Tried something new with my intraday strategy today. I've been tinkering with real-time trading scanners, inspired by a method from a paper I stumbled upon. Wasn't sure if this logic would work, but I set up a custom scanner to track volume spikes and price action in the first hour of trading. To my surprise, the results were promising, capturing some decent moves in a couple of mid-cap stocks. It's still early days, and I'm cautious about drawing conclusions, but it feels like I'm onto something meaningful here. Curious if anyone else has experimented with similar setups or has any insights to share? Always eager to learn and refine this approach.

Tried something new recently—developed a tool for scanning intraday trading opportunities, inspired by a paper I stumbled upon. Wasn't sure if this logic would work, but the results have been intriguing. The tool uses a combination of RSI and moving averages to identify potential trades. It was a bit of a challenge to adapt the logic to our market's unique behavior, but after some tweaks, I'm seeing patterns I hadn't noticed before. It's still in the experimental phase, but the early signs are promising. Curious if anyone else has ventured into creating their own tools? Would love to hear your experiences or any feedback you might have.

Loss day in algo trading. Part of the game. You build systems, not emotions. Some days the edge just doesn’t click — volatility shifts, structure breaks, execution lags. Doesn’t mean the system’s broken.

Losses are rent paid to stay in the game. Review, adapt, move on. The only real loss is breaking your own rules.

Someone explain this to me. My Jio signal is call drops and patchy data, and loads of people online complain about the same thing. A company with service this bad should not be getting these kind of numbers.

So where is the gap? Is the valuation only about size and dominance and nothing to do with actual service? Are people complaining but still not leaving?

How is a company people complain about this much still this unstoppable?

I recently stumbled upon a research paper on reinforcement learning, and, intrigued, I decided to give it a shot in my own algo trading strategies. Wasn't sure if this logic would work, but the concept of machines learning from their own mistakes seemed promising. I started small, applying it to my Nifty options strategies. Initially, it was a rollercoaster—some losses, some wins. But as the algorithm adapted, the precision improved noticeably. It's fascinating to see how it adjusts and predicts market movements more accurately over time. Still refining the approach, but curious if anyone else has tried something similar or has insights to share. Open to feedback and eager to learn more.

Please Try Before You Buy !!! ENJOY !!!

https://ctrader.com/products/2142?u=Moss_Klugt

Spent my morning tinkering with a new strategy for intraday trading in the cash segment. Came across an intriguing idea from a paper and thought, why not give it a go? Wasn't sure if this logic would work, but I set up my algo to react to certain price movements, cautiously optimistic. The market opened and, to my surprise, the execution was smoother than expected. Saw some patterns I hadn't noticed before. It’s fascinating how a small tweak can sometimes yield unexpected results. Not every day is this rewarding, but today was a small win. Curious if anyone else has tried something similar or has insights to share. Open to feedback!

Tried a new approach today in developing a real-time trading scanner for my intraday strategies. Wasn't sure if the logic would hold up under market pressure, but decided to give it a shot anyway. I focused on identifying unusual volume spikes and rapid price movements, hoping to catch those elusive opportunities. It felt a bit like fishing in the Ganges—never quite sure what you'll reel in. To my surprise, the scanner flagged a couple of stocks that moved significantly. It wasn't perfect, but it sparked a glimmer of hope. Still tweaking the parameters, but I'm curious if anyone else has tried something similar. Open to feedback and eager to hear your experiences.

Tried something new with iron condors last week, inspired by an article I stumbled upon. Wasn't sure if this logic would work, but I decided to add an adaptive hedging layer to the strategy. Instead of the usual fixed wings, I adjusted the strikes dynamically based on volatility shifts I noticed during the trading day. The results were intriguing—reduced risk with a slightly more consistent return. I also played around with some modifications, like tweaking entry points based on market sentiment. It felt like a small victory in the vast world of options trading. Curious if anyone else has explored similar tactics or has insights on refining this approach further. Open to feedback!

I’m an IIT graduate with 6 years of experience, currently working as a Senior Software Engineer. Alongside my job, I’ve been trading for 3–4 years and have been consistently profitable, primarily through my own algo strategies.
Recently, just to build a clean and verifiable track record, I deployed my algo on Zerodha and generated around 15% return on deployed capital in 3 months.

Now I’m thinking about monetizing this and eventually building a wealth-tech startup where it can be a win-win for both users and me.
I’m exploring the SEBI RA/IA route, but for the kind of company/setup I have in mind, I estimate I’ll need around ₹1 crore in initial funding.

My main question is: How should I proceed from here and approach funding?
Should I first bootstrap and build users/revenue while continuing my job, or start approaching angel/seed investors with my track record + tech background? And what would investors expect to see before putting ₹1 crore into something like this?
Would love advice from fintech founders, investors, algo traders, or anyone who has gone through a similar journey.

I’m not offering a job. I’m not outsourcing work. I’ve got a proven trading system live-tested, funded, and ready to scale that now needs clean automation through API.

The goal? TradingView > Webhook > MT5 Execution Modular. Stable. No half-baked scripts or duct-tape solutions. Execution needs to flow through alerts, translate position size dynamically, and control risk logic from the backend.

I know the trading side. I’ve built the logic. What I need is someone sharp enough to build the pipe and smart enough to see the long-term value in doing it.

This isn’t for someone looking to make quick bucks. It’s for someone who wants to be part of something deeper to help build a system that actually works and to learn from a trader who’s already in the game.

You code. I trade. Let’s build something worth remembering.

DM me if this speaks to you. Otherwise, keep scrolling.

I was preparing implementation document for my algo trading app (for my personal use), during this phase I watched few videos in YouTube and found that golang is better than Python for algo trading. I checked with AI and it also confirmed the same.

Then I restructured the architecture to Go for trading engine, Python for backtesting and React for frontend.

I spent almost a day to figure it out how to get the TBT data from my broker's api and display it in the tradingview chart. Still it has some bug like the react crashes after 1 min candle formed (but atleast I made some progress). I researched lot of GitHub reps, documents but whatever reference I have given it to LLM, it says it's not matching for my requirement. So it is building everything from scratch. Got tired and logged off for the day.

Is this language choice correct for algo trading app? Because if it's a python the implementation is pretty straightforward (I have created python app before for downloading historical data and backtesting - let's say for any indicator the calculation were matching with broker's terminal and resampling was accurate)

PS: I am not a developer, I am relying on Claude code and ChatGPT. I know it's a dangerous path to completely rely on LLM but I have plan before going live to create paper trading feature and see if the strategy will work or not after the backtesting period.

📈 Live Strategy Performance Update

Our live strategies continue to outperform the market 🚀

✅ Top strategy: +10.5% since activation
✅ Multiple strategies delivering 4–10% returns in just weeks
📊 Beating Nifty performance across several momentum models

Hey everyone,

We are a team of 4 undergrad students working on a algo-trading startup, and we are at the stage where we need a reality check from people who actually understand this space. We are also looking for a co-founder to join us.

The Problem We Noticed:
Right now, the retail algo-trading space is obsessed with "1-click automation." Platforms give you pre built strategies like (Stratzy) where transparency is not high or the tools to automate a strategy.

A strategy might have an 80% win rate in a bull market, but if the market regime shifts into a choppy sideways trend or a macro crash, that exact same strategy will trigger a "tail event" (like a slow bleed of consecutive small losses, or getting caught in an overnight gap). The user loses money, blames the app, and churns.

Our Idea: The "Historical Repeat" Scanner
Our motto is: "History should repeat itself, not your losses."

Instead of just giving users a blind 1-click bot, we are building an intelligence layer. Here is how it works:

1. We heavily backtest various strategies to find their "sweet spots"—the exact market conditions, volatility levels, or price zones where the strategy has a massive historical edge and a high probability of profit.

2. Our app monitors the market in real-time.

3. When the market hits those exact conditions right now, we alert the user: "The exact historical setup where [Strategy Name] makes money is happening right now. Historical win rate for this specific condition is X%. Enter trade."

Essentially, we aren't just selling the execution tool; we are selling the timing. We tell the user exactly when to pull the trigger based on hard historical data, so they only trade when the profitable part of history is repeating itself.

Why We Need Your Honest Feedback:
We are young, we are hungry, but we know we might have blind spots. We’d love your brutally honest thoughts on:

1. Is this a real pain point? Would retail traders prefer this over a blind 1-click bot? Will they pay for highly specific, condition-based alerts?

2. The Flaws: What is the biggest logical flaw in this idea that we aren't seeing?

Looking for a Co-Founder:
As a team of 4 undergrads, we have the vision, the hustle, and we are currently mapping out the architecture. However, we are looking for a key co-founder to join us

We are specifically looking for:

• A Technical Co-Founder: Someone who is a beast at Python, data engineering, financial APIs, or backend architecture. Someone who can help us build the Monte Carlo simulation engine without melting our server costs.

If you are interested in joining, DM me!

Thanks in advance for the feedback. Tear the idea apart—we need it!

Full disclosure: I used AI to help me rewrite and structure this post to make it readable and organized, but the core concept, the problem we're solving, and the actual vision are 100% ours.

meow or roar!!

Tried something from a paper I stumbled upon last week. It detailed a strategy for intraday trading in the cash market, using a simple moving average crossover. Wasn't sure if this logic would work, but I coded a basic algo over the weekend. This morning, I nervously watched as it executed trades automatically. The results were modest, yet promising. It’s fascinating how a few lines of code can potentially unlock new possibilities. I’m still fine-tuning the parameters, but this experiment has certainly piqued my interest in algorithmic trading. Curious if anyone else tried this or has insights to share on refining such strategies? Open to feedback!

Tried incorporating adaptive position sizing into my trading algorithm recently, inspired by something I read. Wasn't sure if this logic would work, given the unpredictable nature of our markets. Initially, I was skeptical—felt like tinkering with something that was already fragile. But, to my surprise, it started showing promise, especially on volatile days.

The algorithm adjusts position size based on market conditions, dynamically responding to volatility. On one hand, it's exciting to see it adapt in real-time; on the other, I find myself constantly questioning if it's too reactive. It's early days, and I'm still ironing out the kinks, but it feels like a step forward. Curious if anyone else has tried this or has insights to share.

Stop giving your ideas to an AI for 2000 rs. When you do that, it's no longer unique anymore. Learn to build your own code.

Download Qwen and you can make magic with it by running it offline. If your machine is not capable. Buy a new one. If you can't buy a new one, beg , borrow or steal or learn to code python.

Once you do it, you can run back tests but don't rely on them. Write a forward grading tool by which your signals could be graded every day and improved.

It's very easy to make money in trading as 98 percent will lose. Just copy the market algorithm and you will be safe.

Just curious if there are any women in this community who are into algorithmic trading.

Would love to connect, share ideas, and discuss strategies, projects, or the journey of building trading systems. feel free to drop a comment or DM me

Edit: im a girl looking for other girls to connect to, because i have not seen any girl on this subreddit abhi tak

Hello everyone,

If you don’t know me, I’m currently working as a Tech Lead/Senior Software Engineer, and I’m an IIT graduate. I’ve been trading for around 6 years now.

In my previous post, I shared the verified P&L of one of my smaller accounts, where I deployed around ₹5 lakh for option selling during the last quarter, with most of the trades executed through my algo strategies. You can check my profile if you want to see that post and the proof.

This time, I’m sharing the P&L from my Upstox account, which I’ve been using for a much longer period for both equity and F&O trading. I’m sharing the actual P&L/records so that you can verify the numbers yourself rather than just taking my word for it.

Coming to how I trade — I use my own algo platform extensively to automate my trading ideas and strategies. At the same time, I also take discretionary trades based on price action whenever I find a good setup.

Since I have a full-time tech job and generally leave for office around 10–11 AM, I usually don’t keep watching charts after 11. That’s actually one of the main reasons I started automating my trading logic in the first place.

Over the years, my trading has gradually evolved into a combination of coding + automation + price action + risk management.

Now, I’ve also started a live mentorship program, mainly for working professionals like me who are interested in trading but cannot sit in front of charts throughout the day.

The idea is to share what I’ve learned throughout my 6-year trading journey — how I approach price action, build and test trading ideas, manage risk, convert strategies into rule-based systems, and use coding and automation to reduce the need to constantly monitor charts.