I will break down the exact framework to use Opus 5.5 to find, test and run profitable trading strategies 24/7, along with the exact resources that helped me personally.
Let's get straight to it.
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If you are running strategies with Opus 5.5 right now, DM me your setup and i will personally review the first 20.
Opus 5.5 is the BEST research analyst you can hire right now.
1M token context. Adaptive thinking on every answer. Built for long-running agentic work and coding. $4 per million input tokens.
Introducing Claude Opus 5.5, the first model in our new Claude 5.5 family. It performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.
00:00 / 00:20
9K
97K
28M
Yes, its knowledge stops in June 2026. That doesn't matter.
No model's memory is fresh enough to trade on. Prices move every second. You don't want a model that remembers the market.
*You want the best reasoner, fed the market live.*
So i plugged Opus 5.5 into Minara AI.
They say: Run your own Wall Street. Live data, a backtest lab with real fees, an Autopilot that trades 24/7, and one rule. The model recommends. You approve. You Win.
*Opus 5.5 is the analyst.* *Minara AI is the trading floor.* *Together they are a research desk that never sleeps.*
This article is the full build, step by step, with every prompt i use. No coding background needed to follow it.
By the end of this article you will know:
- Why Opus 5.5 is the right brain for trading research and what it cannot do alone.
- How every Minara AI feature works, from Copilot to Strategy Studio to Autopilot.
- The exact setup, from an empty account to your first real trade.
- How to find strategy ideas and kill the ones that would lose you money.
- How to run the survivors 24/7 and make the desk smarter every night.
Let's get into it.
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Part 1: The Desk You Are About to Build
Start with the end result.
This is the desk you will have running by the end of this article:
Eight stages. One loop. It never stops.
1. SCAN read live markets, no strategy yet
2. HYPOTHESIZE 5 ideas, each with a reason it works
3. CODE turn the best idea into a strategy
4. BACKTEST real history, real fees, real slippage
5. ATTACK a separate risk review tries to kill it
6. PAPER live data, fake money, weeks not hours
7. LIVE survivors run 24/7 with hard limits
8. REVIEW every fill feeds the next research cycleEight stages. One loop. It never stops.
Two pieces run it.
PIECE ROLE WHAT IT DOES FOR YOU
Opus 5.5 the analyst thinks, researches, writes, reviews
Minara AI the trading floor data, backtests, wallet, autopilot*Opus 5.5 is the brain.* It never touches money directly. It reads, reasons, proposes, and reviews.
*Minara AI is the trading floor.* It gives the brain live data, a backtest lab, a wallet with limits and execution. Part 2 covers it end to end.
Why Opus 5.5 is the right analyst?
Research is long-horizon work. You read a lot, you hold a lot in your head, and you change your mind when the evidence changes. That is what Opus 5.5 was built for.
DESK JOB OPUS 5.5 CAPABILITY
read everything 1M token context window
deep analysis adaptive thinking, always on
long research runs built for long-running agentic work
write strategy code Anthropic's strongest Opus for coding
review the logs up to 128K tokens out per answerThe scores back it. An Elo of 1,822 on Artificial Analysis's agentic knowledge work evaluation, 143 points ahead of Fable 5.1. The first model to beat Vals AI's published reference on LM Training, a long-horizon task. 40% cheaper to run than Opus 5.
What it cannot do alone -
- No live prices.
- No historical candles.
- No backtest engine.
- No wallet. No execution.
That is not a weakness of Opus 5.5. It is true of every model. Data and execution have to come from the floor.
So the real question is not which model. It is where you run it.
One research team tested exactly that. They ran the same model through four different agent setups. On one benchmark, the same model scored anywhere from 10.5% to 68.5%.
Same brain. Different floor. Seven times the result.
That team was Minara AI. Their floor is the one this article runs on.
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Part 2: Running Your Own Wall Street
Minara AI is a trading platform for research, backtesting and execution across crypto, US stocks and commodities.
One account. Perps on Hyperliquid and Lighter. Spot across 8 chains. Tokenized US stocks like AAPL, TSLA, and NVDA. Gold and oil.
You will use six parts of it. Here is each one and its job on the desk.
FEATURE DESK JOB WHAT IT DOES
Harness the brain's seat runs Opus 5.5 with Minara's tools
Chat scan live research with sources
Strategy Studio code + backtest idea → code → backtest → paper
Autopilot live runs strategies 24/7 with stops
Workflow watch alerts and scheduled research
Marketplace earn publish survivors, share profit*Harness (Where Opus 5.5 sits)*
This is how you connect opus 5.5 to Minara AI
Two Harness features you will use:
- Institution Mode runs a roundtable of research and risk agents. It researches only and cannot trade. That makes it your risk officer.
- The safety stack previews every trade, caps size, exposure, and slippage, and has an emergency stop. The model proposes. You approve.
*Strategy Studio. The lab.*
It builds two kinds of strategies.
TYPE ANSWERS EXAMPLE
time-series WHEN to trade one asset ETH 4h breakout
cross-sectional WHICH assets to hold long top 5, short bottom 5You describe the idea in plain English or paste PineScript. Minara writes readable code and backtests it automatically, on Binance data for crypto and FMP data for TradFi, after fees and slippage.
You get return, drawdown, Sharpe, profit factor, win rate and every single trade.
Three things matter for this build:
send trades to AI ask why the losers lost
versions every edit is a new version, live stays untouched
paper mode runs on live data with no real money*Autopilot. The execution desk.*
It runs a strategy 24/7. It opens positions, places a take-profit and stop-loss on every one, trails the stop, and reverses when the signal flips.
It can run Sharpe Guard 2.0, Minara's own trend strategy, or any strategy you deploy from Strategy Studio.
It needs a paid plan, at least $500 available and cross margin. An optional drawdown limit closes everything if it is hit.
Minara is never your counterparty. Every trade fills on a real venue.
*Workflow, Marketplace, and Skills. One line each.*
- Workflow: describe an automation in one sentence and it becomes a pipeline that triggers on prices, wallets, or schedules and alerts you on Telegram.
- Marketplace: publish a strategy that survived and set a profit share. Creators earn a share of the realized profit it makes for subscribers.
- Skills: the official pack that brings Minara into Claude Code, where AgenKit runs too. Part 3 sets it up.
That is the whole floor. Every stage of the desk has a home.
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Part 3: The Setup in 8 Steps
Steps 1 to 6 get you trading. Steps 7 and 8 add the tools you build on later.
*Step 1. Create your account.*
Sign up at minara.ai with Google or email. You get one perps wallet on Hyperliquid and one on Lighter by default.
*Step 2. Fund your wallet.*
Send USDC on Arbitrum only. Anything else is not credited. The minimum deposit is 10 USDC. No crypto yet? Click Deposit and buy with a card through MoonPay or Banxa Pay.
For Autopilot later you need a paid plan and at least $500 available.
*Step 3. Install the Harness.*
Download it from minara.ai for macOS (Apple Silicon), Windows or Linux. Sign in with your Minara account.
*Step 4. Make Opus 5.5 the brain.*
Go to Agent → Model → select Claude Opus 5.5. Set reasoning to High.
*Step 5. Check it can see.*
What is my Minara balance, and what are BTC funding
and open interest doing right now? Cite your sources.If the numbers match the app and each one has a source, you are connected.
*Step 6. Place your first real trade.*
Buy 10 USDC worth of ETH.The agent shows the asset, amount and route first. Approve only if it matches.
One real fill. The whole pipe works, from brain to venue.
*Step 7. Bring the desk into Claude Code.*
Open Claude Code, type /model, select Opus 5.5 and send:
Run "curl -fsSL https://raw.githubusercontent.com/Minara-AI/skills/main/scripts/claudecode-minara-skill-setup.sh | bash" to install Minara CLI, Minara Skills and set following config. Follow the login URL when prompted.Then say "Login to Minara" and authenticate in the browser.
*Step 8. Add AgenKit.*
Get your key at agenkit.xyz (Node 18+), then in the same project:
npx agenkit activate <license-key>
npx agenkit install engineering-kitThe split stays simple. Opus 5.5 thinks. Minara trades. AgenKit builds the tools Minara does not ship, like your gate checker and nightly report. You will use it in steps 14 and 20.
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Part 4: Find the Strategy, Then Try to Kill It
Now the desk does its real job. Finding ideas is half of it. Killing the bad ones is the other half.
Finding Winning Strategies
*Step 9. Scan - No strategy yet.*
Never ask the analyst for a strategy first. Ask for the market.
Scan crypto and US equities with live Minara data.
1. Regime: BTC and ETH trend, funding, open interest,
ETF flows, stablecoin liquidity.
2. Which sectors or assets lead over the last 3 months?
3. Where is positioning crowded? Where is it ignored?
4. Any scheduled catalysts in the next 14 days?
Do NOT propose a strategy yet.
Cite the source and timestamp for every number.
Flag anything stale, missing, or conflicting.*Step 10. Check what you can actually trade.*
Which assets in this sector can I trade in Minara,
and in which strategy universe? Group them.In that same session, the sector ETF that won the scan was not tradable at all. The idea had to become a basket of companies. You want to learn that in minute 3, not after a day of research.
*Step 11. Five hypotheses, each with a loser on the other side.*
From this scan, propose 5 testable trading hypotheses.
For each:
- the signal you can observe
- why it should work
- WHO is on the other side, and why they keep losing
- time-series or cross-sectional
- the exact condition that proves it wrong
Rank by how testable they are, not how exciting."Who is on the other side" is the filter that matters. Forced sellers. Leveraged longs paying funding. Funds that must rebalance on a schedule. If Opus cannot name the loser, there is no edge.
*Step 12. Build it in Strategy Studio.*
Take the best hypothesis and say it in plain English.
One asset, time-series:
Time-series on ETH, 4h. Go long when price breaks the
20-bar high and volatility is below its 100-bar average.
Stop at 2x ATR, take profit at 3x ATR. Long and short.A ranking idea, cross-sectional, on Coin 30:
Combine short-term momentum and price-normalized MACD.
Long the top 5, short the bottom 5, rebalance daily,
equal weight.Minara writes the code and runs the backtest.
Want Opus to write the logic itself? Have it write PineScript and use Code import. One rule: every breakout level uses only completed bars, like ta.highest(high, 20)[1]. Without that [1], the backtest sees the future.
*Step 13. Run the gauntlet.*
Five gates. Fail one, back to research.
*Gate 1. Honest settings.*
Set fees to the venue you will really trade on. Raise slippage above default. Use the longest window. Add warmup bars for long lookbacks.
Fees decide more than people think. Minara rebuilt 236 public TradingView strategies and re-ran them with real Hyperliquid fees. 14 were profitable at zero fees and lost money with fees. All 14 traded more than 200 times a year. Strategies under 25 trades a year kept almost all their return.
In the end, roughly 1 in 7 both reproduced its own backtest and made money after fees.
*Gate 2. Read the right numbers.*
Not total return first.
Read win rate and profit factor together. In that study, most survivors won only 35% to 50% of trades. A few big winners paid for many small losers.
Check long and short separately. For a long-short book, beta should sit near zero.
*Gate 3. The risk officer.*
The person who builds a strategy never signs it off. Open Institution Mode and give the risk seat this brief:
You are the risk officer. Your job is to REJECT this.
Find every reason the backtest could be fake:
- look-ahead in the logic
- profit from a few trades or one symbol
- one market regime carrying the whole curve
- too few trades to trust
- fees or slippage that erase the edge
End with KEEP or KILL and the biggest reason.Then select the worst trades in Strategy Studio and ask the AI why they failed.
Gate 4. Stress test.
A real edge is a plateau, not a spike.
Nudge every parameter a little. If results collapse, it was a lucky number.
Double fees and slippage. If it dies, it never had room for real fills.
Test the most recent year on its own. In Minara's walkthrough, a version that crushed its benchmark over 3 years was losing over the trailing year. The agent flagged it before anything shipped.
Gate 5. Paper trade forward.
Run paper mode for weeks. Compare it with the backtest. If it falls apart, the backtest lied, and you found out for free.
Minara's own Academy puts it plainly: tuning until the backtest looks perfect is overfitting.
Here is the scorecard i use. Tune it to your risk.
GATE KEEP IF
settings venue fees on, slippage raised
sample 100+ closed trades
quality Sharpe > 1, profit factor > 1.3
pain a drawdown you would really sit through
benchmark beats buy and hold
neutral books beta near 0
risk officer KEEP
stress plateau, survives 2x costs
recent still works on the trailing year
forward paper tracks the backtest for weeks*Step 14. Make the scorecard enforce itself.*
Rules you remember at 2 AM get skipped. Rules in code do not.
/agenkit build a gate checker that reads an exported
Minara backtest report and refuses to mark a strategy
deployable unless every scorecard line passes. Tests first.---
Part 5: Run It 24/7 and Make It Smarter Every Night
The survivor goes live. Slowly.
Make System Smarter Every Night
*Step 15. Deploy to Autopilot.*
From Strategy Studio, deploy the strategy. It appears in your Autopilot list next to the official strategies.
Then:
- Open Autopilot and pick your strategy under My.
- Confirm the trading scope: only the assets it should trade.
- Close any isolated margin or out-of-scope positions first. Autopilot needs cross margin.
- Set the initial equity drawdown limit. If it hits, Autopilot closes everything, cancels orders, stops, and emails you.
- Confirm and start.
Start with one strategy, one asset, small size. Scale only after weeks of clean live results.
*Step 16. Benchmark against the house.*
Run your strategy beside Sharpe Guard 2.0, Minara's own trend strategy.
If yours cannot beat a conservative regime-aware trend follower on a risk-adjusted basis, run Sharpe Guard and keep researching.
Step 17. Set up the watchers.
Autopilot trades. Workflows watch. Three to build on day one:
1. Every day at 8 AM UTC: market regime summary
(BTC trend, funding, ETF flows) to my Telegram.
2. If BTC falls 8% from today's open: Telegram
alert immediately.
3. Every Sunday at 6 PM: summary of the week's
biggest smart money moves.*Step 18. Review every night.*
This is what turns a bot into a desk that learns. Run one review a night in the Harness:
Pull my trade history and open positions.
For each live strategy:
1. Compare the last 7 days live vs its backtest.
2. List every losing trade with its market context.
3. Find the root cause pattern, if one exists.
4. Write one rule per real pattern into lessons.md,
with the date and the evidence.
5. Propose at most ONE change per strategy.
Change nothing live. Proposals only.Step 19. Re-gate every change.
A proposal is not a change.
Edit the strategy in Strategy Studio. That creates a new version and leaves the live one untouched. Run it through the gauntlet again. Only if it passes, stop the old version and deploy the new one.
Step 20. Automate the review.
/agenkit build a nightly job that pulls my Minara trade
history, appends lessons.md in a fixed format, and sends
me a one-page report. Read-only access. Tests first.Step 21. Publish what survives.
A strategy with a clean gauntlet and a live track record is an asset. Publish it to the Marketplace, set your profit share, and let it earn while you research the next one.
The honest contract. Screenshot this.
This desk CAN:
give Opus 5.5 live data and execution
turn ideas into readable, versioned code
backtest with real fees and slippage
run a separate risk review
paper trade on live data
run survivors 24/7 with hard stops
alert you on Telegram
feed every fill into tomorrow's researchThis desk CANNOT:
guarantee profit
make a backtest equal live fills
save you from overfitting if you skip gates
trade without your approval
keep workflows running with zero credits
turn a weak idea into an edge---
The Whole System on One Page
STEPS WHAT WHERE
1-2 account, USDC Minara
3-5 Opus 5.5 as the brain Harness
6 first real trade Harness
7-8 terminal desk + engineers Claude Code, AgenKit
9-11 scan, universe, hypotheses Opus 5.5 + Chat
12 build the strategy Strategy Studio
13 5-gate gauntlet Studio + Institution Mode
14 gate checker AgenKit
15-16 go live, benchmark Autopilot
17 watchers Workflow
18-20 review, re-gate, automate Harness, Studio, AgenKit
21 publish survivors Marketplace---
Closing
I started with one question. Can Opus 5.5 find strategies that survive a real backtest?
On its own, no. It is an analyst in an empty room.
Give it a floor and it becomes a desk. Minara AI is that floor: live data, a real lab, a wallet with limits, an autopilot that never sleeps. AgenKit is the engineering team that builds whatever the floor does not ship.
The desk that wins does not ask Opus what to buy.
It scans first. It demands a loser for every idea. It kills six strategies out of seven. It paper trades the survivor. It runs it small. It reviews every fill, every night.
In my previous articles i broke down the mathematical trading models on GPT-6 Astra, the one-person hedge fund architecture, and the millisecond decision layer with Jev. This one adds the piece all of them needed: the desk that finds the strategies in the first place.
So here is the question.
Are you still asking a chatbot what to buy, or are you running a desk that kills six ideas so the seventh can trade 24/7?
There is no wrong answer. But there are very revealing ones.
