# Alphacast

- [Alphacast: Financial Data Analytics for Everyone](https://learn.alphacast.io/introduction.md): Alphacast is a financial data analytics platform for managing datasets and connecting to global data providers via API.
- [Get Started with Alphacast API](https://learn.alphacast.io/quickstart.md): Get your API key, create a repository and dataset, upload data, and download it — all in under five minutes using the Alphacast API.
- [Authenticate with the Alphacast API](https://learn.alphacast.io/authentication.md): Learn how to authenticate Alphacast API requests using your API key via HTTP Basic Auth or as a query parameter, with examples in curl and Python.
- [Repositories: Organizing Your Data in Alphacast](https://learn.alphacast.io/concepts/repositories.md): Repositories are the top-level containers in Alphacast. Every dataset you create belongs to a repository, and you control who can access it.
- [Datasets: Storing and Accessing Time-Series Data](https://learn.alphacast.io/concepts/datasets.md): Datasets hold tabular and time-series data inside Alphacast repositories. Upload CSV files, query with OData filters, and download in CSV, JSON, XLSX, or TSV.
- [Data Providers: Built-In Sources for Market Data](https://learn.alphacast.io/concepts/data-providers.md): Alphacast ships built-in connectors for 30+ external sources — FRED, BLS, World Bank, OECD, IMF, BIS, ECB, Eurostat, Banxico, BCRA, and many more. Browse, search, and pull series through a single unified REST API.
- [Uploading Data: Overview](https://learn.alphacast.io/uploading/overview.md): How dataset uploads work in Alphacast — the manifest, the upload lifecycle, and what to expect after a CSV is processed.
- [The Dataset Manifest](https://learn.alphacast.io/uploading/manifest.md): How to define column types, entity columns, date format, and renames in the dataset manifest that drives every Alphacast upload.
- [Validation Rules and Upload Modes](https://learn.alphacast.io/uploading/validation.md): Every validation rule Alphacast runs against an uploaded CSV, and how the deleteMissingFromDB / onConflictUpdateDB / acceptNewColumns flags control row-level conflict resolution.
- [Connect to External Data Providers](https://learn.alphacast.io/providers/overview.md): Pull series from central banks, statistical agencies, market data sources, and international organizations through a single unified Alphacast API. Browse the live catalog at alphacast.io/explore/apis.
- [Pipeline Steps](https://learn.alphacast.io/steps/overview.md): How pipelines work in Alphacast, and a map of every step available — from importing data to publishing the result.
- [Fetch Alphacast Dataset](https://learn.alphacast.io/steps/fetch-dataset.md): Pull an existing Alphacast dataset into a pipeline as its starting point.
- [Fetch FRED Data](https://learn.alphacast.io/steps/fetch-fred.md): Import economic time series from the St. Louis Fed's FRED database into a pipeline by series code.
- [Fetch Financial Data](https://learn.alphacast.io/steps/fetch-yahoo.md): Import stocks, ETFs, indices and other financial assets from Yahoo Finance into a pipeline by ticker.
- [Fetch Alphacast Series](https://learn.alphacast.io/steps/fetch-series.md): Pull individual Alphacast series into a pipeline by series ID, in wide or long format, with optional scale conversion.
- [Fetch API Explorer Data](https://learn.alphacast.io/steps/fetch-api-provider.md): Import series from any connected external data provider — central banks, statistics offices and international organizations.
- [Select Columns](https://learn.alphacast.io/steps/select-columns.md): Keep only the columns you need, reducing the data processed by every downstream step.
- [Filter Entity](https://learn.alphacast.io/steps/filter-entity.md): Keep only the entities you want to work with — countries, categories or tickers — and drop the rest.
- [Filter Dates](https://learn.alphacast.io/steps/filter-dates.md): Restrict a pipeline to a fixed date range or to a window defined relative to today.
- [Filter Rows](https://learn.alphacast.io/steps/filter-rows.md): Drop rows that don't meet a condition, using the same formula grammar as Calculate Variable.
- [Sort Rows](https://learn.alphacast.io/steps/sort-rows.md): Order rows by one or more columns, each with its own ascending or descending direction.
- [Select Series](https://learn.alphacast.io/steps/select-series.md): Reduce a dataset to specific series — a variable combined with one set of entity values — and flatten them into named columns.
- [Reorder Columns](https://learn.alphacast.io/steps/reorder-columns.md): Set the order in which columns appear in the pipeline output.
- [Rename Columns](https://learn.alphacast.io/steps/rename-columns.md): Give columns new names, or leave them unchanged, before publishing or charting.
- [Regroup Entities](https://learn.alphacast.io/steps/regroup-entities.md): Drop an entity column and aggregate the remaining rows with a formula — the equivalent of GROUP BY.
- [Change Frequency](https://learn.alphacast.io/steps/resample.md): Resample a dataset to a different time frequency, interpolating up or aggregating down.
- [Merge with Dataset](https://learn.alphacast.io/steps/merge.md): Join two datasets on their common entities, choosing which rows survive when they don't match.
- [Append Dataset](https://learn.alphacast.io/steps/append-dataset.md): Stack datasets with the same structure on top of each other, with control over duplicate handling.
- [Unstack (Long to Wide)](https://learn.alphacast.io/steps/unstack-dataset.md): Pivot a long-format dataset to wide format, turning the values of an entity column into columns of their own.
- [Melt (Wide to Long)](https://learn.alphacast.io/steps/melt-dataset.md): Convert a wide-format dataset to long format, collapsing value columns into rows under a new variable column.
- [Rename Values](https://learn.alphacast.io/steps/rename-values.md): Replace values inside an entity or string column — harmonizing country names, categories or labels across sources.
- [Reorder Rows](https://learn.alphacast.io/steps/reorder-rows.md): Put entity values in a deliberate, non-alphabetical order — useful when chart and table legends should follow a specific sequence.
- [Apply Transform](https://learn.alphacast.io/steps/transform.md): Apply prebuilt economic transformations — year-over-year change, constant prices, per capita, % of GDP, seasonal adjustment and currency conversion.
- [Apply Country Transform](https://learn.alphacast.io/steps/change-units.md): Country-specific unit conversions — constant prices, per capita, % of GDP and currency conversion — with explicit control over which country applies.
- [Apply Formula](https://learn.alphacast.io/steps/apply-formula.md): Apply one formula to many columns at once, producing a new suffixed column for each.
- [Calculate Variable](https://learn.alphacast.io/steps/calculate-variable.md): Create new columns with an Excel-like formula parser — arithmetic, row functions, group functions and rolling windows.
- [Formula Reference](https://learn.alphacast.io/steps/formula-reference.md): Every operator, comparison and function available in the Alphacast formula editor — row, group, rolling window and date functions.
- [X13 ARIMA Seasonal Adjustment](https://learn.alphacast.io/steps/arima-sa.md): Seasonally adjust series with X-13ARIMA-SEATS, with control over model order, outliers, log transformation and forecast horizon.
- [Validate Countries](https://learn.alphacast.io/steps/country-validator.md): Check country names against a canonical list and decide what happens to the ones that don't match — fail, remove, fuzzy match or rename.
- [Technical Analysis](https://learn.alphacast.io/steps/talib.md): Estimate 132 technical analysis metrics on financial assets — momentum, volatility, volume, overlap studies and pattern recognition.
- [Portfolio Statistics and Tear Sheets](https://learn.alphacast.io/steps/portfolio-analysis.md): Design and test portfolio and trading strategies — tear sheets from daily returns, plus dynamic rolling stats.
- [Transform with AI](https://learn.alphacast.io/steps/ai-transform.md): Describe a transformation in plain language and let Alphacast write and run the Python that performs it.
- [Summarize with AI](https://learn.alphacast.io/steps/ai-summary.md): Generate a written summary of the data at this point in the pipeline, regenerated only when the underlying data changes.
- [Create AI View](https://learn.alphacast.io/steps/ai-visualization.md): Build a chart, table or mini dashboard from the output of one or more earlier steps, generated and versioned by AI.
- [Publish to Dataset](https://learn.alphacast.io/steps/publish.md): Write pipeline output to a dataset that updates automatically whenever the pipeline runs.
- [Create a Chart](https://learn.alphacast.io/steps/chart-data.md): Turn pipeline output into a chart and customize it without leaving the pipeline.
- [Create a Table](https://learn.alphacast.io/steps/table-view.md): Render pipeline output as a formatted table that can be embedded in a dashboard.
- [Create a Deck of Views](https://learn.alphacast.io/steps/deck-view.md): Build multiple charts and tables over one dataset, sharing a common set of filters, for use in dashboards.
- [Data Snapshot](https://learn.alphacast.io/steps/data-snapshot.md): Capture the pipeline output as a downloadable, embeddable snapshot in CSV, XLSX or TSV.
- [Add Markdown Text](https://learn.alphacast.io/steps/markdown.md): Add formatted notes, documentation and section headers inline in a pipeline.
- [Alphacast REST API Reference](https://learn.alphacast.io/api/overview.md): Explore the Alphacast REST API to manage repositories, datasets, and data providers programmatically from any HTTP client.
- [API Error Codes and Troubleshooting](https://learn.alphacast.io/api/errors.md): Reference for every HTTP error code returned by the Alphacast API — 400, 401, 402, 403, 404, 409, 429, 500, 503, 504 — with causes, example responses, and fixes.
- [GET /repositories — List All Repositories](https://learn.alphacast.io/api/repositories/list.md): Retrieve all repositories accessible to the authenticated user. Returns an array of repository objects with id, name, privacy, and permission fields.
- [GET /repositories/{id} — Get a Repository](https://learn.alphacast.io/api/repositories/get.md): Retrieve a single repository by its numeric ID. Returns the repository object including name, description, privacy, and your permission level.
- [GET /repositories/home — Get Home Repository](https://learn.alphacast.io/api/repositories/home.md): Retrieve the authenticated user's Home repository — the default workspace where pipelines and datasets land when no specific repository is named.
- [POST /repositories — Create a Repository](https://learn.alphacast.io/api/repositories/create.md): Create a new repository to organize datasets. Requires name and privacy. Optionally specify description and team for collaboration.
- [PUT /repositories/{id} — Update a Repository](https://learn.alphacast.io/api/repositories/update.md): Update an existing repository's name, description, or privacy. Requires Owner permission. Returns the updated repository object on success.
- [DELETE /repositories/{id} — Delete a Repository](https://learn.alphacast.io/api/repositories/delete.md): Permanently delete a repository and all its datasets. Requires Owner permission. This action cannot be undone.
- [GET /datasets — List All Datasets](https://learn.alphacast.io/api/datasets/list.md): Retrieve all datasets accessible to the authenticated user across all repositories. Returns an array of dataset objects with metadata.
- [GET /datasets/{id} — Get a Dataset](https://learn.alphacast.io/api/datasets/get.md): Retrieve a single dataset by numeric ID, including its name, repository, description, column schema, date range, and your permission level.
- [GET /datasets/{id}/columns — List Dataset Columns](https://learn.alphacast.io/api/datasets/columns.md): Retrieve just the column schema of a dataset — name, ID, data type, and whether the column is an entity/dimension — without the rest of the dataset metadata.
- [POST /datasets — Create a Dataset](https://learn.alphacast.io/api/datasets/create.md): Create a new dataset in a repository to store tabular or time-series data. Requires name and repositoryId. Optionally add source metadata.
- [DELETE /datasets/{id} — Delete a Dataset](https://learn.alphacast.io/api/datasets/delete.md): Permanently delete a dataset and all its data. Requires Write permission on the dataset. Returns 204 No Content. This action cannot be undone.
- [GET /datasets/{id}/data — Download Dataset Data](https://learn.alphacast.io/api/datasets/data.md): Download dataset rows as CSV, JSON, XLSX, or TSV. Supports OData filtering, column selection, row limits, and transposing the output.
- [PUT /datasets/{id}/data — Upload Data to a Dataset](https://learn.alphacast.io/api/datasets/upload.md): Upload a CSV or gzip-compressed CSV file to add, update, or replace dataset rows. Supports a column manifest, conflict resolution flags, and async processing.
- [GET /providers — List and Browse Data Providers](https://learn.alphacast.io/api/providers/list.md): List all available data providers, browse their hierarchical categories, search for series, and save provider API keys for authenticated access.
- [Fetch Series Data from External Providers](https://learn.alphacast.io/api/providers/series.md): Preview or download complete time-series data from FRED, BLS, World Bank, OECD, IMF, BIS, ECB, Eurostat, and central banks across Latin America, Europe, and Asia using the Alphacast provider API.
- [Alphacast Python SDK](https://learn.alphacast.io/python-sdk/overview.md): Install the Alphacast Python SDK and interact with repositories, datasets, series, and search from your Python code — no need to call the REST API directly.
- [Python SDK Quickstart](https://learn.alphacast.io/python-sdk/quickstart.md): Install the Alphacast Python SDK, create a repository, upload a pandas DataFrame, and download it back — in under five minutes.
- [Authentication](https://learn.alphacast.io/python-sdk/authentication.md): How the Alphacast Python SDK authenticates requests with your API key, plus best practices for storing the key safely.
- [Repositories](https://learn.alphacast.io/python-sdk/repositories.md): Use the Alphacast Python SDK's repository client to list, look up, create, and delete repositories — the top-level containers for your datasets.
- [Datasets](https://learn.alphacast.io/python-sdk/datasets.md): List, look up, and inspect datasets with the Alphacast Python SDK — read metadata, columns, and date stats before uploading or downloading.
- [Series](https://learn.alphacast.io/python-sdk/series.md): Read metadata and data for a single Alphacast series by ID using the Python SDK.
- [Search](https://learn.alphacast.io/python-sdk/search.md): Find datasets across your repositories and Alphacast's public catalog using the Python SDK's search client.
- [Uploading Data](https://learn.alphacast.io/python-sdk/uploading.md): Create datasets, declare their column schema, and upload pandas DataFrames or CSV strings to Alphacast using the Python SDK.
- [Downloading Data](https://learn.alphacast.io/python-sdk/downloading.md): Pull dataset rows into your Python code as a pandas DataFrame, CSV, JSON, or XLSX, with optional date, entity, and variable filters.
- [Process Status](https://learn.alphacast.io/python-sdk/processes.md): Poll the status of an upload process from the Alphacast Python SDK to know when your data is ready.
- [API Reference](https://learn.alphacast.io/python-sdk/reference.md): Method-by-method reference of the Alphacast Python SDK — every public class, method, and parameter.
- [Alphacast MCP Server](https://learn.alphacast.io/mcp/overview.md): Connect Claude Code and Codex to Alphacast. Search the catalog, download datasets, browse 30+ data providers, look up tickers, and build pipelines from natural-language prompts.
- [MCP Quickstart](https://learn.alphacast.io/mcp/quickstart.md): Connect Claude Desktop, Claude Code, or Codex to the Alphacast MCP server in three steps and run your first prompt.
- [MCP Authentication](https://learn.alphacast.io/mcp/authentication.md): How the Alphacast MCP server authenticates: OAuth for the hosted server, or the ALPHACAST_API_KEY environment variable for the local npm package.
- [Connect Claude Desktop](https://learn.alphacast.io/mcp/configuration/claude-desktop.md): Connect Claude Desktop to the Alphacast MCP server — the hosted server as a custom connector (OAuth), or the local npm package via npx.
- [Connect Claude Code](https://learn.alphacast.io/mcp/configuration/claude-code.md): Add the Alphacast MCP server to Claude Code — the hosted server over OAuth, or the local npm package via npx.
- [Connect Codex](https://learn.alphacast.io/mcp/configuration/codex.md): Add the Alphacast MCP server to OpenAI Codex — the hosted server over OAuth, or the local npm package via npx.
- [Connect Cursor](https://learn.alphacast.io/mcp/configuration/cursor.md): Configure Cursor to use the Alphacast MCP server — the hosted server over OAuth, or the local npm package.
- [Other MCP Clients](https://learn.alphacast.io/mcp/configuration/other-clients.md): Connect VS Code, Zed, Windsurf, and any custom MCP client to the Alphacast MCP server — hosted (OAuth) or local (npx).
- [MCP Tools Overview](https://learn.alphacast.io/mcp/tools/overview.md): Reference for all 32 tools exposed by the Alphacast MCP server, grouped by capability: search & datasets, providers, tickers, grapher & charts, and pipelines.
- [Search & Datasets Tools](https://learn.alphacast.io/mcp/tools/search-and-datasets.md): Tools for discovering content in the Alphacast catalog, reading dataset metadata, schemas, and rows, and resolving the workspace's default repository.
- [Provider Tools](https://learn.alphacast.io/mcp/tools/providers.md): Tools for discovering 30+ external data providers (FRED, BLS, World Bank, OECD, IMF, BIS, ECB, Eurostat, and central banks across Latin America, Europe, and Asia), browsing their catalogs, and fetching series.
- [Ticker Tools](https://learn.alphacast.io/mcp/tools/tickers.md): Look up market tickers, ETFs, indexes, and ISINs from an MCP client, fetch their profile metadata, and pull historical price data. Three read-only tools that pair naturally with the fetch-yahoo pipeline step.
- [Grapher & Chart Tools](https://learn.alphacast.io/mcp/tools/grapher.md): Tools for looking up chart-authoring recipes, validating Grapher configurations, and inspecting existing chart metadata. Three read-only tools.
- [Pipeline Tools](https://learn.alphacast.io/mcp/tools/pipelines.md): Build, inspect, edit, and run Alphacast pipelines from natural-language prompts. Thirteen MCP tools for the full pipeline lifecycle — nine read-only, four write — with auto-validation and auto-run after every mutation.
- [Writing better prompts](https://learn.alphacast.io/mcp/prompting.md): How to frame requests so the Alphacast MCP returns exactly the data, pipeline, or diagnostic you need — five guidelines, a broad-to-precise example, copy-paste templates, and the safety pattern for write actions.
- [MCP Examples](https://learn.alphacast.io/mcp/examples.md): End-to-end prompt walkthroughs that combine multiple Alphacast MCP tools to solve realistic discovery and data-fetching tasks.
- [MCP Troubleshooting](https://learn.alphacast.io/mcp/troubleshooting.md): Common errors and fixes when connecting clients to the Alphacast MCP server, authenticating, or running tools.