No, meri strategy mein ai nhi hai. mere codebase mein hai kyonki mai lazy hun itne long codes likhne mein.
strategy simple hai. kuch jyada hi simple isliye effective bhi aur bahut fast bhi
If you need to practice them in colab notebook , try this https://comm.servloci.in/notebooks/21_top_50_technical_indicators.html
If you want to just practice NIFTY or BANK NIFTY monthly series, simple paper trade without risking money, you can try it here. I run it to train my intuition and will be doing for approx 1 year.
Hi
If you need to learn basic about algo trading , you can use it . Over weekend, I will add live notebooks , better data connectors to see things in actions like computing technical indicators and their overlap. place real orders in off hours and then live .
No matter what is condition, Just 1 formula with few variable to rule it all. no worry about intraday , fno etc. When I can get benefit of intraday and cnc in best possible way. build strong base for covered option why to take risky path.
My approach is based on accumulation, isolation, diversification, and mathematical exits, rather than trying to predict exact tops or bottoms.
In simple terms:
Slow collection → isolated positions → mathematical averaging → selective profitable exits → recycle capital → repea
My approach is based on accumulation, isolation, diversification, and mathematical exits, rather than trying to predict exact tops or bottoms.
In simple terms:
Slow collection → isolated positions → mathematical averaging → selective profitable exits → recycle capital → repea
I mostly focus on one simple idea:
Collect slowly on bad days, release gradually on good days.
Instead of trying to solve the market using hundreds of strategies, indicators, and signals, I prefer slow, incremental, dynamic accumulation.
I’m not trying to predict the exact bottom or top. I keep adjusting positions as price moves and try to benefit from the market’s natural oscillations.
It may not look sophisticated, but simplicity is the advantage.
For me, a strategy that is understandable, repeatable, and usable across different market conditions is better than constantly jumping between 100 different strategies.
I’ve also built a simple tool around this approach. If this style of trading interests you, feel free to DM me.
This is intentionally a slow process. I’m not interested in flashy claims or overpromising results.
I would rather under-promise and over-deliver.
https://console.zerodha.com/verified/2e2ae571
As I have always stated , doing CNC or MTF can provide you enough edge so you do not need risky options or intraday. Next Month will be better. >50% capital unused. Just good rotation
Single formula to do intraday , swing and long term depend upon how day ended. Day converted to Weeks and months . Slow accumulation and greedy exit( exit is manual mostly) . You can automate this if needed .
Discussion thread is updated. look into this. this is initial theorem and will be converted to algo. If you need help , dm me
I’m thinking of extending my L1-L2-L3 framework for HNI-sized portfolios.
Example: ₹20 Cr capital, max **₹1L per entry**, max **20 active symbols**.
Instead of averaging everything into one position, every buy becomes an independent lot.
If a selected stock reaches a defined lower range, the system can allocate ₹1L through one of multiple broker accounts. That lot remembers its own broker, entry price, range and recovery target.
So:
* ₹1L = one capital unit * Max 20 symbols * Max 10 lots per symbol * Max exposure/symbol = ₹10L * Max theoretical deployment = ₹2 Cr, only 10% of ₹20 Cr
All broker positions appear inside one **virtual portfolio**, but every lot remains isolated.
If a stock falls further, a new range can create another independent lot. When price recovers, profitable/recovered lots can be cleared individually instead of waiting for the entire averaged position to recover.
Once 10 lots are active for a stock, accumulation stops. Recovery frees slots again.
So brokers are basically execution shards, while L1/L2/L3 controls capital deployment.
The goal isn't to predict the exact bottom or top.
It is to **deploy small amounts gradually, diversify heavily, isolate each entry, and continuously recycle recovered capital.**
Would you consider this safer than conventional averaging for a large portfolio?
Discussion thread is updated. look into this. this is initial theorem and will be converted to algo. If you need help , dm me
I’m thinking of extending my L1-L2-L3 framework for HNI-sized portfolios.
Example: ₹20 Cr capital, max ₹1L per entry, max 20 active symbols.
Instead of averaging everything into one position, every buy becomes an independent lot.
If a selected stock reaches a defined lower range, the system can allocate ₹1L through one of multiple broker accounts. That lot remembers its own broker, entry price, range and recovery target.
So:
All broker positions appear inside one virtual portfolio, but every lot remains isolated.
If a stock falls further, a new range can create another independent lot. When price recovers, profitable/recovered lots can be cleared individually instead of waiting for the entire averaged position to recover.
Once 10 lots are active for a stock, accumulation stops. Recovery frees slots again.
So brokers are basically execution shards, while L1/L2/L3 controls capital deployment.
The goal isn't to predict the exact bottom or top.
It is to deploy small amounts gradually, diversify heavily, isolate each entry, and continuously recycle recovered capital.
Would you consider this safer than conventional averaging for a large portfolio?
I practice this approach daily, and for me trading becomes much simpler when I focus on:
Worst-case analysis + diversification + dynamic grid + a few useful indicators
instead of relying on a screen full of TA indicators trying to predict the next move.
The grid handles the basic reality of markets: price moves in cycles. Indicators can still help, but mainly as inputs to the system — for example, deciding grid distance, quantity, volatility adjustment, accumulation speed, or when to slow down buying.
That feels more practical than expecting indicators to tell me exactly where price will go next.
Most trading systems want to be the rabbit.
Catch the breakout.
Find the perfect entry.
Take a big position.
Hit the target before everyone else.
Servloci is closer to the tortoise.
We don't try to predict the highest point or the lowest point. We react to what the market gives us.
A good stock can fall 10%, 20%, even 50% temporarily. It can also move through many smaller cycles before reaching its long-term destination.
So instead of betting everything on one perfect prediction:
No need to win every trade.
The important thing is to keep moving, keep capital productive, and survive long enough for compounding to matter.
The rabbit asks:
"How much can I make on this trade?"
The tortoise asks:
"How many times can I repeat a small mathematical advantage without destroying my capital?"
That is much closer to the Servloci philosophy.
Slow doesn't necessarily mean low return.
Slow means controlled, repeatable and scalable.
In markets, the finish line isn't the next candle.
It's staying in the race.
I have not applied for anything yet. My current plan is simpler: trade through the Servloci algo appliance, then see what remains at EOD.
The core idea is continuous accumulation across timeframes.
A position does not need to be classified as “intraday”, “swing” or “long-term” before entering.
It starts as accumulation over seconds/minutes:
Seconds/minutes → Intraday accumulation
The appliance keeps accumulating/releasing small quantities based on the current mathematical conditions.
EOD → Remaining quantity becomes holding
Whatever is not released during the day simply carries forward.
Repeated over multiple days → Swing position
The same process continues the next day. New quantities can be accumulated while older quantities can be released independently.
Stayed longer → Long-term holding
If part of the position remains for weeks/months, it naturally becomes a longer-term investment.
So instead of:
>
The model is closer to:
>
Timeframe becomes an outcome, not necessarily an entry decision.
This also fits the L1/L2/L3 idea I have been working on. Capital can be distributed across different drawdown zones instead of treating the entire position as one giant bet.
Another important part is the static-IP appliance architecture.
In a family, we may have multiple independent brokerage profiles. Instead of maintaining a different laptop/server/network setup for every account, the Servloci appliance can become the common execution point.
Each family member still has their own broker account, credentials, permissions, ledger and orders, subject to the broker's rules. But the infrastructure, market intelligence and strategy logic can be shared.
So we can share:
market intelligence + strategy + infrastructure + outcomes
while keeping:
account ownership + capital + positions + broker records
separate.
A static IP makes the execution endpoint predictable. Rather than orders originating from changing home/mobile/cloud IPs, authorized profiles can use a stable execution infrastructure where the broker/API rules permit it.
For me, the interesting part is not trying to perfectly predict whether something is an intraday trade, swing trade or investment.
Just keep managing inventory.
What exits quickly was intraday.
What survives several days became swing.
What survives much longer became long-term.
The system discovers the holding period through execution rather than forcing it beforehand.
Would be interested to know how others think about timeframe as an outcome instead of an input.
I have started taking position here . See where it goes ? I can predict that this will less worse than how much it can fall. Might comes out in green that is possible also. This is not my brilliance. This is process briliance. Cost like brokrage is already factored. The better my selection, superior outcome willl be be. I prefer generally good stock bad day.
More signals, indicators and AI layers add complexity, latency and compute.
Servloci takes a simpler approach: no AI, just deterministic math.
Observe → apply fixed rules → react.
Stocks rarely move only in one direction. Markets move in cycles. A quality stock can still fall 30–50% before recovering.
If your strategy only cares about catching a possible high point, you’re essentially making a bet on direction.
We don’t.
We diversify, control exposure and gradually build positions using L1-L2-L3 drawdown layers.
For CNC/MTF, a fall isn't automatically a stop-loss event. Under predefined limits, it can become the next accumulation layer.
Don’t predict the perfect point. Diversify, layer, control risk and let the position develop gradually.
Whenever I explain this system, one criticism comes up:
>
That criticism is valid if the only logic is: price fell → keep buying.
That is not what I’m trying to build.
Even genuinely high-quality companies can experience large short- or medium-term drawdowns because of sector cycles, temporary earnings pressure, macro conditions, liquidity, sentiment, or simply valuation compression.
And this creates a common problem for long-term investors:
You buy a good company → it falls 15–30% → most of your capital is already committed near the first entry → then you spend months or even years waiting just to get back to breakeven.
My L1/L2/L3 framework tries to handle the drawdown differently.
Instead of treating the entire position as one average price, capital is divided into separate legs.
The important part is that the lower leg does not necessarily need the stock to recover all the way back to your original purchase price.
Imagine:
Initial position: ₹100
Stock falls to ₹82
A deeper leg accumulates around ₹82–85.
If the stock rebounds to ₹90, the original ₹100 position is still underwater.
But the lower leg may already be profitable and can be released.
That capital becomes available again.
This is the part I care about: capital rotation rather than simply waiting for the entire position to heal.
Markets are also cyclic.
A fundamentally sound company rarely moves in one straight line forever. There can be multiple 5–15% waves inside a much larger drawdown.
Instead of ignoring those waves, the framework tries to harvest them.
So the objective isn't:
“Keep averaging until I am right.”
It is:
“Segment the drawdown so different parts of the position can recover independently.”
And diversification makes this much more interesting.
Suppose you have 15–20 carefully selected companies.
Some may be in L1.
Some may temporarily enter L2.
A few may reach L3.
While one company remains depressed, another may recover and release capital.
That capital can then rotate again.
So instead of depending on one stock making one huge recovery, you increasingly depend on many smaller independent recovery events.
Better diversification + better company selection + strict exposure limits should make the framework stronger.
Of course, none of this saves you from buying a structurally broken company.
If earnings collapse permanently, debt becomes unmanageable, governance deteriorates, or the original investment thesis is invalidated, adding more capital can absolutely make the loss worse.
So stock selection and maximum exposure matter more than the rotation algorithm itself.
I don't see L1/L2/L3 as a way to magically eliminate drawdowns.
I see it as a way to turn a large, slow, monolithic drawdown into multiple smaller capital-recovery opportunities.
The goal is:
Accumulate better → release profitable legs earlier → recycle capital → reduce capital lock-up → make recovery more systematic.
If the selection quality and diversification improve, this framework should have more opportunities to “zoom” capital through recovery cycles instead of waiting years for a single average price to come back.
The screenshot is from the working implementation I'm testing now.
Interested in criticism from people who manage long-duration CNC/MTF portfolios—especially cases where you think this structure would fail.
I am interested . What irritate when co-founders just ask for profit sharing not the execution and growth path. If you need to discuss these things, I am good to go. My current work stack is devops stack which amplify financial tech. I am also exploring agentic workflow mixed with common functions to speedup , lower cost and open connect between them. I am also working on ai inner core to embed more feature token to improve llm like putting $_$ so content between get automatic more attention , physics inspired dimensity , memonic keys to find facts faster , derivation engine etc
This is simplifed trading setup. Good enough. No chart. No technical Indicator. Just smart chunking , exit and repeat cycles. This works. Control panel is making it even better. Multiple user, multiple broker , multiple setup just 1 formula that works in all condition https://console.zerodha.com/verified/2e2ae571