Servloci Algo Appliance: Intraday → Swing → Long-Term, without forcing the label upfront. Today with shiprocket

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.

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u/avnish-vikas-devops — 1 day ago

Is cupid risky ? Most of time it depends on how we process

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.

u/avnish-vikas-devops — 2 days ago

Too much AI can make trading worse. Rely on maths

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.

u/avnish-vikas-devops — 3 days ago
▲ 1 r/TakeProfitTrader+1 crossposts

L1/L2/L3 is becoming reality and no platform is even through of this. L1/L2/L3 rotation: my answer to the “you’re just catching a falling knife” criticism

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.

  • L1 handles the normal price zone.
  • L2 becomes active deeper in the drawdown.
  • L3 handles an even deeper zone.
  • Legs can use different CNC/MTF allocations and even different broker accounts.
  • Each leg has its own entry/exit behaviour instead of turning everything into one giant averaged position.

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.

u/avnish-vikas-devops — 7 days ago

Tech and Finance oriented Co-Founders. Any expertise will work

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

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u/avnish-vikas-devops — 7 days ago

Golang Based Simple trading framework utilizing static ip exit route

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

u/avnish-vikas-devops — 7 days ago
▲ 1 r/TakeProfitTrader+1 crossposts

L1 _ L2 _L3 More safer multi broker CNC/MTF strategy

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

https://preview.redd.it/u7tlgzob7bih1.png?width=2454&format=png&auto=webp&s=9360bc5708ad4a6b49df779495da7fe082a0a88d

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.

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u/avnish-vikas-devops — 11 days ago
▲ 4 r/mltraders+1 crossposts

What VPS/setup are you using for your trading system?

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.

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u/avnish-vikas-devops — 12 days ago
▲ 3 r/mltraders+1 crossposts

A simpler way to meet broker static-IP requirements

Updated with Servloci’s Oracle VM installation option included:

Many broker APIs now require a fixed IPv4 or IPv6 address. A common workaround is to create an Oracle Cloud Free Tier VM and route broker traffic through it.

Oracle Free Tier can work, but it has limitations:

  • Free AMD instances may provide only around 0.125 OCPU, effectively one-eighth of a CPU.
  • Free ARM instances offer better resources, but availability is often limited.
  • Free VM capacity may not be available in your preferred region.
  • You still need to manage networking, firewall rules, updates, routing, monitoring, and recovery.
  • Managing separate IP mappings for multiple family members or broker accounts remains complicated.
  • The VM becomes another possible failure point between your application and the broker.

There is also an IP availability problem.

Public IPv4 addresses are limited, so dedicated IPv4 allocation cannot scale indefinitely. IPv6 provides a more scalable alternative, but it can only be used when the broker and its APIs properly support IPv6.

Servloci provides two approaches through:

https://comm.servloci.in/

1. Use a Servloci-provided static IP

Generate a private, one-time command and run it on Ubuntu or Debian. The machine appears in the dashboard with its assigned fixed IPv6 address and broker environments.

  • No cloud login is shared.
  • No SSH key is shared.
  • No broker API key or secret is shared.
  • Your trading application continues running on your own machine, server, or notebook.
  • Only broker-bound traffic uses the assigned static network identity.
  • IPv4 is available where required, subject to limited availability.
  • IPv6 provides a more scalable option where supported by the broker.

2. Create and install your own Oracle VM

For users who prefer Oracle Cloud Free Tier, Servloci also provides an easy provision to create an Oracle VM and install the required networking setup.

This reduces the manual work involved in configuring the VM, installing the agent, setting up routing, and connecting it to the Servloci dashboard.

You can therefore choose between:

  • A Servloci-provided working static IPv4 or IPv6.
  • Your own Oracle Free VM with simplified installation and management.

This is intended for developers who already have their own broker execution system but need a reliable static network identity without sharing broker credentials or manually managing complex networking.

How are you currently handling broker static-IP requirements: Oracle Free Tier, paid VPS, static IPv4, IPv6, VPN, or SOCKS5 routing?

u/avnish-vikas-devops — 17 days ago
▲ 1 r/mltraders+1 crossposts

Building a Technical Analysis Workbench to Support a Live Smart-Order Execution System

I’m building a technical-analysis workbench as a complementary layer to an already running smart-order execution system.

The execution side handles:

  • Smart order chunking
  • Dynamic price-difference thresholds
  • Dynamic quantity sizing
  • Continuous adjustment based on current market conditions

The workbench adds the missing decision context before and during execution:

  • Daily candles and volume
  • SMA / EMA, Bollinger Bands, VWAP, RSI and MACD
  • Trendlines, rays, Fibonacci and marked zones
  • Plain-language bullish, bearish or mixed market context
  • Configurable presets and historical date ranges

The goal is not to make a single “buy/sell” prediction from indicators. It is to provide a more reliable market-state layer around the execution engine.

For example, the engine can treat price relative to SMA200, EMA50 and VWAP differently from a neutral or weakening setup. RSI and MACD then add momentum context, while drawn levels help identify structure and possible reaction zones.

The parameters and outcomes are recorded and fed back into a GRPO-based optimisation system. The intention is to learn which combinations of chart context, chunk size, price diff, quantity, and execution timing improve net outcomes—while penalising excessive risk, churn, drawdown, or bad fills.

In short:

Technical-analysis workbench → market context → smart chunked execution → outcome logging → GRPO feedback and tuning

This is being built as a decision-support and execution-quality system, not as a “guaranteed signal” product. The real test is whether it improves average execution and risk-adjusted outcomes over enough forward-tested trades.

Would you trust a system more if it exposes the context and execution logic like this, rather than just showing a black-box buy/sell call?

u/avnish-vikas-devops — 19 days ago