tune-thresholds

name

tune-thresholds

description

Analyze a group's price volatility and suggest ATR-based swing/confirm thresholds for group.toml. Use this skill when setting up a new group and you need to pick initial thresholds, when signals seem noisy or too infrequent, when the user asks "are my thresholds right?", "why am I getting whipsawed?", "tune thresholds", or any variation that implies the current swing_pct or confirm_pct may not fit the stock's actual volatility. Also use it proactively before first analysis of a newly created group — default thresholds are guesses, ATR-derived ones are calibrated.

Tune Thresholds

Livermore's column transitions depend on swing and confirm thresholds. If these are wrong, everything downstream is wrong — the engine either whipsaws on noise (thresholds too tight) or sleeps through real moves (thresholds too loose). ATR (Average True Range) measures a stock's actual daily volatility, giving us an objective basis for setting these values.

The script

scripts/atr.py computes ATR for each leader in a group and suggests swing_pct / confirm_pct values. It does not modify any files.

./run .claude/skills/tune-thresholds/scripts/atr.py ~/.lafmm/data/semis

./run .claude/skills/tune-thresholds/scripts/atr.py ~/.lafmm/data/us-indices --period 20

./run .claude/skills/tune-thresholds/scripts/atr.py ~/.lafmm/data/energy --multiplier 2.0

Options:

• --period N — ATR averaging window in trading days. Default: 14. Shorter periods (7-10) react faster to volatility changes. Longer periods (20-30) smooth out spikes. 14 is the standard starting point.
• --multiplier X — How many ATR% to use as the swing threshold. Default: 1.5. At 1.0× the swing equals normal noise — any average day could trigger a transition. At 2.0× only unusually large moves register. 1.5× is a reasonable middle ground. Adjust based on how sensitive you want the system to be.

How ATR works

True Range for a single day = the largest of:

• high − low (intraday range)
• |high − previous close| (gap up that retraced)
• |low − previous close| (gap down that retraced)

ATR = simple average of True Range over the last N days.

ATR% = ATR ÷ current price × 100. This normalizes across price levels — a $5 ATR on a $100 stock (5%) means more than a $5 ATR on a $500 stock (1%).

The script uses the higher of the two leaders' ATR% values. This ensures the threshold isn't too tight for the more volatile leader, which would cause false column transitions from its normal daily moves.

How to use the output

The script prints current vs suggested values. Present this to the user and ask before changing anything.

If the user agrees, update group.toml:

swing_pct = 8.7
confirm_pct = 4.4

Key Price thresholds are always 2× the stock thresholds — this is hardcoded in the engine and does not need manual adjustment.

When the defaults are wrong

• Threshold barely exceeds ATR%: the script warns about this. The engine is treating normal noise as signal. Expect frequent whipsaws between NR/NREAC columns with few confirmed UT/DT entries.
• Threshold far exceeds ATR% (>2.5× ratio): the engine is too sluggish. Real trend changes are absorbed as continuation within the current column. The stock could reverse 15% before the engine notices.
• Leaders have very different volatility: one leader is calm, the other is wild. The script uses the higher ATR% — but consider whether these two stocks actually belong in the same group. Livermore paired leaders that moved similarly.

Choosing the period and multiplier

The defaults (period=14, multiplier=1.5) work for most US large-cap equities. Adjust when:

• Small-caps or high-beta stocks: consider --period 10 (faster response) or --multiplier 1.2 (tighter threshold) — these stocks trend faster and need quicker detection
• Stable sectors (utilities, staples): consider --period 20 and --multiplier 1.5-2.0 — these stocks have lower volatility and you want to filter out more noise
• During market stress (VIX > 30): volatility spikes inflate ATR temporarily. Consider using a longer period or waiting for volatility to normalize before tuning

There is no universally correct answer — these are judgment calls. The script gives you the data. You decide.