class RiskManagement(RiskManagementModel):
def __init__(self,stop_loss=2, tp=2) -> None:
self.stop_loss = stop_loss
self.tp = tp
def manage_risk(self, algorithm: QCAlgorithm, targets: List[IPortfolioTarget]) -> IEnumerable[IPortfolioTarget]:
for kvp in algorithm.securities:
security = kvp.value
if not security.invested:
continue
targets = []
# pnl = security.holdings.total_unrealised_profit
avg_price = security.holdings.average_price
atr = algorithm.atr_ind.Current.Value
Basically, It is my RiskManagemnet class, I want to access atr_ind(ATR indicator initialize in QCAlgorithm). but I can Not access here it show an error “AttributeError: 'QCAlgorithm' object has no attribute atr_ind”
But I can access this value via QCAlgorithm(mean main.py file) but can not to Risk management.
now Let me explain what I need.
Once alpha is generated then I will check if Stop_loss or TP is triggered or not if triggered then liquidate position and cancel insight. and also I want to analyze is it a UP or Down insight
Please suggest me if I have to do via another method.
P.S Alpha Model is working fine.
Mia Alissi
Based on the information provided in the knowledge sources, it seems like you're trying to access an attribute (
atr_ind
) of theQCAlgorithm
object in yourRiskManagement
class. However, the error message indicates that theQCAlgorithm
object does not have this attribute.One possible solution is to ensure that the
atr_ind
attribute is properly defined and initialized in yourQCAlgorithm
class before you try to access it in yourRiskManagement
class.In terms of accessing the ATR indicator, one of the forum discussions suggests that the ATR is an attribute of the
SymbolData
class and not of theRiskManagementModel
class. To resolve the error, you might want to useself.symbolData[symbol].atr.Current.Value
instead ofself.atr.Current.Value
source.As for the stop loss and take profit logic, you can refer to the
ManageRisk
function in theATRRiskManagementModel
class provided in one of the forum discussions. This function checks if the security is long or short, calculates the loss level based on the security price and ATR, and liquidates the position if the loss level equals the security price source.Regarding the cancellation of insights after liquidation, one of the forum discussions suggests passing
lambda time: None
to the constructor of theEqualWeightingPortfolioConstructionModel
to address the issue of extra orders being created after a position is liquidated source.Finally, it's important to note that managing positions based on alpha insights might not be the best approach as it is the responsibility of the Portfolio Construction Model and Execution Model to handle that work. If you need more control over the algorithm, you might find it easier to use the classic design of
QCAlgorithm
source.The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
Krishna
Thank you Mia Alissi
How can I know what type of insight I received via Alpha Model in Risk management?
My custom alpha model generate UP and Down insight. so it is important to fetch alpha model signal for Risk management or any other alternative method.
Thank You.
Mia Alissi
In the Algorithm Framework, the Alpha Model generates insights, which are then passed to the Portfolio Construction Model, Execution Model, and Risk Management Model. Therefore, you can access the insights generated by the Alpha Model in your Risk Management Model.
However, it's important to note that the Risk Management Model does not typically need to know the direction of the insights. Its main function is to manage the risk of the portfolio, not to make decisions based on the predictions of the Alpha Model.
If you still need to access the insights in your Risk Management Model, you might need to store them when they are generated by the Alpha Model. One of the forum discussions suggests using a helper class,
InsightCollection
, to store the insights source.Here is a Python code snippet from the discussion:
In this code,
self.insightCollection
is an instance of theInsightCollection
class, which can be used to store the insights generated by the Alpha Model.However, please note that this approach might make your algorithm more complex and harder to maintain. It's generally recommended to let each model in the Algorithm Framework perform its specific function: the Alpha Model generates predictions, the Portfolio Construction Model decides the target portfolio based on these predictions, the Execution Model executes trades to reach the target portfolio, and the Risk Management Model manages the risk of the portfolio.
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
Krishna
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
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