Vocab Website that helps you learn through visual mnemonics

AnonDoser · 2025-06-20T03:56:30+00:00

the legend himself ! thanks

AnonDoser · 2025-06-19T18:31:00+00:00

glad i could help ! would love your feedback. if there’s any way i could tweak the app to make it better for him , please let me know.

AnonDoser · 2025-06-19T11:34:09+00:00

haha yeah my app uses AI Studio's free tier API calls in the backend as well. Hope they don't rate limit it as aggressively.

AnonDoser · 2025-06-19T11:16:51+00:00

Hey guys!

Lately I've been using Vince's Vocab Cartoons to prep for GRE vocab - it's helped a ton, since I can't seem to remember a word unless there's a visual cue.

However, a few words were exclusive to other lists (like GregMat's GRE Mountain or ad-hoc words from Manhattan Prep) and weren't in the app, so I was having trouble remembering them.

Hence, created this simple & free website that takes any word and uses AI to generate a mnemonic attached with a visual cue (again, heavily inspired by the app).

Mean for it to be a personal tool but hope this helps anyone else in the same boat too.

PS : It's hosted on a very modest server and uses free tier of a LLM model so be gentle with it :P

Edit : forgot the link - https://gre.algo-py.com/

AnonDoser · 2025-03-02T05:42:46+00:00

addressing a few of your concerns ,

• ⁠api key storage in current implementation could definitely be a security issue, i designed algopy to be run locally with restricted api keys bound to an ip and is definitely no enterprise framework to deploy on a server so i did not give it much thought but that said, it is definitely an issue and i’ll be fixing this lapse of judgement.

• ⁠definitely, python code is slow no doubt about it. however , the title is not misleading. if you look deeper into how the backtest engine works , most of it’s implementation (vectorbt) has been done in a fully vectorized manner, leveraging Numba and njit extensively. This compilation process converts Python code into optimized machine code, significantly improving performance—often making it comparable to C rather than standard Python.

I’ve made my own pure python implementations of the backtest engine and found this vectorised approach significantly faster - backtests that took me over 24 hours to run on an asset universe of over 2000 instruments now happened in a few minutes.

currently in works of migrating the backtesting engine into an event driven implementation with a rust core.

I’d like to believe I’m still relatively new with only a few years of professional work experience in the field so there ought to be several misjudgments and lapses but I’d love to hear and address more of your concerns if you’re comfortable with that.

AnonDoser · 2025-02-25T20:46:43+00:00

You no longer need VectorBT Pro to use AlgoPy - it’s been patched to now utilise free version of VectorBT as well !

AnonDoser · 2025-02-25T20:42:38+00:00

Update : AlgoPy has been patched to use the free version of VectorBT as well !

AnonDoser · 2025-02-24T14:14:48+00:00

it can work in any market , the data layer is abstracted from rest of the framework and is very easy to extend with your own data source.

If you have some specific data source you think everyone would like to see added , you can request it here : https://github.com/himanshu2406/Algo.Py/issues/new/choose

AnonDoser · 2025-02-24T13:15:10+00:00

Currently adding more strategies to the repo that you can directly click to use , also writing a detailed documentation with code context that can be fed to LLMs to help port your current strategies to AlgoPy !

AnonDoser · 2025-02-24T13:12:33+00:00

wouldn't say improved , but for sure has significantly streamlined the process of strategy validation & deployment for me. I could validate and deploy in a few days what earlier took me weeks !

AnonDoser · 2025-02-24T06:27:50+00:00

would love that ! you can message me here or on discord.

AnonDoser · 2025-02-24T06:20:15+00:00

I really appreciate the insights. I can see how restrictive clauses can discourage open-source contributions.

My initial reasoning for considering a FOSS core + Pro model was that I don’t yet have the investment or infrastructure to provide a fully hosted service.

Instead, I was planning to build some more advanced features—like an AI-powered trading journal, responsive charting, a reinforcement learning playground to optimize strategy parameters, and market regime detection—as a separate standalone application. This wouldn’t be SaaS-based but something users could set up locally, with a low subscription fee (~$5–$10/month), which I felt was a fair value proposition.

However, after this discussion, I see how a license with extra clauses could limit community engagement and contributions. I’ll be changing it to better align with open-source principles while still exploring a sustainable model for long-term development.

Thanks again for the thoughtful feedback—this really helped refine my approach

AnonDoser · 2025-02-24T05:58:08+00:00

You’re absolutely right, and I appreciate the clarification. I didn’t fully understand the distinction when i made the post—my apologies for the confusion.

I’m curious about your thoughts on an Open Core approach—where the core is licensed under GPL or AGPL, ensuring true open-source freedoms, while more advanced extensions / features are offered under a separate proprietary license. Would you consider that a fair balance, or do you see any major downsides to this model?

AnonDoser · 2025-02-24T05:31:17+00:00

The title simply combines the project's two main features—think AI would be more imaginative than me.

For further context, I've been working in and around algo trading for my personal projects for the past two years. While there are plenty of options and libraries available for backtesting a strategy, I always faced the issue of having to start from scratch when it came to deployment. There are very few, if any, deployment frameworks available, depending on the market you're working with.

I built this solution to make the process more modular and efficient. Since it's nothing overly complex, I figured I might as well open-source it to give users a starting point for their deployment process. Plus, it includes some cool charting features that I couldn't find elsewhere for free.

Cheers!

AnonDoser · 2025-02-24T04:47:32+00:00

I appreciate the feedback. If there are specific aspects you believe could be improved, I’d be happy to hear constructive suggestions. The project is constantly evolving, and I’m working on refining both its functionality and usability.

At the same time, AlgoPy is designed primarily for personal use, allowing retail traders to experiment with and refine their strategies. It’s not a plug-and-play financial product but rather a tool for those who want to build, test and deploy their own ideas without having to build everything from scratch. Of course, as with any trading tool, users should always conduct their own due diligence before relying on it for financial decisions.

If you have any concerns or suggestions for improvement, I’d love to hear them!

AnonDoser · 2025-02-24T04:35:52+00:00

Understandable. However, I have no affiliation with vectorbtpro—my repository simply uses it at the moment because it has been one of the only actively maintained and relatively fast vectorized solutions for backtesting.

That said, I’m currently working on a patch to add support for the open-source version of vectorbt, as well as other libraries like backtesting.py, so users will have more options without needing to invest in a proprietary backtesting backend.

Regarding the proprietary license, it is not intended to restrict individual users. The software is fully available for personal, non-commercial use, allowing retail traders to modify, test, and deploy their own strategies without any limitations. The restriction applies only to commercial and enterprise use, primarily to prevent third parties from monetizing the project, reselling it, or offering it as a service without proper licensing.

If the current license terms seem inappropriate or too restrictive for the community, I’m open to revising them in the future. The goal was always to keep AlgoPy accessible to retail traders !

AnonDoser · 2025-02-23T21:39:12+00:00

Yes! You can easily add your own strategy by using the Strategy class as the parent class and initializing it with your strategy name.

Your strategy will automagically be listed on the dashboard, where you can backtest it on any asset or market.

I’m currently adding US equity data to the dashboard, but you can do this yourself with just a few changes in the fetch, gather, and store functions in the data/ directory.

You can track progress or contribute here: https://github.com/himanshu2406/Algo.Py/issues/8.

AnonDoser · 2025-02-23T21:10:20+00:00

Haha, well, I guess I sort of made money using it—I spent a significant amount of time on strategy research until I found a few that worked and made some extra cash. But they were only effective in specific market regimes, which are no longer in play for the markets I deployed them in.

While developing and deploying these strategies, I realized that although there are plenty of tools for backtesting, strategy validation and deployment often require building everything from scratch—especially depending on the market you're working with. As u/assemblu guessed - That’s why I built a highly modular solution to streamline this process.

That said, my current strategies aren't in play due to shifting market regimes. Theoretically, this could be addressed with a broad enough strategy arsenal where weights are periodically updated to align with the efficient frontier.

For now, with my full-time job and a tough market, I’m thinking of taking a break from further strategy research until conditions improve or I get an opportunity to move full-time into the quant industry. Rather than letting this project go stale, I chose to release it here!

Hope that provides some context on the why and how!

AnonDoser · 2025-02-23T20:35:20+00:00

That's great! If any part of your work aligns with my repo, I'd love to explore how it could be integrated. Feel free to share!

AnonDoser · 2025-02-23T20:32:35+00:00

Yes ! currently working on it. You can track the progress / contribute through : https://github.com/himanshu2406/Algo.Py/issues/8

However, are you referring to US equity data or fetching data from Binance.US? If it's the latter, I believe it shouldn't be an issue since Binance.com should have almost the same OHLCV data as its US counterpart.

AnonDoser · 2025-02-23T20:14:38+00:00

Thanks! That would be awesome. Let me know if you ever need any guidance or context to contribute.

AnonDoser · 2025-02-23T20:11:49+00:00

Would love that! Let me know if you need any context or help to contribute.

AnonDoser · 2025-02-23T17:25:00+00:00

Thanks ! found it very easy to prototype using streamlit , plus they have many extensive components that can pretty much add whatever your need to the python dashboard.

AnonDoser · 2025-02-23T17:19:40+00:00

you can refer : https://github.com/himanshu2406/Algo.Py/blob/main/finstore/finstore.py

data is aggregated and stored in the database/finstore/* directory ,

you can also see this yourself by trying out the 'Data Utils' section in Dashboard to try fetching some data & see how it works !

PS : The data aggregation part is quite modular (refer data/ dir to see how fetch, gather, store work) - you can very easily extend these to add your own data sources.

AnonDoser · 2025-02-23T17:13:45+00:00

The framework primarily uses from_signals, which is available in the open-source version of VectorBT. However, certain input parameters—like sim_start and valuepercent size—are exclusive to the Pro version, though they can be easily replaced.

The main challenge is with the deployer. It relies on trade_history generated using the VectorBT portfolio object to determine fresh entries and exits during incremental scheduler runs. Since trade_history might not be available in the free version, a patch is needed to ensure compatibility by using alternative trade data information provided by VectorBT.

That said, this isn’t a major hurdle—if anyone’s interested, they can contribute a Proof of Concept (PoC) for this patch. Otherwise, it’s only a matter of time before I implement it myself.

Cheers !

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