495 lines
14 KiB
Python
495 lines
14 KiB
Python
from decimal import Decimal
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import numpy as np
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from SimCAD import Configuration, configs
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from SimCAD.configuration import exo_update_per_ts, bound_norm_random, \
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ep_time_step
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seed = {
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'z': np.random.RandomState(1)
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# 'a': np.random.RandomState(2),
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# 'b': np.random.RandomState(3),
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# 'c': np.random.RandomState(3)
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}
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#Signals
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# Pr_signal
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#if s['P_Ext_Markets'] != 0:
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#Pr_signal = s['Z']/s['P_Ext_Markets']
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#else Pr_signal = 0
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# if Pr_signal < s['Z']/s['Buy_Log']:
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beta = Decimal('0.25') #agent response gain
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beta_LT = Decimal('0.1') #LT agent response gain
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alpha = Decimal('0.091') #21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
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max_withdraw_factor = Decimal('0.9')
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external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
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# Stochastic process factors
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correction_factor = Decimal('0.01')
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volatility = Decimal('5.0')
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# Buy_Log_signal =
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# Z_signal =
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# Price_signal =
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# TDR_draw_signal =
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# P_Ext_Markets_signal =
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# Behaviors per Mechanism
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# BEHAVIOR 1: EMH Trader
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EMH_portion = Decimal('0.250')
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EMH_Ext_Hold = Decimal('42000.0')
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def b1m1(step, sL, s):
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# y = 'P_Ext_Markets'
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# Psignal_ext = s['P_Ext_Markets'] / s['Z']
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# Psignal_int = s['Buy_Log'] / s['Z']
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# if Psignal_ext < Psignal_int:
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# return beta*(Psignal_int - Psignal_ext) * s['Z'] # Deposited amount in TDR
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# else:
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# return 0 # Decimal(0.000001)
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# return (y,x)
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theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
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if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
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return beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
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elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
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return 0
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else:
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return 0
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def b1m2(step, sL, s):
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theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
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if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
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return 0
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elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
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return beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
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else:
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return 0
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# BEHAVIOR 3: Herding
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# BEHAVIOR 4: HODLers
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HODL_belief = Decimal('10.0')
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HODL_portion = Decimal('0.250')
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HODL_Ext_Hold = Decimal('4200.0')
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def b4m2(step, sL, s):
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theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
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if s['Price'] < 1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
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return beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
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elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
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return 0
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else:
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return 0
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# BEHAVIOR 2: Withdraw TDR and burn Zeus
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# Selling Agent- Arbitrage on TDR ext v TDR int signals
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# def b2m1(step, sL, s):
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# Psignal_ext = s['P_Ext_Markets'] / s['Z']
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# Psignal_int = s['Buy_Log'] / s['Z']
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# if Psignal_ext > Psignal_int:
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# # withdrawn amount in TDR, subject to TDR limit
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# return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor)
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# else:
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# return 0 #- Decimal(0.000001)
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# return 0
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# BEHAVIOR 1: Deposit TDR and mint Zeus
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# Buying Agent- Arbitrage on Price and Z signals
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# def b1m2(step, sL, s):
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# # Psignal_ext = s['P_Ext_Markets'] / s['Z']
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# # Psignal_int = s['Buy_Log'] / s['Z']
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# # if Psignal_ext > Psignal_int:
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# # # withdrawn amount in TDR, subject to TDR limit
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# # return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor)
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# # else:
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# # return 0 #- Decimal(0.000001)
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# #
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# # LT more valuable than ST = deposit TDR and mint Z
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# Psignal_LT = s['Price'] / s['Z']
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# if Psignal_LT > 1:
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# return beta_LT*(Psignal_LT - 1) * s['Z']
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# else:
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# return 0
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# Behavior will go here- b2m2, putting in mech 3: b1m3 for debugging
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# def b2m2(step, sL, s):
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# # Psignal_LT = s['Price'] / s['Z']
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# # if Psignal_LT > 1:
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# test = np.arange(1,10)
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# return test
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# Selling Agent- Arbitrage on Price and Z signals
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# def b1m3(step, sL, s):
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# Psignal_LT = s['Price'] / s['Z']
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# if Psignal_LT < 1:
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# return - np.minimum(beta_LT*(Psignal_LT - 1) * s['Z'], s['Z']*max_withdraw_factor)
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# else:
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# return 0
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# def b2m3(step, sL, s):
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# return 0
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def dummy_behavior(step, sL, s):
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return 0
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def s1_dummy(step, sL, s, _input):
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y = 'Z'
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x = s['Z']
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return (y, x)
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def s2_dummy(step, sL, s, _input):
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y = 'Price'
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x = s['Price']
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return (y, x)
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def s3_dummy(step, sL, s, _input):
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y = 'Buy_Log'
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x = s['Buy_Log']
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return (y, x)
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def s4_dummy(step, sL, s, _input):
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y = 'Sell_Log'
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x = s['Sell_Log']
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return (y, x)
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def s5_dummy(step, sL, s, _input):
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y = 'Trans'
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x = s['Trans']
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return (y, x)
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def s6_dummy(step, sL, s, _input):
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y = 'P_Ext_Markets'
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x = s['P_Ext_Markets']
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return (y, x)
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# Internal States per Mechanism
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# Deposit TDR/Mint Zeus
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# def s1m1(step, sL, s, _input):
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# s['Z'] = s['Z'] + _input
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# STATES
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# ZEUS Fixed Supply
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def s1m1(step, sL, s, _input):
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y = 'Z'
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x = s['Z'] #+ _input # / Psignal_int
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return (y, x)
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def s2m1(step, sL, s, _input):
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y = 'Price'
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x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
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#x= alpha * s['Z'] + (1 - alpha)*s['Price']
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return (y, x)
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def s3m1(step, sL, s, _input):
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y = 'Buy_Log'
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x = _input # / Psignal_int
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return (y, x)
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def s4m2(step, sL, s, _input):
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y = 'Sell_Log'
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x = _input # / Psignal_int
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print('s4m2 ',type(_input))
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return (y, x)
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def s3m3(step, sL, s, _input):
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y = 'Buy_Log'
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x = s['Buy_Log'] + _input # / Psignal_int
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return (y, x)
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# Price Update
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def s2m3(step, sL, s, _input):
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print('s2m3 ')
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print(type(s['Sell_Log']))
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print(type(s['Z']))
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y = 'Price'
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x = s['Price'] + s['Buy_Log']/s['Z'] - s['Sell_Log']/s['Z']
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#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
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return (y, x)
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def s6m1(step, sL, s, _input):
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y = 'P_Ext_Markets'
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x = s['P_Ext_Markets'] - _input
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#x= alpha * s['Z'] + (1 - alpha)*s['Price']
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return (y, x)
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def s2m2(step, sL, s, _input):
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y = 'Price'
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x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
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#x= alpha * s['Z'] + (1 - alpha)*s['Price']
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return (y, x)
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# def s1m1(step, sL, s, _input):
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# Psignal_int = s['Buy_Log'] / s['Z']
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# y = 'Z'
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# x = s['Z'] + _input / Psignal_int
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# return (y, x)
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# def s2m1(step, sL, s, _input):
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# y = 'Price'
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# x= alpha * s['Z'] + (1 - alpha)*s['Price']
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# return (y, x)
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# def s3m1(step, sL, s, _input):
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# y = 'Buy_Log'
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# x = s['Buy_Log'] + _input # Input already in TDR * s['Z']
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# return (y, x)
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# # Withdraw TDR/Burn Zeus
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# def s1m2(step, sL, s, _input):
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# Psignal_int = s['Buy_Log'] / s['Z']
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# y = 'Z'
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# x = s['Z'] #+ _input / Psignal_int
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# return (y, x)
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# def s2m2(step, sL, s, _input):
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# y = 'Price'
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# x= alpha * s['Z'] + (1 - alpha)*s['Price']
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# return (y, x)
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# def s3m2(step, sL, s, _input):
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# y = 'Buy_Log'
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# x = s['Buy_Log'] + _input #* s['Z']
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# # y = 'Buy_Log'
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# # x = s['Buy_Log'] + _input
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# return (y, x)
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# def s1m3(step, sL, s, _input):
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# Psignal_int = s['Buy_Log'] / s['Z']
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# y = 'Z'
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# x = s['Z'] #+ _input / Psignal_int
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# return (y, x)
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# def s2m3(step, sL, s, _input):
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# y = 'Price'
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# x= alpha * s['Z'] + (1 - alpha)*s['Price']
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# return (y, x)
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# def s3m3(step, sL, s, _input):
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# y = 'Buy_Log'
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# x = s['Buy_Log'] #+ _input #* s['Z']
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# # y = 'Buy_Log'
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# # x = s['Buy_Log'] + _input
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# return (y, x)
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# def s3m4(step, sL, s, _input):
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# y = 'Buy_Log'
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# x = s['Buy_Log']*(1-external_draw) + s['Sell_Log']*external_draw # _input #* s['Z']
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# # y = 'Buy_Log'
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# # x = s['Buy_Log'] + _input
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# return (y, x)
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# def s1m3(step, sL, s, _input):
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# s['Z'] = s['Z'] + _input
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# def s2m3(step, sL, s, _input):
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# s['Price'] = s['Price'] + _input
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# Exogenous States
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proc_one_coef_A = -125
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proc_one_coef_B = 125
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# def es3p1(step, sL, s, _input):
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# s['s3'] = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
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# def es4p2(step, sL, s, _input):
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# s['P_Ext_Markets'] = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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# def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
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# s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
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def es3p1(step, sL, s, _input):
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y = 's3'
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x = s['s3'] + 1
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return (y, x)
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# def es4p2(step, sL, s, _input):
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# y = 'P_Ext_Markets'
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# # bound_norm_random defined in utils.py
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# #x = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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# expected_change = correction_factor*(s['P_Ext_Markets']-s['Buy_Log'])
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# vol = np.random.randint(1,volatility)
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# change = expected_change * vol
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# # change_float = (np.random.normal(expected_change,volatility*expected_change) #Decimal('1.0')
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# #change = Decimal.from_float(change_float)
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# x = s['P_Ext_Markets'] + change
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# return (y, x)
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# A change in belief of actual price, passed onto behaviors to make action
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def es4p2(step, sL, s, _input):
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y = 'P_Ext_Markets'
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x = s['P_Ext_Markets'] + bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
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return (y,x)
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def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
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y = 'timestamp'
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x = ep_time_step(s, s['timestamp'], seconds=1)
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return (y, x)
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#Environment States
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# def stochastic(reference, seed, correction = 0.01):
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# series = np.zeros(len(reference))
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# series[0] = reference[0]
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# for i in range(1,len(reference)):
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# expected_change = correction*(reference[i]-series[i-1])
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# normalized_expected_change = np.abs(expected_change)*(reference[i])/(reference[i-1])
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# seed_int = seed.randint(1,10)
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# change = np.random.normal(expected_change,seed_int*normalized_expected_change)
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# series[i] = series[i-1]+change
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# # avoid negative series returns
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# if series[i] <= 0:
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# series[i] = .01
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# #series[i] = series[i-1]+change
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# return [series,seed_int]
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# ref3 = np.arange(1,1000)*.1
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# test = stochastic(ref3,seed['b'])
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# def env_a(ref3,seed['b']):
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# return stochastic(ref3,seed['b'])
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def env_a(x):
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return 100
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def env_b(x):
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return 21000000
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# def what_ever(x):
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# return x + 1
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# Genesis States
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state_dict = {
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'Z': Decimal(21000000.0),
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'Price': Decimal(100.0), # Initialize = Z for EMA
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'Buy_Log': Decimal(0.0),
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'Sell_Log': Decimal(0.0),
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'Trans': Decimal(0.0),
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'P_Ext_Markets': Decimal(25000.0),
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# 's2': Decimal(0.0),
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# 's3': Decimal(0.0),
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# 's4': Decimal(0.0),
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'timestamp': '2018-10-01 15:16:24'
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}
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# exogenous_states = {
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# # "s3": es3p1,
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# "P_Ext_Markets": es4p2,
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# "timestamp": es5p2
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# }
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exogenous_states = exo_update_per_ts(
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{
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# "s3": es3p1,
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"P_Ext_Markets": es4p2,
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"timestamp": es5p2
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}
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)
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env_processes = {
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# "s3": env_proc('2018-10-01 15:16:25', env_a),
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# "P_Ext_Markets": env_proc('2018-10-01 15:16:25', env_b)
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}
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# test return vs. non-return functions as lambdas
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# test fully defined functions
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mechanisms = {
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"m1": {
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"behaviors": {
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"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
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# "b2": b2m1
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},
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"states": {
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"Z": s1m1,
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"Price": s2_dummy,
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"Buy_Log": s3m1,
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"Sell_Log":s4_dummy,
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"Trans": s5_dummy,
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"P_Ext_Markets": s6_dummy
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}
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},
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"m2": {
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"behaviors": {
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"b1": b1m2,
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"b4": b4m2
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},
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"states": {
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"Z": s1_dummy,
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"Price": s2_dummy,
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"Buy_Log": s3_dummy,
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"Sell_Log":s4m2,
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"Trans": s5_dummy,
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"P_Ext_Markets": s6_dummy
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}
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},
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"m3": {
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"behaviors": {
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# "b1": b1m2,
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# "b4": b4m2
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},
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"states": {
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"Z": s1_dummy,
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"Price": s2m3,
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"Buy_Log": s3_dummy,
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"Sell_Log":s4_dummy,
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"Trans": s5_dummy,
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"P_Ext_Markets": s6_dummy
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}
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},
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# "m3": {
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# "behaviors": {
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# "b1": b1m3,
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# "b2": b2m3
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# },
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# "states": {
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# "Z": s1m3,
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# "Price": s2m3,
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# "Buy_Log": s3m3,
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# "Sell_Log": s4_dummy,
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# "Trans": s5_dummy,
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# "P_Ext_Markets": s6_dummy
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# }
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# },
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# "m4": {
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# "behaviors": {
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# "dummy": dummy_behavior
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|
# },
|
|
# "states": {
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|
# "Z": s1_dummy,
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|
# "Price": s2_dummy,
|
|
# "Buy_Log": s3m4,
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|
# "Sell_Log": s4_dummy,
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|
# "Trans": s5_dummy,
|
|
# "P_Ext_Markets": s6_dummy
|
|
# }
|
|
# },
|
|
# "m3": {
|
|
# "behaviors": {
|
|
# "b1": b1m3,
|
|
# "b2": b2m3
|
|
# },
|
|
# "states": {
|
|
# "Z": s1m3,
|
|
# "Price": s2m3,
|
|
# }
|
|
# }
|
|
#treat environmental processes as a mechanism
|
|
"ep": {
|
|
"behaviors": {
|
|
"dummy": dummy_behavior
|
|
},
|
|
"states": {
|
|
"Z": s1_dummy,
|
|
"Price": s2_dummy,
|
|
"Buy_Log": s3_dummy,
|
|
"Sell_Log": s4_dummy,
|
|
"Trans": s5_dummy,
|
|
"P_Ext_Markets": es4p2
|
|
}
|
|
}
|
|
}
|
|
|
|
sim_config = {
|
|
"N": 1,
|
|
"T": range(1000)
|
|
}
|
|
|
|
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|