BUG: run value for Genesis State always last run for large datasets

.gitignore

@@ -9,4 +9,5 @@ SimCAD.egg-info
 __pycache__
 Pipfile
 Pipfile.lock
-scrapbox/
+scrapbox/
+results/

SimCAD/engine/__init__.py

@@ -63,8 +63,9 @@ class Executor:
         # Dimensions: N x r x mechs
 
         def single_proc_exec(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns):
-            simulation, states_list, config = simulation_execs.pop(), states_lists.pop(), configs_structs.pop()
+            l = [simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns]
-            env_processes, T, N = env_processes_list.pop(), Ts.pop(), Ns.pop()
+            simulation, states_list, config, env_processes, T, N = list(map(lambda x: x.pop(), l))
+            # print(states_list)
             result = simulation(states_list, config, env_processes, T, N)
             return flatten(result)
 

SimCAD/engine/simulation.py

@@ -1,6 +1,7 @@
 from copy import deepcopy
 from fn.op import foldr, call
-
+import pprint
+pp = pprint.PrettyPrinter(indent=4)
 
 class Executor:
     def __init__(self, behavior_ops):
@@ -61,6 +62,7 @@ class Executor:
     def block_gen(self, states_list, configs, env_processes, t_step, run):
         m_step = 0
         states_list_copy = deepcopy(states_list)
+        # print(states_list_copy)
         # remove copy
         genesis_states = states_list_copy[-1]
         genesis_states['mech_step'], genesis_states['time_step'] = m_step, t_step
@@ -82,6 +84,7 @@ class Executor:
         time_seq = [x + 1 for x in time_seq]
         simulation_list = [states_list]
         for time_step in time_seq:
+            # print(run)
             pipe_run = self.block_gen(simulation_list[-1], configs, env_processes, time_step, run)
             _, *pipe_run = pipe_run
             simulation_list.append(pipe_run)
@@ -94,9 +97,13 @@ class Executor:
         pipe_run = []
         for run in range(runs):
             run += 1
-            head, *tail = self.pipe(states_list, configs, env_processes, time_seq, run)
+            # print("Run: "+str(run))
-            head[-1]['mech_step'], head[-1]['time_step'], head[-1]['run'] = 0, 0, run
+            states_list_copy = deepcopy(states_list) # WHY ???
-            simulation_list = [head] + tail
+            head, *tail = self.pipe(states_list_copy, configs, env_processes, time_seq, run)
-            pipe_run += simulation_list
+            genesis = head.pop()
+            genesis['mech_step'], genesis['time_step'], genesis['run'] = 0, 0, run
+            first_timestep = [genesis] + tail.pop(0)
+            pipe_run += [first_timestep] + tail
+            del states_list_copy
 
         return pipe_run

SimCAD/utils/configuration.py

@@ -79,11 +79,6 @@ def sum_dict_values():
 # 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/config3.py

@@ -6,7 +6,7 @@ from SimCAD.utils.configuration import exo_update_per_ts, proc_trigger, bound_no
     ep_time_step
 
 seed = {
-    'z': np.random.RandomState(1)
+   'z': np.random.RandomState(1)
 }
 
 # Signals
@@ -19,7 +19,7 @@ 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')
+volatility = Decimal('5.0')
 
 # Buy_Log_signal =
 # Z_signal =

sandboxUX/config4.py

@@ -0,0 +1,243 @@
+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 = .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)
+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('0.250')
+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
+Herd_portion = Decimal('0.250')
+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))
+        return {'herd_sell': sell, 'herd_buy': 0}
+      #  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))
+        return {'herd_sell': 0, 'herd_buy': buy}
+    else:
+        return {'herd_sell': 0, 'herd_buy': 0}
+
+# 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'] + _input['herd_buy'] # / Psignal_int
+    return (y, x)
+
+
+def s4m2(step, sL, s, _input):
+    y = 'Sell_Log'
+    x = _input['sell_order1'] + _input['sell_order2'] + _input['herd_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'] + s['Buy_Log'] /s['Z'] - s['Sell_Log']/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 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),
+    'Price_Signal': 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 = {}
+
+exogenous_states = exo_update_per_ts(
+    {
+    "P_Ext_Markets": es4p2,
+    "timestamp": es5p2
+    }
+)
+
+sim_config = {
+    "N": 20,
+    "T": range(1000)
+}
+
+# test return vs. non-return functions as lambdas
+# test fully defined functions
+mechanisms = {
+    "m1": {
+        "behaviors": {
+            "b1": b1m1,
+            "b3": b3m2
+        },
+        "states": {
+            "Z": s1m1,
+            "Buy_Log": s3m1
+        }
+    },
+    "m2": {
+         "behaviors": {
+            "b1": b1m2,
+            "b3": b3m2,
+            "b4": b4m2
+         },
+         "states": {
+             "Sell_Log": s4m2
+        }
+    },
+    "m3": {
+            "behaviors": {
+        },
+            "states": {
+                "Price": s2m3,
+                "Price_Signal": s5m3
+        }
+    }
+}
+
+configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms))

sandboxUX/sim_test.py

@@ -2,8 +2,8 @@ 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 sandboxUX import config3
+# from sandboxUX import config4
 from SimCAD import configs
 
 # ToDo: pass ExecutionContext with execution method as ExecutionContext input
@@ -18,15 +18,16 @@ 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)
+# result.to_csv('~/Projects/DiffyQ-SimCAD/results/config4.csv', sep=',')
 print(tabulate(result, headers='keys', tablefmt='psql'))
 print()
-#
+
-# print("Simulation Run 2: Pairwise Execution")
+print("Simulation Run 2: Pairwise Execution")
-# print()
+print()
-# multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)
+multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)
-# run2 = Executor(multi_proc_ctx, configs)
+run2 = Executor(multi_proc_ctx, configs)
-# run2_raw_results = run2.main()
+run2_raw_results = run2.main()
-# for raw_result in run2_raw_results:
+for raw_result in run2_raw_results:
-#     result = pd.DataFrame(raw_result)
+    result = pd.DataFrame(raw_result)
-#     print(tabulate(result, headers='keys', tablefmt='psql'))
+    print(tabulate(result, headers='keys', tablefmt='psql'))
-# print()
+print()