| compatibility | Python 3.8+, standard library only. No network. No third-party packages. |
| description | Discovers and out-of-sample validates recurring price patterns for one DSE ticker with anti-overfitting safeguards (train/holdout split, minimum occurrences, Bonferroni multiple-testing correction, auto-retirement of degraded patterns, manipulation-footprint warnings). Use when the user asks to mine, backtest, or validate chart patterns/setups, "which patterns actually work on this stock", "is this breakout signal reliable", or wants historical edge and forward-return statistics for a Dhaka Stock Exchange ticker. |
| license | Apache-2.0 |
| metadata | {"author":"stock-buddy","github-path":"skills/pattern-miner","github-pinned":"main","github-ref":"refs/heads/main","github-repo":"https://github.com/kuntal-r-d/my-skills","github-tree-sha":"96eeeb86c9ab165374269ac34604f1aaa6dd487c","mode":["momentum"],"prd_refs":["PRD-002:REQ-107","PRD-001:REQ-052"],"version":"0.1.0"} |
| name | pattern-miner |
Pattern Miner
When to use
Activate when a user wants to know which rule-based price patterns have a validated edge
on a specific DSE stock: "mine patterns for GP", "backtest the 20-day breakout on BEXIMCO",
"does the oversold RSI bounce work here?", "which of my setups survive out-of-sample?".
This is the Pattern Miner with anti-overfitting (PRD-002 REQ-107; PRD-001 REQ-052).
What it does
Tests a fixed library of rule-based candidate patterns and reports only those that survive
the safeguards in references/PATTERNS.md:
- 20-day high breakout — close makes a new 20-bar high.
- RSI oversold bounce — RSI(14) crosses up through 30.
- Golden cross — MA50 crosses above MA200.
- Upper-Bollinger close — close above the upper 20/2 Bollinger band.
For each pattern it finds every historical occurrence, splits the series into a train
window (first 70%) and an out-of-sample holdout window (last 30%), and computes the
forward return over forward_days and the success rate (P(forward return > 0)) on each
window. A pattern is validated only if total occurrences ≥ min_occurrences and the
success rate clears success_threshold on both train and holdout. Degradation on the
holdout triggers retired (auto-retirement). A Bonferroni correction tightens the
threshold by the number of patterns tested. Occurrences with volume > 5× average or a
circuit-limit-sized move raise a manipulation_footprint warning.
How to run
python3 scripts/mine.py --input data.json --pretty
cat data.json | python3 scripts/mine.py
Reads ohlcv (required, ≥120 bars recommended) and optional params
(min_occurrences default 8, forward_days default 5, success_threshold default 0.6).
Indicator math lives in scripts/indicators.py.
Interpreting output
Returns patterns[] each with occurrences, train_success, holdout_success,
avg_forward_return_pct, status (validated / retired / insufficient_data), and
warnings[]; plus validated_count, a methodology_note, and flags[]. If nothing
survives, validated_count is 0 — the skill is deliberately honest about a null result.
Educational analysis only, never financial advice.
