Temporal
Trigger.dev
n8n.io
Pipedream
Amazon AWS
Apache Airflow
Aditya Protocol
Molted
Backtrader
QuantConnect
Quantopian
CloudQuant
QuantRocket
Intrinio
Gekko Plus
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Temporal
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Based on our record, Temporal should be more popular than Backtrader. It has been mentiond 16 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Durable execution โ the pattern implemented by Temporal, Inngest, Rivet Actors, and now Cloudflare Workflows โ treats waiting as a continuation rather than a loop:. - Source: dev.to / 4 days ago
Two specific moves stand out in Duncan's account. The first is durable execution, via Temporal โ Mercury replaced fragile cron-and-database state machines with workflow code whose failure semantics are platform-handled (replay, retry, timeout, cancellation). Mercury open-sourced its hs-temporal-sdk, which wraps Temporal's official Rust Core SDK via FFI and provides a Haskell-native API. The dovetail with Haskell's... - Source: dev.to / about 1 month ago
We picked Temporal as the first reference engine on purpose. Temporal has the strictest execution model we know of โ a V8 sandbox, determinism constraints, replay-driven recovery. If our port contract holds up against that, easier engines โ an in-memory test double, a BullMQ queue, or JSON-first platforms like Inngest or Restate โ plug in through the same two interfaces. We're shipping Temporal first; the rest is... - Source: dev.to / about 2 months ago
The trick is to find whatever metadata channel the queue already gives you and use that and thankfully, almost every mature queue has one (probably because of this scenario). SQS has message attributes, Temporal has context propagators built into the SDK, and Hatchet (which we use to run our workflows) has a metadata field called additionalMetadata. - Source: dev.to / 3 months ago
A typical production stack for teams using Claude or Gemini as the reasoning layer includes an LLM provider API, an orchestration layer (n8n, Temporal, or a custom Python service), application infrastructure (a server running the orchestration code), and a data layer (a database for storing results). Each boundary introduces a failure point. When the LLM provider changes its rate limits, as OpenAI did repeatedly... - Source: dev.to / 4 months ago
I do like what I see and hear about backtrader.com. I would say they are a notable exception to my general rule of not trusting or using backtesting frameworks. However, I still think it is important to understand how the framework you are using works. So if you are using backtrader for backtesting you still need to put in the time to understand the backtesting engine. Source: over 3 years ago
What about backtrader.com? And I feel like it would be step 2 after you at least have something to backtrade and test haha. Source: over 3 years ago
Backtesting is basically applying your strategy on historical price data to see if it makes money. I've used Backtrader it works decently well: https://backtrader.com/. Source: almost 5 years ago
Trigger.dev - Trigger workflows from APIs, on a schedule, or on demand. API calls are easy with authentication handled for you. Add durable delays that survive server restarts.
QuantConnect - QuantConnect provides a free algorithm backtesting tool and financial data so engineers can design algorithmic trading strategies. We are democratizing algorithm trading technology to empower investors.
n8n.io - Free and open fair-code licensed node based Workflow Automation Tool. Easily automate tasks across different services.
Quantopian - Your algorithmic investing platform
Pipedream - Integration platform for developers
CloudQuant - Crowd based algorithmic trading development and backtesing for stock market trading.