Articles on crypto market intelligence, trading APIs, AI agents, derivatives data, and building algorithmic trading systems.
Options traders watch dealer gamma to know when the market amplifies a move or mean-reverts. Perps have no options chain — so we built the analog from market-maker inventory and on-chain liquidation density.
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Exchange-deposit alerts lag the decision. On Hyperliquid the positions are on-chain, so you can read the live margin book of every $100k+ account — net bias, conviction, and the coins whales hold most.
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An aggregate long/short ratio hides who is actually positioned. This endpoint splits every coin's book by trader type — market maker, whale, and everyone else — so you know whose money is on each side.
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Fanning out two calls per coin across 200+ perps is slow and rate-limit-hungry. This endpoint batches the whole universe's risk model — regime, liquidation risk, volatility and sizing — into one response.
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Would you let a black box size your trades? The model-card endpoint exposes exactly what the regime engine is, how it validated, and whether it's drifting — so your agent can decide whether to trust today's call.
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Most 'historical regimes' are relabeled with hindsight — which quietly poisons any backtest. This timeline is labeled by the same model that runs live, fold-honest, every day since 2019.
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The price history that trains serious quant models isn't behind a paywall — it's a free, checksummed archive on Binance's own CDN. Here's how to pull years of perp klines and funding in an afternoon.
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You can't backtest a regime strategy without knowing the past regime at every hour. Now you can download it — the full 6-regime market history, hourly since 2020, with now/4h/24h probabilities, in one Parquet.
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Most regime tools hand you one label and hide the uncertainty. The quant engine returns a calibrated probability across six market states every hour — so your bot knows the difference between 'definitely ranging' and 'a coin-flip between range and breakout'.
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Price is what you see; depth is what you actually get filled at. This feed exposes live order-book liquidity — depth within bps of mid, spread, and imbalance — so your agent sizes orders the book can actually absorb.
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A point forecast tells your bot nothing about tail risk. The quant engine runs 1,000 Monte Carlo paths off the live regime every hour and returns the full distribution of tomorrow — including the odds of a 5% drawdown.
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Knowing today's regime is table stakes. The quant engine also returns the probability of every transition tomorrow plus six conditional forecasts — direction, volatility, funding, liquidation risk, OI and breadth.
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