Angular Orderbook Heatmap

1import {
2    SciChartSurface,
3    NumericAxis,
4    ENumericFormat,
5    ZoomPanModifier,
6    ZoomExtentsModifier,
7    MouseWheelZoomModifier,
8    NumberRange,
9    OhlcDataSeries,
10    FastCandlestickRenderableSeries,
11    SciChartJsNavyTheme,
12    DateTimeNumericAxis,
13    CursorModifier,
14    CursorTooltipSvgAnnotation,
15    EDataSeriesType,
16    ESeriesType,
17    FastMountainRenderableSeries,
18    GradientParams,
19    IRenderableSeries,
20    OhlcSeriesInfo,
21    Point,
22    SeriesInfo,
23    HeatmapColorMap,
24    UniformHeatmapRenderableSeries,
25    UniformHeatmapDataSeries,
26    EXyDirection,
27} from "scichart";
28
29import { appTheme } from "../../../theme";
30
31// Data file paths (files are copied via webpack.config.js)
32const ohlcFilePath = "LTCUSDT_OHLC.csv";
33const orderbookLevels = "orderbook_levels.csv";
34
35const baseUrl =
36    typeof window !== "undefined" &&
37    !window.location.hostname.includes("scichart.com") &&
38    !window.location.hostname.includes("localhost")
39        ? "https://www.scichart.com/demo"
40        : "";
41
42/** OHLCV candlestick data structure */
43type TCandleData = {
44    xValues: number[];
45    openValues: number[];
46    highValues: number[];
47    lowValues: number[];
48    closeValues: number[];
49    volumeValues: number[];
50};
51
52/**
53 * Loads OHLCV candlestick data from CSV file
54 * @returns Promise resolving to parsed candle data arrays
55 */
56async function loadCandleData(): Promise<TCandleData> {
57    const xValues: number[] = [];
58    const openValues: number[] = [];
59    const highValues: number[] = [];
60    const lowValues: number[] = [];
61    const closeValues: number[] = [];
62    const volumeValues: number[] = [];
63
64    try {
65        const filepath = baseUrl + ohlcFilePath;
66        const response = await fetch(filepath);
67
68        if (!response.ok) {
69            throw new Error(`HTTP error! status: ${response.status}`);
70        }
71
72        const csvText = await response.text();
73        const lines = csvText.split("\n");
74
75        // Parse each data row (skip header at index 0)
76        for (let i = 1; i < lines.length; i++) {
77            const line = lines[i].trim();
78            if (!line) continue;
79
80            const rowData = line.split(",");
81
82            if (rowData.length >= 6) {
83                const priceBar = {
84                    date: Number.parseInt(rowData[0]),
85                    open: Number.parseFloat(rowData[1]),
86                    high: Number.parseFloat(rowData[2]),
87                    low: Number.parseFloat(rowData[3]),
88                    close: Number.parseFloat(rowData[4]),
89                    volume: Number.parseFloat(rowData[5]),
90                };
91
92                // Only add valid numeric data
93                if (
94                    !isNaN(priceBar.date) &&
95                    !isNaN(priceBar.open) &&
96                    !isNaN(priceBar.high) &&
97                    !isNaN(priceBar.low) &&
98                    !isNaN(priceBar.close) &&
99                    !isNaN(priceBar.volume)
100                ) {
101                    xValues.push(priceBar.date);
102                    openValues.push(priceBar.open);
103                    highValues.push(priceBar.high);
104                    lowValues.push(priceBar.low);
105                    closeValues.push(priceBar.close);
106                    volumeValues.push(priceBar.volume);
107                }
108            }
109        }
110
111        return {
112            xValues,
113            openValues,
114            highValues,
115            lowValues,
116            closeValues,
117            volumeValues,
118        };
119    } catch (error) {
120        console.error("Error loading candle data:", error);
121        throw error;
122    }
123}
124
125/** Parsed order book heatmap data structure */
126type TParsedHeatmapData = {
127    /** 2D array of order values (Z-axis intensity) */
128    zValues: number[][];
129    /** Unix timestamps for X-axis cell positions */
130    xCellOffsets: number[];
131    /** Price levels for Y-axis cell positions */
132    yCellOffsets: number[];
133    /** Minimum non-zero value in the heatmap */
134    minValue: number;
135    /** Maximum value in the heatmap */
136    maxValue: number;
137};
138
139/**
140 * Loads order book depth heatmap data from CSV file
141 * CSV format: First row contains timestamps, subsequent rows contain price level and order values
142 * @returns Promise resolving to parsed heatmap data with Z-values and axis offsets
143 */
144async function loadHeatmapData(): Promise<TParsedHeatmapData> {
145    const zValues: number[][] = [];
146    let xCellOffsets: number[] = [];
147    const yCellOffsets: number[] = [];
148    let minValue = Infinity;
149    let maxValue = -Infinity;
150
151    try {
152        const dataFile = baseUrl + orderbookLevels;
153        const response = await fetch(dataFile);
154        const csvText = await response.text();
155        const lines = csvText.split("\n");
156
157        for (let i = 0; i < lines.length; i++) {
158            const line = lines[i].trim();
159            if (!line) continue;
160
161            const rowData = line.split(",");
162
163            if (i === 0) {
164                // Header row contains Unix timestamps for each column
165                const [_, ...cellOffsets] = rowData;
166                xCellOffsets = cellOffsets.map((timestampString: string) => {
167                    const timestamp = Number.parseInt(timestampString);
168                    return timestamp || 0;
169                });
170            } else {
171                // Data rows: first column is price level, remaining columns are order values
172                const [price, ...zValuesRow] = rowData;
173
174                if (!Number.isNaN(Number.parseInt(price))) {
175                    const rowValues = zValuesRow.map((val: string) => {
176                        const numVal = Number.parseFloat(val) || 0;
177                        // Track min/max for color map scaling (exclude zeros)
178                        if (numVal > 0) {
179                            minValue = Math.min(minValue, numVal);
180                            maxValue = Math.max(maxValue, numVal);
181                        }
182                        return numVal;
183                    });
184
185                    zValues.push(rowValues);
186                    yCellOffsets.push(Number.parseFloat(price));
187                }
188            }
189        }
190
191        // Default range if no valid values found
192        if (minValue === Infinity) {
193            minValue = 0;
194            maxValue = 1;
195        }
196
197        return { zValues, xCellOffsets, yCellOffsets, minValue, maxValue };
198    } catch (error) {
199        console.error("Error loading heatmap data:", error);
200        throw error;
201    }
202}
203
204/**
205 * Creates an Order Book Heatmap chart with candlestick overlay
206 * Demonstrates combining UniformHeatmapRenderableSeries with FastCandlestickRenderableSeries
207 * to visualize order book depth alongside price action
208 *
209 * @param rootElement - HTML element ID or element to render the chart into
210 * @returns Promise resolving to the chart surface and candlestick series
211 */
212export const drawExample = async (rootElement: string | HTMLDivElement) => {
213    const { sciChartSurface, wasmContext } = await SciChartSurface.create(rootElement, {
214        theme: new SciChartJsNavyTheme(),
215    });
216
217    // X-Axis: DateTimeNumericAxis for continuous time data
218    // Note: For stocks/forex with market hours, use DiscontinuousDateAxis to collapse weekend gaps
219    const xAxis = new DateTimeNumericAxis(wasmContext, {
220        cursorLabelFormat: ENumericFormat.Date_HHMMSS,
221    });
222    sciChartSurface.xAxes.add(xAxis);
223
224    // Y-Axis: Price axis with currency formatting
225    const priceAxis = new NumericAxis(wasmContext, {
226        labelFormat: ENumericFormat.Decimal,
227        labelPrecision: 2,
228        labelPrefix: "$",
229    });
230    sciChartSurface.yAxes.add(priceAxis);
231
232    // Load data from CSV files
233    const { xValues, openValues, highValues, lowValues, closeValues, volumeValues } = await loadCandleData();
234    const { zValues, xCellOffsets, yCellOffsets, minValue, maxValue } = await loadHeatmapData();
235
236    // Heatmap color gradient: dark background -> white (low values) -> red (high values)
237    const gradientStops = [
238        { offset: 0, color: appTheme.DarkIndigo },
239        { offset: 0.03, color: appTheme.ForegroundColor },
240        { offset: 0.4, color: appTheme.VividRed },
241    ];
242
243    // Color map scales Z-values to gradient colors
244    const colorMap = new HeatmapColorMap({
245        minimum: minValue,
246        maximum: maxValue,
247        gradientStops,
248    });
249
250    // Calculate average X-axis step size for uniform heatmap spacing
251    let totalStep = 0;
252    let stepCount = 0;
253    for (let i = 1; i < xCellOffsets.length; i++) {
254        totalStep += xCellOffsets[i] - xCellOffsets[i - 1];
255        stepCount++;
256    }
257    const averageXStep = stepCount > 0 ? totalStep / stepCount : xCellOffsets[1] - xCellOffsets[0];
258
259    // Calculate average Y-axis step size for uniform heatmap spacing
260    let totalYStep = 0;
261    let yStepCount = 0;
262    for (let i = 1; i < yCellOffsets.length; i++) {
263        totalYStep += yCellOffsets[i] - yCellOffsets[i - 1];
264        yStepCount++;
265    }
266    const averageYStep = yStepCount > 0 ? totalYStep / yStepCount : yCellOffsets[1] - yCellOffsets[0];
267
268    // Create heatmap data series with uniform cell spacing
269    const heatmapDataSeries = new UniformHeatmapDataSeries(wasmContext, {
270        xStart: xCellOffsets[0],
271        xStep: averageXStep,
272        yStart: yCellOffsets[0],
273        yStep: averageYStep,
274        zValues,
275        dataSeriesName: "Order Value",
276    });
277
278    // Calculate data ranges for axis configuration
279    const candleXRange = [Math.min(...xValues), Math.max(...xValues)];
280    const heatmapXRange = [Math.min(...xCellOffsets), Math.max(...xCellOffsets)];
281
282    // Create heatmap series with semi-transparency to show candlesticks through it
283    const heatmapSeries = new UniformHeatmapRenderableSeries(wasmContext, {
284        opacity: 0.4,
285        dataSeries: heatmapDataSeries,
286        colorMap,
287        stroke: appTheme.PaleSkyBlue,
288    });
289    // Disable texture filtering for crisp cell boundaries
290    heatmapSeries.useLinearTextureFiltering = false;
291
292    sciChartSurface.renderableSeries.add(heatmapSeries);
293
294    // Set Y-axis to show full price range from heatmap data
295    const heatmapYRange = [Math.min(...yCellOffsets), Math.max(...yCellOffsets)];
296    priceAxis.visibleRange = new NumberRange(heatmapYRange[0], heatmapYRange[1]);
297
298    // Set X-axis to show overlapping time range between candle and heatmap data
299    const overlapStart = Math.max(candleXRange[0], heatmapXRange[0]);
300    const overlapEnd = Math.min(candleXRange[1], heatmapXRange[1]);
301
302    if (overlapStart < overlapEnd) {
303        xAxis.visibleRange = new NumberRange(overlapStart, overlapEnd);
304    } else {
305        // No overlap - default to heatmap range
306        xAxis.visibleRange = new NumberRange(heatmapXRange[0], heatmapXRange[1]);
307    }
308
309    // Create candlestick series with OHLC data
310    const candleDataSeries = new OhlcDataSeries(wasmContext, {
311        xValues,
312        openValues,
313        highValues,
314        lowValues,
315        closeValues,
316        dataSeriesName: "LTC/USDT",
317    });
318
319    const candlestickSeries = new FastCandlestickRenderableSeries(wasmContext, {
320        dataSeries: candleDataSeries,
321        stroke: appTheme.ForegroundColor,
322        strokeThickness: 2,
323        brushUp: appTheme.VividGreen + "AA",
324        brushDown: appTheme.MutedRed + "AA",
325        strokeUp: appTheme.VividGreen,
326        strokeDown: appTheme.MutedRed,
327        dataPointWidth: 0.8,
328        isVisible: true,
329        opacity: 1,
330    });
331
332    sciChartSurface.renderableSeries.add(candlestickSeries);
333    console.log("Added candlestick series with", candleDataSeries.count(), "data points");
334
335    // Add chart interaction modifiers
336    sciChartSurface.chartModifiers.add(
337        new ZoomExtentsModifier(),
338        new ZoomPanModifier({
339            enableZoom: true,
340            horizontalGrowFactor: 0.005,
341            verticalGrowFactor: 0,
342            xyDirection: EXyDirection.XDirection,
343        }),
344        new MouseWheelZoomModifier({ xyDirection: EXyDirection.XDirection }),
345        new CursorModifier({
346            crosshairStroke: appTheme.PaleOrange + 55,
347            axisLabelFill: appTheme.PaleOrange + 55,
348            tooltipLegendTemplate: getTooltipLegendTemplate,
349        })
350    );
351
352    return { sciChartSurface, candlestickSeries };
353};
354
355/**
356 * Transforms series for the SciChart overview/navigator component
357 * Converts candlestick series to mountain series for cleaner overview display
358 * @param defaultSeries - The original renderable series
359 * @returns Transformed series for overview, or undefined to hide
360 */
361const getOverviewSeries = (defaultSeries: IRenderableSeries) => {
362    if (defaultSeries.type === ESeriesType.CandlestickSeries) {
363        // Display candlestick data as a mountain series in the overview for cleaner visualization
364        return new FastMountainRenderableSeries(defaultSeries.parentSurface.webAssemblyContext2D, {
365            dataSeries: defaultSeries.dataSeries,
366            fillLinearGradient: new GradientParams(new Point(0, 0), new Point(0, 1), [
367                { color: appTheme.VividSkyBlue + "77", offset: 0 },
368                { color: "Transparent", offset: 1 },
369            ]),
370            stroke: appTheme.VividSkyBlue,
371        });
372    }
373    // Hide other series (e.g., heatmap) from overview
374    return undefined;
375};
376
377/** Configuration for SciChart overview/navigator component */
378export const sciChartOverview = {
379    theme: appTheme.SciChartJsTheme,
380    transformRenderableSeries: getOverviewSeries,
381};
382
383/**
384 * Custom tooltip template for CursorModifier
385 * Displays OHLC values for candlestick series and order values for heatmap
386 * @param seriesInfos - Array of series info objects for series under cursor
387 * @param svgAnnotation - The tooltip annotation instance
388 * @returns SVG string for tooltip content
389 */
390const getTooltipLegendTemplate = (seriesInfos: SeriesInfo[], svgAnnotation: CursorTooltipSvgAnnotation) => {
391    let outputSvgString = "";
392
393    seriesInfos.forEach((seriesInfo, index) => {
394        const y = 20 + index * 20;
395        const textColor = seriesInfo.stroke;
396        let legendText = seriesInfo.formattedYValue;
397
398        // Format OHLC data for candlestick series
399        if (seriesInfo.dataSeriesType === EDataSeriesType.Ohlc) {
400            const o = seriesInfo as OhlcSeriesInfo;
401            legendText = `Open=${o.formattedOpenValue} High=${o.formattedHighValue} Low=${o.formattedLowValue} Close=${o.formattedCloseValue}`;
402        }
403        // Format heatmap data showing price level and order count
404        else if (seriesInfo.seriesName === "Order Value") {
405            legendText = `${seriesInfo.formattedYValue} Orders: ${seriesInfo.hitTestInfo.zValue}`;
406        }
407
408        outputSvgString += `<text x="8" y="${y}" font-size="13" font-family="Verdana" fill="${textColor}">
409          ${seriesInfo.seriesName}: ${legendText}
410      </text>`;
411    });
412
413    return `<svg width="100%" height="100%">
414                <g transform=translate(5,5)>
415               ${
416                   outputSvgString ? `<rect width="480px" height="50px" fill="#000000" opacity="0.4" rx="5" />` : ``
417               }
418                ${outputSvgString}
419                <g>
420             </svg>`;
421};
422