Super Algorithm

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Customizing Chart Data ​

You can customize the information shown on the charts when backtesting or monitoring your strategy during live trading.

TIP

During a live trading session, the chart is updated in real time, similar to other live charting solutions, allowing you to monitor your trading strategy.

Backtest Dashboard

1. Configuring the Chart Schema ​

In your sma_sample_strategy.py add imports for ChartSchema, ChartPointDataType, set_chart_schema, chart:

python
...
from superalgorithm.types import (
    Bar,
    OrderType,
    PositionType,
    ChartSchema,
    ChartPointDataType
)
...
from superalgorithm.utils.logging import set_chart_schema, chart

Create the chart schema in the init function. We will plot the candlesticks of the price data, and the moving averages.

python
class SMAStrategy(BaseStrategy):

    def init(self):
        self.on("5m", self.on_5)
        self.sma = SMA(14)
        self.sma_slow = SMA(200)

        set_chart_schema(
            [
                ChartSchema(
                    "BTC/USDT", ChartPointDataType.OHLCV, "candlestick", "orange"
                ),
                ChartSchema("sma", ChartPointDataType.FLOAT, "line", "red"),
                ChartSchema("sma_slow", ChartPointDataType.FLOAT, "line", "blue"),
            ]
        )

2. Populate Chart Data ​

We now populate the charts with data every time we receive an update in our 5m handler.

python
    async def on_5(self, bar: Bar):

        self.sma.add(bar.close)
        self.sma_slow.add(bar.close)

        chart("BTC/USDT", bar.ohlcv)
        chart("sma", self.sma[-1])
        chart("sma_slow", self.sma_slow[-1])

        await self.trade_logic()

3. Test the Chart ​

Now run your backtest.py again to see the chart:

SUPER_STRATEGY_ID=<choose a unique name for your strategy> backtest.py

4. Adding entry and exit points ​

Inside the set_chart_schema function add two additional configurations of type scatter:

py
ChartSchema("long", ChartPointDataType.FLOAT, "scatter", chart_color="green"),
ChartSchema("short", ChartPointDataType.FLOAT, "scatter", chart_color="red"),
```

Then, modify the buy and sell code blocks to plot the scatter points when we go long or short.

python
    async def trade_logic(self):

        ...

        if buy:
            await self.open("BTC/USDT", PositionType.LONG, 0.1, OrderType.LIMIT, close)
            chart("long", close)

        if sell:
            await self.close("BTC/USDT", PositionType.LONG, 0.1, OrderType.LIMIT, close)
            chart("short", close)

Re-run your backtest to see the results.