Alpha

// NullAlphaModel doesn't emit insights.
AddAlpha(new NullAlphaModel());

# NullAlphaModel doesn't emit insights.
self.add_alpha(NullAlphaModel())

// Emit price UP insight that expires in 30 days.
	AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(30)));

# Emit price UP insight that expires in 30 days.
	self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(30)))

// Add HistoricalReturnsAlphaModel to leverage historical return data for generating alpha signals, identifying trends based on past performance.
AddAlpha(new HistoricalReturnsAlphaModel());

# Add HistoricalReturnsAlphaModel to leverage historical return data for generating alpha signals, identifying trends based on past performance.
self.add_alpha(HistoricalReturnsAlphaModel())

// Add EmaCrossAlphaModel to generate trading insights based on the crossing of fast and slow exponential moving averages identifying trends and potential reversals. The duration of insights is set by the resolution and fast_period to ensure timely and relevant trading signals.
AddAlpha(new EmaCrossAlphaModel());

# Add EmaCrossAlphaModel to generate trading insights based on the crossing of fast and slow exponential moving averages identifying trends and potential reversals. The duration of insights is set by the resolution and fast_period to ensure timely and relevant trading signals.
self.add_alpha(EmaCrossAlphaModel())

// Use MacdAlphaModel to generate insights based on MACD crossovers, emitting up signals when the MACD signal line is 1% above the security price and down signals when it is 1% below, while canceling insights if within 1% capturing significant trend changes while avoiding noise from minor fluctuations.
AddAlpha(new MacdAlphaModel());

# Use MacdAlphaModel to generate insights based on MACD crossovers, emitting up signals when the MACD signal line is 1% above the security price and down signals when it is 1% below, while canceling insights if within 1% capturing significant trend changes while avoiding noise from minor fluctuations.
self.add_alpha(MacdAlphaModel())

// The RsiAlphaModel generates insights based on the RSI indicator: emits a down insight when RSI exceeds 70 (overbought) and an up insight when RSI falls below 30 (oversold). This helps identify potential price reversals based on momentum.
AddAlpha(new RsiAlphaModel());

# The RsiAlphaModel generates insights based on the RSI indicator: emits a down insight when RSI exceeds 70 (overbought) and an up insight when RSI falls below 30 (oversold). This helps identify potential price reversals based on momentum.
self.add_alpha(RsiAlphaModel())

// Use BasePairsTradingAlphaModel to analyze security pairs from the universe, generating long/short insights based on deviations from a 500-period EMA of their price ratio to capture mean-reversion opportunities.
AddAlpha(new BasePairsTradingAlphaModel());

# Use BasePairsTradingAlphaModel to analyze security pairs from the universe, generating long/short insights based on deviations from a 500-period EMA of their price ratio to capture mean-reversion opportunities.
self.add_alpha(BasePairsTradingAlphaModel())

// Add PearsonCorrelationPairsTradingAlphaModel to rank and trade the most correlated security pairs based on Pearson correlation coefficients, using a similar insight logic as BasePairsTradingModel to capture mean-reversion opportunities.
AddAlpha(new PearsonCorrelationPairsTradingAlphaModel());

# Add PearsonCorrelationPairsTradingAlphaModel to rank and trade the most correlated security pairs based on Pearson correlation coefficients, using a similar insight logic as BasePairsTradingModel to capture mean-reversion opportunities.
self.add_alpha(PearsonCorrelationPairsTradingAlphaModel())