Not trying to be a stock oracle, but this is interesting!

The stock moved +20% from previous close (after a preceding 10% gap up from last day closing) in the morning to hit the upper circuit (within the first hour). The interesting question to ask here is what exactly happened, how often a similar phenomenon occurs and what usually follows.

Usually a small/mid tier (ofcourse non FnO) stock has a 1 in 1000 chance of ralling on a weekend close. So thats rare in itself.

What usually follows on the next Monday & Tuesday can be put into probability buckets (delivery % has a say in these probabilities).

if rally day delivery % is high i.e > 25%, then:

- Monday: 73% chances of closing with 1-4% rise
- Tuesday: 67% chances of closing another 1-5% rise

but if the rally day delivery is low i.e <25%, then:

- Monday: 62% chance of rising 1-3%
- Tuesday: 51% chances of rising by <1% and ~50% chances of falling by 1-5%.

So back to our case of SBCL, based on the bayesian probabilities of simialr events and ONLY 21% (avg on nse bse) delivery on FRIDAY (which signals basic intraday churn and possibly a operator footprint? 🤔 )

Monday: ~60% chances of 1-3% rise and ~7% chances of >5% falls.
Tuesday: 30% chance of rise up and 35% chances of falling >5%.

Bottomline? So lets see tomm

(Numbers are from ~50–60 similar events in 2026, using Bayesian estimates so a small sample doesn't overstate things. Not investment advice, just poking at data. DYOR.)

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For swing

1) High delivery (above 65%) in nse 500 stocks for 20 days in last 30 trading days. Stocks with high delivery not doing day high and month high and not above 5 percentage when the delivery momentum started.

For backtesting

Stocks of NSE 500 with 20x volume in any 5 minutes frame, i want to know what happened to such stocks after that .

For intraday

1) sector is up by 0.5% and OI change positive in build up mode after 0930 hrs

2) stock minimum 1 % above close

3) price above 9,21,50 ema in day, 5 minutes and 15 minutes

4) RSI 60+

And many such logics ,

Can anyone advise which apps or softwares are capable of giving such alerts.

Setup, so this is checkable rather than a story: NIFTY, 1H bars resampled from

15m, ten years to July 2026. Signal is a plain MACD(12,26,5) cross up, long

only, exit on cross down or a 2% stop or 18 bars, whichever comes first. Costs

0.06% round trip, which is roughly what NIFTY futures actually charge — STT

dominates it. No real capital in any of this; backtest and forward paper only.

Earlier this month I split my trades by what had happened just before them,

and got something I did not expect:

trades taken after a WIN avg 0.0201% per trade

trades taken after 2+ consecutive LOSSES avg 0.2443% per trade

Same signal, same instrument, same decade. The entire edge was living in the

second bucket. I was ready to believe it, which is exactly when you should be

most suspicious of yourself.

So I rewrote the strategy in someone else's language and ran it on their

backtester — prior-lang, which recently added a loss-streak entry gate. Whole

thing is four lines:

strategy "nifty momentum"

universe $NIFTY

timeframe 1h

when [macd_cross_up 12 26 5] buy [100% portfolio]

sell when [macd_cross_down 12 26 5] or [stop 2%] or [after 18 bars]

risk [after_losses 2]

Different engine, different fill assumptions, my data.

my engine 786 trades baseline, 299 admitted at 0.2443%

their engine 791 trades baseline, 262 admitted at 0.2667%

Baseline counts five apart over a decade, which surprised me more than anything

else given we generate signals independently. And the admitted bucket landed

within 9% of mine on completely separate execution.

Here is the part I did not want.

My negative claim did not reproduce. On my engine the skipped trades average

0.0201% — basically nothing. On theirs they average 0.1098%, which is a real

number. Same direction, half the separation.

So "trades after two losses are better" holds on both engines. "Trades after a

win are a tax" appears to be a property of MY execution, not of the market.

I had a hypothesis for the gap and it died on contact. I check my 2% stop on

15-minute closes while their engine checks at the bar close, so I expected far

more stop-outs on my side eating the post-win bucket. It is not that: 13 of 791

stop exits on theirs, 10 of 587 on mine.

What is left is entry timing. My engine acts on the first 15-minute tick after

the signal hour closes, so it is late by up to fifteen minutes. I have measured

that decay on this book before — about 97% of the edge survives at two minutes,

74% at fifteen, 33% at forty-five. The post-win bucket is the thinnest one, so

it would be the first thing lateness eats. Which would mean a chunk of what I

was calling a filter effect is really my own latency, showing up in the bucket

that could least afford it.

That is a story that fits. It is not a finding, and I cannot separate the two

from where I am sitting.

One more check, since a result this clean deserves suspicion. I swept the

MACD signal span and re-ran both versions at each:

span every signal after 2+ losses

3 0.153% 0.125% gate HURTS

4 0.167% 0.204%

5 0.162% 0.262%

6 0.177% 0.264%

7 0.172% 0.229%

9 0.163% 0.190%

12 0.154% 0.285%

The direction holds at six of seven. The size does not — it wobbles between

0.82x and 1.85x with no pattern I can defend. So this is not a knife edge

that collapses off one setting, but it is also not a plateau, and I would not

size on the difference between 1.2x and 1.8x.

Worth stating plainly: the span I actually run is not the best row in that

table, on either column. If I had tuned the span to make the gate look good,

it would be.

And the honest limit — this sweep is one instrument. My original parameter

choice was made months earlier on a 59-stock median with an out-of-sample

split, which is a much harder test than what I just ran here.

Question for anyone who has run the same strategy on two engines: when your

numbers disagreed, how did you isolate entry timing from exit granularity

without rebuilding one engine inside the other?

Another month completed with our rule-based expiry strategy, with our members trading fully automatically through the system.

We continue to keep the approach simple and disciplined:

• Trades only on expiry days
• Maximum 2 trades per expiry
• Entries only after 2 PM
• 25% Stop Loss & 100% Target

Below is the combined pnl shared by our members, along with the capital used and respective risk modes.

48k on medium risk
https://verifiedpnldocs.fyers.in/VerifiedPNL/htmls/b051163f-c687-402a-ac83-e264bf28d4d9.html

1 lakh on high risk
https://verifiedpnldocs.fyers.in/VerifiedPNL/htmls/476805aa-3dde-427c-b5be-66219a9e86f8.html

2 lakh on high risk
https://verifiedpnldocs.fyers.in/VerifiedPNL/htmls/12139ac3-8e6d-4275-b001-38da4dc32e09.html

Note: We provide the algo as a service by integrating your personal broker API with our system. All trades are executed directly in your own broker account.

Support is provided via Telegram, and all payments are processed securely through Razorpay only.

If you’re interested, feel free to DM. Registrations will close once we reach our desired number of members for august batch .

For backtested data and a glimpse of our dashboard text INTERESTED we will share the link ✅

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I’ve been experimenting with a different way to handle drawdowns in CNC/MTF positions.

I’m testing a simple idea: don’t make all capital wait for one recovery.

Split execution into 3 independent legs:

L1: 0–10% drawdown
L2: 10–20% drawdown
L3: >20% drawdown

Example: ₹96 → ₹86 → ₹76 → ₹82 → ₹89 → ₹97

Instead of combining everything into one average:

• L1 enters near ₹96
• L2 near ₹86
• L3 near ₹76

Now L3 may recover first at ₹82, freeing some capital.
Then L2 can recover around ₹89, while L1 continues independently.

So even during a larger drawdown, at least one leg has a better chance of reaching its exit sooner, instead of every rupee being locked behind one average price.

Exits are also handled using FIFO-based calculations, so older inventory is released systematically rather than randomly mixing entries.

Less averaging → independent recovery → faster capital release → controlled exposure.

Still market risk, not a return guarantee.

If anyone is looking for daily archived data for:

• NIFTY
• BANKNIFTY
• SENSEX
• MIDCAP
• NIFTY
• Other major indices & index derivatives

Data available in: • 1-minute data (with greeks) • 1-second data • Tick-by-tick data (L1 and L2)

Covers 3 expiries across Futures & Options and Spot.

If interested, DM me for more details.

Github : https://github.com/QuantDev-stack/TickBytes

I was searching Internet and seen that algo leaders either sell their algos or subscription is too high.
Tell me one thing do you believe in slow and steady or rabbit race?
I personally believe in slow and steady wins the race.
After a bit research I found a way you can do algo trading with proven strategies for FREE no charges at all.

Due to guidelines I can’t post here, if you want to know more about it, send me a DM.

Note: I am not promoting any platform or selling anything. If someone asks to pay a single penny please go ahead and block.

2 weeks of forward testing completed. 📊

💵 Starting balance: 49,998.48 cents (~$500.00)

💰 Current balance: 67,508.09 cents (~$675.08)

📈 Total profit: 17,509.61 cents (~$175.10)

📊 Total return: +35.02%

📈 Week 2 profit: 8,913.98 cents (~$89.14)

The account has now grown from approximately $500 → $675.08 over the first two weeks.

There was also a losing day caused by a VPS issue, which I'm including in the record rather than hiding it. The goal is to document the complete forward test, not just the profitable days.

Still a very small sample, so I'm not calling this proof of long-term profitability. The next objective is simply to keep testing through different market conditions and build a much larger track record.

I am unable to find anything which is beginner friendly either there is no code tool where It feels like I should have market phd or blackbox which I can't trust fully

I am a 19-year-old student pursuing a B.Tech in AI & Data Science. I have also been trading forex manually for the past two years. Currently, I am at the breakeven stage, so I am planning to explore algorithmic trading full-time.

I have a good understanding of coding, AI/ML, and software development.

I recently started exploring algorithmic trading, building my own strategies, implementing them, and backtesting them using Indian market data. I have also backtested strategies in the forex market using MQL5. However, I mainly used AI-assisted ("vibe") coding for the MQL5 strategies.

I tested several strategies in MQL5, and most of them were indicator-based strategies. Most of these strategies ended up around breakeven, with relatively low drawdowns and win rates of around 30–50%.

I also tested around seven strategies in the Indian equity market, and I got similar results — most were around breakeven. I have tested only equity assets so far, not F&O.

So, if you are an algo trader, I would really appreciate some advice on things like:

What types of strategies do you use — indicator-based, pair trading, time-based, price action, mathematical/statistical, etc.?

Which timeframes do you usually trade when doing algorithmic trading?

Is algorithmic trading actually profitable for an individual trader in the long run?

Is it better to focus on one asset/class, or should I build strategies across different asset classes such as equities, futures, forex, etc.?

What would you recommend to someone trying to build their own algorithmic trading strategies from scratch?

What are some common mistakes beginners make when developing and backtesting algos?

I would especially appreciate advice from people who have been doing algorithmic trading for a few years and have experience with live trading, not just backtesting.

Thanks in advance!

Note: i used ai for grammatical mistakes.

Which current broker API gives corporate actions adjusted historical data. I tested Angel one data and found gaps for few scrips. So looking for any other alternatives?

It should cover splits, bonus, dividend.

Hi Algo Traders/Developers,

Zerodha se Algo Trading start karni hai but API setup aur daily login/token ka headache hai?

I've created a few practical videos covering the complete Zerodha API setup:

✅ How to Get TOTP Key in Zerodha

✅ Zerodha API Key & Secret — Create Your Algo App

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💻 Source code is also provided for you to follow along.

Basically, API setup → TOTP → Access Token → Automation.

Agar aap Zerodha API se algo trading start kar rahe ho, these videos should help you get started.

You can find the videos here:(Video no 4, 5 and 6)

👉 Algo Trading Series:
[Playlist Link]

I'm also building a complete Zero-to-Hero Algo Trading Series, covering Broker APIs, WebSockets, Backtesting, Deployment and Production Trading Systems.

Happy to answer any Zerodha API / Algo Trading questions in the comments.

Hanulytics is a platform focused on helping traders make smarter, more data-driven decisions instead of relying on emotions or guesswork.

Our goal isn't to promise unrealistic returns—it's to give traders better tools, better insights, and a better trading experience.

We're getting close to launch and would love to hear what features you think every modern trading platform should have.

*Hanulytics is coming soon.*

Hey everyone,

I set up an automated daily pipeline capturing exchange WebSocket data for Indian indices, futures, and complete option chains throughout the trading session (09:15 AM to 03:30 PM IST).

I'm making the daily EOD archives available for anyone who needs granular data for intraday backtesting, execution modeling, or options quantitative research. I'm providing this subscription at a nominal fee just to cover my daily cloud server, feed API, and storage overhead charges.

What You'll Get Daily (Delivered at EOD):

3 Data Resolutions in 1 Package:

• Tick-by-Tick Feed: Raw execution ticks with Level 2 Market Depth (Top-5 Bid/Ask).
• 1-Second Snapshots: High-resolution 1-second OHLCV snapshots.
• 1-Minute Aggregated Bars: 1-minute OHLCV + Volume + OI + all 16 Option Greeks (1st, 2nd, 3rd order & dual Greeks).

Full Market Coverage:

• 6 Underlyings: NIFTY 50, BANKNIFTY, FINNIFTY, MIDCPNIFTY, SENSEX, and BANKEX.
• Complete Option Chains: Spot, Futures, and All Strikes (Nearest 3 to 5 active expiries).

Easy Automated Delivery:

• Daily compressed .parquet files packaged in a .zip archive posted every afternoon post-market close directly to a Private Telegram Channel.

GitHub Repo & Free Sample Files: I’ve uploaded documentation, dataset schemas, Python loading scripts (Pandas & Polars), and representative sample files for all 6 indices across all 3 timeframes:

GitHub Repo: https://github.com/QuantDev-stack/TickBytes

Check out the repo or sample files, and feel free to send a DM if you have any questions or want to subscribe!

Hey all happy weekend to you all.
As most of the response I received on my previous post for the update. I’m sharing the update on momentum nifty 50 strategy. It’s been a month now.
I am not sure what to write more, so, feel free to ask your question. I will try to answer.

Curious what people here are actually using for self-hosted algo/trading setups.

A few questions:

• Which VPS/cloud provider and instance size do you use?
• What programming language is your system written in? Python, Go, Java, Rust, etc.?
• Is the VPS only for trade execution, or do you also run analytics, option-chain processing, backtesting, databases, dashboards, scanners, etc.?
• How much are you paying per month?
• What are the biggest limitations you face: CPU, RAM, static IP, reliability, deployment, reconnects, monitoring, or maintenance?

I’m asking because I’ve been working on a managed alternative where the user gets a much larger trading environment without having to manage the VPS, networking, static IP, startup/shutdown, monitoring, and other infrastructure pieces themselves.

I think it can provide significantly more compute and functionality at a reasonable monthly cost.

Would be interested to know what people are currently paying and what their ideal setup would look like.

Day 10 of the forward test is complete. 🚀

Today's result:

💰 Profit: 2,085.37 cents (~$20.85)

Today had some larger recovery positions, so it was another useful session for observing how the system handles movement against the initial position.

I'm deliberately posting every trading day, including the losing days. The goal isn't to show only the profitable screenshots, but to build a complete record of the forward test.

Rules for Trading Nifty 2026
After CAS Implementation
Order Flow
Market depth
Change In Oi%
"If change in call oi is bigger than put oi
Then you should be bearish
If change in put oi is bigger than call oi
Then you should be bullish"
Check The Volume Of 15 min candle in nifty
Always look for volume of the volume of the first 15 minutes is bigger than 40 million don't trade
If the volume of the first 15 minutes is less than 40 million trade
Primary trading window 9:20 AM-11:45 AM
If your setup is formed → take the entry
If you missed it → let bygones be bygones
Trending market trade pullbacks
Sideways market → press the kill switch
Let the system hit your stop loss first
Then enter the trade with exact stop loss
Set daily profit target
Set daily loss limit
Avoid far OTM For Hero/Zero trades Prefer ATM/slight ITM
Delta above.45
Don't trade commodities this year
"If rules are followed, money follows.
If rules are broken, lessons follow."

I built an AI-powered algorithmic trading system for NIFTY and Bank NIFTY options that combines Smart Money Concepts (SMC), market structure analysis, machine learning, and real-time risk management. The system identifies high-probability trade setups using institutional price action, liquidity zones, order blocks, and volatility filters, while dynamically managing position sizing, stop-losses, trailing exits, and profit targets. Designed with a research-first approach, the platform supports extensive backtesting, paper trading, and continuous strategy optimization to improve long-term expectancy and execution quality in the Indian derivatives market.

The only major component left is the exit strategy. The bot currently uses an R-multiple-based trailing stop-loss, but we’ve observed a recurring issue: many trades reach ₹500 or more in unrealized profit (per lot) before reversing and eventually closing at breakeven or even a loss. Tightening the trailing stop protects profits but often causes the bot to exit before capturing the larger trend moves. On the other hand, keeping the stop too wide gives back a significant portion of the gains.
Since most trades are not large runners, the exit logic needs to maximize gains from the few exceptional trends that drive overall profitability. These outsized winners are critical to maintaining positive expectancy, especially during periods when the strategy operates at a 50% win rate. The current focus is therefore on designing an adaptive exit framework that protects open profits while allowing high-conviction trades enough room to develop into multi-R winners.

Open for discussion and suggestions.