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How to use test_asset_list method in avocado
Best Python code snippet using avocado_python
RelativePerformance.py
Source:RelativePerformance.py 
1#!/usr/bin/env python2# coding: utf-83# In[24]:4import gc5import os6import sys7module_path = os.path.abspath(os.path.join('../..'))8if module_path not in sys.path:9 sys.path.append(module_path)10import numpy as np11import pandas as pd12import vectorbt as vbt13from numba import njit14from math import e15from vectorbt.generic.nb import diff_nb16from lib.utils import directory_to_data_frame_list, ExtendedPortfolio, create_windows, get_best_pairs17from lib.utils import is_notebook18# In[ ]:19current_dir = os.path.dirname(os.path.realpath("__file__"))20directory = "/home/ec2-user/data/long/"21ohlcv_series_list = directory_to_data_frame_list(directory)22# concatenamos los dfs23names = list(map(lambda t: t[0], ohlcv_series_list))24dfs = list(map(lambda t: t[1].get(["Close", "Volume"]), ohlcv_series_list))25ohlc_dict = {26#'Open':'first',27#'High':'max',28#'Low':'min',29'Close': 'last',30'Volume': 'sum'31}32# Resampleamos la información a candles más chicas para reducir la memora necesaria33# además solo agarramos close y volumne34for i in range(len(dfs)):35 dfs[i] = dfs[i].resample('5T', closed='left', label='left').apply(ohlc_dict)36ov_df = pd.concat(dfs, axis=1, keys=names)37# borramos las filas que tengan nan(parece que algunos pueden estar desalineados)38ov_df.dropna(inplace=True)39ov_df.columns.set_names(["symbol", "value"], inplace=True)40del ohlcv_series_list, names, dfs41ov_df.head()42# In[ ]:43figure, windows = create_windows(ohlc=ov_df, n=10, window_len=0.6, right_set_len=0.3*0.9)44(in_windows, _), (out_windows, _) = windows45del _46print("Done creating windows")47# In[ ]:48if is_notebook():49 figure.show()50del figure, ov_df51# In[ ]:52portfolio_kwargs = dict(53 direction='longonly',54 freq='m',55)56# creamos el indicador para el lr y otro para el wlr57# lo hago por separado para poder calcular el mlr58# con data de varios activos #del mercado59# y luego solo utiliza lr con los que me interesa60@njit61def lr_nb(close):62 c_log = np.log(close)63 return diff_nb(c_log)64LR = vbt.IndicatorFactory(65 input_names=['close'],66 output_names=['lr']67).from_apply_func(lr_nb, use_ray=True)68@njit69def wlr_nb(volume, lr):70 mkt_vol = volume.sum(axis=1)71 mkt_ratio = (volume.T / mkt_vol).T72 return lr * mkt_ratio73WLR = vbt.IndicatorFactory(74 input_names=['volume', 'lr'],75 output_names=['wlr']76).from_apply_func(wlr_nb, use_ray=True)77 #creamos el indicador para las bandas78@njit79def mkt_band_nb(mkt_lr, upper_filter, lower_filter):80 filtered = np.where(mkt_lr >= upper_filter, mkt_lr, np.nan)81 filtered = np.where(mkt_lr <= -lower_filter, mkt_lr, filtered)82 return filtered83MKT_BANDS = vbt.IndicatorFactory(84 input_names=['mkt_lr'],85 param_names=['upper_filter', 'lower_filter'],86 output_names=['filtered']87).from_apply_func(mkt_band_nb, use_ray=True)88# In[ ]:89# lr = log_return90# wlr = weighted log_return = lr * (Vi / Vmercado)91# mkt_lr = sum(wlr)92in_close = in_windows.xs('Close', level='value', axis=1)93in_volume = in_windows.xs('Volume', level='value', axis=1)94lr_ind = LR.run(in_close)95wlr_ind = WLR.run(in_volume, lr_ind.lr)96mkt_lr = wlr_ind.wlr.sum(axis=1, level="split_idx", skipna=False)97print("Done calculating mkt_lr")98del in_volume, in_windows # esto no se usa más99lr_ind.lr.head()100# In[ ]:101mkt_lr.head()102# In[ ]:103if is_notebook():104 # Grafico un resultadoo arbitrario selecionando filtros arbitrarios para ver como ejemplo el funcionamiento de la estategia105 split_index = 5106 _mkt_lr_arb = mkt_lr[split_index] # agarro el mkt_lr de algúna ventana107 lr_ada = lr_ind.lr[(split_index, "ADA")] # agarro el lr de ADA en esa ventana108 # borramos el mkt cuando está entre 0.0005 y - 0.0005109 filtered = np.where(_mkt_lr_arb >= 0.0005, _mkt_lr_arb, np.nan)110 filtered = np.where(_mkt_lr_arb <= -0.0005, _mkt_lr_arb, filtered)111 fig = pd.DataFrame({112 "lr_ada" : lr_ada,113 "mkt_lr": _mkt_lr_arb,114 "mkt_lr_filtered" : filtered115 }).vbt.plot()116 pd.DataFrame({117 "entries": np.where(filtered >= lr_ada, _mkt_lr_arb, np.nan), # compramos cuando el mercado está por encima de ada118 "exits": np.where(filtered <= lr_ada, _mkt_lr_arb, np.nan)119 }).vbt.scatterplot(fig=fig).show()120 del _mkt_lr_arb, lr_ada, filtered, fig121 gc.collect()122# In[ ]:123# Acá filtramos los thresholds del mkt_lr a partir del cual compramos o vendemos.124upper_fltr = np.linspace(0.00001, 0.003, 50, endpoint=False)125lower_fltr = np.linspace(0.00001, 0.005, 50, endpoint=False)126mkt_bands_ind = MKT_BANDS.run(mkt_lr=mkt_lr, upper_filter=upper_fltr , lower_filter=lower_fltr,127 per_column=False,128 param_product=True,129 short_name="mkt")130del upper_fltr, lower_fltr, mkt_lr131gc.collect()132print("Done calculating mkt_bands")133# In[ ]:134# Ya generamos todos los datos necesarios, ahora vamos a correr todas las simulaciones para cada assets que nos135# interesa testear136# para que no muera por memoria a la mitad y perder todo lo porcesado hasta el momento, me aseguro de que todas137# las keys existan en el df138test_asset_list = ["ADA", "BTC"]139assert( set(test_asset_list).issubset(in_close.columns.get_level_values(level="symbol").unique()))140# In[ ]:141# Recolectamos el close y el lr de cada uno para poder borrar de memoria el df grande de todos los close y los lrs que no usamos142# puesto que close y lr son varias veces más grandes que el lr y close individual143_lrs = {}144_close = {}145for asset in test_asset_list:146 _lrs[asset] = lr_ind.lr.xs(asset, level='symbol', axis=1)147 _close[asset] = in_close.xs(asset, level='symbol', axis=1)148 print(f"Done separating close and lrs for {asset}")149del in_close, lr_ind150in_close = _close151in_lrs = _lrs152gc.collect()153# In[ ]:154# corremos la simulación para cada asset155def dropnan(s):156 return s[~np.isnan(s)]157in_best_fltr_pairs = {}158params_names = mkt_bands_ind.level_names159for asset in test_asset_list:160 lr = in_lrs[asset]161 close = in_close[asset]162 entries = mkt_bands_ind.filtered_above(lr, crossover=True)163 exits = mkt_bands_ind.filtered_below(lr, crossover=True)164 del lr, in_lrs[asset]165 gc.collect()166 print(f"Running optimizing for {asset}")167 port = ExtendedPortfolio.from_signals(close, entries, exits, **portfolio_kwargs, max_logs=0)168 del entries, exits, close, in_close[asset]169 gc.collect()170 print(f"Done optimizing {asset}")171 172 # buscamos la mejor combinación de filtros173 in_best_fltr_pairs[asset] = get_best_pairs(port.expected_log_returns(), *params_names)174 # ploteamos la performace de todas las combinanciones175 if is_notebook():176 elr_volume = dropnan(port.expected_log_returns()).vbt.volume(title=f"{asset}'s Expected Log Return").show()177 sharpe_volume = dropnan(port.sharpe_ratio()).vbt.volume(title=f"{asset}'s Sharpe Ratio").show()178 del port179 gc.collect()180 print(f"Done plotting {asset}")181del mkt_bands_ind182gc.collect()183# In[ ]:184# acá arranca la parte de correr las simulaciones con los datos del out y los parámetros ya optimizados185out_close = out_windows.xs('Close', level='value', axis=1)186out_volume = out_windows.xs('Volume', level='value', axis=1)187lr_ind = LR.run(out_close)188wlr_ind = WLR.run(out_volume, lr_ind.lr)189mkt_lr = wlr_ind.wlr.sum(axis=1, level="split_idx", skipna=False)190_lrs = {}191_close = {}192for asset in test_asset_list:193 _lrs[asset] = lr_ind.lr.xs(asset, level='symbol', axis=1)194 _close[asset] = out_close.xs(asset, level='symbol', axis=1)195 print(f"Done separating close and lrs for {asset}")196del out_close, lr_ind, out_windows, wlr_ind197out_close = _close198out_lrs = _lrs199gc.collect()200for asset in test_asset_list:201 # para cada activo de los que me interesa tradear simulo el resultado de ser corrido con los parámetros optimizados202 in_best_pairs = np.array(in_best_fltr_pairs[asset])203 upper_fltr = in_best_pairs[:,0]204 lower_fltr = in_best_pairs[:,1]205 mkt_bands_ind = MKT_BANDS.run(mkt_lr=mkt_lr, upper_filter=upper_fltr , lower_filter=lower_fltr,206 per_column=True,207 param_product=False,208 short_name="mkt")209 lr = out_lrs[asset]210 close = out_close[asset]211 entries = mkt_bands_ind.filtered_above(lr, crossover=True)212 exits = mkt_bands_ind.filtered_below(lr, crossover=True)213 del lr, out_lrs[asset], mkt_bands_ind214 port = ExtendedPortfolio.from_signals(close, entries, exits, **portfolio_kwargs, max_logs=0)215 exp_plot = port.expected_log_returns().vbt.plot(title=f"{asset}'s Expected Log Return")216 sharpe_plot = port.sharpe_ratio().vbt.plot(title=f"{asset}'s Sharpe ratio")217 if is_notebook():218 exp_plot.show()219 sharpe_plot.show()220 else:221 exp_plot.write_html(f"{current_dir}/{asset}_simulation_exp_log_ret.html")222 sharpe_plot.write_html(f"{current_dir}/{asset}_simulation_sharpe-ratio.html")223 print(f"Done simulating {asset}")224# In[ ]:225# un pequeño test para asegurarnos que todas las cuentas den226_py = pd.DataFrame({227 'Close': [1,e,e**2],228 'Volume': [1,2,1]229})230_thon = pd.DataFrame({231 'Close': [e**2,e,1],232 'Volume': [1,4,10]233})234_test_df = pd.concat([_py,_thon], axis=1, keys=["Py", "Thon"])235_test_df.columns.set_names(["asset", "value"], inplace=True)236close = _test_df.xs('Close', level='value', axis=1)237volume = _test_df.xs('Volume', level='value', axis=1)238_test_lrInd = LR.run(close)239_test_wlrInd = WLR.run(volume, _test_lrInd.lr)240exp_py_lr = np.array([np.nan, 1, 1])241exp_thon_lr = np.array([np.nan, -1, -1])242assert (np.allclose(exp_py_lr, _test_lrInd.lr["Py"], equal_nan=True))243assert (np.allclose(exp_thon_lr, _test_lrInd.lr["Thon"], equal_nan=True))244exp_py_vr = np.array([0.5, 1/3, 1/11])245exp_thon_vr = np.array([0.5, 2/3, 10/11])246exp_py_wlr = exp_py_lr * exp_py_vr247exp_thon_wlr = exp_thon_lr * exp_thon_vr248assert (np.allclose(exp_py_wlr, _test_wlrInd.wlr["Py"], equal_nan=True))249assert (np.allclose(exp_thon_wlr, _test_wlrInd.wlr["Thon"], equal_nan=True))250# falta testear el cálculo de mkt_lr251_test_mkt_lr = _test_wlrInd.wlr.sum(axis=1, skipna=False)252exp_mkt_lr = exp_py_wlr + exp_thon_wlr...
test_plugin.py
Source:test_plugin.py
1#2# Copyright (c) 2019 One Identity3#4# Permission is hereby granted, free of charge, to any person obtaining a copy5# of this software and associated documentation files (the "Software"), to6# deal in the Software without restriction, including without limitation the7# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or8# sell copies of the Software, and to permit persons to whom the Software is9# furnished to do so, subject to the following conditions:10#11# The above copyright notice and this permission notice shall be included in12# all copies or substantial portions of the Software.13#14# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR15# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,16# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE17# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER18# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING19# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS20# IN THE SOFTWARE.21#22from copy import deepcopy23import pytest24from textwrap import dedent25import unittest.mock26from ..plugin import SafeguardPlugin27from ..safeguard import SafeguardException28from safeguard.sessions.plugin_impl.test_utils.plugin import (29 assert_plugin_hook_result,30 check_that_data_is_serializable,31 minimal_parameters,32 update_cookies,33)34def test_checkout_password_with_gateway_credentials(gateway_config, safeguard_lock, generate_params):35 plugin = SafeguardPlugin(gateway_config)36 params = generate_params()37 checkout_result = plugin.get_password_list(**deepcopy(params))38 check_that_data_is_serializable(checkout_result)39 update_cookies(params, checkout_result)40 plugin.authentication_completed(**minimal_parameters(params))41 plugin.session_ended(**minimal_parameters(params))42 checkout_result_cookie = checkout_result["cookie"]43 assert "access_request_id" in checkout_result_cookie44 assert "access_token" in checkout_result_cookie45 assert checkout_result["passwords"] # not None and has at least 1 element46def test_checkout_password_with_explicit_credentials(explicit_config, safeguard_lock, generate_params):47 plugin = SafeguardPlugin(explicit_config)48 params = generate_params()49 checkout_result = plugin.get_password_list(**deepcopy(params))50 check_that_data_is_serializable(checkout_result)51 update_cookies(params, checkout_result)52 plugin.authentication_completed(**minimal_parameters(params))53 plugin.session_ended(**minimal_parameters(params))54 checkout_result_cookie = checkout_result["cookie"]55 assert "access_request_id" in checkout_result_cookie56 assert "access_token" in checkout_result_cookie57 assert checkout_result["passwords"] # not None and has at least 1 element58def test_checkout_password_with_token(token_config, safeguard_lock, safeguard_client, generate_params):59 plugin = SafeguardPlugin(token_config)60 safeguard_client.authenticate()61 params = generate_params(session_cookie={"token": safeguard_client.access_token})62 checkout_result = plugin.get_password_list(**deepcopy(params))63 check_that_data_is_serializable(checkout_result)64 update_cookies(params, checkout_result)65 plugin.authentication_completed(**minimal_parameters(params))66 plugin.session_ended(**minimal_parameters(params))67 checkout_result_cookie = checkout_result["cookie"]68 assert "access_request_id" in checkout_result_cookie69 assert "access_token" in checkout_result_cookie70 assert checkout_result["passwords"] # not None and has at least 1 element71def test_get_password_list_returns_the_correct_response(explicit_config, dummy_sg_client_factory, generate_params):72 plugin = SafeguardPlugin(explicit_config, safeguard_client_factory=dummy_sg_client_factory)73 params = generate_params()74 result = plugin.get_password_list(**deepcopy(params))75 assert_plugin_hook_result(76 result,77 {78 "cookie": {"access_token": "the_access_token", "access_request_id": "the_access_request_id"},79 "passwords": ["the_password"],80 },81 )82def test_raises_exception_if_access_request_id_is_not_presented(explicit_config, dummy_sg_client_factory):83 plugin = SafeguardPlugin(explicit_config, safeguard_client_factory=dummy_sg_client_factory)84 with pytest.raises(SafeguardException) as exc_info:85 plugin.session_ended(cookie={"account": "x"}, session_cookie={}, session_id="the_session_id")86 assert exc_info.match("Missing access_request_id")87class SaveAssets(SafeguardPlugin):88 def __init__(self, configuration, safeguard_client_factory):89 super().__init__(configuration, safeguard_client_factory)90 self.test_asset_list = []91 def do_get_password_list(self):92 self.test_asset_list.append(self.asset)93def test_assets_suffix(explicit_config, dummy_sg_client_factory, generate_params):94 config = dedent(95 """96 [domain_asset_mapping]97 bar.baz=acme.com98 [assets]99 domain_suffix=baz100 """101 )102 plugin = SaveAssets(config, dummy_sg_client_factory)103 params = generate_params(server_hostname="foo.bar", server_domain="bar")104 plugin.get_password_list(**deepcopy(params))...
tests.py
Source:tests.py
1from rest_framework import status2from rest_framework.test import APITestCase3class AccountTests(APITestCase):4 def test_asset_list(self):5 response = self.client.post('/api/v1/rest-auth/registration/', {6 'username': 'francisco213422',7 'password1': 'Francisco123',8 'password2': 'Francisco123',9 'email': 'fra123@gmail.com',10 'email2': 'fra123@gmail.com',11 'first_name': 'fanasdasd',12 'last_name': 'asddasdasj',13 'avatar': 1,14 }, format='json')15 response = self.client.post('/api/v1/rest-auth/login/',16 {'username': 'francisco213422',17 'password': 'Francisco123'},18 format='json')...
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