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Full-investment constraint

As explained in Build a Robo-Advisor with Python (From Scratch)

A full-investment constraint requires the total amount invested to equal a specified amount.

A full-investment constraint requires the portfolio’s total investment to equal a specified amount. In asset allocation, portfolio weights must sum to 1; in rebalancing, position values must equal the desired investment value. section 10.3.1 section 13.3.2

The constraint plays two closely related roles:

  • Asset allocation: keep all capital represented by the optimized weights while other constraints limit short positions or portfolio volatility. section 10.3.1
  • Rebalancing: force optimized dollar positions to equal the amount available for the target portfolio. section 13.3.2
  • Constraint classes can be combined with objectives such as maximizing expected return, minimizing tracking error, or minimizing taxes. section 10.3.1 section 13.3.2

A $10,000 target portfolio

A target portfolio containing 60% VTI and 40% AGG has positions of $6,000 and $4,000. The full-investment constraint requires their optimized positions to sum to $10,000, while separate constraints can limit deviations from those targets. section 13.3.2

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes
Defines separate weight-based and dollar-position implementations, but defers solver mechanics and broader constraint design to surrounding classes.
Examples
Uses unit-sum asset weights and a $10,000 VTI–AGG rebalancing portfolio as concrete instances.
What it assumes
Assumes portfolio variables and optimization objectives, while introducing the constraint-class interface and its generated solver expression.
What you can do after
Builds a reusable Python constraint that keeps optimized weights or positions fully invested.
How it connects
Connects portfolio optimization with long-only constraints, volatility constraints, rebalancing, tracking error, and tax minimization.
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