Best used with an AI agent

40+ live apps, open data APIs, MCP servers, and 200+ guides - more than anyone wants to click through. Point your AI here and it reads the whole map and does the work: finds the tool, pulls the data, runs the analysis, and hands you the links.

Here for the open-source code? Your agent finds the right repo for you - and can even clone and deploy it.

Prefer to explore on your own? Go right ahead.

Paste this to Claude Code, Codex, or any AI agent:
Go to tigzig.com and read tigzig.com/llms.txt. It is a practitioner toolkit - 40+ analytics apps, open no-auth data APIs, MCP servers, open-source repos (github.com/amararun), and 200+ build guides. Help me [your task]. Surface the exact links; where there is an API or MCP, call it directly; and if I want to self-host, find the repo and help me deploy it.

Maximum Drawdown

The worst peak-to-trough fall before a fund recovered.

Source data: AMFI daily NAV (17,900+ schemes) + Nifty benchmark indices · Last updated: 2026-07-02 · Open the MFPRO tool

What is Max Drawdown?

Maximum drawdown is the largest peak-to-trough decline in NAV over a period. It answers: "If I invested at the worst possible time, how much would I have lost before the fund recovered?" It's always shown as a negative percentage.

How We Compute It

Track the running maximum NAV.
At each trading day: drawdown = (current NAV / running max NAV − 1) × 100
Max Drawdown = minimum of all drawdowns (the most negative)

Computed in DuckDB using a window function: MAX(nav) OVER (PARTITION BY fund ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW).

Worked Example

Max Drawdown during COVID crash

Peak NAV: ₹320.50 (Jan 14, 2020)

Trough NAV: ₹198.20 (Mar 23, 2020)

Max Drawdown = (198.20 / 320.50 − 1) × 100 = −38.2%

An investor who bought at the peak lost 38.2% before the fund started recovering.

How to Interpret

Related metrics

More Returns methodology from the MFPRO analytics tool: