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Five key points:
1. Orders arrive in clusters. Hawkes processes model how a trade can temporarily increase subsequent buying or selling activity, with that effect gradually decaying.
2. More activity doesn’t necessarily mean more fills for you. Your fill rate depends on both order-arrival intensity and the probability that an incoming order reaches your quote. Other market makers replenishing liquidity or changing prices can offset increased activity.
3. Quote safety and fair value are separate questions. A burst may justify canceling orders, moving quotes away, or reducing size without establishing that the market’s fair value has changed.
4. Cancellation latency matters. Even if you detect dangerous flow correctly, your exposed quote can still get filled before the cancellation takes effect.
5. Raw trade counts can exaggerate intensity. One aggressive order can generate multiple fills. In his Binance sample, 43.7% of aggregated trades contained multiple fills, accounting for 78.4% of raw fills.
1. Orders arrive in clusters. Hawkes processes model how a trade can temporarily increase subsequent buying or selling activity, with that effect gradually decaying.
2. More activity doesn’t necessarily mean more fills for you. Your fill rate depends on both order-arrival intensity and the probability that an incoming order reaches your quote. Other market makers replenishing liquidity or changing prices can offset increased activity.
3. Quote safety and fair value are separate questions. A burst may justify canceling orders, moving quotes away, or reducing size without establishing that the market’s fair value has changed.
4. Cancellation latency matters. Even if you detect dangerous flow correctly, your exposed quote can still get filled before the cancellation takes effect.
5. Raw trade counts can exaggerate intensity. One aggressive order can generate multiple fills. In his Binance sample, 43.7% of aggregated trades contained multiple fills, accounting for 78.4% of raw fills.
做市實戰筆記 #6：互相激發下的訂單流強度
AS 跟 GLFT 通常把限價單成交率轉換成訂單，一旦庫存偏了，報價就往減倉方向移動，看起來很完美。
但研究發現，當一筆有影響力的市價買單進來，後面可能拖著一串同方向的買單接踵而至
然而經典設定通常沒有把剛剛發生的市價單，當成接下來市價單會變密的直接原因。
Hawkes 過程補充說明事件發生後，會暫時提高後續事件的到達強度，如果沒有新的激發，多出來的強度再逐漸衰減，整個系統要穩定，衰減必須夠快，不能只有激發沒有消退。
Cartea、Jaimungal、Ricci 在 2018 年的論文，把這套結構接到訂單簿上，買賣市價單會成群到達，訂單簿形狀與短期價格漂移也跟著變。
自我激發是同側成群，互相激發則是兩側會影響彼此，買單進來，賣單的強度也可能跟著上升。
這裡有一個很重要的區分，市價單變多，不等於你的掛單一定更容易成交。論文把你自己的成交率拆成兩部分，市價單的到達強度，乘上這筆單進來後能不能打到你所在報價位置的條件機率，後者還受到訂單簿形狀影響。
一筆有影響力的市價單進來，可能讓後續市價單更密，但同時其他做市商也會補單、改價，讓原本那個價格位置更不容易被打到，一個往上，一個往下，最後成交率增加還是減少，不能只看到達強度。
這也是為什麼強度不能直接當成毒性。買賣兩側一起變密，可能只是短暫的交易活動增加
所以我想把兩個問題分開
第一個是這張限價單還安不安全，要不要撤掉、後退，或停止加倉
第二個是公允價有沒有真的改變，是否值得重新決定下一張單的中心價格，強度升高可以是第一個問題的警訊，但不直接回答第二個問題。
這也接回上一篇的報價過期問題。
就算判斷某一側正在成群到達，從看到事件到撤單真正生效還有一段延遲，舊報價可能已經被吃掉，預測到了卻來不及撤，和完全沒預測到，最終都可能是一次有毒成交。
我的 bot 先不把這個強度當成方向預測，而是把近期成群事件當作風控輸入，同側成交突然變密時，優先檢查正在暴露的掛單，對可能繼續增加風險的方向減少尺寸，必要時停止新增庫存。
公開資料裡看到的成交筆數，未必等於獨立市價單的數量，同一張主動單可能拆成很多筆成交，把每筆都當成一次獨立激發，強度就會被算得過高。
如下面這兩張圖在講同一件事：你在公開行情裡數到的「成交筆數」，常常不是「來了幾張獨立的市價單」。
左圖看的是，Binance 會把同一張主動單、同一個價位、短時間內打到的多筆成交，先包成一筆彙總成交。但 43.7% 的彙總裡其實都不止一筆，而且這些「一包多筆」就佔了全部原始成交的 78.4%。如果你把裡面每一筆都當成一次獨立事件，後面的到達強度會被算高。
右圖每個點是某一秒有多少成交，全體每秒大約是 1.75 倍，最忙的那 1% 秒更到 2.54 倍。流量越擠，這個落差越大，也正好是最容易把「一張單被拆成很多筆成交」誤看成「單在自我激發、愈來愈密」的時候。
強度告訴我，接下來可能有更多單要來，但那些單會不會打到我、成交後價格會往哪走，需要依照數據分析訂單簿
來源：Cartea, Jaimungal, Ricci (2018), Algorithmic Trading, Stochastic Control, and Mutually Exciting Processes, SIAM Review, 60(3), 673–703。Hawkes (1971), Spectra of Some Self-Exciting and Mutually Exciting Point Processes, Biometrika, 58(1), 83–90。View quoted post on X
Congrats on @IsleFinance team's recent achievement!
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As an early liquidity supporter of the Injective ecosystem, Anti Capital is proud to have been part of the journey. Excited to see $INJ reach a broader investor base!
As an early liquidity supporter of the Injective ecosystem, Anti Capital is proud to have been part of the journey. Excited to see $INJ reach a broader investor base!
Just the beginningView quoted post on X
