def setUp(self):
tickers = [BloombergTicker("AAPL US Equity")]
all_fields = PriceField.ohlcv()
self._mocked_prices_arr = self._make_mock_data_array(
tickers, all_fields)
self._price_provider_mock = PresetDataProvider(self._mocked_prices_arr,
self.data_start_date,
self.data_end_date,
self.frequency)
risk_estimation_factor = 0.05
data_handler = Mock()
data_handler.get_last_available_price.return_value = None
alpha_model = self.DummyAlphaModel(risk_estimation_factor,
data_handler)
ts = self._test_trading_session_init()
# Mock the backtest result in order to be able to compare transactions
self.transactions = []
ts.monitor.record_transaction.side_effect = lambda transaction: self.transactions.append(
transaction)
self.portfolio = ts.portfolio
AlphaModelStrategy(ts, {alpha_model: tickers}, use_stop_losses=True)
ts.start_trading()
def setUp(self):
all_fields = PriceField.ohlcv()
self._mocked_prices_arr = self._make_mock_data_array(self.tickers, all_fields)
self._price_provider_mock = PresetDataProvider(self._mocked_prices_arr,
self.data_start_date, self.data_end_date, self.frequency)
self.timer = SettableTimer()
self.alpha_model_type = DummyAlphaModel
def _get_demo_data_provider(frequency: Frequency):
frequency_to_data_file = {
Frequency.MIN_1: "intraday_data.csv",
Frequency.DAILY: "daily_data.csv"
}
input_file = join(get_starting_dir_abs_path(), "input",
frequency_to_data_file[frequency])
data, start_date, end_date = _acquire_data(input_file)
return PresetDataProvider(data, start_date, end_date, frequency)
def _get_demo_data_provider(frequency: Frequency):
frequency_to_data_file = {
Frequency.MIN_1: "intraday_data.csv",
Frequency.DAILY: "daily_data.csv"
}
input_file = join(
pathlib.Path(__file__).parent.absolute(), "input",
frequency_to_data_file[frequency])
data, start_date, end_date = _acquire_data(input_file)
return PresetDataProvider(data, start_date, end_date, frequency)
def _setup_data_handler(self, volume_value: Optional[float]) -> DataHandler:
dates = pd.date_range(str_to_date("2019-12-01"), str_to_date("2020-01-30"), freq='D')
prices_data_frame = QFDataFrame(data={PriceField.Volume: [volume_value] * len(dates)},
index=dates)
prices_data_array = tickers_dict_to_data_array({
self.ticker: prices_data_frame,
}, [self.ticker], [PriceField.Volume])
data_provider = PresetDataProvider(prices_data_array, dates[0], dates[-1], Frequency.DAILY)
timer = SettableTimer(dates[-1])
return DailyDataHandler(data_provider, timer)
def setUp(self):
all_fields = PriceField.ohlcv()
self._mocked_prices_arr = self._make_mock_data_array(
self.tickers, all_fields)
self._price_provider_mock = PresetDataProvider(self._mocked_prices_arr,
self.data_start_date,
self.end_date)
risk_estimation_factor = 0.05
self.alpha_model = DummyAlphaModel(risk_estimation_factor)
self.ts = self._test_trading_session_init()
model_tickers_dict = {self.alpha_model: self.tickers}
AlphaModelStrategy(self.ts, model_tickers_dict, use_stop_losses=True)
self.ts.start_trading()
def _mock_data_provider(self):
dates_index = pd.bdate_range(start=self.prices_start_date,
end=self.test_end_date)
fields_number = len(self.fields)
tickers = [
BloombergTicker("MSFT US Equity"),
BloombergTicker("AUDUSD Curncy"),
BloombergTicker(
"HOZ9 Comdty"), # Mock three contracts for Heating Oil
BloombergTicker("HOF0 Comdty"),
BloombergTicker("HOG0 Comdty"),
]
# Mock price data array
values = [
[ # 02-01-2020
# MSFT US Equity, AUDUSD Curncy, Heating Oil (3 contracts) prices
[90],
[90],
[90],
[90],
[90]
],
[ # 03-01-2020
[95], [95], [85], [100], [85]
],
[ # 06-01-2020
[110],
[100],
[90],
[100],
[90] # The Heating Oil contract will create an open position
],
[ # 07-01-2020
[90],
[110],
[95],
[np.nan],
[
95
] # Rolling for Heating Oil occurs, new Heating Oil contract is bought
],
[ # 08-01-2020
[80],
[120],
[100],
[100],
[
100
] # Previous Heating Oil is sold (there was no price on the 7th)
],
[ # 09-01-2020
[80], [120], [100], [100], [100]
],
]
# Open, High, Low and Close prices are equal every day
values = [[price * fields_number for price in day_prices]
for day_prices in values]
mocked_prices = QFDataArray.create(dates_index,
tickers,
self.fields,
data=values)
# Mock expiration dates
exp_dates = {
BloombergFutureTicker("Heating Oil", "HO{} Comdty", 1, 1, 100):
QFDataFrame(data={
ExpirationDateField.FirstNotice: [
str_to_date("2019-12-01"),
str_to_date("2020-01-07"),
str_to_date("2020-02-03")
],
ExpirationDateField.LastTradeableDate: [
str_to_date("2019-12-01"),
str_to_date("2020-01-07"),
str_to_date("2020-02-03")
],
},
index=[
BloombergTicker("HOZ9 Comdty"),
BloombergTicker("HOF0 Comdty"),
BloombergTicker("HOG0 Comdty")
])
}
return PresetDataProvider(data=mocked_prices,
exp_dates=exp_dates,
start_date=str_to_date("2019-12-01"),
end_date=self.test_end_date,
frequency=self.frequency)