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PairOptimizer: removing dead code comments
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@sklbancor
sklbancor committed Sep 21, 2023
1 parent 627c474 commit 62c94be
Showing 1 changed file with 1 addition and 151 deletions.
152 changes: 1 addition & 151 deletions fastlane_bot/tools/optimizer/pairoptimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -41,132 +41,6 @@ class PairOptimizer(CPCArbOptimizer):
def kind(self):
return "pair"

# @dataclass
# class PairOptimizerResult(OptimizerBase.OptimizerResult):
# """
# results of the pairs optimizer

# :curves: list of curves used in the optimization, possibly wrapped in CPCInverter objects*
# :dxdyfromp_vec_f: vector of tuples (dx, dy), as a function of p
# :dxdyfromp_sum_f: sum of the above, also as a function of p
# :dxdyfromp_valx_f: valx = dy/p + dx, also as a function of p
# :dxdyfromp_valy_f: valy = dy + p*dx/p, also as a function of p
# :p_optimal: optimal p value

# *the CPCInverter object ensures that all curves in the list correspond to the same quote
# conventions, according to the primary direction of the pair (as determined by the Pair
# object). Accordingly, tknx and tkny are always the same for all curves in the list, regardless
# of the quote direction of the pair. The CPCInverter object abstracts this away, but of course
# only for functions that are accessible through it.
# """

# NONEFUNC = lambda x: None

# curves: list = field(repr=False, default=None)
# dxdyfromp_vec_f: any = field(repr=False, default=NONEFUNC)
# dxdyfromp_sum_f: any = field(repr=False, default=NONEFUNC)
# dxdyfromp_valx_f: any = field(repr=False, default=NONEFUNC)
# dxdyfromp_valy_f: any = field(repr=False, default=NONEFUNC)
# p_optimal: float = field(repr=False, default=None)
# errormsg: str = field(repr=True, default=None)

# def __post_init__(self, *args, **kwargs):
# super().__post_init__(*args, **kwargs)
# # print("[PairOptimizerResult] post_init")
# assert (
# self.p_optimal is not None or self.errormsg is not None
# ), "p_optimal must be set unless errormsg is set"
# if self.method is None:
# self.method = "pair"

# @property
# def is_error(self):
# return self.errormsg is not None

# def detailed_error(self):
# return self.errormsg

# def status(self):
# return "error" if self.is_error else "converged"

# def dxdyfromp_vecs_f(self, p):
# """returns dx, dy as separate vectors instead as a vector of tuples"""
# return tuple(zip(*self.dxdyfromp_vec_f(p)))

# @property
# def tknx(self):
# return self.curves[0].tknx

# @property
# def tkny(self):
# return self.curves[0].tkny

# @property
# def tknxp(self):
# return self.curves[0].tknxp

# @property
# def tknyp(self):
# return self.curves[0].tknyp

# @property
# def pair(self):
# return self.curves[0].pair

# @property
# def pairp(self):
# return self.curves[0].pairp

# @property
# def dxdy_vecs(self):
# return self.dxdyfromp_vecs_f(self.p_optimal)

# @property
# def dxvalues(self):
# return self.dxdy_vecs[0]

# dxv = dxvalues

# @property
# def dyvalues(self):
# return self.dxdy_vecs[1]

# dyv = dyvalues

# @property
# def dxdy_vec(self):
# return self.dxdyfromp_vec_f(self.p_optimal)

# @property
# def dxdy_sum(self):
# return self.dxdyfromp_sum_f(self.p_optimal)

# @property
# def dxdy_valx(self):
# return self.dxdyfromp_valx_f(self.p_optimal)

# valx = dxdy_valx

# @property
# def dxdy_valy(self):
# return self.dxdyfromp_valy_f(self.p_optimal)

# valy = dxdy_valy

# def trade_instructions(self, ti_format=None):
# """returns list of TradeInstruction objects"""
# result = (
# CPCArbOptimizer.TradeInstruction.new(
# curve_or_cid=c, tkn1=self.tknx, amt1=dx, tkn2=self.tkny, amt2=dy
# )
# for c, dx, dy in zip(self.curves, self.dxvalues, self.dyvalues)
# if dx != 0 or dy != 0
# )
# assert ti_format != CPCArbOptimizer.TIF_DFAGGR, "TIF_DFAGGR not implemented for convex optimization"
# assert ti_format != CPCArbOptimizer.TIF_DFPG, "TIF_DFPG not implemented for convex optimization"
# return CPCArbOptimizer.TradeInstruction.to_format(result, ti_format=ti_format)


SO_DXDYVECFUNC = "dxdyvecfunc"
SO_DXDYSUMFUNC = "dxdysumfunc"
SO_DXDYVALXFUNC = "dxdyvalxfunc"
Expand Down Expand Up @@ -278,19 +152,6 @@ def optimize(self, targettkn=None, result=None, *, params=None):
# allows to mask certain long portions of the result if desired, the same way
# the main margpoptimizer does it; however, this not currently considered necessary
if p_optimal.is_error:
# return self.PairOptimizerResult(
# result=None,
# time=time.time() - start_time,
# curves=curves_t,
# dxdyfromp_vec_f=dxdyfromp_vec_f,
# dxdyfromp_sum_f=dxdyfromp_sum_f,
# dxdyfromp_valx_f=dxdyfromp_valx_f,
# dxdyfromp_valy_f=dxdyfromp_valy_f,
# p_optimal=None,
# errormsg=p_optimal.errormsg,
# method=method,
# optimizer=self,
# )
return self.MargpOptimizerResult(
method=method,
optimizer=NOMR(self),
Expand All @@ -305,18 +166,7 @@ def optimize(self, targettkn=None, result=None, *, params=None):
n_iterations=None,
errormsg="bisection did not converge",
)
# return self.PairOptimizerResult(
# result=full_result,
# time=time.time() - start_time,
# curves=curves_t,
# dxdyfromp_vec_f=dxdyfromp_vec_f,
# dxdyfromp_sum_f=dxdyfromp_sum_f,
# dxdyfromp_valx_f=dxdyfromp_valx_f,
# dxdyfromp_valy_f=dxdyfromp_valy_f,
# p_optimal=float(p_optimal),
# method=method,
# optimizer=self,
# )

return self.MargpOptimizerResult(
method=method,
optimizer=NOMR(self),
Expand Down

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