StoxGPT – type "add RSI" and the indicator appears on the chart

Hacker News

Hi HN, I built *StoxGPT*, a TradingView-powered chart where you control everything by chat. Example: > *You*: add RSI > *Chart*: (RSI indicator appears) > *You*: change ticker to AMZN > *Chart*: (switches symbols) No menus, no hotkeys—just natural language mapped to the TradingView JS API. --- ### Why? I got tired of drilling through panels to add indicators or tweak settings. A chatbot front-end felt faster, so I wired GPT-3.5-Turbo to TradingView’s `widget.activeChart()` calls. --- ### How it works * *React + Next.js* front-end * *Dummy OHLCV generator* (open-source) for this demo * Simple command grammar → LLM → function-calling layer → TradingView injection * Hosted on Vercel; cold start ~ 400 ms --- ### What I’m exploring next * *Plaid auth* → live Robinhood balances & fundamentals * Back-testing via OpenAI function calls * Sharing indicator “recipes” between users --- ### Looking for feedback * Would live data make this a daily driver? * Any killer feature missing? * Is the chat modality actually faster for you? Thanks in advance—happy to answer anything!

Visit product View on Hacker NewsJul 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, openai, open · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

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

TrustMRRFits verified-revenue profile · Strong signals: users, trading · Missing: mobile apps, ios, personal

66%66% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users, calls · Missing: plus, platform, intuitive

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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