Update margpoptimizer.py

fastlane_bot/tools/optimizer/margpoptimizer.py

@@ -116,7 +116,8 @@ def optimize(self, sfc=None, *, pstart=None, mode=None, result=None, params=None
 
         :sfc:               the self financing constraint to use (2)
         :pstart:            dict or tuple of starting price for optimization (3)
-        :mode:              mode of operation (MO_MODE_REL or MO_MODE_ABS)
+        :mode:              mode of operation (MO_MODE_REL for relative convergence, or 
+                            MO_MODE_ABS for absolute)
         :result:            the result type (see MO_XXX constants below) (4)
         :params:            dict of optional parameters (see table below) (4)
 
@@ -144,7 +145,6 @@ def optimize(self, sfc=None, *, pstart=None, mode=None, result=None, params=None
         ==================  =========================================================================
         parameter           meaning
         ==================  =========================================================================
-        crit                criterion: MO_MODE_REL (relative; default) or MO_MODE_ABS (absolute)
         norm                norm for convergence crit (MO_NORML1, MO_NORML2, MO_NORMLINF)
         epsr                relative convergence threshold (default: MO_EPSR)
         epsa                absolute convergence threshold (default: MO_EPSA)
@@ -188,8 +188,8 @@ def optimize(self, sfc=None, *, pstart=None, mode=None, result=None, params=None
         # lambdas
         P      = lambda item: params.get(item, None) if params is not None else None
         get    = lambda p, ix: p[ix] if ix is not None else 1       # safe get from tuple
-        dxdy_f = lambda r: (np.array(r[0:2]))                       # extract dx, dy from result
-        tn     = lambda t: t.split("-")[0]                          # token name, eg WETH-xxxx -> WETH
+        # dxdy_f = lambda r: (np.array(r[0:2]))                       # extract dx, dy from result
+        # tn     = lambda t: t.split("-")[0]                          # token name, eg WETH-xxxx -> WETH
 
 
         # epsilons and maxiter
@@ -215,20 +215,30 @@ def optimize(self, sfc=None, *, pstart=None, mode=None, result=None, params=None
         pairs = self.curve_container.pairs(standardize=False)
         curves_by_pair = {pair: tuple(c for c in curves_t if c.pair == pair) for pair in pairs }
         pairs_t = tuple(tuple(p.split("/")) for p in pairs)
-        # pstart legacy handling
+
+
+        if P("verbose") or P("debug"):
+            print(f"[margp_optimizer] targettkn = {targettkn}")
+
+        # legacy paramters handling (pstart, crit)
         if not pstart is None:
             assert P("pstart") is None, "pstart must not be in params dict if pstart is provided as argument"
         else:
             pstart = P("pstart")
             if not P("pstart") is None:
                 print(f"[margp_optimizer] WARNING - providing `pstart` as parameter is deprecated; use `pstart` variable instead")
 
-
-
-        # pstart
-        if P("verbose") or P("debug"):
-            print(f"[margp_optimizer] targettkn = {targettkn}")
+        if not mode is None:
+            assert P("crit") is None, "crit must not be in params dict if mode is provided as argument"
+            crit = mode
+        else:
+            crit = P("crit")
+            if not P("crit") is None:
+                print(f"[margp_optimizer] WARNING - providing `crit` as parameter is deprecated; use `mode` instead")
+            if crit is None:
+                crit = self.MO_MODE_REL
 
+        # pstart
         pstart = P("pstart")
         if not pstart is None:
             if P("debug"):