我试图实现WorldQuant（https://arxiv.org/pdf/1601.00991.pdf）发布的101个量化交易因子。
一个典型的因子是关于处理股票的价格和数量信息以及时间维度和股票维度。以alpha因子#4为例：（-1 * Ts_Rank（rank（low），9））。这是一个动量alpha信号。low是股票在一定时间段内的最低价格的面板。rank是对面板的每一行进行排名的横截面过程（时间快照）。Ts_Rank是将_rank面板的每一列（股票）移动到指定窗口的时间序列过程。
直观地说，Pandas Dataframe 或NumPy矩阵应该适合101个alpha因子的实现。下面是我目前为止使用NumPy得到的最佳实现。但是，性能太低了。在我的Intel core i7 windows机器上，用5000（交易日期）x 200（股票）矩阵作为输入运行alpha #4因子大约需要45秒。
我还遇到了DolphinDB，这是一个具有内置分析功能的时间序列数据库（* https://www.dolphindb.com/downloads.html *）。对于相同的Alpha#4因子，DolphinDB仅运行了0.04秒，比NumPy版本快1000倍。但是，DolphinDB是一个商业软件，有人知道更好的Python实现吗？或者有什么技巧可以改进我当前的python代码，以获得与DolphinDB相当的性能？

• • Numpy实现**（* 基于https://github.com/yli188/WorldQuant_alpha101_code *）

import numpy as np

def rankdata(a, method='average', *, axis=None):
    # this rankdata refer to scipy.stats.rankdata (https://github.com/scipy/scipy/blob/v1.9.1/scipy/stats/_stats_py.py#L9047-L9153)
    if method not in ('average', 'min', 'max', 'dense', 'ordinal'):
        raise ValueError('unknown method "{0}"'.format(method))

    if axis is not None:
        a = np.asarray(a)
        if a.size == 0:
            np.core.multiarray.normalize_axis_index(axis, a.ndim)
            dt = np.float64 if method == 'average' else np.int_
            return np.empty(a.shape, dtype=dt)
        return np.apply_along_axis(rankdata, axis, a, method)

    arr = np.ravel(np.asarray(a))
    algo = 'mergesort' if method == 'ordinal' else 'quicksort'
    sorter = np.argsort(arr, kind=algo)

    inv = np.empty(sorter.size, dtype=np.intp)
    inv[sorter] = np.arange(sorter.size, dtype=np.intp)

    if method == 'ordinal':
        return inv + 1

    arr = arr[sorter]
    obs = np.r_[True, arr[1:] != arr[:-1]]
    dense = obs.cumsum()[inv]

    if method == 'dense':
        return dense

    # cumulative counts of each unique value
    count = np.r_[np.nonzero(obs)[0], len(obs)]

    if method == 'max':
        return count[dense]

    if method == 'min':
        return count[dense - 1] + 1

    # average method
    return .5 * (count[dense] + count[dense - 1] + 1)

def rank(x):
    return rankdata(x,method='min',axis=1)/np.size(x, 1)

def rolling_rank(na):
    return rankdata(na.transpose(),method='min',axis=0)[-1].transpose()    

def ts_rank(x, window=10):
    a_rolled = np.lib.stride_tricks.sliding_window_view(x, window,axis = 0)
    return np.append(np.full([window-1,np.size(x, 1)],np.nan),rolling_rank(a_rolled),axis = 0)

def alpha004(data):
    return -1 * ts_rank(rank(data), 9)

import time

# The input is a 5000 by 200 matrix, where the row index represents trade date and the column index represents security ID. 
data=np.random.random((5000, 200))
start_time = time.time()
alpha004(data)
print("--- %s seconds ---" % (time.time() - start_time))

--- 44.85099506378174 seconds ---

• • 海豚数据库实现**

def WQAlpha4(low){
    return -mrank(rowRank(low, percent=true), true, 9)
}

// The input is a 5000 by 200 matrix, where the row index represents trade date and the column index represents security ID.
low = rand(1000.0,5000:200);
timer WQAlpha4(low);

Time elapsed: 44.036 ms (0.044s)