Beat the stock market with plans you can enter automatically while away

Hacker News

Hi HN, I submitted an Ask HN earlier with a response so I figured I would throw up a web page and start posting some of the plans that I was working on for the market [1]. I started posting some plans to Reddit last week to create a log, you can see them here in my profile post [2]. I use a customized system which monitors different data from the different providers for the underlying stocks I am monitoring. It looks at volatility, spreads, and everything else it deems relevant. Ask me any questions. Please feel free to provide feedback as well. [1] https://news.ycombinator.com/item?id=17270396 [2] https://www.reddit.com/user/marketgodfather [3] http://plainsitemanagement.com/index.html

Visit product View on Hacker NewsJun 18, 2018

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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

43%43% 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

11%11% 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.

Incorrect prediction on native model

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