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I vibe coded a complex trading app

Hacker News

I vibe coded a complex trading app

Hey HN, I just released Tradofire, a crypto trading app I built using a lot of cursor chatgpt and claude for over a year. The main idea of the app is very simple - to swipe on pre-prepared trading setups (like tinder) and if you right swipe it opens a position. The execution of this simple idea was much more complex and tbh I could not have done it without AI. Here's a quick rundown of the main features: Swipe-to-Trade UI: Quickly initiate long or short positions with intuitive swipe gestures. Real-Time Trade Signals: Get curated crypto signals from the market delivered proactively, helping you spot opportunities early. Built-in Risk Controls: Automated stop-loss and take-profit orders embedded directly into each trade to simplify risk management. Paper Trading Mode: Simulate trades without risk to refine strategies before deploying real capital. Easy Exchange Integration: Seamlessly connect your live trading account when you're ready to go live. The goal was to create a focused tool for traders who want quick access to high-quality trade ideas and minimal friction in execution. I’d love your feedback on the product and the idea itself. You can check it out here: https://tradofire.com Feedback appreciated thanks a lot!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: cursor, claude, chatgpt · Missing: mac, agents, macos
56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: intuitive · Missing: plus, platform, reviews
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: trading · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: just released, ide, io · Missing: https docs, excited, exist
39%39% 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: profit, active · Missing: arr, mrr, revenue
13%13% 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, crypto · Missing: web3, cryptocurrency, make money
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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