An Open-Source Tool to Build, Test, and Deploy Stock-Trading Algorithms

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Hey guys, TL;DR: Check out the tool at https://github.com/Finned-Digital-Solutions/hft-ext ! Feel free to drop a ! Or just join the Discord: (see repo Discord badge on GitHub for link), your choice. I've built an open-source tool which allows anyone to get started algotrading on a paper or live account for free. The tool includes everything you need to get started with aggregating and analyzing data through the Yahoo Finance and Alpaca APIs, and on top of that is completely free! It supports new strategies in an easily extensible way, meaning that you can get your own strategies up, running, and tested in just minutes. My goal was initially to test out some high frequency trading strategies that I came up with, but along the way I realized that I could add methods on to support any custom strategy. Now that I feel confident with the documentation I've written, the code, and the GUI, I want to share it with all of you. Some cool features that I haven't mentioned already are: - realtime: monitors stocks by the minute to buy, sell, and hold based on a strategy. - oh, and the stocks it monitors are completely configurable: currently, the defaults are about 500 of the largest companies by market cap. - comes out of the box with an ARIMA strategy: basically determines the probability of increase or decrease to influence actions. - growing community on Discord. - free, open-source, and non-tracking. Other tools are expensive, log your data and/or strategies, or just are a black-box for extensibility. I plan to keep updating this as the project gains traction. I am open to any suggestions and feature requests through GitHub issues.

Visit product View on Hacker NewsFeb 21, 2023

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

80%80% 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: new, code, apis · Missing: mac, agents, macos

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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.

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

46%46% 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

38%38% 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

13%13% 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.

Correct prediction on native model

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