behavior id bug

.gitignore

@@ -8,4 +8,5 @@ ui/__pycache__
 SimCAD.egg-info
 __pycache__
 Pipfile
-Pipfile.lock
+Pipfile.lock
+scrapbox/

README.md

@@ -34,13 +34,20 @@ Step 2. Import Package & Run:
 import pandas as pd
 from tabulate import tabulate
 
-from SimCAD.engine import ExecutionContext, Executor
+from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
-from sandboxUX import config1, config2
+# from sandboxUX import config1, config2
+from SimCAD import configs
+
+# ToDo: pass ExecutionContext with execution method as ExecutionContext input
+
+exec_mode = ExecutionMode()
+
 
 print("Simulation Run 1")
 print()
-single_config = [config1]
+single_config = [configs[0]]
-run1 = Executor(ExecutionContext, single_config)
+single_proc_ctx = ExecutionContext(exec_mode.single_proc)
+run1 = Executor(single_proc_ctx, single_config)
 run1_raw_result = run1.main()
 result = pd.DataFrame(run1_raw_result)
 print(tabulate(result, headers='keys', tablefmt='psql'))
@@ -48,8 +55,8 @@ print()
 
 print("Simulation Run 2: Pairwise Execution")
 print()
-configs = [config1, config2]
+multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)
-run2 = Executor(ExecutionContext, configs)
+run2 = Executor(multi_proc_ctx, configs)
 run2_raw_results = run2.main()
 for raw_result in run2_raw_results:
     result = pd.DataFrame(raw_result)

SimCAD/__init__.py

@@ -4,7 +4,7 @@ from SimCAD.utils.configuration import dict_elemwise_sum
 
 configs = []
 
-
+#Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms)
 class Configuration:
     def __init__(self, sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms, behavior_ops=[foldr(dict_elemwise_sum())]):
         self.sim_config = sim_config

SimCAD/utils/configProcessor.py

@@ -10,8 +10,9 @@ def state_identity(k):
     return lambda step, sL, s, _input: (k, s[k])
 
 
+# fix
 def b_identity(step, sL, s):
-    return 0
+    return {'identity': 0}
 
 
 def behavior_identity(k):
@@ -19,7 +20,7 @@ def behavior_identity(k):
 
 
 def key_filter(mechanisms, keyname):
-    return [ v[keyname] for k, v in mechanisms.items() ]
+    return [v[keyname] for k, v in mechanisms.items()]
 
 
 def fillna_with_id_func(identity, df, col):

SimCAD/utils/configuration.py

@@ -75,10 +75,15 @@ def foldr_dict_vals(f, d):
 def sum_dict_values():
     return foldr_dict_vals(add)
 
-
+# AttributeError: 'int' object has no attribute 'keys'
+# config7c
 @curried
 def dict_op(f, d1, d2):
-
+    print('d1')
+    print(d1)
+    print('d2')
+    print(d2)
+    print()
     def set_base_value(target_dict, source_dict, key):
         if key not in target_dict:
             return get_base_value(type(source_dict[key]))

sandboxUX/config1.py

@@ -1,6 +1,5 @@
 from decimal import Decimal
 import numpy as np
-from fn.op import foldr
 
 from SimCAD import Configuration, configs
 from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, bound_norm_random, \
@@ -119,7 +118,7 @@ env_processes = {
 # [1, 2] = {'b1': ['a'], 'b2', [1]} =
 # behavior_ops = [ behavior_to_dict, print_fwd, sum_dict_values ]
 # behavior_ops = [foldr(dict_elemwise_sum())]
-# behavior_ops = []
+# behavior_ops = [foldr(lambda a, b: a + b)]
 
 # need at least 1 behaviour and 1 state function for the 1st mech with behaviors
 # mechanisms = {}

sandboxUX/config2.py

@@ -98,6 +98,7 @@ state_dict = {
 }
 
 # 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,

sandboxUX/config3.py

@@ -0,0 +1,218 @@
+from decimal import Decimal
+import numpy as np
+
+from SimCAD import Configuration, configs
+from SimCAD.utils.configuration 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 = 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')
+    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}
+
+
+# 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)
+
+# Exogenous States
+proc_one_coef_A = -125
+proc_one_coef_B = 125
+
+# A change in belief of actual price, passed onto behaviors to make action
+def es4p2(step, sL, s, _input):
+    y = 'P_Ext_Markets'
+    x = s['P_Ext_Markets'] +  bound_norm_random(seed['z'], proc_one_coef_A, proc_one_coef_B)
+
+    return (y,x)
+
+
+def es5p2(step, sL, s, _input): # accept timedelta instead of timedelta params
+    y = 'timestamp'
+    x = ep_time_step(s, s['timestamp'], seconds=1)
+    return (y, x)
+
+#Environment States
+# NONE
+
+# Genesis States
+state_dict = {
+    'Z': Decimal(21000000.0),
+    'Price': Decimal(100.0),  # Initialize = Z for EMA
+    'Buy_Log': Decimal(0.0),
+    'Sell_Log': Decimal(0.0),
+    '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,
+    "timestamp": 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
+        },
+        "states": {
+            "Z": s1m1,
+            "Buy_Log": s3m1
+        }
+    },
+    # "m2": {
+    #      "behaviors": {
+    #         "b1": b1m2,
+    #         # "b4": b4m2
+    #      },
+    #      "states": {
+    #          "Sell_Log": s4m2
+    #     }
+    # },
+    # "m3": {
+    #         "behaviors": {
+    #     },
+    #         "states": {
+    #             "Price": s2m3
+    #     }
+    # }
+}
+
+configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))

sandboxUX/sim_test.py

@@ -2,13 +2,14 @@ import pandas as pd
 from tabulate import tabulate
 
 from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
-from sandboxUX import config1, config2
+# from sandboxUX import config1, config2
 from SimCAD import configs
 
 # ToDo: pass ExecutionContext with execution method as ExecutionContext input
 
 exec_mode = ExecutionMode()
 
+
 print("Simulation Run 1")
 print()
 single_config = [configs[0]]