diff --git a/SimCAD/engine/__init__.py b/SimCAD/engine/__init__.py
index 11b2031..47b7cac 100644
--- a/SimCAD/engine/__init__.py
+++ b/SimCAD/engine/__init__.py
@@ -8,7 +8,6 @@ from SimCAD.engine.simulation import Executor as SimExecutor
 
 
 class ExecutionMode:
-
     single_proc = 'single_proc'
     multi_proc = 'multi_proc'
 
diff --git a/SimCAD/engine/simulation.py b/SimCAD/engine/simulation.py
index 1c22286..dc051ec 100644
--- a/SimCAD/engine/simulation.py
+++ b/SimCAD/engine/simulation.py
@@ -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 = []
diff --git a/sandbox/barlin/config3.py b/sandbox/barlin/config3.py
index 14f45fd..12c0db5 100644
--- a/sandbox/barlin/config3.py
+++ b/sandbox/barlin/config3.py
@@ -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
diff --git a/sandbox/barlin/config4.py b/sandbox/barlin/config4.py
index eb62774..9bcbf16 100644
--- a/sandbox/barlin/config4.py
+++ b/sandbox/barlin/config4.py
@@ -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
diff --git a/sandbox/barlin/config6atemp.py b/sandbox/barlin/config6atemp.py
new file mode 100644
index 0000000..f3ff426
--- /dev/null
+++ b/sandbox/barlin/config6atemp.py
@@ -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))
\ No newline at end of file
diff --git a/sandbox/barlin/config6aworks.py b/sandbox/barlin/config6aworks.py
new file mode 100644
index 0000000..f3ff426
--- /dev/null
+++ b/sandbox/barlin/config6aworks.py
@@ -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))
\ No newline at end of file
diff --git a/sandbox/sim_test.py b/sandbox/sim_test.py
index 93f5ed3..935b49f 100644
--- a/sandbox/sim_test.py
+++ b/sandbox/sim_test.py
@@ -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()
\ No newline at end of file
+# 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()
\ No newline at end of file