Python三角套利算法

如何解决Python三角套利算法

我希望创建一个基于 Python 的三角套利算法。我查看过的所有资源都与 Binace 等上的 Coin Exchanges 相关。我只是在寻找一个“引擎”，因为我可以简单地提供矩阵表（或任何其他选项，将不胜感激）

我正在编写我从 stackoverflow 中删除的代码（如下所示）

    from typing import Tuple,List
    from math import log

    '''
    rates = [
        [1,0.23,0.26,17.41],[4.31,1,1.14,75.01],[3.79,0.88,65.93],[0.057,0.013,0.015,1],'''

    rates = [
        [1,2363.353782,1492.716318,7844.449626,10.980451,75149.558747],[0.00042,0.634884,3.309109,0.004684,32.1756],[0.0006,1.5656,5.2004,0.007319,50.117612],[0.000126,0.300385,0.191139,0.001401,9.603383],[0.0897,211.9355,135.762445,706.204,6785.758382],[0.0000001,0.0308,0.019833,0.1035,0.000146,]

    currencies = ('YFIN','YFLR','xUSD','FXRP','FLTC','FDOGE')

    #currencies = ('PLN','EUR','USD','RUB')


    def negate_logarithm_convertor(graph: Tuple[Tuple[float]]) -> List[List[float]]:
        ''' log of each rate in graph and negate it'''
        result = [[-log(edge) for edge in row] for row in graph]
        return result


    def arbitrage(currency_tuple: tuple,rates_matrix: Tuple[Tuple[float,...]]):
        ''' Calculates arbitrage situations and prints out the details of this calculations'''

        trans_graph = negate_logarithm_convertor(rates_matrix)

        # Pick any source vertex -- we can run Bellman-Ford from any vertex and get the right result

        source = 0
        n = len(trans_graph)
        min_dist = [float('inf')] * n
        
        min_dist[source] = source

        # 'Relax edges |V-1| times'
        for _ in range(n-1):
            for source_curr in range(n):
                for dest_curr in range(n):
                    if min_dist[dest_curr] > min_dist[source_curr] + trans_graph[source_curr][dest_curr]:
                        min_dist[dest_curr] = min_dist[source_curr] + trans_graph[source_curr][dest_curr]

        # if we can still relax edges,then we have a negative cycle
        for source_curr in range(n):
            for dest_curr in range(n):
                if min_dist[dest_curr] > min_dist[source_curr] + trans_graph[source_curr][dest_curr]:
                    #print(str(source_curr) + " END : " + str(dest_curr))
                    print('Found arbitrage: {0} --> {1}'.format(currency_tuple[source_curr],currency_tuple[dest_curr]))
            
        
    if __name__ == "__main__":
        arbitrage(currencies,rates)

输出：发现套利：FXRP --> YFLR

然而 - 这实际上并没有带来套利。

我正在寻找一个结果，无论“序列大小”如何，都可以提供所有可能的套利机会

希望结果示例： 'xUSD --> FXRP --> FLTC --> xUSD

我还看了： How to use Python and Pandas to find bests opportunities in triangular arbitrage 尽管这段代码看起来最终得到了修复，但它并没有解释套利过程。

所以我想知道是否有人有 python 套利计算的例子——而不是与所有加密货币交易所等的整个框架连接。

干杯