Quant Backtester
helio / quant-backtesterDescribe the strategy logic, the instrument, and the parameters. Your AI teammate implements and backtests it, then scans for signals during market hours so you're not watching a screen all day.
What is Quant Backtester?
- ·Turns a strategy description into tested code: Implements the described logic and runs it against a specified historical window — never a guess dressed up as a result.
- ·Labels its own assumptions: Data range and known limitations stated plainly, so a strong backtest reads as a hypothesis, not a guarantee.
- ·Scans continuously during market hours: Watches signal conditions as they change, not on a fixed check-in schedule.
- ·Alerts, never executes: Every signal produces a prompt for a human decision — no trade is ever placed automatically.
What does Quant Backtester produce?
| 🧮Backtest implementation | Strategy logic turned into runnable code against historical data. |
| 📊Performance metrics | Reported with the data range and assumptions labeled. |
| 🔔Signal alert | Fires the moment an entry/exit condition is met, not on a fixed schedule. |
| 🛡️Risk-rule check | Position size and stop-loss rules evaluated alongside every signal. |
The SOP, step by step.
- 01
Take the strategy description
Logic, instrument, and parameters, in plain language.
- 02
Implement the backtest
Turn the description into runnable code against historical data.
- 03
Run and measure
Performance metrics over the specified historical window.
- 04
Label the assumptions
Data range and known limitations stated plainly — never presented as a guarantee.
- 05
Load the signal conditions
The specific rules that define an entry or exit.
- 06
Scan continuously during market hours
Not on a fixed interval — as conditions actually change.
- 07
Check risk rules
Position size and stop-loss conditions evaluated alongside the signal.
- 08
Alert, don't execute
A signal produces an alert for you to act on — it never places a trade on its own.
What it produces, in practice.
Illustrative example generated for this page — actual output depends on your real data.
📉 Backtest Report · Moving-Average Strategy
Sample · illustrativeWindow: 2020-01 – 2025-12 (assumes 0.03% commission, 0.1% slippage).
| Metric | Value |
|---|---|
| Annualized return | 14.2% |
| Max drawdown | -22.6% |
| Sharpe ratio | 1.03 |
| Win rate | 47% |
Parameter sensitivity: stable for the short-window parameter between 3–7 days; performance degrades sharply past 10 days — flagged as a possible overfit.
Risk rules: position sizing and stop-loss are built into the backtest logic.
Live scan: checks entry conditions every 30 minutes during market hours; a signal produces an alert, never a trade.
Watch Quant set up a new automation.
What Quant Backtester connects to.
Market data API
Coming soonPulls historical and live pricing data for the instruments you're tracking.
Local code directory
ConnectedImplements and version-controls the backtest alongside your existing research code.
Questions
- Does this give investment advice?
- No. It implements and backtests strategies you describe and reports what happened — it never recommends a trade or claims a result will repeat. Every real decision is yours.
- Can it execute trades automatically?
- No. Signal detection produces an alert only. Trade execution is a deliberate, separate step you control.
- How do I know the backtest isn't overfit?
- Every result is labeled with its data range and assumptions. Treat a strong backtest as a hypothesis to validate further, not a guarantee — the report says so explicitly.
- What data sources does it support?
- It connects to whatever market data API or historical dataset you provide access to — it doesn't come bundled with a specific paid data source.
Stop watching the market by hand.
Describe your strategy — Helio backtests it and scans for signals during market hours. You still make the call.
Ask about this automation