Portfolio Statistics and Tear Sheets

Design and test portfolio and trading strategies — tear sheets from daily returns, plus dynamic rolling stats.

Alphacast pipelines can be used to design and test portfolio and trading strategies. The Portfolio Analysis step produces tear sheets from daily returns, and dynamic rolling statistics across timeframes.

1. Load data and calculate daily returns

Any dataset works. As an example, load Yahoo Finance data and compute daily returns for three assets:

1. Open the pipeline editor

   Create new → Pipeline.

2. Add Yahoo Finance as the source

   Add the tickers you want to analyze — MSFT, AAPL, GOOG — and select the period.

3. Calculate the daily return

   Add a Calculate Variable step with the formula:

   pct_change(@Close, 1, @Ticker)
   

   

   

   

2. Calculate the tear sheet

Add a Portfolio Analysis step, choose the metrics to compute, and select the column holding the daily returns — pct_change in this example.

Available statistics: annual return, cumulative returns, annual volatility, Sharpe ratio, Calmar ratio, stability, max drawdown, Omega ratio, Sortino ratio, skew, kurtosis, tail ratio, daily value at risk, and total return.

Tear sheet on

A single tear sheet is calculated for the whole sample period.

Tear sheet off

Statistics are estimated recursively for each day of the sample, each one computed from that day up to the most recent one. Two extra parameters apply:

Parameter	Meaning
dropLastN	Statistics are not calculated for timeframes beginning in the last N days. Defaults to excluding 252 trading days.
Skip days	Start days are sampled every N days rather than daily. Matters for performance. Defaults to 10 days.

The result looks like this dataset.

Optional: add technical indicators

The Technical Analysis step estimates 132 metrics on the same price data — cycle, momentum, volatility and volume indicators, overlap studies, pattern recognition and statistics functions.

Next steps

Technical analysis

132 technical indicators for the same assets.

Publish dataset

Save the tear sheet as an auto-updating dataset.