Exclude too-small trades from optimizers

- Exclude trades smaller than pool's minimum trade size from optimization
  in get_arb_trades and multipair_optimal arbitrage
- Scale price errors by target price
- Return price errors as dicts with trading pair as key
- Add ArbTrade class to templates.trader

changelog.d/20231102_145659_nagakingg_exclude_trades.rst

@@ -0,0 +1,15 @@
+Added
+-----
+- Added ArbTrade class to templates.trader. It is a Trade object with a 
+  target price field, and a method to change the amount_in.
+
+Changed
+-------
+- Changed arbitrage optimizers to exclude trades smaller than a 
+  pool's minimum trade size from optimization. This reduces variability 
+  in optimization and prevents some potential errors.
+
+- Changed price errors returned by Traders to dicts indicating the
+  trading pair for each error value.
+
+- Changed price errors to be normalized by target price. 

curvesim/metrics/metrics.py

@@ -99,7 +99,7 @@ def compute_arb_metrics(self, **kwargs):
 
         profits = self._compute_profits(prices, trade_data.trades)
         price_error = trade_data.price_errors.apply(
-            lambda errors: sum(abs(e) for e in errors)
+            lambda errors: sum(abs(e) for e in errors.values())
         )
 
         results = concat([profits, price_error], axis=1)

curvesim/pipelines/common/__init__.py

@@ -6,6 +6,7 @@
 
 from curvesim.logging import get_logger
 from curvesim.metrics import metrics as Metrics
+from curvesim.templates.trader import ArbTrade
 
 logger = get_logger(__name__)
 DEFAULT_METRICS = [
@@ -55,25 +56,22 @@ def post_trade_price_error(dx, coin_in, coin_out, price_target):
     trades = []
 
     for pair in prices:
-        i, j = pair
-
-        if pool.price(i, j) - prices[pair] > 0:
-            price = prices[pair]
-            coin_in, coin_out = i, j
-        elif pool.price(j, i) - 1 / prices[pair] > 0:
-            price = 1 / prices[pair]
-            coin_in, coin_out = j, i
-        else:
-            trades.append((0, pair, prices[pair]))
+        coin_in, coin_out, target_price = _get_arb_direction(pair, pool, prices[pair])
+
+        lower_bound = pool.get_min_trade_size(coin_in)
+        profit_per_unit = post_trade_price_error(
+            lower_bound, coin_in, coin_out, target_price
+        )
+        if profit_per_unit <= 0:
+            trades.append(ArbTrade(coin_in, coin_out, 0, target_price))
             continue
 
-        high = pool.get_max_trade_size(coin_in, coin_out)
-        bounds = (0, high)
+        upper_bound = pool.get_max_trade_size(coin_in, coin_out)
         try:
             res = root_scalar(
                 post_trade_price_error,
-                args=(coin_in, coin_out, price),
-                bracket=bounds,
+                args=(coin_in, coin_out, target_price),
+                bracket=(lower_bound, upper_bound),
                 method="brentq",
             )
             size = int(res.root)
@@ -85,11 +83,26 @@ def post_trade_price_error(dx, coin_in, coin_out, price_target):
                 coin_in,
                 coin_out,
                 pool_price,
-                price,
-                pool_price - price,
+                target_price,
+                pool_price - target_price,
             )
             size = 0
 
-        trades.append((size, (coin_in, coin_out), price))
+        trades.append(ArbTrade(coin_in, coin_out, size, target_price))
 
     return trades
+
+
+def _get_arb_direction(pair, pool, market_price):
+    i, j = pair
+    price_error_i = pool.price(i, j) - market_price
+    price_error_j = pool.price(j, i) - 1 / market_price
+
+    if price_error_i >= price_error_j:
+        target_price = market_price
+        coin_in, coin_out = i, j
+    else:
+        target_price = 1 / market_price
+        coin_in, coin_out = j, i
+
+    return coin_in, coin_out, target_price

curvesim/pipelines/simple/trader.py

@@ -41,22 +41,23 @@ def compute_trades(self, prices):
         best_trade = None
         price_error = None
         for t in trades:
-            size, coins, price_target = t
-            i, j = coins
-            min_trade_size = pool.get_min_trade_size(i)
-            if size <= min_trade_size:
+            coin_in, coin_out, amount_in, price_target = t
+            min_trade_size = pool.get_min_trade_size(coin_in)
+            if amount_in <= min_trade_size:
                 continue
             with pool.use_snapshot_context():
-                out_amount, _ = pool.trade(i, j, size)
+                amount_out, _ = pool.trade(coin_in, coin_out, amount_in)
                 # assume we transacted at "infinite" depth at target price
                 # on the other exchange to obtain our in-token
-                profit = out_amount - size * price_target
+                profit = amount_out - amount_in * price_target
                 if profit > max_profit:
                     max_profit = profit
-                    best_trade = Trade(i, j, size)
-                    price_error = pool.price(i, j) - price_target
+                    best_trade = Trade(coin_in, coin_out, amount_in)
+                    price_error = (
+                        pool.price(coin_in, coin_out) - price_target
+                    ) / price_target
 
         if not best_trade:
-            return [], {"price_errors": []}
+            return [], {"price_errors": {}}
 
-        return [best_trade], {"price_errors": [price_error]}
+        return [best_trade], {"price_errors": {(coin_in, coin_out): price_error}}

curvesim/pipelines/vol_limited_arb/strategy.py

@@ -41,4 +41,6 @@ def _compute_volume_limits(sample, vol_mult):
 
     limits = {key: volumes[key] * vol_mult[key] for key in volumes}
     reversed_limits = {(j, i): lim * prices[(i, j)] for (i, j), lim in limits.items()}
-    return {**limits, **reversed_limits}
+    all_limits = {**limits, **reversed_limits}
+
+    return {key: int(val * 10**18) for key, val in all_limits.items()}

curvesim/pipelines/vol_limited_arb/trader.py

@@ -1,3 +1,5 @@
+from pprint import pformat
+
 from numpy import isnan
 from scipy.optimize import least_squares
 
@@ -71,37 +73,32 @@ def multipair_optimal_arbitrage(  # noqa: C901  pylint: disable=too-many-locals
     res : scipy.optimize.OptimizeResult
         Results object from the numerical optimizer.
     """
-    init_trades = get_arb_trades(pool, prices)
+    all_trades = get_arb_trades(pool, prices)
+    input_trades, skipped_trades = _apply_volume_limits(all_trades, limits, pool)
 
-    # Limit trade size, add size bounds
-    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
-        limited_init_trades.append(t)
+    if not input_trades:
+        price_errors = _make_price_errors(skipped_trades=skipped_trades, pool=pool)
+        return [], price_errors, None
 
-    # Order trades in terms of expected size
-    limited_init_trades = sorted(limited_init_trades, reverse=True, key=lambda t: t[0])
-    sizes, coins, price_targets, lo, hi = zip(*limited_init_trades)
+    input_trades = _sort_trades_by_size(input_trades)
+    least_squares_inputs = _make_least_squares_inputs(input_trades, limits)
 
-    def post_trade_price_error_multi(dxs, price_targets, coins):
+    def post_trade_price_error_multi(amounts_in, price_targets, coin_pairs):
         with pool.use_snapshot_context():
-            for k, pair in enumerate(coins):
-                if isnan(dxs[k]):
+            for coin_pair, amount_in in zip(coin_pairs, amounts_in):
+                if isnan(amount_in):
                     dx = 0
                 else:
-                    dx = int(dxs[k])
+                    dx = int(amount_in)
 
-                coin_in, coin_out = pair
-                min_size = pool.get_min_trade_size(coin_in)
+                min_size = pool.get_min_trade_size(coin_pair[0])
                 if dx > min_size:
-                    pool.trade(coin_in, coin_out, dx)
+                    pool.trade(*coin_pair, dx)
 
             errors = []
-            for k, pair in enumerate(coins):
-                price = pool.price(*pair, use_fee=True)
-                errors.append(price - price_targets[k])
+            for coin_pair, price_target in zip(coin_pairs, price_targets):
+                price = pool.price(*coin_pair, use_fee=True)
+                errors.append(price - price_target)
 
         return errors
 
@@ -111,38 +108,109 @@ def post_trade_price_error_multi(dxs, price_targets, coins):
     try:
         res = least_squares(
             post_trade_price_error_multi,
-            x0=sizes,
-            args=(price_targets, coins),
-            bounds=(lo, hi),
+            **least_squares_inputs,
             gtol=10**-15,
             xtol=10**-15,
         )
 
-        # Format trades into tuples, ignore if dx=0
-        dxs = res.x
-
-        for k, amount_in in enumerate(dxs):
+        # Record optimized trades
+        for trade, amount_in in zip(input_trades, res.x):
             if isnan(amount_in):
                 continue
 
             amount_in = int(amount_in)
-            coin_in, coin_out = coins[k]
-            min_size = pool.get_min_trade_size(coin_in)
+            min_size = pool.get_min_trade_size(trade.coin_in)
             if amount_in > min_size:
-                trades.append(Trade(coin_in, coin_out, amount_in))
+                trades.append(Trade(trade.coin_in, trade.coin_out, amount_in))
 
-        errors = res.fun
+        price_errors = _make_price_errors(input_trades, res.fun, skipped_trades, pool)
 
     except Exception:
-        logger.error(
-            "Optarbs args: x0: %s, lo: %s, hi: %s, prices: %s",
-            sizes,
-            lo,
-            hi,
-            price_targets,
-            exc_info=True,
-        )
-        errors = post_trade_price_error_multi([0] * len(sizes), price_targets, coins)
-        res = []
+        logger.error("Opt Arbs:\n %s", pformat(least_squares_inputs), exc_info=True)
+        price_errors = _make_price_errors(skipped_trades=all_trades, pool=pool)
+        res = None
+
+    return trades, price_errors, res
+
+
+def _apply_volume_limits(arb_trades, limits, pool):
+    """
+    Returns list of ArbTrades with amount_in set to min(limit, amount_in). Any trades
+    limited to less than the pool's minimum trade size are excluded.
+    """
+
+    limited_arb_trades = []
+    excluded_trades = []
+    for trade in arb_trades:
+        pair = trade.coin_in, trade.coin_out
+        limited_amount_in = min(limits[pair], trade.amount_in)
+        lim_trade = trade.replace_amount_in(limited_amount_in)
+
+        if lim_trade.amount_in > pool.get_min_trade_size(lim_trade.coin_in):
+            limited_arb_trades.append(lim_trade)
+        else:
+            excluded_trades.append(lim_trade)
+
+    return limited_arb_trades, excluded_trades
+
+
+def _sort_trades_by_size(trades):
+    """Sorts trades by amount_in."""
+    sorted_trades = sorted(trades, reverse=True, key=lambda t: t.amount_in)
+    return sorted_trades
+
+
+def _make_least_squares_inputs(trades, limits):
+    """
+    Returns a dict of trades, bounds, and targets formatted as kwargs for least_squares.
+    """
 
-    return trades, errors, res
+    coins_in, coins_out, amounts_in, price_targets = zip(*trades)
+    coin_pairs = tuple(zip(coins_in, coins_out))
+
+    low_bound = [0] * len(trades)
+    high_bound = [limits[pair] + 1 for pair in coin_pairs]
+
+    return {
+        "x0": amounts_in,
+        "kwargs": {"price_targets": price_targets, "coin_pairs": coin_pairs},
+        "bounds": (low_bound, high_bound),
+    }
+
+
+def _make_price_errors(trades=None, trade_errors=None, skipped_trades=None, pool=None):
+    """
+    Returns a dict mapping coin pairs to price errors.
+
+    Parameters
+    ----------
+    trades :
+        Trades input into the least_squares optimizer
+
+    trade_errors :
+        Price errors returned by the optimizer
+
+    skipped_trades :
+        Trades excluded from optimization
+
+    pool :
+        The pool used to compute the pool price for skipped trades
+
+    Returns
+    -------
+    price_errors: Dict
+        Maps coin pairs (tuple) to price_errors
+    """
+    price_errors = {}
+    if trades:
+        for trade, price_error in zip(trades, trade_errors):
+            coin_pair = trade.coin_in, trade.coin_out
+            price_errors[coin_pair] = price_error / trade.price_target
+
+    if skipped_trades:
+        for trade in skipped_trades:
+            coin_pair = trade.coin_in, trade.coin_out
+            price_error = pool.price(*coin_pair, use_fee=True) - trade.price_target
+            price_errors[coin_pair] = price_error / trade.price_target
+
+    return price_errors

curvesim/templates/trader.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+from dataclasses import fields
 from typing import Union
 
 from curvesim.logging import get_logger
@@ -16,8 +17,19 @@ class Trade:
     amount_in: int
 
     def __iter__(self):
-        # pylint: disable=no-member
-        return (getattr(self, attr) for attr in self.__slots__)
+        return (getattr(self, field.name) for field in fields(self))
+
+
+@dataclass(frozen=True, slots=True)
+class ArbTrade(Trade):
+    """Trade object specifying an arbitrage trade."""
+
+    price_target: float
+
+    def replace_amount_in(self, new_amount_in):
+        """Returns self, replacing amount_in."""
+        coin_in, coin_out, _, price_target = self
+        return ArbTrade(coin_in, coin_out, new_amount_in, price_target)
 
 
 @dataclass(slots=True)
@@ -31,8 +43,7 @@ class TradeResult:
     fee: int
 
     def __iter__(self):
-        # pylint: disable=no-member
-        return (getattr(self, attr) for attr in self.__slots__)
+        return (getattr(self, field.name) for field in fields(self))
 
     def set_attrs(self, **kwargs):
         """Sets multiple attributes defined by keyword arguments."""