From Idea to Pseudocode: A Standard Format for Describing Strategies
A four-part structure of entry, exit, position sizing and filters, so AI can translate it correctly.
AI quant research · All content free
Replace gut feel with AI Quant
Replace gut feel with AI Quant
Turn trading into a backtestable system
SmartQuant focuses on crypto quant research: strategy breakdowns, signal reviews, backtesting methods and risk control. We don't predict prices; we teach you how to turn an idea into a strategy that can be verified, executed and stopped out.
All figures below are historical backtest data and do not constitute a promise of returns.
128+Public strategies
61.4%Avg. backtest win rate
9,200+Historical signal reviews
SQ-Composite portfolio equityBacktest 2023–2025
Strategy portfolioBTC benchmark
The chart is drawn from historical backtest data and only illustrates the methodology; it does not represent future returns.
Get Started
Three steps from confused to running your first strategy
Order matters: understand first, then backtest, then go live with small capital. Skip any step and the odds of losing money rise sharply.
01
Learn the quant basics
Get three things straight first: strategy logic (where signals come from), risk metrics (max drawdown, Sharpe) and trading costs (fees + slippage). The Quant Academy has 4 essential intro pieces, about 2 hours of reading.
02
Backtest with AI strategies
Use AI to translate a strategy idea into code, then backtest it over at least one full bull-bear cycle. Focus on drawdown and trade count, not just return.
03
Validate live with small capital
Run 4–8 weeks with an amount you could afford to lose entirely, and compare live results against the backtest. Only discuss scaling up once the gap is stable, and always set a stop loss first.
Research Library
Latest research and tutorials
Organised by section, each piece labelled with method, window and limitations. Research is updated continuously and free to read.
How to Prompt an LLM Properly for Strategy Code
Supplying data structures and constraints works far better than simply asking it to write a strategy.
Vectorized vs Bar-by-bar: Pitfalls of Both Implementations
How look-ahead bias quietly slips into your code, with a self-check list.
Engineering Structure for Multi-Timeframe and Multi-Asset Strategies
How to organize data alignment, signal merging and state management.
What Quant Trading Really Is: Explained in 3 Examples
Understand rule-based trading through the three simplest cases: grid, moving average and arbitrage.
What AI Can and Cannot Do in Quant
Predicting price is a false need; AI is genuinely good at feature engineering and code translation.
Exchange APIs and Data Collection Basics
The difference between candlesticks, order book depth and trade prints, plus least-privilege API configuration.
Reading Four Core Metrics: Annualized Return, Drawdown, Win Rate, Sharpe
Why looking only at annualized return is the biggest beginner trap.
Building a Minimal Backtest Engine from Scratch
Complete implementations of three modules: data loading, order matching and performance statistics.
Modeling Fees and Slippage: Why Your Backtest Is Fake
Why most high-frequency strategies flip from profit to loss once costs are included.
Hands-on Walk-Forward Validation
The standard method for judging whether a strategy fits noise or captures a real pattern.
Spotting Overfitting: Parameter Plateaus and Sensitivity Analysis
Good parameters should be a broad plateau, not a needle tip.
Position Sizing: Practical Use of Fractional Kelly
Why full Kelly in crypto markets means liquidation sooner or later.
A Three-Layer Stop-Loss System: Trade, Strategy, Account
How to set circuit-breaker rules so losing streaks don't lead to emotional averaging in.
The Real Cost of Leverage: Volatility Drag
The mathematics behind why high leverage loses over the long run.
Black Swan Drills and Disconnection Contingency Plans
Designing automatic handling for exchange outages, de-pegging and API rate limits.
Airdrop Roadmap: Cost Accounting from Testnet to Mainnet
Treat airdrop farming as a business with real costs — how to count gas and time.
Multi-Wallet Management and Sybil Detection
Common linkage signals, and how project teams actually judge them.
Automation Scripts: Balancing Efficiency and Safety
Where the risk boundary sits, and the basics of private-key hygiene.
Airdrop Expectations: Why Most Projects Are Not Worth Farming
Estimate expected value from historical data and filter out 80% of low-value projects.
Why SmartQuant
Why we're worth your time
The biggest problem with quant content is showing off returns while ignoring risk. SmartQuant does the opposite: we explain the losses first.
Tested strategies
Every strategy is run through a full backtest on real historical market data, with parameters, windows and trade counts published. No cherry-picked curves.
Data-driven backtests
A unified data source and cost model (fees + slippage), with Walk-Forward validation to avoid fake returns from parameter overfitting.
Risk control
All content must state max drawdown and risk level. Tutorials cover loss control before return amplification, and leverage content includes liquidation math.
Free and open
All research and tutorials are free and public. No paid groups, no managed accounts, no management fees. Core tools will keep a free tier after launch.
Beta Access
AI quant tool beta signup
Automated strategy generation · one-click backtests · signal alerts · risk dashboard
Leave your email and you'll be in the first batch notified when the beta opens. Core features keep a permanent free tier.
Used only for beta notifications. No investment advice, unsubscribe anytime.
FAQ
FAQ
Can I do AI quant with no coding background?
Yes, but not in the wrong order. First use the Quant Academy to understand strategy logic, risk metrics and trading costs, then use AI to help translate ideas into code. AI can write code for you, but it can't judge whether a strategy is sound — going live without understanding the logic means handing your capital to a black box you can't read.
Are the return and win rate figures on this site real?
The data comes from backtests on real historical market data, but to be clear: this is historical backtest data and does not constitute any promise of returns. Backtests inherently suffer from overfitting, survivorship bias and understated slippage, so live performance is usually worse. We publish windows, parameters and trade counts precisely so you can check them yourself instead of taking them on faith.
How much starting capital does quant trading need?
Validate the logic on a paper account first, then run 4–8 weeks live with an amount whose total loss wouldn't affect your life. The goal at this stage isn't profit, it's measuring the gap between live and backtest (fill prices, latency, fees). Once that gap is stable and manageable, you can discuss scaling up.
Will SmartQuant recommend trades, manage my money, or charge fees?
None of those. SmartQuant is a content and research platform: no managed accounts, no management fees, no buy or sell recommendations, and no promise of any returns. The upcoming AI quant tools (strategies, backtests, signal alerts) will keep a free tier, and all trading decisions and consequences rest with the user.
Risk notice:Quantitative trading carries the risk of loss. All strategies, signals and performance figures on this site are historical backtest data and do not constitute a promise of returns, nor investment advice. Crypto markets are highly volatile, so validate with small capital first and comply with the laws of your jurisdiction.