Methodology

How the PnLock engine works.

A plain-English explanation of the stop models, quality score, portfolio analytics, and re-entry scoring behind PnLock.

Principles

PnLock is decision support, not automation. Every number below is an estimate built from public market data, designed to make a manual decision more disciplined, never to predict the future or place trades for you.

  • Stops are anchored to the current price (the latest close), not your entry, so protection reflects where the stock is now.
  • A model returns nothing rather than a bad answer: if a computed level would sit at or above the current price, it is shown as unavailable.
  • Nothing here is financial advice, and past volatility never guarantees future behaviour.

The six stop models

Profit Lock computes up to six exit levels for the same position, each with a different philosophy, then lets you compare them side by side.

ModelHow it places the exit
Conservative ShieldA fixed percentage below the current price. Simple and predictable; ignores volatility.
Balanced Defender1.5× the 14-day ATR below price. A volatility-aware default for most stocks.
Volatility Guard2× the 21-day ATR below price. Roomier and more patient for longer holds.
Momentum ProtectorJust under the 20-day swing low, the most recent level buyers defended.
Crash HunterAt the 50-day moving average, a trend-break line for clearly uptrending names.
Adaptive ProtectorA Chandelier Exit: 3× ATR below the 22-day high. Trails up with the trend, never down.

The quality score (0–100)

Every candidate stop is graded across four sub-scores that sum to 100, then mapped to an A–F grade. The score answers "is this a sensible level?", not "will this trade win?".

  • Grades: A/B = sound, C = workable, D/F = likely to misfire, reconsider the model or re-run.
ComponentWeightWhat it checks
Noise tolerance40 ptsIs the stop far enough from price that ordinary daily wiggles won’t trip it?
Distance from price30 ptsIs the distance sensible as a multiple of ATR, neither hair-trigger nor far too loose?
Model agreement20 ptsHow tightly do the other models cluster around this level?
Freshness10 ptsHow recent is the data and analysis behind the level?

Portfolio analytics

Portfolio Analytics decomposes holdings, including the companies inside your ETFs, where data is available, and maps exposure across several dimensions.

  • Concentration is graded with a Herfindahl-Hirschman Index (HHI) over holding weights, so "many tickers" doesn’t get mistaken for "diversified".
  • Exposure is broken out by holding, sector, industry, country, currency, market-cap band, and portfolio beta.
  • ETF passthrough uses issuer-fed holdings where available and falls back to a top-10 estimate otherwise.

Re-entry scoring

Reentry Intelligence runs five checks on a stopped-out name and rolls them into a 0–100 readiness score with a Re-enter / Watching / Wait status.

  • Stabilisation, has disorderly price action calmed down?
  • Volatility reset, are daily ranges contracting from the panic?
  • Higher-low structure, is price carving constructive higher lows?
  • Volume context, is participation confirming the recovery?
  • Market regime, is the broad market (SPY) supportive rather than hostile?

Data & limitations

Models run on daily OHLC candles from market-data providers; enrichment (sector, market cap, ETF composition) comes from public sources and is cached.

  • Disclosure feeds (SEC Form 4, US lawmaker trades) are public, delayed, and can be amended, verify before acting.
  • Backtests are historical simulations and can mislead if overfit; they are not proof of future results.
  • All levels are estimates from past data. News gaps and regime shifts can move straight through any stop.