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Barlin 12/03/18

This commit is contained in:
Joshua E. Jodesty 2018-12-03 11:16:54 -05:00
parent 8e699b199d
commit 9139e148fa
7 changed files with 660 additions and 20 deletions
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1
SimCAD/engine/__init__.py
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@ -8,7 +8,6 @@ from SimCAD.engine.simulation import Executor as SimExecutor
class ExecutionMode:
single_proc = 'single_proc'
multi_proc = 'multi_proc'

2
SimCAD/engine/simulation.py
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@ -74,7 +74,6 @@ class Executor:
return states_list
# rename pipe
def block_pipeline(self, states_list, configs, env_processes, time_seq, run):
time_seq = [x + 1 for x in time_seq]
@ -89,7 +88,6 @@ class Executor:
return simulation_list
# Del _ / head
def simulation(self, states_list, configs, env_processes, time_seq, runs):
pipe_run = []

9
sandbox/barlin/config3.py
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@ -1,8 +1,9 @@
from decimal import Decimal
import numpy as np
from datetime import timedelta
from SimCAD import Configuration, configs
from SimCAD.configuration import exo_update_per_ts, bound_norm_random, \
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
ep_time_step
seed = {
@ -146,9 +147,11 @@ def es4p2(step, sL, s, _input):
return (y,x)
def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
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, s['timestamp'], seconds=1)
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
return (y, x)
#Environment States

9
sandbox/barlin/config4.py
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@ -1,8 +1,9 @@
from decimal import Decimal
import numpy as np
from datetime import timedelta
from SimCAD import Configuration, configs
from SimCAD.configuration import exo_update_per_ts, bound_norm_random, \
from SimCAD.configuration.utils import exo_update_per_ts, proc_trigger, bound_norm_random, \
ep_time_step
seed = {
@ -170,9 +171,11 @@ def es4p2(step, sL, s, _input):
return (y,x)
def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
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, s['timestamp'], seconds=1)
x = ep_time_step(s, dt_str=s['timestamp'], fromat_str=ts_format, _timedelta=t_delta)
return (y, x)
#Environment States

318
sandbox/barlin/config6atemp.py Normal file
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@ -0,0 +1,318 @@
from decimal import Decimal
import numpy as np
from datetime import timedelta
from SimCAD import Configuration, configs
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))

318
sandbox/barlin/config6aworks.py Normal file
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@ -0,0 +1,318 @@
from decimal import Decimal
import numpy as np
from datetime import timedelta
from SimCAD import Configuration, configs
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))

23
sandbox/sim_test.py
View File

@ -2,9 +2,10 @@ import pandas as pd
from tabulate import tabulate
from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
from sandbox.validation import config1, config2
# from sandbox.validation import config1, config2
# from sandbox import config4
# from sandbox import config_zx
from sandbox.barlin import config6atemp #config6aworks,
from SimCAD import configs
# ToDo: pass ExecutionContext with execution method as ExecutionContext input
@ -23,13 +24,13 @@ result = pd.DataFrame(run1_raw_result)
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
print("Simulation Run 2: Pairwise Execution")
print()
multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)
# configs = [config1, config1]
run2 = Executor(multi_proc_ctx, configs)
run2_raw_results = run2.main()
for raw_result in run2_raw_results:
result = pd.DataFrame(raw_result)
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
# print("Simulation Run 2: Pairwise Execution")
# print()
# multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)
# # configs = [config1, config1]
# run2 = Executor(multi_proc_ctx, configs)
# run2_raw_results = run2.main()
# for raw_result in run2_raw_results:
# result = pd.DataFrame(raw_result)
# print(tabulate(result, headers='keys', tablefmt='psql'))
# print()