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A crypto social trading platform that force people to practice stoploss

Hacker News

A crypto social trading platform that force people to practice stoploss

Hi HN, I was watching an interview of Caroline Ellison (CEO of Alameda Research) the other day and found it interesting that she never uses stoploss (no wonder shit happened). One of the most important skills for traders is know how to set up stoploss and stick to it when stoploss is about to hit. I see that most crypto traders don't follow this principle. On the other hand, there is a segment in fintech called prop-trade challenge. This is a proven business model in forex & stock trading with multiple platforms having 100k+ active users (such as Topstep or FTMO). These platforms evaluate day traders’ performance in real-time simulated accounts, in which those who pass the evaluation/challenge earn a funded trading account (while still being monitored against its max drawdown rule). I realized that prop-trade challenge can be integrated with copy-trade functions and create a better version of crypto social trading platform than the current Etoro model. That's why I built Tordess (a “misspelling” of tortoise). We launched our MVP (open-beta) on October 15th, acquired 700+ beta users/testers as of today and secured affiliate partnership with 2 major Vietnamese crypto trading & prop-trade (Facebook) groups that have more than 200 thousand members. Since launch, several top traders on our platform have produced 100%+ return on simulated accounts, without breaking our rule of maximum 10% drawdown. While it is amazing how talented retail traders are able to achieve such results in crypto bear market, it also shows the potential of fixing social trading (copy-trade) model by combining prop-trade challenges. If you find this interesting, please give Tordess a try. Our competition mode is always for free. Let me know if you have any feedback or questions!

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Actual performance

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
90%90% 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.
TrustMRRFits verified-revenue profile · Strong signals: users, trading, way · Missing: mobile apps, ios, personal
64%64% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
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.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, user, open · Missing: mac, agents, macos
26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: crypto · Missing: web3, chat, cryptocurrency
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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