AI quant research assistant

From a trading idea to a backtested strategy in minutes.

Describe a strategy in plain language. W3Quant writes the code, backtests it on real market data, and shows you the numbers — Sharpe, drawdown and P&L — so you can decide what to trade.

See how it works
Built for crypto perpetuals — BTCUSDT, ETHUSDT, SOLUSDT.
W3Quantnew strategy
Backtest ResultBTCUSDT · 2021 → 2025
P&L
Final value
Sharpe
Max DD
01How it works

Three steps from prompt to a strategy you can read.

The same workflow every time — no notebooks, no data wrangling, no boilerplate.

01step

Describe the idea

Type a strategy in plain English — entry, exit and risk rules, or just the gist. The agent fills in the rest and asks if anything is ambiguous.

02step

Generate & backtest

W3Quant writes runnable strategy code, then backtests it on real historical data and returns P&L, Sharpe, Calmar and max drawdown.

03step

Review and refine

Read the equity curve and metrics, ask for an optimization pass, and iterate until the strategy clears your bar.

W3Quantoptimizing
This is solid — can you push the Sharpe higher?
Analyzing strengths & weaknesses…
02Optimize · the difference

A weak strategy, pushed over the bar.

Backtesting tells you where a strategy stands. Optimization moves it. The agent reads the equity curve, finds what's dragging on returns, and re-tunes the rules — faster signals, tighter risk, a volatility filter — then re-runs to prove the gain.

  • 01
    Diagnoses the weaknessWhipsaws, lookahead bias, oversized risk — the agent names what's holding the strategy back.
  • 02
    Re-tunes & re-runsIt rewrites parameters and rules, then backtests again so every change is measured, not guessed.
  • 03
    Clears your portfolio barA typical pass adds ~0.3 Sharpe target — often enough to take a strategy from "skip" to "trade".
03By the numbers

Specialized for one job, and measured on it.

~5 min
idea → first backtest
illustrative
~90%
prompts generate correct strategy code
target
+0.3
avg Sharpe gain per optimization pass
target
~1 min
backtest per year, per symbol
target
04W3Quant vs. general-purpose AI

A general chatbot can draft code. It can't backtest it.

How a quant-specialized assistant compares to leading general-purpose models on the tasks that actually ship a strategy. All figures illustrative — for comparison, not a verified benchmark.

Capability
W3Quant
GPT‑4oClaudeGemini
Natural language → runnable strategy code
Built-in backtesting on real market data
AI optimization / Sharpe uplift
One-click deploy to live trading
Quant guardrails (lookahead-bias checks)
Avg Sharpe of generated strategies1.920.610.740.58
full partial / code only not supported· Sharpe figures are illustrative placeholders.
Early access open

Build your first strategy today.

Describe an idea, get the code and the backtest, and decide what's worth trading — in one session.

See how it works
W3Quant can make mistakes. Always review generated strategies before trading.