# Quick Start

##### - disclaimer

The PathLit API is currently in alpha preview. What does it mean? - first of all, it's free as we are still figuring out how the final product should look like to best fit our audience use case. We are learning what our users need in order to help them solve problems, and really appreciate your feedback.

Second, the platform performance is not (yet) optimised. Concretely, you can expect some endpoints to respond anywhere from 5 to 15 seconds per call.

Finally, despite being in early stages, the computations are tested and validated, thus reliable.

## Using the PathLit Engine API#

Let's imagine a scenario where you are allocating some instruments: "ADBE", "T", "CSCO", "JNJ", "AMZN", "BRK-B", "BAC", "PG", "KO", "AAPL", "JPM", "MSFT"

##### info

If you haven't done it yet, you need to create an account in order to generate an API key (or token). It's really fast and easy.

### (optional) Retrieving the supported symbols#

PathLit currently supports around 800 symbols, you can retrieve them and make sure the one you at interested in are there (the response is truncated):

curl --location --request GET 'https://engine.pathlit.io/v1/timeseries/info' \--header 'x-api-key: xxxxxxxx'

### Get the weights for the symbols#

curl --location --request GET 'https://engine.pathlit.io/v1/timeseries/info' \--header 'x-api-key: xxxxxxxx' \--data-raw '{  "tickers": [      "ADBE",      "T",      "CSCO",      "JNJ",      "AMZN",      "BRK-B",      "BAC",      "PG",      "KO",      "AAPL",      "JPM",      "MSFT"  ]}'

## What's next?#

Now that we have generated some weights we can go to the model section to understand how to make the best of the API response: what is l1r.l2d.l3ewp, l1r.l2w.l3rpp and how was it computed.

If a deeper dive is needed, the optimisation section is where you want to go.

If you wonder what are the use cases the PathLit engine can help with, please visit the use cases page.