http://blog.dailycodingproblem.com/how-to-find-arbitrage-opportunities-in-python/

arbitrage.py

from math import log

def arbitrage(table):
    transformed_graph = [[-log(edge) for edge in row] for row in graph]

    # Pick any source vertex -- we can run Bellman-Ford from any vertex and
    # get the right result
    source = 0
    n = len(transformed_graph)
    min_dist = [float('inf')] * n

    min_dist[source] = 0

    # Relax edges |V - 1| times
    for i in range(n - 1):
        for v in range(n):
            for w in range(n):
                if min_dist[w] > min_dist[v] + transformed_graph[v][w]:
                    min_dist[w] = min_dist[v] + transformed_graph[v][w]

    # If we can still relax edges, then we have a negative cycle
    for v in range(n):
        for w in range(n):
            if min_dist[w] > min_dist[v] + transformed_graph[v][w]:
                return True

    return False

What does an example input look like?

arbitrage([
    [1.000, 0.013, 0.015],
    [0.013, 1.000, 1.160],
    [0.015, 1.160, 1.000]
])

is this correct? because Im not sure I understand how to format the input for a graph

Also this article and snippet inspired a Bellman Ford implementation in Rust+wasm: https://github.com/drbh/wasm-bellman-ford