Merge branch 'master' of https://github.com/BlockScience/DiffyQ-SimCAD
This commit is contained in:
Joshua E. Jodesty
commit
45530ae91f
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@ -8,12 +8,4 @@ __pycache__
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Pipfile
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Pipfile.lock
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results
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.mypy_cache
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notebooks
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simulations/validation/base_config1.py
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simulations/validation/base_config2.py
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simulations/validation/config_1.py
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simulations/validation/config_2.py
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simulations/barlin
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simulations/scrapbox
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simulations/zx
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.mypy_cache
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@ -0,0 +1,494 @@
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from decimal import Decimal
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import numpy as np
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from SimCAD import Configuration, configs
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from SimCAD.configuration import exo_update_per_ts, bound_norm_random, \
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ep_time_step
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seed = {
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'z': np.random.RandomState(1)
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# 'a': np.random.RandomState(2),
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# 'b': np.random.RandomState(3),
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# 'c': np.random.RandomState(3)
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}
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#Signals
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# Pr_signal
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#if s['P_Ext_Markets'] != 0:
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#Pr_signal = s['Z']/s['P_Ext_Markets']
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#else Pr_signal = 0
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# if Pr_signal < s['Z']/s['Buy_Log']:
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beta = Decimal('0.25') #agent response gain
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beta_LT = Decimal('0.1') #LT agent response gain
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alpha = Decimal('0.091') #21 day EMA forgetfullness between 0 and 1, closer to 1 discounts older obs quicker, should be 2/(N+1)
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max_withdraw_factor = Decimal('0.9')
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external_draw = Decimal('0.01') # between 0 and 1 to draw Buy_Log to external
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# Stochastic process factors
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correction_factor = Decimal('0.01')
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volatility = Decimal('5.0')
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# Buy_Log_signal =
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# Z_signal =
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# Price_signal =
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# TDR_draw_signal =
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# P_Ext_Markets_signal =
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# Behaviors per Mechanism
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# BEHAVIOR 1: EMH Trader
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EMH_portion = Decimal('0.250')
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EMH_Ext_Hold = Decimal('42000.0')
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def b1m1(step, sL, s):
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# y = 'P_Ext_Markets'
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# Psignal_ext = s['P_Ext_Markets'] / s['Z']
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# Psignal_int = s['Buy_Log'] / s['Z']
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# if Psignal_ext < Psignal_int:
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# return beta*(Psignal_int - Psignal_ext) * s['Z'] # Deposited amount in TDR
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# else:
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# return 0 # Decimal(0.000001)
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# return (y,x)
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theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
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if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
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return beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
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elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
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return 0
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else:
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return 0
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def b1m2(step, sL, s):
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theta = (s['Z']*EMH_portion*s['Price'])/(s['Z']*EMH_portion*s['Price'] + EMH_Ext_Hold * s['P_Ext_Markets'])
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if s['Price'] < (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
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return 0
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elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
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return beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
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else:
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return 0
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# BEHAVIOR 3: Herding
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# BEHAVIOR 4: HODLers
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HODL_belief = Decimal('10.0')
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HODL_portion = Decimal('0.250')
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HODL_Ext_Hold = Decimal('4200.0')
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def b4m2(step, sL, s):
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theta = (s['Z']*HODL_portion*s['Price'])/(s['Z']*HODL_portion*s['Price'] + HODL_Ext_Hold * s['P_Ext_Markets'])
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if s['Price'] < 1/HODL_belief*(theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
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return beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
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elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
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return 0
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else:
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return 0
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# BEHAVIOR 2: Withdraw TDR and burn Zeus
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# Selling Agent- Arbitrage on TDR ext v TDR int signals
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# def b2m1(step, sL, s):
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# Psignal_ext = s['P_Ext_Markets'] / s['Z']
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# Psignal_int = s['Buy_Log'] / s['Z']
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# if Psignal_ext > Psignal_int:
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# # withdrawn amount in TDR, subject to TDR limit
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# return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor)
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# else:
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# return 0 #- Decimal(0.000001)
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# return 0
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# BEHAVIOR 1: Deposit TDR and mint Zeus
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# Buying Agent- Arbitrage on Price and Z signals
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# def b1m2(step, sL, s):
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# # Psignal_ext = s['P_Ext_Markets'] / s['Z']
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# # Psignal_int = s['Buy_Log'] / s['Z']
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# # if Psignal_ext > Psignal_int:
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# # # withdrawn amount in TDR, subject to TDR limit
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# # return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor)
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# # else:
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# # return 0 #- Decimal(0.000001)
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# #
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# # LT more valuable than ST = deposit TDR and mint Z
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# Psignal_LT = s['Price'] / s['Z']
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# if Psignal_LT > 1:
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# return beta_LT*(Psignal_LT - 1) * s['Z']
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# else:
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# return 0
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# Behavior will go here- b2m2, putting in mech 3: b1m3 for debugging
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# def b2m2(step, sL, s):
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# # Psignal_LT = s['Price'] / s['Z']
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# # if Psignal_LT > 1:
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# test = np.arange(1,10)
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# return test
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# Selling Agent- Arbitrage on Price and Z signals
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# def b1m3(step, sL, s):
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# Psignal_LT = s['Price'] / s['Z']
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# if Psignal_LT < 1:
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# return - np.minimum(beta_LT*(Psignal_LT - 1) * s['Z'], s['Z']*max_withdraw_factor)
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# else:
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# return 0
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# def b2m3(step, sL, s):
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# return 0
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def dummy_behavior(step, sL, s):
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return 0
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def s1_dummy(step, sL, s, _input):
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y = 'Z'
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x = s['Z']
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return (y, x)
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def s2_dummy(step, sL, s, _input):
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y = 'Price'
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x = s['Price']
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return (y, x)
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def s3_dummy(step, sL, s, _input):
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y = 'Buy_Log'
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x = s['Buy_Log']
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return (y, x)
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def s4_dummy(step, sL, s, _input):
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y = 'Sell_Log'
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x = s['Sell_Log']
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return (y, x)
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def s5_dummy(step, sL, s, _input):
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y = 'Trans'
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x = s['Trans']
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return (y, x)
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def s6_dummy(step, sL, s, _input):
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y = 'P_Ext_Markets'
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x = s['P_Ext_Markets']
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return (y, x)
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# Internal States per Mechanism
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# Deposit TDR/Mint Zeus
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# def s1m1(step, sL, s, _input):
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# s['Z'] = s['Z'] + _input
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# STATES
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# ZEUS Fixed Supply
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def s1m1(step, sL, s, _input):
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y = 'Z'
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x = s['Z'] #+ _input # / Psignal_int
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return (y, x)
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def s2m1(step, sL, s, _input):
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y = 'Price'
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x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
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#x= alpha * s['Z'] + (1 - alpha)*s['Price']
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return (y, x)
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def s3m1(step, sL, s, _input):
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y = 'Buy_Log'
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x = _input # / Psignal_int
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return (y, x)
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def s4m2(step, sL, s, _input):
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y = 'Sell_Log'
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x = _input # / Psignal_int
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print('s4m2 ',type(_input))
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return (y, x)
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def s3m3(step, sL, s, _input):
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y = 'Buy_Log'
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x = s['Buy_Log'] + _input # / Psignal_int
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return (y, x)
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# Price Update
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def s2m3(step, sL, s, _input):
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print('s2m3 ')
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print(type(s['Sell_Log']))
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print(type(s['Z']))
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y = 'Price'
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x = s['Price'] + s['Buy_Log']/s['Z'] - s['Sell_Log']/s['Z']
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#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
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return (y, x)
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def s6m1(step, sL, s, _input):
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y = 'P_Ext_Markets'
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x = s['P_Ext_Markets'] - _input
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#x= alpha * s['Z'] + (1 - alpha)*s['Price']
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return (y, x)
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def s2m2(step, sL, s, _input):
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y = 'Price'
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x = (s['P_Ext_Markets'] - _input) /s['Z'] *10000
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#x= alpha * s['Z'] + (1 - alpha)*s['Price']
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return (y, x)
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# def s1m1(step, sL, s, _input):
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# Psignal_int = s['Buy_Log'] / s['Z']
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# y = 'Z'
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# x = s['Z'] + _input / Psignal_int
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# return (y, x)
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# def s2m1(step, sL, s, _input):
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# y = 'Price'
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# x= alpha * s['Z'] + (1 - alpha)*s['Price']
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# return (y, x)
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# def s3m1(step, sL, s, _input):
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# y = 'Buy_Log'
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# x = s['Buy_Log'] + _input # Input already in TDR * s['Z']
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# return (y, x)
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# # Withdraw TDR/Burn Zeus
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# def s1m2(step, sL, s, _input):
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# Psignal_int = s['Buy_Log'] / s['Z']
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# y = 'Z'
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# x = s['Z'] #+ _input / Psignal_int
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# return (y, x)
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# def s2m2(step, sL, s, _input):
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# y = 'Price'
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# x= alpha * s['Z'] + (1 - alpha)*s['Price']
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# return (y, x)
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# def s3m2(step, sL, s, _input):
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# y = 'Buy_Log'
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# x = s['Buy_Log'] + _input #* s['Z']
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# # y = 'Buy_Log'
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# # x = s['Buy_Log'] + _input
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# return (y, x)
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# def s1m3(step, sL, s, _input):
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# Psignal_int = s['Buy_Log'] / s['Z']
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# y = 'Z'
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# x = s['Z'] #+ _input / Psignal_int
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# return (y, x)
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# def s2m3(step, sL, s, _input):
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# y = 'Price'
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# x= alpha * s['Z'] + (1 - alpha)*s['Price']
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# return (y, x)
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# def s3m3(step, sL, s, _input):
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# y = 'Buy_Log'
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# x = s['Buy_Log'] #+ _input #* s['Z']
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# # y = 'Buy_Log'
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# # x = s['Buy_Log'] + _input
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# return (y, x)
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# def s3m4(step, sL, s, _input):
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# y = 'Buy_Log'
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# x = s['Buy_Log']*(1-external_draw) + s['Sell_Log']*external_draw # _input #* s['Z']
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# # y = 'Buy_Log'
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# # x = s['Buy_Log'] + _input
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# return (y, x)
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# def s1m3(step, sL, s, _input):
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# s['Z'] = s['Z'] + _input
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# def s2m3(step, sL, s, _input):
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# s['Price'] = s['Price'] + _input
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# Exogenous States
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proc_one_coef_A = -125
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proc_one_coef_B = 125
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# def es3p1(step, sL, s, _input):
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# s['s3'] = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
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# def es4p2(step, sL, s, _input):
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# s['P_Ext_Markets'] = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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# def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
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# s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
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def es3p1(step, sL, s, _input):
|
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y = 's3'
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x = s['s3'] + 1
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return (y, x)
|
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# def es4p2(step, sL, s, _input):
|
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# y = 'P_Ext_Markets'
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# # bound_norm_random defined in utils.py
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# #x = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
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# expected_change = correction_factor*(s['P_Ext_Markets']-s['Buy_Log'])
|
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# vol = np.random.randint(1,volatility)
|
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# change = expected_change * vol
|
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# # change_float = (np.random.normal(expected_change,volatility*expected_change) #Decimal('1.0')
|
||||
# #change = Decimal.from_float(change_float)
|
||||
# x = s['P_Ext_Markets'] + change
|
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|
||||
# return (y, x)
|
||||
|
||||
# A change in belief of actual price, passed onto behaviors to make action
|
||||
def es4p2(step, sL, s, _input):
|
||||
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)
|
||||
|
||||
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 stochastic(reference, seed, correction = 0.01):
|
||||
# series = np.zeros(len(reference))
|
||||
# series[0] = reference[0]
|
||||
# for i in range(1,len(reference)):
|
||||
# expected_change = correction*(reference[i]-series[i-1])
|
||||
# normalized_expected_change = np.abs(expected_change)*(reference[i])/(reference[i-1])
|
||||
# seed_int = seed.randint(1,10)
|
||||
# change = np.random.normal(expected_change,seed_int*normalized_expected_change)
|
||||
|
||||
# series[i] = series[i-1]+change
|
||||
# # avoid negative series returns
|
||||
# if series[i] <= 0:
|
||||
# series[i] = .01
|
||||
# #series[i] = series[i-1]+change
|
||||
|
||||
# return [series,seed_int]
|
||||
# ref3 = np.arange(1,1000)*.1
|
||||
# test = stochastic(ref3,seed['b'])
|
||||
|
||||
# def env_a(ref3,seed['b']):
|
||||
# return stochastic(ref3,seed['b'])
|
||||
def env_a(x):
|
||||
return 100
|
||||
def env_b(x):
|
||||
return 21000000
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# 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),
|
||||
'Trans': Decimal(0.0),
|
||||
'P_Ext_Markets': Decimal(25000.0),
|
||||
|
||||
# 's2': Decimal(0.0),
|
||||
# 's3': Decimal(0.0),
|
||||
# 's4': Decimal(0.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
# exogenous_states = {
|
||||
# # "s3": es3p1,
|
||||
# "P_Ext_Markets": es4p2,
|
||||
# "timestamp": es5p2
|
||||
# }
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
# "s3": es3p1,
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
env_processes = {
|
||||
# "s3": env_proc('2018-10-01 15:16:25', env_a),
|
||||
# "P_Ext_Markets": env_proc('2018-10-01 15:16:25', env_b)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
|
||||
# "b2": b2m1
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
"Price": s2_dummy,
|
||||
"Buy_Log": s3m1,
|
||||
"Sell_Log":s4_dummy,
|
||||
"Trans": s5_dummy,
|
||||
"P_Ext_Markets": s6_dummy
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b4": b4m2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1_dummy,
|
||||
"Price": s2_dummy,
|
||||
"Buy_Log": s3_dummy,
|
||||
"Sell_Log":s4m2,
|
||||
"Trans": s5_dummy,
|
||||
"P_Ext_Markets": s6_dummy
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
# "b1": b1m2,
|
||||
# "b4": b4m2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1_dummy,
|
||||
"Price": s2m3,
|
||||
"Buy_Log": s3_dummy,
|
||||
"Sell_Log":s4_dummy,
|
||||
"Trans": s5_dummy,
|
||||
"P_Ext_Markets": s6_dummy
|
||||
}
|
||||
},
|
||||
# "m3": {
|
||||
# "behaviors": {
|
||||
# "b1": b1m3,
|
||||
# "b2": b2m3
|
||||
# },
|
||||
# "states": {
|
||||
# "Z": s1m3,
|
||||
# "Price": s2m3,
|
||||
# "Buy_Log": s3m3,
|
||||
# "Sell_Log": s4_dummy,
|
||||
# "Trans": s5_dummy,
|
||||
# "P_Ext_Markets": s6_dummy
|
||||
# }
|
||||
# },
|
||||
# "m4": {
|
||||
# "behaviors": {
|
||||
# "dummy": dummy_behavior
|
||||
# },
|
||||
# "states": {
|
||||
# "Z": s1_dummy,
|
||||
# "Price": s2_dummy,
|
||||
# "Buy_Log": s3m4,
|
||||
# "Sell_Log": s4_dummy,
|
||||
# "Trans": s5_dummy,
|
||||
# "P_Ext_Markets": s6_dummy
|
||||
# }
|
||||
# },
|
||||
# "m3": {
|
||||
# "behaviors": {
|
||||
# "b1": b1m3,
|
||||
# "b2": b2m3
|
||||
# },
|
||||
# "states": {
|
||||
# "Z": s1m3,
|
||||
# "Price": s2m3,
|
||||
# }
|
||||
# }
|
||||
#treat environmental processes as a mechanism
|
||||
"ep": {
|
||||
"behaviors": {
|
||||
"dummy": dummy_behavior
|
||||
},
|
||||
"states": {
|
||||
"Z": s1_dummy,
|
||||
"Price": s2_dummy,
|
||||
"Buy_Log": s3_dummy,
|
||||
"Sell_Log": s4_dummy,
|
||||
"Trans": s5_dummy,
|
||||
"P_Ext_Markets": es4p2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sim_config = {
|
||||
"N": 1,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|
||||
|
|
@ -0,0 +1,443 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
|
||||
from SimCAD import Configuration, configs
|
||||
from SimCAD.configuration import exo_update_per_ts, bound_norm_random, \
|
||||
ep_time_step
|
||||
|
||||
# behavior_ops = []
|
||||
# behavior_ops = [foldr(dict_elemwise_sum())]
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1)
|
||||
# 'a': np.random.RandomState(2),
|
||||
# 'b': np.random.RandomState(3),
|
||||
# 'c': np.random.RandomState(3)
|
||||
}
|
||||
|
||||
#Signals
|
||||
# Pr_signal
|
||||
#if s['P_Ext_Markets'] != 0:
|
||||
#Pr_signal = s['Z']/s['P_Ext_Markets']
|
||||
#else Pr_signal = 0
|
||||
# if Pr_signal < s['Z']/s['Buy_Log']:
|
||||
beta = Decimal('0.25') #agent response gain
|
||||
beta_LT = Decimal('0.1') #LT agent response gain
|
||||
alpha = Decimal('0.091') #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
|
||||
|
||||
# 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')
|
||||
# y = 'P_Ext_Markets'
|
||||
# Psignal_ext = s['P_Ext_Markets'] / s['Z']
|
||||
# Psignal_int = s['Buy_Log'] / s['Z']
|
||||
# if Psignal_ext < Psignal_int:
|
||||
# return beta*(Psignal_int - Psignal_ext) * s['Z'] # Deposited amount in TDR
|
||||
# else:
|
||||
# return 0 # Decimal(0.000001)
|
||||
# return (y,x)
|
||||
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
|
||||
|
||||
|
||||
# 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 2: Withdraw TDR and burn Zeus
|
||||
# Selling Agent- Arbitrage on TDR ext v TDR int signals
|
||||
# def b2m1(step, sL, s):
|
||||
# Psignal_ext = s['P_Ext_Markets'] / s['Z']
|
||||
# Psignal_int = s['Buy_Log'] / s['Z']
|
||||
# if Psignal_ext > Psignal_int:
|
||||
# # withdrawn amount in TDR, subject to TDR limit
|
||||
# return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor)
|
||||
# else:
|
||||
# return 0 #- Decimal(0.000001)
|
||||
# return 0
|
||||
|
||||
# BEHAVIOR 1: Deposit TDR and mint Zeus
|
||||
# Buying Agent- Arbitrage on Price and Z signals
|
||||
# def b1m2(step, sL, s):
|
||||
# # Psignal_ext = s['P_Ext_Markets'] / s['Z']
|
||||
# # Psignal_int = s['Buy_Log'] / s['Z']
|
||||
# # if Psignal_ext > Psignal_int:
|
||||
# # # withdrawn amount in TDR, subject to TDR limit
|
||||
# # return - np.minimum(beta*(Psignal_ext - Psignal_int) * s['Z'],s['Buy_Log']*max_withdraw_factor)
|
||||
# # else:
|
||||
# # return 0 #- Decimal(0.000001)
|
||||
# #
|
||||
# # LT more valuable than ST = deposit TDR and mint Z
|
||||
# Psignal_LT = s['Price'] / s['Z']
|
||||
# if Psignal_LT > 1:
|
||||
# return beta_LT*(Psignal_LT - 1) * s['Z']
|
||||
# else:
|
||||
# return 0
|
||||
|
||||
# Behavior will go here- b2m2, putting in mech 3: b1m3 for debugging
|
||||
# def b2m2(step, sL, s):
|
||||
# # Psignal_LT = s['Price'] / s['Z']
|
||||
# # if Psignal_LT > 1:
|
||||
# test = np.arange(1,10)
|
||||
# return test
|
||||
|
||||
# Selling Agent- Arbitrage on Price and Z signals
|
||||
# def b1m3(step, sL, s):
|
||||
# Psignal_LT = s['Price'] / s['Z']
|
||||
# if Psignal_LT < 1:
|
||||
# return - np.minimum(beta_LT*(Psignal_LT - 1) * s['Z'], s['Z']*max_withdraw_factor)
|
||||
# else:
|
||||
# return 0
|
||||
|
||||
|
||||
# def b2m3(step, sL, s):
|
||||
# return 0
|
||||
|
||||
# Internal States per Mechanism
|
||||
# Deposit TDR/Mint Zeus
|
||||
# def s1m1(step, sL, s, _input):
|
||||
# s['Z'] = s['Z'] + _input
|
||||
|
||||
|
||||
# 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'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
def s4m2(step, sL, s, _input):
|
||||
y = 'Sell_Log'
|
||||
x = _input['sell_order1'] + _input['sell_order2'] # / 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'] * 1/s['Z'] - s['Sell_Log']/s['Z']
|
||||
#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int
|
||||
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)
|
||||
|
||||
# def s1m1(step, sL, s, _input):
|
||||
# Psignal_int = s['Buy_Log'] / s['Z']
|
||||
# y = 'Z'
|
||||
# x = s['Z'] + _input / Psignal_int
|
||||
# return (y, x)
|
||||
|
||||
# def s2m1(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
# def s3m1(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log'] + _input # Input already in TDR * s['Z']
|
||||
# return (y, x)
|
||||
|
||||
# # Withdraw TDR/Burn Zeus
|
||||
# def s1m2(step, sL, s, _input):
|
||||
# Psignal_int = s['Buy_Log'] / s['Z']
|
||||
# y = 'Z'
|
||||
# x = s['Z'] #+ _input / Psignal_int
|
||||
# return (y, x)
|
||||
|
||||
# def s2m2(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
# def s3m2(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log'] + _input #* s['Z']
|
||||
# # y = 'Buy_Log'
|
||||
# # x = s['Buy_Log'] + _input
|
||||
# return (y, x)
|
||||
|
||||
# def s1m3(step, sL, s, _input):
|
||||
# Psignal_int = s['Buy_Log'] / s['Z']
|
||||
# y = 'Z'
|
||||
# x = s['Z'] #+ _input / Psignal_int
|
||||
# return (y, x)
|
||||
|
||||
# def s2m3(step, sL, s, _input):
|
||||
# y = 'Price'
|
||||
# x= alpha * s['Z'] + (1 - alpha)*s['Price']
|
||||
# return (y, x)
|
||||
|
||||
# def s3m3(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log'] #+ _input #* s['Z']
|
||||
# # y = 'Buy_Log'
|
||||
# # x = s['Buy_Log'] + _input
|
||||
# return (y, x)
|
||||
|
||||
# def s3m4(step, sL, s, _input):
|
||||
# y = 'Buy_Log'
|
||||
# x = s['Buy_Log']*(1-external_draw) + s['Sell_Log']*external_draw # _input #* s['Z']
|
||||
# # y = 'Buy_Log'
|
||||
# # x = s['Buy_Log'] + _input
|
||||
# return (y, x)
|
||||
|
||||
# def s1m3(step, sL, s, _input):
|
||||
# s['Z'] = s['Z'] + _input
|
||||
# def s2m3(step, sL, s, _input):
|
||||
# s['Price'] = s['Price'] + _input
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = -125
|
||||
proc_one_coef_B = 125
|
||||
# def es3p1(step, sL, s, _input):
|
||||
# s['s3'] = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
||||
# def es4p2(step, sL, s, _input):
|
||||
# s['P_Ext_Markets'] = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
|
||||
# def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
|
||||
# s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
|
||||
def es3p1(step, sL, s, _input):
|
||||
y = 's3'
|
||||
x = s['s3'] + 1
|
||||
return (y, x)
|
||||
# def es4p2(step, sL, s, _input):
|
||||
# y = 'P_Ext_Markets'
|
||||
# # bound_norm_random defined in utils.py
|
||||
|
||||
# #x = s['P_Ext_Markets'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
|
||||
# expected_change = correction_factor*(s['P_Ext_Markets']-s['Buy_Log'])
|
||||
# vol = np.random.randint(1,volatility)
|
||||
# change = expected_change * vol
|
||||
# # change_float = (np.random.normal(expected_change,volatility*expected_change) #Decimal('1.0')
|
||||
# #change = Decimal.from_float(change_float)
|
||||
# x = s['P_Ext_Markets'] + change
|
||||
|
||||
# return (y, x)
|
||||
|
||||
# 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
|
||||
# def stochastic(reference, seed, correction = 0.01):
|
||||
# series = np.zeros(len(reference))
|
||||
# series[0] = reference[0]
|
||||
# for i in range(1,len(reference)):
|
||||
# expected_change = correction*(reference[i]-series[i-1])
|
||||
# normalized_expected_change = np.abs(expected_change)*(reference[i])/(reference[i-1])
|
||||
# seed_int = seed.randint(1,10)
|
||||
# change = np.random.normal(expected_change,seed_int*normalized_expected_change)
|
||||
|
||||
# series[i] = series[i-1]+change
|
||||
# # avoid negative series returns
|
||||
# if series[i] <= 0:
|
||||
# series[i] = .01
|
||||
# #series[i] = series[i-1]+change
|
||||
|
||||
# return [series,seed_int]
|
||||
# ref3 = np.arange(1,1000)*.1
|
||||
# test = stochastic(ref3,seed['b'])
|
||||
|
||||
# def env_a(ref3,seed['b']):
|
||||
# return stochastic(ref3,seed['b'])
|
||||
def env_a(x):
|
||||
return 100
|
||||
def env_b(x):
|
||||
return 21000000
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# 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),
|
||||
'Trans': Decimal(0.0),
|
||||
'P_Ext_Markets': Decimal(25000.0),
|
||||
|
||||
# 's2': Decimal(0.0),
|
||||
# 's3': Decimal(0.0),
|
||||
# 's4': Decimal(0.0),
|
||||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
# exogenous_states = {
|
||||
# # "s3": es3p1,
|
||||
# "P_Ext_Markets": es4p2,
|
||||
# "timestamp": es5p2
|
||||
# }
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
# "s3": es3p1,
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
env_processes = {
|
||||
# "s3": env_proc('2018-10-01 15:16:25', env_a),
|
||||
# "P_Ext_Markets": env_proc('2018-10-01 15:16:25', env_b)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
|
||||
# "b2": b2m1
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
# "Price": s2_dummy,
|
||||
"Buy_Log": s3m1,
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b4": b4m2
|
||||
},
|
||||
"states": {
|
||||
"Sell_Log":s4m2,
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
},
|
||||
"states": {
|
||||
"Price": s2m3,
|
||||
}
|
||||
},
|
||||
# "m3": {
|
||||
# "behaviors": {
|
||||
# "b1": b1m3,
|
||||
# "b2": b2m3
|
||||
# },
|
||||
# "states": {
|
||||
# "Z": s1m3,
|
||||
# "Price": s2m3,
|
||||
# "Buy_Log": s3m3,
|
||||
# }
|
||||
# },
|
||||
# "m4": {
|
||||
# "behaviors": {
|
||||
# },
|
||||
# "states": {
|
||||
# }
|
||||
# },
|
||||
# "m3": {
|
||||
# "behaviors": {
|
||||
# "b1": b1m3,
|
||||
# "b2": b2m3
|
||||
# },
|
||||
# "states": {
|
||||
# "Z": s1m3,
|
||||
# "Price": s2m3,
|
||||
# }
|
||||
# }
|
||||
#treat environmental processes as a mechanism
|
||||
"ep": {
|
||||
"behaviors": {
|
||||
},
|
||||
"states": {
|
||||
"P_Ext_Markets": es4p2
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sim_config = {
|
||||
"N": 1,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))
|
||||
|
|
@ -18,7 +18,7 @@ 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=',')
|
||||
# result.to_csv('~/Projects/DiffyQ-SimCAD/results/config.csv', sep=',')
|
||||
print()
|
||||
print("Tensor Field:")
|
||||
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
|
||||
|
|
|
|||
|
|
@ -0,0 +1,171 @@
|
|||
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
|
||||
)
|
||||
)
|
||||
|
|
@ -0,0 +1,180 @@
|
|||
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
|
||||
)
|
||||
)
|
||||
|
|
@ -0,0 +1,178 @@
|
|||
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 5
|
||||
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
|
||||
}
|
||||
)
|
||||
|
||||
# ToDo: make env proc trigger field agnostic
|
||||
# ToDo: input json into function renaming __name__
|
||||
env_processes = {
|
||||
"s3": 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
|
||||
)
|
||||
)
|
||||
|
|
@ -0,0 +1,180 @@
|
|||
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
|
||||
)
|
||||
)
|
||||
Loading…
Reference in New Issue