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Paths

Computes the dollar returns of a portfolio for the actual time series

This builds upon and uses /weights for computation. It takes a time-series of unlimited instruments as an input and splits it into two sets of data - a training set and a test set. The training set is the first 252 days long, the remaining days provided are used for the test set.

The training set is fed into the /weights endpoint to obtain the weights for the portfolio to be constructed on the first day of the test set. The test set prices are then used to track the portfolio performance.

The output is a JSON-formatted time-series of end-of-day portfolio performance in dollar terms.

note

This endpoint is experimental so please take note that the processing performance hasn't been fine tuned yet, i.e. do expect up to 30 seconds to get a response from the engine. The computation are however production ready

Method#

POST /v1/optimiser/paths

Parameters#

ticker(s) symbol(s) REQUIRED - one or many symbols. For example the following symbols are valid: AAPL,HOG,KO

info

The list of tickers supported by the PathLit engine are advertised at /v1/timeseries/info

Response attributes#

info

Want to learn more about the attributes? a deep dive is available at this location, under the quant section

AttributeHigh level explanation
l1r.l2d.l3ewpAssets aggregated by daily log-returns, allocated using an equally-weighted strategy
l1r.l2d.l3gmvpAssets aggregated by daily log-returns allocated using a global minimum-variance strategy
l1r.l2d.l3hrpAssets aggregated by daily log-returns allocated using a hierarchical risk-parity strategy
l1r.l2d.l3ivpAssets aggregated by daily log-returns allocated using an inverse-volatility strategy
l1r.l2d.l3mdcpAssets aggregated by daily log-returns allocated using an maximum-decorrelation strategy
l1r.l2d.l3mdpAssets aggregated by daily log-returns allocated using an maximum-diversified strategy
l1r.l2d.l3rppAssets aggregated by daily log-returns allocated using an risk-parity strategy
l1r.l2m.l3ewpAssets aggregated by monthly log-returns, allocated using an equally-weighted strategy
l1r.l2m.l3gmvpAssets aggregated by monthly log-returns allocated using a global minimum-variance strategy
l1r.l2m.l3hrpAssets aggregated by monthly log-returns allocated using a hierarchical risk-parity strategy
l1r.l2m.l3ivpAssets aggregated by monthly log-returns allocated using an inverse-volatility strategy
l1r.l2m.l3mdcpAssets aggregated by monthly log-returns allocated using an maximum-decorrelation strategy
l1r.l2m.l3mdpAssets aggregated by monthly log-returns allocated using an maximum-diversified strategy
l1r.l2m.l3rppAssets aggregated by monthly log-returns allocated using an risk-parity strategy
l1r.l2w.l3ewpAssets aggregated by monthly log-returns, allocated using an equally-weighted strategy
l1r.l2w.l3gmvpAssets aggregated by weekly log-returns allocated using a global minimum-variance strategy
l1r.l2w.l3hrpAssets aggregated by weekly log-returns allocated using a hierarchical risk-parity strategy
l1r.l2w.l3ivpAssets aggregated by weekly log-returns allocated using an inverse-volatility strategy
l1r.l2w.l3mdcpAssets aggregated by weekly log-returns allocated using an maximum-decorrelation strategy
l1r.l2w.l3mdpAssets aggregated by weekly log-returns allocated using an maximum-diversified strategy
l1r.l2w.l3rppAssets aggregated by weekly log-returns allocated using an risk-parity strategy

Note#

n/a

Example#

curl--request POST 'https://engine.pathlit.io/v1/optimiser/paths' \--header 'x-api-key: xxxxxxxxxxxxxxxxxxxxx' \--header 'Content-Type: application/json' \--data-raw '{    "tickers": [        "AAPL",        "HOG",        "KO"    ]}'