Hey there all, I have been manually trading charts for 3 years and in June last year started algotrading journey. I have built a bot of my own, however, semi-automated before each trade. I'm about to start a better (probably), fully automated, test-able version of the original application. This time though, I'm thinking if it would be good to share progress and thoughts that go with it while working on the app with a like minded individual(s). So drop me a message if you are interested in knowing more of what I've in mind. You can be a contributor too! I'm using python right now as it meets current needs. I use AI, with concise prompts, to accelerate code writing (while reviewing all code and making necessary updates) Thanks.

https://www.youtube.com/watch?v=FGLuyx0aM-I Before watching this video I was too focused on winrate. This guy spells it out in an easy to understand way.

Someone on algotrading mentioned that some of these pdfs are available on github - https://github.com/PlamenStilyianov/FinMathematics https://github.com/PlamenStilyianov/Quant Tons of useful info in there.. already improved my models by 5 percentage points.

Hey everyone, we had our first intro meeting last Friday. About 5-6 of us were able to make it and had a cool conversation! We left with some takeaways for ideas on how to contribute to the group. We figure - we can do presentations on our algo projects and have them peer reviewed by the group - we can work on a project that tackles a pillar of algorithmic trading as a group. let me know what you think would be best! Attatched are the meeting notes: Meeting Purpose Introductory call to connect on algorithmic trading projects and challenges. Key Takeaways - The "Edge" Debate: The group debated where a retail edge exists. Nik argued for using high-resolution MBO data to detect institutional order flow before patterns form, while Surya advocated for simple, robust price-action strategies that avoid parameter optimization and overfitting. - Data Quality is Paramount: Alpaca's free data is unreliable (UEX feed covers only ~3% of the market), making its $90/mo SIP feed or a premium vendor like DataBento necessary for accurate backtesting. - Alpaca's PDT Trap: Alpaca's margin-account-only structure forces all users into Pattern Day Trader (PDT) rules, requiring a $25k balance to avoid trade limits—a critical, often overlooked detail. - Surya's High-Return Strategy: A simple, 15-min price-action strategy shows >30% annual returns on Tesla since 2021. The group will analyze its logic to understand why it works on specific stocks and how to generalize it. Topics Member Intros & Project Status - Jared: Developer (ex-MSFT/IBM) focused on a prop firm (FTMO) challenge and local algo development. - Zach: Scientist learning Python for quant finance; building a backtesting engine but struggling to find a profitable strategy. - Dennis: Medical genetics grad starting a data science Master's; seeking a clear roadmap for algo trading. - Nik: Data analytics veteran building complex options/futures strategies; paper trading results don't match backtests, prompting a shift to simpler futures strategies. - Surya: Python developer with a backtested strategy showing >30% annual returns on Tesla since 2021.

Hey everyone - join the google meet call! meet.google.com/uyr-baxs-vpn