--- title: "Plotting Indicators" description: "LEAN provides helper methods to make it simple to create indicator plots." canonical: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/plotting-indicators markdown: https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/plotting-indicators.md language: python source: https://github.com/QuantConnect/Documentation/tree/master/03%20Writing%20Algorithms/28%20Indicators/05%20Plotting%20Indicators modified: 2026-11-02 index: https://www.quantconnect.com/llms.txt --- # Plotting Indicators [Documentation](https://www.quantconnect.com/docs/v2.md) > [Writing Algorithms](https://www.quantconnect.com/docs/v2/writing-algorithms.md) > Indicators > Plotting Indicators > Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page. ## Introduction LEAN provides helper methods to make it simple to create indicator plots. ## Plot Update Events To plot all of the values of some indicators, in the `initialize` method, call the `plot_indicator` method. The method plots each indicator value as the indicator updates. The method accepts up to four indicators. ```python symbol = self.add_equity("SPY") sma_short = self.sma(symbol, 10) sma_long = self.sma(symbol, 20) self.plot_indicator("", sma_short, sma_long) ``` ## Plot Current Values To plot the current value of indicators, call the `plot` method. The method accepts up to four indicators. ```python # In Initialize symbol = self.add_equity("SPY") sma_short = self.sma(symbol, 10) sma_long = self.sma(symbol, 20) # In OnData self.plot("", sma_short, sma_long) ``` ## View Charts The following table describes where you can access your charts, depending on how to deploy your algorithms: | Location | Algorithm Lab Algorithms | CLI Cloud Algorithms | CLI Local Algorithms | | --- | --- | --- | --- | | [Backtest results page](https://www.quantconnect.com/docs/v2/cloud-platform/backtesting/results.md) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | | | [Live results page](https://www.quantconnect.com/docs/v2/cloud-platform/live-trading/results.md) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | | | [/backtests/read endpoint](https://www.quantconnect.com/docs/v2/cloud-platform/api-reference/backtest-management/read-backtest/backtest-statistics.md) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | | | [/live/read endpoint](https://www.quantconnect.com/docs/v2/cloud-platform/api-reference/live-management/read-live-algorithm/live-algorithm-statistics.md) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | | | [ReadBacktest method](https://www.quantconnect.com/docs/v2/research-environment/meta-analysis/backtest-analysis.md) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | | | [ReadLiveAlgorithm method](https://www.quantconnect.com/docs/v2/research-environment/meta-analysis/live-analysis.md) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | | | Local `JSON` file in your ` / backtests / ` or ` / live / ` directory | | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | ![green check](https://cdn.quantconnect.com/i/tu/check.png) | ## Chart Limits Not all indicators share the same base type(T), so you may not be able to plot them together since some indicators require points while others require `TradeBars`. ```python # Plot indicators that extend the "Indicator" type self.plot_indicators("All Indicator Values", sma, rsi) self.plot("Current Indicator Values", sma, rsi); # Plot indicators that extend the "TradeBarIndicator" type self.plot_indicators("All Indicator Values", atr, aroon) self.plot("Current Indicator Values", atr, aroon); ``` If your indicator plots are complex, call the `plot` method with one indicator and plot its `current.value` property. For more information about plotting, see [Charting](https://www.quantconnect.com/docs/v2/writing-algorithms/charting.md). ## Examples The following examples demonstrate some common practices for plotting indicators. ### Example 1: SMA Ribbon The following algorithm trades [simple moving average](https://www.quantconnect.com/docs/v2/writing-algorithms/indicators/supported-indicators/simple-moving-average.md) (SMA) crossings, which involves five SMA indicators to form an SMA ribbon. To visualize the ribbon, it plots each indicator with a different color. ```python class PlottingIndicatorAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) # Add SPY data to update indicators and trade. self._spy = self.add_equity("SPY", Resolution.DAILY).symbol # Create a chart to plot the ribbon. To better visualize the plot, use different colors per series. chart = Chart("Ribbon") chart.add_series(Series("Price", SeriesType.CANDLE, "$")) chart.add_series(Series("SMA10", SeriesType.LINE, "$", Color.VIOLET)) chart.add_series(Series("SMA20", SeriesType.LINE, "$", Color.BLUE)) chart.add_series(Series("SMA30", SeriesType.LINE, "$", Color.GREEN)) chart.add_series(Series("SMA40", SeriesType.LINE, "$", Color.YELLOW)) chart.add_series(Series("SMA50", SeriesType.LINE, "$", Color.RED)) # Create 5 SMA indicators to create the SMA ribbon. self._smas = {n: self._create_sma(n) for n in [10, 20, 30, 40, 50]} def _create_sma(self, n: int) -> SimpleMovingAverage: sma = self.sma(self._spy, n, Resolution.DAILY) # Plot the indicator value to create the ribbon when the indicator updates. sma.updated += lambda sender, point: self.plot("Ribbon", f"SMA{n}", point.value) # Warm up the indicator so it's ready to use immediately. self.warm_up_indicator(self._spy, sma) return sma def on_data(self, slice: Slice) -> None: bar = slice.bars.get(self._spy) if bar: # Plot the candlestick when you get the daily bar. self.plot("Ribbon", "Price", bar.open, bar.high, bar.low, bar.close) # Follow strong upward trend. if self._is_uptrend(bar.close, self._smas): self.set_holdings(self._spy, 1) # Follow strong downward trend. elif self._is_downtrend(bar.close, self._smas): self.set_holdings(self._spy, -1) # Liquidate positions if the trend is less deterministic. else: self.liquidate() def _is_uptrend(self, price: float, smas: List[SimpleMovingAverage]) -> bool: # Uptrend occurs when price is above all SMAs and shorter SMAs are above longer SMAs. smas_list = [x[1].current.value for x in sorted(smas.items(), key=lambda x: x[0])] return all([smas_list[i] < smas_list[i-1] for i in range(1, len(smas_list))]) and price > smas_list[0] def _is_downtrend(self, price: float, smas: List[SimpleMovingAverage]) -> bool: # Downtrend occurs when price is below all SMAs and shorter SMAs are below longer SMAs. smas_list = [x[1].current.value for x in sorted(smas.items(), key=lambda x: x[0])] return all([smas_list[i] > smas_list[i-1] for i in range(1, len(smas_list))]) and price < smas_list[0] ```