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absolut path issue

This commit is contained in:
Joshua E. Jodesty 2019-01-10 14:02:16 -05:00
parent ae25a9ff04 f9f945c20f
commit 141680e3a1
19 changed files with 373 additions and 1146 deletions
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8
.gitignore vendored
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@ -1,8 +1,12 @@
demos
SimCAD
simularions
setup.py
build
.ipynb_checkpoints
.DS_Store
.idea
notebooks/.ipynb_checkpoints
notebooks/multithreading.ipynb
SimCAD.egg-info
__pycache__
Pipfile

119
LICENSE.txt Normal file
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@ -0,0 +1,119 @@
TRIAL LICENSE AGREEMENT
BACKGROUND
Company has developed and intends to market and license a certain software product and service called ”SimCAD” which,
among other things, is a scientific engineering simulation tool (“Software”). Company wishes to provide access, on a
trial basis, to users of a “beta” version of the Software to test and provide feedback to Company. Licensee wishes to
participate in Company’s beta trial of the Software and to provide feedback to Company with respect to Licensee’s use
thereof.
Accordingly, the parties hereby agree as follows:
1. BETA PRODUCT.
This Agreement applies to any pre­release version of the Software and any updates and changes thereto during the Term
(collectively, “Beta Product”). As an essential condition of this Agreement, Licensee understands and acknowledges that:
(a) Licensee is participating in a beta test of the Beta Product; (b) the Beta Product has not been field tested or
trialed; and (c) the Beta Product may not operate properly or be error free and may not perform all functions for
which it is intended or represented.
2. FEEDBACK.
As a condition of this Agreement, during the Term of this Agreement, Licensee agrees to provide Company with comments,
feedback, criticisms, and suggestions for changes to the Beta Product (“Feedback”), and to help Company identify errors
or malfunctions, and performance issues, in the operation of the Beta Product, as Company may reasonably request. All
rights to any Feedback or other intellectual property derived from Licensee’s use of or relating to the Beta Product,
as well any data collected from the use of the Beta Product, belong solely to Company and Licensee hereby irrevocably
assigns all such rights to Company. Company reserves the right to use all Feedback and data collected as a result of the
use of the Beta Product to advertise and promote the Company and the Software.
3. LICENSE AND RESERVATION OF RIGHTS.
3.1 Subject to the terms and conditions set forth in this Agreement, Company hereby grants Licensee, and Licensee
accepts, during the Term, a non­exclusive, royalty­free, revocable, non­transferable, limited license to access and use
the Beta Product for its internal, non­commercial use for evaluation purposes only, and to give permission to employees
of Licensee and employees of Licensee’s subsidiaries (“Permitted Users”) to use the Beta Product in accordance with the
foregoing.
3.2 The Beta Product and the Software comprise the intellectual property of Company. All right, title and interest in
and to the Beta Product (and, more generally, in and to the Software), and to all Feedback and data arising from its
use, in whole or in part, and all patent, copyright, trade­marks, trade secret and all other intellectual and industrial
property rights therein and the structure, sequence and organization of same, and the media on which such material is
contained belong exclusively to Company. Licensee and its Permitted Users will not, directly or indirectly: reverse
engineer, decompile, disassemble or otherwise attempt to discover the source code, object code or underlying structure,
ideas, know­how or algorithms relevant to the Beta Product; modify, adapt, alter, edit, correct, translate, publish,
sell, transfer, assign, convey, rent, lease, loan, pledge, sublicense, distribute, export, enhance or create derivative
works based on the Beta Product; or remove, alter, cover or otherwise obscure any proprietary notices or labels
displayed on or within the Beta Product any documentation relating thereto.
4. DISCLAIMER.
4.1 COMPANY MAKES NO WARRANTIES, WHETHER EXPRESS, IMPLIED, STATUTORY OR OTHERWISE, WITH RESPECT TO THE BETA PRODUCT,
INCLUDING, BUT NOT LIMITED TO, THE AVAILABILITY, QUALITY OR PERFORMANCE OF THE BETA PRODUCT. COMPANY SPECIFICALLY
DISCLAIMS ALL EXPRESS, STATUTORY AND IMPLIED WARRANTIES AND CONDITIONS, INCLUDING, WITHOUT LIMITATION (A) THE IMPLIED
WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON­INFRINGEMENT, (B) ANY WARRANTIES AGAINST HIDDEN
OR LATENT DEFECTS, (C) AND ANY WARRANTIES AND CONDITIONS ARISING OUT OF COURSE OF DEALING OR USAGE OF TRADE AND (D) ANY
WARRANTY OR REPRESENTATION THAT THE BETA PRODUCT IS ERROR­FREE, VIRUS­FREE, SECURE, UNINTERRUPTED, OR FREE FROM
UNAUTHORIZED ACCESS (INCLUDING, BUT NOT LIMITED TO, THIRD PARTY HACKERS OR DENIAL OF SERVICE ATTACKS). THE BETA PRODUCT
IS SUPPLIED ON AN “AS IS”, “AS AVAILABLE” BASIS WITHOUT WARRANTY.
4.2 NEITHER PARTY SHALL BE LIABLE FOR SPECIAL, INCIDENTAL, PUNITIVE, CONSEQUENTIAL OR INDIRECT DAMAGES OR LOSS
(INCLUDING DEATH AND PERSONAL INJURY), IRRESPECTIVE OF THEIR CAUSE, NOTWITHSTANDING THAT A PARTY HAS BEEN ADVISED OF THE
POSSIBILITY OF SUCH LOSS OR DAMAGE, NOR FOR ANY CLAIMS FOR SUCH LOSS OR DAMAGE INSTITUTED AGAINST A PARTY OR ITS
CUSTOMERS BY ANY THIRD PARTY.
5. CONFIDENTIALITY
5.1 All Confidential Information disclosed by either party shall be kept by the receiving party in strict confidence and
shall not be disclosed to any third party without the disclosing party’s express written consent. For purposes of this
Agreement, “Confidential Information” means all information regarding either party’s business which has been marked or
is otherwise communicated as being “proprietary” or “confidential” or which reasonably should be known by the receiving
party to be proprietary or confidential information. Without limiting the generality of the foregoing, Confidential
Information of Company includes non­public information regarding features, functionality and performance of the Beta
Product, including all Feedback and related data. Notwithstanding the foregoing, each party’s confidentiality
obligations hereunder shall not apply to information that: (a) is already known to the receiving party without a
pre­existing restriction as to disclosure; (b) is or becomes publicly available without fault of the receiving party;
(c) is rightfully obtained by the receiving party from a third party without restriction as to disclosure, or is
approved for release by written authorization of the disclosing party; (d) is developed independently by the receiving
party without use of the disclosing party’s Confidential Information; or (e) is required to be disclosed by law or
regulation, including, but not limited to, supplying such information or making such statements or disclosures relating
to this Agreement before any competent court, governmental agency or authority in response to a lawful requirement or
request from a court of governmental agency or authority, provided that the disclosing party shall give the other party
prompt notice of such request, to the extent practicable, so that the other party may seek (at its sole cost and
expense) an appropriate protective order or similar relief.
5.2 In the event of a breach of Sections 2, 3 or this Section 5, the non­breaching party shall be entitled to seek
equitable relief to protect its interests, including, but not limited to, injunctive relief. In the event of expiration
or earlier termination of this Agreement, each party shall immediately return to the other party such other party’s
Confidential Information, or at such other party’s option, destroy any remaining Confidential Information and certify
that such destruction has taken place.
6. FEES; EXPENSES.
Neither party shall be entitled to any compensation in connection with this Agreement or its use or provision of the
Beta Product. Each party shall bear its own costs and expenses arising from this Agreement and its use or provision of
the Beta Product, as the case may be.
7. TERM OF AGREEMENT.
This Agreement shall begin on the Effective Date and shall continue until it has been terminated (such period, the
“Term”). Either party shall have the right to terminate this Agreement at any time on one (1) month written notice to
the other party, or in the case of a breach of this Agreement by Licensee or its Permitted Users, Company may terminate
this Agreement immediately on written notice to Licensee. Upon termination of this Agreement, all rights granted to
Licensee (and any Permitted User) under this Agreement will immediately terminate and Licensee (and all Permitted Users)
must immediately cease all use of the Beta Product at such time. Notwithstanding any termination of this Agreement,
Sections 2, 3.2, 4, 5, 6, this Section 7 and Section 8 shall survive and remain binding on the parties.
8. MISCELLANEOUS.
This Agreement shall be governed by and construed in accordance with the laws of the State of New York. All disputes
relating to this Agreement shall be resolved in the federal and state courts of New York County, New York and the
parties submit to the jurisdiction of such courts. This Agreement does not create any agency, partnership, or joint
venture relationship between Licensee and Company. This Agreement is the entire understanding of the parties with
respect to the subject matter hereof and supersedes any previous or contemporaneous communications, representations,
warranties, discussions, arrangements or commitments, whether oral or written with respect to such subject matter. This
Agreement cannot be amended except by a written amendment that expressly refers to this Agreement and is signed by an
authorized representative of each party. This Agreement may be executed in one or more counterparts, including via
facsimile or email (or any other electronic means such as “.pdf” or “.tiff” files), each of which shall be deemed an
original, and all of which shall constitute one and the same Agreement.

72
README.md
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@ -1,52 +1,57 @@
# SimCad
**Dependencies:**
**Warning**:
**Do not** publish this package / software to **any** software repository **except** one permitted by BlockScience.
**Description:**
SimCAD is a differential games based simulation software package for research, validation, and Computer \
Aided Design of economic systems. An economic system is treated as a state based model and defined through a \
set of endogenous and exogenous state variables which are updated through mechanisms and environmental \
processes, respectively. Behavioral models, which may be deterministic or stochastic, provide the evolution of \
the system within the action space of the mechanisms. Mathematical formulations of these economic games \
treat agent utility as derived from state rather than direct from action, creating a rich dynamic modeling framework.
Simulations may be run with a range of initial conditions and parameters for states, behaviors, mechanisms, \
and environmental processes to understand and visualize network behavior under various conditions. Support for \
A/B testing policies, monte carlo analysis and other common numerical methods is provided.
**1. Install Dependencies:**
```bash
pip install -r requirements.txt
```
**Project:**
Example Runs:
`/simulations/sim_test.py`
Example Configurations:
`/simulations/validation/`
**User Interface: Simulation Configuration**
Configurations:
```bash
/DiffyQ-SimCAD/ui/config.py
```
**Build Tool & Package Import:**
Step 1. Build & Install Package locally:
```bash
pip install .
pip install -e .
```
* [Package Creation Tutorial](https://python-packaging.readthedocs.io/en/latest/minimal.html)
Step 2. Import Package & Run:
**2. Configure Simulation:**
Intructions:
`/Simulation.md`
Examples:
`/simulations/validation/*`
**3. Import SimCAD & Run Simulation:**
Example:
`/demos/sim_test.py` or `test.ipynb`
```python
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact same order
from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1, config2
from SimCAD import configs
# ToDo: pass ExecutionContext with execution method as ExecutionContext input
exec_mode = ExecutionMode()
exec_mode = ExecutionMode()
print("Simulation Execution 1")
print()
first_config = [configs[0]] # from config1
@ -54,7 +59,7 @@ 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=',')
print()
print("Tensor Field:")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
@ -76,14 +81,7 @@ for raw_result, tensor_field in run2.main():
print()
```
Same can be run in Jupyter .
The above can be run in Jupyter.
```bash
jupyter notebook
```
Notebooks Directory:
`/DiffyQ-SimCAD/notebooks/`
**Warning**:
**Do Not** publish this package / software to **Any** software repository **except** [DiffyQ-SimCAD's staging branch](https://github.com/BlockScience/DiffyQ-SimCAD/tree/staging) or its **Fork**

6
SimCAD/configuration/__init__.py
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@ -49,7 +49,6 @@ class Processor:
self.state_identity = id.state_identity
self.apply_identity_funcs = id.apply_identity_funcs
# Make returntype chosen by user.
def create_matrix_field(self, mechanisms, key):
if key == 'states':
identity = self.state_identity
@ -62,11 +61,8 @@ class Processor:
else:
return pd.DataFrame({'empty': []})
# Maybe Refactor to only use dictionary BUT I used dfs to fill NAs. Perhaps fill
def generate_config(self, state_dict, mechanisms, exo_proc):
# ToDo: include False / False case
# ToDo: Use Range multiplier instead for loop iterator
def no_update_handler(bdf, sdf):
if (bdf.empty == False) and (sdf.empty == True):
bdf_values = bdf.values.tolist()
@ -98,4 +94,4 @@ class Processor:
sdf_values, bdf_values = only_ep_handler(state_dict)
zipped_list = list(zip(sdf_values, bdf_values))
return list(map(lambda x: (x[0] + exo_proc, x[1]), zipped_list))
return list(map(lambda x: (x[0] + exo_proc, x[1]), zipped_list))

7
SimCAD/configuration/utils/__init__.py
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@ -8,7 +8,6 @@ class TensorFieldReport:
def __init__(self, config_proc):
self.config_proc = config_proc
# ??? dont for-loop to apply exo_procs, use exo_proc struct
def create_tensor_field(self, mechanisms, exo_proc, keys=['behaviors', 'states']):
dfs = [self.config_proc.create_matrix_field(mechanisms, k) for k in keys]
df = pd.concat(dfs, axis=1)
@ -34,7 +33,6 @@ def proc_trigger(trigger_step, update_f, step):
return lambda x: x
# accept timedelta instead of timedelta params
t_delta = timedelta(days=0, minutes=0, seconds=30)
def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
dt = datetime.strptime(dt_str, dt_format)
@ -42,7 +40,6 @@ def time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
return t.strftime(dt_format)
# accept timedelta instead of timedelta params
t_delta = timedelta(days=0, minutes=0, seconds=1)
def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = t_delta):
if s['mech_step'] == 0:
@ -54,8 +51,8 @@ def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = t_delta
def exo_update_per_ts(ep):
@curried
def ep_decorator(f, y, step, sL, s, _input):
if s['mech_step'] + 1 == 1: # inside f body to reduce performance costs
if s['mech_step'] + 1 == 1:
return f(step, sL, s, _input)
else:
return (y, s[y])
return {es: ep_decorator(f, es) for es, f in ep.items()}
return {es: ep_decorator(f, es) for es, f in ep.items()}

7
SimCAD/configuration/utils/behaviorAggregation.py
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@ -27,7 +27,7 @@ def foldr_dict_vals(f, d):
def sum_dict_values():
return foldr_dict_vals(add)
# AttributeError: 'int' object has no attribute 'keys'
@curried
def dict_op(f, d1, d2):
def set_base_value(target_dict, source_dict, key):
@ -42,7 +42,4 @@ def dict_op(f, d1, d2):
def dict_elemwise_sum():
return dict_op(add)
# class BehaviorAggregation:
return dict_op(add)

5
SimCAD/engine/__init__.py
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@ -45,7 +45,6 @@ class Executor:
def execute(self):
config_proc = Processor()
create_tensor_field = TensorFieldReport(config_proc).create_tensor_field
@ -65,6 +64,7 @@ class Executor:
config_idx += 1
# Dimensions: N x r x mechs
if self.exec_context == ExecutionMode.single_proc:
@ -77,4 +77,5 @@ class Executor:
results = []
for result, mechanism, ep in list(zip(simulations, mechanisms, eps)):
results.append((flatten(result), create_tensor_field(mechanism, ep)))
return results
return results

19
SimCAD/engine/simulation.py
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@ -1,10 +1,7 @@
from copy import deepcopy
from fn.op import foldr, call
from SimCAD.utils import rename
from SimCAD.engine.utils import engine_exception
id_exception = engine_exception(KeyError, KeyError, None)
@ -14,7 +11,6 @@ class Executor:
self.state_update_exception = state_update_exception
self.behavior_update_exception = behavior_update_exception
# Data Type reduction
def get_behavior_input(self, step, sL, s, funcs):
ops = self.behavior_ops[::-1]
@ -27,18 +23,16 @@ class Executor:
for state in state_dict.keys():
if state in list(env_processes.keys()):
env_state = env_processes[state]
if (env_state.__name__ == '_curried') or (env_state.__name__ == 'proc_trigger'): # might want to change
if (env_state.__name__ == '_curried') or (env_state.__name__ == 'proc_trigger'):
state_dict[state] = env_state(step)(state_dict[state])
else:
state_dict[state] = env_state(state_dict[state])
def mech_step(self, m_step, sL, state_funcs, behavior_funcs, env_processes, t_step, run):
last_in_obj = sL[-1]
_input = self.state_update_exception(self.get_behavior_input(m_step, sL, last_in_obj, behavior_funcs))
# ToDo: add env_proc generator to `last_in_copy` iterator as wrapper function
last_in_copy = dict([self.behavior_update_exception(f(m_step, sL, last_in_obj, _input)) for f in state_funcs])
for k in last_in_obj:
@ -47,8 +41,7 @@ class Executor:
del last_in_obj
# make env proc trigger field agnostic
self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp']) # mutating last_in_copy
self.apply_env_proc(env_processes, last_in_copy, last_in_copy['timestamp'])
last_in_copy["mech_step"], last_in_copy["time_step"], last_in_copy['run'] = m_step, t_step, run
sL.append(last_in_copy)
@ -59,8 +52,6 @@ class Executor:
def mech_pipeline(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
states_list = [genesis_states]
@ -75,7 +66,6 @@ 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]
simulation_list = [states_list]
@ -87,12 +77,11 @@ class Executor:
return simulation_list
# Del _ / head
def simulation(self, states_list, configs, env_processes, time_seq, runs):
pipe_run = []
for run in range(runs):
run += 1
states_list_copy = deepcopy(states_list) # WHY ???
states_list_copy = deepcopy(states_list)
head, *tail = self.block_pipeline(states_list_copy, configs, env_processes, time_seq, run)
genesis = head.pop()
genesis['mech_step'], genesis['time_step'], genesis['run'] = 0, 0, run
@ -100,4 +89,4 @@ class Executor:
pipe_run += [first_timestep_per_run] + tail
del states_list_copy
return pipe_run
return pipe_run

8
SimCAD/engine/utils.py
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@ -31,11 +31,3 @@ def engine_exception(ErrorType, error_message, exception_function, try_function)
except ErrorType:
print(error_message)
return exception_function
# def exception_handler(f, m_step, sL, last_mut_obj, _input):
# try:
# return f(m_step, sL, last_mut_obj, _input)
# except KeyError:
# print("Exception")
# return f(m_step, sL, sL[-2], _input)

10
SimCAD/utils/__init__.py
View File

@ -1,5 +1,3 @@
# from fn.func import curried
def pipe(x):
return x
@ -20,13 +18,7 @@ def flatmap(f, items):
def key_filter(l, keyname):
return [v[keyname] for k, v in l.items()]
# @curried
def rename(new_name, f):
f.__name__ = new_name
return f
#
# def rename(newname):
# def decorator(f):
# f.__name__ = newname
# return f
# return decorator

37
demos/sim_test.py Normal file
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@ -0,0 +1,37 @@
import pandas as pd
from tabulate import tabulate
# The following imports NEED to be in the exact order
from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
from simulations.validation import config1, config2
from SimCAD import configs
exec_mode = ExecutionMode()
print("Simulation Execution 1")
print()
first_config = [configs[0]] # from config1
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)
print()
print("Tensor Field:")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()
print("Simulation Execution 2: Pairwise Execution")
print()
multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
for raw_result, tensor_field in run2.main():
result = pd.DataFrame(raw_result)
print()
print("Tensor Field:")
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
print("Output:")
print(tabulate(result, headers='keys', tablefmt='psql'))
print()

137
demos/test.ipynb Normal file
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@ -0,0 +1,137 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"# The following imports NEED to be in the exact order\n",
"from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
"from simulations.validation import config1, config2\n",
"from SimCAD import configs\n",
"\n",
"exec_mode = ExecutionMode()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Simulation Execution 1\")\n",
"print()\n",
"first_config = [configs[0]] # from config1\n",
"single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)\n",
"run1 = Executor(exec_context=single_proc_ctx, configs=first_config)\n",
"run1_raw_result, raw_tensor_field = run1.main()\n",
"result = pd.DataFrame(run1_raw_result)\n",
"tensor_field = pd.DataFrame(raw_tensor_field)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Tensor Field:\")\n",
"tensor_field"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Output:\")\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Simulation Execution 2: Pairwise Execution\")\n",
"print()\n",
"multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\n",
"run2 = Executor(exec_context=multi_proc_ctx, configs=configs)\n",
"results = []\n",
"tensor_fields = []\n",
"for raw_result, raw_tensor_field in run2.main():\n",
" results.append(pd.DataFrame(raw_result))\n",
" tensor_fields.append(pd.DataFrame(raw_tensor_field))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"print(\"Tensor Field A:\")\n",
"tensor_fields[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Output A:\")\n",
"results[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Tensor Field B:\")\n",
"tensor_fields[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Output B:\")\n",
"results[1]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

78
notebooks/test.ipynb
View File

@ -1,78 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Simulation Run 2: Pairwise Execution\")\n",
"print()\n",
"multi_proc_ctx = ExecutionContext(exec_mode.multi_proc)\n",
"run2 = Executor(multi_proc_ctx, configs)\n",
"run2_raw_results = run2.main()\n",
"for raw_result in run2_raw_results:\n",
" result = pd.DataFrame(raw_result)\n",
" print(tabulate(result, headers='keys', tablefmt='psql'))\n",
"print()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(\"Simulation Run 1\")\n",
"print()\n",
"single_config = [configs[0]]\n",
"single_proc_ctx = ExecutionContext(exec_mode.single_proc)\n",
"run1 = Executor(single_proc_ctx, single_config)\n",
"run1_raw_result = run1.main()\n",
"result = pd.DataFrame(run1_raw_result)\n",
"result"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"from tabulate import tabulate\n",
"\n",
"from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
"from sandboxUX import config1, config2\n",
"from SimCAD import configs\n",
"\n",
"# ToDo: pass ExecutionContext with execution method as ExecutionContext input\n",
"\n",
"exec_mode = ExecutionMode()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

1
requirements.txt
View File

@ -1,4 +1,3 @@
pathos
pipenv
fn
tabulate

4
setup.py
View File

@ -19,5 +19,5 @@ setup(name='SimCAD',
author='Joshua E. Jodesty',
author_email='joshua@block.science',
license='licenses',
packages=['SimCAD'],
zip_safe=False)
packages=['SimCAD']
)

494
simulations/scrapbox/config7c.py
View File

@ -1,494 +0,0 @@
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
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):
# 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)):
return beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
return 0
else:
return 0
def b1m2(step, sL, s):
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 0
elif s['Price'] > (theta*EMH_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*EMH_portion*(1-theta)):
return beta * theta*EMH_Ext_Hold * s['P_Ext_Markets']/(s['Price']*EMH_portion*(1-theta))
else:
return 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):
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)):
return beta * theta*HODL_Ext_Hold * s['P_Ext_Markets']/(s['Price']*HODL_portion*(1-theta))
elif s['Price'] > (theta*HODL_Ext_Hold * s['P_Ext_Markets'])/(s['Z']*HODL_portion*(1-theta)):
return 0
else:
return 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
def dummy_behavior(step, sL, s):
return 0
def s1_dummy(step, sL, s, _input):
y = 'Z'
x = s['Z']
return (y, x)
def s2_dummy(step, sL, s, _input):
y = 'Price'
x = s['Price']
return (y, x)
def s3_dummy(step, sL, s, _input):
y = 'Buy_Log'
x = s['Buy_Log']
return (y, x)
def s4_dummy(step, sL, s, _input):
y = 'Sell_Log'
x = s['Sell_Log']
return (y, x)
def s5_dummy(step, sL, s, _input):
y = 'Trans'
x = s['Trans']
return (y, x)
def s6_dummy(step, sL, s, _input):
y = 'P_Ext_Markets'
x = s['P_Ext_Markets']
return (y, x)
# 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) /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 # / Psignal_int
return (y, x)
def s4m2(step, sL, s, _input):
y = 'Sell_Log'
x = _input # / Psignal_int
print('s4m2 ',type(_input))
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):
print('s2m3 ')
print(type(s['Sell_Log']))
print(type(s['Z']))
y = 'Price'
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 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,
"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))

443
simulations/scrapbox/config8c.py
View File

@ -1,443 +0,0 @@
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))

35
simulations/validation/config1.py
View File

@ -7,6 +7,7 @@ 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),
@ -14,8 +15,8 @@ seed = {
'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):
@ -31,15 +32,15 @@ def b1m3(step, sL, s):
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]
x = _input['param1']
return (y, x)
def s2m1(step, sL, s, _input):
y = 's2'
x = _input['param2'] #+ [Coef2 x 5]
x = _input['param2']
return (y, x)
def s1m2(step, sL, s, _input):
@ -60,6 +61,7 @@ def s2m3(step, sL, s, _input):
x = _input['param2']
return (y, x)
# Exogenous States
proc_one_coef_A = 0.7
proc_one_coef_B = 1.3
@ -90,6 +92,7 @@ def env_b(x):
# def what_ever(x):
# return x + 1
# Genesis States
genesis_states = {
's1': Decimal(0.0),
@ -99,6 +102,7 @@ genesis_states = {
'timestamp': '2018-10-01 15:16:24'
}
# remove `exo_update_per_ts` to update every ts
exogenous_states = exo_update_per_ts(
{
@ -108,33 +112,20 @@ exogenous_states = exo_update_per_ts(
}
)
# 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,
"b1": b1m1,
"b2": b2m1
},
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
"states": {
"s1": s1m1,
"s2": s2m1
}
@ -161,11 +152,13 @@ mechanisms = {
}
}
sim_config = {
"N": 2,
"T": range(5)
}
configs.append(
Configuration(
sim_config=sim_config,
@ -175,4 +168,4 @@ configs.append(
env_processes=env_processes,
mechanisms=mechanisms
)
)
)

29
simulations/validation/config2.py
View File

@ -15,8 +15,8 @@ seed = {
'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):
@ -27,7 +27,6 @@ def b1m2(step, sL, s):
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):
@ -62,6 +61,7 @@ def s2m3(step, sL, s, _input):
x = _input['param2']
return (y, x)
# Exogenous States
proc_one_coef_A = 0.7
proc_one_coef_B = 1.3
@ -92,6 +92,7 @@ def env_b(x):
# def what_ever(x):
# return x + 1
# Genesis States
genesis_states = {
's1': Decimal(0.0),
@ -101,8 +102,8 @@ genesis_states = {
'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,
@ -111,32 +112,20 @@ exogenous_states = exo_update_per_ts(
}
)
# 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,
"b1": b1m1,
# "b2": b2m1
},
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
"states": {
"s1": s1m1,
# "s2": s2m1
}
@ -163,11 +152,13 @@ mechanisms = {
}
}
sim_config = {
"N": 2,
"T": range(5)
}
configs.append(
Configuration(
sim_config=sim_config,
@ -177,4 +168,4 @@ configs.append(
env_processes=env_processes,
mechanisms=mechanisms
)
)
)