mcpbeat Sign in

Return Calculations Agent Skill

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.

6k tokens
context cost
the whole folder, loaded on every use
2
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
161
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/JoelLewis/finance_skills --skill return-calculations

What comes with it

14 423 bytes besides the instruction
scripts/return_calculations.py

The instruction itself

16 sections, as written by the author

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

Other skills for the same job

different authors, same section of the catalogue
Deeptools
by christophacham
×3

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

21k tokens scripts
Customer io Automation
by christophacham
×2

Automate customer engagement workflows including broadcast triggers, message analytics, segment management, and newsletter tracking through Customer.io via Composio

2k tokens needs MCP
Deeptools
by K-Dense-AI
×1

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

23k tokens scripts
Pathogen Variant Surveillance
by K-Dense-AI
×1

Query live pathogen genomic surveillance data through the GenSpectrum LAPIS API to find which viral lineages are circulating now, how fast they are growing, and what mutations they carry. Use whenever a question depends on the current state of a pathogen population rather than on remembered facts - which SARS-CoV-2 variant is dominant, whether a Pango lineage is still designated or has been withdrawn, what clade or genotype of H5N1 is in a host or region, whether a PCR primer or assay target still matches circulating sequence, or how a lineage's prevalence has moved week to week. Triggers include "variant surveillance", "genomic surveillance", "what variant is circulating", "dominant variant", "Pango lineage", "lineage prevalence", "growth advantage", "SARS-CoV-2 variant", "XFG", "clade 2.3.4.4b", "H5N1 genotype", "influenza clade", "RSV/mpox/measles/dengue lineage", "CoV-Spectrum", "LAPIS", "Nextclade", "pango-designation", and any request to report what a pathogen population looks like today.

26k tokens scripts
Deeptools
by ComeOnOliver
×1

NGS analysis toolkit. BAM to bigWig conversion, QC (correlation, PCA, fingerprints), heatmaps/profiles (TSS, peaks), for ChIP-seq, RNA-seq, ATAC-seq visualization.

24k tokens scripts
Product Strategy
by phuryn

Create a comprehensive product strategy using the 9-section Product Strategy Canvas — vision, segments, costs, value propositions, trade-offs, metrics, growth, capabilities, and defensibility. Use when building a product strategy, creating a strategic plan, or defining product direction.

1k tokens
Performance Report
by anthropics
vendor

Build a marketing performance report with key metrics, trend analysis, wins and misses, and prioritized optimization recommendations. Use when wrapping a campaign, when preparing weekly, monthly, or quarterly channel summaries for stakeholders, or when you need data translated into an executive summary with next-period priorities.

5k tokens
Saas Revenue Growth Metrics
by deanpeters

Calculate SaaS revenue, retention, and growth metrics. Use when diagnosing momentum, churn, expansion, or product-market-fit signals.

9k tokens

How to use it

Copy the folder

Take joellewis/return-calculations from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.