139 lines
3.6 KiB
Python
139 lines
3.6 KiB
Python
#import numpy as np
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#import itertools
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#import json
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#import math
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#import collections
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from common import *
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import matplotlib
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import matplotlib.pyplot as plt
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from scipy.optimize import minimize
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from gekko import GEKKO
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"""
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Gekko function.
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Function to minimize
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"""
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def to_optimize(x, S, a, nb_cycles, M, m):
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local_gain = 0
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t = m.if2(a, -1, 1) # type of trade
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tax = m.if2(a, 0.035, 0.045) # type of tax
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for i in range(1, M):
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mult = m.if2( x[0] - t*i, 0, x[0] - t*i)
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delta = x[i+1] - x[i]
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local_gain += t*mult*delta*( (S+a*nb_cycles) - (S+a*(x[i] + x[i+1] + 1)/2)*(1+t*tax*mult))
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return -local_gain # We minimize the opposite function
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"""
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GEKKO optimizer
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"""
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def optimize(wma_coins, coin_prices, nb_cycles, M):
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nb_rows, nb_cols = len(wma_coins.keys()), M
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# Transform dict to lists
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coin_prices_list = [coin_prices[coin]["price"] for coin in coin_prices.keys()]
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wma_coins_list = [wma_coins[coin] for coin in coin_prices.keys()]
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m = GEKKO(remote=False)
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x = m.Array(m.Var, (nb_rows, nb_cols + 1), lb=0, ub=144, integer=True) # Lower bound is 0: no order, upper bound is 10: hyper-multicoining
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# Set multicoin bounds
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for i in range(nb_rows):
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x[i][0].value = 0
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x[i][0].lower = -10
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x[i][0].upper = 10
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# constraints on cycles
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for i in range(nb_rows):
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for j in range(1, nb_cols):
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m.Equation(x[i][j] <= x[i][j+1])
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# EQUATIONS
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for i in range(nb_rows):
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m.Obj(to_optimize(x[i], coin_prices_list[i], wma_coins_list[i], nb_cycles, M, m))
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m.options.SOLVER=3
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m.options.IMODE = 3
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m.options.COLDSTART=1
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#m.solver_options = ['maximum_iterations 10000']
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#m.Minimize(to_optimize(x, nb_rows, nb_cols, coin_prices_list, wma_coins_list, nb_cycles, m))
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m.solve(disp=True)
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return x
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#def save_result(res):
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#
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# print(res)
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# d = dict()
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# for i in range(len(L)):
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# d[L[i]] = round(int(res[i].value[0]))
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#
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#
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# with open(PATH_OUT, "w") as f:
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# json.dump(d, f)
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def display_strategy(x):
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s = {}
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m = -1
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index = 0
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toprint = ""
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for i in range(len(x)):
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if x[i] != m:
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toprint = "[Change@" + str(i) + "\t:x" + str(int(x[i])) + "]"
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m = x[i]
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s[i] = x[i]
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print(toprint)
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return s
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def plot_results(x, s):
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fig, ax = plt.subplots()
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M = len(x)
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X = [i for i in range(M)]
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# Naive Strategies to plot
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n_strats = [i for i in range(1, 5)]
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# Plot the straight strategies
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for n in n_strats:
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xx = [n for _ in range(M)]
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Y = [to_optimize_(xx, m) for m in X]
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ax.scatter(X, Y, marker = "+", label = "Constant x{}".format(n))
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# Plot optimized strategy
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Y = [to_optimize_(x, m) for m in X]
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ax.scatter(X, Y, marker = "^", label = "Optimized")
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for cycle, n in s.items():
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ax.annotate("x{}".format(int(n)), textcoords = "offset pixels", xytext = (0, 10), xy = (X[cycle], Y[cycle]))
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# Display parameters
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textstr = '\n'.join((
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r'Coin price at adjustment: {}'.format(S),
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r'Tax baseline: {}'.format(T),
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r'Slope extrapolation: {}'.format(a),
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r'Number of cycles: {}'.format(M)))
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props = dict(boxstyle='round', facecolor='white', alpha=0.5)
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# place a text box in upper left in axes coords
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ax.text(0.12, 0.98, textstr, transform=ax.transAxes, fontsize=14,
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verticalalignment='top', bbox=props)
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#ax.set_yscale("log")
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#ax.set_xscale("log")
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ax.legend(loc="upper left")
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ax.set_xlabel("Cycle")
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ax.set_ylabel("Profit")
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ax.set_title("Profit using naive and optimized strategies")
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ax.grid(True)
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plt.show()
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