| 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)
- Split history: train = first 70%, holdout = last 30% (out-of-sample).
- 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.
- For each occurrence with a full forward window, record the
forward_days return; success =
return > 0.
- 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.
