# Kvants Studio: Quantitative AI Trading Agent builder > 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. No code required. - Website: https://kvants.ai - Product (the app): https://studio.kvants.ai - Vaults product: https://app.kvants.ai - Contact: hello@kvants.ai - Positioning: Quantitative AI Trading Agent builder - One-line: plain English in, an inspectable quant model out, then backtest, validate, paper trade, and export. --- ## Positioning: a model, not a mood Most AI trading agents trade on headlines and sentiment, which cannot be backtested. Kvants Studio puts a quantitative model inside the agent: signals, position sizing, and risk rules that are visible, editable, and validated against history with modeled costs before you rely on them. Built in plain English. You stay in control. - Headline: "Trading agents with a quant model inside." - Kicker: "Quantitative AI Trading Agent builder." - Free to start. No card required. AI assistance is metered: you pay only for the compute the AI actually uses. --- ## Services - Kvants Studio: Quantitative AI Trading Agent builder - Kvants Backtester: Advanced backtesting for trading strategies - TradingView Pine Script v6 export: Seamless integration with TradingView - .kvants.json export: Export trading agent configurations - KVAI token: Utility token for the Kvants ecosystem - Strategy Brain: AI-powered strategy generation and optimization ## Audience This site is for quantitative traders, financial institutions, hedge funds, and individual investors interested in building, backtesting, and deploying AI-driven trading agents. Users are typically familiar with financial markets, trading strategies, and may have an interest in AI, machine learning, or algorithmic trading. They seek tools to automate and optimize their trading decisions. ## How it works (the builder workflow) 1. Describe the agent's strategy: the market, entries, exits, and risk rules, in plain English. Or start from a curated template. 2. Review the model: the AI drafts the quantitative model; every signal, parameter, and limit is visible and editable, and the co-pilot explains each assumption. 3. Test it: backtests with modeled costs, walk-forward analysis, and crisis-period stress replays. 4. Run it your way: paper trade it, or export to TradingView Pine Script v6 or a portable .kvants.json file. Full five-stage flow: Idea → Build → Test → Validate → Export. - 01 Idea: shape the thesis. Start from a curated template, or describe the strategy in plain English. - 02 Build: review the model. The AI drafts it; inspect every signal, parameter, and risk rule, and tune them. - 03 Test: challenge the assumptions. Run backtests with modeled fees, slippage, and funding, then walk-forward checks. - 04 Validate: paper trade first. Simulated fills on live market data. No real funds are used; live results can differ materially. - 05 Export: take the model with you. Export TradingView Pine Script v6 or a portable .kvants.json file. It runs where you decide. --- ## 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. - 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. Example agent (a wired model): BTC/ETH price feed → EMA fast (12) and EMA slow (26) → RSI (14) → crossover and threshold conditions → AND gate → entry long → percent sizing with leverage → ATR stop loss and R-multiple take profit. Every node is inspectable and editable. --- ## Why a quant model beats a news-driven agent (six problems it solves) 1. THE 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. 2. TRUST: 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. 3. OVERFITTING: 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. 4. GETTING 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. 5. PORTABILITY: 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. 6. ADAPTATION: 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. --- ## The 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. - Runs on NautilusTrader. - Modes: single-asset, multi-asset, parameter sweep, walk-forward, and crisis-period replay. - 16+ transparent performance metrics: Sharpe, Sortino, drawdown, profit factor, and more, 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. - Example modeled metrics for a strategy: Return, Max Drawdown, Sharpe, Sortino, Win Rate, and Trades, each computed with fees, funding, slippage, walk-forward, crisis replay, and multi-asset sweeps applied. --- ## By the numbers - 0 lines of code required - 71 building blocks - 16 templates - 25 signals - 14 venues modeled - 16+ transparent performance metrics --- ## Markets and venues - US equities: build, backtest, and paper trade via Alpaca paper accounts. - Crypto: build, backtest, and paper trade for major crypto markets with simulated fills on live market data. - Venues modeled for data and execution include Bybit, Binance, OKX, Hyperliquid, Coinbase, and Alpaca, among others (14 venues modeled). - Both asset classes use the same plain-English builder, backtest engine, and validation workflow. --- ## Strategy templates (16 curated) 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. Examples: - Momentum Crossover (Trend, Crypto): EMA(12)/EMA(26) crossover with an RSI > 50 filter. Stop and take-profit rules included. BTC 4h. - SPY SMA 50/200 Crossover (Stocks, Alpaca): golden-cross long entry, death-cross exit on SPY daily. 100% equity sizing, no leverage. Testable on Alpaca paper. - EMA Trend Flip (Trend, Crypto): long on EMA(20) > EMA(50), flip short on the reverse. Always in market. 1.5% stop, ATR-based sizing. BTC 1h. --- ## Pricing Start free. Scale when your workflow needs it. Build, backtest, and export on the free tier. AI features run on metered credits funded with KVAI deposits. KVAI is a consumption token: Kvants does not sell it, locked KVAI only unlocks higher AI effort tiers, and holding it earns nothing. - Operator ($0, free forever): build, test, and export strategies with metered AI assistance. Full builder + all 16 templates, up to 10 saved strategies, Alpaca paper trading, single-asset backtest + walk-forward, Pine v6 export with validation + AI repair, BYOK model choice via OpenRouter. - Quant (Waitlist): for research that outgrows one asset. Everything in Operator, multi-asset backtests + sweep heatmaps, crisis-stress + walk-forward gates, higher AI effort tiers, Strategy Brain (regime + signal map). - Desk (Custom): private integrations and dedicated research infrastructure. Everything in Quant, custom data and research integrations, custom risk workflows, dedicated support + SLA. --- ## Frequently asked questions Q: What is Kvants Studio? A: 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. Q: Can I create a trading strategy just by describing it in plain English? A: 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. Q: What makes a Kvants agent different from a news-trading AI agent? A: 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. Q: Can Kvants Studio handle both stocks and crypto? A: 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. Q: Do I need to know Python or Pine Script to use Kvants Studio? A: 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. Q: How does the Kvants Backtester validate strategies? A: 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. Q: Can Kvants Studio export to TradingView? A: 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. Q: How much does Kvants Studio cost? A: 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. Q: What is KVAI and what is it used for? A: 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. Q: Is Kvants Studio investment advice? A: 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. --- ## Newsletter Practical quant research, delivered. Product updates and practical notes on building and testing systematic strategies. No signals, no tips, no financial advice. Subscribe at https://kvants.ai. --- ## Important disclaimers Marketing communication. Kvants Studio is software for building and testing trading strategies. It is not investment, financial, legal, or tax advice, and nothing on the site 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. --- ## Machine-readable resources - Structured data (JSON-LD) is embedded on the homepage: Organization, Brand, WebSite, SoftwareApplication, Service, SiteNavigation (ItemList), WebPage, BreadcrumbList, FAQPage, and HowTo. - Sitemap: https://kvants.ai/sitemap.xml - Robots: https://kvants.ai/robots.txt - Short summary: https://kvants.ai/llms.txt