Alpha Vantage data downloader

versión 0.11 (15.7 MB) por Artem Lenskiy
Currently the toolbox implements functions to download company fundamentals and economic indicators.

39 descargas

Actualizada 15 Sep 2021

De GitHub

Ver licencia en GitHub

alphavantage-matlab

Donwload cashflow reports

% replace the "demo" apikey below with your own key from https://www.alphavantage.co/support/#api-key
keyAV = "demo";
symbols = ["TSLA","XPEV", "NIO"]; % Define symbols of interest
cashflowReports = getFundamentals(symbols, "CASH_FLOW", keyAV); % Donwload reports
% convert company reports to a single table
cashflowTable = extractFields(cashflowReports, ["CASH_FLOW", "quarterlyReport"]);

Predict selected cashflow indicators

% Variables to predict
indicatorsOfInterest  = [ "operatingCashflow",...
                    "changeInInventory",...
                    "netIncome"];
    
for k = 1:length(symbols)
    % Retrieve records for a specific ticker
    reportPerCompany = findbyValue(cashflowTable, "Symbol", symbols{k});
    % preprocess 
    options = struct("extrapolate", "linear",...
                     "removeMissingBy", "column",...
                     "toCategorical", "",...
                     "removeColumns", ["reportedCurrency", "Symbol",...
                                       "proceedsFromIssuanceOfCommonStock"]);
    reportPerCompanyProcessed = preprocess(reportPerCompany, options);
    

    rawData         = reportPerCompanyProcessed(:, indicatorsOfInterest).Variables;
    Mdl             = varm(length(indicatorsOfInterest), 2);
    %Mdl.Trend       = NaN;   % Estimate trend
    [normData, means, stds] = normalize(rawData); % normalise the data
    EstMdl          = estimate(Mdl, normData);
    numOfQs          = 4;    % Forecast numOfQs quarters
    futureDates     = dateshift(reportPerCompanyProcessed.fiscalDateEnding(end)...
                            ,'end','quarter', 1:numOfQs); % Dates to predict
    
    futureSim       = simulate(EstMdl, numOfQs,'Y0', normData,'NumPaths',2000);
    futureSim       = (futureSim .*  stds) + means; % Denormalise
    futureSimMean   = mean(futureSim, 3);           % Calculate means
    futureSimStd    = std(futureSim, 0, 3);         % Calculate std deviations
    
    % Plot the predictions
    figure('color', 'white', 'position', [0, 0, 400, 800]), hold('on');
    for l = 1:length(varsOfInterest)
        subplot(length(varsOfInterest),1, l), hold on
        plot(reportPerCompanyProcessed.fiscalDateEnding, rawData(:,l),'k', 'LineWidth', 3);
        plot([reportPerCompanyProcessed.fiscalDateEnding(end) futureDates],...
                [rawData(end,l); futureSimMean(:, l)],'r', 'LineWidth', 3)
        plot([reportPerCompanyProcessed.fiscalDateEnding(end) futureDates],...
                [rawData(end,l); futureSimMean(:, l)] + [0; futureSimStd(:, l)],'b', 'LineWidth', 3)
        plot([reportPerCompanyProcessed.fiscalDateEnding(end) futureDates],...
                [rawData(end,l); futureSimMean(:, l)] - [0; futureSimStd(:, l)],'b', 'LineWidth', 3);
        title(varsOfInterest{l});
    end
    sgtitle(symbols{k});
end

Download Economic Indicators

treasury_yield_3month = getEconomicIndicators("TREASURY_YIELD", keyAV, struct("interval", "daily", "maturity", "3month"));
treasury_yield_5year  = getEconomicIndicators("TREASURY_YIELD", keyAV, struct("interval", "daily", "maturity", "5year"));
treasury_yield_10year = getEconomicIndicators("TREASURY_YIELD", keyAV, struct("interval", "daily", "maturity", "10year"));

Plot economic indicators

figure('color', 'white'), hold on;
plot(treasury_yield_3month.data.date,treasury_yield_3month.data.value, 'LineWidth', 2);
plot(treasury_yield_5year.data.date, treasury_yield_5year.data.value,  'LineWidth', 2);
plot(treasury_yield_10year.data.date,treasury_yield_10year.data.value, 'LineWidth', 2);
xlabel('date'), ylabel('percent');
title('Treasury yields');
legend({'3 month', '5 year', '10 year'});

Donwload SnP500

snp500list = readtable("snp500list.csv");
load reports.mat % comment this line to donwload the data
%reports = getFundamentals(snp500list.Symbol, "ALL", keyAV); % uncomment

Summary of SnP500

% preprocess
overviewTable = extractFields(reports, "OVERVIEW");
sectorsLabels = unique(overviewTable.Sector);
removeColumns = ["Symbol","AssetType", "Name", "Description", "Currency",...
                 "Country","Industry", "Address", "FiscalYearEnd",...
                 "LatestQuarter", "DividendDate", "ExDividendDate",...
                 "LastSplitDate"];

options = struct("extrapolate", "linear",...
                     "removeMissingBy", "row",...
                     "toCategorical", ["Exchange", "Sector"],...
                     "removeColumns", removeColumns);


[overviewTableTirm, ind] = preprocess(overviewTable, options);
sectors = unique(overviewTableTirm.Sector);


% plot pie chart 
colors = lines(length(sectorsLabels));
figure('color', 'white', 'Position', [1, 1, 800, 600]), 
p = subplot(2,2,[1,3]); pie(histcounts(overviewTableTirm.Sector));p.Colormap = lines(7);
lgnd = legend(sectorsLabels, 'Location', 'northoutside'); title(lgnd, 'SnP500');

% plot distributions of selected indicators per sector
colName = {'Beta', 'DividendYield'};
for l = 1:2
    subplot(2,2,2*l), hold on,
    for k = 1:size(sectors,1)
        overviewPerSector{k} = findbyValue(overviewTableTirm, "Sector", sectors(k));
        [m, x] = ksdensity(overviewPerSector{k}.(colName{l}),  'Kernel', 'epanechnikov'); 
        plot(x, m,'color', colors(k, :), 'linewidth', 5)
        area(x, m, 'FaceColor', colors(k, :), 'FaceAlpha', 0.2);
        title(colName{l});
    end
end

Citar como

Artem Lenskiy (2022). Alpha Vantage data downloader (https://github.com/Lenskiy/alphavantage-matlab/releases/tag/v0.11), GitHub. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2021a
Compatible con cualquier versión
Compatibilidad con las plataformas
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Para consultar o informar de algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o informar de algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.