Indicators
Manual Indicators
Introduction
Manual indicators are indicators that don't automatically update from the underlying security data. Manual updates let you customize how you update the indicator, such as using a custom field or using Renko Bars. It's not always necessary to create manual indicators though. Automatic indicators are easier to create and update.
Naming Convention
The class name to create a manual indicator is the indicator name spelled in pascal case. For example, to create a manual simple moving average indicator, use the SimpleMovingAverage class.
Create Manual Indicators
To create manual indicators, instantiate an indicator with its constructor. To view all of the available indicators and their constructors, see Supported Indicators.
// Create an indicator private Delay _delay; _delay = new Delay(20); // Create a candlestick pattern private TwoCrows _twoCrows; _twoCrows = new TwoCrows();
# Create an indicator self.delay = Delay(20) # Create a candlestick pattern self.two_crows = TwoCrows()
You can track indicators by their name. To name a manual indicator, pass a string as the first argument to the constructor.
// Name an indicator
_delay = new Delay("AAPL Past Price", 20);
Log(_delay.Name);
// Name a candlestick pattern
_twoCrows = new TwoCrows("Two Crows Pattern");
Log(_twoCrows.Name); # Name an indicator
self.delay = Delay("AAPL Past Price", 20)
self.log(self.delay.name)
# Name a candlestick pattern
self.two_crows = TwoCrows("Two Crows Pattern")
self.log(self.two_crows.name)
If you don't name an indicator, it's given a default name. For example, the default name for a Delay indicator is "Delay(period)".
Manual Updates
With manual updates, you control what data you use to update the indicator. For instance, you can use the 3:30 PM price in your daily moving average instead of the daily closing price or you can use the maximum temperature of the past 10 cloudy days.
To update an indicator, call the Updateupdate method. The Updateupdate method expects one of the following arguments:
- Time/decimal pair
IndicatorDataPointQuoteBarTradeBar- Custom data bar
To view what data type you should use to update an indicator, see Supported Indicators.
You can update indicators at any point in your algorithm, but the most common places are during the OnDataon_data event handler or during a consolidation event handler.
def initialize(self) -> None:
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
self._rsi = RelativeStrengthIndex(10, MovingAverageType.SIMPLE)
self.ad = AccumulationDistribution()
self.consolidator = TradeBarConsolidator(timedelta(3)) # 10 x 3 daily trade bars to warm up the RSI.
self.consolidator = QuoteBarConsolidator(timedelta(3)) # 10 x 3 daily quote bars to warm up the RSI.
self.consolidator = RenkoBarConsolidator(1) # Renko consolidator that emits a bar when the price moves $1
# Update the AD indicator with the consolidated bar
self.consolidator.data_consolidated += self.on_data_consolidated
self.subscription_manager.add_consolidator(self._symbol, self.consolidator)
def on_data_consolidated(self, sender, bar):
self.ad.update(bar)
def on_data(self, slice: Slice) -> None:
# Update the RSI indicator value with the new input close price every day
if slice.bars.contains_key(self._symbol):
bar = slice.bars[self._symbol]
self.rsi.update(bar.end_time, bar.close)
private Symbol _symbol;
private RelativeStrengthIndex _rsi;
private AccumulationDistribution _ad;
public override void Initialize()
{
_symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_rsi = new RelativeStrengthIndex(10, MovingAverageType.Simple);
_ad = new AccumulationDistribution();
var consolidator = new TradeBarConsolidator(TimeSpan.FromDays(3)); // 10 x 3 daily trade bars to warm up the RSI.
consolidator = new QuoteBarConsolidator(TimeSpan.FromDays(3)); // 10 x 3 daily quote bars to warm up the RSI.
consolidator = new RenkoBarConsolidator(1); // Renko consolidator that emits a bar when the price moves $1
//Update the AD indicator with the consolidated bar
consolidator.DataConsolidated += (_, bar) => _ad.Update(bar);
SubscriptionManager.AddConsolidator(_symbol, consolidator);
}
public override void OnData(Slice slice)
{
// Update the RSI indicator value with the new input close price every day
if (slice.Bars.ContainsKey(_symbol))
{
var bar = slice.Bars[_symbol];
_rsi.Update(bar.EndTime, bar.Close);
}
}
Automatic Updates
With automatic updates, your indicators automatically update with the security data on a schedule you set. To configure automatic updates, create a consolidator and then call the RegisterIndicatorregister_indicator method. If you register an indicator for automatic updates, don't call the indicator's Updateupdate method or else the indicator will receive double updates.
# Create a security subscription
self._symbol = self.add_equity("SPY", Resolution.MINUTE).symbol
# Create a manual indicator
self.indicator = RelativeStrengthIndex(10, MovingAverageType.SIMPLE)
# Create a consolidator
consolidator = TradeBarConsolidator(3) # 10 x 3 min trade bars to warm up the RSI.
consolidator = QuoteBarConsolidator(4) # 10 x 4 min quote bars to warm up the RSI
consolidator = RenkoConsolidator(1) # Renko consolidator that emits a bar when the price moves $1
# Register the indicator to update with the consolidated data
self.register_indicator(self._symbol, self.indicator, consolidator) // Create a security subscription
_symbol = AddEquity("SPY", Resolution.Hour);
// Create a manual indicator
_indicator = new RelativeStrengthIndex(10, MovingAverageType.Simple);
// Create a consolidator
var consolidator = new TradeBarConsolidator(3); // 10 x 3 min trade bars to warm up the RSI.
consolidator = new QuoteBarConsolidator(4); // 10 x 4 min quote bars to warm up the RSI.
consolidator = new RenkoConsolidator(1); // Renko consolidator that emits a bar when the price moves $1
// Register the indicator to update with the consolidated data
RegisterIndicator(_symbol, _indicator, consolidator);
Data point indicators use only a single price data in their calculations. By default, those indicators use the closing price. For assets with TradeBar data, that price is the TradeBar close price. For assets with QuoteBar data, that price is the mid-price of the bid closing price and the ask closing price. To create an indicator with the other fields like the Open, High, Low, or Close, provide a selector argument to the RegisterIndicatorregister_indicator method.
self.register_indicator(self._symbol, self.indicator, consolidator, Field.HIGH)
RegisterIndicator(_symbol, _rsi, consolidator, Field.High);
The Field class has the following selector properties:
To create a custom selector, define a function that calculates the value.
RegisterIndicator(_symbol, _indicator, _consolidator, x =>
{
var bar = x as IBaseDataBar;
return (bar.Low + bar.High) / 2;
});
To stop automatically updating an indicator, pass the indicator to the DeregisterIndicatorderegister_indicator property method.
DeregisterIndicator(_indicator); // Alias: // UnregisterIndicator(_indicator);
self.deregister_indicator(self.indicator) # Alias: # self.unregister_indicator(self.indicator)
Warm Up Indicators
Indicators use historical data to compute their value. Before you start trading with an indicator, warm it up. There are several ways to warm-up manual indicators.
Manual Indicator Warm-up
If you have access to the QCAlgorithm object, you can manually warmup indicators with a history request.
private SimpleMovingAverage _sma;
// Create a manual indicator.
_sma = SimpleMovingAverage(20);
// Get 20 bars of daily data.
var history = algorithm.History(_symbol, 20, Resolution.Daily);
// Manually warm up the indicator with the historical close prices.
foreach (var bar in history)
{
sma.Update(bar.Time, bar.Close);
}
# Create a manual indicator.
self._sma = SimpleMovingAverage(20)
# Get 20 bars of daily data.
history = algorithm.history[TradeBar](self._symbol, 20, Resolution.DAILY)
# Manually warm up the indicator with the historical close prices.
for bar in history:
self._sma.update(bar.end_time, bar.close)
Warm-up Helper Method
If an indicator inherits (implements) the IIndicatorWarmUpPeriodProvider interface, you can warm it up with the WarmUpIndicatorwarm_up_indicator method.
// Warm up the indicator using the warm-up helper. _sma = SimpleMovingAverage(20); algorithm.WarmUpIndicator(_symbol, _sma);
# Warm up the indicator using the warm-up helper. self._sma = SimpleMovingAverage(20) algorithm.warm_up_indicator(self._symbol, self._sma)
To warm up the indicator with a resolution that's different from the security resolution, pass a resolution or TimeSpantimedelta argument to the WarmUpIndicatorwarm_up_indicator method. The resolution you provide should be greater than or equal to the security resolution. For example, if the security has minute resolution data, you should warm up the indicator with data that spans at least one minute.
// Warm-up with daily bars. algorithm.WarmUpIndicator(_symbol, _sma, Resolution.Daily); // Warm-up with 3-day bars. algorithm.WarmUpIndicator(_symbol, _sma, TimeSpan.FromDays(3));
# Warm-up with daily bars. algorithm.warm_up_indicator(self._symbol, self._sma, Resolution.DAILY) # Warm-up with 3-day bars. algorithm.warm_up_indicator(self._symbol, self._sma, timedelta(days=3))
The WarmUpIndicatorwarm_up_indicator method uses the default Valuevalue of the historical data to warm up the indicator.
In most cases, this is the closing price. To warm up the indicator with an alternative price field, pass a Field argument to the method.
// Warm up the indicator using daily high prices instead of close prices. algorithm.WarmUpIndicator(_symbol, _sma, Resolution.Daily, Field.High);
# Warm up the indicator using daily high prices instead of close prices. algorithm.warm_up_indicator(self._symbol, self._sma, Resolution.DAILY, Field.HIGH)
Some indicators require the prices of two assets to compute their value (for example, Beta).
In this case, pass a list of the Symbol objects to the method.
// Use the warm-up helper for an indicator that needs the data of 2 assets.
var spy = AddEquity("SPY").Symbol;
var aapl = AddEquity("AAPL").Symbol;
var beta = new Beta("", aapl, spy, 21);
WarmUpIndicator(new[] {aapl, spy}, beta, Resolution.Daily); # Use the warm-up helper for an indicator that needs the data of 2 assets.
self._spy = self.add_equity("SPY").symbol
self._aapl = self.add_equity('AAPL').symbol
self._beta = Beta("", self._aapl, self._spy, 21)
self.warm_up_indicator([self._aapl, self._spy], self._beta, Resolution.DAILY)
Algorithm Warm-up
If you create indicators at the beginning of your algorithm, you can set an algorithm warm-up period to warm up the indicators. When you set an algorithm warm-up period, the engine pumps data in and automatically updates all the indicators from before the start date of the algorithm. To ensure that all the indicators are ready after the algorithm warm-up period, choose a lookback period that contains sufficient data.
private SimpleMovingAverage _sma;
// In the Initialize method, request data and construct the indicators you need.
var symbol = AddEquity("SPY", Resolution.Daily).Symbol;
_sma = SimpleMovingAverage(20);
SetWarmUp(20);
// In the OnData event handler, update your indicators.
// Use the IsWarmingUp flag to skip some code execution.
_sma.Update(data["SPY"]); // Delete this line if you registered the indicator for automatic updates.
if (IsWarmingUp)
{
return;
} # In the initialize() method, request data and construct the indicators you need.
self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
self._sma = SimpleMovingAverage(20)
self.set_warm_up(20)
# In the on_data event handler, update your indicators.
# Use the self.is_warming_up flag to skip some code execution.
self._sma.update(data[self._symbol]) # Delete this line if you registered the indicator for automatic updates.
if self.is_warming_up:
return
Timing Considerations
In some cases, you might create and warm up the indicator during its sampling period. For example, say the security resolution is minute, the indicator resolution is daily, and you create and warm-up the indicator at noon without using the SetWarmUpset_warm_up method. In this example, the history request that gathers data to warm up the indicator won't contain any data from the current day. Furthermore, if you set up a consolidator to update the indicator, the consolidator also won't aggregate any data from before noon. It doesn't cause issues if the indicator only uses the close price to calculate the indicator value (like the simple moving average indicator) because the first consolidated bar that updates the indicator will have the correct close price. However, if the indicator uses more than just the close price to calculate its value (like the True Range indicator), the open, high, and low values of the first consolidated bar may be incorrect, causing the initial indicator values to be incorrect.
Examples
The following examples demonstrate some common practices for using manual indicators.
Example 1: Universe Selection
The following algorithm selects the stocks within 5% of the 1-year maximum price among the top 100 liquid stocks. To do so, we make use of Maximum indicator to do so. Then, we hold the stocks with price > EMA > SMA, which indicates an upward accelerating trend.
public class ManualIndicatorAlgorithm : QCAlgorithm
{
private Dictionary<Symbol, Maximum> _maximumBySymbol = new();
private Dictionary<Symbol, SymbolData> _symbolData = new();
public override void Initialize()
{
SetStartDate(2021, 1, 1);
SetEndDate(2021, 2, 1);
// Select a popularity-based universe with indicators in a Selection function.
AddUniverse(Selection);
// Set a scheduled event to rebalance daily on the daily indicator signals.
Schedule.On(
DateRules.EveryDay(),
TimeRules.At(9, 31),
Rebalance
);
}
private IEnumerable<Symbol> Selection(IEnumerable<Fundamental> fundamentals)
{
var selected = new List<Symbol>();
// Initially filtered for the top 100 liquid stocks first.
var filtered = fundamentals.OrderByDescending(f => f.DollarVolume)
.Take(100)
.ToList();
foreach (var f in filtered)
{
if (!_maximumBySymbol.TryGetValue(f.Symbol, out var maximum))
{
maximum = new(252);
// Warm up the Maximum indicator for its readiness to use immediately.
var history = History<TradeBar>(f.Symbol, 252, Resolution.Daily);
foreach (var bar in history)
{
maximum.Update(bar.EndTime, bar.Close);
}
_maximumBySymbol[f.Symbol] = maximum;
}
else
{
// Update the indicator with the last known adjusted price daily.
maximum.Update(f.EndTime, f.AdjustedPrice);
}
// Select to trade if the current price is within 5% of the maximum price of the last year.
// Close to the maximum price provides evidence of high popularity for the fund to support the trend.
if (f.AdjustedPrice >= maximum * 0.95m)
{
selected.Add(f.Symbol);
}
}
return selected;
}
private void Rebalance()
{
var symbolsToBuy = new List<Symbol>();
foreach (var (symbol, symbolData) in _symbolData)
{
// Buy the stocks whose prices are above the EMA and the EMA is above the SMA, meaning their trend is upward accelerating.
if (symbolData.IsReady && Securities[symbol].Price > symbolData.Ema && symbolData.Ema > symbolData.Sma)
{
symbolsToBuy.Add(symbol);
}
}
// Equally invest in selected stocks to dissipate capital risk evenly.
var count = symbolsToBuy.Count;
if (count > 0)
{
var targets = symbolsToBuy.Select(symbol => new PortfolioTarget(symbol, 1m / count)).ToList();
// Liquidate the positions that are not on an upward trend or are not popular anymore.
SetHoldings(targets, liquidateExistingHoldings: true);
}
}
public override void OnSecuritiesChanged(SecurityChanges changes)
{
foreach (var added in changes.AddedSecurities)
{
// Instantiate a new instance of SymbolData object to hold indicators to use.
_symbolData[added.Symbol] = new SymbolData(this, added.Symbol);
}
foreach (var removed in changes.RemovedSecurities)
{
// Remove the data subscription of indicators to release computation resources when leaving the universe.
if (_symbolData.Remove(removed.Symbol, out var symbolData))
{
symbolData.Dispose();
}
}
}
private class SymbolData
{
private QCAlgorithm _algorithm;
public Symbol Symbol { get; set; }
public ExponentialMovingAverage Ema { get; set; }
public SimpleMovingAverage Sma { get; set; }
public bool IsReady => Ema.IsReady && Sma.IsReady;
public SymbolData(QCAlgorithm algorithm, Symbol symbol)
{
_algorithm = algorithm;
Symbol = symbol;
// Create an EMA and an SMA manual indicator for trade signal generation.
Ema = new ExponentialMovingAverage(20);
Sma = new SimpleMovingAverage(20);
// Warm up the indicators to ensure their readiness to use them immediately.
algorithm.WarmUpIndicator(symbol, Ema, Resolution.Daily);
algorithm.WarmUpIndicator(symbol, Sma, Resolution.Daily);
// Subscribe to the indicators to update daily price data for updated trade signal generation.
algorithm.RegisterIndicator(symbol, Ema, Resolution.Daily);
algorithm.RegisterIndicator(symbol, Sma, Resolution.Daily);
}
public void Dispose()
{
Ema.Reset();
Sma.Reset();
// Cancel data subscription to release computation resources.
_algorithm.DeregisterIndicator(Ema);
_algorithm.DeregisterIndicator(Sma);
}
}
} class ManualIndicatorAlgorithm(QCAlgorithm):
maximum_by_symbol = {}
def initialize(self) -> None:
self.set_start_date(2021, 1, 1)
self.set_end_date(2021, 2, 1)
# Select a popularity-based universe with indicators in a Selection function.
self._universe = self.add_universe(self.selection)
# Set a scheduled event to rebalance daily on the daily indicator signals.
self.schedule.on(
self.date_rules.every_day(),
self.time_rules.at(9, 31),
self.rebalance
)
def selection(self, fundamentals: list[Fundamental]) -> list[Symbol]:
selected = []
# Initially filtered for the top 100 liquid stocks first.
filtered = sorted(fundamentals, key=lambda f: f.dollar_volume, reverse=True)[:100]
for f in filtered:
if f.symbol not in self.maximum_by_symbol:
self.maximum_by_symbol[f.symbol] = Maximum(252)
# Warm up the Maximum indicator to its readiness to use immediately.
history = self.history[TradeBar](f.symbol, 252, Resolution.DAILY)
for bar in history:
self.maximum_by_symbol[f.symbol].update(bar.end_time, bar.close)
else:
# Update the indicator with the last known adjusted price daily.
self.maximum_by_symbol[f.symbol].update(f.end_time, f.adjusted_price)
# Select to trade if the current price is within 5% of the maximum price of the last year.
# Close to the maximum price provides evidence of high popularity for the fund to support the trend.
if f.adjusted_price >= self.maximum_by_symbol[f.symbol].current.value * 0.95:
selected.append(f.symbol)
return selected
def rebalance(self) -> None:
def to_buy(symbol):
security = self.securities[symbol]
# Buy the stocks whose prices are above the EMA and the EMA is above the SMA, meaning their trend is upward accelerating.
return security.price > security.ema.current.value > security.sma.current.value
symbols_to_buy = [symbol for symbol in self._universe.selected if to_buy(symbol)]
# Equally invest in the selected stocks to dissipate the capital risk evenly.
count = len(symbols_to_buy)
if count > 0:
targets = [PortfolioTarget(symbol, 1 / count) for symbol in symbols_to_buy]
# Liquidate the positions that are not on an upward trend or are not popular anymore.
self.set_holdings(targets, liquidate_existing_holdings=True)
def on_securities_changed(self, changes: SecurityChanges) -> None:
for added in changes.added_securities:
symbol = added.symbol
# Create an EMA and an SMA manual indicator for trade signal generation.
added.ema = ExponentialMovingAverage(20)
added.sma = SimpleMovingAverage(20)
# Warm up the indicators to ensure their readiness to use them immediately.
self.warm_up_indicator(symbol, added.ema, Resolution.DAILY)
self.warm_up_indicator(symbol, added.sma, Resolution.DAILY)
# Subscribe to the indicators to update daily price data for updated trade signal generation.
self.register_indicator(symbol, added.ema, Resolution.DAILY)
self.register_indicator(symbol, added.sma, Resolution.DAILY)
for removed in changes.removed_securities:
# Cancel data subscription to release computation resources when leaving the universe.
self.deregister_indicator(removed.ema)
self.deregister_indicator(removed.sma)
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