308 lines
10 KiB
Python
308 lines
10 KiB
Python
from decimal import Decimal
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import numpy as np
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from datetime import timedelta
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from SimCAD import Configuration, configs
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from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, 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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}
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# Signals
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# Pr_signal
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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 = .67, 2 block moving average
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alpha = Decimal('0.67')
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# 21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
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# 21 * 3 mech steps, 2/64 = 0.03125
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alpha_2 = Decimal('0.03125')
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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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#alpha * s['Zeus_ST'] + (1 - alpha)*s['Zeus_LT']
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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('0.20')
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EMH_Ext_Hold = Decimal('42000.0')
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def b1m1(step, sL, s):
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# print('b1m1')
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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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buy = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
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price = s['Price']
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return {'EMH_buy': buy, 'EMH_buy_P': price}
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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 {'EMH_buy': 0}
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else:
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return {'EMH_buy': 0}
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def b1m2(step, sL, s):
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# print('b1m2')
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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 {'EMH_sell': 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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sell = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
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price = s['Price']
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return {'EMH_sell': sell, 'EMH_sell_P': price}
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else:
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return {'EMH_sell': 0}
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# BEHAVIOR 3: Herding
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Herd_portion = Decimal('0.20')
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Herd_Ext_Hold = Decimal('42000.0')
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Herd_UB = Decimal('0.10') # UPPER BOUND
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Herd_LB = Decimal('0.10') # LOWER BOUND
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def b3m2(step, sL, s):
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theta = (s['Z']*Herd_portion*s['Price'])/(s['Z']*Herd_portion*s['Price'] + Herd_Ext_Hold * s['P_Ext_Markets'])
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# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
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if (s['Price'] - s['Price_Signal']) < - Herd_LB:
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sell = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
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price = s['Price'] - (s['Price_Signal'] / s['Price'])
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return {'herd_sell': sell, 'herd_buy': 0, 'herd_sell_P': price}
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# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
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elif (s['Price'] - s['Price_Signal']) > Herd_UB:
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buy = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
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price = s['Price'] + (s['Price'] / s['Price_Signal'])
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return {'herd_sell': 0, 'herd_buy': buy, 'herd_buy_P': price}
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else:
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return {'herd_sell': 0, 'herd_buy': 0}
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# BEHAVIOR 4: HODLers
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HODL_belief = Decimal('10.0')
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HODL_portion = Decimal('0.20')
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HODL_Ext_Hold = Decimal('4200.0')
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def b4m2(step, sL, s):
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# print('b4m2')
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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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sell = beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
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price = s['Price']
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return {'HODL_sell': sell, 'HODL_sell_P': price}
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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 {'HODL_sell': 0}
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else:
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return {'HODL_sell': 0}
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# BEHAVIOR 7: Endogenous Information Updating (EIU)
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# Short Term Price Signal, Lower Threshold = BOT-like
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EIU_portion = Decimal('0.20')
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EIU_Ext_Hold = Decimal('42000.0')
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EIU_UB = Decimal('0.50') # UPPER BOUND
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EIU_LB = Decimal('0.50') # LOWER BOUND
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def b7m2(step, sL, s):
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theta = (s['Z']*EIU_portion*s['Price'])/(s['Z']*EIU_portion*s['Price'] + EIU_Ext_Hold * s['P_Ext_Markets'])
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# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
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if (s['Price'] - s['Price_Signal']) < - EIU_LB:
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sell = beta * theta*EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EIU_portion*(1-theta))
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price = s['Price'] + (s['Price_Signal'] / s['Price'])
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return {'EIU_sell': sell, 'EIU_buy': 0, 'EIU_sell_P': price}
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# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
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elif (s['Price'] - s['Price_Signal']) > EIU_UB:
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buy = beta * theta* EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* EIU_portion*(1-theta))
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price = s['Price'] - (s['Price'] / s['Price_Signal'])
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return {'EIU_sell': 0, 'EIU_buy': buy, 'EIU_buy_P': price}
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else:
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return {'EIU_sell': 0, 'EIU_buy': 0}
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# BEHAVIOR 7b: Endogenous Information Updating (EIU)
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# Longer Term Price Signal, Higher Threshold = Human-Like
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HEIU_portion = Decimal('0.20')
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HEIU_Ext_Hold = Decimal('42000.0')
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HEIU_UB = Decimal('2.0') # UPPER BOUND
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HEIU_LB = Decimal('2.0') # LOWER BOUND
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def b7hm2(step, sL, s):
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theta = (s['Z']*HEIU_portion*s['Price'])/(s['Z']*HEIU_portion*s['Price'] + HEIU_Ext_Hold * s['P_Ext_Markets'])
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# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
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if (s['Price'] - s['Price_Signal_2']) < - HEIU_LB:
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sell = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HEIU_portion*(1-theta))
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price = s['Price'] + (s['Price_Signal_2'] / s['Price'])
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return {'HEIU_sell': sell, 'HEIU_buy': 0, 'HEIU_sell_P': price}
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# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
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elif (s['Price'] - s['Price_Signal_2']) > HEIU_UB:
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buy = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* HEIU_portion*(1-theta))
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price = s['Price'] - (s['Price'] / s['Price_Signal_2'])
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return {'HEIU_sell': 0, 'HEIU_buy': buy, 'HEIU_buy_P': price}
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else:
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return {'HEIU_sell': 0, 'HEIU_buy': 0}
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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['EMH_buy']) / 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['EMH_buy'] + _input['herd_buy'] + _input['EIU_buy'] + _input['HEIU_buy'] # / 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['EMH_sell'] + _input['HODL_sell'] + _input['herd_sell'] + _input['EIU_sell'] + _input['HEIU_sell'] # / Psignal_int
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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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y = 'Price'
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#var1 = Decimal.from_float(s['Buy_Log'])
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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 s5m3(step, sL, s, _input):
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y = 'Price_Signal'
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x = alpha * s['Price'] + (1 - alpha)*s['Price_Signal']
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return (y, x)
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def s6m3(step, sL, s, _input):
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y = 'Price_Signal_2'
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x = alpha_2 * s['Price'] + (1 - alpha_2)*s['Price_Signal_2']
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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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# Exogenous States
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proc_one_coef_A = -125
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proc_one_coef_B = 125
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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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# NONE
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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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'Price_Signal': Decimal(100.0),
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'Price_Signal_2': Decimal(100.0),
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'Trans': Decimal(0.0),
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'P_Ext_Markets': Decimal(25000.0),
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'timestamp': '2018-10-01 15:16:24'
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}
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def env_proc_id(x):
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return x
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env_processes = {
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# "P_Ext_Markets": env_proc_id
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}
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exogenous_states = exo_update_per_ts(
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{
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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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sim_config = {
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"N": 1,
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"T": range(1000)
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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,
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"b3": b3m2,
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"b7": b7m2,
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"b7h": b7hm2
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},
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"states": {
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"Z": s1m1,
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"Buy_Log": s3m1
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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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"b3": b3m2,
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"b4": b4m2,
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"b7": b7m2,
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"b7h": b7hm2
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},
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"states": {
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"Sell_Log": s4m2
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}
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},
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"m3": {
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"behaviors": {
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},
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"states": {
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"Price": s2m3,
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"Price_Signal": s5m3,
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"Price_Signal_2": s6m3,
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}
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}
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}
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configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms)) |