Equity Price Normalization Settings

Article Strategy library · Author: QuantConnect

Summary

This regression example shows how an equity subscription can choose its data normalization mode at the time the symbol is added. It demonstrates raw, adjusted, and total-return modes for three equities, then checks that each subscription uses the requested setting. The algorithm also monitors prices against expected ranges during a single trading day, making the example a compact way to validate both subscription configuration and resulting data.

The document is an implementation example rather than a trading strategy or performance study. Its price ranges apply only to the stated symbols and date, and it does not explain how to choose a normalization mode for a particular analysis. Researchers should account for the way raw, adjusted, and total-return data represent corporate actions when comparing prices or calculating returns.

Key ideas

An equity’s data normalization mode can be set directly when adding its subscription.
The example contrasts raw, adjusted, and total-return data using separate symbols.
Subscription settings are checked against the expected normalization mode.
Expected price ranges provide an additional validation of the received data.
The sample’s symbols and price ranges are illustrative and date-specific.

Tags

Equities Backtesting

Full text

# SetEquityDataNormalizationModeOnAddEquity


# SetEquityDataNormalizationModeOnAddEquity









This regression algorithm has examples of how to add an equity indicating the directly with the method instead of using the method.

## Source (Apache-2.0)

```python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from AlgorithmImports import *

### <summary>
### This regression algorithm has examples of how to add an equity indicating the <see cref="DataNormalizationMode"/>
### directly with the <see cref="QCAlgorithm.add_equity"/> method instead of using the <see cref="Equity.SET_DATA_NORMALIZATION_MODE"/> method.
### </summary>
class SetEquityDataNormalizationModeOnAddEquity(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2013, 10, 7)
        self.set_end_date(2013, 10, 7)

        spy_normalization_mode = DataNormalizationMode.RAW
        ibm_normalization_mode = DataNormalizationMode.ADJUSTED
        aig_normalization_mode = DataNormalizationMode.TOTAL_RETURN

        self._price_ranges = {}

        spy_equity = self.add_equity("SPY", Resolution.MINUTE, data_normalization_mode=spy_normalization_mode)
        self.check_equity_data_normalization_mode(spy_equity, spy_normalization_mode)
        self._price_ranges[spy_equity] = (167.28, 168.37)

        ibm_equity = self.add_equity("IBM", Resolution.MINUTE, data_normalization_mode=ibm_normalization_mode)
        self.check_equity_data_normalization_mode(ibm_equity, ibm_normalization_mode)
        self._price_ranges[ibm_equity] = (135.864131052, 136.819606508)

        aig_equity = self.add_equity("AIG", Resolution.MINUTE, data_normalization_mode=aig_normalization_mode)
        self.check_equity_data_normalization_mode(aig_equity, aig_normalization_mode)
        self._price_ranges[aig_equity] = (48.73, 49.10)

    def on_data(self, slice):
        for equity, (min_expected_price, max_expected_price) in self._price_ranges.items():
            if equity.has_data and (equity.price < min_expected_price or equity.price > max_expected_price):
                raise AssertionError(f"{equity.symbol}: Price {equity.price} is out of expected range [{min_expected_price}, {max_expected_price}]")

    def check_equity_data_normalization_mode(self, equity, expected_normalization_mode):
        subscriptions = [x for x in self.subscription_manager.subscriptions if x.symbol == equity.symbol]
        if any([x.data_normalization_mode != expected_normalization_mode for x in subscriptions]):
            raise AssertionError(f"Expected {equity.symbol} to have data normalization mode {expected_normalization_mode} but was {subscriptions[0].data_normalization_mode}")



```

Shown in full with attribution under the source's licence. Licence: Apache-2.0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.

Selecting Equity Price Normalization When Adding Symbols

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