testing fun

engine/run.py

@@ -1,5 +1,6 @@
 #from ui.config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
-from ui.toyConfig import state_dict, mechanisms, exogenous_states, env_processes, sim_config
+#from ui.toyConfig import state_dict, mechanisms, exogenous_states, env_processes, sim_config
+from ui.simpleBC_Config import state_dict, mechanisms, exogenous_states, env_processes, sim_config
 #from ui.<config_filename> import state_dict, mechanisms, exogenous_states, env_processes, sim_config
 from engine.configProcessor import generate_config
 from engine.mechanismExecutor import simulation

ui/simpleBC_Config.py

@@ -0,0 +1,168 @@
+from engine.utils import bound_norm_random, ep_time_step, env_proc
+
+import numpy as np
+from decimal import Decimal
+
+alpha = Decimal('.7') #forgetting param
+theta = Decimal('.75') #weight param for rational price
+beta = Decimal('0.5') #agant response gain
+gamma = Decimal('.03') #action friction param
+delta = Decimal('.3') #bounds on price change
+omega = Decimal('.5') #bound on burn frac per period
+
+seed = {
+    'z': np.random.RandomState(1),
+    'a': np.random.RandomState(2),
+    'b': np.random.RandomState(3),
+    'c': np.random.RandomState(3)
+}
+
+# Behaviors per Mechanism
+
+#arbit X Bond
+def b1m1(step, sL, s):
+    #returns "delta p"
+    if s['Price']< s['Pool']/s['Supply']-gamma:
+        return  (s['Pool']/s['Supply']-s['Price'])/s['Price']*s['Pool']*beta
+    else :
+        return 0
+    
+
+#invest X Bond
+def b2m1(step, sL, s):
+    #returns "delta p"
+    if s['Belief']< (alpha*s['Belief']+s['Pool']/s['Supply'])*(1-alpha):
+        return  s['Supply']*((alpha*s['Belief']+s['Pool']/s['Supply'])*(1-alpha)-s['Belief'])*beta
+    else :
+        return 0
+
+#arbit X Burn
+def b1m2(step, sL, s):
+    #returns "delta s"
+    if Decimal('1')/s['Price']< s['Supply']/s['Pool']-gamma:
+        return  (s['Supply']/s['Pool']-Decimal('1')/s['Price'])*s['Price']*s['Supply']*beta
+    else :
+        return 0
+
+#invest X Burn 
+def b2m2(step, sL, s):
+    #returns "delta s"
+    if Decimal('1')/s['Belief']< Decimal('1')/s['Price']:
+        return  np.min([ s['Pool']*(Decimal('1')/s['Price']-Decimal('1')/s['Belief'])*beta, omega*s['Supply']])
+    else :
+        return 0
+
+#
+#def b1m3(step, sL, s):
+#    return s['s1']
+#def b2m3(step, sL, s):
+#    return s['s2']
+
+
+# Internal States per Mechanism
+
+#Pool X Bond
+def s1m1(step, sL, s, _input):
+    #_input = "delta p"
+    s['Pool'] = s['Pool']+_input
+
+#Supply X Bond     
+def s2m1(step, sL, s, _input):
+    #_input = "delta p"
+    s['Supply'] = s['Supply']+_input*s['Supply']/s['Pool'] 
+
+# Pool X Burn   
+def s1m2(step, sL, s, _input):
+    #_input is "delta s"
+    s['Pool'] = s['Pool']- _input*s['Pool']/s['Supply'] 
+
+# Supply X Burn    
+def s2m2(step, sL, s, _input):
+    s['Supply'] = s['Supply'] - _input
+
+#def s1m3(step, sL, s, _input):
+#    s['s1'] = s['s1']+Decimal(.25)*(s['s2']-s['s1']) + Decimal(.25)*(_input-s['s1'])
+#
+#def s2m3(step, sL, s, _input):
+#    s['s2'] = s['s2']+Decimal(.25)*(s['s1']-s['s2']) + Decimal(.25)*(_input-s['s2'])
+
+# Exogenous States
+proc_one_coef_A = -delta
+proc_one_coef_B = delta
+def es3p1(step, sL, s, _input):
+    rv = bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
+    s['Price'] = theta*s['Price'] * (Decimal('1')+rv) +(Decimal('1')-theta)*s['Pool']/s['Supply'] 
+def es4p2(step, sL, s, _input):
+    s['Belief'] = alpha*s['Belief']+s['Pool']/s['Supply']*(Decimal('1')-alpha)
+
+def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
+    s['timestamp'] = ep_time_step(s, s['timestamp'], seconds=1)
+
+# Environment States
+#from numpy.random import randn as rn
+def env_a(x):
+    return 3
+def env_b(x):
+    return 7
+# def what_ever(x):
+#     return x + 1
+
+# Genesis States
+state_dict = {
+    'Pool': Decimal(10.0),
+    'Supply': Decimal(5.0),
+    'Price': Decimal(.01),
+    'Belief': Decimal(10.0),
+    'timestamp': '2018-10-01 15:16:24'
+}
+
+exogenous_states = {
+    "Price": es3p1,
+    "Belief": es4p2,
+    "timestamp": es5p2
+}
+
+env_processes = {
+    "Price": env_proc('2018-10-01 15:16:25', env_a),
+    "Belief": env_proc('2018-10-01 15:16:25', env_b)
+}
+
+# test return vs. non-return functions as lambdas
+# test fully defined functions
+mechanisms = {
+    "bond": {
+        "behaviors": {
+            "arbit": b1m1, # lambda step, sL, s: s['s1'] + 1,
+            "invest": b2m1
+        },
+        "states": {
+            "Pool": s1m1,
+            "Supply": s2m1,
+        }
+    },
+    "burn": {
+        "behaviors": {
+            "arbit": b1m2,
+            "invest": b2m2
+        },
+        "states": {
+            "Pool": s1m2,
+            "Supply": s2m2,
+        }
+    },
+#    "m3": {
+#        "behaviors": {
+#            "b1": b1m3,
+#            "b2": b2m3
+#        },
+#        "states": {
+#            "s1": s1m3,
+#            "s2": s2m3,
+#        }
+#    }
+}
+
+sim_config = {
+    "N": 1,
+    "R": 1000
+}

ui/toyConfig.py

@@ -83,7 +83,7 @@ env_processes = {
 # test return vs. non-return functions as lambdas
 # test fully defined functions
 mechanisms = {
-    "m1": {
+    "mech1": {
         "behaviors": {
             "b1": b1m1, # lambda step, sL, s: s['s1'] + 1,
             "b2": b2m1