Custom Data Workflows

Overview

Custom datasets allow you to upload and manage your own time-series data for use with Alfa. This guide provides step-by-step instructions for creating, managing, and uploading data to your custom datasets.

Any data you upload is shared with your team.

Currently, we only support macroeconomic data (i.e. data that is not tied to any specific stock.)

Managing Datasets

Creating a Dataset

To create a new dataset, you’ll need to provide a name. The system will return a unique dataset ID that you’ll use for future operations.

Make the API Request

Send a PUT request to create a new dataset:

create_dataset.py

1 import requests
2 
3 BASE_URL = "https://alfa.boosted.ai/client"
4 API_KEY = "YOUR_API_KEY_HERE"
5 
6 response = requests.put(
7     f"{BASE_URL}/v2/datasets/create-macro-dataset",
8     headers={"x-api-key": API_KEY},
9     json={"dataset_name": "My Custom Dataset"}
10 )
11 
12 dataset_id = response.json()["dataset_id"]

Handle the Response

The API will return a response containing:

dataset_id: A unique identifier for your new dataset
Store this ID as you’ll need it for future operations

Fetching Dataset Details

You can retrieve detailed information about a specific dataset, including its metadata and features.

Make the API Request

Send a GET request with your dataset ID:

get_dataset_info.py

1 import request
2 
3 BASE_URL = "https://alfa.boosted.ai/client"
4 API_KEY = "YOUR_API_KEY_HERE"
5 
6 
7 response = requests.get(
8     f"{BASE_URL}/v2/datasets/{dataset_id}/get-info",
9     headers={"x-api-key": API_KEY}
10 )
11 
12 dataset_info = response.json()

Understand the Response

The response includes:

dataset_info: Basic metadata about the dataset
- dataset_id: Unique identifier
- dataset_name: Name you provided
- dataset_type: Type of dataset (GLOBAL or STOCK)
- data_start: Earliest data point
- data_end: Latest data point
- created_time: When the dataset was created
- last_updated: Last modification time
- approx_records: Approximate number of records
- status: Current status (READY, INGESTING, ERROR, UNKNOWN)
- owner_info: Information about the owner of the dataset and your access level
custom_features: List of features/columns in the dataset
upload_info: Information about any ongoing uploads

Listing All Datasets

You can view all your custom datasets, sorted by last update time.

Make the API Request

Send a GET request to list all datasets:

list_datasets.py

1 import requests
2 
3 BASE_URL = "https://alfa.boosted.ai/client"
4 API_KEY = "YOUR_API_KEY_HERE"
5 
6 response = requests.get(
7     f"{BASE_URL}/v2/datasets/list-datasets",
8     headers={"x-api-key": API_KEY}
9     params={"limit": 100}
10 )
11 
12 datasets = response.json()["dataset_listing"]

Understand the Response

The response contains a list of datasets, each with:

dataset_id: Unique identifier
dataset_name: Dataset name
dataset_type: Type of dataset
data_start: Earliest data point
data_end: Latest data point
created_time: Creation timestamp
last_updated: Last modification timestamp
approx_records: Approximate record count
status: Current dataset status

Deleting a Dataset

You can permanently delete a dataset when it’s no longer needed.

Once a dataset is deleted, it cannot be recovered and existing workplans using the data will no longer function.

Make the API Request

Send a DELETE request with your dataset ID:

delete_dataset.py

1 import requests
2 
3 BASE_URL = "https://alfa.boosted.ai/client"
4 API_KEY = "YOUR_API_KEY_HERE"
5 
6 response = requests.delete(
7     f"{BASE_URL}/v2/datasets/{dataset_id}",
8     headers={"x-api-key": API_KEY}
9 )

Handle the Response

The API will return a success response if the deletion was successful

Uploading Data into an Existing Dataset

Uploading CSV Data

You can upload time-series data to your dataset using a CSV file.

Prepare Your CSV File

Ensure your CSV file has:

A date column for time-series data in YYYY-MM-DD form
One or more feature columns with numeric values
Consistent date formatting

Make the API Request

Send a POST request with your CSV file:

your_data.csv

1 date,value1,value2,value3
2 2024-01-01,1.0,2.0,3.0
3 2024-01-02,1.7,2.2,3.3
4 2024-01-03,1.4,2.1,3.9

upload_data.py

1 import requests
2 
3 BASE_URL = "https://alfa.boosted.ai/client"
4 API_KEY = "YOUR_API_KEY_HERE"
5 
6 with open("your_data.csv", "rb") as f:
7     files = {"file": f}
8     data = {
9         "date_column": "date",
10         "feature_columns": "value1,value2,value3"
11     }
12     
13     response = requests.post(
14         f"{BASE_URL}/v2/datasets/{dataset_id}/upload-macro-data",
15         headers={"x-api-key": API_KEY},
16         files=files,
17         data=data
18     )
19 
20 upload_info = response.json()

Handle the Response

The API returns:

dataset_id: The ID of the dataset receiving the upload
upload_id: A unique ID to track the upload progress
Store the upload_id to check the upload status

Checking Upload Status

You can monitor the progress of your data upload.

Make the API Request

Send a GET request with your dataset ID and upload ID:

check_upload_status.py

1 import requests
2 
3 BASE_URL = "https://alfa.boosted.ai/client"
4 API_KEY = "YOUR_API_KEY_HERE"
5 
6 response = requests.get(
7     f"{BASE_URL}/v2/datasets/{dataset_id}/get-info",
8     headers={"x-api-key": API_KEY},
9     params={"upload_id": upload_id}
10 )
11 
12 upload_status = response.json()["upload_info"]

Understand the Response

The response includes:

upload_id: The ID of the upload
warnings: Any warnings during processing
errors: Any errors that occurred
successful_rows: Number of rows successfully processed
total_rows: Total number of rows in the upload
upload_status: Current status (PROCESSING, ABORTED, SUCCESS, WARNING, ERROR)

Monitor Progress

Poll the status endpoint until upload_status is not “PROCESSING”
Check warnings and errors for any issues

The upload process may take some time depending on the size of your CSV file. It’s recommended to implement a polling mechanism to check the status periodically.

When uploading data, ensure your CSV file is properly formatted and contains all required columns. This will help avoid processing errors and warnings.

Using Your Data

You can use this like any other variable in Alfa now! Use the @ typeahead or # selector button to choose the variable for inclusion in your workplans.