Qlib

Qlib (pip install pyqlib) is a quant research framework from Microsoft. It stores OHLCV in its own .bin format, builds features with an expression engine (Ref($close, 60) / $close), trains models from a zoo of baselines (LightGBM, transformers, and more), and runs portfolio-style backtests with its own analysis reports. It is built for research on many instruments over long histories.

What it adds to Moby. Cross-sectional research. Backtesting.py tests one token at a time; Qlib ranks a universe. Export candles for the Solana tokens your agent follows, convert them with scripts/dump_bin.py, and test whether a feature set predicts next-day returns across the group.

How to pair it. Pull get_token_ohlcv for a basket (your watchlist, the top of get_screener_leaderboard, or wallets' holdings from get_whale_wallet_holdings), write one CSV per token with date, open, high, low, close, volume, dump to Qlib format and run a workflow. Use the result to shape a Moby strategy, not to pick a trade. Moby returns candles newest-first; reverse them before you feed them in.

What it does not do. It does not trade and does not read Moby directly. Its data collectors, examples and benchmarks are for stocks; on short, thin memecoin histories a model will overfit, so keep it to tokens with weeks of candles. It is heavy to install (LightGBM, Cython) and the main branch has unreleased breaking changes; stick to the PyPI release.