If you understand how to configure your agent properly, you’re defining how your strategy behaves in the market.

This guide breaks down every field inside Agent Configuration, and how to think about it like a trader, not just a user.

— — —

1. Trading Budget & Basic Config

This section defines the operating framework of your agent. It answers three core questions:

• How much capital is being used?
• How many signals can be active?
• How does the agent behave when signals arrive?

1.1, Number of Sessions

Quant mode operates on 1-hour trading sessions.

👉 What this controls:

• How long your agent stays active (Running time in hours)
• How many cycles it processes signals

This is essentially your time horizon control.

1.2, Total Volume (USD), Max Signals, Average volume

Example:

• Total volume = $45
• Max signals = 3 (defines how many trades your agent can run at the same time).
• Average volume = $15 (position size per trade: Average Volume = Total Volume ÷ Max Signals)

👉 What’s happening:
Your capital is distributed across signals, not deployed all at once.

👉 Why it matters:

• Prevents over-commitment
• Keeps your exposure structured
• Forces discipline in allocation

Instead of reacting to every signal, your agent operates within a bounded decision space.
The problem isn’t lack of signals. It’s too many signals hitting at once.

1.3, Signal Direction

This controls whether your agent trades:

• Long
• Short
• Or Both

It determines:

• Which signals are allowed into your system
• How your agent reacts when new signals appear

1.4, Close Position Mode (Critical)

This is one of the most important parts of the entire configuration.

It defines:
👉 How your agent exits trades

Oraichain designed multiple modes to:

• Protect PnL
• React faster to new signals
• Avoid “dead positions”

* Auto Exit or Switch (New Signal Gated)

👉 Logic:

• Same direction signal → keep position, update logic
• Opposite direction signal → close and switch

👉 What this does:

• Keeps your agent adaptive
• Prevents holding outdated positions
• Aligns execution with current signals

This is ideal if you want your agent to continuously follow the market.

* Manual Exit Mode

👉 Logic:

• No forced closing
• No automatic flipping

👉 You control when to exit, when to hold.

This is useful if you want:

• More discretion
• Less automation

👉 Key idea:
Quant Terminal doesn’t remove control,
It lets you choose how much control to keep.

2. Risk control

2.1, TP % and SL %

Take Profit and Stop Loss define your risk boundaries.

Key detail: Even after applying a model: TP/SL remain editable

2.2, Auto Break-even

This is one of the most practical risk tools.

This feature is disabled by default and can be enabled as needed.

👉 How it works:

• You set a trigger (e.g. +0.5%)
• When price reaches it → Stop Loss moves to entry

👉 Result:

• You eliminate downside risk on that trade
• You protect gains if price reverses

👉 Purpose:
Lock safety once the trade is working.

Or simply: Protect the bag.

2.3, Reversed Quant Signals

This allows you to invert the model’s signals.

This feature is disabled by default and can be enabled as needed.

• Model says Long → you take Short
• Model says Short → you take Long

Useful when:

• Market regime changes
• Model underperforms in current conditions.

2.4, Skip Hours (Local Time)

Markets don’t behave the same 24/7. There are:

• Dead hours
• Low liquidity windows
• High-risk volatility spikes

👉 This setting allows you to:

• Skip specific time ranges
• Avoid trading in poor conditions

Your agent becomes selective, not always-on.

3. Advanced setup

3.1, Model type

This is where you choose your signal engine.

To identify which models are profitable and best match your trading style, please review the model rate available on the Model Marketplace.

Try different models and find the one that works best for you.

Different market conditions may require different models for optimal performance.

Oraichain separates:

• Model = what to trade
• Config = how to trade

👉 Important:
Applying a model does NOT lock your settings. You can still adjust:

• TP/SL
• Exit logic
• Risk parameters

This gives you flexibility to adapt execution without changing strategy.

3.2, Volatility

Markets move in different regimes:

• Low volatility → slow, range-bound
• High volatility → fast, aggressive

👉 This slider lets you define: Which volatility range your agent operates in

3.3, Whitelist & Blacklist Tokens

This defines your tradable universe.

• Whitelist → only trade selected tokens
• Blacklist → exclude specific tokens

Agent Configuration on Oraichain Quant Terminal was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Oraichain Quant Terminal packs multiple functions into one place: Trading Agent, Vault, Staking, etc.

In this guide, we focus on one of the most practical features on the terminal: creating your own Trading Agent.

This is the part of Quant Terminal that lets you build an autonomous trading workflow without coding a bot from scratch.

You set the rules, choose how the agent behaves, and decide how hands-on you want to be. The flow is simple: set up your account, configure the agent, start it, then monitor or step in manually when needed.

What is a Trading Agent on Quant Terminal?

A Trading Agent is more than a signal feed.

On Quant Terminal, the agent works inside a rules-based framework. It reads model-driven signals, follows the configuration you set, executes trades on supported venues, and manages positions based on your chosen logic. That means you are not just watching signals. You are turning those signals into an execution system that matches your own strategy and risk preferences.

Before You Create Your First Agent

Before anything else, you need to prepare the trading account for the venue you want to use.

Start by connecting your wallet to quant.orai.io and funding the account tied to the venue where you want the agent to run. From there, the setup flow is straightforward:

• Open Portfolio, deposit funds into the venue you want to trade on
• Generate your API key
• Create or edit your config
• Save config and start agent

Deposit to the Correct Venue Account

Open Portfolio tab. Choose your trading venue.
Each venue has its own trading account flow, so funds need to be deposited into the specific account tied to that DEX or execution environment. If you want to run the agent on a certain venue, make sure the funds are deposited to the right place first.

Generate Your API Key

After depositing money into your account, choose Set up config in Dashboard tab.

Then generate your API key.

You only need to do this once for a trading account. After that, you can move through the setup without repeating the same step again.

How to Set Up Your Trading Agent

Once the account is ready, head to the configuration panel and customize the setup based on your trading idea.

Each agent can run with a different configuration, but the overall setup logic stays the same across standard DEX agents. The main exception is the XAU Quant Agent, which follows a simpler fixed-model setup.

Core Configuration Fields

Number of Sessions

This controls how the agent distributes trading activity across configured sessions.

Total Volume (USD)

This is the total capital allocation the agent is allowed to use for trading. In simple terms, it sets the overall size the agent can deploy under that configuration.

Max Number of Signals

This controls the maximum number of signals the agent is allowed to act on. It is enforced per session, which helps cap how many trade opportunities the agent can take within each trading window.

Average Volume

This refers to the average size per signal or per trade allocation under the config.

Signal Direction

This determines which side of the market the agent is allowed to trade. Users can choose from:

• Long only
• Short only
• Both directions

This is useful when you want the agent to stay aligned with your market bias instead of trading every setup it sees.

Close Position Mode

This is one of the most important settings because it defines how the agent exits or rotates positions.

1. Manual Exit

In this mode, the agent does not automatically close or flip a position just because a new opposite signal appears. The position stays open until you manually close it, or until TP/SL is triggered.

This is the most flexible mode for users who want more control after the trade is opened.

2. Auto Exit by Close Candle

Here, the agent closes the position when the candle closes based on the session logic tied to the strategy.

This mode fits traders who want the system to respect the original signal window and avoid holding positions beyond that session.

3. Auto Exit / Switch

This mode lets the agent react faster when new signals arrive.

The logic works like this:

• If the new signal is in the same direction, the position stays open and TP/SL can be updated
• If the new signal is in the opposite direction, the current position closes and a new one opens
• If a symbol no longer appears in the new signal batch, the agent can close that position.

This setup is useful for traders who want the agent to stay responsive to model changes without waiting for manual intervention.

TP and SL

TP and SL are your Take Profit and Stop Loss settings. These define the basic profit target and downside limit for each position.

You can also edit the TP and SL of an active trade in the Trade tab even while the agent is running. That gives you flexibility to keep the automation live while still adjusting risk manually when needed.

Auto Break-even

Turn on Auto Break-even automatically moves the stop loss to the entry price once price reaches your chosen x trigger.

Once price hits your selected x% trigger, the SL moves to entry. You can enable this in Setup Agent Config → Auto Break-even.

This helps protect capital once a trade starts moving in your favor. It is a simple but useful setting for reducing the chance of a winning trade turning back into a loss.

Apply Reversed Quant Signals

This setting tells the agent to invert the model’s original direction.

If the quant model gives a long signal, the agent can take it as a short. If the model gives a short signal, the agent can take it as a long. This is mainly useful for users who want to test the opposite expression of a model view.

Skip Hours (Local Time)

Skip Hours lets you define time windows when the agent should not open new positions.

The logic is simple:

• If no hours are selected, the agent can trade at all hours
• Selected time ranges are applied daily using your local time
• Internally, those hours are stored in UTC

This is useful if you want to avoid lower-conviction hours, low-liquidity periods, or time windows that do not fit your trading style.

Advanced Configuration

Quant Terminal also gives you more detailed filters to shape how the agent behaves.

Model Type

Model Type determines which quant model the agent follows.

You can use Model Marketplace to browse live or in-testing models and compare stats like PnL, max drawdown, and win rate before choosing the one that fits your style. This makes it easier to match the agent to your own risk appetite instead of trading blindly.

Whitelist Tokens

Whitelist Tokens limits the agent to trading only selected assets.

This is useful when you want the agent to focus on a smaller set of tokens instead of scanning a broader universe.

Blacklist Tokens

Blacklist Tokens excludes specific assets from the agent’s trading universe.

This helps when there are tokens you do not want exposure to, even if the model generates signals on them.

Volatility

Volatility acts as a market condition filter.

In practice, this helps narrow which trades the agent takes based on volatility conditions, so the setup can better match the type of market movement you want to trade.

Save Config, Then Start the Agent

Once the setup is ready, save the configuration and click Start Agent.

That is the step that actually turns your setup into a live trading agent.

If you want to review the config later, you can open it again from the config panel. You can also click the eye icon next to setup config to view the saved settings.

How to Monitor Your Agent After It Goes Live

Once the agent is running, the Trade tab becomes the main place to track what it is doing.

This is where you monitor each position.

The workflow stays flexible. You can let the agent run on its own, or you can still step in manually when needed. That is one of the more practical parts of Quant Terminal. Automation does not lock you out of your own trades.

What About the XAU Quant Agent?

The XAU Quant Agent is built to be much easier to start with.

Unlike standard DEX agents, it does not require the same level of setup because the flow runs on two fixed models designed specifically for XAU trading. If you want a faster way to get exposure to this strategy without spending time tuning every detail, this is one of the easiest entry points on Quant Terminal.

With just a few clicks (choosing risk mode and start your agent), you can get started and see why the XAU Agent has been drawing attention lately.

Final Takeaway

Creating your own Trading Agent on Oraichain Quant Terminal is really about turning a model into a trading workflow that fits your own rules.

You choose the capital allocation, define how many signals the agent can take, decide how positions should be handled, and set filters that match your style. Then you start the agent and let it trade with the structure you built.

That is the real edge here. Not just automation for the sake of automation, but automation you can shape around your own strategy, risk, and execution style.

And getting started is easy at quant.orai.io.

Guide: Create Your Own Trading Agent was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

A signal tells you what might be worth doing. Execution decides what actually happens when you try to do it.

That gap matters a lot in crypto. Markets move fast, liquidity shifts quickly, and prices are fragmented across venues. The same setup can lead to very different results depending on how and where it gets executed.

Two traders can follow the same signal and still end up with different PnL. One gets in cleanly, sizes properly, and exits with discipline. The other gets a worse fill, chases the move, or reacts too slowly. Same signal. Different execution. Different outcome.

That is why execution is not just a technical detail. It is part of the edge.

Why crypto makes execution harder

Crypto is not a clean environment.

It runs 24/7, liquidity is uneven, spreads can widen fast, and depth can disappear when volatility picks up. A setup may look perfect on the chart but become much harder to capture in live conditions.

This is where execution starts breaking down:

Slippage

Even if the trade idea is right, a bad fill can weaken the whole setup.

Latency

If your process is too manual, the market often moves before you do.

Liquidity shifts

Some pairs look tradable until real size hits the book.

Venue differences

Fees, spreads, and order book depth can make the same trade behave differently across platforms.

Emotional interference

Hesitation, overreaction, and changing the plan mid-trade can damage performance more than a weak signal does.

Why traders focus too much on signals

Signals are easier to sell.

They look exciting, easy to share, and easy to debate. Some traders trust signals built on quantitative models. Others rely on price history, chart reaction, or pattern recognition. But no matter how the signal is generated, it still needs solid execution to turn into real results.

That is where many traders get stuck. They keep searching for better signals while the real issue is how those signals get deployed.

In a lot of cases, the problem is not signal quality. It is execution quality.

What better execution actually looks like

Good execution is not just speed. It is having a workflow that reduces friction between idea and action.

That usually means:

• clear trade rules
• disciplined position sizing
• consistent deployment
• built-in risk controls
• adapting to real market conditions

Without that, even a strong signal can get wasted.

This is exactly why execution matters so much in products like Oraichain Quant Terminal.

The goal is not just to surface trading signals. It is to help traders move from signal to execution in a more structured way, with less noise, less delay, and less manual friction. In crypto, that workflow matters a lot. Because edge is not only about spotting opportunities. It is about acting on them well.

For traders using AI or quant systems, this becomes even more important. A model can generate a strong idea, but if execution is slow, inconsistent, or poorly managed, the value of that signal drops fast.

Final thought

Signals get attention. Execution gets results.

In crypto, the real difference often comes down to whether a trader can carry a good idea through live market conditions without losing the edge along the way. That is why better trading is not just about finding smarter signals. It is about having a better system to execute them.

And that is exactly the layer Oraichain Quant Terminal is built to improve.

Why Execution Matters More Than Signals in Crypto was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

1. Spot opportunities faster

Quant Terminal helps you identify potential setups earlier by scanning market data, ranking signals, and narrowing down what is worth attention. Instead of manually checking charts and filtering noise yourself, you get a shorter path to the setups that matter.

2. Pick strategies with more confidence

Finding a setup is one thing. Knowing how to trade it is another.

Quant Terminal helps users review and apply trading models based on performance and market fit. The process becomes more systematic and less reactive.

Instead of asking what to chase next, you can focus on which model fits current conditions and whether the setup is worth deploying.

3. Execute without switching between endless tabs

Most traders know the pain of fragmented workflow. Research is in one place, signals in another, and orders somewhere else.

With Quant Terminal, research, strategy selection, and execution happen in one place. You can execute directly from the terminal instead of bouncing across tools.

Less tab overload. Less friction. Faster action.

4. Trade with more structure

A lot of traders spend too much time trying to be right and not enough time building a repeatable process.

Quant Terminal helps you create more structure around execution with rules for take profit, stop loss, exits, and position handling.

That makes trading less chaotic and reduces emotional decision-making when the market gets ugly.

5. Manage risk more seriously

Anyone can talk about entries. Serious traders care about survival.

Oraichain Quant Terminal supports risk management controls like TP and SL logic, automated exits, and position handling. It is built to help users not only find trades, but also manage downside when real capital is on the line.

That is where a quant system stops being interesting and starts being useful.

6. Monitor performance in real time

A good workflow does not end after the order is filled.

Quant Terminal lets users track positions, PnL, and strategy behavior in real time so they can see what is working, what is underperforming, and what needs adjustment.

For traders and researchers, that creates a much clearer feedback loop.

7. Put idle capital to work

Not every user wants to be in active trading mode all the time.

Quant Terminal also opens access to structured vault products for more passive deployment, including stablecoin-based strategies tied to themes like gold.

So the product is not only for active trading. It also gives users another way to keep capital productive.

8. Build a cleaner end-to-end trading workflow

This is the biggest answer to the question.

With Oraichain Quant Terminal, you can scan for opportunities, choose models, execute trades, manage risk, monitor performance, and deploy capital more efficiently through one terminal.

That matters because most traders do not need more disconnected tools. They need a tighter path from data to decision and from decision to execution.

Why this matters now

Everyone has access to information. Everyone sees charts, headlines, and market noise.

The real edge is no longer just access to data. It is the ability to process, decide, and act on that data with less delay and less emotional friction.

That is why Quant Terminal stands out. It helps traders move faster without becoming more random, and trade with more structure without making the experience feel rigid.

In simple terms

Oraichain Quant Terminal is for users who want to:

• find opportunities faster
• reduce manual clicking
• trade with more structure
• manage risk more seriously
• keep execution closer to data, not just vibes
• put capital to work more efficiently

For traders and researchers, the real value is simple: it compresses the workflow into one command center.

Final thoughts

Oraichain Quant Terminal is not interesting because it adds more noise to crypto trading.

It is interesting because it removes some of the mess.

In a market where speed, discipline, and execution quality matter more than ever, having one place to move from market discovery to strategy selection to execution to monitoring is a real advantage.

What Can You Really Do with Oraichain Quant Terminal? was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Whether you trade manually or use AI to help, you still face the same market reality: a lot of the biggest risks are outside your control.

A strategy can look strong on paper and still get wrecked in live conditions. That is because performance is never driven by signal alone. Once capital is live, everything else starts to matter too: market regime, slippage, liquidity, execution quality, venue conditions, and all the real-world friction that backtests cannot fully capture.

What traders are actually up against

Trade performance can change for many reasons:

• changing market conditions
• slippage and execution quality
• liquidity shifts
• venue-specific conditions
• real-world trading friction

The market does not stay still. Conditions rotate. A model that works well in one regime can lose its edge in another. Even a solid setup can get dragged down by poor fills, thin liquidity, or unstable execution.

That is why live trading often feels harder than the model looked in theory.

The risks traders cannot ignore

This is where most damage happens.

The main risks include:

• strategy underperformance
• market volatility
• smart contract risk
• venue or counterparty risk
• execution risk
• oracle risk
• liquidity constraints
• operational issues
• partial or full loss of principal

These risks do not disappear just because a system is automated or AI-powered. Automation can improve speed and structure, but it does not remove uncertainty. The market can still move against you. Infrastructure can still fail. Execution can still break down.

The goal is not to pretend risk is gone. The goal is to manage it better.

So how should traders deal with it?

The first step is mindset.

Do not treat a backtest like a promise. Treat it like a reference point. It shows how a strategy behaved in certain past conditions, not how it will always behave in the future.

The second step is risk control.

Traders need clear rules for how positions are entered, managed, and exited. That includes defining loss tolerance, protecting upside when trades move in your favor, and adjusting exposure when market conditions become less stable.

The third step is adaptability.

Markets change fast. Risk management should change with them. Static setups can help, but traders also need tools that let them respond when volatility expands, liquidity gets worse, or price action starts behaving differently.

Why risk management matters more than prediction

In live markets, being right is not enough.

A good call can still turn into a bad trade if execution is poor. A profitable setup can still lose money if liquidity disappears. A strong model can still underperform when the regime changes.

That is why risk management is not a side feature. It is the core survival layer.

Backtests show potential. Risk management decides whether you last.

A practical example

This is also the thinking behind tools built into systems like Oraichain Quant Terminal (quant.orai.io)

The point is not only to generate signals, but to help traders manage risk more deliberately through things like configurable TP and SL, multiple exit modes, break-even protection, trailing stop, and position-level adjustments during live trading.

Those tools do not remove market risk. They help traders respond to it with more structure.

And that is what matters most.

Final thought

Live markets are messy. Always have been.

Manual traders face that. AI-assisted traders face that too.

The real challenge is not just finding opportunity. It is surviving volatility, handling uncertainty, and protecting capital when the market stops behaving nicely.

Because in the end, the traders who stay in the game are usually not the ones with the prettiest backtest.

They are the ones who know how to manage risk when reality hits.

In complex markets, survival starts with Risk Management? was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

You’re not gonna quit trading, be real 😏

You just want less chart babysitting and fewer “why did I click that?” moments, but still earn something.

So yeah: trade less manually. Here’s a way.

You don’t have to quit trading. Relax.

Just aim for this: still play the market, but stop refreshing the chart 200 times a day, and make enough to feel like it was worth the stress. 😉

Wanna get out of manual trading? Follow the steps below.

— -

Step 1: Stop thinking “more screen time = more profit”

Manual trading rewards the worst habits in you.

You know the ones:

• overtrading when you’re bored
• FOMO entries when price starts moving
• “just one more” revenge trade after a loss
• micromanaging positions because you can’t sit still

So the goal isn’t “trade more.”

It’s trade less, but with better decisions.

Step 2: Don’t replace yourself with a dumb bot

People hear “automation” and instantly think “bot.”

And most bots really are just: fixed rules running forever.

They’ll keep doing the same thing day after day… even when the market clearly changed.

That’s not what you want.

What you actually want is closer to an agent:

• automated (so you’re not clicking day & night)
• but not “set it and forget it nonsense”
• configurable, so you can keep risk inside your comfort zone
• runs on real logic, not random signals

Step 3: Use an agent + configs, not vibes

This is where Oraichain Quant Terminal fits.

I’m not gonna pretend I’ve tried every product out there.

But I do know the difference between:

A bot that repeats the same thing daily…

vs

An agent that executes based on quant calculation + models + backtests, and lets you tune how aggressive it is.

In normal human words, it means:

• it runs on DEX automatically, so you don’t have to sit there all day (and keep switching tabs like a maniac)
• you can customize configs so your PnL risk stays in a range you can actually accept
• it runs across multiple DEX (and they’re adding more)
• you can run multiple agents at once (up to 4 for now, more later)

The only “bot-like” part is: it executes for you.

Everything else is more like: you set the boundaries, it follows them consistently (with quant logic), and yeah, that’s usually more reliable than whatever your emotions decide in the moment.

Step 4: Accept the one truth nobody likes

Nothing can guarantee you “always win.”

Market is chaos.

But you can remove the one thing that’s almost guaranteed to screw you:

your emotions at the worst timing.

Quant won’t “ensure win rate.”

But it can help you run a strategy that’s more likely to keep you alive when the market is terrible.

And honestly? Staying alive is already a flex in crypto.

Step 5: Start small, watch behavior, then scale

Don’t go full degen on day one.

Try Quant now while it’s still free.

Run conservative settings. Watch how it behaves across a few market conditions first.

If you need proof people are actually using it:

• 1,000+ users in the first month after release
• $80M+ trading volume generated by quant agents
• users keep coming back daily to turn agents on

Again: not “guaranteed profit.”

More like: a way to stop self-sabotaging.

If you don’t trust your emotions,

believe in something else 😉

👉 quant.orai.io

How to get out of manual trading was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

AI is already part of trading. That is no longer the interesting part.

Retail investors’ use of AI tools rose 46% year over year in eToro’s 2025 Retail Investor Beat, and exchange data cited by Finance Magnates showed 67% of Gen Z crypto traders on MEXC activated at least one AI-powered trading bot in Q2 2025. At the same time, McKinsey’s 2025 State of AI said organizations are seeing a growing proliferation of agentic AI. In other words, the market is already shifting from “use AI sometimes” to “build AI into the workflow.”

And trading is one of the clearest places where that shift makes sense.

A recent academic paper even found that stock trading volume declined during ChatGPT outages, especially around fresh corporate news, which is early evidence that investors are already relying on generative AI for professional tasks. So yes, people are already using AI for trading work. The next step is not just asking AI for help. It is giving it a job inside a structured system.

That is where the idea of your own trading agent starts to matter.

What is a trading agent?

A trading agent is a system that does more than give you signals.

It can watch the market, process data, follow strategy logic, make decisions inside defined rules, and execute actions without needing you to manually do every step. MIT Sloan describes agentic AI as systems that orchestrate tasks through one or more agents, while ISACA draws the line this way: AI agents handle specific tasks, while agentic AI systems can think, plan, and act with greater independence.

Is it just a bot?

Not really.

A bot usually follows a fixed script. It does X when Y happens. Clean, simple, limited.

A trading agent works inside a broader decision framework. It can combine market data, strategy logic, risk constraints, execution rules, and user preferences in one operating loop.

The easiest way to think about it is this:

A bot executes instructions. A trading agent operates within a structured decision framework.

That is why “agent” is not just a fancier word. It suggests a more flexible and more useful system.

Why do you need your own trading agent?

Because the market is already too fast, too noisy, and too continuous for purely manual execution to stay efficient.

Here is the practical case.

1. The market moves too fast

Crypto trades 24/7 across fragmented venues. If your workflow depends on you being online, focused, and fast every time, you are already at a disadvantage.

2. Humans are inconsistent

People hesitate. People panic. People override their own plan mid-trade. A rules-based system does not solve everything, but it does reduce emotional drift.

3. Crypto never sleeps

Quant and agentic systems are naturally better suited to markets that do not close, because they can monitor and react continuously. This is one of the core reasons quant trading has become more relevant in crypto.

4. It cuts repetitive manual work

Less chart babysitting. Less clicking. Less checking five tabs just to stay organized.

5. It helps you trade with rules, not impulse

That is the whole point of quant trading in the first place: more structure, less improvisation.

6. It can follow your preferences

The right trading agent should not force a one-size-fits-all setup. It should work with your model choice, your risk tolerance, your constraints, and your level of participation.

What makes your own trading agent different?

A lot of “AI trading agent” products give you one of two things:

• raw code that normal users could never use
• a black-box agent you can use, but not fully align with your needs

That is not the same as having your own agent.

The real value is not just “using an agent.”

The value is having one configured your way:

• your chosen model
• your setup
• your risk configuration
• your constraints
• your participation level

That is where Oraichain Quant Terminal has a stronger angle.

Oraichain’s own Quant Terminal materials describe it as a single terminal for quant agents, built around a data layer, agent testbed, execution workers, and a risk and trade management UI. The stated goal is not just to find a tiny edge, but to turn that edge into “survivable PnL” through sizing, TP/SL, and risk-first controls.

That makes the product feel less like a demo and more like an operating environment.

How to have your own trading agent on Quant Terminal

This is the practical part.

Step 1: Access Quant Terminal

Start on Quant Terminal, connect your wallet, and fund the account you want to trade from.

Then choose the DEX venue where you want your agent to run. Quant Terminal has publicly referenced execution connectivity to venues like Lighter, HyperLiquid,etc. and also announced support for running up to four agents at once across Lighter and Hyperliquid.

Step 2: Choose your model or strategy route

You can pick from the Model Marketplace or go the custom-config route, depending on how hands-on you want to be. Oraichain’s own posts describe the marketplace as a “shop” of live and in-testing quant models with stats like PnL, max drawdown, and win rate.

Step 3: Configure your agent

Set the parameters that define how the agent behaves:

• risk preferences
• sizing
• constraints
• stop loss or exit logic
• how active you want your own role to be

This is where the “your own agent” part becomes real.

Step 4: Activate the agent

Once live, the agent begins running inside the defined framework.

Oraichain has explicitly said traders can configure Quant Terminal to run these models fully automatically or semi-automatically, which is exactly the kind of user-control layer that makes agentic trading usable for more than just developers.

Step 5: Monitor and adjust when needed

The point is not to remove the user completely. It is to reduce manual burden while keeping user control.

You can still review activity, adjust settings, and step in when needed. The system just does not depend on you clicking every single action yourself.

What can your trading agent do for you on Quant Terminal?

At a practical level, your agent can help:

• read model-driven signals
• execute trades automatically
• manage risk based on your config
• use stop loss or preset logic to exit losing positions
• handle profit-taking logic
• reduce the need for constant screen-watching

Oraichain’s product messaging around Quant Terminal leans into exactly this: automated market scanning, quant model deployment, and onchain execution from one command center.

Why Quant Terminal is a good place to start

Because it is not trying to be just one more signal feed. It is trying to connect the full workflow in one place:

• AI plus quant logic
• real-time market data
• agent-based execution
• user-defined control
• onchain deployment
• risk and trade management

That “single terminal” framing is consistent across Oraichain’s own materials, from the technical architecture post to product launch messaging.

And that is the bigger point.

Most users do not need more tools. They need a cleaner path from data → strategy → execution.

Oraichain Quant Terminal solved your problem: quant.orai.io

How to Have Your Own Trading Agent was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Quant trading is no longer something only hedge funds or PhDs care about. In crypto, it is becoming one of the clearest ways to trade a 24/7 market with more discipline, more speed, and less emotional noise.

And now the conversation is moving again.

The question is no longer just whether traders should use AI in their workflow. Many already do. The bigger question is what happens when that workflow becomes agentic. What changes when an AI-powered quant agent can monitor data, generate signals, execute trades, and manage risk inside a structured system?

From quant trading to agentic trading

At its core, quant trading is a systematic way to trade using mathematical models, statistical analysis, historical data, and automated rules. The goal is simple: make decisions based more on data and logic, and less on emotion or instinct.

That becomes even more relevant in crypto.

Crypto markets run 24/7, move fast, and are fragmented across venues. There is more volatility, more public data, more noise, and more opportunities that only show up if you can process information quickly and act with consistency. That is why quant trading in crypto often appears in strategies like market making, arbitrage, trend following, mean reversion, and signal-based portfolio rotation.

Then comes AI quant trading.

AI adds another layer by helping process larger, messier, and more complex datasets. It can support pattern detection, prediction, portfolio construction, risk monitoring, and signal refinement. But AI alone is not the point. Markets are noisy, regimes change, and raw AI without structure can go off track fast. That is why AI works best inside a rules-based system.

And that leads to the next step: agentic quant trading.

A quant trading AI agent is not just a model that spits out signals. It is a system that moves through the workflow itself: reading data, analyzing opportunities, generating decisions, managing positions, and executing trades within defined rules and risk constraints.

This does not mean fully autonomous trading is always better. In practice, higher levels of automation introduce new risks: model drift, unexpected edge cases, and execution errors can scale faster when humans are not directly in the loop.

The real shift is not toward removing humans entirely, but toward designing systems where humans define the rules, and agents handle the repetition and speed within those boundaries.

That is the difference:
from using AI as a tool
to using AI as part of the operating layer.

Why quant trading matters

Most traders do not fail because they have zero ideas. They fail because execution breaks down.

You know the pattern:

• you see the setup, but react too late
• you hesitate because the market is moving too fast
• you override your own plan mid-trade
• you miss the entry, chase the move, then blame the strategy

Quant trading matters because it solves for exactly those weaknesses.

Why it matters:

• Data is more reliable than feelings. Markets do not care about conviction if the numbers do not support it.
• Speed matters. Machines can scan, compare, and react faster than humans.
• Consistency matters. Rules do not get tired, panic, or revenge-trade.
• Scale matters. Quant systems can track more assets, more signals, and more conditions than one person can.
• 24/7 coverage matters. Crypto never sleeps, so fully manual workflows are structurally weaker from the start.

Of course, quant trading is not risk-free. Models can break, code can fail, and regime changes can hit hard. But for many trading tasks, quant systems are simply better suited than purely discretionary execution.

The real gap: most users do not have a full quant workflow

This is where things get more practical.

A lot of users already use AI for trading in some way. They ask for market summaries, indicator explanations, sentiment reads, or trade ideas. That is useful, but it is still only one layer of the stack.

A real quant workflow is much bigger:

• data collection
• data processing
• signal generation
• model logic
• risk constraints
• execution
• position monitoring
• portfolio management

That is not easy to build alone. It takes infrastructure, technical skill, and constant maintenance.

And that is exactly why the future of quant trading is not just smarter models. It is better systems.

How Oraichain Quant Terminal fits in

Oraichain Quant Terminal is not just “AI for trading.” It is closer to an agentic quant workflow built for crypto users.

Instead of stopping at insight, it is designed around the path from data → model → signal → execution.

Conceptually, it brings together:

• data ingestion and processing
• quant models
• AI-assisted intelligence
• agent-based execution
• user-defined constraints
• onchain deployment
• risk and portfolio management

That is the difference.

A lot of products help users watch the market. Some help interpret it. Fewer help run the workflow. Quant Terminal is built around that full loop.

The shift is already happening

You probably already use AI somewhere in your trading workflow:

• summarizing news
• reading charts faster
• comparing narratives
• brainstorming setups

But agentic trading is a different category.

It is AI and quant systems helping run the workflow itself.

That is the shift Oraichain Quant Terminal is aiming at:

• from scattered tools to connected infrastructure
• from manual reaction to system-driven execution
• from occasional AI help to an agentic quant trading environment

Final thought

Quant trading started as a way to make trading more measurable, more repeatable, and less emotional.

Crypto made that approach even more relevant. AI is now pushing it one step further, from model-assisted trading into agentic systems that can monitor, decide, and act inside a rules-based framework.

That is where Oraichain Quant Terminal fits.

Not as a generic AI trading app, but as a product direction that connects quant methods, AI, data, and execution into a workflow crypto users can actually use.

Quant Trading in Crypto is becoming a systems game? was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

Oraichain Quant Terminal has been live for a while, and information about it is already available across Oraichain channels. This article is here to shorten the learning curve.

At a glance, here’s what you need to know when first exploring Quant Terminal to support your trading.

Why Oraichain Quant Terminal exists

Modern crypto trading has a tooling problem before it has a strategy problem.

Users often have to switch between dashboards, wallets, signal feeds, exchanges, and portfolio trackers just to keep up. The workflow is fragmented, the learning curve is steep, and useful information is often too scattered to act on in time.

The market itself adds more pressure. Crypto runs 24/7, moves fast, and trades across fragmented venues where liquidity and price discovery are not always unified. That makes decision-making harder, especially when human execution is still exposed to fatigue, bias, and noise.

Oraichain Quant Terminal exists to solve this at the workflow level.

What Oraichain Quant Terminal is

Oraichain has long focused on the intersection of blockchain infrastructure, data-centric systems, and AI-enabled applications on decentralized rails. Quant Terminal follows that same direction in trading.

At its core, Oraichain Quant Terminal is an AI-powered trading platform where users can activate their own quantitative agent, configure how it should operate, and let it trade on their behalf within a rules-based system.

In practice, Oraichain Quant Terminal combines:

• AI for intelligent decision support
• quantitative systems for structured trading logic
• real-time data for live market responsiveness
• onchain execution for actual trade deployment
• user configuration for risk, behavior, and control

It is built for crypto traders and researchers who want a workflow shaped less by instinct and more by data, rules, and systematic methods.

How it works at a high level

At a high level, the system starts with data.

Market data is collected, processed, and transformed into a more usable structure. Quantitative models then analyze that data to identify patterns, opportunities, and trading logic. AI helps surface trading intelligence from that model layer so the output becomes more actionable.

From there, the system moves into execution.

The platform supports portfolio construction through internal tools, model logic, user-defined constraints, and strategy configuration. That produces a trading list the agent can act on automatically, with embedded risk controls already in place.

For a more technical breakdown, follow Oraichain’s upcoming deep dives.

The core product idea: your own quant agent

This is the easiest way to understand Quant Terminal.

On Oraichain Quant Terminal, the user does not just read the market. The user can activate a quant agent built to trade within a defined framework.

What the user does

• choose a model or strategy route
• configure key parameters
• define risk preferences and constraints
• decide how actively they want to participate

One of the core product ideas is simple: less manual micromanagement, more structured automation.

What the agent does

• reads model-driven signals
• executes trades automatically
• manages risk based on config
• exits losing positions through stop loss or other preset rules
• handles profit-taking or claim logic automatically

This means the platform is not built around constant manual clicking. It is built around delegated execution with user control.

That changes the experience in a meaningful way:

• less screen-watching
• less repeated manual work
• less emotional interference
• more consistency in execution

Users can still be involved. But they do not have to babysit the system every step of the way.

The platform in practice

Oraichain Quant Terminal is easier to understand as an operating environment with multiple connected functions.

Dashboard

The dashboard gives users a live view of:

• agent information
• setup configuration
• portfolio state
• account-level overview

Model Marketplace

This is where users explore Oraichain’s tested quant models and choose how they want to deploy them as part of their trading workflow.

Portfolio

The portfolio layer helps users track:

• trading history
• main trading account
• open and closed positions
• strategy activity in one place

Vaults

Users can deposit USDC into vault strategies and track how capital is being executed across those vaults. This gives users another way to participate in structured strategies without managing every trade manually.

Current vault include:

• Polymarket Vault
• XAU Alpha Vault
• Stable Yield Vault
• Quant Signal Vault
• Delta Neutral Vault

Staking

Users can also stake ORAI inside the same environment, track their positions, and access additional utility and yield opportunities.

Supported execution environment

Quant Terminal is built for actual execution, not passive observation. It currently supports trading across Lighter, Hyperliquid, and Paradex, with network support across Solana, BNB Chain, Arbitrum, and Oraichain.

This makes it a cross-venue trading environment, not just a single-market interface.

What makes Quant Terminal different

Quant Terminal takes a different approach. It connects the path from data to decision to execution inside one system.

1. It is all-in-one in the way that actually matters

Not just multiple tabs under one brand.

It brings research, signals, agent activity, execution, vaults, and tracking closer together in one workflow.

2. Quant models are there to act, not just to decorate

The model is not there to make the interface look smart. It is part of the execution flow itself.

3. The AI layer is infrastructural, not cosmetic

A lot of products mention AI because the market likes the word. That is not enough.

Here, AI works inside a quantitative framework with rules, constraints, and execution logic around it. That makes it more grounded and more useful.

4. Real-time data matters because timing matters

In crypto, delayed context is often useless context.

Quant Terminal is designed around live market responsiveness, so the system is not operating on stale information while the market has already moved.

5. Onchain execution closes the gap between insight and action

A signal that stays on a dashboard is just information. A system that can move from intelligence into execution is much more powerful.

6. It makes AI in trading more credible

This is not an AI chatbot throwing out vague opinions.

The stronger point is that intelligence here is shaped through:

• quant methods
• model logic
• constraints
• execution rules
• risk configurations

That is a much more serious way to apply AI in trading.

The bigger vision

The broader vision behind Oraichain Quant Terminal is consistent with Oraichain’s longer-term direction: building more intelligent infrastructure for crypto users.

The next workflow looks different:

• data-driven
• model-assisted
• execution-aware
• more automated
• more accessible to normal users

That is where Oraichain Quant Terminal fits.

Quant Terminal closes part of that gap by packaging AI, quantitative methods, real-time data, user-defined control, and onchain execution into a system users can actually use.

An Overview of Oraichain Quant Terminal was originally published in Quant Terminal on Medium, where people are continuing the conversation by highlighting and responding to this story.

2026 is a utility year. Oraichain’s focus in H1 is to build Oraichain Quant Terminal as the interface layer for stablecoin-first capital deployment. Terminal builds directly on Oraichain’s accumulated strengths: data oracle infrastructure, agentic AI workflows, and cross-chain execution. The mission stays consistent: decentralize access to data and AI, and build intelligent systems that create real value and accrue back to ORAI.

TL;DR

• Stablecoin-first: we develop Oraichain Quant Terminal around stablecoin use-cases, including quant trading, funding, yield, and risk-managed deployment.
• AI copilots with a control plane: strategies mining, copilots propose, users approve, Terminal executes, and reports.
• Cross-chain execution: unify fragmented liquidity and opportunities across chains.
• H1 deliverables: Quant Marketplace, Meme Mode, Backtest Playground, Stablecoin yield tools, plus Mainnet, wallet, and ORAI value capture upgrades.

Stablecoin-first Era

Stablecoins are the clearest market fit crypto has produced so far. They deliver what users actually wanted from day one: digital fiats that move globally, settle instantly, and work 24/7. That is why stablecoins are not “one more narrative.” They are becoming the base currency of on-chain activity.

This shift becomes even more obvious as Wall Street goes deeper on-chain. The institutional push is not mainly about memes or speculation. It is about infrastructure that survives real markets: settlement rails, collateral mobility, predictable accounting, and the ability to move capital outside banking hours. Stablecoins are the bridge TradFi can adopt fastest because they behave like digital cash. Funding, settlement, collateral, treasury operations all become simpler when the base layer is programmable money.

For retail, the implication is straightforward. The mainstream on-chain user journey becomes stablecoin-first. People hold stablecoins, deploy stablecoins, and expect the experience to be safe and repeatable. When institutions bring real market practice on-chain, they also bring discipline: data processing, decision frameworks, execution quality, and risk controls. Retail needs that discipline too, but with tools that are accessible.

That is the gap Oraichain Quant Terminal is built to close.

Yield: Trading, Farming, and Carry

If stablecoins are the base layer, yield becomes the battleground.

As stablecoin balances grow, holding stablecoins starts to feel like holding cash. Then the next expectation appears: cash should earn. This is where the market becomes competitive. Not just on “how high the APY looks,” but on how returns are generated, how risks are controlled, and how efficiently capital can be deployed after fees.

In a more mature DeFi market, there are many ways platforms will try to deliver returns. Some are closer to passive yield, like lending and farming. Some are strategy-driven, like carry and hedged structures. And some are active, like quant trading and systematic execution. Quant trading belongs in this conversation because active strategies can produce strong yields too, when the edge is real, risk-managed, and net positive after costs.

Retail gets hurt today not because yield opportunities do not exist, but because the workflow is chaotic. Liquidity is fragmented. Execution costs hide in every swap, bridge, and rebalance. Risk is easy to underestimate until the market turns. The next era of DeFi will reward products that add structure and discipline: explain where returns come from, set constraints, execute efficiently, and track outcomes so users can learn.

This is why the terminal category matters. Yield is no longer a single product. It is an operating workflow.

Oraichain H1 Roadmap

H1 2026 is execution-focused. We are building Oraichain Quant Terminal into a stablecoin-first operating workflow for retail users, and strengthening the base layer so real usage scales safely and accrues back into ORAI. The roadmap is split into two tracks: product delivery in the Terminal, and foundation work across Mainnet, OWallet, and value capture.

Track 1: Oraichain Quant Terminal

Quant Terminal is built as a control plane, not a black box. Users remain in control of their wallet (non-custodial wallet), their risk boundaries, and their strategy choices. The system focuses on repeatable workflows, execution quality, and performance transparency, with AI copilots that assist decision-making and monitoring without removing accountability.

H1 deliverables:

• Quant Marketplace: multiple trading models and multiple timeframes, plus real-time performance views that help users compare, investigate, and choose with evidence.
• Meme Mode: sentiment and mindshare signals that detect attention shocks and convert them into structured trading triggers with clear risk controls.
• Backtest Playground: a simple testing environment to replay strategy behavior and understand results net of fees before scaling exposure.
• Stablecoin yield tools (phased): expand beyond trading into stablecoin yield strategies first, then roll out LP, lending, hedging, grid trading, and arbitrage modules with clear cost and risk breakdowns.
• Report & Monitoring: improve monitoring, reporting, and execution reliability across the full loop that already exists today, from signal to execution to tracking.

Track 2: Mainnet, OWallet, and ORAI Value Capture

In parallel, Oraichain will continue to maintain the Oraichain Network as a reliable data oracle infrastructure and strengthen ORAI value capture with sustainable tokenomics. We focus on security, stablecoin usability, clear utility, and revenue alignment that can sustain long-term adoption.

H1 deliverables:

• Mainnet security and reliability upgrades: ongoing patches, operational hardening, and oracle service stability improvements.
• Quant oracle readiness: prepare mechanisms to put data and quant decisions on-chain for proof and transparency over time.
• Sustain ecosystem programs: address the alignment of FDC and GPU staking programs.
• Strengthen ORAI value capture: clearer ORAI utility and revenue alignment, including fee discounts, priority access where appropriate, payment-related utility, and a revenue path that supports ORAI buy-backs over time.

Closing

As blockchain and crypto move from experimentation to real adoption, 2026 marks a clear shift in what the market rewards: usable products, measurable performance, and infrastructure that can carry real capital.

Stablecoins are becoming the base layer of on-chain finance. Yield is where users will compete and where platforms will differentiate. AI copilots will become the default way people navigate markets that never sleep.

Oraichain is built for this moment. We are shipping Oraichain Quant Terminal as the stablecoin-first interface layer for retail capital deployment, backed by reliable data oracle infrastructure and a clearer path for ORAI to accrue value from real usage.

More than anything, we want this vision to land in the hands of real users. Decentralization should feel practical, and intelligent systems should be accessible. Oraichain Terminal is built to put institutional-grade workflows, data, and automation on open rails, so a wider, creative crowd can deploy strategies with discipline and confidence.

FAQ: https://oraichain.notion.site/Oraichain-H1-2026-Roadmap-FAQs-2fb248af3290802bb43dfb6c73c5c21e?source=copy_link

Oraichain H1 2026 Roadmap was originally published in Oraichain on Medium, where people are continuing the conversation by highlighting and responding to this story.