Grid Historical Backtest

Last updated: 2026-09-23

Direct answer

The Grid Historical Backtest runs your range, grid count, capital and fee over past closes in a daily simulation, returning final equity, return, sells, win rate, max drawdown and fees with an equity curve; results are historical only and do not guarantee future performance.

The Grid Historical Backtest runs your parameters over past prices: enter the range, grids, capital and fee, choose real exchange klines (daily/hourly), the built-in demo series or paste your own historical closes, and get final equity, return, sells, win rate, max drawdown and fees, with an equity curve.

Live mode fetches ETH / BTC / SOL / XRP daily or hourly closes from public exchange market endpoints through this site's read-only Worker proxy, cached at the edge for about an hour; the built-in demo series exists to show the workflow — it is not real market data.

Data source

Symbol (live mode)

Interval (live mode)

Bars to fetch (30–1000)

History prices (paste mode: close per line or date,close)

Lower price (USDT)

Upper price (USDT)

Number of grids

Capital (USDT)

Fee rate per side (%)

Assumptions & notes

Simplified daily model using closes only: a buy fills at its level price when price falls through it; a sell fills when price rises above its level.
Slippage, partial fills, order-book depth and funding are not modeled; live results are usually worse.
Grids buy with capital per grid = capital ÷ grids and stop once cash is exhausted.
Historical backtest results do not guarantee future performance.

Key data & facts

Fill rules (close granularity): falls through a buy level → buy at level; rises above a sell level → sell at levelCapital per grid = capital ÷ grids; buying stops once cash is exhausted
Return: (final equity − capital) ÷ capital
Max drawdown: max(peak-to-trough decline)
Equity definition: cash + held coins × today's close
Example: 2500–3500, 50 grids: demo series ≈ 240 points1000 USDT at 0.05%, returning return and max drawdown

What is the Grid Historical Backtest?

A backtest answers how these parameters would have behaved: not just profit, but trade frequency, win rate, peak capital usage and max drawdown.

This implementation is a deterministic daily simulation with transparent rules — paste data and it is fully reproducible.

How to use it

Choose “live market data” and pick the symbol (ETH/BTC/SOL/XRP), interval (daily/hourly) and how many bars; or “paste your own history”, or try the built-in demo series to learn the flow.
Enter the range, grid count, capital and one-side fee rate.
Click Calculate and read return and final equity first, then drawdown and capital usage.
Re-run with other ranges or grid counts to compare historical behaviour.

Model rules

Fill rules (close granularity)

Price falls through a buy level → buy at level (equal capital per grid)
Price rises above a sell level → sell at level and book net

Equity = cash + held coins × today's close.

Key metrics

Return = (final equity − capital) ÷ capital
Max drawdown = max(peak-to-trough decline)

Worked example

On the built-in demo series (~240 points, range 2500–3500, 50 grids, 1000 USDT, 0.05%), the tool returns final equity, total return, sells and max drawdown plus the equity curve.

Switch to “live market data” and pull real ETH daily closes (365 bars ≈ one year): if the same parameters land in a similar return band, they are at least plausible for that sample — otherwise adjust.

Practical tips

Draw conclusions from real data, not the demo series. Prefer “live market data” to pull exchange history; the demo only teaches the workflow.
Read returns next to drawdown. High returns with deep drawdown and heavy usage are common; return alone flatters a strategy.

FAQ

Where does the live market data come from?

Through this site's read-only Worker proxy, from public exchange market endpoints: ETH/BTC/SOL/XRP daily or hourly closes, cached at the edge for about an hour. Only closes are used for the backtest; no orders are involved.

Is the backtest accurate?

It is a deterministic model at close granularity with ideal level fills, ignoring slippage and partial fills, so it usually overstates performance. Minute-level and order-book data would be needed for finer accuracy.

Is the demo series real market data?

No. It is a synthetic deterministic waveform for demonstrating the workflow. Do not base any conclusion on it.

What format should pasted data follow?

One number per line (a close), or “date,close”; the parser takes the last token of each line. Use at least 20 points.

Why do fees look low in the backtest?

Fees depend on how many sells the grid triggers. In one-way moves triggers are rare, so fees — and returns — are both low. Always read fees with returns.

Sources & references

What Is Backtesting?Investopedia