Tops Hide, Bottoms Don't: an Asymmetry We Did Not Expect

This is the mirror of the previous study: the fall from a top to a bottom, 27,180 windows, the same pipeline to the letter. The structure repeats — and one number does not. Tops hide from the order book four times more often than bottoms do.

Full walkthrough — streamed from YouTube.

① The mirror image of the previous study

The zigzag with a 2.3% threshold cuts the price into legs: every leg runs from one turning point to the next. Take only the falling half — the turn itself (±15 minutes around the pivot) plus the whole leg that follows it — and cluster only those windows, with the feature scale recomputed on this subset so that nothing from outside leaks in.

| the set | value | |---|---| | windows of 210 s | 27,180 | | reversal windows in them | 431 (1.59%) | | legs of the zigzag in the dataset | 120, average 33.9 h |

One objection has to be said out loud before any number: the zigzag knows the future. A leg is only a leg once the next turn has happened. So a class found inside one half of a move is not, by itself, something a live bot can use — it first has to know which half it is in. We measure that separately, and the answer is that the book barely knows.

Everything in this series feeds one goal: an automated trading bot that reads the order book instead of the price. Which is why this study is a measurement, not a strategy: it tells us how much of the structure we see is really about the direction of the move.

② The structure repeats exactly

Same result as on the whole dataset, and that is the point of running it: the split does not change when we restrict it to one half of the move.

| split | classes | the reversal-rich one | reversals in it | versus base | |---|---|---|---|---| | k-means | K=2 | 6,928 windows (25%) | 312 | ×2.84 | | HDBSCAN | 5 + noise | 6,335 windows | 273 | ×2.72 |

The quiet classes hold almost no reversals at all, the noise holds 156 of them at ×0.50. Stability is 0.764 against 0.207 for the hard null.

Everything in this series feeds one goal: an automated trading bot that reads the order book instead of the price. A filter that works the same way in both halves of a move is a filter that does not need to know the direction — which is the only kind we can put in a bot.

③ Counted as turning points

Counting reversal windows is misleading on its own. Windows step every 210 seconds, so up to nine of them fit into the ±15-minute band around a single pivot: one cluster of windows easily looks like nine separate events. So everything gets recounted per turning point, with the metric being the share of a turn's windows that a class holds — 1.0 if the turn lies in it entirely, 0.03 if it holds one window out of thirty.

| | windows | turning points touched | where it holds the largest share | turns lying in it entirely | |---|---|---|---|---| | noise | 156 | 43 | 22 | 3 | | class 0 | 0 | 0 | 0 | 0 | | class 1 | 0 | 0 | 0 | 0 | | class 2 | 0 | 0 | 0 | 0 | | class 3 | 273 | 51 | 33 | 12 | | class 4 | 2 | 2 | 0 | 0 |

431 reversal windows belong to 55 distinct turns — 7.84 windows per turn. The noise holds an average share of 0.365 of a turn.

Everything in this series feeds one goal: an automated trading bot that reads the order book instead of the price. For a bot this is the difference between "we catch 200 signals" and "we catch 30 events, several times each" — and only the second number is real.

④ The asymmetry: a top is a quieter event

Everything about the two halves matches: the same K, nearly the same class sizes, nearly the same enrichment of reversals. The asymmetry is in what the clustering fails to place.

| | bottoms (rising half) | tops (falling half) | |---|---|---| | average share of a turn's windows landing in noise | 0.092 | 0.365 | | turns with any window in noise | 12 of 54 | 43 of 55 | | turns with half or more in noise | 5 | 22 | | turns lying entirely in noise | 0 | 3 |

At a bottom the book always says something: not one bottom disappears completely. At a top three do, and half the tops have most of their windows unplaced. A top, it seems, is more often a quiet event — buyers simply stop arriving — while a bottom is loud, because someone has to step in front of a falling price.

What is next. Before trusting any of this, it has to survive being rebuilt on different data — so the following study redoes the whole clustering week by week, 25 times from scratch, and compares each week with the global answer. Everything in this series feeds one goal: an automated trading bot that reads the order book instead of the price. And a bot that shorts tops on an order-book signal needs to know that its signal is weaker there, by design.

Comments

Discussion is powered by GitHub. Enable it by adding secrets/giscus.json (repo IDs from giscus.app).