125 lines
5.8 KiB
Plaintext
125 lines
5.8 KiB
Plaintext
|
|
|
|
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. |