simple-back | A simple daily python backtester | Data Visualization library
kandi X-RAY | simple-back Summary
kandi X-RAY | simple-back Summary
A simple daily python backtester that works out of the box.
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Top functions reviewed by kandi - BETA
- Get price for a given symbol
- Check if the key is in the cache
- Set a cache value
- Return cached value for key
- Get the key for the given key
- Fetch historical data
- Return a list of revisions for the given title
- Transform symbol
- Returns the highest order of the current event
- Return the order of an event
- Return a list of dates for the given symbol
- Remove a cache entry
simple-back Key Features
simple-back Examples and Code Snippets
Lines of Code : 29License : Weak Copyleft (MPL-2.0)
Copyfrom simple_back.backtester import BacktesterBuilder
builder = (
BacktesterBuilder()
.name('JNUG 20-Day Crossover')
.balance(10_000)
.calendar('NYSE')
.compare(['JNUG']) # strategies to compare with
.live_progress() # show a progre pip install simple_back
Community Discussions
Trending Discussions on simple-back
QUESTION
What I'm doing: I'm building a system in which one Cloud Pub/Sub topic will be read by dozens of Apache Beam pipelines in streaming mode. Each time I deploy a new pipeline, it should first process several years of historic data (stored in BigQuery).
The problem: If I replay historic data into the topic whenever I deploy a new pipeline (as suggested here), it will also be delivered to every other pipeline currently reading the topic, which would be wasteful and very costly. I can't use Cloud Pub/Sub Seek (as suggested here) as it stores a maximum of 7 days history (more details here).
The question: What is the recommended pattern to replay historic data into new Apache Beam streaming pipelines with minimal overhead (and without causing event time/watermark issues)?
Current ideas: I can currently think of three approaches to solving the problem, however, none of them seem very elegant and I have not seen any of them mentioned in the documentation, common patterns (part 1 or part 2) or elsewhere. They are:
Ideally, I could use Flatten to merge the real-time
ReadFromPubSubwith a one-offBigQuerySource, however, I see three potential issues: a) I can't account for data that has already been published to Pub/Sub, but hasn't yet made it into BigQuery, b) I am not sure whether theBigQuerySourcemight inadvertently be rerun if the pipeline is restarted, and c) I am unsure whetherBigQuerySourceworks in streaming mode (per the table here).I create a separate replay topic for each pipeline and then use Flatten to merge the
ReadFromPubSubs for the main topic and the pipeline-specific replay topic. After deployment of the pipeline, I replay historic data to the pipeline-specific replay topic.I create dedicated topics for each pipeline and deploy a separate pipeline that reads the main topic and broadcasts messages to the pipeline-specific topics. Whenever a replay is needed, I can replay data into the pipeline-specific topic.
ANSWER
Answered 2019-Mar-08 at 22:47Out of your three ideas:
The first one will not work because currently the Python SDK does not support unbounded reads from bounded sources (meaning that you can't add a
ReadFromBigQueryto a streaming pipeline).The third one sounds overly complicated, and maybe costly.
I believe your best bet at the moment is as you said, to replay your table into an extra PubSub topic that you Flatten with your main topic, as you rightly pointed out.
I will check if there's a better solution, but for now, option #2 should do the trick.
Also, I'd refer you to an interesting talk from Lyft on doing this for their architecture (in Flink).
QUESTION
The post sets the Max Poll Records to 1 to guarantee the events in one flow file come from the same partition. https://community.hortonworks.com/articles/223849/simple-backup-and-restore-of-kafka-messages-via-ni.html
Does that mean if using Message Demarcator, the events in the same FlowFile can be from different partitions?
from the source code I think the above thinking is true? https://github.com/apache/nifi/blob/ea9b0db2f620526c8dd0db595cf8b44c3ef835be/nifi-nar-bundles/nifi-kafka-bundle/nifi-kafka-0-9-processors/src/main/java/org/apache/nifi/processors/kafka/pubsub/ConsumerLease.java#L366
...ANSWER
Answered 2019-Jan-18 at 16:08When using a demarcator it creates a bundle per topic/partition, so you will get flow files where all messages are from the same topic partition:
The reason that post set max pool records to 1 was explained in the post, it was because the key of the messages is only available when there is 1 message per flow file, and they needed the key in this case. In general, it is better to not do this and to have many messages per flow file.
QUESTION
I would like to use RxJava (RxJava2 actually) to periodically send network (HTTP) requests. A first attempt would look more or less like this (adapted from this source):
...ANSWER
Answered 2018-Aug-02 at 15:48Use repeatWhen
QUESTION
New F# developer, long time C# developer. As an exercise in learning F#, I'm working my way through Eric Lippert's series on graph coloring translating from his C# to F#. I'm currently working on part two.
The original C# is in the blog post - here's the F# translation so far - but it doesn't compile:
...ANSWER
Answered 2017-Jan-03 at 21:49I think the problem is that the this reference is created as a reference to the current value of the struct, so that you can modify the struct (if you wanted and the struct was mutable).
This causes problem inside seq { .. } because this generates code inside another class and so the compiler fails to pass the reference to the "this" instance.
If you assign the this value an ordinary local variable, then the code works fine:
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