

Here are the 100 alpha factors grouped by the five dimensions:

### 趋势动能形态因子 (Trend Momentum)
```python
ts_delta(ts_zscore(close, 20), 10)
ts_regression(ts_zscore(close, 20), ts_step(1), 100, rettype=1)
ts_std_dev(ts_delta(close, 5), 20)
ts_zscore(ts_regression(close, ts_step(1), 60, rettype=0), 12)
ts_delta(ts_std_dev(close, 20), 10)
ts_corr(ts_delta(close, 5), ts_step(1), 20)
ts_regression(ts_zscore(close, 12), ts_step(1), 60, rettype=4)
ts_delta(ts_regression(close, ts_step(1), 30, rettype=1), 15)
ts_scale(ts_delta(close, 10), 60)
ts_zscore(ts_delta(close, 20), 60)
ts_regression(ts_delta(close, 5), ts_step(1), 30, rettype=1)
ts_std_dev(ts_regression(close, ts_step(1), 20, rettype=1), 40)
ts_delta(ts_zscore(close, 14), 7)
ts_regression(ts_zscore(close, 10), ts_step(1), 50, rettype=3)
ts_corr(ts_delta(close, 10), ts_step(1), 30)
ts_zscore(ts_regression(close, ts_step(1), 25, rettype=0), 30)
ts_delta(ts_regression(ts_zscore(close, 15), ts_step(1), 40, rettype=1), 10)
ts_std_dev(ts_zscore(close, 20), 10)
ts_regression(ts_zscore(close, 5), ts_step(1), 20, rettype=1)
ts_delta(ts_std_dev(ts_zscore(close, 10), 20), 5)
```

### 行业质量与市场环境适配度因子 (Industry Quality & Market Fit)
```python
market_cap * inverse(ts_std_dev(close, 60))
market_cap * ts_zscore(close, 120)
market_cap / ts_std_dev(volume, 30)
ts_zscore(market_cap, 240) * inverse(ts_zscore(ts_std_dev(close, 30), 240))
market_cap * ts_correlation(close, vwap, 60)
market_cap * inverse(ts_max(ts_std_dev(close, 20), 120))
market_cap / ts_sum(ts_std_dev(close, 10), 60)
ts_zscore(market_cap, 360) * inverse(ts_zscore(ts_std_dev(close, 30), 360))
market_cap * ts_correlation(close, volume, 60)
market_cap * inverse(ts_std_dev(ts_zscore(close, 20), 60))
market_cap / ts_std_dev(ts_zscore(close, 10), 60)
ts_zscore(market_cap, 480) * inverse(ts_zscore(ts_std_dev(close, 30), 480))
market_cap * ts_correlation(ts_zscore(close, 20), ts_zscore(volume, 20), 60)
market_cap * inverse(ts_std_dev(ts_zscore(close, 10), 60))
market_cap / ts_std_dev(ts_zscore(close, 15), 90)
ts_zscore(market_cap, 600) * inverse(ts_zscore(ts_std_dev(close, 30), 600))
market_cap * ts_correlation(ts_zscore(close, 30), ts_zscore(volume, 30), 60)
market_cap * inverse(ts_std_dev(ts_zscore(close, 20), 90))
market_cap / ts_std_dev(ts_zscore(close, 25), 120)
ts_zscore(market_cap, 720) * inverse(ts_zscore(ts_std_dev(close, 30), 720))
```

### 行业内部分化与领导力因子 (Intra-Industry Dynamics)
```python
group_std_dev(close, industry)
group_corr(close, group_rank(market_cap, industry), 12)
group_zscore(ts_zscore(close, 20), industry)
group_mean(ts_zscore(close, 10), industry)
group_std_dev(ts_zscore(close, 15), industry)
group_corr(ts_zscore(close, 5), group_rank(market_cap, industry), 20)
group_zscore(ts_delta(close, 10), industry)
group_mean(ts_delta(close, 5), industry)
group_std_dev(ts_delta(close, 20), industry)
group_corr(ts_delta(close, 15), group_rank(market_cap, industry), 30)
group_zscore(ts_zscore(close, 10), industry)
group_mean(ts_zscore(close, 5), industry)
group_std_dev(ts_zscore(close, 20), industry)
group_corr(ts_zscore(close, 10), group_rank(market_cap, industry), 20)
group_zscore(ts_delta(close, 5), industry)
group_mean(ts_delta(close, 10), industry)
group_std_dev(ts_delta(close, 15), industry)
group_corr(ts_delta(close, 25), group_rank(market_cap, industry), 40)
group_zscore(ts_zscore(close, 15), industry)
group_mean(ts_zscore(close, 20), industry)
```

### 超买超卖与情绪反转因子 (Overbought/Oversold & Reversal)
```python
ts_zscore(close, 14)
ts_delta(ts_zscore(close, 14), 5)
ts_zscore(ts_delta(close, 5), 20)
ts_std_dev(ts_zscore(close, 14), 20)
ts_zscore(ts_std_dev(close, 14), 20)
ts_delta(ts_std_dev(close, 14), 5)
ts_zscore(ts_delta(close, 1), 5)
ts_zscore(ts_delta(close, 5), 5)
ts_zscore(ts_delta(close, 10), 5)
ts_zscore(ts_delta(close, 15), 5)
ts_zscore(ts_delta(close, 20), 5)
ts_zscore(ts_delta(close, 25), 5)
ts_zscore(ts_delta(close, 30), 5)
ts_zscore(ts_delta(close, 35), 5)
ts_zscore(ts_delta(close, 40), 5)
ts_zscore(ts_delta(close, 45), 5)
ts_zscore(ts_delta(close, 50), 5)
ts_zscore(ts_delta(close, 55), 5)
ts_zscore(ts_delta(close, 60), 5)
ts_zscore(ts_delta(close, 65), 5)
```

### 行业间关联与动量溢出因子 (Inter-Industry Momentum Spillover)
```python
ts_delta(group_mean(ts_zscore(close, 20), industry), 1)
ts_corr(group_mean(ts_zscore(close, 20), industry), ts_step(1), 12)
group_zscore(ts_delta(group_mean(close, industry), 1), industry)
ts_regression(group_mean(ts_zscore(close, 20), industry), ts_step(1), 12, rettype=1)
ts_std_dev(group_mean(ts_zscore(close, 20), industry), 12)
ts_zscore(ts_delta(group_mean(close, industry), 5), 12)
ts_corr(group_mean(ts_zscore(close, 10), industry), ts_step(1), 12)
group_zscore(ts_delta(group_mean(close, industry), 10), industry)
ts_regression(group_mean(ts_zscore(close, 10), industry), ts_step(1), 12, rettype=1)
ts_std_dev(group_mean(ts_zscore(close, 10), industry), 12)
ts_zscore(ts_delta(group_mean(close, industry), 15), 12)
ts_corr(group_mean(ts_zscore(close, 15), industry), ts_step(1), 12)
group_zscore(ts_delta(group_mean(close, industry), 20), industry)
ts_regression(group_mean(ts_zscore(close, 15), industry), ts_step(1), 12, rettype=1)
ts_std_dev(group_mean(ts_zscore(close, 15), industry), 12)
ts_zscore(ts_delta(group_mean(close, industry), 25), 12)
ts_corr(group_mean(ts_zscore(close, 20), industry), ts_step(1), 12)
group_zscore(ts_delta(group_mean(close, industry), 30), industry)
ts_regression(group_mean(ts_zscore(close, 20), industry), ts_step(1), 12, rettype=1)
ts_std_dev(group_mean(ts_zscore(close, 20), industry), 12)
```

Each set of factors targets a specific dimension of industry selection, leveraging WebSim's time series and group functions to capture nuanced market dynamics. These factors are designed to work within WorldQuant's platform constraints while exploring innovative alpha sources beyond traditional metrics.