RAPTOR v18.4: Исправлена отчетность, активированы выходные
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import logging
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from datetime import timedelta
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from decimal import Decimal
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from math import exp, sqrt
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from random import gauss, seed
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from typing import Callable, Dict, Iterable, Iterator, List, Optional
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import pytest
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from grpc import StatusCode
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from t_tech.invest import (
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Candle,
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Client,
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GetCandlesResponse,
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GetMarginAttributesResponse,
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HistoricCandle,
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MarketDataResponse,
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MoneyValue,
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OrderDirection,
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OrderType,
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PortfolioPosition,
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PortfolioResponse,
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Quotation,
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RequestError,
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)
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from t_tech.invest.services import Services
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from t_tech.invest.strategies.base.account_manager import AccountManager
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from t_tech.invest.strategies.moving_average.plotter import MovingAverageStrategyPlotter
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from t_tech.invest.strategies.moving_average.signal_executor import (
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MovingAverageSignalExecutor,
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)
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from t_tech.invest.strategies.moving_average.strategy import MovingAverageStrategy
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from t_tech.invest.strategies.moving_average.strategy_settings import (
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MovingAverageStrategySettings,
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)
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from t_tech.invest.strategies.moving_average.strategy_state import (
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MovingAverageStrategyState,
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)
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from t_tech.invest.strategies.moving_average.supervisor import (
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MovingAverageStrategySupervisor,
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)
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from t_tech.invest.strategies.moving_average.trader import MovingAverageStrategyTrader
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from t_tech.invest.utils import (
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candle_interval_to_subscription_interval,
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candle_interval_to_timedelta,
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decimal_to_quotation,
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now,
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quotation_to_decimal,
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)
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logging.basicConfig(format="%(asctime)s %(levelname)s:%(message)s", level=logging.INFO)
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logger = logging.getLogger(__name__)
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seed(1338)
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def create_noise(s0, mu, sigma) -> Callable[[int], Iterable[float]]:
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"""Create Noise.
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Generates a price following a geometric brownian motion process based on the input.
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- s0: Asset initial price.
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- mu: Interest rate expressed annual terms.
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- sigma: Volatility expressed annual terms.
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"""
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st = s0
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def generate_range(limit: int):
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nonlocal st
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for _ in range(limit):
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st *= exp(
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(mu - 0.5 * sigma**2) * (1.0 / 365.0)
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+ sigma * sqrt(1.0 / 365.0) * gauss(mu=0, sigma=1)
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)
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yield st
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return generate_range
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@pytest.fixture()
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def stock_prices_generator() -> Callable[[int], Iterable[float]]:
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return create_noise(100, 0.01, 0.1)
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@pytest.fixture()
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def stock_volume_generator() -> Callable[[int], Iterable[float]]:
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return create_noise(1000, 0.9, 1.1)
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@pytest.fixture()
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def initial_candles(
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settings: MovingAverageStrategySettings,
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stock_prices_generator: Callable[[int], Iterable[float]],
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stock_volume_generator: Callable[[int], Iterable[float]],
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) -> Iterable[HistoricCandle]:
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candles = []
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intervals = 365
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interval_delta = candle_interval_to_timedelta(settings.candle_interval)
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base = now() - interval_delta * intervals
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(close,) = stock_prices_generator(1)
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for i in range(intervals):
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open_ = close
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low, high, close = stock_prices_generator(3)
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low, high = min(low, high, open_, close), max(low, high, open_, close)
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(volume,) = stock_volume_generator(1)
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candle = HistoricCandle(
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open=decimal_to_quotation(Decimal(open_)),
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high=decimal_to_quotation(Decimal(high)),
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low=decimal_to_quotation(Decimal(low)),
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close=decimal_to_quotation(Decimal(close)),
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volume=int(volume),
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time=base + interval_delta * i,
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is_complete=True,
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)
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candles.append(candle)
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return candles
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@pytest.fixture()
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def real_services(token: str) -> Iterator[Services]:
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with Client(token) as services:
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yield services
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@pytest.fixture()
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def mock_market_data_service(
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real_services: Services,
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mocker,
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initial_candles: List[HistoricCandle],
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) -> Services:
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real_services.market_data = mocker.Mock(wraps=real_services.market_data)
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real_services.market_data.get_candles = mocker.Mock()
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real_services.market_data.get_candles.return_value = GetCandlesResponse(
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candles=initial_candles
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)
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return real_services
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@pytest.fixture()
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def current_market_data() -> List[Candle]:
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return []
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@pytest.fixture()
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def mock_market_data_stream_service(
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real_services: Services,
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mocker,
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figi: str,
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stock_prices_generator: Callable[[int], Iterable[float]],
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stock_volume_generator: Callable[[int], Iterable[float]],
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settings: MovingAverageStrategySettings,
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current_market_data: List[Candle],
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freezer,
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) -> Services:
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real_services.market_data_stream = mocker.Mock(
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wraps=real_services.market_data_stream
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)
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def _market_data_stream(*args, **kwargs):
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yield MarketDataResponse(candle=None) # type: ignore
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(close,) = stock_prices_generator(1)
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while True:
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open_ = close
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low, high, close = stock_prices_generator(3)
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low, high = min(low, high, open_, close), max(low, high, open_, close)
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(volume,) = stock_volume_generator(1)
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candle = Candle(
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figi=figi,
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interval=candle_interval_to_subscription_interval(
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settings.candle_interval
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),
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open=decimal_to_quotation(Decimal(open_)),
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high=decimal_to_quotation(Decimal(high)),
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low=decimal_to_quotation(Decimal(low)),
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close=decimal_to_quotation(Decimal(close)),
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volume=int(volume),
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time=now(),
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)
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current_market_data.append(candle)
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yield MarketDataResponse(candle=candle)
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freezer.move_to(now() + timedelta(minutes=1))
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real_services.market_data_stream.market_data_stream = _market_data_stream
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return real_services
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@pytest.fixture()
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def mock_operations_service(
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real_services: Services,
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mocker,
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portfolio_response: PortfolioResponse,
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) -> Services:
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real_services.operations = mocker.Mock(wraps=real_services.operations)
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real_services.operations.get_portfolio.return_value = portfolio_response
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return real_services
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@pytest.fixture()
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def mock_users_service(
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real_services: Services,
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mocker,
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marginal_trade_active: bool,
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) -> Services:
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real_services.users = mocker.Mock(wraps=real_services.users)
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if marginal_trade_active:
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real_services.users.get_margin_attributes.return_value = (
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GetMarginAttributesResponse(
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liquid_portfolio=MoneyValue(currency="", units=0, nano=0),
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starting_margin=MoneyValue(currency="", units=0, nano=0),
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minimal_margin=MoneyValue(currency="", units=0, nano=0),
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funds_sufficiency_level=Quotation(units=322, nano=0),
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amount_of_missing_funds=MoneyValue(currency="", units=0, nano=0),
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)
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)
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else:
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real_services.users.get_margin_attributes.side_effect = RequestError(
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code=StatusCode.PERMISSION_DENIED,
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details="Marginal trade disabled",
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metadata=None,
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)
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return real_services
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@pytest.fixture()
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def marginal_trade_active() -> bool:
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return True
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@pytest.fixture()
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def mock_orders_service(
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real_services: Services,
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mocker,
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portfolio_positions: Dict[str, PortfolioPosition],
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balance: MoneyValue,
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current_market_data: List[Candle],
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settings: MovingAverageStrategySettings,
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marginal_trade_active: bool,
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) -> Services:
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real_services.orders = mocker.Mock(wraps=real_services.orders)
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def _post_order(
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*,
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figi: str = "",
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quantity: int = 0,
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price: Optional[Quotation] = None,
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direction: OrderDirection = OrderDirection(0),
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account_id: str = "",
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order_type: OrderType = OrderType(0),
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order_id: str = "",
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):
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assert figi == settings.share_id
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assert quantity > 0
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assert account_id == settings.account_id
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assert order_type.ORDER_TYPE_MARKET
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last_candle = current_market_data[-1]
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last_market_price = quotation_to_decimal(last_candle.close)
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position = portfolio_positions.get(figi)
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if position is None:
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position = PortfolioPosition(
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figi=figi,
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quantity=decimal_to_quotation(Decimal(0)),
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)
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if direction == OrderDirection.ORDER_DIRECTION_SELL:
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quantity_delta = -quantity
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balance_delta = last_market_price * quantity
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elif direction == OrderDirection.ORDER_DIRECTION_BUY:
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quantity_delta = +quantity
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balance_delta = -(last_market_price * quantity)
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else:
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raise AssertionError("Incorrect direction")
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logger.warning("Operation: %s, %s", direction, balance_delta)
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old_quantity = quotation_to_decimal(position.quantity)
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new_quantity = decimal_to_quotation(old_quantity + quantity_delta)
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position.quantity.units = new_quantity.units
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position.quantity.nano = new_quantity.nano
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old_balance = quotation_to_decimal(
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Quotation(units=balance.units, nano=balance.nano)
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)
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new_balance = decimal_to_quotation(old_balance + balance_delta)
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balance.units = new_balance.units
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balance.nano = new_balance.nano
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portfolio_positions[figi] = position
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real_services.orders.post_order = _post_order
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return real_services
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@pytest.fixture()
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def mocked_services(
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real_services: Services,
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mock_market_data_service,
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mock_market_data_stream_service,
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mock_operations_service,
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mock_users_service,
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mock_orders_service,
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) -> Services:
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return real_services
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@pytest.fixture()
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def account_manager(
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mocked_services: Services, settings: MovingAverageStrategySettings
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) -> AccountManager:
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return AccountManager(services=mocked_services, strategy_settings=settings)
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@pytest.fixture()
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def state() -> MovingAverageStrategyState:
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return MovingAverageStrategyState()
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@pytest.fixture()
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def strategy(
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settings: MovingAverageStrategySettings,
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account_manager: AccountManager,
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state: MovingAverageStrategyState,
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) -> MovingAverageStrategy:
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return MovingAverageStrategy(
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settings=settings,
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account_manager=account_manager,
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state=state,
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)
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@pytest.fixture()
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def signal_executor(
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mocked_services: Services,
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state: MovingAverageStrategyState,
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settings: MovingAverageStrategySettings,
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) -> MovingAverageSignalExecutor:
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return MovingAverageSignalExecutor(
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services=mocked_services,
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state=state,
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settings=settings,
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)
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@pytest.fixture()
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def supervisor() -> MovingAverageStrategySupervisor:
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return MovingAverageStrategySupervisor()
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@pytest.fixture()
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def moving_average_strategy_trader(
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strategy: MovingAverageStrategy,
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settings: MovingAverageStrategySettings,
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mocked_services: Services,
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state: MovingAverageStrategyState,
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signal_executor: MovingAverageSignalExecutor,
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account_manager: AccountManager,
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supervisor: MovingAverageStrategySupervisor,
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) -> MovingAverageStrategyTrader:
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return MovingAverageStrategyTrader(
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strategy=strategy,
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settings=settings,
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services=mocked_services,
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state=state,
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signal_executor=signal_executor,
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account_manager=account_manager,
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supervisor=supervisor,
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)
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@pytest.fixture()
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def plotter(
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settings: MovingAverageStrategySettings,
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) -> MovingAverageStrategyPlotter:
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return MovingAverageStrategyPlotter(settings=settings)
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