I have read the below In&Out post and found that the condition for exiting the TLT/IEF is not so good.
self.count and self.outday will reset to zero when the server crashes and algorithm restarts.
Is there any way to store the values and ensure the value could be used when the algorithm restart?
Start New Discussion 
Back
Reward Discussion 
if exit:
self.bull = False
self.outday = self.count
if self.count >= self.outday + wait_days:
self.bull = True
self.count += 1
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.
Vidal boreal,
I do have a version without self.count, but I promised my clients not to share this code.
Here is one method to save some data in your object store. You can test this out in a research notebook as well.
2
import json
from json import JSONEncoder
class EncodeNumpyArray(JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
return obj.tolist()
return JSONEncoder.default(self, obj)
try:
self.ObjectStore.ContainsKey('some_key')
except:
# Probably debugging in research notebook
qb = QuantBook()
self = qb
# Can loop through values and cache
#some_list = ['some_iter_key']
#for some_iter_var in some_list:
some_iter_var = 'some_iter_key'
f_key = f'{some_iter_var}'
value = np.float32([0.0]) # some new value to append to list
if(self.ObjectStore.ContainsKey(f_key)):
decodedWeights = json.loads(self.ObjectStore.Read(f_key))
cached_value = np.float32(decodedWeights)
new_value = np.append(cached_value,value)
encoded_json_params = json.dumps(new_value, cls=EncodeNumpyArray)
self.ObjectStore.Save(f_key, encoded_json_params)
print(encoded_json_params)
#[0.0, 0.0, 0.0, 0.0] # multiple runs will append based on cached value and current value
else:
# if first time need to create it
encoded_json_params = json.dumps(value, cls=EncodeNumpyArray)
self.ObjectStore.Save(f_key, encoded_json_params)
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.
To unlock posting to the community forums please complete at least 30% of Boot Camp.
You can
continue your Boot Camp training progress from the terminal. We
hope to see you in the community soon!
QuantConnect™ 2023. All Rights Reserved
|
|