How to use an Hourly and Daily resolution in an algorithm

Here's a clear example for Ryan on how to use the same indicators at both Hourly and Daily resolutions in QuantConnect, including automatic and manual (with consolidators) methods:

from AlgorithmImports import *
from datetime import timedelta

class DualResolutionIndicatorsAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2021, 1, 1)
        self.set_end_date(2022, 1, 1)

        # Add SPY at hourly resolution
        self._symbol = self.add_equity("SPY", Resolution.HOUR).symbol

        # Hourly indicators
        self._hourly_sma = self.sma(self._symbol, 14, Resolution.HOUR)
        self._hourly_rsi = self.rsi(self._symbol, 14, MovingAverageType.WILDERS, Resolution.HOUR)
        self._hourly_obv = self.obv(self._symbol, Resolution.HOUR)

        # Daily indicators (to be updated manually via a consolidator)
        self._daily_sma = SimpleMovingAverage(14)
        self._daily_rsi = RelativeStrengthIndex(14, MovingAverageType.WILDERS)
        self._daily_obv = OnBalanceVolume("DailyOBV")

        # Consolidator: build daily bars from hourly data
        daily_consolidator = TradeBarConsolidator(timedelta(days=1))
        daily_consolidator.data_consolidated += self.on_daily_bar
        self.subscription_manager.add_consolidator(self._symbol, daily_consolidator)

    def on_daily_bar(self, sender, bar: TradeBar) -> None:
        # Manually update daily indicators
        self._daily_sma.update(bar.end_time, bar.close)
        self._daily_rsi.update(bar.end_time, bar.close)
        self._daily_obv.update(bar)

    def on_data(self, data: Slice) -> None:
        # Log indicator values once they're ready
        if self._hourly_sma.is_ready and self._daily_sma.is_ready:
            self.log(f"Hourly SMA: {self._hourly_sma.current.value:.2f} | Daily SMA: {self._daily_sma.current.value:.2f}")

        if self._hourly_rsi.is_ready and self._daily_rsi.is_ready:
            self.log(f"Hourly RSI: {self._hourly_rsi.current.value:.2f} | Daily RSI: {self._daily_rsi.current.value:.2f}")

        if self._hourly_obv.is_ready and self._daily_obv.is_ready:
            self.log(f"Hourly OBV: {self._hourly_obv.current.value:.2f} | Daily OBV: {self._daily_obv.current.value:.2f}")

How this works:

• The Hourly indicators (SMA, RSI, OBV) use QuantConnect's built-ins with the resolution specified.
• The Daily indicators are created as indicator objects and updated with daily bars generated by a consolidator (since your subscription is at hour resolution).
• The algorithm logs both sets of indicator values as soon as they're ready.

This approach:

• Combines the power of two different timeframes elegantly.
• Is scalable for other custom indicators or resolutions.
• Demonstrates how to bridge the gap between high-frequency subscriptions and lower frequency indicator needs.

Let me know if you want this extended for multiple symbols, universes, or other indicator types!

Sources: Custom Resolutions - QuantConnect.com, Hybrid Algorithms - QuantConnect.com, Updating Indicators - QuantConnect.com, Multiple Symbol Consolidation Algorithm, Manual Indicators - QuantConnect.com

Wow thanks Mia.

Appreciate the detailed answer.

I have an idea of how I would expand this using the coarse universe filter and list comprehension, but I'm curious how you would approach extending this using a coarse filter to include all equity with daily volume above 100,000 and price over 25 and then filtering that coarse universe into a smaller subset using the daily SMA consolidator you've described above.

Thank you!