PineCone – A bundler for splitting PineScript into multiple files

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I built a module bundler for PineScript (TradingView's scripting language). The problem: TradingView doesn't support multi-file projects. As indicators grow complex, you end up with 1000+ line files that are painful to maintain. Pinecone lets you split code across multiple .pine files using import/export directives, then bundles everything into a single TradingView-compatible script. It handles automatic namespacing to prevent variable collisions between modules, deduplicates external library imports, and includes watch mode for development. Built with Python. Uses the pynescript library for AST parsing and manipulation. I had to work around some upstream parser bugs with generic type syntax, which was an interesting challenge. GitHub: https://github.com/claudianadalin/pinecone Blog post with more technical details: https://www.claudianadalin.com/blog/building-pinecone This is a niche tool, but if you've ever built complex TradingView indicators, you know the pain. Would love feedback on the approach.

Visit product View on Hacker NewsDec 28, 2025

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77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: single, using, code · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: trading · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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