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Return Calculations

Community
JoelLewis
return-calculations

Compute and compare investment return metrics including TWR, MWR (dollar-weighted IRR on portfolio cash flows), CAGR, and annualized returns. Use when the user asks about portfolio performance calculation, comparing manager returns, linking sub-period returns, understanding why different return methods give different numbers, converting returns across time periods, or computing the IRR of an investor's own contributions and withdrawals. Also trigger when users mention 'how much did I make', 'annual return', 'compound growth', 'dollar-weighted vs time-weighted', 'what was my rate of return', 'geometric vs arithmetic mean', 'log returns', or ask about the effect of cash flows on reported returns. For project or loan IRR, NPV, and generic 'solve for the rate' problems, use time-value-of-money instead.

Overview

PublisherJoelLewis
Repositoryfinance_skills
Skill namereturn-calculations
Stars
217
Forks
37
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by JoelLewis on GitHub. Read the source before you install it.

Installation

Install the Return Calculations AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/JoelLewis/finance_skills/tree/main/plugins/core/skills/return-calculations
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/JoelLewis/finance_skills.git /tmp/finance_skills
mkdir -p .claude/skills
cp -r /tmp/finance_skills/plugins/core/skills/return-calculations .claude/skills/return-calculations
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Return Calculations in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Return Calculations on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Return Calculations is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Return Calculations

Core Concepts

Simple (Holding Period) Return

$$R = \frac{V_{end} - V_{begin} + D}{V_{begin}}$$

where D = distributions (dividends, interest) received during the period. If V_end already reflects reinvested distributions, do not add D again.

Mean and Log Return Conventions

  • Arithmetic mean R_a = (1/n) * sum(R_i) — unbiased estimate of the expected single-period return (use for forward-looking inputs, e.g., mean-variance optimization). Always >= geometric mean; overstates realized compound growth.
  • Geometric mean R_g = [prod(1 + R_i)]^(1/n) - 1 — the correct measure of realized multi-period compound growth. The gap below the arithmetic mean approximates sigma^2 / 2 (volatility drag).
  • Log return r = ln(V_end / V_begin) — time-additive (r_total = r_1 + ... + r_n), so preferred for statistical modeling and multi-period aggregation. Convert with R_simple = e^r - 1 and r = ln(1 + R_simple). Log returns are additive across time but NOT across assets.

CAGR (Compound Annual Growth Rate)

$$CAGR = \left(\frac{V_{end}}{V_{begin}}\right)^{1/n} - 1$$

where n is measured in years. The annualized geometric growth rate between two valuations with no intermediate cash flows.

Time-Weighted Return (TWR)

Chain-links sub-period returns calculated between each external cash flow, removing the effect of cash flow timing. TWR measures the manager's investment skill independent of investor deposit/withdrawal decisions, and is the GIPS standard for manager performance.

$$1 + R_{TWR} = \prod_{i=1}^{n}(1 + R_i), \qquad R_i = \frac{V_{end,i}}{V_{begin,i} + CF_i} - 1$$

Exact TWR requires a portfolio valuation on every cash flow date.

Modified Dietz Return

When valuations on each cash flow date are unavailable, Modified Dietz approximates the period return by day-weighting each external cash flow within the period:

$$R_{MD} = \frac{V_{end} - V_{begin} - CF_{net}}{V_{begin} + \sum_i CF_i \times w_i}, \qquad w_i = \frac{CD - D_i}{CD}$$

where CF_net = sum of external cash flows, CD = calendar days in the period, and D_i = day of flow i (so w_i is the fraction of the period the flow was invested). It is a money-weighted approximation; chain-linking Modified Dietz sub-period returns approximates TWR. Accuracy degrades when flows are large relative to portfolio value or markets are volatile within the period — revalue on large-flow dates instead.

Money-Weighted Return (MWR / IRR)

The internal rate of return that sets the NPV of all investor cash flows (contributions, withdrawals, and terminal value) to zero:

$$0 = \sum_{t=0}^{T} \frac{CF_t}{(1 + r)^t}$$

MWR reflects the actual investor experience because it is sensitive to the timing and magnitude of cash flows. Solved numerically (Newton-Raphson or bisection).

Annualization

$$R_{annual} = (1 + R_{period})^{periods_per_year} - 1$$

For example, a 2% quarterly return annualizes to (1.02)^4 - 1 = 8.24%.

Sub-Period Linking

$$(1 + R_{total}) = \prod_{i=1}^{n}(1 + R_i)$$

The foundational identity behind TWR and CAGR.

Worked Examples

Example 1: Computing CAGR from a 5-Year Investment

Given: An investment of $10,000 grows to $16,105.10 over exactly 5 years with no intermediate cash flows.

Calculate: The compound annual growth rate (CAGR).

Solution:

CAGR = (V_end / V_begin)^(1/n) - 1
CAGR = (16,105.10 / 10,000)^(1/5) - 1
CAGR = (1.610510)^(0.2) - 1
CAGR = 1.10 - 1
CAGR = 0.10 = 10%

The investment grew at a compound annual rate of 10% per year.

Verification: $10,000 * (1.10)^5 = $10,000 * 1.61051 = $16,105.10

Example 2: TWR vs MWR Divergence with Poorly Timed Cash Flow

Given: A fund has the following history:

  • Start of Year 1: Portfolio value = $100,000
  • End of Year 1: Portfolio value = $120,000 (return = +20%)
  • Start of Year 2: Investor deposits $100,000, bringing portfolio to $220,000
  • End of Year 2: Portfolio value = $198,000 (return = -10%)

Calculate: Both TWR and MWR, and explain the divergence.

Solution:

Time-Weighted Return (TWR):

Sub-period 1 return: R_1 = (120,000 - 100,000) / 100,000 = +20%
Sub-period 2 return: R_2 = (198,000 - 220,000) / 220,000 = -10%

TWR (cumulative) = (1 + 0.20) * (1 + (-0.10)) - 1
                  = 1.20 * 0.90 - 1
                  = 1.08 - 1
                  = +8.0%

TWR (annualized) = (1.08)^(1/2) - 1 = 3.92%

Money-Weighted Return (MWR / IRR): Cash flows from the investor's perspective:

  • t=0: -$100,000 (initial investment)
  • t=1: -$100,000 (additional deposit)
  • t=2: +$198,000 (terminal value)

Solve: -100,000 + (-100,000)/(1+r) + 198,000/(1+r)^2 = 0

This is quadratic in x = 1/(1+r); the positive root gives r = -0.66815% (verifiable with the bundled script or any IRR solver).

NPV check at r = -0.0066815:

-100,000 + (-100,000)/0.9933185 + 198,000/0.9933185^2
= -100,000 - 100,672.65 + 200,672.65
= 0.00  (exact)

The MWR is approximately -0.67% annualized.

Interpretation: The TWR of +3.92% annualized reflects the manager's skill: the fund gained 20% then lost 10%, netting +8% over two years. The MWR of approximately -0.67% reflects the investor's experience: more money was at risk during the losing year (Year 2) because of the large deposit, so the investor's dollar-weighted outcome was slightly negative. This divergence highlights why TWR is preferred for evaluating manager performance, while MWR better describes the specific investor's realized result.

Common Pitfalls

  • Confusing arithmetic and geometric means: the arithmetic mean is always greater than or equal to the geometric mean (AM-GM inequality). Using arithmetic mean to project compounded growth overstates terminal wealth.
  • Using arithmetic mean for multi-period compounding: always use geometric mean or CAGR when describing compound growth over multiple periods.
  • Annualizing returns from very short periods: annualizing a 2% weekly return yields (1.02)^52 - 1 = 180%, which amplifies noise and is misleading. Annualization is most meaningful for periods of at least one year.
  • Ignoring cash flow timing when TWR is appropriate: MWR conflates manager skill with investor timing decisions. Use TWR for manager evaluation.
  • Double-counting dividends: if the ending value V_end already includes reinvested dividends, do not add D separately in the holding period return formula.
  • Trusting Modified Dietz with large intra-period flows: when a single flow exceeds roughly 10% of portfolio value, revalue the portfolio on the flow date rather than day-weighting.

Running the Script

scripts/return_calculations.py provides a Returns class with static methods for every formula above (holding period return, TWR, MWR/IRR via Newton's method, Modified Dietz is straightforward to compose from these, CAGR, annualization, linking, arithmetic/geometric means, log-return conversions).

  • Run: uv run scripts/return_calculations.py (PEP 723 inline metadata resolves numpy automatically), or python3 scripts/return_calculations.py with numpy installed.
  • Bare invocation (or --verify) prints a demo of all functions and asserts the worked-example values above (Example 1 CAGR = 10%, Example 2 TWR = +8.0% cumulative / 3.92% annualized, MWR = -0.6682%), exiting nonzero on any mismatch.
  • --help lists the available functions and import usage.
  • For programmatic use, import rather than run: from return_calculations import Returns.

Cross-References

  • time-value-of-money (core plugin): NPV, IRR, and discounting concepts overlap with MWR calculations; owns project/loan IRR
  • statistics-fundamentals (core plugin): Arithmetic and geometric means, return distribution analysis

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Return Calculations AI skill do?

Compute and compare investment return metrics including TWR, MWR (dollar-weighted IRR on portfolio cash flows), CAGR, and annualized returns. Use when the user asks about portfolio performance calculation, comparing manager returns, linking sub-period returns, understanding why different return methods give different numbers, converting returns across time periods, or computing the IRR of an investor's own contributions and withdrawals. Also trigger when users mention 'how much did I make', 'annual return', 'compound growth', 'dollar-weighted vs time-weighted', 'what was my rate of return',...

Why use Return Calculations on TypingMind?

Because you install it once and use it with any model. Return Calculations is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Return Calculations in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JoelLewis/finance_skills/tree/main/plugins/core/skills/return-calculations. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Return Calculations?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Return Calculations?

As many as you like. As long as a model supports skills, you can use Return Calculations with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Return Calculations AI skill free?

Yes. It is published on GitHub by JoelLewis under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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