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Plots and series ​

For lines, histograms, or areas to appear on the chart, two things are required:

  1. Declare the visual shape in DECLARATION["plots"] (name, type, color, pane).
  2. Return the numeric values in series from the df= branch of main().

The key in series must match exactly the source of the plot. Otherwise, the frontend draws nothing.

Minimum contract ​

python
DECLARATION = {
    "type": "indicator",
    "inputs": [
        {"name": "period", "type": "int", "default": 14, "min": 1, "max": 100},
    ],
    "plots": [
        {
            "name": "sma",             # internal name
            "source": "sma",           # must match the key in series
            "type": "line",
            "color": "#22D3EE",
            "lineWidth": 2,
        },
    ],
    "pane": "overlay",
}


def _sma_series(values, period):
    out = []
    for i in range(len(values)):
        if i + 1 < period:
            out.append(None)
        else:
            out.append(sum(values[i - period + 1:i + 1]) / period)
    return out


def _build_chart(df, params):
    period = int((params or {}).get("period", 14))
    closes = list(df["close"])
    return {
        "plots": DECLARATION["plots"],
        "series": {
            "sma": _sma_series(closes, period),
        },
    }


def main(df=None, sdk=None, params={}):
    if df is not None:
        return _build_chart(df, params)
    return DECLARATION

Rules for series ​

Length = number of candles ​

Each array in series must have exactly the same length as df. The frontend aligns by index. An array that is shorter or longer misaligns every point.

python
closes = list(df["close"])
sma_array = _sma_series(closes, period)
assert len(sma_array) == len(closes)  # always true if _sma_series is correct

None during warmup ​

Before there is enough data to compute, use None. The frontend does not draw a point there.

python
# An SMA of period 14 cannot be computed on the first 13 candles.
[None, None, ..., None, first_sma, ...]

Never use 0 or NaN for warmup. The frontend draws a point at 0 (visually distorted) or breaks on NaN.

Accepted types ​

Each value must be float or None. Numpy conversions also work (np.float64), but numpy arrays do not -- convert them with .tolist():

python
# Wrong
closes_np = np.array(closes)
sma_np = pd.Series(closes_np).rolling(period).mean()
return {"series": {"sma": sma_np}}  # return value must be JSON-serializable

# Correct
return {"series": {"sma": sma_np.tolist()}}   # converts to list
# or
return {"series": {"sma": [float(x) if not pd.isna(x) else None for x in sma_np]}}

Multiple plots ​

Declare multiple entries in plots and return multiple keys in series:

python
DECLARATION = {
    "plots": [
        {"name": "ma_fast", "source": "ma_fast", "type": "line", "color": "#22D3EE"},
        {"name": "ma_slow", "source": "ma_slow", "type": "line", "color": "#F59E0B"},
        {"name": "volume",  "source": "volume",  "type": "histogram", "color": "#64748B"},
    ],
    "pane": "overlay",
}

def _build_chart(df, params):
    closes = list(df["close"])
    volumes = list(df["volume"])
    return {
        "plots": DECLARATION["plots"],
        "series": {
            "ma_fast": _sma_series(closes, 9),
            "ma_slow": _sma_series(closes, 21),
            "volume":  volumes,  # already has 1 point per candle
        },
    }

Plot types ​

typeVisualTypical use
"line"Continuous lineMoving averages, RSI, VWAP
"histogram"Vertical bars (positive/negative)MACD hist, Volume
"dots"Discrete pointsBuy/sell signals
"area"Area filled to zeroVolume, ATR
"arrows"Up/down arrowsSignal markers
"circles"CirclesPivots, extremes

Histograms with conditional colors ​

The engine accepts colorExpression on histogram columns. Use it for a two-color histogram (green positive, red negative):

python
{
    "name": "hist",
    "source": "hist",
    "type": "histogram",
    "colorExpression": "value >= 0 ? '#22C55E' : '#EF4444'",
}

If you prefer not to use the expression, leave only color and the frontend applies the same color to every bar (positive or negative).

Dots for visual signals ​

When you want to mark specific points (not a continuous series), fill only the relevant indices and leave the rest as None:

python
signals = [None] * len(closes)
for i in range(len(closes)):
    if closes[i] > closes[i-1] * 1.02:  # spike
        signals[i] = closes[i]

return {"series": {"spike_marker": signals}}

In the DECLARATION:

python
{"name": "spike_marker", "source": "spike_marker", "type": "dots", "color": "#22C55E"}

Reusing the same computation for plots and trading ​

Idiomatic pattern: the df= branch builds the entire series (for the chart) and the sdk= branch calls a variant that only computes the last point:

python
def _sma(values, period):
    """Last point only. Efficient for on_bar."""
    if len(values) < period:
        return None
    return sum(values[-period:]) / period


def _sma_series(values, period):
    """Full series. Used only in build_chart."""
    # ... (full implementation)


def _build_chart(df, params):
    period = int((params or {}).get("period", 14))
    closes = list(df["close"])
    return {
        "plots": DECLARATION["plots"],
        "series": {"sma": _sma_series(closes, period)},
    }


def on_bar_strategy(sdk, params):
    period = int((params or {}).get("period", 14))
    closes = [c["close"] for c in sdk.candles]
    sma = _sma(closes, period)  # current point only
    if sma is None:
        return
    # ... trading logic

Common errors ​

  • Plot does not appear: check source of the plot against the key in series. Both must be exactly equal, case-sensitive.
  • Misaligned line: the series array has a length different from len(df). Use None for warmup instead of omitting.
  • All-gray histogram: colorExpression is missing for differentiating positive/negative. Configure it or accept a single color.
  • Plot "jumping" between points: None in the middle of the series (after warmup). The frontend interprets it as a break. For continuous lines, ensure a dense computation.
  • Returning {"series": {...}} without "plots": the frontend needs both in the return of the df= branch. Although the engine already has DECLARATION["plots"], the contract is to return it explicitly.

Next steps ​