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calc.py
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CMSN = 0.02
from itertools import combinations
from functools import reduce
import operator
import math
import matplotlib.pyplot as plt
import numpy as np
import sys
def convert_odds(x,percent=False):
if x == None: return None
if isinstance(x,str): x = x.replace(" ","").replace("\n","").replace("%","")
try:
y = float(x)
except (ValueError):
if "/" in x:
y = float(x.split("/")[0])/float(x.split("/")[1]) + 1
else: return None
if percent: y = 100/y
return y
def ratio(back_odds,lay_odds,cmsn):
return back_odds - (1 + back_odds*(lay_odds - 1)/(lay_odds - cmsn))
def back_stakes(odds):
if odds == []: return -1
comb = list(combinations(odds,len(odds) - 1))
zs = sum(product(a for a in z) for z in comb)
return [product(x for x in odds if not x is y)/zs for y in odds]
def back_ratio(odds):
if odds == []: return -1
odds = [convert_odds(x) for x in odds]
st = back_stakes(odds)
return st[0]*odds[0] - sum(st)
def product(iterable):
return reduce(operator.mul, iterable, 1)
def lay_stake(back_odds,lay_odds,cmsn):
return back_odds/(lay_odds-cmsn)
def liability(back_odds,lay_odds,cmsn):
return 1 + lay_stake(back_odds,lay_odds,cmsn)*(lay_odds - 1)
def liablity_ratio(back_odds,lay_odds,cmsn):
return ratio(back_odds,lay_odds,cmsn)/liability(back_odds,lay_odds,cmsn)
##### COMPLEX METHODS FOR VARIABLE TURNOVER #####
def r_w(t,back_odds,lay_odds):
'''
ratio of profit for each back win, variable on lay stake t
'''
return (back_odds) - t*(lay_odds - 1)
def r_l(t,back_odds,lay_odds):
'''
ratio of profit for the lay win, variable on lay stake t
'''
return t*(1-CMSN)
def powersum(base,power):
return sum(base**n for n in range(power))
def _max_full_bets(odds,turnover):
i = 0
while True:
r = turnover - powersum(odds,i)
if r < 0: return i - 1
else: i += 1
def px(t,back_odds,lay_odds,max_turnover,fair_odds=None):
if fair_odds==None: fair_odds = (back_odds + lay_odds)/2
p = 1/fair_odds
q = 1-p
max_full_bets = _max_full_bets(back_odds,max_turnover)
turnover_remaining = max_turnover - powersum(back_odds,max_full_bets)
# so E(X) = (r_l*q) + ... + (r_w*back_odds*p)^max_full_bets*(r_l*q) + final_bet_win + final_bet_loss
rw = r_w(t,back_odds,lay_odds)
rl = r_l(t,back_odds,lay_odds)
EX = []
for i in range(max_full_bets):
x = rl*back_odds**(i) - t*(lay_odds-1)*powersum(back_odds,i)
EX.append((q*p**i,x))
released_amount = back_odds**(max_full_bets) - t*(lay_odds-1)*powersum(back_odds,max_full_bets)
EX.append((q*p**max_full_bets,(released_amount + (rl-1)*turnover_remaining))) # final bet loss
EX.append((p**(max_full_bets + 1),(released_amount + (rw-1)*turnover_remaining))) # final bet win
#print(t,EX)
return EX
def max_liabiity(t,back_odds,lay_odds,max_turnover):
max_full_bets = _max_full_bets(back_odds,max_turnover)
turnover_remaining = max_turnover - powersum(back_odds,max_full_bets)
return 1 + powersum(back_odds,max_full_bets)*t*(lay_odds-1) + turnover_remaining*t*(lay_odds-1)
if __name__ == "__main__":
if "-a" in sys.argv:
req_liability = float(input("liability? "))
back_odds = float(input("back odds? "))
lay_odds = input("lay odds? ")
if lay_odds[-1] == "%":
lay_odds = 100/float(lay_odds[:-1])
else: lay_odds = float(lay_odds)
unit_lay_stake = lay_stake(back_odds,lay_odds,CMSN)
unit_liability = liability(back_odds, lay_odds, CMSN)
back_stake = req_liability / unit_liability
lay_stake = back_stake*unit_lay_stake
profit = ((back_stake*back_odds)-(back_stake+((lay_odds-1)*lay_stake)))
profit2= (lay_stake-(CMSN*lay_stake)-back_stake)
print("back stake: ",int(round(back_stake*100))/100)
print("lay stake: ",int(round(lay_stake*100))/100)
print("liability: ",int(round(req_liability*100))/100)
print("profit: ",int(round(profit*100))/100)
print("alt. profit: ",int(round(profit2*100))/100)
raise SystemExit
turnover = float(input("turnover? "))
back_stake = int(float(input("back stake? "))*100)
back_odds = float(input("back odds? "))
lay_odds = input("lay odds? ")
if lay_odds[-1] == "%":
lay_odds = 100/float(lay_odds[:-1])
else: lay_odds = float(lay_odds)
if turnover == 1.0:
lay_stake = ((back_odds*back_stake)/(lay_odds-CMSN))
profit = ((back_stake*back_odds)-(back_stake+((lay_odds-1)*lay_stake)))
profit2= (lay_stake-(CMSN*lay_stake)-back_stake)
liability = (((lay_odds-1)*lay_stake)+back_stake)
print("lay stake: ",int(round(lay_stake))/100)
print("liability: ",int(round(liability))/100)
print("profit: ",int(round(profit))/100)
print("alt. profit: ",int(round(profit2))/100)
else:
liability_cap = float(input("liabiliity cap? "))
fair_odds = float(input("fair odds? "))
x = np.arange(0,1.5,0.01)
y = np.arange(0,1.5,0.01)
z = np.arange(0,1.5,0.01)
w = np.arange(0,1.5,0.01)
liability_cap_t = 0
for i in range(len(x)):
px_i = px(x[i],back_odds,lay_odds,turnover,fair_odds=fair_odds)
mu = sum(p*x for (p,x) in px_i)
y[i] = sum(p*(x-mu)**2 for (p,x) in px_i)
z[i] = min(x for (p,x) in px_i)
w[i] = max_liabiity(x[i],back_odds,lay_odds,turnover)
if w[i] < liability_cap: liability_cap_t = x[i]
px_liability_cap = px(liability_cap_t,back_odds,lay_odds,turnover,fair_odds=fair_odds)
ex_liability_cap = sum(p*x for (p,x) in px_liability_cap)
print("\nHighest value under liability cap at t={0:.4f}, E(X)={1:.4f}".format(liability_cap_t,ex_liability_cap))
print("Max Profit: {}, Min Profit: {}".format(max(x for (p,x) in px_liability_cap),min(x for (p,x) in px_liability_cap)))
print("Maximum Liability: {}".format(max_liabiity(liability_cap_t,back_odds,lay_odds,turnover)))
print([x for (p,x) in px_liability_cap])
ordinary_t = back_odds/(lay_odds-CMSN)
px_ordinary = px(ordinary_t,back_odds,lay_odds,turnover,fair_odds=fair_odds)
ex_ordinary = sum(p*x for (p,x) in px_ordinary)
print("\nNormal at t={0:.4f}, E(X)={1:.4f}".format(ordinary_t,ex_ordinary))
print("Max Profit: {}, Min Profit: {}".format(max(x for (p,x) in px_ordinary),min(x for (p,x) in px_ordinary)))
print("Maximum Liability: {}".format(max_liabiity(ordinary_t,back_odds,lay_odds,turnover)))
print([x for (p,x) in px_ordinary])
a,b,c = np.polyfit(x,y,2)
min_var_t = -b/(2*a)
px_min_var = px(min_var_t,back_odds,lay_odds,turnover,fair_odds=fair_odds)
ex_min_var = sum(p*x for (p,x) in px_min_var)
print("\nMinimum Variance at t={0:.2f}, E(X)={1:.4f}".format(min_var_t,ex_min_var))
print("Max Profit: {}, Min Profit: {}".format(max(x for (p,x) in px_min_var),min(x for (p,x) in px_min_var)))
print("Maximum Liability: {}".format(max_liabiity(min_var_t,back_odds,lay_odds,turnover)))
print([x for (p,x) in px_min_var])
max_guaranteed_t = sorted(zip(x,z),key=lambda t: t[1])[-1][0]
px_max_guaranteed = px(max_guaranteed_t,back_odds,lay_odds,turnover,fair_odds=fair_odds)
ex_max_guaranteed = sum(p*x for (p,x) in px_max_guaranteed)
print("\nMaximum Guaranteed Return at t={0:.4f}, E(X)={1:.4f}".format(max_guaranteed_t,ex_max_guaranteed))
print("Max Profit: {}, Min Profit: {}".format(max(x for (p,x) in px_max_guaranteed),min(x for (p,x) in px_max_guaranteed)))
print("Maximum Liability: {}".format(max_liabiity(max_guaranteed_t,back_odds,lay_odds,turnover)))
print([x for (p,x) in px_max_guaranteed])
fig, ax = plt.subplots()
ax.plot(x,y)
ax.plot(x,z)
ax.plot(x,w)
plt.show()