Alpha
Supported Models
Introduction
This page describes the pre-built Alpha models in LEAN. The number of models grows over time. To add a model to LEAN, make a pull request to the GitHub repository. If none of these models perform exactly how you want, create a custom Alpha model.
Null Model
The NullAlphaModel doesn't emit any insights. It's the default Alpha model.
AddAlpha(new NullAlphaModel());
self.AddAlpha(NullAlphaModel())
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.
Constant Model
The ConstantAlphaModel always returns the same insight for each security.
AddAlpha(new ConstantAlphaModel(type, direction, period));
self.AddAlpha(ConstantAlphaModel(type, direction, period))
The following table describes the arguments the model accepts:
| Argument | Data Type | Description | Default Value |
|---|---|---|---|
type | InsightType | The type of insight | |
direction | InsightDirection | The direction of the insight | |
period | TimeSpantimedelta | The period over which the insight will come to fruition | |
magnitude | double?float/NoneType | The predicted change in magnitude as a +/- percentage | nullNone |
confidence | double?float/NoneType | The confidence in the insight | nullNone |
weight | double?float/NoneType | The portfolio weight of the insights | nullNone |
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.
Historical Returns Model
The HistoricalReturnsAlphaModel buys securities that have a positive trailing return and sells securities that have a negative trailing return. It sets the magnitude of the Insight objects to the trailing rate of change.
AddAlpha(new HistoricalReturnsAlphaModel());
self.AddAlpha(HistoricalReturnsAlphaModel())
The following table describes the arguments the model accepts:
| Argument | Data Type | Description | Default Value |
|---|---|---|---|
lookback | int | Historical return lookback period | 1 |
resolution | Resolution | The resolution of historical data | Resolution.Daily |
This model cancels all the active insights it has emit for a security when the security has a 0% historical return.
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.
EMA Cross Model
The EmaCrossAlphaModel uses an exponential moving average (EMA) cross to create insights. When the fast EMA crosses above the slow EMA, it emits up insights. When the fast EMA crosses below the slow EMA, it emits down insights. It sets the duration of Insight objects to be the product of the resolution and fastPeriod arguments.
AddAlpha(new EmaCrossAlphaModel());
self.AddAlpha(EmaCrossAlphaModel())
The following table describes the arguments the model accepts:
| Argument | Data Type | Description | Default Value |
|---|---|---|---|
fastPeriod | int | The fast EMA period | 12 |
slowPeriod | int | The slow EMA period | 26 |
resolution | Resolution | The resolution of data sent into the EMA indicators | Resolution.Daily |
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.
MACD Model
The MacdAlphaModel emits insights based on moving average convergence divergence (MACD) crossovers. If the MACD signal line is 1% above the security price, the model emits an up insight. If the MACD signal line is 1% below the security price, the model emits a down insight. If the MACD signal line is within 1% of the security price, the model cancels all the active insights it has emitted for the security.
AddAlpha(new MacdAlphaModel());
self.AddAlpha(MacdAlphaModel())
The following table describes the arguments the model accepts:
| Argument | Data Type | Description | Default Value |
|---|---|---|---|
fastPeriod | int | The MACD fast period | 12 |
slowPeriod | int | The MACD slow period | 26 |
signalPeriod | int | The smoothing period for the MACD signal | 9 |
movingAverageType | MovingAverageType | The type of moving average to use in the MACD | MovingAverageType.Exponential |
resolution | Resolution | The resolution of data sent into the MACD indicator | Resolution.Daily |
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.
RSI Model
The RsiAlphaModel generates insights based on the relative strength index (RSI) indicator values. When the RSI value passes above 70, the model emits a down insight. When the RSI value passes below 30, the model emits an up insight. The model uses the Wilder moving average type and sets the duration of Insight objects to be the product of the resolution and period arguments.
AddAlpha(new RsiAlphaModel());
self.AddAlpha(RsiAlphaModel())
The following table describes the arguments the model accepts:
| Argument | Data Type | Description | Default Value |
|---|---|---|---|
period | int | The RSI indicator period | 14 |
resolution | Resolution | The resolution of data sent into the RSI indicator | Resolution.Daily |
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.
Base Pairs Trading Model
The BasePairsTradingAlphaModel analyzes every possible pair combination from securities that the Universe Selection model selects. This model calculates a ratio between the two securities by dividing their historical prices over a lookback window. It then calculates the mean of this ratio by taking the 500-period EMA of the quotient. When the ratio diverges far enough from the mean ratio, this model emits generates alternating long ratio/short ratio insights emitted as a group to capture the reversion of the ratio.
AddAlpha(new BasePairsTradingAlphaModel());
self.AddAlpha(BasePairsTradingAlphaModel())
The following table describes the arguments the model accepts:
| Argument | Data Type | Description | Default Value |
|---|---|---|---|
lookback | int | Lookback period of the analysis | 1 |
resolution | Resolution | Analysis resolution | Resolution.Daily |
threshold | decimalfloat | The percent [0, 100] deviation of the ratio from the mean before emitting an insight | 1 |
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.
Pearson Correlation Pairs Trading Model
The PearsonCorrelationPairsTradingAlphaModel ranks every pair combination by its Pearson correlation coefficient and trades the pair with the highest correlation. This model follows the same insight logic as the BasePairsTradingModel.
AddAlpha(new PearsonCorrelationPairsTradingAlphaModel());
self.AddAlpha(PearsonCorrelationPairsTradingAlphaModel())
The following table describes the arguments the model accepts:
| Argument | Data Type | Description | Default Value |
|---|---|---|---|
lookback | int | Lookback period of the analysis | 15 |
resolution | Resolution | Analysis resolution | Resolution.Minute |
threshold | decimalfloat | The percent [0, 100] deviation of the ratio from the mean before emitting an insight | 1 |
minimumCorrelation | float | The minimum correlation to consider a tradable pair | 0.5 |
To view the implementation of this model, see the LEAN GitHub repositoryLEAN GitHub repository.
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