cleanup
This commit is contained in:
Markus
parent
437fd81f08
commit
d9c1b50de9
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@ -1,220 +0,0 @@
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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 configs
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from SimCAD.configuration import Configuration
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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 = 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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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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return {'buy_order1': buy}
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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 {'buy_order1': 0}
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else:
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return {'buy_order1': 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 {'sell_order1': 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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return {'sell_order1': sell}
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else:
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return {'sell_order1': 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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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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return {'sell_order2': sell}
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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 {'sell_order2': 0}
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else:
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return {'sell_order2': 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['buy_order1']) / 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['buy_order1'] # / 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['sell_order1'] + _input['sell_order2'] # / 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'] * 1/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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# 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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ts_format = '%Y-%m-%d %H:%M:%S'
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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def es5p2(step, sL, s, _input):
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y = 'timestamp'
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x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
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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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'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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},
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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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"b4": b4m2
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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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}
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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))
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@ -1,247 +0,0 @@
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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 configs
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from SimCAD.configuration import Configuration
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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') # 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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#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('0.250')
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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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return {'buy_order1': buy}
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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 {'buy_order1': 0}
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else:
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return {'buy_order1': 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 {'sell_order1': 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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return {'sell_order1': sell}
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else:
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return {'sell_order1': 0}
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# BEHAVIOR 3: Herding
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Herd_portion = Decimal('0.250')
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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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return {'herd_sell': sell, 'herd_buy': 0}
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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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return {'herd_sell': 0, 'herd_buy': buy}
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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('0.250')
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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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return {'sell_order2': sell}
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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 {'sell_order2': 0}
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else:
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return {'sell_order2': 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['buy_order1']) / 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['buy_order1'] + _input['herd_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['sell_order1'] + _input['sell_order2'] + _input['herd_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 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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ts_format = '%Y-%m-%d %H:%M:%S'
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t_delta = timedelta(days=0, minutes=0, seconds=1)
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def es5p2(step, sL, s, _input):
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y = 'timestamp'
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x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
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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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'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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|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
sim_config = {
|
||||
"N": 20,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1,
|
||||
"b3": b3m2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
"Buy_Log": s3m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b3": b3m2,
|
||||
"b4": b4m2
|
||||
},
|
||||
"states": {
|
||||
"Sell_Log": s4m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
},
|
||||
"states": {
|
||||
"Price": s2m3,
|
||||
"Price_Signal": s5m3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|
||||
|
|
@ -1,267 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1)
|
||||
}
|
||||
|
||||
# Signals
|
||||
# Pr_signal
|
||||
beta = Decimal('0.25') # agent response gain
|
||||
beta_LT = Decimal('0.1') # LT agent response gain
|
||||
# alpha = .67, 2 block moving average
|
||||
alpha = Decimal('0.67') # 21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
|
||||
max_withdraw_factor = Decimal('0.9')
|
||||
external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
|
||||
|
||||
|
||||
#alpha * s['Zeus_ST'] + (1 - alpha)*s['Zeus_LT']
|
||||
|
||||
# Stochastic process factors
|
||||
correction_factor = Decimal('0.01')
|
||||
volatility = Decimal('5.0')
|
||||
|
||||
# Buy_Log_signal =
|
||||
# Z_signal =
|
||||
# Price_signal =
|
||||
# TDR_draw_signal =
|
||||
# P_Ext_Markets_signal =
|
||||
|
||||
# Behaviors per Mechanism
|
||||
|
||||
# BEHAVIOR 1: EMH Trader
|
||||
EMH_portion = Decimal('0.250')
|
||||
EMH_Ext_Hold = Decimal('42000.0')
|
||||
|
||||
|
||||
def b1m1(step, sL, s):
|
||||
# print('b1m1')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
buy = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
return {'buy_order1': buy}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
return {'buy_order1': 0}
|
||||
else:
|
||||
return {'buy_order1': 0}
|
||||
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
# print('b1m2')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
return {'sell_order1': 0}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
sell = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
return {'sell_order1': sell}
|
||||
else:
|
||||
return {'sell_order1': 0}
|
||||
|
||||
# BEHAVIOR 3: Herding
|
||||
Herd_portion = Decimal('0.250')
|
||||
Herd_Ext_Hold = Decimal('42000.0')
|
||||
Herd_UB = Decimal('0.10') # UPPER BOUND
|
||||
Herd_LB = Decimal('0.10') # LOWER BOUND
|
||||
def b3m2(step, sL, s):
|
||||
theta = (s['Z']*Herd_portion*s['Price'])/(s['Z']*Herd_portion*s['Price'] + Herd_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - Herd_LB:
|
||||
|
||||
sell = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
return {'herd_sell': sell, 'herd_buy': 0}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > Herd_UB:
|
||||
buy = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
return {'herd_sell': 0, 'herd_buy': buy}
|
||||
else:
|
||||
return {'herd_sell': 0, 'herd_buy': 0}
|
||||
|
||||
# BEHAVIOR 4: HODLers
|
||||
HODL_belief = Decimal('10.0')
|
||||
HODL_portion = Decimal('0.250')
|
||||
HODL_Ext_Hold = Decimal('4200.0')
|
||||
|
||||
|
||||
def b4m2(step, sL, s):
|
||||
# print('b4m2')
|
||||
theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < 1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
sell = beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
|
||||
return {'sell_order2': sell}
|
||||
elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
return {'sell_order2': 0}
|
||||
else:
|
||||
return {'sell_order2': 0}
|
||||
|
||||
# BEHAVIOR 7: Endogenous Information Updating (EIU)
|
||||
EIU_portion = Decimal('0.250')
|
||||
EIU_Ext_Hold = Decimal('42000.0')
|
||||
EIU_UB = Decimal('0.50') # UPPER BOUND
|
||||
EIU_LB = Decimal('0.50') # LOWER BOUND
|
||||
def b7m2(step, sL, s):
|
||||
theta = (s['Z']*EIU_portion*s['Price'])/(s['Z']*EIU_portion*s['Price'] + EIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - EIU_LB:
|
||||
|
||||
sell = beta * theta*EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EIU_portion*(1-theta))
|
||||
return {'EIU_sell': sell, 'EIU_buy': 0}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > EIU_UB:
|
||||
buy = beta * theta* EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* EIU_portion*(1-theta))
|
||||
return {'EIU_sell': 0, 'EIU_buy': buy}
|
||||
else:
|
||||
return {'EIU_sell': 0, 'EIU_buy': 0}
|
||||
|
||||
# STATES
|
||||
# ZEUS Fixed Supply
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 'Z'
|
||||
x = s['Z'] #+ _input # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s2m1(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x = (s['P_Ext_Markets'] - _input['buy_order1']) / s['Z'] * 10000
|
||||
# #x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
|
||||
def s3m1(step, sL, s, _input):
|
||||
y = 'Buy_Log'
|
||||
x = _input['buy_order1'] + _input['herd_buy'] + _input['EIU_buy'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
def s4m2(step, sL, s, _input):
|
||||
y = 'Sell_Log'
|
||||
x = _input['sell_order1'] + _input['sell_order2'] + _input['herd_sell'] + _input['EIU_sell'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s3m3(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log'] + _input # / Psignal_int
|
||||
# return (y, x)
|
||||
|
||||
|
||||
# Price Update
|
||||
def s2m3(step, sL, s, _input):
|
||||
|
||||
y = 'Price'
|
||||
#var1 = Decimal.from_float(s['Buy_Log'])
|
||||
x = s['Price'] + s['Buy_Log'] /s['Z'] /(Decimal('0.10') * s['Price']) - s['Sell_Log'] / s['Z'] / (Decimal('0.10')*s['Price'])
|
||||
#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
def s5m3(step, sL, s, _input):
|
||||
y = 'Price_Signal'
|
||||
x = alpha * s['Price'] + (1 - alpha)*s['Price_Signal']
|
||||
return (y, x)
|
||||
|
||||
def s6m1(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] - _input
|
||||
#x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
return (y, x)
|
||||
|
||||
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 'Price'
|
||||
x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
|
||||
#x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = -125
|
||||
proc_one_coef_B = 125
|
||||
|
||||
# A change in belief of actual price, passed onto behaviors to make action
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] + bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
|
||||
|
||||
return (y,x)
|
||||
|
||||
|
||||
def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, s['timestamp'], seconds=1)
|
||||
return (y, x)
|
||||
|
||||
#Environment States
|
||||
# NONE
|
||||
|
||||
# Genesis States
|
||||
state_dict = {
|
||||
'Z': Decimal(21000000.0),
|
||||
'Price': Decimal(100.0), # Initialize = Z for EMA
|
||||
'Buy_Log': Decimal(0.0),
|
||||
'Sell_Log': Decimal(0.0),
|
||||
'Price_Signal': Decimal(100.0),
|
||||
'Trans': Decimal(0.0),
|
||||
'P_Ext_Markets': Decimal(25000.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
def env_proc_id(x):
|
||||
return x
|
||||
|
||||
env_processes = {
|
||||
# "P_Ext_Markets": env_proc_id
|
||||
}
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
sim_config = {
|
||||
"N": 100,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1,
|
||||
"b3": b3m2,
|
||||
"b7": b7m2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
"Buy_Log": s3m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b3": b3m2,
|
||||
"b4": b4m2,
|
||||
"b7": b7m2
|
||||
},
|
||||
"states": {
|
||||
"Sell_Log": s4m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
},
|
||||
"states": {
|
||||
"Price": s2m3,
|
||||
"Price_Signal": s5m3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|
||||
|
|
@ -1,300 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1)
|
||||
}
|
||||
|
||||
# Signals
|
||||
# Pr_signal
|
||||
beta = Decimal('0.25') # agent response gain
|
||||
beta_LT = Decimal('0.1') # LT agent response gain
|
||||
# alpha = .67, 2 block moving average
|
||||
alpha = Decimal('0.67')
|
||||
# 21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
|
||||
# 21 * 3 mech steps, 2/64 = 0.03125
|
||||
alpha_2 = Decimal('0.03125')
|
||||
max_withdraw_factor = Decimal('0.9')
|
||||
external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
|
||||
|
||||
|
||||
#alpha * s['Zeus_ST'] + (1 - alpha)*s['Zeus_LT']
|
||||
|
||||
# Stochastic process factors
|
||||
correction_factor = Decimal('0.01')
|
||||
volatility = Decimal('5.0')
|
||||
|
||||
# Buy_Log_signal =
|
||||
# Z_signal =
|
||||
# Price_signal =
|
||||
# TDR_draw_signal =
|
||||
# P_Ext_Markets_signal =
|
||||
|
||||
# Behaviors per Mechanism
|
||||
|
||||
# BEHAVIOR 1: EMH Trader
|
||||
EMH_portion = Decimal('0.20')
|
||||
EMH_Ext_Hold = Decimal('42000.0')
|
||||
|
||||
|
||||
def b1m1(step, sL, s):
|
||||
# print('b1m1')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
buy = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
return {'buy_order1': buy}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
return {'buy_order1': 0}
|
||||
else:
|
||||
return {'buy_order1': 0}
|
||||
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
# print('b1m2')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
return {'sell_order1': 0}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
sell = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
return {'sell_order1': sell}
|
||||
else:
|
||||
return {'sell_order1': 0}
|
||||
|
||||
# BEHAVIOR 3: Herding
|
||||
Herd_portion = Decimal('0.20')
|
||||
Herd_Ext_Hold = Decimal('42000.0')
|
||||
Herd_UB = Decimal('0.10') # UPPER BOUND
|
||||
Herd_LB = Decimal('0.10') # LOWER BOUND
|
||||
def b3m2(step, sL, s):
|
||||
theta = (s['Z']*Herd_portion*s['Price'])/(s['Z']*Herd_portion*s['Price'] + Herd_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - Herd_LB:
|
||||
|
||||
sell = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
return {'herd_sell': sell, 'herd_buy': 0}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > Herd_UB:
|
||||
buy = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
return {'herd_sell': 0, 'herd_buy': buy}
|
||||
else:
|
||||
return {'herd_sell': 0, 'herd_buy': 0}
|
||||
|
||||
# BEHAVIOR 4: HODLers
|
||||
HODL_belief = Decimal('10.0')
|
||||
HODL_portion = Decimal('0.20')
|
||||
HODL_Ext_Hold = Decimal('4200.0')
|
||||
|
||||
|
||||
def b4m2(step, sL, s):
|
||||
# print('b4m2')
|
||||
theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < 1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
sell = beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
|
||||
return {'sell_order2': sell}
|
||||
elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
return {'sell_order2': 0}
|
||||
else:
|
||||
return {'sell_order2': 0}
|
||||
|
||||
# BEHAVIOR 7: Endogenous Information Updating (EIU)
|
||||
# Short Term Price Signal, Lower Threshold = BOT-like
|
||||
EIU_portion = Decimal('0.20')
|
||||
EIU_Ext_Hold = Decimal('42000.0')
|
||||
EIU_UB = Decimal('0.50') # UPPER BOUND
|
||||
EIU_LB = Decimal('0.50') # LOWER BOUND
|
||||
def b7m2(step, sL, s):
|
||||
theta = (s['Z']*EIU_portion*s['Price'])/(s['Z']*EIU_portion*s['Price'] + EIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - EIU_LB:
|
||||
|
||||
sell = beta * theta*EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EIU_portion*(1-theta))
|
||||
return {'EIU_sell': sell, 'EIU_buy': 0}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > EIU_UB:
|
||||
buy = beta * theta* EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* EIU_portion*(1-theta))
|
||||
return {'EIU_sell': 0, 'EIU_buy': buy}
|
||||
else:
|
||||
return {'EIU_sell': 0, 'EIU_buy': 0}
|
||||
|
||||
# BEHAVIOR 7b: Endogenous Information Updating (EIU)
|
||||
# Longer Term Price Signal, Higher Threshold = Human-Like
|
||||
HEIU_portion = Decimal('0.20')
|
||||
HEIU_Ext_Hold = Decimal('42000.0')
|
||||
HEIU_UB = Decimal('2.0') # UPPER BOUND
|
||||
HEIU_LB = Decimal('2.0') # LOWER BOUND
|
||||
def b7hm2(step, sL, s):
|
||||
theta = (s['Z']*HEIU_portion*s['Price'])/(s['Z']*HEIU_portion*s['Price'] + HEIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal_2']) < - HEIU_LB:
|
||||
|
||||
sell = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HEIU_portion*(1-theta))
|
||||
return {'HEIU_sell': sell, 'HEIU_buy': 0}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal_2']) > HEIU_UB:
|
||||
buy = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* HEIU_portion*(1-theta))
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': buy}
|
||||
else:
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': 0}
|
||||
|
||||
# STATES
|
||||
# ZEUS Fixed Supply
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 'Z'
|
||||
x = s['Z'] #+ _input # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s2m1(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x = (s['P_Ext_Markets'] - _input['buy_order1']) / s['Z'] * 10000
|
||||
# #x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
|
||||
def s3m1(step, sL, s, _input):
|
||||
y = 'Buy_Log'
|
||||
x = _input['buy_order1'] + _input['herd_buy'] + _input['EIU_buy'] + _input['HEIU_buy'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
def s4m2(step, sL, s, _input):
|
||||
y = 'Sell_Log'
|
||||
x = _input['sell_order1'] + _input['sell_order2'] + _input['herd_sell'] + _input['EIU_sell'] + _input['HEIU_sell'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s3m3(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log'] + _input # / Psignal_int
|
||||
# return (y, x)
|
||||
|
||||
|
||||
# Price Update
|
||||
def s2m3(step, sL, s, _input):
|
||||
|
||||
y = 'Price'
|
||||
#var1 = Decimal.from_float(s['Buy_Log'])
|
||||
x = s['Price'] + s['Buy_Log'] /s['Z']/(Decimal('1.25') ) - s['Sell_Log']/s['Z']/(Decimal('1.25') )
|
||||
#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
def s5m3(step, sL, s, _input):
|
||||
y = 'Price_Signal'
|
||||
x = alpha * s['Price'] + (1 - alpha)*s['Price_Signal']
|
||||
return (y, x)
|
||||
|
||||
def s6m3(step, sL, s, _input):
|
||||
y = 'Price_Signal_2'
|
||||
x = alpha_2 * s['Price'] + (1 - alpha_2)*s['Price_Signal_2']
|
||||
return (y, x)
|
||||
|
||||
def s6m1(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] - _input
|
||||
#x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
return (y, x)
|
||||
|
||||
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 'Price'
|
||||
x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
|
||||
#x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = -125
|
||||
proc_one_coef_B = 125
|
||||
|
||||
# A change in belief of actual price, passed onto behaviors to make action
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] + bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
|
||||
|
||||
return (y,x)
|
||||
|
||||
|
||||
def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, s['timestamp'], seconds=1)
|
||||
return (y, x)
|
||||
|
||||
#Environment States
|
||||
# NONE
|
||||
|
||||
# Genesis States
|
||||
state_dict = {
|
||||
'Z': Decimal(21000000.0),
|
||||
'Price': Decimal(100.0), # Initialize = Z for EMA
|
||||
'Buy_Log': Decimal(0.0),
|
||||
'Sell_Log': Decimal(0.0),
|
||||
'Price_Signal': Decimal(100.0),
|
||||
'Price_Signal_2': Decimal(100.0),
|
||||
'Trans': Decimal(0.0),
|
||||
'P_Ext_Markets': Decimal(25000.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
def env_proc_id(x):
|
||||
return x
|
||||
|
||||
env_processes = {
|
||||
# "P_Ext_Markets": env_proc_id
|
||||
}
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
sim_config = {
|
||||
"N": 100,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1,
|
||||
"b3": b3m2,
|
||||
"b7": b7m2,
|
||||
"b7h": b7hm2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
"Buy_Log": s3m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b3": b3m2,
|
||||
"b4": b4m2,
|
||||
"b7": b7m2,
|
||||
"b7h": b7hm2
|
||||
},
|
||||
"states": {
|
||||
"Sell_Log": s4m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
},
|
||||
"states": {
|
||||
"Price": s2m3,
|
||||
"Price_Signal": s5m3,
|
||||
"Price_Signal_2": s6m3,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|
||||
|
|
@ -1,309 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1)
|
||||
}
|
||||
|
||||
# Signals
|
||||
# Pr_signal
|
||||
beta = Decimal('0.25') # agent response gain
|
||||
beta_LT = Decimal('0.1') # LT agent response gain
|
||||
# alpha = .67, 2 block moving average
|
||||
alpha = Decimal('0.67')
|
||||
# 21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
|
||||
# 21 * 3 mech steps, 2/64 = 0.03125
|
||||
alpha_2 = Decimal('0.03125')
|
||||
max_withdraw_factor = Decimal('0.9')
|
||||
external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
|
||||
|
||||
|
||||
#alpha * s['Zeus_ST'] + (1 - alpha)*s['Zeus_LT']
|
||||
|
||||
# Stochastic process factors
|
||||
correction_factor = Decimal('0.01')
|
||||
volatility = Decimal('5.0')
|
||||
|
||||
# Buy_Log_signal =
|
||||
# Z_signal =
|
||||
# Price_signal =
|
||||
# TDR_draw_signal =
|
||||
# P_Ext_Markets_signal =
|
||||
|
||||
# Behaviors per Mechanism
|
||||
|
||||
# BEHAVIOR 1: EMH Trader
|
||||
EMH_portion = Decimal('0.20')
|
||||
EMH_Ext_Hold = Decimal('42000.0')
|
||||
|
||||
|
||||
def b1m1(step, sL, s):
|
||||
# print('b1m1')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
buy = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'EMH_buy': buy, 'EMH_buy_P': price}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
return {'EMH_buy': 0}
|
||||
else:
|
||||
return {'EMH_buy': 0}
|
||||
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
# print('b1m2')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
return {'EMH_sell': 0}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
sell = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'EMH_sell': sell, 'EMH_sell_P': price}
|
||||
else:
|
||||
return {'EMH_sell': 0}
|
||||
|
||||
# BEHAVIOR 3: Herding
|
||||
Herd_portion = Decimal('0.20')
|
||||
Herd_Ext_Hold = Decimal('42000.0')
|
||||
Herd_UB = Decimal('0.10') # UPPER BOUND
|
||||
Herd_LB = Decimal('0.10') # LOWER BOUND
|
||||
def b3m2(step, sL, s):
|
||||
theta = (s['Z']*Herd_portion*s['Price'])/(s['Z']*Herd_portion*s['Price'] + Herd_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - Herd_LB:
|
||||
|
||||
sell = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price_Signal'] / s['Price'])
|
||||
return {'herd_sell': sell, 'herd_buy': 0, 'herd_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > Herd_UB:
|
||||
buy = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price'] / s['Price_Signal'])
|
||||
return {'herd_sell': 0, 'herd_buy': buy, 'herd_buy_P': price}
|
||||
else:
|
||||
return {'herd_sell': 0, 'herd_buy': 0}
|
||||
|
||||
# BEHAVIOR 4: HODLers
|
||||
HODL_belief = Decimal('10.0')
|
||||
HODL_portion = Decimal('0.20')
|
||||
HODL_Ext_Hold = Decimal('4200.0')
|
||||
|
||||
|
||||
def b4m2(step, sL, s):
|
||||
# print('b4m2')
|
||||
theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < 1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
sell = beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'HODL_sell': sell, 'HODL_sell_P': price}
|
||||
elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
return {'HODL_sell': 0}
|
||||
else:
|
||||
return {'HODL_sell': 0}
|
||||
|
||||
# BEHAVIOR 7: Endogenous Information Updating (EIU)
|
||||
# Short Term Price Signal, Lower Threshold = BOT-like
|
||||
EIU_portion = Decimal('0.20')
|
||||
EIU_Ext_Hold = Decimal('42000.0')
|
||||
EIU_UB = Decimal('0.50') # UPPER BOUND
|
||||
EIU_LB = Decimal('0.50') # LOWER BOUND
|
||||
def b7m2(step, sL, s):
|
||||
theta = (s['Z']*EIU_portion*s['Price'])/(s['Z']*EIU_portion*s['Price'] + EIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - EIU_LB:
|
||||
|
||||
sell = beta * theta*EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EIU_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price_Signal'] / s['Price'])
|
||||
return {'EIU_sell': sell, 'EIU_buy': 0, 'EIU_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > EIU_UB:
|
||||
buy = beta * theta* EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* EIU_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price'] / s['Price_Signal'])
|
||||
return {'EIU_sell': 0, 'EIU_buy': buy, 'EIU_buy_P': price}
|
||||
else:
|
||||
return {'EIU_sell': 0, 'EIU_buy': 0}
|
||||
|
||||
# BEHAVIOR 7b: Endogenous Information Updating (EIU)
|
||||
# Longer Term Price Signal, Higher Threshold = Human-Like
|
||||
HEIU_portion = Decimal('0.20')
|
||||
HEIU_Ext_Hold = Decimal('42000.0')
|
||||
HEIU_UB = Decimal('2.0') # UPPER BOUND
|
||||
HEIU_LB = Decimal('2.0') # LOWER BOUND
|
||||
def b7hm2(step, sL, s):
|
||||
theta = (s['Z']*HEIU_portion*s['Price'])/(s['Z']*HEIU_portion*s['Price'] + HEIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal_2']) < - HEIU_LB:
|
||||
|
||||
sell = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HEIU_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price_Signal_2'] / s['Price'])
|
||||
return {'HEIU_sell': sell, 'HEIU_buy': 0, 'HEIU_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal_2']) > HEIU_UB:
|
||||
buy = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* HEIU_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price'] / s['Price_Signal_2'])
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': buy, 'HEIU_buy_P': price}
|
||||
else:
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': 0}
|
||||
|
||||
# STATES
|
||||
# ZEUS Fixed Supply
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 'Z'
|
||||
x = s['Z'] #+ _input # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s2m1(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x = (s['P_Ext_Markets'] - _input['EMH_buy']) / s['Z'] * 10000
|
||||
# #x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
|
||||
def s3m1(step, sL, s, _input):
|
||||
y = 'Buy_Log'
|
||||
x = _input['EMH_buy'] + _input['herd_buy'] + _input['EIU_buy'] + _input['HEIU_buy'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
def s4m2(step, sL, s, _input):
|
||||
y = 'Sell_Log'
|
||||
x = _input['EMH_sell'] + _input['HODL_sell'] + _input['herd_sell'] + _input['EIU_sell'] + _input['HEIU_sell'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s3m3(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log'] + _input # / Psignal_int
|
||||
# return (y, x)
|
||||
|
||||
|
||||
# Price Update
|
||||
def s2m3(step, sL, s, _input):
|
||||
|
||||
y = 'Price'
|
||||
#var1 = Decimal.from_float(s['Buy_Log'])
|
||||
x = s['Price'] + (s['Buy_Log'] /s['Z'] ) - (s['Sell_Log']/s['Z'] )
|
||||
#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
def s5m3(step, sL, s, _input):
|
||||
y = 'Price_Signal'
|
||||
x = alpha * s['Price'] + (1 - alpha)*s['Price_Signal']
|
||||
return (y, x)
|
||||
|
||||
def s6m3(step, sL, s, _input):
|
||||
y = 'Price_Signal_2'
|
||||
x = alpha_2 * s['Price'] + (1 - alpha_2)*s['Price_Signal_2']
|
||||
return (y, x)
|
||||
|
||||
def s6m1(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] - _input
|
||||
#x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s2m2(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
|
||||
# x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = -125
|
||||
proc_one_coef_B = 125
|
||||
|
||||
# A change in belief of actual price, passed onto behaviors to make action
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] + bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
|
||||
|
||||
return (y,x)
|
||||
|
||||
|
||||
def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, s['timestamp'], seconds=1)
|
||||
return (y, x)
|
||||
|
||||
#Environment States
|
||||
# NONE
|
||||
|
||||
# Genesis States
|
||||
state_dict = {
|
||||
'Z': Decimal(21000000.0),
|
||||
'Price': Decimal(100.0), # Initialize = Z for EMA
|
||||
'Buy_Log': Decimal(0.0),
|
||||
'Sell_Log': Decimal(0.0),
|
||||
'Price_Signal': Decimal(100.0),
|
||||
'Price_Signal_2': Decimal(100.0),
|
||||
'Trans': Decimal(0.0),
|
||||
'P_Ext_Markets': Decimal(25000.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
def env_proc_id(x):
|
||||
return x
|
||||
|
||||
env_processes = {
|
||||
# "P_Ext_Markets": env_proc_id
|
||||
}
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
sim_config = {
|
||||
"N": 1,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1,
|
||||
"b3": b3m2,
|
||||
"b7": b7m2,
|
||||
"b7h": b7hm2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
"Buy_Log": s3m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b3": b3m2,
|
||||
"b4": b4m2,
|
||||
"b7": b7m2,
|
||||
"b7h": b7hm2
|
||||
},
|
||||
"states": {
|
||||
"Sell_Log": s4m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
},
|
||||
"states": {
|
||||
"Price": s2m3,
|
||||
"Price_Signal": s5m3,
|
||||
"Price_Signal_2": s6m3,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|
||||
|
|
@ -1,319 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1)
|
||||
}
|
||||
|
||||
# Signals
|
||||
# Pr_signal
|
||||
beta = Decimal('0.25') # agent response gain
|
||||
beta_LT = Decimal('0.1') # LT agent response gain
|
||||
# alpha = .67, 2 block moving average
|
||||
alpha = Decimal('0.67')
|
||||
# 21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
|
||||
# 21 * 3 mech steps, 2/64 = 0.03125
|
||||
alpha_2 = Decimal('0.03125')
|
||||
max_withdraw_factor = Decimal('0.9')
|
||||
external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
|
||||
|
||||
|
||||
#alpha * s['Zeus_ST'] + (1 - alpha)*s['Zeus_LT']
|
||||
|
||||
# Stochastic process factors
|
||||
correction_factor = Decimal('0.01')
|
||||
volatility = Decimal('5.0')
|
||||
|
||||
# Buy_Log_signal =
|
||||
# Z_signal =
|
||||
# Price_signal =
|
||||
# TDR_draw_signal =
|
||||
# P_Ext_Markets_signal =
|
||||
|
||||
# Behaviors per Mechanism
|
||||
|
||||
# BEHAVIOR 1: EMH Trader
|
||||
EMH_portion = Decimal('0.20')
|
||||
EMH_Ext_Hold = Decimal('42000.0')
|
||||
|
||||
|
||||
def b1m1(step, sL, s):
|
||||
# print('b1m1')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
buy = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'EMH_buy': buy, 'EMH_buy_P': price}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
price = 0
|
||||
return {'EMH_buy': 0, 'EMH_buy_P': price}
|
||||
else:
|
||||
price = 0
|
||||
return {'EMH_buy': 0, 'EMH_buy_P': price}
|
||||
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
# print('b1m2')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
return {'EMH_sell': 0}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
sell = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'EMH_sell': sell, 'EMH_sell_P': price}
|
||||
else:
|
||||
return {'EMH_sell': 0}
|
||||
|
||||
# BEHAVIOR 3: Herding
|
||||
Herd_portion = Decimal('0.20')
|
||||
Herd_Ext_Hold = Decimal('42000.0')
|
||||
Herd_UB = Decimal('0.10') # UPPER BOUND
|
||||
Herd_LB = Decimal('0.10') # LOWER BOUND
|
||||
def b3m2(step, sL, s):
|
||||
theta = (s['Z']*Herd_portion*s['Price'])/(s['Z']*Herd_portion*s['Price'] + Herd_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - Herd_LB:
|
||||
|
||||
sell = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price_Signal'] / s['Price'])
|
||||
return {'herd_sell': sell, 'herd_buy': 0, 'herd_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > Herd_UB:
|
||||
buy = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price'] / s['Price_Signal'])
|
||||
return {'herd_sell': 0, 'herd_buy': buy, 'herd_buy_P': price}
|
||||
else:
|
||||
return {'herd_sell': 0, 'herd_buy': 0, 'herd_buy_P':0}
|
||||
|
||||
# BEHAVIOR 4: HODLers
|
||||
HODL_belief = Decimal('10.0')
|
||||
HODL_portion = Decimal('0.20')
|
||||
HODL_Ext_Hold = Decimal('4200.0')
|
||||
|
||||
|
||||
def b4m2(step, sL, s):
|
||||
# print('b4m2')
|
||||
theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < 1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
sell = beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'HODL_sell': sell, 'HODL_sell_P': price}
|
||||
elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
return {'HODL_sell': 0}
|
||||
else:
|
||||
return {'HODL_sell': 0}
|
||||
|
||||
# BEHAVIOR 7: Endogenous Information Updating (EIU)
|
||||
# Short Term Price Signal, Lower Threshold = BOT-like
|
||||
EIU_portion = Decimal('0.20')
|
||||
EIU_Ext_Hold = Decimal('42000.0')
|
||||
EIU_UB = Decimal('0.50') # UPPER BOUND
|
||||
EIU_LB = Decimal('0.50') # LOWER BOUND
|
||||
def b7m2(step, sL, s):
|
||||
theta = (s['Z']*EIU_portion*s['Price'])/(s['Z']*EIU_portion*s['Price'] + EIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - EIU_LB:
|
||||
|
||||
sell = beta * theta*EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EIU_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price_Signal'] / s['Price'])
|
||||
return {'EIU_sell': sell, 'EIU_buy': 0, 'EIU_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > EIU_UB:
|
||||
buy = beta * theta* EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* EIU_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price'] / s['Price_Signal'])
|
||||
return {'EIU_sell': 0, 'EIU_buy': buy, 'EIU_buy_P': price}
|
||||
else:
|
||||
return {'EIU_sell': 0, 'EIU_buy': 0}
|
||||
|
||||
# BEHAVIOR 7b: Endogenous Information Updating (EIU)
|
||||
# Longer Term Price Signal, Higher Threshold = Human-Like
|
||||
HEIU_portion = Decimal('0.20')
|
||||
HEIU_Ext_Hold = Decimal('42000.0')
|
||||
HEIU_UB = Decimal('2.0') # UPPER BOUND
|
||||
HEIU_LB = Decimal('2.0') # LOWER BOUND
|
||||
def b7hm2(step, sL, s):
|
||||
theta = (s['Z']*HEIU_portion*s['Price'])/(s['Z']*HEIU_portion*s['Price'] + HEIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal_2']) < - HEIU_LB:
|
||||
|
||||
sell = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HEIU_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price_Signal_2'] / s['Price'])
|
||||
return {'HEIU_sell': sell, 'HEIU_buy': 0, 'HEIU_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal_2']) > HEIU_UB:
|
||||
buy = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* HEIU_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price'] / s['Price_Signal_2'])
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': buy, 'HEIU_buy_P': price}
|
||||
else:
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': 0}
|
||||
|
||||
# STATES
|
||||
# ZEUS Fixed Supply
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 'Z'
|
||||
x = s['Z'] #+ _input # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s2m1(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x = (s['P_Ext_Markets'] - _input['EMH_buy']) / s['Z'] * 10000
|
||||
# #x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
|
||||
def s3m1(step, sL, s, _input):
|
||||
y = 'Buy_Log'
|
||||
x = np.zeros(4)
|
||||
x[0] = _input['EMH_buy']
|
||||
x[1] = _input['EMH_buy_P']
|
||||
x[2] = _input['herd_buy']
|
||||
x[3] = _input['herd_buy_P']
|
||||
# = _input['EMH_buy'] + _input['herd_buy'] + _input['EIU_buy'] + _input['HEIU_buy'] # / Psignal_int
|
||||
return (y, x) #[0], x[1])
|
||||
|
||||
|
||||
def s4m2(step, sL, s, _input):
|
||||
y = 'Sell_Log'
|
||||
x = _input['EMH_sell'] + _input['HODL_sell'] + _input['herd_sell'] + _input['EIU_sell'] + _input['HEIU_sell'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s3m3(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log'] + _input # / Psignal_int
|
||||
# return (y, x)
|
||||
|
||||
|
||||
# Price Update
|
||||
def s2m3(step, sL, s, _input):
|
||||
|
||||
y = 'Price'
|
||||
#var1 = Decimal.from_float(s['Buy_Log'])
|
||||
|
||||
x = s['Price'] + (Decimal(s['Buy_Log'][0])) / s['Z'] # - (s['Sell_Log']/s['Z'] ) # for buy log term /s['Z'] )
|
||||
#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
def s5m3(step, sL, s, _input):
|
||||
y = 'Price_Signal'
|
||||
x = alpha * s['Price'] + (1 - alpha)*s['Price_Signal']
|
||||
return (y, x)
|
||||
|
||||
def s6m3(step, sL, s, _input):
|
||||
y = 'Price_Signal_2'
|
||||
x = alpha_2 * s['Price'] + (1 - alpha_2)*s['Price_Signal_2']
|
||||
return (y, x)
|
||||
|
||||
def s6m1(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] - _input
|
||||
#x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s2m2(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
|
||||
# x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = -125
|
||||
proc_one_coef_B = 125
|
||||
|
||||
# A change in belief of actual price, passed onto behaviors to make action
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] + bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
|
||||
|
||||
return (y,x)
|
||||
|
||||
|
||||
ts_format = '%Y-%m-%d %H:%M:%S'
|
||||
t_delta = timedelta(days=0, minutes=0, seconds=1)
|
||||
def es5p2(step, sL, s, _input):
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
|
||||
return (y, x)
|
||||
|
||||
#Environment States
|
||||
# NONE
|
||||
|
||||
# Genesis States
|
||||
state_dict = {
|
||||
'Z': Decimal(21000000.0),
|
||||
'Price': Decimal(100.0), # Initialize = Z for EMA
|
||||
'Buy_Log': Decimal(0.0),
|
||||
'Sell_Log': Decimal(0.0),
|
||||
'Price_Signal': Decimal(100.0),
|
||||
'Price_Signal_2': Decimal(100.0),
|
||||
'Trans': Decimal(0.0),
|
||||
'P_Ext_Markets': Decimal(25000.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
def env_proc_id(x):
|
||||
return x
|
||||
|
||||
env_processes = {
|
||||
# "P_Ext_Markets": env_proc_id
|
||||
}
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
sim_config = {
|
||||
"N": 1,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1,
|
||||
"b3": b3m2,
|
||||
"b7": b7m2,
|
||||
"b7h": b7hm2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
"Buy_Log": s3m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b3": b3m2,
|
||||
"b4": b4m2,
|
||||
"b7": b7m2,
|
||||
"b7h": b7hm2
|
||||
},
|
||||
"states": {
|
||||
"Sell_Log": s4m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
},
|
||||
"states": {
|
||||
"Price": s2m3,
|
||||
"Price_Signal": s5m3,
|
||||
"Price_Signal_2": s6m3,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|
||||
|
|
@ -1,319 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1)
|
||||
}
|
||||
|
||||
# Signals
|
||||
# Pr_signal
|
||||
beta = Decimal('0.25') # agent response gain
|
||||
beta_LT = Decimal('0.1') # LT agent response gain
|
||||
# alpha = .67, 2 block moving average
|
||||
alpha = Decimal('0.67')
|
||||
# 21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
|
||||
# 21 * 3 mech steps, 2/64 = 0.03125
|
||||
alpha_2 = Decimal('0.03125')
|
||||
max_withdraw_factor = Decimal('0.9')
|
||||
external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
|
||||
|
||||
|
||||
#alpha * s['Zeus_ST'] + (1 - alpha)*s['Zeus_LT']
|
||||
|
||||
# Stochastic process factors
|
||||
correction_factor = Decimal('0.01')
|
||||
volatility = Decimal('5.0')
|
||||
|
||||
# Buy_Log_signal =
|
||||
# Z_signal =
|
||||
# Price_signal =
|
||||
# TDR_draw_signal =
|
||||
# P_Ext_Markets_signal =
|
||||
|
||||
# Behaviors per Mechanism
|
||||
|
||||
# BEHAVIOR 1: EMH Trader
|
||||
EMH_portion = Decimal('0.20')
|
||||
EMH_Ext_Hold = Decimal('42000.0')
|
||||
|
||||
|
||||
def b1m1(step, sL, s):
|
||||
# print('b1m1')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
buy = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'EMH_buy': buy, 'EMH_buy_P': price}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
price = 0
|
||||
return {'EMH_buy': 0, 'EMH_buy_P': price}
|
||||
else:
|
||||
price = 0
|
||||
return {'EMH_buy': 0, 'EMH_buy_P': price}
|
||||
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
# print('b1m2')
|
||||
theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
return {'EMH_sell': 0}
|
||||
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
|
||||
sell = beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'EMH_sell': sell, 'EMH_sell_P': price}
|
||||
else:
|
||||
return {'EMH_sell': 0}
|
||||
|
||||
# BEHAVIOR 3: Herding
|
||||
Herd_portion = Decimal('0.20')
|
||||
Herd_Ext_Hold = Decimal('42000.0')
|
||||
Herd_UB = Decimal('0.10') # UPPER BOUND
|
||||
Herd_LB = Decimal('0.10') # LOWER BOUND
|
||||
def b3m2(step, sL, s):
|
||||
theta = (s['Z']*Herd_portion*s['Price'])/(s['Z']*Herd_portion*s['Price'] + Herd_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - Herd_LB:
|
||||
|
||||
sell = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price_Signal'] / s['Price'])
|
||||
return {'herd_sell': sell, 'herd_buy': 0, 'herd_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > Herd_UB:
|
||||
buy = beta * theta*Herd_Ext_Hold * s['P_Ext_Markets']/(s['Price']*Herd_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price'] / s['Price_Signal'])
|
||||
return {'herd_sell': 0, 'herd_buy': buy, 'herd_buy_P': price}
|
||||
else:
|
||||
return {'herd_sell': 0, 'herd_buy': 0, 'herd_buy_P':0}
|
||||
|
||||
# BEHAVIOR 4: HODLers
|
||||
HODL_belief = Decimal('10.0')
|
||||
HODL_portion = Decimal('0.20')
|
||||
HODL_Ext_Hold = Decimal('4200.0')
|
||||
|
||||
|
||||
def b4m2(step, sL, s):
|
||||
# print('b4m2')
|
||||
theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
|
||||
if s['Price'] < 1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
sell = beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
|
||||
price = s['Price']
|
||||
return {'HODL_sell': sell, 'HODL_sell_P': price}
|
||||
elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
|
||||
return {'HODL_sell': 0}
|
||||
else:
|
||||
return {'HODL_sell': 0}
|
||||
|
||||
# BEHAVIOR 7: Endogenous Information Updating (EIU)
|
||||
# Short Term Price Signal, Lower Threshold = BOT-like
|
||||
EIU_portion = Decimal('0.20')
|
||||
EIU_Ext_Hold = Decimal('42000.0')
|
||||
EIU_UB = Decimal('0.50') # UPPER BOUND
|
||||
EIU_LB = Decimal('0.50') # LOWER BOUND
|
||||
def b7m2(step, sL, s):
|
||||
theta = (s['Z']*EIU_portion*s['Price'])/(s['Z']*EIU_portion*s['Price'] + EIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal']) < - EIU_LB:
|
||||
|
||||
sell = beta * theta*EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EIU_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price_Signal'] / s['Price'])
|
||||
return {'EIU_sell': sell, 'EIU_buy': 0, 'EIU_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal']) > EIU_UB:
|
||||
buy = beta * theta* EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* EIU_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price'] / s['Price_Signal'])
|
||||
return {'EIU_sell': 0, 'EIU_buy': buy, 'EIU_buy_P': price}
|
||||
else:
|
||||
return {'EIU_sell': 0, 'EIU_buy': 0}
|
||||
|
||||
# BEHAVIOR 7b: Endogenous Information Updating (EIU)
|
||||
# Longer Term Price Signal, Higher Threshold = Human-Like
|
||||
HEIU_portion = Decimal('0.20')
|
||||
HEIU_Ext_Hold = Decimal('42000.0')
|
||||
HEIU_UB = Decimal('2.0') # UPPER BOUND
|
||||
HEIU_LB = Decimal('2.0') # LOWER BOUND
|
||||
def b7hm2(step, sL, s):
|
||||
theta = (s['Z']*HEIU_portion*s['Price'])/(s['Z']*HEIU_portion*s['Price'] + HEIU_Ext_Hold * s['P_Ext_Markets'])
|
||||
# if s['Price'] - s['Price_Signal'] < (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)) - Herd_LB:
|
||||
if (s['Price'] - s['Price_Signal_2']) < - HEIU_LB:
|
||||
|
||||
sell = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HEIU_portion*(1-theta))
|
||||
price = s['Price'] + (s['Price_Signal_2'] / s['Price'])
|
||||
return {'HEIU_sell': sell, 'HEIU_buy': 0, 'HEIU_sell_P': price}
|
||||
# elif s['Price'] > Herd_UB - (theta*Herd_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*Herd_portion*(1-theta)):
|
||||
elif (s['Price'] - s['Price_Signal_2']) > HEIU_UB:
|
||||
buy = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* HEIU_portion*(1-theta))
|
||||
price = s['Price'] - (s['Price'] / s['Price_Signal_2'])
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': buy, 'HEIU_buy_P': price}
|
||||
else:
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': 0}
|
||||
|
||||
# STATES
|
||||
# ZEUS Fixed Supply
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 'Z'
|
||||
x = s['Z'] #+ _input # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s2m1(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x = (s['P_Ext_Markets'] - _input['EMH_buy']) / s['Z'] * 10000
|
||||
# #x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
|
||||
def s3m1(step, sL, s, _input):
|
||||
y = 'Buy_Log'
|
||||
x = np.zeros(4)
|
||||
x[0] = _input['EMH_buy']
|
||||
x[1] = _input['EMH_buy_P']
|
||||
x[2] = _input['herd_buy']
|
||||
x[3] = _input['herd_buy_P']
|
||||
# = _input['EMH_buy'] + _input['herd_buy'] + _input['EIU_buy'] + _input['HEIU_buy'] # / Psignal_int
|
||||
return (y, x) #[0], x[1])
|
||||
|
||||
|
||||
def s4m2(step, sL, s, _input):
|
||||
y = 'Sell_Log'
|
||||
x = _input['EMH_sell'] + _input['HODL_sell'] + _input['herd_sell'] + _input['EIU_sell'] + _input['HEIU_sell'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s3m3(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log'] + _input # / Psignal_int
|
||||
# return (y, x)
|
||||
|
||||
|
||||
# Price Update
|
||||
def s2m3(step, sL, s, _input):
|
||||
|
||||
y = 'Price'
|
||||
#var1 = Decimal.from_float(s['Buy_Log'])
|
||||
|
||||
x = s['Price'] + (Decimal(s['Buy_Log'][0] )) /s['Z'] # - (s['Sell_Log']/s['Z'] ) # for buy log term /s['Z'] )
|
||||
#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
def s5m3(step, sL, s, _input):
|
||||
y = 'Price_Signal'
|
||||
x = alpha * s['Price'] + (1 - alpha)*s['Price_Signal']
|
||||
return (y, x)
|
||||
|
||||
def s6m3(step, sL, s, _input):
|
||||
y = 'Price_Signal_2'
|
||||
x = alpha_2 * s['Price'] + (1 - alpha_2)*s['Price_Signal_2']
|
||||
return (y, x)
|
||||
|
||||
def s6m1(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] - _input
|
||||
#x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
return (y, x)
|
||||
|
||||
|
||||
# def s2m2(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
|
||||
# x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = -125
|
||||
proc_one_coef_B = 125
|
||||
|
||||
# A change in belief of actual price, passed onto behaviors to make action
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 'P_Ext_Markets'
|
||||
x = s['P_Ext_Markets'] + bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
|
||||
|
||||
return (y,x)
|
||||
|
||||
|
||||
ts_format = '%Y-%m-%d %H:%M:%S'
|
||||
t_delta = timedelta(days=0, minutes=0, seconds=1)
|
||||
def es5p2(step, sL, s, _input):
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
|
||||
return (y, x)
|
||||
|
||||
#Environment States
|
||||
# NONE
|
||||
|
||||
# Genesis States
|
||||
state_dict = {
|
||||
'Z': Decimal(21000000.0),
|
||||
'Price': Decimal(100.0), # Initialize = Z for EMA
|
||||
'Buy_Log': Decimal(0.0),
|
||||
'Sell_Log': Decimal(0.0),
|
||||
'Price_Signal': Decimal(100.0),
|
||||
'Price_Signal_2': Decimal(100.0),
|
||||
'Trans': Decimal(0.0),
|
||||
'P_Ext_Markets': Decimal(25000.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
def env_proc_id(x):
|
||||
return x
|
||||
|
||||
env_processes = {
|
||||
# "P_Ext_Markets": env_proc_id
|
||||
}
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
sim_config = {
|
||||
"N": 1,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1,
|
||||
"b3": b3m2,
|
||||
"b7": b7m2,
|
||||
"b7h": b7hm2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
"Buy_Log": s3m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b3": b3m2,
|
||||
"b4": b4m2,
|
||||
"b7": b7m2,
|
||||
"b7h": b7hm2
|
||||
},
|
||||
"states": {
|
||||
"Sell_Log": s4m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
},
|
||||
"states": {
|
||||
"Price": s2m3,
|
||||
"Price_Signal": s5m3,
|
||||
"Price_Signal_2": s6m3,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|
||||
|
|
@ -1,44 +0,0 @@
|
|||
import pandas as pd
|
||||
from tabulate import tabulate
|
||||
|
||||
# The following imports NEED to be in the exact same order
|
||||
from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
|
||||
from simulations.validation import config1, config2
|
||||
# from simulations.validation import base_config1, base_config2
|
||||
# from simulations.barlin import config4
|
||||
# from simulations.zx import config_zx
|
||||
# from simulations.barlin import config6atemp #config6aworks,
|
||||
from SimCAD import configs
|
||||
|
||||
# ToDo: pass ExecutionContext with execution method as ExecutionContext input
|
||||
|
||||
exec_mode = ExecutionMode()
|
||||
|
||||
|
||||
print("Simulation Execution 1")
|
||||
print()
|
||||
first_config = [configs[0]] # from config1
|
||||
single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
|
||||
run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
|
||||
run1_raw_result, tensor_field = run1.main()
|
||||
result = pd.DataFrame(run1_raw_result)
|
||||
# result.to_csv('~/Projects/DiffyQ-SimCAD/results/config4.csv', sep=',')
|
||||
print()
|
||||
print("Tensor Field:")
|
||||
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
|
||||
print("Output:")
|
||||
print(tabulate(result, headers='keys', tablefmt='psql'))
|
||||
print()
|
||||
|
||||
print("Simulation Execution 2: Pairwise Execution")
|
||||
print()
|
||||
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
|
||||
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
|
||||
for raw_result, tensor_field in run2.main():
|
||||
result = pd.DataFrame(raw_result)
|
||||
print()
|
||||
print("Tensor Field:")
|
||||
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
|
||||
print("Output:")
|
||||
print(tabulate(result, headers='keys', tablefmt='psql'))
|
||||
print()
|
||||
|
|
@ -1,171 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1),
|
||||
'a': np.random.RandomState(2),
|
||||
'b': np.random.RandomState(3),
|
||||
'c': np.random.RandomState(3)
|
||||
}
|
||||
|
||||
# Behaviors per Mechanism
|
||||
# Different return types per mechanism ?? *** No ***
|
||||
def b1m1(step, sL, s):
|
||||
return {'param1': 1}
|
||||
def b2m1(step, sL, s):
|
||||
return {'param1': 1}
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
return {'param1': 1, 'param2': 2}
|
||||
def b2m2(step, sL, s):
|
||||
return {'param1': 1, 'param2': 4}
|
||||
|
||||
def b1m3(step, sL, s):
|
||||
return {'param1': 1, 'param2': np.array([10, 100])}
|
||||
def b2m3(step, sL, s):
|
||||
return {'param1': 1, 'param2': np.array([20, 200])}
|
||||
|
||||
# deff not more than 2
|
||||
# Internal States per Mechanism
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = s['s1'] + _input['param1']
|
||||
return (y, x)
|
||||
def s2m1(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + _input['param1']
|
||||
return (y, x)
|
||||
|
||||
def s1m2(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = s['s1'] + _input['param1']
|
||||
return (y, x)
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + _input['param1']
|
||||
return (y, x)
|
||||
|
||||
def s1m3(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = s['s1'] + _input['param1']
|
||||
return (y, x)
|
||||
def s2m3(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + _input['param1']
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = 0.7
|
||||
proc_one_coef_B = 1.3
|
||||
|
||||
def es3p1(step, sL, s, _input):
|
||||
y = 's3'
|
||||
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 's4'
|
||||
x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
ts_format = '%Y-%m-%d %H:%M:%S'
|
||||
t_delta = timedelta(days=0, minutes=0, seconds=1)
|
||||
def es5p2(step, sL, s, _input):
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
|
||||
return (y, x)
|
||||
|
||||
|
||||
# Environment States
|
||||
def env_a(x):
|
||||
return 10
|
||||
def env_b(x):
|
||||
return 10
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# Genesis States
|
||||
genesis_states = {
|
||||
's1': Decimal(0.0),
|
||||
's2': Decimal(0.0),
|
||||
's3': Decimal(1.0),
|
||||
's4': Decimal(1.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
# remove `exo_update_per_ts` to update every ts
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"s3": es3p1,
|
||||
"s4": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
# make env proc trigger field agnostic
|
||||
|
||||
# ToDo: Bug - Can't use environments without proc_trigger. TypeError: 'int' object is not callable
|
||||
# "/Users/jjodesty/Projects/DiffyQ-SimCAD/SimCAD/engine/simulation.py"
|
||||
env_processes = {
|
||||
# "s3": env_a,
|
||||
# "s4": env_b
|
||||
"s3": proc_trigger('2018-10-01 15:16:25', env_a),
|
||||
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
|
||||
}
|
||||
|
||||
# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
|
||||
# mechanisms = {}
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
|
||||
"b2": b2m1
|
||||
},
|
||||
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
|
||||
"s1": s1m1,
|
||||
"s2": s2m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b2": b2m2
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m2,
|
||||
"s2": s2m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
"b1": b1m3,
|
||||
"b2": b2m3
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m3,
|
||||
"s2": s2m3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sim_config = {
|
||||
"N": 2,
|
||||
"T": range(5)
|
||||
}
|
||||
|
||||
configs.append(
|
||||
Configuration(
|
||||
sim_config=sim_config,
|
||||
state_dict=genesis_states,
|
||||
seed=seed,
|
||||
exogenous_states=exogenous_states,
|
||||
env_processes=env_processes,
|
||||
mechanisms=mechanisms
|
||||
)
|
||||
)
|
||||
|
|
@ -1,180 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1),
|
||||
'a': np.random.RandomState(2),
|
||||
'b': np.random.RandomState(3),
|
||||
'c': np.random.RandomState(3)
|
||||
}
|
||||
|
||||
# Behaviors per Mechanism
|
||||
# Different return types per mechanism ?? *** No ***
|
||||
def b1m1(step, sL, s):
|
||||
return {'param1': 1}
|
||||
def b2m1(step, sL, s):
|
||||
return {'param2': 4}
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
return {'param1': 'a', 'param2': 2}
|
||||
def b2m2(step, sL, s):
|
||||
return {'param1': 'b', 'param2': 4}
|
||||
|
||||
|
||||
def b1m3(step, sL, s):
|
||||
return {'param1': ['c'], 'param2': np.array([10, 100])}
|
||||
def b2m3(step, sL, s):
|
||||
return {'param1': ['d'], 'param2': np.array([20, 200])}
|
||||
|
||||
|
||||
# Internal States per Mechanism
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m1(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
def s1m2(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
def s1m3(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m3(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = 0.7
|
||||
proc_one_coef_B = 1.3
|
||||
|
||||
def es3p1(step, sL, s, _input):
|
||||
y = 's3'
|
||||
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 's4'
|
||||
x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
ts_format = '%Y-%m-%d %H:%M:%S'
|
||||
t_delta = timedelta(days=0, minutes=0, seconds=1)
|
||||
def es5p2(step, sL, s, _input):
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
|
||||
return (y, x)
|
||||
|
||||
|
||||
# Environment States
|
||||
def env_a(x):
|
||||
return 10
|
||||
def env_b(x):
|
||||
return 10
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# Genesis States
|
||||
genesis_states = {
|
||||
's1': Decimal(0.0),
|
||||
's2': Decimal(0.0),
|
||||
's3': Decimal(1.0),
|
||||
's4': Decimal(1.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
# remove `exo_update_per_ts` to update every ts
|
||||
# why `exo_update_per_ts` here instead of `env_processes`
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"s3": es3p1,
|
||||
"s4": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
# make env proc trigger field agnostic
|
||||
env_processes = {
|
||||
"s3": proc_trigger('2018-10-01 15:16:25', env_a),
|
||||
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
|
||||
}
|
||||
|
||||
# lambdas
|
||||
# genesis Sites should always be there
|
||||
# [1, 2]
|
||||
# behavior_ops = [ foldr(_ + _), lambda x: x + 0 ]
|
||||
|
||||
|
||||
# [1, 2] = {'b1': ['a'], 'b2', [1]} =
|
||||
# behavior_ops = [behavior_to_dict, print_fwd, sum_dict_values]
|
||||
# behavior_ops = [foldr(dict_elemwise_sum())]
|
||||
# behavior_ops = []
|
||||
|
||||
# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
|
||||
# mechanisms = {}
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
|
||||
# "b2": b2m1
|
||||
},
|
||||
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
|
||||
"s1": s1m1,
|
||||
# "s2": s2m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
# "b2": b2m2
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m2,
|
||||
# "s2": s2m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
"b1": b1m3,
|
||||
"b2": b2m3
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m3,
|
||||
"s2": s2m3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sim_config = {
|
||||
"N": 2,
|
||||
"T": range(5)
|
||||
}
|
||||
|
||||
configs.append(
|
||||
Configuration(
|
||||
sim_config=sim_config,
|
||||
state_dict=genesis_states,
|
||||
seed=seed,
|
||||
exogenous_states=exogenous_states,
|
||||
env_processes=env_processes,
|
||||
mechanisms=mechanisms
|
||||
)
|
||||
)
|
||||
|
|
@ -1,176 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1),
|
||||
'a': np.random.RandomState(2),
|
||||
'b': np.random.RandomState(3),
|
||||
'c': np.random.RandomState(3)
|
||||
}
|
||||
|
||||
# Behaviors per Mechanism
|
||||
# Different return types per mechanism ?? *** No ***
|
||||
def b1m1(step, sL, s):
|
||||
return {'param1': 1}
|
||||
def b2m1(step, sL, s):
|
||||
return {'param2': 4}
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
return {'param1': 'a', 'param2': 2}
|
||||
def b2m2(step, sL, s):
|
||||
return {'param1': 'b', 'param2': 4}
|
||||
|
||||
def b1m3(step, sL, s):
|
||||
return {'param1': ['c'], 'param2': np.array([10, 100])}
|
||||
def b2m3(step, sL, s):
|
||||
return {'param1': ['d'], 'param2': np.array([20, 200])}
|
||||
|
||||
# deff not more than 2
|
||||
# Internal States per Mechanism
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1'] #+ [Coef1 x 5]
|
||||
return (y, x)
|
||||
def s2m1(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2'] #+ [Coef2 x 5]
|
||||
return (y, x)
|
||||
|
||||
def s1m2(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
def s1m3(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m3(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = 0.7
|
||||
proc_one_coef_B = 1.3
|
||||
|
||||
def es3p1(step, sL, s, _input):
|
||||
y = 's3'
|
||||
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 's4'
|
||||
x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
ts_format = '%Y-%m-%d %H:%M:%S'
|
||||
t_delta = timedelta(days=0, minutes=0, seconds=1)
|
||||
def es5p2(step, sL, s, _input):
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
|
||||
return (y, x)
|
||||
|
||||
|
||||
# Environment States
|
||||
def env_a(x):
|
||||
return 10
|
||||
def env_b(x):
|
||||
return 10
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# Genesis States
|
||||
genesis_states = {
|
||||
's1': Decimal(0.0),
|
||||
's2': Decimal(0.0),
|
||||
's3': Decimal(1.0),
|
||||
's4': Decimal(1.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
# remove `exo_update_per_ts` to update every ts
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"s3": es3p1,
|
||||
"s4": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
# make env proc trigger field agnostic
|
||||
env_processes = {
|
||||
"s3": proc_trigger('2018-10-01 15:16:25', env_a),
|
||||
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
|
||||
}
|
||||
|
||||
# lambdas
|
||||
# genesis Sites should always be there
|
||||
# [1, 2]
|
||||
# behavior_ops = [ foldr(_ + _), lambda x: x + 0 ]
|
||||
|
||||
# [1, 2] = {'b1': ['a'], 'b2', [1]} =
|
||||
# behavior_ops = [ behavior_to_dict, print_fwd, sum_dict_values ]
|
||||
# behavior_ops = [foldr(dict_elemwise_sum())]
|
||||
# behavior_ops = [foldr(lambda a, b: a + b)]
|
||||
|
||||
# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
|
||||
# mechanisms = {}
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
|
||||
"b2": b2m1
|
||||
},
|
||||
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
|
||||
"s1": s1m1,
|
||||
"s2": s2m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b2": b2m2
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m2,
|
||||
"s2": s2m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
"b1": b1m3,
|
||||
"b2": b2m3
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m3,
|
||||
"s2": s2m3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sim_config = {
|
||||
"N": 2,
|
||||
"T": range(5)
|
||||
}
|
||||
|
||||
configs.append(
|
||||
Configuration(
|
||||
sim_config=sim_config,
|
||||
state_dict=genesis_states,
|
||||
seed=seed,
|
||||
exogenous_states=exogenous_states,
|
||||
env_processes=env_processes,
|
||||
mechanisms=mechanisms
|
||||
)
|
||||
)
|
||||
|
|
@ -1,180 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1),
|
||||
'a': np.random.RandomState(2),
|
||||
'b': np.random.RandomState(3),
|
||||
'c': np.random.RandomState(3)
|
||||
}
|
||||
|
||||
# Behaviors per Mechanism
|
||||
# Different return types per mechanism ?? *** No ***
|
||||
def b1m1(step, sL, s):
|
||||
return {'param1': 1}
|
||||
def b2m1(step, sL, s):
|
||||
return {'param2': 4}
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
return {'param1': 'a', 'param2': 2}
|
||||
def b2m2(step, sL, s):
|
||||
return {'param1': 'b', 'param2': 4}
|
||||
|
||||
|
||||
def b1m3(step, sL, s):
|
||||
return {'param1': ['c'], 'param2': np.array([10, 100])}
|
||||
def b2m3(step, sL, s):
|
||||
return {'param1': ['d'], 'param2': np.array([20, 200])}
|
||||
|
||||
|
||||
# Internal States per Mechanism
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m1(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
def s1m2(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
def s1m3(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m3(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = 0.7
|
||||
proc_one_coef_B = 1.3
|
||||
|
||||
def es3p1(step, sL, s, _input):
|
||||
y = 's3'
|
||||
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 's4'
|
||||
x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
ts_format = '%Y-%m-%d %H:%M:%S'
|
||||
t_delta = timedelta(days=0, minutes=0, seconds=1)
|
||||
def es5p2(step, sL, s, _input):
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
|
||||
return (y, x)
|
||||
|
||||
|
||||
# Environment States
|
||||
def env_a(x):
|
||||
return 10
|
||||
def env_b(x):
|
||||
return 10
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# Genesis States
|
||||
genesis_states = {
|
||||
's1': Decimal(0.0),
|
||||
's2': Decimal(0.0),
|
||||
's3': Decimal(1.0),
|
||||
's4': Decimal(1.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
# remove `exo_update_per_ts` to update every ts
|
||||
# why `exo_update_per_ts` here instead of `env_processes`
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"s3": es3p1,
|
||||
"s4": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
# make env proc trigger field agnostic
|
||||
env_processes = {
|
||||
"s3": proc_trigger('2018-10-01 15:16:25', env_a),
|
||||
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
|
||||
}
|
||||
|
||||
# lambdas
|
||||
# genesis Sites should always be there
|
||||
# [1, 2]
|
||||
# behavior_ops = [ foldr(_ + _), lambda x: x + 0 ]
|
||||
|
||||
|
||||
# [1, 2] = {'b1': ['a'], 'b2', [1]} =
|
||||
# behavior_ops = [behavior_to_dict, print_fwd, sum_dict_values]
|
||||
# behavior_ops = [foldr(dict_elemwise_sum())]
|
||||
# behavior_ops = []
|
||||
|
||||
# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
|
||||
# mechanisms = {}
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
|
||||
# "b2": b2m1
|
||||
},
|
||||
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
|
||||
"s1": s1m1,
|
||||
# "s2": s2m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
# "b2": b2m2
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m2,
|
||||
# "s2": s2m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
"b1": b1m3,
|
||||
"b2": b2m3
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m3,
|
||||
"s2": s2m3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sim_config = {
|
||||
"N": 2,
|
||||
"T": range(5)
|
||||
}
|
||||
|
||||
configs.append(
|
||||
Configuration(
|
||||
sim_config=sim_config,
|
||||
state_dict=genesis_states,
|
||||
seed=seed,
|
||||
exogenous_states=exogenous_states,
|
||||
env_processes=env_processes,
|
||||
mechanisms=mechanisms
|
||||
)
|
||||
)
|
||||
|
|
@ -1,156 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
from SimCAD.configuration import Configuration
|
||||
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1),
|
||||
'a': np.random.RandomState(2),
|
||||
'b': np.random.RandomState(3),
|
||||
'c': np.random.RandomState(3)
|
||||
}
|
||||
|
||||
# Behaviors per Mechanism
|
||||
def b1m1(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
def b2m1(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
def b2m2(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
|
||||
def b1m3(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
def b2m3(step, sL, s):
|
||||
return s['s2'] + 1
|
||||
|
||||
|
||||
# Internal States per Mechanism
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = s['s1'] + _input
|
||||
return (y, x)
|
||||
def s2m1(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + _input
|
||||
return (y, x)
|
||||
|
||||
def s1m2(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = s['s1'] + _input
|
||||
return (y, x)
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + _input
|
||||
return (y, x)
|
||||
|
||||
def s1m3(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = s['s1'] + _input
|
||||
return (y, x)
|
||||
def s2m3(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + s['s3'] + _input
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = 0.7
|
||||
proc_one_coef_B = 1.3
|
||||
|
||||
def es3p1(step, sL, s, _input):
|
||||
y = 's3'
|
||||
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 's4'
|
||||
x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
|
||||
y = 'timestamp'
|
||||
x = ep_time_step(s, s['timestamp'], seconds=1)
|
||||
return (y, x)
|
||||
|
||||
|
||||
# Environment States
|
||||
def env_a(x):
|
||||
return 10
|
||||
def env_b(x):
|
||||
return 10
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# Genesis States
|
||||
state_dict = {
|
||||
's1': Decimal(0.0),
|
||||
's2': Decimal(0.0),
|
||||
's3': Decimal(1.0),
|
||||
's4': Decimal(1.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"s3": es3p1,
|
||||
"s4": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
env_processes = {
|
||||
"s3": proc_trigger('2018-10-01 15:16:25', env_a),
|
||||
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
|
||||
}
|
||||
|
||||
# lambdas
|
||||
# genesis Sites should always be there
|
||||
# [1, 2]
|
||||
# User Defined Aggregate Function
|
||||
behavior_udaf = [ foldr(_ + _), lambda x: x + 0 ]
|
||||
# need at least 1 behaviour and 1 state function for the 1st mech with behaviors
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
|
||||
"b2": b2m1
|
||||
},
|
||||
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
|
||||
"s1": s1m1,
|
||||
"s2": s2m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b2": b2m2
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m2,
|
||||
"s2": s2m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
"b1": b1m3,
|
||||
"b2": b2m3
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m3,
|
||||
"s2": s2m3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sim_config = {
|
||||
"N": 2,
|
||||
"T": range(5)
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms, behavior_udaf))
|
||||
Loading…
Reference in New Issue