Orderbook feature pipeline and strategy#81
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Polymarket orderbook data pipeline (dataset/polymarket_orderbook/): end-to-end pipeline to collect and featurise Polymarket orderbook data. Three stages orchestrated by run_pipeline.py:
Parser — converts polymarket_markets_1y.jsonl → markets.parquet / tokens.parquet / filtered_token_ids.parquet, filtering to the top N markets by volume
Fetch — async fetcher that hits the Dome API to collect raw orderbook snapshots per token, stored as raw_orderbook/ob_<token_id>.parquet (alternatively can download a precollected set of orderbook data from google drive)
Feature generation — builds feat_orderbook/feat_<token_id>.parquet per token with snapshot-level features: best bid/ask, spread, depth (top-3 and total), order imbalance, mid-price deltas, and time-to-expiry
OrderbookAlpha (alpha/OrderbookAlpha.py): IAlpha implementation that time-aligns the pre-computed orderbook snapshots to OHLCV candles via a backward merge_asof.
OrderbookImbalanceStrategy (strategy/OrderbookImbalanceStrategy.py): baseline trading strategy using OrderbookAlpha.