update forecast ai page
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MuslemRahimi
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@ -41,10 +41,10 @@
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AI Score Definition
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</h3>
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<div class=" p-2">
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Revenue, also called sales, is the amount of money a company
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receives from its business activities, such as sales of products
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or services. Revenue does not take any expenses into account and
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is therefore different from profits.
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The AI Score uses fundamental and technical indicators, along
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with statistical data, to predict if a stock will be bullish in
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the next quarter. It ranges from 1 to 10, with higher scores
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indicating a greater likelihood of the prediction being correct.
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</div>
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<!--
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<div class="px-2">
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@ -67,10 +67,10 @@
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AI Trend Forecast Definition
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</h3>
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<div class=" p-2">
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Revenue, also called sales, is the amount of money a company
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receives from its business activities, such as sales of products
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or services. Revenue does not take any expenses into account and
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is therefore different from profits.
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The AI Trend Model forecasts stock trends by analyzing
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historical price data. It detects patterns, seasonality, and
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trends to predict future price movements, making it useful for
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predicting price targets for the next 12 months.
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</div>
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<!--
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<div class="px-2">
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@ -16,128 +16,172 @@
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return `Q${quarter} '${year}`;
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};
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const price = data?.getStockQuote?.price?.toFixed(2) || 0;
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const calculatePriceChange = (targetPrice) =>
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targetPrice && price ? ((targetPrice / price - 1) * 100)?.toFixed(2) : 0;
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const avgPriceTarget = data?.getPriceAnalysis?.avgPriceTarget || 0;
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const medianPriceTarget = data?.getPriceAnalysis?.medianPriceTarget || 0;
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const lowPriceTarget = data?.getPriceAnalysis?.lowPriceTarget || 0;
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const highPriceTarget = data?.getPriceAnalysis?.highPriceTarget || 0;
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function prepareDataset() {
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price = data?.getStockQuote?.price?.toFixed(2) || 0;
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const lowChange = calculatePriceChange(lowPriceTarget);
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const medianChange = calculatePriceChange(medianPriceTarget);
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const avgChange = calculatePriceChange(avgPriceTarget);
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const highChange = calculatePriceChange(highPriceTarget);
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avgPriceTarget = data?.getPriceAnalysis?.avgPriceTarget || 0;
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medianPriceTarget = data?.getPriceAnalysis?.medianPriceTarget || 0;
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lowPriceTarget = data?.getPriceAnalysis?.lowPriceTarget || 0;
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highPriceTarget = data?.getPriceAnalysis?.highPriceTarget || 0;
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// Assume data.getHistoricalPrice contains objects with a "time" field (e.g. "2015-01-02")
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const historicalData = data?.getHistoricalPrice || [];
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lowChange = calculatePriceChange(lowPriceTarget);
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medianChange = calculatePriceChange(medianPriceTarget);
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avgChange = calculatePriceChange(avgPriceTarget);
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highChange = calculatePriceChange(highPriceTarget);
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const backtestList = data?.getAIScore?.backtest || [];
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// Assume data.getHistoricalPrice contains objects with a "time" field (e.g. "2015-01-02")
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historicalData = data?.getHistoricalPrice || [];
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// Append the latest historical date (using "time") if available. Note that this entry may not include a score.
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if (historicalData && historicalData.length) {
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const latest = historicalData.at(-1);
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backtestList.push({ date: latest.time });
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backtestList = data?.getAIScore?.backtest || [];
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// Append the latest historical date (using "time") if available. Note that this entry may not include a score.
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if (historicalData && historicalData?.length) {
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const latest = historicalData?.at(-1);
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backtestList?.push({ date: latest.time });
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const seenDates = new Set();
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backtestList = backtestList?.filter((item) => {
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// Check if the date is already seen
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if (seenDates?.has(item?.date)) {
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// If yes, skip this duplicate (delete the last one)
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return false;
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}
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// Otherwise, record the date and keep the item
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seenDates?.add(item?.date);
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return true;
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});
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}
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processedData = backtestList?.map((item) => {
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const dateStr = item.date;
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const targetTime = new Date(dateStr).getTime();
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let closestPoint = historicalData[0];
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let minDiff = Infinity;
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historicalData.forEach((point) => {
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const pointTime = new Date(point.time).getTime();
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const diff = Math.abs(pointTime - targetTime);
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if (diff < minDiff) {
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minDiff = diff;
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closestPoint = point;
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}
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});
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// Base data point with x as the target date timestamp and y as the close price from the closest historical point
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const dataPoint = { x: targetTime, y: closestPoint.close };
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// If a score is provided, add marker configuration based on its value.
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if (item.hasOwnProperty("score")) {
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let markerColor, markerSymbol;
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if (item.score > 6) {
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// Bullish: green marker with an upward triangle
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markerColor = "#2ecc71";
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markerSymbol = "triangle-up";
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} else if (item.score < 5) {
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// Bearish: red marker with a downward triangle
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markerColor = "#e74c3c";
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markerSymbol = "triangle-down";
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} else {
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// Neutral (score exactly 5): yellow marker with a circle
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markerColor = "#f1c40f";
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markerSymbol = "circle";
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}
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dataPoint.marker = {
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symbol: markerSymbol,
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radius: 4,
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fillColor: markerColor,
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lineWidth: 2,
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lineColor: $mode === "light" ? "black" : "white",
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};
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dataPoint.dataLabels = {
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enabled: true,
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format: String(item.score),
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style: {
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color: $mode === "light" ? "black" : "white",
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fontWeight: "bold",
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fontSize: "14px",
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},
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y: -10,
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};
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}
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return dataPoint;
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});
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tableDates = processedData
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?.slice(0, -1)
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?.map((item) => formatDateToQuarter(item?.x));
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tableScore = processedData
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?.slice(0, -1)
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?.map((item) => item?.dataLabels?.format);
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// Compute percentage change
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tableQuarterChange = processedData
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?.slice(0, -1)
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?.map((item, index, arr) => {
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const prevY = arr[index - 1]?.y; // Get the previous value
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if (prevY == null || item.y == null) return null; // Handle missing values
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const change = ((item.y - prevY) / prevY) * 100; // Calculate percentage change
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return {
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quarter: tableDates[index],
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change: Number(change?.toFixed(2)), // Format to 2 decimal places
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};
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})
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?.filter(Boolean); // Remove null values
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// Compute Average Return
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returns = processedData
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?.slice(1) // Skip the first value since there's no previous value for it
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?.map((item, index) => {
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const prevY = processedData[index]?.y;
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if (prevY == null || item.y == null) return null;
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const returnPercentage = ((item.y - prevY) / prevY) * 100;
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return returnPercentage;
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})
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.filter(Boolean); // Remove null values
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avgReturn =
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returns?.reduce((sum, returnPercentage) => sum + returnPercentage, 0) /
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returns?.length;
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}
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let price;
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let avgPriceTarget;
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let medianPriceTarget;
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let lowPriceTarget;
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let highPriceTarget;
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let lowChange;
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let medianChange;
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let avgChange;
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let highChange;
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let historicalData;
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let backtestList;
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// For each backtest entry, find the historical price with the closest available time.
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// Then, if a score exists, attach a marker and data label based on the score.
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const processedData = backtestList?.map((item) => {
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const dateStr = item.date;
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const targetTime = new Date(dateStr).getTime();
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let closestPoint = historicalData[0];
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let minDiff = Infinity;
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let processedData = [];
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historicalData.forEach((point) => {
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const pointTime = new Date(point.time).getTime();
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const diff = Math.abs(pointTime - targetTime);
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if (diff < minDiff) {
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minDiff = diff;
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closestPoint = point;
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}
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});
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let tableDates;
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// Base data point with x as the target date timestamp and y as the close price from the closest historical point
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const dataPoint = { x: targetTime, y: closestPoint.close };
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// If a score is provided, add marker configuration based on its value.
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if (item.hasOwnProperty("score")) {
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let markerColor, markerSymbol;
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if (item.score > 6) {
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// Bullish: green marker with an upward triangle
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markerColor = "#2ecc71";
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markerSymbol = "triangle-up";
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} else if (item.score < 5) {
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// Bearish: red marker with a downward triangle
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markerColor = "#e74c3c";
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markerSymbol = "triangle-down";
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} else {
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// Neutral (score exactly 5): yellow marker with a circle
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markerColor = "#f1c40f";
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markerSymbol = "circle";
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}
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dataPoint.marker = {
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symbol: markerSymbol,
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radius: 4,
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fillColor: markerColor,
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lineWidth: 2,
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lineColor: $mode === "light" ? "black" : "white",
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};
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dataPoint.dataLabels = {
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enabled: true,
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format: String(item.score),
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style: {
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color: $mode === "light" ? "black" : "white",
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fontWeight: "bold",
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fontSize: "14px",
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},
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y: -10,
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};
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}
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return dataPoint;
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});
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const tableDates = processedData
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?.slice(0, -1)
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?.map((item) => formatDateToQuarter(item.x));
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const tableScore = processedData
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?.slice(0, -1)
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?.map((item) => item?.dataLabels?.format);
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let tableScore;
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// Compute percentage change
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const tableQuarterChange = processedData
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?.slice(0, -1)
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.map((item, index, arr) => {
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const prevY = arr[index - 1]?.y; // Get the previous value
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if (prevY == null || item.y == null) return null; // Handle missing values
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const change = ((item.y - prevY) / prevY) * 100; // Calculate percentage change
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return {
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quarter: tableDates[index],
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change: Number(change?.toFixed(2)), // Format to 2 decimal places
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};
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})
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?.filter(Boolean); // Remove null values
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let tableQuarterChange;
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// Compute Average Return
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const returns = processedData
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?.slice(1) // Skip the first value since there's no previous value for it
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?.map((item, index) => {
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const prevY = processedData[index]?.y;
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if (prevY == null || item.y == null) return null;
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const returnPercentage = ((item.y - prevY) / prevY) * 100;
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return returnPercentage;
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})
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.filter(Boolean); // Remove null values
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let returns;
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const avgReturn =
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returns?.reduce((sum, returnPercentage) => sum + returnPercentage, 0) /
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returns?.length;
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let avgReturn;
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function getAIScorePlot() {
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const solidData = processedData.slice(0, -1);
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@ -422,7 +466,8 @@
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let configScore = null;
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$: {
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if ($mode) {
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if ($stockTicker || $mode) {
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prepareDataset();
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configScore = getAIScorePlot() || null;
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config = getPriceForecastChart() || null;
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}
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