Merge 'staging's' core architecture into 'param-sweep-multi-proc'
This commit is contained in:
Joshua E. Jodesty
commit
e06cb00536
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@ -1,12 +1,17 @@
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.idea
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.ipynb_checkpoints
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.DS_Store
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.idea
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notebooks/.ipynb_checkpoints
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notebooks/multithreading.ipynb
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notebooks
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SimCAD.egg-info
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__pycache__
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Pipfile
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Pipfile.lock
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results
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.mypy_cache
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simulations/scrapbox
|
||||
*.csv
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||||
*.txt
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simulations/.ipynb_checkpoints
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||||
|
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build
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||||
SimCAD.egg-info
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||||
69
README.md
69
README.md
|
|
@ -1,52 +1,53 @@
|
|||
# SimCad
|
||||
**Warning**:
|
||||
**Do not** publish this package / software to **any** software repository **except** one permitted by BlockScience.
|
||||
|
||||
**Dependencies:**
|
||||
**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 \
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||||
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.
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||||
|
||||
Simulations may be run with a range of initial conditions and parameters for states, behaviors, mechanisms, \
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||||
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.
|
||||
|
||||
SimCAD is written in Python 3.
|
||||
|
||||
**1. Install Dependencies:**
|
||||
```bash
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||||
pip install -r requirements.txt
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||||
python3 setup.py sdist bdist_wheel
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||||
pip install dist/SimCAD-0.1-py3-none-any.whl
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||||
```
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||||
|
||||
**Project:**
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||||
**2. Configure Simulation:**
|
||||
|
||||
Example Runs:
|
||||
`/simulations/sim_test.py`
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||||
Intructions:
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||||
`/Simulation.md`
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||||
|
||||
Example Configurations:
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||||
`/simulations/validation/`
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||||
Examples:
|
||||
`/simulations/validation/*`
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||||
|
||||
**User Interface: Simulation Configuration**
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||||
**3. Import SimCAD & Run Simulation:**
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||||
|
||||
Configurations:
|
||||
```bash
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||||
/DiffyQ-SimCAD/ui/config.py
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||||
```
|
||||
Examples: `/simulations/example_run.py` or `/simulations/example_run.ipynb`
|
||||
|
||||
**Build Tool & Package Import:**
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||||
|
||||
Step 1. Build & Install Package locally:
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||||
```bash
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||||
pip install .
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||||
pip install -e .
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||||
```
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||||
* [Package Creation Tutorial](https://python-packaging.readthedocs.io/en/latest/minimal.html)
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||||
|
||||
Step 2. Import Package & Run:
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||||
`/simulations/example_run.py`:
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```python
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import pandas as pd
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from tabulate import tabulate
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||||
# The following imports NEED to be in the exact same order
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# The following imports NEED to be in the exact order
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from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
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from simulations.validation import config1, config2
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from validation import config1, config2
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||||
from SimCAD import configs
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||||
|
||||
# ToDo: pass ExecutionContext with execution method as ExecutionContext input
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||||
|
||||
exec_mode = ExecutionMode()
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||||
|
||||
|
||||
exec_mode = ExecutionMode()
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||||
|
||||
|
||||
print("Simulation Execution 1")
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||||
print()
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||||
first_config = [configs[0]] # from config1
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||||
|
|
@ -54,7 +55,6 @@ single_proc_ctx = ExecutionContext(context=exec_mode.single_proc)
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run1 = Executor(exec_context=single_proc_ctx, configs=first_config)
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||||
run1_raw_result, tensor_field = run1.main()
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||||
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'))
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||||
|
|
@ -76,14 +76,7 @@ for raw_result, tensor_field in run2.main():
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|||
print()
|
||||
```
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||||
|
||||
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**
|
||||
|
|
|
|||
|
|
@ -1 +1,2 @@
|
|||
configs = []
|
||||
name = "SimCAD"
|
||||
configs = []
|
||||
|
|
|
|||
|
|
@ -51,7 +51,6 @@ class Processor:
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|||
self.state_identity = id.state_identity
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||||
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
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||||
|
|
@ -64,11 +63,8 @@ class Processor:
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|||
else:
|
||||
return pd.DataFrame({'empty': []})
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|
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# Maybe Refactor to only use dictionary BUT I used dfs to fill NAs. Perhaps fill
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def generate_config(self, state_dict, mechanisms, exo_proc):
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||||
|
||||
# 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()
|
||||
|
|
@ -100,4 +96,4 @@ 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))
|
||||
|
|
|
|||
|
|
@ -12,7 +12,6 @@ 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)
|
||||
|
|
@ -31,7 +30,6 @@ def state_update(y, x):
|
|||
|
||||
|
||||
def bound_norm_random(rng, low, high):
|
||||
# Add RNG Seed
|
||||
res = rng.normal((high+low)/2,(high-low)/6)
|
||||
if (res<low or res>high):
|
||||
res = bound_norm_random(rng, low, high)
|
||||
|
|
@ -46,7 +44,6 @@ 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)
|
||||
|
|
@ -54,7 +51,6 @@ 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:
|
||||
|
|
@ -63,22 +59,6 @@ def ep_time_step(s, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = t_delta
|
|||
return dt_str
|
||||
|
||||
|
||||
def exo_update_per_ts(ep):
|
||||
@curried
|
||||
def ep_decorator(fs, y, step, sL, s, _input):
|
||||
# print(s)
|
||||
if s['mech_step'] + 1 == 1: # inside f body to reduce performance costs
|
||||
if isinstance(fs, list):
|
||||
pool = ThreadPool(nodes=len(fs))
|
||||
fx = pool.map(lambda f: f(step, sL, s, _input), fs)
|
||||
return groupByKey(fx)
|
||||
else:
|
||||
return fs(step, sL, s, _input)
|
||||
else:
|
||||
return (y, s[y])
|
||||
return {es: ep_decorator(f, es) for es, f in ep.items()}
|
||||
|
||||
|
||||
def mech_sweep_filter(mech_field, mechanisms):
|
||||
mech_dict = dict([(k, v[mech_field]) for k, v in mechanisms.items()])
|
||||
return dict([
|
||||
|
|
@ -129,6 +109,7 @@ def sweep_states(state_type, states, in_config):
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|||
|
||||
return configs
|
||||
|
||||
|
||||
def param_sweep(config, raw_exogenous_states):
|
||||
return flatMap(
|
||||
sweep_states('environmental', config.env_processes),
|
||||
|
|
@ -139,4 +120,22 @@ def param_sweep(config, raw_exogenous_states):
|
|||
sweep_mechs('behaviors', config)
|
||||
)
|
||||
)
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def exo_update_per_ts(ep):
|
||||
@curried
|
||||
def ep_decorator(f, y, step, sL, s, _input):
|
||||
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()}
|
||||
|
||||
|
||||
# def ep_decorator(f, y, step, sL, s, _input):
|
||||
# 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()}
|
||||
|
|
|
|||
|
|
@ -28,8 +28,7 @@ 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):
|
||||
def set_base_value(target_dict, source_dict, key):
|
||||
|
|
@ -45,6 +44,3 @@ def dict_op(f, d1, d2):
|
|||
|
||||
def dict_elemwise_sum():
|
||||
return dict_op(add)
|
||||
|
||||
|
||||
# class BehaviorAggregation:
|
||||
|
|
@ -11,7 +11,6 @@ class ExecutionMode:
|
|||
multi_proc = 'multi_proc'
|
||||
|
||||
|
||||
# ToDo: switch / rename self.name & context ??
|
||||
class ExecutionContext:
|
||||
def __init__(self, context=ExecutionMode.multi_proc):
|
||||
self.name = context
|
||||
|
|
@ -43,9 +42,7 @@ class Executor:
|
|||
self.configs = configs
|
||||
self.main = self.execute
|
||||
|
||||
|
||||
def execute(self):
|
||||
|
||||
config_proc = Processor()
|
||||
create_tensor_field = TensorFieldReport(config_proc).create_tensor_field
|
||||
|
||||
|
|
@ -65,8 +62,6 @@ class Executor:
|
|||
|
||||
config_idx += 1
|
||||
|
||||
# Dimensions: N x r x mechs
|
||||
|
||||
if self.exec_context == ExecutionMode.single_proc:
|
||||
tensor_field = create_tensor_field(mechanisms.pop(), eps.pop())
|
||||
result = self.exec_method(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)
|
||||
|
|
@ -77,4 +72,5 @@ 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
|
||||
|
|
|
|||
|
|
@ -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]
|
||||
|
||||
|
|
@ -27,12 +23,11 @@ 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]
|
||||
|
||||
|
|
@ -40,10 +35,13 @@ class Executor:
|
|||
|
||||
# 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 f in state_funcs:
|
||||
print(f(1,2,3,4))
|
||||
last_in_copy = [self.behavior_update_exception(f(m_step, sL, last_in_obj, _input)) for f in state_funcs]
|
||||
print(last_in_copy)
|
||||
exit()
|
||||
#
|
||||
# for f in state_funcs:
|
||||
# print(f(1,2,3,4))
|
||||
# exit()
|
||||
|
||||
for k in last_in_obj:
|
||||
if k not in last_in_copy:
|
||||
|
|
@ -51,8 +49,7 @@ 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)
|
||||
|
|
@ -63,8 +60,6 @@ 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]
|
||||
|
|
@ -91,12 +86,13 @@ class Executor:
|
|||
|
||||
return simulation_list
|
||||
|
||||
|
||||
# ToDo: Muiltithreaded Runs
|
||||
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
|
||||
|
|
@ -104,4 +100,4 @@ class Executor:
|
|||
pipe_run += [first_timestep_per_run] + tail
|
||||
del states_list_copy
|
||||
|
||||
return pipe_run
|
||||
return pipe_run
|
||||
|
|
|
|||
|
|
@ -54,6 +54,7 @@ def key_filter(l, keyname):
|
|||
return [v[keyname] for k, v in l.items()]
|
||||
|
||||
|
||||
|
||||
def groupByKey(l):
|
||||
d = defaultdict(list)
|
||||
for key, value in l:
|
||||
|
|
@ -70,4 +71,4 @@ def rename(new_name, f):
|
|||
# def decorator(f):
|
||||
# f.__name__ = newname
|
||||
# return f
|
||||
# return decorator
|
||||
# return decorator
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -0,0 +1,150 @@
|
|||
SOFTWARE LICENSE AGREEMENT
|
||||
|
||||
This Software License Agreement (the “Agreement”) is entered into as of December __ 2018, (the “Effective Date”) between
|
||||
Dapper Labs, Inc., a Canadian corporation having its principal place of business at 980-350 Howe Street,
|
||||
Vancouver, BC V6Z 1N9 (“DLI”) and BlockScience, Inc., a California corporation with an address at 471 McAuley Street,
|
||||
Oakland, CA 94609 (“BlockScience”). This Agreement includes the attached Exhibit A.
|
||||
|
||||
WHEREAS, DLI and BlockScience are parties to that certain Professional Services Agreement dated March 23, 2018 (the
|
||||
“PSA”), pursuant to which BlockScience performed and is currently performing professional services and other development
|
||||
work for DLI;
|
||||
|
||||
WHEREAS, as part of BlockScience’s performance under the PSA, BlockScience developed certain “behaviour archetypes” and
|
||||
“configuration of the Null Model”, which the parties agree are “Work Product” under the PSA;
|
||||
|
||||
WHEREAS, the parties agree that BlockScience’s proprietary SimCAD software tool is considered “Contractor Technology”
|
||||
under the PSA; and
|
||||
|
||||
WHEREAS, the parties wish to enter into this Agreement to clarify DLI’s rights to use the SimCAD software tool on a
|
||||
going-forward basis.
|
||||
|
||||
NOW, THEREFORE, for good and valuable consideration, the receipt and sufficiency of which is hereby acknowledged, DLI
|
||||
and BlockScience agree as follows:
|
||||
|
||||
1. DEFINITIONS
|
||||
|
||||
(a) “Affiliate” means any entity that, directly or indirectly through one or more intermediaries, controls, is
|
||||
controlled by, or is under common control with, DLI.
|
||||
|
||||
(b) “Documentation” means any manuals, documentation and other supporting materials related to the Software.
|
||||
Documentation is considered part of the related Software.
|
||||
|
||||
(c) “Intellectual Property Rights” means patent rights (including patent applications and disclosures), copyrights,
|
||||
trade marks, trade secrets, know-how and any other intellectual property rights recognized in any country or
|
||||
jurisdiction in the world.
|
||||
|
||||
(d) “Software” means the object and source code versions of BlockScience’s proprietary SimCAD software product more
|
||||
fully described in Exhibit A. Software includes the applicable Documentation, as well as any Updates.
|
||||
|
||||
(e) “Update” means any bug fix, error correction, patch, modification, enhancement, update, upgrade, replacement,
|
||||
successor product, new version, new release, or derivative work of or to the Software.
|
||||
|
||||
(f) “Zeus” means the decentralized synchronous computational network developed by DLI, as such name or reference may be
|
||||
changed from time to time at DLI’s sole discretion.
|
||||
|
||||
2. SOFTWARE LICENSE
|
||||
|
||||
(a) License Grant. BlockScience hereby grants to DLI and its Affiliates a worldwide, non-exclusive, royalty-free,
|
||||
irrevocable, perpetual license to (i) download, install, use, execute, access, copy, perform, and modify, the Software
|
||||
in connection with the Zeus project; (ii) distribute and display the Software internally amongst DLI and its Affiliates,
|
||||
its and their employees, contractors, and agents, subject to the use of reasonable efforts to maintain the confidential
|
||||
status of the non-public aspects of the Software display; and (iii) create derivative works of the Software in
|
||||
connection with the Zeus project, provided that any such derivative works may only be used in connection with the Zeus
|
||||
project. For the sake of clarity, nothing in this Agreement (including, without limitation, this Section 2) will create
|
||||
any liability to DLI for or restrict DLI’s ability to externally distribute python scripts containing the “input”
|
||||
configuration files specific to the Zeus project, as well as the notebooks with the resulting “output” data from the
|
||||
Software, all of which may be distributed, displayed, and shared publicly at DLI’s discretion.
|
||||
|
||||
(b) Ownership; Limited Rights. As between the parties, BlockScience owns and retains all right, title and interest in
|
||||
and to the Software, and all Intellectual Property Rights therein. DLI’s rights in the Software are limited to those
|
||||
expressly granted in Section 2(a) and in the PSA. BlockScience reserves all rights and licenses in the Software not
|
||||
expressly granted to DLI herein and in the PSA.
|
||||
|
||||
(c) Delivery. BlockScience will deliver a copy of the Software and Documentation to DLI on the Effective Date. The
|
||||
delivery may be made in electronic form, or via hardcopy medium (e.g., a CD).
|
||||
|
||||
(d) Updates. BlockScience will deliver Updates to DLI as and when such Updates become available. The obligation to
|
||||
deliver Updates will continue for as long as the PSA remains in force; upon termination or expiration of the PSA,
|
||||
BlockScience’s obligation to provide Updates will automatically terminate.
|
||||
|
||||
(e) Support. BlockScience will provide reasonable technical support for the Software, to help DLI manage any support
|
||||
issues that arise. The obligation to provide support will continue for as long as the PSA remains in force; upon
|
||||
termination or expiration of the PSA, BlockScience’s obligation to provide support will automatically terminate.
|
||||
|
||||
3. NO FEES.
|
||||
|
||||
There are no fees owed by DLI for the license granted or the Updates or support provided by BlockScience
|
||||
pursuant to this Agreement. Each party will bear its own costs and expenses arising out of or relating to its
|
||||
obligations, efforts and performance under this Agreement.
|
||||
|
||||
4. LIMITED WARRANTY; DISCLAIMER
|
||||
|
||||
(a) Limited Warranty. BlockScience represents and warrants as follows: (i) that it has the right to enter into this
|
||||
Agreement, and to perform its obligations hereunder, without violating the terms of any other agreement; (ii) that the
|
||||
Software, and any Updates, do not and will not infringe, violate, or misappropriate the Intellectual Property Rights of
|
||||
any third party; (iii) that the Software and any Updates do not and will not contain any virus, malware, spyware, trojan
|
||||
horse, or other malicious code; and (iv) that the Software and each Update will substantially conform to its
|
||||
Documentation.
|
||||
|
||||
(b) Disclaimer. EXCEPT AS OTHERWISE SET FORTH IN THIS AGREEMENT, BLOCKSCIENCE DISCLAIMS ALL OTHER WARRANTIES, EXPRESS OR
|
||||
IMPLIED, RELATED TO THE SOFTWARE, INCLUDING ANY IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR
|
||||
PURPOSE.
|
||||
|
||||
5. TERM & TERMINATION
|
||||
|
||||
(a) Term. This Agreement begins on the Effective Date, and will continue in effect until one of us terminates it in
|
||||
accordance with Section 5(b).
|
||||
|
||||
(b) Termination for Breach. Either party may terminate this Agreement if the other party breaches any material term or
|
||||
condition of this Agreement, and the breaching party fails to cure the breach within thirty (30) days of receiving
|
||||
written notice of it.
|
||||
|
||||
(c) Survival. Sections 2 through 7 will survive termination or expiration of this Agreement.
|
||||
|
||||
6. INDEMNIFICATION.
|
||||
|
||||
BlockScience will defend, indemnify, and hold DLI harmless from and against any claim, damage, loss,
|
||||
liability, expense and cost (including, without limitation, reasonable attorneys’ fees) incurred by or brought against
|
||||
DLI arising out of or related to: (i) any claim that the Software infringes or misappropriates the Intellectual Property
|
||||
Rights of that third party; or (ii) BlockScience’s breach of its limited warranties in Section 4(a).
|
||||
|
||||
7. GENERAL TERMS
|
||||
|
||||
(a) Entire Agreement; Waiver. This Agreement is the entire understanding of the parties, and supersedes any and all
|
||||
prior agreements or understandings between the parties as to its subject matter. It may be amended or modified, or
|
||||
provisions waived, only in a writing signed by both parties. The waiver of a breach of any provision of this Agreement
|
||||
will not operate or be interpreted as a waiver of any other or subsequent breach.
|
||||
|
||||
(b) Acknowledgement. BlockScience acknowledges and agrees that the “behaviour archetypes” and “configuration of the Null
|
||||
Model” referenced in the PSA are considered “Work Product” under the PSA.
|
||||
|
||||
(c) Governing Law. This Agreement will be construed, interpreted and applies in accordance with the internal laws of
|
||||
British Columbia, Canada (excluding its body of law controlling conflicts of law). Any legal action or proceeding
|
||||
arising under or related to this Agreement will be brought exclusively in the federal or provincial courts located in
|
||||
Vancouver, British Columbia, and the parties irrevocably consent to personal jurisdiction and venue there.
|
||||
|
||||
(d) Severability. If any provision of this Agreement is held to be invalid or unenforceable for any reason, that
|
||||
provision will be enforced to the maximum extent permitted by law, and the remaining provisions will continue in full
|
||||
force and effect.
|
||||
|
||||
(e) Miscellaneous. This Agreement may be executed in one or more counterparts, with the same effect as if the parties
|
||||
had signed the same document. Each counterpart so executed will be deemed to be an original, and all such counterparts
|
||||
will be construed together and will constitute one Agreement. The prevailing party in any action or legal proceeding
|
||||
arising out of this Agreement will be entitled to recover from the other party all reasonable costs and expenses
|
||||
incurred in connection with such action or proceeding, including reasonable attorneys’ fees and court costs. In the
|
||||
event of a direct conflict between the terms of this Agreement and the PSA with respect to the DLI’s rights in and to
|
||||
the Software, the terms of this Agreement will control.
|
||||
|
||||
EXHIBIT A
|
||||
|
||||
SOFTWARE
|
||||
|
||||
Software Name: SimCAD tool
|
||||
|
||||
Software Description: SimCAD is a Monte-Carlo 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. Simulations can 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.
|
||||
|
|
@ -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.
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,42 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"assert pd.__version__ == '0.23.4'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,55 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'ui'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-1-a6e895c51fc0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\engine\\run.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mui\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstate_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmechanisms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexogenous_states\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msim_config\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigProcessor\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgenerate_config\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmechanismExecutor\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0msimulation\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mflatten\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'ui'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from engine import run\n",
|
||||
"run.main()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
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|
|
@ -1,482 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## SimCAD Application Notebook\n",
|
||||
"## Experiment Type 2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Name of Config File or System Description\n",
|
||||
"#### 20 MonteCarlo Runs \n",
|
||||
"#### Behaviors: EMHers, Herders, HODLers, EIUers, and Human EIUers"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Simulation Run 1\n",
|
||||
"single_proc: [<SimCAD.Configuration object at 0x000001EA1AAA6630>]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "TypeError",
|
||||
"evalue": "unsupported operand type(s) for *: 'float' and 'decimal.Decimal'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-1-0d9ea96d7f5c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0msingle_proc_ctx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExecutionContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexec_mode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msingle_proc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[0mrun1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExecutor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msingle_proc_ctx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msingle_config\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0mrun1_raw_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrun1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun1_raw_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;31m# print(tabulate(result, headers='keys', tablefmt='psql'))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\SimCAD\\engine\\__init__.py\u001b[0m in \u001b[0;36mexecute\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_context\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mExecutionMode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msingle_proc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 73\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0msingle_proc_exec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimulation_execs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstates_lists\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfigs_structs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_processes_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 74\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_context\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mExecutionMode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmulti_proc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfigs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\SimCAD\\engine\\__init__.py\u001b[0m in \u001b[0;36msingle_proc_exec\u001b[1;34m(simulation_execs, states_lists, configs_structs, env_processes_list, Ts, Ns)\u001b[0m\n\u001b[0;32m 67\u001b[0m \u001b[0msimulation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstates_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[1;31m# print(states_list)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 69\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msimulation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstates_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 70\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\SimCAD\\engine\\simulation.py\u001b[0m in \u001b[0;36msimulation\u001b[1;34m(self, states_list, configs, env_processes, time_seq, runs)\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[1;31m# print(\"Run: \"+str(run))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[0mstates_list_copy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstates_list\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# WHY ???\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m \u001b[0mhead\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mtail\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstates_list_copy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime_seq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 103\u001b[0m \u001b[0mgenesis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhead\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[0mgenesis\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'mech_step'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgenesis\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'time_step'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgenesis\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'run'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\SimCAD\\engine\\simulation.py\u001b[0m in \u001b[0;36mpipe\u001b[1;34m(self, states_list, configs, env_processes, time_seq, run)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtime_step\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtime_seq\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 87\u001b[0m \u001b[1;31m# print(run)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m \u001b[0mpipe_run\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblock_gen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msimulation_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtime_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 89\u001b[0m \u001b[0m_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0mpipe_run\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpipe_run\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[0msimulation_list\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpipe_run\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\SimCAD\\engine\\simulation.py\u001b[0m in \u001b[0;36mblock_gen\u001b[1;34m(self, states_list, configs, env_processes, t_step, run)\u001b[0m\n\u001b[0;32m 72\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mconfig\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mconfigs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 73\u001b[0m \u001b[0ms_conf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb_conf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m \u001b[0mstates_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmech_step\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstates_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms_conf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb_conf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0menv_processes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 75\u001b[0m \u001b[0mm_step\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\SimCAD\\engine\\simulation.py\u001b[0m in \u001b[0;36mmech_step\u001b[1;34m(self, m_step, sL, state_funcs, behavior_funcs, env_processes, t_step, run)\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;31m# *** add env_proc value here as wrapper function ***\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mlast_in_copy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexception_handler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_in_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_input\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mstate_funcs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlast_in_obj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\SimCAD\\engine\\simulation.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;31m# *** add env_proc value here as wrapper function ***\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mlast_in_copy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexception_handler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_in_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_input\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mstate_funcs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlast_in_obj\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\SimCAD\\engine\\simulation.py\u001b[0m in \u001b[0;36mexception_handler\u001b[1;34m(self, f, m_step, sL, last_mut_obj, _input)\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mexception_handler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mm_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_mut_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 29\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm_step\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlast_mut_obj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_input\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 31\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Exception\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;32m~\\staging\\21f1155\\sandboxUX\\config6b.py\u001b[0m in \u001b[0;36ms2m3\u001b[1;34m(step, sL, s, _input)\u001b[0m\n\u001b[0;32m 179\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'Price'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 180\u001b[0m \u001b[1;31m#var1 = Decimal.from_float(s['Buy_Log'])\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 181\u001b[1;33m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Price'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Buy_Log'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Z'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m0.1\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Sell_Log'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Z'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Buy_Log'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Sell_Log'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 182\u001b[0m \u001b[1;31m#+ np.divide(s['Buy_Log'],s['Z']) - np.divide() # / Psignal_int\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from tabulate import tabulate\n",
|
||||
"\n",
|
||||
"from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
|
||||
"from sandboxUX import config6b #, config2\n",
|
||||
"from SimCAD import configs\n",
|
||||
"\n",
|
||||
"# ToDo: pass ExecutionContext with execution method as ExecutionContext input\n",
|
||||
"\n",
|
||||
"exec_mode = ExecutionMode()\n",
|
||||
"\n",
|
||||
"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",
|
||||
"df = pd.DataFrame(run1_raw_result)\n",
|
||||
"# print(tabulate(result, headers='keys', tablefmt='psql'))\n",
|
||||
"# print()\n",
|
||||
"\n",
|
||||
"# 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": [
|
||||
"#df = pd.DataFrame(run1_raw_result)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Standard Library Imports\n",
|
||||
"#import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib as mpl\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import seaborn as sns\n",
|
||||
"#from tabulate import tabulate\n",
|
||||
"\n",
|
||||
"sns.set_style('whitegrid')\n",
|
||||
"\n",
|
||||
"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# SimCAD Specific\n",
|
||||
"# from SimCAD.engine import ExecutionMode, ExecutionContext, Executor\n",
|
||||
"# from sandboxUX import config1 , config2\n",
|
||||
"# from SimCAD import configs"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Convert data type of output to float. MPL works OK with strings, seaborn does not\n",
|
||||
"names = df.keys()[:-3] # [:-3] only affects state variables\n",
|
||||
"for n in names:\n",
|
||||
" df[n]=df[n].apply(float)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Check\n",
|
||||
"df.head(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.iloc[2995:3005]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.tail(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.corr()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aggregate_dimension = 'time_step'\n",
|
||||
"\n",
|
||||
"mean_df = df.groupby(aggregate_dimension).mean().reset_index()\n",
|
||||
"median_df = df.groupby(aggregate_dimension).median().reset_index()\n",
|
||||
"std_df = df.groupby(aggregate_dimension).std().reset_index()\n",
|
||||
"min_df = df.groupby(aggregate_dimension).min().reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mean_df.head(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"mean_df.tail(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def dist_plot(x, y,lx=False,ly=False, suppMin=False): \n",
|
||||
" plt.figure(figsize=(12,8))\n",
|
||||
" if not(suppMin):\n",
|
||||
" plt.plot(mean_df[x].values, mean_df[y].values,\n",
|
||||
" mean_df[x].values,median_df[y].values,\n",
|
||||
" mean_df[x].values,mean_df[y].values+std_df[y].values,\n",
|
||||
" mean_df[x].values,min_df[y].values)\n",
|
||||
" plt.legend(['mean', 'median', 'mean+ 1*std', 'min'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
|
||||
" \n",
|
||||
" else:\n",
|
||||
" plt.plot(mean_df[x].values, mean_df[y].values,\n",
|
||||
" mean_df[x].values,median_df[y].values,\n",
|
||||
" mean_df[x].values,mean_df[y].values+std_df[y].values,\n",
|
||||
" mean_df[x].values,mean_df[y].values-std_df[y].values)\n",
|
||||
" plt.legend(['mean', 'median', 'mean+ 1*std', 'mean - 1*std'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
|
||||
"\n",
|
||||
" plt.xlabel(x)\n",
|
||||
" plt.ylabel(y)\n",
|
||||
" if lx:\n",
|
||||
" plt.xscale('log')\n",
|
||||
" \n",
|
||||
" if ly:\n",
|
||||
" plt.yscale('log')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dist_plot('time_step', 'P_Ext_Markets',suppMin=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dist_plot('time_step', 'Price',suppMin=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(12,8))\n",
|
||||
"plt.plot(mean_df['time_step'][1:],mean_df['Price'][1:]) #, df['Zeus_LT']], figsize=(15,10)) #, logy=True)\n",
|
||||
"plt.plot(mean_df['time_step'][1:],(1/250)*mean_df['P_Ext_Markets'][1:])\n",
|
||||
"#plt.plot(df['time_step'],df['Zeus_LT'])\n",
|
||||
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(np.std(mean_df))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(12,8))\n",
|
||||
"plt.plot(mean_df['time_step'][1:],mean_df['Buy_Log'][1:]) #, df['Zeus_LT']], figsize=(15,10)) #, logy=True)\n",
|
||||
"plt.plot(mean_df['time_step'][1:],mean_df['Sell_Log'][1:])\n",
|
||||
"#plt.plot(df['time_step'],df['Zeus_LT'])\n",
|
||||
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"buy_delta = mean_df['Buy_Log'].diff()\n",
|
||||
"sell_delta = mean_df['Sell_Log'].diff()\n",
|
||||
"ext_delta = mean_df['P_Ext_Markets'].diff()\n",
|
||||
"# df_delta['Buy_Log'] = buy_delta\n",
|
||||
"# df_delta['Sell_Log'] = sell_delta\n",
|
||||
"# df_delta = df_delta.append(ext_delta)\n",
|
||||
"# df_delta.head()\n",
|
||||
"sell_delta.head(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(12,8))\n",
|
||||
"plt.plot(mean_df['time_step'][1:],buy_delta[1:]) #, df['Zeus_LT']], figsize=(15,10)) #, logy=True)\n",
|
||||
"plt.plot(mean_df['time_step'][1:],sell_delta[1:])\n",
|
||||
"plt.plot(mean_df['time_step'][1:],ext_delta[1:])\n",
|
||||
"plt.ylim(-400,400)\n",
|
||||
"#plt.plot(df['time_step'],df['Zeus_LT'])\n",
|
||||
"plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sns.pairplot(mean_df)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(12,8))\n",
|
||||
"plt.plot(mean_df['time_step'],mean_df['Z']/mean_df['P_Ext_Markets'])\n",
|
||||
"plt.title('Z per External Stock Market Price')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plt.figure(figsize=(12,8))\n",
|
||||
"# plt.plot(df['time_step'],(df['TDR_Int']-df['TDR_Ext'])/df['TDR_Ext'])\n",
|
||||
"# plt.title('Availability of TDR arbitrage opportunity')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# plt.figure(figsize=(12,8))\n",
|
||||
"# plt.plot(df['time_step'],(df['Zeus_LT']/df['Zeus_ST']-1))\n",
|
||||
"# plt.title('Availability of LT vs ST arbitrage opportunity')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# vol_df = df.rolling(window = 21).mean()\n",
|
||||
"vol_df = pd.DataFrame()\n",
|
||||
"rolling_days = 63 # days = number * mechanisms\n",
|
||||
"for n in names:\n",
|
||||
" vol_df[n] = mean_df[n].rolling(rolling_days).mean().shift()\n",
|
||||
" \n",
|
||||
"vol_df = vol_df.dropna() #(vol_df.iloc[0:rolling_days])\n",
|
||||
"# vol_df[n].iloc[:rolling_days], axis=1)\n",
|
||||
"vol_df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(12,8))\n",
|
||||
"plt.plot(vol_df['Z'])\n",
|
||||
"plt.title('Rolling Average of Z')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(12,8))\n",
|
||||
"plt.plot(vol_df['P_Ext_Markets'])\n",
|
||||
"plt.title('Rolling Average of External Stock Market Price')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"plt.figure(figsize=(12,8))\n",
|
||||
"plt.plot(vol_df['Price'])\n",
|
||||
"plt.plot(vol_df['P_Ext_Markets']/250)\n",
|
||||
"plt.legend()\n",
|
||||
"plt.title('Rolling Average of Zeus Price')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"Price\"].min()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"Price\"].max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,202 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Simulation Run 1\n",
|
||||
"\n",
|
||||
"single_proc: [<SimCAD.Configuration object at 0x10fc1a8d0>]\n",
|
||||
"+----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------+\n",
|
||||
"| | mech_step | run | s1 | s2 | s3 | s4 | time_step | timestamp |\n",
|
||||
"|----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------|\n",
|
||||
"| 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 2018-10-01 15:16:24 |\n",
|
||||
"| 1 | 1 | 1 | 1 | 4 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 2 | 2 | 1 | ab | 6 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 3 | 3 | 1 | ['c', 'd'] | [ 30 300] | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 4 | 1 | 1 | 1 | 4 | 9.94373 | 10.4365 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 5 | 2 | 1 | ab | 6 | 9.94373 | 10.4365 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 6 | 3 | 1 | ['c', 'd'] | [ 30 300] | 9.94373 | 10.4365 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 7 | 1 | 1 | 1 | 4 | 7.81956 | 10.5372 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 8 | 2 | 1 | ab | 6 | 7.81956 | 10.5372 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 9 | 3 | 1 | ['c', 'd'] | [ 30 300] | 7.81956 | 10.5372 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 10 | 1 | 1 | 1 | 4 | 9.10218 | 8.57362 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 11 | 2 | 1 | ab | 6 | 9.10218 | 8.57362 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 12 | 3 | 1 | ['c', 'd'] | [ 30 300] | 9.10218 | 8.57362 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 13 | 1 | 1 | 1 | 4 | 7.46976 | 8.33579 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 14 | 2 | 1 | ab | 6 | 7.46976 | 8.33579 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 15 | 3 | 1 | ['c', 'd'] | [ 30 300] | 7.46976 | 8.33579 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 16 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 2018-10-01 15:16:24 |\n",
|
||||
"| 17 | 1 | 2 | 1 | 4 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 18 | 2 | 2 | ab | 6 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 19 | 3 | 2 | ['c', 'd'] | [ 30 300] | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 20 | 1 | 2 | 1 | 4 | 10.5029 | 9.91726 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 21 | 2 | 2 | ab | 6 | 10.5029 | 9.91726 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 22 | 3 | 2 | ['c', 'd'] | [ 30 300] | 10.5029 | 9.91726 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 23 | 1 | 2 | 1 | 4 | 9.19497 | 9.29545 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 24 | 2 | 2 | ab | 6 | 9.19497 | 9.29545 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 25 | 3 | 2 | ['c', 'd'] | [ 30 300] | 9.19497 | 9.29545 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 26 | 1 | 2 | 1 | 4 | 8.22219 | 9.25471 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 27 | 2 | 2 | ab | 6 | 8.22219 | 9.25471 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 28 | 3 | 2 | ['c', 'd'] | [ 30 300] | 8.22219 | 9.25471 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 29 | 1 | 2 | 1 | 4 | 7.47478 | 8.81306 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 30 | 2 | 2 | ab | 6 | 7.47478 | 8.81306 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 31 | 3 | 2 | ['c', 'd'] | [ 30 300] | 7.47478 | 8.81306 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"+----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------+\n",
|
||||
"\n",
|
||||
"Simulation Run 2: Pairwise Execution\n",
|
||||
"\n",
|
||||
"multi_proc: [<SimCAD.Configuration object at 0x10fc1a8d0>, <SimCAD.Configuration object at 0x10fc1aeb8>]\n",
|
||||
"+----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----+\n",
|
||||
"| | b1 | b2 | s1 | s2 | es1 | es2 | es3 | m |\n",
|
||||
"|----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----|\n",
|
||||
"| 0 | <function b1m1 at 0x10faedd08> | <function b2m1 at 0x10fc230d0> | <function s1m1 at 0x10fc23378> | <function s2m1 at 0x10fc23400> | <function curried.<locals>._curried at 0x10fc23ae8> | <function curried.<locals>._curried at 0x10fc23b70> | <function curried.<locals>._curried at 0x10fc23bf8> | 1 |\n",
|
||||
"| 1 | <function b1m2 at 0x10fc23158> | <function b2m2 at 0x10fc231e0> | <function s1m2 at 0x10fc23488> | <function s2m2 at 0x10fc23510> | <function curried.<locals>._curried at 0x10fc23ae8> | <function curried.<locals>._curried at 0x10fc23b70> | <function curried.<locals>._curried at 0x10fc23bf8> | 2 |\n",
|
||||
"| 2 | <function b1m3 at 0x10fc23268> | <function b2m3 at 0x10fc232f0> | <function s1m3 at 0x10fc23598> | <function s2m3 at 0x10fc23620> | <function curried.<locals>._curried at 0x10fc23ae8> | <function curried.<locals>._curried at 0x10fc23b70> | <function curried.<locals>._curried at 0x10fc23bf8> | 3 |\n",
|
||||
"+----+--------------------------------+--------------------------------+--------------------------------+--------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----+\n",
|
||||
"+----+--------------------------------+--------------------------------+--------------------------------+------------------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----+\n",
|
||||
"| | b1 | b2 | s1 | s2 | es1 | es2 | es3 | m |\n",
|
||||
"|----+--------------------------------+--------------------------------+--------------------------------+------------------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----|\n",
|
||||
"| 0 | <function b1m1 at 0x10fc23d08> | <function b2m1 at 0x10fc23d90> | <function s1m1 at 0x10fc290d0> | <function state_identity.<locals>.<lambda> at 0x10d4f6598> | <function curried.<locals>._curried at 0x10fc29840> | <function curried.<locals>._curried at 0x10fc298c8> | <function curried.<locals>._curried at 0x10fc29950> | 1 |\n",
|
||||
"| 1 | <function b1m2 at 0x10fc23e18> | <function b2m2 at 0x10fc23ea0> | <function s1m2 at 0x10fc291e0> | <function state_identity.<locals>.<lambda> at 0x10d4f6598> | <function curried.<locals>._curried at 0x10fc29840> | <function curried.<locals>._curried at 0x10fc298c8> | <function curried.<locals>._curried at 0x10fc29950> | 2 |\n",
|
||||
"| 2 | <function b1m3 at 0x10fc23f28> | <function b2m3 at 0x10fc29048> | <function s1m3 at 0x10fc292f0> | <function s2m3 at 0x10fc29378> | <function curried.<locals>._curried at 0x10fc29840> | <function curried.<locals>._curried at 0x10fc298c8> | <function curried.<locals>._curried at 0x10fc29950> | 3 |\n",
|
||||
"+----+--------------------------------+--------------------------------+--------------------------------+------------------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----------------------------------------------------+-----+\n",
|
||||
"+----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------+\n",
|
||||
"| | mech_step | run | s1 | s2 | s3 | s4 | time_step | timestamp |\n",
|
||||
"|----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------|\n",
|
||||
"| 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 2018-10-01 15:16:24 |\n",
|
||||
"| 1 | 1 | 1 | 1 | 4 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 2 | 2 | 1 | ab | 6 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 3 | 3 | 1 | ['c', 'd'] | [ 30 300] | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 4 | 1 | 1 | 1 | 4 | 12.2922 | 10.8846 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 5 | 2 | 1 | ab | 6 | 12.2922 | 10.8846 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 6 | 3 | 1 | ['c', 'd'] | [ 30 300] | 12.2922 | 10.8846 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 7 | 1 | 1 | 1 | 4 | 12.3433 | 11.8439 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 8 | 2 | 1 | ab | 6 | 12.3433 | 11.8439 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 9 | 3 | 1 | ['c', 'd'] | [ 30 300] | 12.3433 | 11.8439 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 10 | 1 | 1 | 1 | 4 | 10.9634 | 13.8687 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 11 | 2 | 1 | ab | 6 | 10.9634 | 13.8687 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 12 | 3 | 1 | ['c', 'd'] | [ 30 300] | 10.9634 | 13.8687 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 13 | 1 | 1 | 1 | 4 | 11.5544 | 13.9381 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 14 | 2 | 1 | ab | 6 | 11.5544 | 13.9381 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 15 | 3 | 1 | ['c', 'd'] | [ 30 300] | 11.5544 | 13.9381 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 16 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 2018-10-01 15:16:24 |\n",
|
||||
"| 17 | 1 | 2 | 1 | 4 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 18 | 2 | 2 | ab | 6 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 19 | 3 | 2 | ['c', 'd'] | [ 30 300] | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 20 | 1 | 2 | 1 | 4 | 9.98087 | 9.45464 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 21 | 2 | 2 | ab | 6 | 9.98087 | 9.45464 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 22 | 3 | 2 | ['c', 'd'] | [ 30 300] | 9.98087 | 9.45464 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 23 | 1 | 2 | 1 | 4 | 11.1536 | 7.9925 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 24 | 2 | 2 | ab | 6 | 11.1536 | 7.9925 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 25 | 3 | 2 | ['c', 'd'] | [ 30 300] | 11.1536 | 7.9925 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 26 | 1 | 2 | 1 | 4 | 10.3195 | 8.77766 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 27 | 2 | 2 | ab | 6 | 10.3195 | 8.77766 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 28 | 3 | 2 | ['c', 'd'] | [ 30 300] | 10.3195 | 8.77766 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 29 | 1 | 2 | 1 | 4 | 10.3288 | 7.81118 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 30 | 2 | 2 | ab | 6 | 10.3288 | 7.81118 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 31 | 3 | 2 | ['c', 'd'] | [ 30 300] | 10.3288 | 7.81118 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"+----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------+\n",
|
||||
"+----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------+\n",
|
||||
"| | mech_step | run | s1 | s2 | s3 | s4 | time_step | timestamp |\n",
|
||||
"|----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------|\n",
|
||||
"| 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 2018-10-01 15:16:24 |\n",
|
||||
"| 1 | 1 | 1 | 1 | 0 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 2 | 2 | 1 | ab | 0 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 3 | 3 | 1 | ['c', 'd'] | [ 30 300] | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 4 | 1 | 1 | 1 | [ 30 300] | 9.94373 | 10.4365 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 5 | 2 | 1 | ab | [ 30 300] | 9.94373 | 10.4365 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 6 | 3 | 1 | ['c', 'd'] | [ 30 300] | 9.94373 | 10.4365 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 7 | 1 | 1 | 1 | [ 30 300] | 7.81956 | 10.5372 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 8 | 2 | 1 | ab | [ 30 300] | 7.81956 | 10.5372 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 9 | 3 | 1 | ['c', 'd'] | [ 30 300] | 7.81956 | 10.5372 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 10 | 1 | 1 | 1 | [ 30 300] | 9.10218 | 8.57362 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 11 | 2 | 1 | ab | [ 30 300] | 9.10218 | 8.57362 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 12 | 3 | 1 | ['c', 'd'] | [ 30 300] | 9.10218 | 8.57362 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 13 | 1 | 1 | 1 | [ 30 300] | 7.46976 | 8.33579 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 14 | 2 | 1 | ab | [ 30 300] | 7.46976 | 8.33579 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 15 | 3 | 1 | ['c', 'd'] | [ 30 300] | 7.46976 | 8.33579 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 16 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 2018-10-01 15:16:24 |\n",
|
||||
"| 17 | 1 | 2 | 1 | 0 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 18 | 2 | 2 | ab | 0 | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 19 | 3 | 2 | ['c', 'd'] | [ 30 300] | 10 | 10 | 1 | 2018-10-01 15:16:25 |\n",
|
||||
"| 20 | 1 | 2 | 1 | [ 30 300] | 10.5029 | 9.91726 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 21 | 2 | 2 | ab | [ 30 300] | 10.5029 | 9.91726 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 22 | 3 | 2 | ['c', 'd'] | [ 30 300] | 10.5029 | 9.91726 | 2 | 2018-10-01 15:16:26 |\n",
|
||||
"| 23 | 1 | 2 | 1 | [ 30 300] | 9.19497 | 9.29545 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 24 | 2 | 2 | ab | [ 30 300] | 9.19497 | 9.29545 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 25 | 3 | 2 | ['c', 'd'] | [ 30 300] | 9.19497 | 9.29545 | 3 | 2018-10-01 15:16:27 |\n",
|
||||
"| 26 | 1 | 2 | 1 | [ 30 300] | 8.22219 | 9.25471 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 27 | 2 | 2 | ab | [ 30 300] | 8.22219 | 9.25471 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 28 | 3 | 2 | ['c', 'd'] | [ 30 300] | 8.22219 | 9.25471 | 4 | 2018-10-01 15:16:28 |\n",
|
||||
"| 29 | 1 | 2 | 1 | [ 30 300] | 7.47478 | 8.81306 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 30 | 2 | 2 | ab | [ 30 300] | 7.47478 | 8.81306 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"| 31 | 3 | 2 | ['c', 'd'] | [ 30 300] | 7.47478 | 8.81306 | 5 | 2018-10-01 15:16:29 |\n",
|
||||
"+----+-------------+-------+------------+-----------+----------+----------+-------------+---------------------+\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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()\n",
|
||||
"\n",
|
||||
"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",
|
||||
"print(tabulate(result, headers='keys', tablefmt='psql'))\n",
|
||||
"print()\n",
|
||||
"\n",
|
||||
"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()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
wheel
|
||||
pathos
|
||||
pipenv
|
||||
fn
|
||||
tabulate
|
||||
22
setup.py
22
setup.py
|
|
@ -1,11 +1,23 @@
|
|||
from setuptools import setup
|
||||
from setuptools import setup, find_packages
|
||||
|
||||
long_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."
|
||||
|
||||
setup(name='SimCAD',
|
||||
version='0.1',
|
||||
description='Sim-Cad Enigne',
|
||||
description="SimCAD: a differential games based simulation software package for research, validation, and \
|
||||
Computer Aided Design of economic systems",
|
||||
long_description = long_description,
|
||||
url='https://github.com/BlockScience/DiffyQ-SimCAD',
|
||||
author='Joshua E. Jodesty',
|
||||
author_email='joshua@block.science',
|
||||
# license='?????',
|
||||
packages=['SimCAD'],
|
||||
zip_safe=False)
|
||||
# license='LICENSE',
|
||||
packages=find_packages() #['SimCAD']
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,220 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
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)
|
||||
}
|
||||
|
||||
# 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)
|
||||
|
||||
|
||||
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),
|
||||
'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
|
||||
},
|
||||
"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))
|
||||
|
|
@ -1,247 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
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)
|
||||
}
|
||||
|
||||
# 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)
|
||||
|
||||
|
||||
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),
|
||||
'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))
|
||||
|
|
@ -1,267 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
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)
|
||||
}
|
||||
|
||||
# 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}
|
||||
|
||||
# BEHAVIOR 7: Endogenous Information Updating (EIU)
|
||||
EIU_portion = Decimal('0.250')
|
||||
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))
|
||||
return {'EIU_sell': sell, 'EIU_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']) > EIU_UB:
|
||||
buy = beta * theta* EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* EIU_portion*(1-theta))
|
||||
return {'EIU_sell': 0, 'EIU_buy': buy}
|
||||
else:
|
||||
return {'EIU_sell': 0, 'EIU_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['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'] + _input['EIU_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'] + _input['EIU_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'] /(Decimal('0.10') * s['Price']) - s['Sell_Log'] / s['Z'] / (Decimal('0.10')*s['Price'])
|
||||
#+ 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 = {
|
||||
# "P_Ext_Markets": env_proc_id
|
||||
}
|
||||
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"P_Ext_Markets": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
sim_config = {
|
||||
"N": 100,
|
||||
"T": range(1000)
|
||||
}
|
||||
|
||||
# test return vs. non-return functions as lambdas
|
||||
# test fully defined functions
|
||||
mechanisms = {
|
||||
"m1": {
|
||||
"behaviors": {
|
||||
"b1": b1m1,
|
||||
"b3": b3m2,
|
||||
"b7": b7m2
|
||||
},
|
||||
"states": {
|
||||
"Z": s1m1,
|
||||
"Buy_Log": s3m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
"b3": b3m2,
|
||||
"b4": b4m2,
|
||||
"b7": b7m2
|
||||
},
|
||||
"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))
|
||||
|
|
@ -1,300 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
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)
|
||||
}
|
||||
|
||||
# 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))
|
||||
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('0.20')
|
||||
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))
|
||||
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('0.20')
|
||||
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))
|
||||
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 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))
|
||||
return {'EIU_sell': sell, 'EIU_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']) > EIU_UB:
|
||||
buy = beta * theta* EIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* EIU_portion*(1-theta))
|
||||
return {'EIU_sell': 0, 'EIU_buy': buy}
|
||||
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))
|
||||
return {'HEIU_sell': sell, 'HEIU_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_2']) > HEIU_UB:
|
||||
buy = beta * theta* HEIU_Ext_Hold * s['P_Ext_Markets']/(s['Price']* HEIU_portion*(1-theta))
|
||||
return {'HEIU_sell': 0, 'HEIU_buy': buy}
|
||||
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['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'] + _input['EIU_buy'] + _input['HEIU_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'] + _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'] + s['Buy_Log'] /s['Z']/(Decimal('1.25') ) - s['Sell_Log']/s['Z']/(Decimal('1.25') )
|
||||
#+ 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)
|
||||
|
||||
|
||||
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),
|
||||
'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": 100,
|
||||
"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))
|
||||
|
|
@ -1,309 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
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)
|
||||
}
|
||||
|
||||
# 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)):
|
||||
return {'EMH_buy': 0}
|
||||
else:
|
||||
return {'EMH_buy': 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 {'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}
|
||||
|
||||
# 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 = _input['EMH_buy'] + _input['herd_buy'] + _input['EIU_buy'] + _input['HEIU_buy'] # / Psignal_int
|
||||
return (y, x)
|
||||
|
||||
|
||||
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'] + (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 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)
|
||||
|
||||
|
||||
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),
|
||||
'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))
|
||||
|
|
@ -1,319 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
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)
|
||||
}
|
||||
|
||||
# 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))
|
||||
|
|
@ -1,319 +0,0 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
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)
|
||||
}
|
||||
|
||||
# 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))
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,28 +1,20 @@
|
|||
import pandas as pd
|
||||
from tabulate import tabulate
|
||||
|
||||
# The following imports NEED to be in the exact same order
|
||||
# The following imports NEED to be in the exact order
|
||||
from SimCAD.engine import ExecutionMode, ExecutionContext, Executor
|
||||
from simulations.validation import config1 #, config2
|
||||
# from simulations.validation import base_config1, base_config2
|
||||
# from simulations.barlin import config4
|
||||
# from simulations.zx import config_zx
|
||||
# from simulations.barlin import config6atemp #config6aworks,
|
||||
from simulations.validation import config1, config2
|
||||
from SimCAD import configs
|
||||
|
||||
# ToDo: pass ExecutionContext with execution method as ExecutionContext input
|
||||
|
||||
exec_mode = ExecutionMode()
|
||||
|
||||
|
||||
print("Simulation Execution 1: Config 1")
|
||||
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)
|
||||
# result.to_csv('~/Projects/DiffyQ-SimCAD/results/config4.csv', sep=',')
|
||||
print()
|
||||
print("Tensor Field:")
|
||||
print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
|
||||
|
|
@ -30,7 +22,6 @@ print("Output:")
|
|||
print(tabulate(result, headers='keys', tablefmt='psql'))
|
||||
print()
|
||||
|
||||
|
||||
# print("Simulation Execution 2: Pairwise Execution")
|
||||
# multi_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)
|
||||
# run2 = Executor(exec_context=multi_proc_ctx, configs=configs)
|
||||
|
|
@ -41,4 +32,4 @@ print()
|
|||
# print(tabulate(tensor_field, headers='keys', tablefmt='psql'))
|
||||
# print("Output:")
|
||||
# print(tabulate(result, headers='keys', tablefmt='psql'))
|
||||
# print()
|
||||
# print()
|
||||
|
|
@ -16,6 +16,7 @@ pp = pprint.PrettyPrinter(indent=4)
|
|||
# ToDo: handle single param sweep
|
||||
beta =[Decimal(1), Decimal(2)]
|
||||
|
||||
|
||||
seed = {
|
||||
'z': np.random.RandomState(1),
|
||||
'a': np.random.RandomState(2),
|
||||
|
|
@ -23,8 +24,8 @@ 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):
|
||||
|
|
@ -46,11 +47,11 @@ 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)
|
||||
|
||||
|
||||
|
|
@ -84,6 +85,7 @@ 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
|
||||
|
|
@ -123,6 +125,7 @@ def env_b(x):
|
|||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
|
||||
# Genesis States
|
||||
genesis_states = {
|
||||
's1': Decimal(0.0),
|
||||
|
|
@ -132,6 +135,7 @@ genesis_states = {
|
|||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
|
||||
# remove `exo_update_per_ts` to update every ts
|
||||
raw_exogenous_states = {
|
||||
"s3": es3p1, #sweep(beta, es3p1),
|
||||
|
|
@ -144,6 +148,7 @@ exogenous_states['s3'] = rename('parameterized', es3p1)
|
|||
# ToDo: make env proc trigger field agnostic
|
||||
# ToDo: input json into function renaming __name__
|
||||
triggered_env_b = proc_trigger('2018-10-01 15:16:25', env_b)
|
||||
|
||||
env_processes = {
|
||||
"s3": env_a, #sweep(beta, env_a, 'env_a'),
|
||||
"s4": rename('parameterized', triggered_env_b) #sweep(beta, triggered_env_b)
|
||||
|
|
@ -161,17 +166,17 @@ env_processes = {
|
|||
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": rename('parameterized', s2m1) #s2m1(1) #sweep(beta, s2m1)
|
||||
"s2": sweep(beta, s2m1) #rename('parameterized', s2m1) #s2m1(1) #sweep(beta, s2m1)
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": rename('parameterized', b1m2), #b1m2(1) #sweep(beta, b1m2),
|
||||
"b1": sweep(beta, b1m2), #rename('parameterized', b1m2), #b1m2(1) #sweep(beta, b1m2),
|
||||
"b2": b2m2
|
||||
},
|
||||
"states": {
|
||||
|
|
@ -191,6 +196,28 @@ mechanisms = {
|
|||
}
|
||||
}
|
||||
|
||||
def mech_sweep(mechanisms):
|
||||
sweep_lists = []
|
||||
new_mechanisms = deepcopy(mechanisms)
|
||||
for mech, update_types in new_mechanisms.items():
|
||||
for update_type, fkv in update_types.items():
|
||||
for sk, vfs in fkv.items():
|
||||
if isinstance(vfs, list):
|
||||
for vf in vfs:
|
||||
sweep_lists.append((sk,vf))
|
||||
|
||||
zipped_sweep_lists = []
|
||||
it = iter(sweep_lists)
|
||||
the_len = len(next(it))
|
||||
if all(len(l) == the_len for l in it):
|
||||
zipped_sweep_lists = list(zip(*sweep_lists))
|
||||
else:
|
||||
raise ValueError('not all lists have same length!')
|
||||
|
||||
return sweep_lists
|
||||
|
||||
pp.pprint(mech_sweep(mechanisms))
|
||||
|
||||
sim_config = {
|
||||
"N": 2,
|
||||
"T": range(5)
|
||||
|
|
@ -204,7 +231,7 @@ def parameterize_mechanism(mechanisms, param):
|
|||
for update_type, fkv in update_types.items():
|
||||
for sk, vf in fkv.items():
|
||||
if vf.__name__ == 'parameterized':
|
||||
print(vf.__name__)
|
||||
# print(vf.__name__)
|
||||
new_mechanisms[mech][update_type][sk] = vf(param)
|
||||
|
||||
del mechanisms
|
||||
|
|
@ -228,21 +255,24 @@ def s2m1(param, a, b, c, d):
|
|||
return (y, x)
|
||||
|
||||
# print(s2m1(1)(1))
|
||||
pp.pprint(parameterize_mechanism(mechanisms, 1))
|
||||
# pp.pprint(mechanisms)
|
||||
# pp.pprint(parameterize_mechanism(mechanisms, 1))
|
||||
# print(sweep(beta, s2m1))
|
||||
|
||||
# pp.pprint(parameterize_states(raw_exogenous_states, 1))
|
||||
# pp.pprint(parameterize_states(env_processes, 1))
|
||||
|
||||
|
||||
configs.append(
|
||||
Configuration(
|
||||
sim_config=sim_config,
|
||||
state_dict=genesis_states,
|
||||
seed=seed,
|
||||
exogenous_states=exogenous_states,
|
||||
env_processes=env_processes,
|
||||
mechanisms=parameterize_mechanism(mechanisms, 1)
|
||||
)
|
||||
)
|
||||
# configs.append(
|
||||
# Configuration(
|
||||
# sim_config=sim_config,
|
||||
# state_dict=genesis_states,
|
||||
# seed=seed,
|
||||
# exogenous_states=exogenous_states,
|
||||
# env_processes=env_processes,
|
||||
# mechanisms=parameterize_mechanism(mechanisms, 1)
|
||||
# )
|
||||
# )
|
||||
|
||||
# def sweep_config(config, params):
|
||||
# new_config = deepcopy(config)
|
||||
|
|
@ -283,4 +313,4 @@ configs.append(
|
|||
# pp.pprint(g.exogenous_states)
|
||||
# print()
|
||||
# pp.pprint(g.mechanisms)
|
||||
# print()
|
||||
# print()
|
||||
|
|
|
|||
|
|
@ -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
|
||||
)
|
||||
)
|
||||
)
|
||||
|
|
|
|||
|
|
@ -15,48 +15,49 @@ seed = {
|
|||
}
|
||||
|
||||
# Behaviors per Mechanism
|
||||
# Different return types per mechanism ?? *** No ***
|
||||
def b1m1(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
return {'param1': 1}
|
||||
def b2m1(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
return {'param2': 4}
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
return {'param1': 'a', 'param2': 2}
|
||||
def b2m2(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
return {'param1': 'b', 'param2': 4}
|
||||
|
||||
def b1m3(step, sL, s):
|
||||
return s['s1'] + 1
|
||||
return {'param1': ['c'], 'param2': np.array([10, 100])}
|
||||
def b2m3(step, sL, s):
|
||||
return s['s2'] + 1
|
||||
|
||||
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 = s['s1'] + _input
|
||||
x = _input['param1'] #+ [Coef1 x 5]
|
||||
return (y, x)
|
||||
def s2m1(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + _input
|
||||
x = _input['param2'] #+ [Coef2 x 5]
|
||||
return (y, x)
|
||||
|
||||
def s1m2(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = s['s1'] + _input
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + _input
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
def s1m3(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = s['s1'] + _input
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m3(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = s['s2'] + s['s3'] + _input
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
|
|
@ -73,22 +74,24 @@ def es4p2(step, sL, s, _input):
|
|||
x = s['s4'] * bound_norm_random(seed['b'], proc_one_coef_A, proc_one_coef_B)
|
||||
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
|
||||
def env_a(x):
|
||||
return 10
|
||||
return 5
|
||||
def env_b(x):
|
||||
return 10
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# Genesis States
|
||||
state_dict = {
|
||||
genesis_states = {
|
||||
's1': Decimal(0.0),
|
||||
's2': Decimal(0.0),
|
||||
's3': Decimal(1.0),
|
||||
|
|
@ -96,6 +99,7 @@ state_dict = {
|
|||
'timestamp': '2018-10-01 15:16:24'
|
||||
}
|
||||
|
||||
# remove `exo_update_per_ts` to update every ts
|
||||
exogenous_states = exo_update_per_ts(
|
||||
{
|
||||
"s3": es3p1,
|
||||
|
|
@ -104,17 +108,26 @@ exogenous_states = exo_update_per_ts(
|
|||
}
|
||||
)
|
||||
|
||||
# ToDo: make env proc trigger field agnostic
|
||||
# ToDo: input json into function renaming __name__
|
||||
env_processes = {
|
||||
"s3": proc_trigger('2018-10-01 15:16:25', env_a),
|
||||
"s3": env_a,
|
||||
"s4": proc_trigger('2018-10-01 15:16:25', env_b)
|
||||
}
|
||||
|
||||
# lambdas
|
||||
# genesis Sites should always be there
|
||||
# [1, 2]
|
||||
# User Defined Aggregate Function
|
||||
behavior_udaf = [ foldr(_ + _), lambda x: x + 0 ]
|
||||
# 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": {
|
||||
|
|
@ -153,4 +166,13 @@ sim_config = {
|
|||
"T": range(5)
|
||||
}
|
||||
|
||||
configs.append(Configuration(sim_config, state_dict, seed, exogenous_states, env_processes, mechanisms, behavior_udaf))
|
||||
configs.append(
|
||||
Configuration(
|
||||
sim_config=sim_config,
|
||||
state_dict=genesis_states,
|
||||
seed=seed,
|
||||
exogenous_states=exogenous_states,
|
||||
env_processes=env_processes,
|
||||
mechanisms=mechanisms
|
||||
)
|
||||
)
|
||||
|
|
@ -0,0 +1,180 @@
|
|||
from decimal import Decimal
|
||||
import numpy as np
|
||||
from datetime import timedelta
|
||||
|
||||
from SimCAD import configs
|
||||
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),
|
||||
'b': np.random.RandomState(3),
|
||||
'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):
|
||||
return {'param2': 4}
|
||||
|
||||
def b1m2(step, sL, s):
|
||||
return {'param1': 'a', 'param2': 2}
|
||||
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):
|
||||
return {'param1': ['d'], 'param2': np.array([20, 200])}
|
||||
|
||||
|
||||
# Internal States per Mechanism
|
||||
def s1m1(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m1(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
def s1m2(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m2(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
def s1m3(step, sL, s, _input):
|
||||
y = 's1'
|
||||
x = _input['param1']
|
||||
return (y, x)
|
||||
def s2m3(step, sL, s, _input):
|
||||
y = 's2'
|
||||
x = _input['param2']
|
||||
return (y, x)
|
||||
|
||||
# Exogenous States
|
||||
proc_one_coef_A = 0.7
|
||||
proc_one_coef_B = 1.3
|
||||
|
||||
def es3p1(step, sL, s, _input):
|
||||
y = 's3'
|
||||
x = s['s3'] * bound_norm_random(seed['a'], proc_one_coef_A, proc_one_coef_B)
|
||||
return (y, x)
|
||||
|
||||
def es4p2(step, sL, s, _input):
|
||||
y = 's4'
|
||||
x = s['s4'] * bound_norm_random(seed['b'], 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
|
||||
def env_a(x):
|
||||
return 10
|
||||
def env_b(x):
|
||||
return 10
|
||||
# def what_ever(x):
|
||||
# return x + 1
|
||||
|
||||
# Genesis States
|
||||
genesis_states = {
|
||||
's1': Decimal(0.0),
|
||||
's2': Decimal(0.0),
|
||||
's3': Decimal(1.0),
|
||||
's4': Decimal(1.0),
|
||||
'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,
|
||||
"s4": es4p2,
|
||||
"timestamp": es5p2
|
||||
}
|
||||
)
|
||||
|
||||
# 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,
|
||||
# "b2": b2m1
|
||||
},
|
||||
"states": { # exclude only. TypeError: reduce() of empty sequence with no initial value
|
||||
"s1": s1m1,
|
||||
# "s2": s2m1
|
||||
}
|
||||
},
|
||||
"m2": {
|
||||
"behaviors": {
|
||||
"b1": b1m2,
|
||||
# "b2": b2m2
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m2,
|
||||
# "s2": s2m2
|
||||
}
|
||||
},
|
||||
"m3": {
|
||||
"behaviors": {
|
||||
"b1": b1m3,
|
||||
"b2": b2m3
|
||||
},
|
||||
"states": {
|
||||
"s1": s1m3,
|
||||
"s2": s2m3
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
sim_config = {
|
||||
"N": 2,
|
||||
"T": range(5)
|
||||
}
|
||||
|
||||
configs.append(
|
||||
Configuration(
|
||||
sim_config=sim_config,
|
||||
state_dict=genesis_states,
|
||||
seed=seed,
|
||||
exogenous_states=exogenous_states,
|
||||
env_processes=env_processes,
|
||||
mechanisms=mechanisms
|
||||
)
|
||||
)
|
||||
Loading…
Reference in New Issue