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 informaciÃ³n a candles mÃ¡s chicas para reducir la memora necesaria33# ademÃ¡s 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 mÃ¡s99lr_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 algÃºna ventana107    lr_ada = lr_ind.lr[(split_index, "ADA")] # agarro el lr de ADA en esa ventana108    # borramos el mkt cuando estÃ¡ 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 estÃ¡ 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# AcÃ¡ 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 mÃ¡s 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 simulaciÃ³n 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 combinaciÃ³n 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# acÃ¡ arranca la parte de correr las simulaciones con los datos del out y los parÃ¡metros 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 parÃ¡metros 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 pequeÃ±o 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 cÃ¡lculo 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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