QTSurfer

Quantitative trading strategy backtesting platform.

Write trading strategies in Java, submit them to be compiled, run backtests against historical exchange data, and visualize results with millions of data points — all through a REST API.

API Documentation

qtsurfer.github.io — Interactive OpenAPI documentation

How It Works

Strategy (Java) ──► Compile ──► Prepare Data ──┬─► Execute ──► Signals (Parquet) ──► Visualize
                                               └─► Execute Sweep ──► Ranked trials

1. Write a trading strategy in Java using the strategy SDK (indicators, signals, execution)
2. Compile it via POST /strategy — no build tools needed on the client
3. Prepare historical market data via POST /backtest/{exchange}/{type}/prepare — returns a jobId
4. Execute either one backtest via POST /backtest/{exchange}/{type}/execute or a parameter sweep via POST /backtest/{exchange}/{type}/executeSweep/{prepareJobId}
5. Inspect the result or ranked sweep trials; individual backtest signals are stored as Parquet files and loaded in-browser via DuckDB-WASM

Strategy Example

import com.wualabs.qtsurfer.engine.strategy.AbstractTickerStrategy;
import com.wualabs.qtsurfer.engine.strategy.AbstractOnChangeListener;
import com.wualabs.qtsurfer.engine.strategy.event.signal.InfoStrategySignal;
import com.wualabs.qtsurfer.engine.indicators.helpers.WindowTimeRTIndicator.WindowTime;
import com.wualabs.qtsurfer.engine.indicators.helpers.group.InstrumentGroupRTIndicator;
import com.wualabs.qtsurfer.engine.core.state.StateStoreSupport;

public class EmaCrossStrategy extends AbstractTickerStrategy {

    @Override
    protected void setupIndicators(InstrumentGroupRTIndicator indicators) {
        indicators
            .addPrice()
            .ema("fast", 20)
            .ema("slow", 50)
            .window("fast", WindowTime.s1, new CrossListener(indicators));
    }

    private class CrossListener extends AbstractOnChangeListener {
        public CrossListener(InstrumentGroupRTIndicator indicators) {
            super(EmaCrossStrategy.this, indicators);
        }

        @Override
        public void onChange(StateStoreSupport s, double prev, double actual) {
            initStore(s);
            double price = indicators.getValue("price");
            double fast  = indicators.getValue("fast");
            double slow  = indicators.getValue("slow");

            InfoStrategySignal signal = createInfoSignal();
            signal.set("price", price);
            signal.set("fast", fast);
            signal.set("slow", slow);

            boolean wasBullish = store.is("bullish");
            boolean isBullish = fast > slow;

            if (isBullish && !wasBullish) {
                store.set("bullish");
                signal.set("_m", "position", "belowBar", "shape", "arrowUp",
                    "color", "#26a69a", "text", "BUY");
            } else if (!isBullish && wasBullish) {
                store.unset("bullish");
                signal.set("_m", "position", "aboveBar", "shape", "arrowDown",
                    "color", "#ef5350", "text", "SELL");
            }

            emitSignal(signal);
        }
    }
}

API Quick Start

All endpoints require JWT authentication (Authorization: Bearer <token>).

Compile a strategy

curl -X POST https://api.qtsurfer.com/v1/strategy \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: text/plain" \
  --data-binary @MyStrategy.java
# → {"strategyId": "2ul144qe9tlwzu5anhwvc6"}

Prepare market data

curl -X POST https://api.qtsurfer.com/v1/backtest/binance/ticker/prepare \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"instrument":"BTC/USDT","from":"2026-03-14","to":"2026-03-15"}'
# → 202 {"jobId": "5ikYAMIO..."}

Poll until completion:

curl https://api.qtsurfer.com/v1/backtest/binance/ticker/prepare/$PREPARE_JOB_ID \
  -H "Authorization: Bearer $TOKEN"
# → {"status": "Completed", ...}

Execute backtest

curl -X POST https://api.qtsurfer.com/v1/backtest/binance/ticker/execute \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"prepareJobId":"5ikYAMIO...","strategyId":"2ul144qe9tlwzu5anhwvc6"}'
# → 202 {"jobId": "4GmNN0i9..."}

Poll results

curl https://api.qtsurfer.com/v1/backtest/binance/ticker/execute/$EXECUTE_JOB_ID \
  -H "Authorization: Bearer $TOKEN"
# → {"state": {"status": "Completed", "completed": 85058},
#    "results": {"pnlTotal": 42.75, "totalTrades": 156, "winRate": 58.33,
#                "sharpeRatio": 1.245, "sortinoRatio": 1.872, "cagr": 0.1534,
#                "maxDrawdown": 12.50, "maxDrawdownPercent": 8.75,
#                "iops": 101346.81, "signalsUrl": "https://storage.qtsurfer.com/..."}}

The response includes yield metrics (PnL, win rate, Sharpe, Sortino, CAGR, max drawdown) and a signalsUrl pointing to a Parquet file with all emitted signals, ready for visualization.

Execute a parameter sweep

The sweep reuses the same prepared dataset. Its path requestId is the jobId returned by the existing prepare endpoint.

curl -X POST "https://api.qtsurfer.com/v1/backtest/binance/ticker/executeSweep/$PREPARE_JOB_ID" \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "strategyId": "2ul144qe9tlwzu5anhwvc6",
    "sweep": {
      "sampler": "grid",
      "objective": "sharpe",
      "params": {
        "rsiPeriod": {"from": 7, "to": 28, "step": 1},
        "useTrendFilter": {"values": [true, false]}
      }
    }
  }'
# → 202 {"sweepId":"swp_95e47a7f0966ce11","requestId":"5ikYAMIO...",
#        "totalRuns":44,"shards":1,"seed":487221,"queued":true}

Poll the ranked leaderboard:

curl "https://api.qtsurfer.com/v1/backtest/binance/ticker/executeSweep/$PREPARE_JOB_ID/$SWEEP_ID?objective=sharpe&order=ranked" \
  -H "Authorization: Bearer $TOKEN"
# → {"status":"RUNNING","progress":{"done":31,"total":44,...},
#    "leaderboard":[{"runIx":12,"rank":1,"params":{...},"sharpe":1.84,...}]}

Use order=natural to retrieve every available row in stable runIx order, without leaderboard truncation. The server returns the effective random seed so sampled sweeps can be reproduced exactly.

Identical prepare and execute requests are idempotent. Repeated sweeps return the same sweepId with queued: false instead of enqueueing duplicate work.

Key Technologies

Layer	Technology
Strategy runtime	Java
Signal storage	Apache Parquet, S3-compatible object storage
Visualization	svelte-timeseries (DuckDB-WASM + ECharts)

Data Sources

Type	Description
ticker	Real-time bid/ask/last/volume
kline	Candlestick OHLCV
frate	Funding rates (futures)

Related Projects

Repository	Description
svelte-timeseries	OSS Svelte component for time-series visualization

License

Apache-2.0