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Epoch: No-Code Hub for Algorithmic Trading (Now with AI Assistance)

Hacker News

Epoch: No-Code Hub for Algorithmic Trading (Now with AI Assistance)

Hey HN, We’re excited to introduce Epoch, a platform designed for both professional and novice traders to build, backtest, deploy, and share algorithmic trading strategies—without writing code or managing infrastructure/data. Unlike traditional platforms that limit you to rule-based strategies, Epoch is expanding the horizon with full support for machine learning algorithms like XGBoost, deep learning, and reinforcement learning, bringing institutional-grade automation to everyone. What We’ve Released So Far 1⃣ Strategy Builder – Our no-code solution lets you construct trading strategies effortlessly. Select assets from futures, stocks, crypto, and FX (we provide market data). Define trade logic: open long/short positions, manage risk, and control position sizing. 2⃣ Algorithm Blueprint – A visual scripting system for building trade signal algorithms. Includes all major technical indicators and integrates high-performance C++ code on the backend. 3⃣ LLM-Powered Micro-Interactions – We just rolled out AI-assisted strategy building, making life even easier! Describe your idea in text, and Epoch helps you construct it. We’re looking for honest feedback from the HN community—your insights will help us refine and improve Epoch. Would love to hear what you think! Check us out and let’s discuss: https://epoch.trade/ Looking forward to your thoughts!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% 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: mac, visual, code · Missing: agents, macos, agent
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: trading · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
48%48% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, builder · Missing: plus, intuitive, reviews
42%42% 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
14%14% 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, introduce · 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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