mirror of
https://github.com/OpenBB-finance/OpenBB.git
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* Introduce integration test modularity * Fix Python 3.9 error * Add TODO * Update generators * Handle endpoints that only feature one provider that is not installed * Fix failing unit test
321 lines
9.2 KiB
Python
321 lines
9.2 KiB
Python
import base64
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import json
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import random
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from typing import Literal
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import pytest
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import requests
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from extensions.tests.conftest import parametrize
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from openbb_core.env import Env
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from openbb_core.provider.utils.helpers import get_querystring
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# pylint:disable=redefined-outer-name
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data: dict = {}
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def get_headers():
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if "headers" in data:
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return data["headers"]
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userpass = f"{Env().API_USERNAME}:{Env().API_PASSWORD}"
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userpass_bytes = userpass.encode("ascii")
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base64_bytes = base64.b64encode(userpass_bytes)
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data["headers"] = {"Authorization": f"Basic {base64_bytes.decode('ascii')}"}
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return data["headers"]
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def request_data(menu: str, symbol: str, provider: str):
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"""Randomly pick a symbol and a provider and get data from the selected menu."""
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url = f"http://0.0.0.0:8000/api/v1/{menu}/price/historical?symbol={symbol}&provider={provider}"
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result = requests.get(url, headers=get_headers(), timeout=10)
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return result.json()["results"]
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def get_stocks_data():
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if "stocks_data" in data:
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return data["stocks_data"]
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symbol = random.choice(["AAPL", "NVDA", "MSFT", "TSLA", "AMZN", "V"]) # noqa: S311
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provider = random.choice(["fmp", "polygon", "yfinance"]) # noqa: S311
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data["stocks_data"] = request_data("equity", symbol=symbol, provider=provider)
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return data["stocks_data"]
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def get_crypto_data():
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if "crypto_data" in data:
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return data["crypto_data"]
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# TODO : add more crypto providers and symbols
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symbol = random.choice(["BTC"]) # noqa: S311
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provider = random.choice(["fmp"]) # noqa: S311
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data["crypto_data"] = request_data(
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menu="crypto",
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symbol=symbol,
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provider=provider,
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)
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return data["crypto_data"]
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def get_data(menu: Literal["equity", "crypto"]):
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funcs = {"equity": get_stocks_data, "crypto": get_crypto_data}
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return funcs[menu]()
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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({"data": "", "target": "high"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_normality(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/normality?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "high"}, "equity"),
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({"data": "", "target": "high"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_capm(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/capm?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"threshold_start": "",
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"threshold_end": "",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"threshold_start": "0.1",
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"threshold_end": "1.6",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_omega_ratio(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/omega_ratio?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close", "window": "5"}, "equity"),
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({"data": "", "target": "high", "window": "10"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_kurtosis(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/kurtosis?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"fuller_reg": "c",
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"kpss_reg": "ct",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"fuller_reg": "ct",
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"kpss_reg": "c",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_unitroot_test(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/unitroot_test?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close", "rfr": "", "window": ""}, "equity"),
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({"data": "", "target": "high", "rfr": "0.5", "window": "250"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_sharpe_ratio(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/sharpe_ratio?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"target_return": "",
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"window": "",
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"adjusted": "",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "close",
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"target_return": "0.5",
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"window": "275",
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"adjusted": "true",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_sortino_ratio(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/sortino_ratio?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close", "window": "220"}, "equity"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_skewness(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/skewness?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=60, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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(
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{
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"data": "",
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"target": "close",
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"window": "10",
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"quantile_pct": "",
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},
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"equity",
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),
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(
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{
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"data": "",
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"target": "high",
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"window": "50",
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"quantile_pct": "0.6",
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},
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"crypto",
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),
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],
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)
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@pytest.mark.integration
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def test_quantitative_quantile(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/quantile?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params, data_type",
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[
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({"data": "", "target": "close"}, "equity"),
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({"data": "", "target": "high"}, "crypto"),
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],
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)
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@pytest.mark.integration
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def test_quantitative_summary(params, data_type):
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params = {p: v for p, v in params.items() if v}
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data = json.dumps(get_data(data_type))
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/quantitative/summary?{query_str}"
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result = requests.post(url, headers=get_headers(), timeout=10, data=data)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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