Teaching the Network to See Tops — Sell Pivots on BTC, One Change and Eighteen Training Runs

The bottom-finding network that powers our buy-only bot was rebuilt for the other side of the market with exactly one change: the label is now a 2.3% top zone instead of a bottom zone. Same canvas, same split, same eighteen training runs with different loss functions. Tops turned out to be rarer and harder: on validation only 62 of 5,265 ten-minute steps sit in a top zone. Plain losses reached an F1 of 0.047–0.084 against a base of 0.023; the "ladder" target that accepts a two-hour neighbourhood reached 0.19–0.21 against 0.13. Fine-tuning further did not help, and one loss never fired at all.

① One change: the label is a top

The study was asked to repeat every step of the bottom work "as precisely as possible, changing one thing". So the canvas — 1,100 records of 100 seconds, 8 order-book features, about 30 hours — is byte-for-byte the input of the bottom network. The split is the same: training 21.02–30.06 (15,878 steps every 10 minutes), a 200-hour gap, held-out 08.07–12.08 (4,445 steps), validation 15.08–21.09 (5,265 steps), all BTC.

The label mirrors the bottom label. A top is a 2.3% zigzag pivot where at least one of its two legs lasts 7 hours or more; the zone is ±5% of the incoming leg's duration around it, and only where price is within 10% of the leg's move from the top. Training has 66 tops (279 steps in zone); held-out 6 tops (123 steps); validation 12 tops (62 steps). That is about one step in eighty-five on validation — a very thin target.

② Eighteen runs: plain losses barely see tops, the ladder sees more

The networks are trained from scratch with four losses — binary cross-entropy (`bce`), positively weighted (`pos`), `focal` and `dice` — and then fine-tuned from the chosen `bce` epoch with distance-aware losses: `far`, `ladder`, and `ladder2`, which counts a hit anywhere within ±2 hours of the zone, with ten different "silence" weights. The epoch is picked on held-out data by F1 at the best threshold.

Among plain losses, `pos` is best both on held-out (F1 0.141, base 0.054) and on validation: average precision 0.056 against a zone frequency of 0.012, AUC 0.787, F1 0.084 — 3.7 times the always-yes answer, but it still raises 800 alarms on validation for 62 zone steps. The `ladder2` family reaches F1 0.187–0.214 on validation with precision 0.15–0.19: about one alarm in six lands within two hours of a top. Those numbers are measured on the wider ±2-hour target, so they are not directly comparable with the plain ones.

③ What did not work — and why that matters for the bot

The study's own verdict is blunt. The top-ranked run on held-out, `l3`, is its epoch 0 — the weights it started from (`ladder2 ×10`, epoch 25). Training it further on a ladder of 2 to 37 steps changed nothing. `far` likewise stayed on its starting point. `dice` produced no signal at all on held-out or validation. The useful information came from the first two rounds.

Tops are harder than bottoms here for a simple reason visible in the data: there are fewer of them that last, and validation, a month in which BTC rose about 29%, had only twelve. A network that is right one time in six still needs a filter before it can trade. That filter is the subject of the next two articles: instead of trusting one threshold, the sell signal becomes a sum of probabilities over a window, and the window and threshold are picked where the result is stable, not where it peaks.

Reproduce this study

• Research log (.md, Ukrainian): goal, data, plan, scripts, every confirmed stage and table — enough to rerun the study
• Reproduction kit (.zip): the study's scripts, project rules and base scripts that build every class

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