About Kvants Studio

Kvants Studio is the quantitative AI trading agent builder: you describe a trading thesis in plain English and the AI drafts the agent's quantitative model, the signals, position sizing, and risk rules you can inspect, edit, backtest, and validate. Unlike news-driven AI agents that react to headlines and cannot be backtested, a Kvants agent is built on an explicit quantitative model. The Kvants Backtester runs it on NautilusTrader with modeled fees, slippage, and funding, and with walk-forward and crisis-period validation. You then paper trade it on Alpaca paper for US equities or with simulated fills on live crypto markets, and export it to TradingView Pine Script v6 or a portable .kvants.json file. You review and control every rule yourself. No code required.

Product category
Quantitative AI trading agent builder: a plain-English (natural-language) quant strategy builder, backtester, and validation workspace for US equities and crypto
What makes it different
The agent is built around an explicit, inspectable quantitative model (signals, sizing, risk rules), not headlines or sentiment, so it can be backtested and validated with modeled costs
Asset classes
US equities via Alpaca paper accounts and major crypto markets with simulated fills on live market data
Builder
71 building blocks, 25 signals, and 16 curated strategy templates; every signal, parameter, and risk rule is visible and editable; 0 lines of code required
Backtest engine
NautilusTrader with single-asset, multi-asset, parameter-sweep, walk-forward, and crisis-period modes, using modeled fees, slippage, and funding across 14 venues, with 16+ transparent performance metrics
Validation
Walk-forward analysis to catch curve-fitting and crisis-period replay of stressed regimes such as March 2020 and May 2022; simulated results are hypothetical and are not a reliable indicator of future results
Run and export targets
Paper trading on Alpaca paper and simulated crypto fills; TradingView Pine Script v6 export with deterministic logic translation and guarded AI compile-error repair; portable .kvants.json format
AI capabilities
Plain-English model drafting, transparent reasoning, guided iteration and walk-forward comparison, and Strategy Brain cross-session memory that tracks regime context, performance drift, and parameter sensitivity
Pricing
Free to start on the Operator tier; AI features run on metered credits funded with KVAI deposits, so you pay only for the AI compute you use; bring-your-own-key model choice via OpenRouter supported
KVAI token and VIP tiers
KVAI is the usage token for AI compute. Deposit it to fund AI credits; each request holds an estimate, settles on actual compute used, and refunds the rest, with a full refund if the request fails. KVAI you hold or lock also sets a VIP tier from VIP 0 to VIP 4 that unlocks higher AI effort levels and deeper studio functions (more reasoning, longer outputs, more tool calls, more refine passes). VIP status is access only.
Built for
Systematic traders, quant researchers, emerging fund managers, and AI-native investors building stocks plus crypto strategies in one workspace

The quantitative AI trading agent builder

Trading agents with a quant model inside

Most AI trading agents trade on headlines. Kvants Studio puts a quantitative model inside the agent: signals, sizing, and risk rules you can inspect, backtest, and validate. Built in plain English. You stay in control.

Try the demo

Free to start. No card required. AI assistance is metered: you pay only for the compute the AI actually uses.

studio.kvants.ai / try_me.kv

This is the agent's model. Drag any node, or click to edit its parameters ↓
Price Feed
symbolBTC
venueBybit
tf1h
EMA Fast
period12
sourceclose
EMA Slow
period26
sourceclose
RSI
period14
sourceclose
ATR
period14
Crossover
dirup
Threshold
level50
dirabove
AND Gate
inputs2
Entry Long
sidebuy
% Sizing
size%20
lev3
Stop Loss
typeatr
mult2
Take Profit
typer_mult
R2

Build and test for the markets you already trade

square

AI Strategy Creation

Describe your thesis. AI drafts the model.

Tell Kvants AI what you want to trade. It drafts the agent's quantitative model, runs the first backtest, and explains every assumption. You review, you decide.

Shadow

Plain English to editable model

One idea in, a fully wired model out, ready to inspect and backtest.

Transparent reasoning

Ask why a signal fired, or which condition blocked a trade.

Guided iteration

Test variants, compare walk-forward splits, keep what holds out of sample.

Risk-aware guardrails

Position sizing and stop-loss rules are part of every draft.

K
Kvants AI · FABLE 5online · faster than your dev friend
Tell me what you want to trade...
01THE MODEL

Give the agent a model, not a mood.

News-driven agents act on text they cannot test. A Kvants agent is built on quantitative rules: every signal, size, and stop is explicit, editable, and auditable.

The old wayKvants
// strategy.pine
strategy("RSI Reversion", overlay=false)
length = 14
rsi = ta.rsi(close, length)
if (rsi < 30)
    strategy.entry("long", ...)
if (rsi > 70)
    strategy.close("long")
RSI
period14
sourceclose
↓
Entry Long
02TRUST

A backtest that pays its fees.

Modeled fees, slippage, spreads, and funding keep the simulation honest, so you evaluate the strategy against realistic execution constraints, not a fantasy.

FantasyReality
+847% · 0 drawdown · 0 fees* if no slippage, no funding, no friction
commissions−0.55%
funding−0.21%
slippage−0.08%
03OVERFITTING

Catch the curve-fit before it costs you.

Walk-forward analysis separates durable logic from historical coincidence before you commit more research time to it.

Curve-fitWalk-forward
Backtest +412%
vs
Live −58%
"But it looked so good on history…"
ROLLING WINDOWS · SHARPE BY PERIOD
IS Q1
2.4
OOS Q1
2.1
IS Q2
2.2
OOS Q2
1.9
IS Q3
2.0
OOS Q3
1.8
IS Q4
2.3
OOS Q4
2.0
✓ Edge holds out-of-sample
04GETTING STARTED

An AI co-pilot that drafts the model.

Describe the thesis. The co-pilot drafts the quantitative model, runs the first backtest, and explains every signal, so you refine the rules instead of just accepting them.

Where do I start?Just describe it
∅
untitled_strategy.pine
// what do I even put here
Mean reversion on ETH. RSI < 30 buy, > 50 sell, 2% stop.
Done. Wired 7 nodes. Sharpe 1.84 over 18 months.
Make it better.
05PORTABILITY

Your model leaves with you.

Export the agent's model as TradingView Pine Script v6 or a portable .kvants.json file, or prove it in simulated paper trading first. Your logic, your accounts, your decision.

Locked inPortable
$ pm2 start strategy.py
[ERROR] ccxt.AuthenticationError
[ERROR] api_key invalid for binance
$ ssh root@vps-3...
connection timed out
$ tail -f /var/log/strat.log
positions stuck open · 3:47am
▌
strategySMA 50/200 · AAPL
venueAlpaca · live
modeOAuth-linked
statusRunning · 12d 06h
heartbeat2s ago
P&L today+1.84%
06ADAPTATION

A brain that keeps the model under review.

Strategy Brain tracks regime context, performance drift, and parameter sensitivity, so the agent's model stays observable, auditable, and challenged.

Static & frozenLearning brain
Locked since Apr 2024
params.json · never updated
market regime: unknown
last review: never
Strategy Brain
● Bull Trending · 0.78

"Momentum signal strongest above EMA-200 with rising volume. RSI <25 holds as high-confidence exit zone in this regime."

✦ Tightened trailing stop 3% → 1.8%. Sharpe +0.32
halfcircle

Kvants Backtester

Backtests built to be challenged.

Modeled fills, fees, funding, and slippage. The point of a backtest is not a pretty curve. It is finding out what breaks before you trust the model.

Return+248.6%
Fees modeled
max dd−12.4%
Funding modeled
Sharpe2.14
Slippage modeled
Sortino3.02
Walk-forward
Win Rate57.8%
Crisis replay
Trades284
Multi-asset + sweeps

16+ transparent performance metrics

Sharpe, Sortino, drawdown, profit factor, from assumptions you choose.

Walk-forward validation

Challenge overfit parameters on unseen windows before you rely on them.

Crisis-period replay

Replay March 2020, May 2022, and other stressed regimes to review how the logic behaved.

Clear diagnostics on zero trades

If a strategy makes no trades, Kvants pinpoints the condition that blocked it.

The builder workflow

How you build with Kvants

Describe the agent's strategy: market, entries, exits, and risk rules

Describe the agent's strategy: market, entries, exits, and risk rules

Review the model: every signal, parameter, and limit is visible and editable

Review the model: every signal, parameter, and limit is visible and editable

Test it: backtests with modeled costs, walk-forward, and stress replays

Test it: backtests with modeled costs, walk-forward, and stress replays

Run it your way: paper trade it, or export to Pine Script v6 or .kvants.json

Run it your way: paper trade it, or export to Pine Script v6 or .kvants.json

0
Lines of code required
71
Building blocks

The workflow

From market thesis to tested agent.

Draft, review, validate, paper trade, export. Every rule stays visible and every decision stays yours.

01IDEA

Shape the thesis

Start from a curated template, or describe the strategy you want in plain English.

02BUILD

Review the model

The AI drafts the model. Inspect every signal, parameter, and risk rule, and tune them.

03TEST

Challenge the assumptions

Run backtests with modeled fees, slippage, and funding, then walk-forward checks.

04VALIDATE

Paper trade first

Simulated fills on live market data. No real funds are used; live results can differ materially.

05EXPORT

Take the model with you

Export TradingView Pine Script v6 or a portable .kvants.json file. It runs where you decide.

Templates

Start from a curated template

Sixteen curated templates across crypto and Alpaca stocks. Clone the model, tune the parameters, and test every assumption yourself. Templates are starting points, not recommendations.

Trend · Crypto

Momentum Crossover

EMA(12) / EMA(26) crossover with an RSI > 50 filter. Stop and take-profit rules included. BTC 4h.

Sharpe2.14
Return+142%
Max DD−14%
Stocks · Alpaca

SPY SMA 50/200 Crossover

Golden-cross long entry, death-cross exit on SPY daily. 100% equity sizing, no leverage. Testable on Alpaca paper.

Sharpe1.81
Return+98%
Max DD−11%
Trend · Crypto

EMA Trend Flip

Long on EMA(20) > EMA(50), flip short on the reverse. Always in market. 1.5% stop, ATR based sizing. BTC 1h.

Sharpe2.41
Return+187%
Max DD−16%

FAQ

What is Kvants Studio?

What is Kvants Studio?

Kvants Studio is the quantitative AI trading agent builder. You describe a trading thesis in plain English, Kvants drafts the agent's quantitative model, and you backtest it with modeled costs, validate it with walk-forward and crisis-stress checks, paper trade it with simulated fills, and export it. You review and control every rule yourself.

Can I create a trading strategy just by describing it in plain English?

Yes. You type the idea in plain English, the AI drafts a quantitative model from the Kvants building-block catalog, and you inspect, refine, backtest, and paper trade it. Writing code is not required.

What makes a Kvants agent different from a news-trading AI agent?

Most AI trading agents react to headlines and sentiment, which cannot be backtested. A Kvants agent is built around an explicit quantitative model: signals, position sizing, and risk rules that are visible, editable, and validated against history with modeled costs before you rely on them.

Can Kvants Studio handle both stocks and crypto?

Yes. You can build, backtest, and paper trade strategies for US equities through Alpaca paper accounts and for major crypto markets with simulated fills on live market data. Both use the same plain-English builder, backtest engine, and validation workflow.

Do I need to know Python or Pine Script to use Kvants Studio?

No. You describe the strategy in plain English and Kvants drafts an editable model. Kvants Studio can export finished strategies to TradingView Pine Script v6, but writing code is not required.

How does the Kvants Backtester validate strategies?

It runs strategies through NautilusTrader with single-asset, multi-asset, parameter-sweep, walk-forward, and crisis-period modes, using modeled fees, slippage, and funding. Output includes an equity curve, trade table, monthly returns heatmap, and a validation gate report. Simulated results are hypothetical and are not a reliable indicator of future results.

Can Kvants Studio export to TradingView?

Yes. Kvants Studio exports strategies to TradingView Pine Script v6. The deterministic exporter preserves the strategy's logic; AI repair is reserved for compile errors and guarded so logic changes are not silently accepted. A portable .kvants.json export is also available.

How much does Kvants Studio cost?

Kvants Studio is free to start. AI features run on metered credits funded with KVAI deposits, so you pay for the AI compute you actually use. KVAI is a consumption token.

What is KVAI and what is it used for?

KVAI is the usage token for AI compute inside Kvants Studio. Deposit KVAI to fund your AI credits: every AI request places a hold for its estimated cost, settles for the compute actually used, and refunds the rest. If a request fails, the full hold is returned. KVAI also sets your VIP tier, from VIP 0 to VIP 4, based on the KVAI you hold or lock. Higher tiers unlock higher AI effort levels and deeper studio functions: more reasoning, longer outputs, more tool calls, and more refine passes per request. VIP status is access only.

Is Kvants Studio investment advice?

No. Kvants Studio is self-directed software for building and testing trading strategies. You author, test, and control every strategy yourself, and nothing the product or this site produces is a recommendation to trade. Trading involves substantial risk of loss, and simulated results are not a reliable indicator of future results.

Subscribe Coins

Stay in the loop

Practical quant research, delivered.

Product updates and practical notes on building and testing systematic strategies. No signals, no tips, no financial advice.

Marketing communication. Kvants Studio is software for building and testing trading strategies. It is not investment, financial, legal, or tax advice, and nothing on this page is a recommendation to trade. You author, test, and control every strategy yourself. Trading stocks, crypto assets, and derivatives carries a high risk of loss; you can lose everything you commit. Backtested and simulated figures are hypothetical and are not a reliable indicator of future results. Feature and venue availability varies by jurisdiction, and you are responsible for making sure your use of Kvants is lawful where you live and where you trade.

TelegramXLinkedInInstagram
Subscribe to be in touch
Copyright © 2026 Kvants.ai – All Rights Reserved.