Exclude trading pair from multipair_optimal_arbitrage if volume limit is 0

Version Information

All curvesim versions for all Python versions and OSs

What's your issue about?

In the volume limited arbitrage pipeline, when a trading pair is limited to 0 volume, the trading size for that pair is still included as a free parameter to estimate in multipair_optimal_arbitrage. This can cause scipy.optimize.least_squares to hang or raise a ValueError "'x' is not within the trust region".

How can it be fixed?

Any trade with a size limit less than a pool's minimum trade size will never be executed during optimization, so they can be excluded from the least_squares optimization. When there are no eligible trades, optimization can/should be skipped.

The most simple adjustment is this, but:

this whole section can be made cleaner
the price error output needs to be adjusted so it includes price errors for all pairs even if they are excluded from optimization

def multipair_optimal_arbitrage(pool, prices, limits):
    init_trades = get_arb_trades(pool, prices)

    limited_init_trades = []
    for t in init_trades:
        size, pair, price_target = t
        limit = int(limits[pair] * 10**18)
        t = min(size, limit), pair, price_target, 0, limit + 1
        if t[0] > pool.get_min_trade_size(pair[0]):  # Only include trade if it is possible for it to be executed
            limited_init_trades.append(t)

    if not limited_init_trades: # Skip optimization if no eligible trades
        return [], [], []

[...]

To do this more cleanly, I propose: 1) Reorganize the above proposed code:

Make get_arb_trades return a list of Trade objects (currently it uses amorphous tuples)
Rewrite for clarity using the Trade object attribute names
Encapsulate all into a function that returns a list of dicts that can be passed as kwargs to least_squares

2) Introduce a get_price_error() function that combines the price errors returned by least_squares with price errors for any excluded pairs

Currently, succesful optimization returns res.fun (i.e., post_trade_price_error_multi for the optimized trades, taken from least_squares results object)
When the optimizer fails, we return post_trade_price_error_multi for a series of null trades:
```
    errors = post_trade_price_error_multi([0] * len(sizes), price_targets, coins)
```
- get_price_error(err.fun, skipped_pairs) could concat optimizer results (if any) with the null trades method (if needed) to handle all cases (i.e., all pairs traded, some pairs traded, no pairs traded, optimizer error)

curveresearch / curvesim