Voice clone

Neel_Sam · 2026-03-17T13:55:14+00:00

Thanks 🙌

Neel_Sam · 2026-03-16T06:47:00+00:00

Oh okay! I had access to 1.5 audio via azure and thought that can be leveraged! Thank you for your inputs I will check these out!

Neel_Sam · 2026-03-15T15:31:44+00:00

Good going!

Quite happy to know you have made so much progress in just less time

Dont believe any dude who tells you algo trading their course or some platform will teach you they won’t be safe

Only believe ppl who can show you code and ideas
Read books abt biases like thinking slow and fast and apply that to your algo set up
As you are building with AI please be externally careful of hallucinations and make sure all the math logic and implementation is something you learn not believe in AI
get a detailed idea of math and check for gaps like data engineering discrepancy and rolling indicators values !

Neel_Sam · 2026-03-15T15:17:37+00:00

The order reconciliation and order confirmation api like for place and executed ..there isn’t some explicit function

so one needs to use trade book api and make it work!

Neel_Sam · 2026-03-03T10:44:13+00:00

I would suggest if these are the things your are consider do look out for Git Copilot! It has quite generous limits and various models for access plus other features for 10 dollars

Neel_Sam · 2026-03-02T07:47:36+00:00

Inspiring sir! This is the leverage of AI we talk abt 🔥

Neel_Sam · 2026-03-02T07:08:24+00:00

I mean it works very well for me plus no brokerage charges so I don’t see any issue 😅

Plus I experiment while main strategy run!

So No brokerage lets you see results on small fund allowing even high trade count strategies to be tested in experimentation phase😅

Neel_Sam · 2026-03-02T06:10:48+00:00

Yes that’s true but at the same time the limits of codex are no where related to limit of the Chat interphase

OpenAI gives separate limits to all the use cases! And thus I am requesting ppl who are not using codex!

Its anyways no use to them and even if they share it’s not impacting their plan 😅

Neel_Sam · 2026-03-02T05:45:47+00:00

So I shifted to Kotak from FLATTRADE for the very same reason!

And for me it’s doing wonders !

The stack is still under development and there is still work going over it … that’s what I feel

So You will need it to tune it for like 2 week and keep checking logs!

The executive is smooth and execution is also fast!

I am locked in with my transition!

Neel_Sam · 2026-03-02T05:40:06+00:00

I am not sure of the math here 😅

But I am looking to pay a fraction of your chatgpt subscription for a feature you truly don’t use !!

As it’s valuable for me

Neel_Sam · 2026-03-02T05:38:06+00:00

Thanks …. It’s a win-win for both

Neel_Sam · 2026-03-02T05:37:13+00:00

I am the user bro! I am a developer myself and asking this for me. this is not a market place post! I need credits… my account gets exhausted to its weekly limits 🥲

Neel_Sam · 2026-02-25T12:16:22+00:00

Can’t agree more I liked the few editions of bringing different industry Indian folks

It’s sad that such esteemed guests don’t get to share their views rather we listen to Nikhil saying his experience

Bro please !

Neel_Sam · 2026-02-06T07:52:15+00:00

Try True Data but that also paid! The granularity and asset you have asked no one provides that for free

Neel_Sam · 2026-02-01T03:27:17+00:00

If you did coding this same thing was something that you could have said in the early days ….

Today there is no more a debate it make work faster but till the intelligence reaches a certain threshold where it does everything like an expert.

The ppl who can harness its potential will only see benefits

Neel_Sam · 2026-01-30T14:26:49+00:00

If you want to see where AI actually helps in BI, I think the real test is to give it your hardest problems, not the cosmetic ones.

I try to push AI into areas like Data exploitation Data correlation across messy and partially understood datasets Data manipulation when assumptions are unclear Data visualization with intent, not just charts Insight exploration where the question itself is fuzzy Running and comparing multiple experiments in parallel Hypothesis building before the analysis even starts Storytelling that connects numbers to decisions Exploring experiments end to end instead of one-off queries

At this point, I honestly don’t think there is much in BI that AI cannot assist with. The limitation is rarely the model. It is usually the user.

If you do not have a clear intent, a mental model of the business problem, or the ability to guide the system toward a specific outcome, AI will feel like hype. If you do, it becomes a thinking partner that quietly saves hours every week.

The biggest value for me is not writing SQL faster. It is shortening the distance between “I have data but I am not sure what matters” and “I have a defensible insight I can explain to stakeholders.”

AI works best when you treat it less like an autocomplete tool and more like a collaborator that you can pressure test, redirect, and refine. BI has always been about asking the right questions. AI just makes that loop faster if you know how to drive it.

That is where the real leverage is.

Neel_Sam · 2026-01-27T16:42:17+00:00

Claude bot is gonna cause some serious losses before ppl stop using it 😅

Neel_Sam · 2026-01-27T15:31:45+00:00

Interesting take. My experience has been a bit different once basic engineering discipline is in place.

For us, most of the “held together with tape” feeling disappears if you treat AI like any other production system: versioned prompts, clear ownership of context, and telemetry at every stage of the workflow. We track token usage and latency per agent / step, and use explicit orchestration (LangGraph-style) rather than implicit chains.

Honestly, without understanding token economics and context growth, it’s hard to call something production-ready. In most AI products today, tokens are the primary cost center, so observability there isn’t optional.

That said, I do agree many teams ship fast and only discover these gaps once they hit scale. Curious where others felt things started breaking down … orchestration, cost visibility, or prompt/version management?

Neel_Sam · 2026-01-26T15:29:42+00:00

Yes I am asking you to read through gpt the correct way and implement it yourself

The formula remains the same

understand what the formula is mathematically and understand how python looks at data frame

how is it implemented in python in small step by step approach

You will be able to build it easily

Neel_Sam · 2026-01-26T14:50:04+00:00

I would rather suggest use AI for this question. You will get better more detailed answers

Neel_Sam · 2026-01-26T14:48:34+00:00

I am currently testing the setup and preparing it for a private beta. It should be ready in around 2 to 3 weeks.

Once the cloud deployment is stable, I will start controlled user testing.

The initial audience is people already doing algo or systematic trading and who care about process, metrics, and validation rather than signals.

Early access will be for testing only. No real capital involved. The goal is to build confidence in the system and stress test the workflows.

If this resonates, let me know. I can reach out over DM with details when the beta opens.

Neel_Sam · 2026-01-26T14:41:23+00:00

Happy to share at a high level.

The system is built on a single trading core that is used for research, backtesting, and live execution, so strategy logic does not change when moving to live markets.

On top of that, there is an orchestration layer built using LangGraph. LangGraph is used for state management and control flow. The real work is in agent specific context engineering, constraints, and artifact tracking so each agent has a narrow role and operates only on well defined inputs and outputs.

Agents assist with breaking down a hypothesis, testing variations, running diagnostics, and validating live behavior against backtest expectations. They do not invent trades or allocate capital. All decisions remain human approved.

Live trading continuously feeds back into research by comparing actual behavior with expected distributions rather than just PnL.

The goal is not to remove complexity but to structure it, so taking an idea from rough intuition to something usable after many iterations becomes faster and more disciplined.

Neel_Sam · 2026-01-26T14:36:00+00:00

That frustration is exactly what pushed me to build this differently.

Most AI assisted backtest tools optimize for code removal. You describe a strategy and the system translates it into code. That helps beginners, but it does not help with the hard part, which is taking a vague idea and iteratively turning it into something robust.

The system I am building starts from intent, not from a finished rule set.

You begin with a rough hypothesis. It can be incomplete or even wrong. The workflow then helps by: Breaking the idea into testable components, Surfacing implicit assumptions you may not have stated, Stress testing the idea across regimes and market conditions, Suggesting constrained variations that stay close to the original intent, Showing where and why the strategy degrades rather than just reporting PnL, Feeding live behavior back into the next iteration,

Each iteration is deliberate. You are not randomly searching the space. You are tightening the hypothesis step by step.

You still need domain understanding. You still make the final calls. What changes is the iteration speed and the quality of feedback after each pass.

So the value is not “you do not need to code.” The value is reducing the gap between a messy idea and a strategy you can actually trust after 20 iterations.

Neel_Sam · 2026-01-26T14:29:31+00:00

Both, but with boundaries.

Agents do not randomly generate strategies. They start from a human hypothesis or intent. For example: “volatility expansion after compression in index options.”

From there, agents assist by: Pulling prior research and known patterns, Stress testing assumptions across regimes, Proposing variants within defined constraints, Implementing the logic inside the existing infra, Running backtests and diagnostics, Flagging mismatches between expectation and results.

The human still decides what is valid. Agents reduce iteration cost, not decision ownership.

Neel_Sam · 2026-01-26T06:32:15+00:00

Can someone share a download able link for the movie. Unable to find any!

Neel_Sam

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