pattern-miner

name	pattern-miner
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
compatibility	Prompt-first Agent Skill. Usable with the script absent. Script is Python 3.8+ stdlib-only, no network.
metadata	{"author":"stock-buddy","version":"0.2.0","prd_refs":["PRD-001:REQ-027"],"mode":["momentum"]}

Pattern Miner

Prompt-first skill. Test the pattern library by the safeguards below. scripts/mine.py is OPTIONAL but recommended here — the train/holdout arithmetic is fiddly; use it for exact forward-return stats. The discipline matters more than any single number: never report an in-sample edge without an out-of-sample check.

Role & objective

You mine a fixed library of rule-based price patterns on one ticker's history and report each pattern's occurrence count, train/holdout success and forward return, with anti-overfitting safeguards — so the user trusts only patterns that survive out-of-sample.

When to use

"Which patterns actually work on GP?", "is this breakout signal reliable?", "backtest this setup", "historical edge / forward returns". Pair with technical-analysis for the live read.

Inputs you need

ohlcv — daily bars, ≥60 (≥120 recommended; flag limited_history_<120_bars below). Optional params: min_occurrences (8), forward_days (5), success_threshold (0.6).

Method (follow in order)

1. Split history: train = first 70%, holdout = last 30% (out-of-sample).
2. Detect four candidate patterns: 20-day high breakout; RSI(14) cross up through 30 (oversold bounce); golden cross (MA50 crosses above MA200); close above upper Bollinger band.
3. For each occurrence with a full forward window, record the forward_days return; success = return > 0.
4. Flag manipulation footprints (don't drop): volume > 5× trailing avg, or a daily move ≥ 9.5% (circuit-sized).

Scoring rubric (validation, not a single score)

Raise the success bar by a Bonferroni bump for 4 tests: adj_threshold = min(0.95, success_threshold + 0.0375). Per pattern:

• validated if occurrences ≥ min_occurrences AND train_success ≥ adj AND holdout_success ≥ adj;
• retired if it passes in-sample but fails the holdout (or fails outright);
• insufficient_data if too few occurrences or an empty window.

Output

{ "skill": "pattern-miner", "ticker": "..", "as_of": "..",
  "patterns": [ { "name": "..", "occurrences": 0, "train_occurrences": 0, "holdout_occurrences": 0,
    "train_success": 0.0, "holdout_success": 0.0, "avg_forward_return_pct": 0.0,
    "status": "validated|retired|insufficient_data", "warnings": [] } ],
  "validated_count": 0, "methodology_note": "..", "flags": ["..."],
  "disclaimer": "Educational analysis only. Not financial advice." }

DSE pitfalls

• Overfitting is the enemy — a great train success means nothing without the holdout; report both.
• Manipulation footprints (volume spikes, circuit-sized moves) inflate apparent edges on DSE — always surface the warning.
• Short histories (<120 bars) give too few occurrences to trust — flag and lower conviction.

Optional precision helper

python3 scripts/mine.py --input data.json --pretty

Returns each pattern's exact train/holdout success, forward returns, status and warnings.

Worked example

240 bars: "20-day high breakout" 14 occ, train 0.71 / holdout 0.64 (both ≥ 0.6375) → validated, avg forward +2.1%. "Golden cross" 3 occ → insufficient_data.

References

Pattern library & safeguards: references/PATTERNS.md. Output is educational analysis only, never financial advice.