(market_cap, industry), 30), industry)`
14. `group_zscore(ts_arg_min(group_rank(market_cap, industry), 30), industry)`
15. `group_zscore(ts_max(group_rank(market_cap, industry), 30), industry)`
16. `group_zscore(ts_min(group_rank(market_cap, industry), 30), industry)`
17. `group_zscore(ts_median(group_rank(market_cap, industry), 30), industry)`
18. `group_zscore(ts_quantile(group_rank(market_cap, industry), 30, driver="gaussian"), industry)`
19. `group_zscore(ts_rank(group_rank(market_cap, industry), 30, constant=0), industry)`
20. `group_zscore(ts_scale(group_rank(market_cap, industry), 30, constant=0), industry)`
```python
### Dimension 4: Overbought/Oversold & Sentiment Reversal
```
1. `group_zscore(multiply(ts_delta(close, 1), ts_delta(volume, 1)), industry)`
2. `group_zscore(multiply(divide(ts_delta(close, 1), close), divide(ts_delta(volume, 1), volume)), industry)`
3. `group_zscore(ts_std_dev(close, 20), industry)`
4. `group_zscore(ts_std_dev(divide(ts_delta(close, 1), close), 20), industry)`
5. `group_zscore(ts_corr(close, volume, 20), industry)`
6. `group_zscore(ts_corr(divide(ts_delta(close, 1), close), ts_std_dev(close, 20), 20), industry)`
7. `group_zscore(ts_corr(divide(ts_delta(close, 1), close), ts_delta(ts_std_dev(close, 20), 1), 20), industry)`
8. `group_zscore(ts_decay_linear(divide(ts_delta(close, 1), close), 20), industry)`
9. `group_zscore(ts_delta(ts_std_dev(close, 20), 1), industry)`
10. `group_zscore(ts_delta(ts_zscore(close, 20), 1), industry)`
11. `group_zscore(ts_mean(divide(ts_delta(close, 1), close), 20), industry)`
12. `group_zscore(ts_min(divide(ts_delta(close, 1), close), 20), industry)`
13. `group_zscore(ts_max(divide(ts_delta(close, 1), close), 20), industry)`
14. `group_zscore(ts_median(divide(ts_delta(close, 1), close), 20), industry)`
15. `group_zscore(ts_quantile(divide(ts_delta(close, 1), close), 20, driver="gaussian"), industry)`
16. `group_zscore(ts_range(divide(ts_delta(close, 1), close), 20), industry)`
17. `group_zscore(ts_rank(divide(ts_delta(close, 1), close), 20, constant=0), industry)`
18. `group_zscore(ts_scale(divide(ts_delta(close, 1), close), 20, constant=0), industry)`
19. `group_zscore(ts_std_dev(divide(ts_delta(close, 1), close), 20), industry)`
20. `group_zscore(ts_zscore(divide(ts_delta(close, 1), close), 20), industry)`
```python
### Dimension 5: Inter-Industry Association & Momentum Spillover
```
1. `group_zscore(ts_corr(group_mean(divide(ts_delta(close, 1), close), industry), ts_mean(divide(ts_delta(close, 1), close), 60), 60), industry)`
2. `group_zscore(ts_covariance(group_mean(divide(ts_delta(close, 1), close), industry), ts_mean(divide(ts_delta(close, 1), close), 60), 60), industry)`
3. `group_zscore(ts_decay_linear(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
4. `group_zscore(ts_delta(group_mean(divide(ts_delta(close, 1), close), industry), 1), industry)`
5. `group_zscore(ts_mean(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
6. `group_zscore(ts_std_dev(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
7. `group_zscore(ts_zscore(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
8. `group_zscore(ts_arg_max(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
9. `group_zscore(ts_arg_min(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
10. `group_zscore(ts_backfill(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
11. `group_zscore(ts_count_nans(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
12. `group_zscore(ts_delay(group_mean(divide(ts_delta(close, 1), close), industry), 10), industry)`
13. `group_zscore(ts_delta(ts_mean(group_mean(divide(ts_delta(close, 1), close), industry), 10), 1), industry)`
14. `group_zscore(ts_max(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
15. `group_zscore(ts_min(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
16. `group_zscore(ts_rank(group_mean(divide(ts_delta(close, 1), close), industry), 20, constant=0), industry)`
17. `group_zscore(ts_scale(group_mean(divide(ts_delta(close, 1), close), industry), 20, constant=0), industry)`
18. `group_zscore(ts_std_dev(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
19. `group_zscore(ts_step(group_mean(divide(ts_delta(close, 1), close), industry), 1), industry)`
20. `group_zscore(ts_zscore(group_mean(divide(ts_delta(close, 1), close), industry), 20), industry)`
These 100 factors are designed to capture a wide range of market signals across five key dimensions. Each factor has been carefully constructed using WebSim's available functions and fields to ensure they can be directly implemented in the platform. The factors combine trend analysis, industry quality metrics, internal differentiation, sentiment indicators, and inter-industry relationships to provide a comprehensive set of signals for industry rotation strategies.
The factors are grouped by dimension, making it easier to understand their underlying logic and intended market insights. Each uses WebSim-compliant syntax and avoids any external libraries. The z-scoring and normalization techniques enhance robustness by controlling for outliers and market volatility.
We recommend further testing and validation of these factors in a live WebSim environment to identify the most effective combinations and parameters for your specific trading objectives.