An analytical model for long/short equity decisions, built with the methodological rigour of the top quant funds. Rigorous methodology, observable signals, no black box, the system flags the setups — you operate.
Below: the operating system, in real time. Auto-refreshes every 10 minutes. SIGNALS for active setups, MACRO for the broader market context.
| TICKER | NAME | EDGE ↓ ? | R/R ? | SIGNAL ? | MACRO ? | TRIGGER ? | STOP LOSS ? | TAKE PROFIT ? |
|---|---|---|---|---|---|---|---|---|
| Connecting to markets… | ||||||||
| SECTOR | ETF | CONTEXT ? | REL.STR 20d ? | % CHG 20d |
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nanoHedgeLABS continuously scans a curated universe of US and Italian equities. Every trading day, every ticker is re-evaluated through a disciplined methodology that combines four guiding principles and five recognisable market patterns. Nothing opaque: each piece is named and observable.
Within those four principles, the system watches for five recurring market situations. None is sufficient on its own — conviction comes from how they combine.
A trader who is right sixty per cent of the time, but whose winners are the same size as their losers, makes barely enough to cover transaction costs. A trader who is right thirty per cent of the time, but whose winners are three times their losers, ends the year ahead.[a]
The second trader trusts asymmetry instead of frequency — and that is harder, because being wrong seven times out of ten is psychologically uncomfortable, even when the maths are working.
The system that follows is built around that single observation. Its purpose is not to win more often than the market — it is to make sure that when it wins, it wins more than it loses when it's wrong, and that the difference is reliable enough across regimes to survive a bad month.
Equation (1) is the entire ranking machinery, simplified. α(i) is the per-ticker asymmetry score in [0, 100]; w is a seven-dimensional weight vector under the operator’s control (the sliders in the terminal sidebar); 𝟙(triggers) is the indicator of which patterns are currently firing. The result is then thresholded into three bands — HIGH · MEDIUM · LOW — and that is what the operator sees.
Kα and Kw are fixed structural constants — calibrated once, never exposed.
The four principles that follow are how that single observation translates into a daily, mechanical scan of the market.
Every system decision was backtested across six years of market data, with realistic transaction costs and slippage. What follows is what we measure, not what we promise. Hover the "?" next to each figure for what it actually means.
Figures aligned with the average of quantitative long/short equity funds. No management fees, no lock-up, no black box.
Order-of-magnitude estimates from the 6-year backtest and the walk-forward analysis. These are not promises.
Market conditions move the outcomes.
A note on sizing. —
A curated sample of ten real setups from the backtest — three currently open, four reached their target, two were stopped out, one timed out. Nothing idealised: what the system saw, where it suggested exiting, what actually happened.
Transparency note. Showing real setups with their full life-cycle — entry, evolution, exit — is a commitment most quant operators avoid. It means publicly carrying the wins and the losses. These ten sit here exactly as the backtest produced them.
Sample drawn from the full 12,365-trade backtest (2020-2026). Typical holding horizon: ~20 trading days, with a hard timeout at 60 trading days. The aggregate statistics — Profit Factor, Sharpe, hit rate — live in §05.
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