nanoHedgeLABS scans a vast equity universe, ranks long/short setups and keeps market context and risk beside every result. One transparent research terminal.
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This is a research screen, not a trading screen. It refreshes every 10 minutes. Start with SIGNALS: each row is a model classification, shown with its trigger, Edge band, R/R — Reward / Risk, a measured comparison between space to the first objective and distance to invalidation — macro context and technical references. Use MACRO to understand the broader context around those rows. A row helps you inspect what the model observed; it never tells you what to do.
| TICKER | NAME | EDGE ↓ ? | R/R ? | CLASSIFICATION ? | MACRO ? | TRIGGERS ? | INVALIDATION ? | 1ST OBJECTIVE ? |
|---|---|---|---|---|---|---|---|---|
| Connecting to markets… | ||||||||
| SECTOR | ETF | CONTEXT ? | REL.STR 20d ? | % CHG 20d |
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The nanoHedgeLABS team has defined a curated equity universe. Every ten minutes, the system applies the same technical and macro analysis to every covered ticker, then reduces that work to a focused research list. It identifies and ranks market configurations for further inspection; it does not recommend, open or manage a trade.
The flow above is the compact version. These six notes explain what the model examines at each stage, how the stages connect and why none of them is a standalone instruction.
The process begins with a consistent daily record: two years of candlesticks for every covered ticker. From that record the model derives price behaviour, moving averages, range, volume structure and other measurements. Input is collection and normalization, not a verdict on the name.
Every instrument passes through the same preparation path. The engine checks the sequence, aligns the observations and turns raw OHLCV data into comparable features so that later stages evaluate like with like.
The weekly view establishes the broader structure before the daily chart is assessed. BULL, BEAR and NEUTRAL describe whether the larger path is supportive, adverse or mixed, so short-term observations are read within a defined market regime rather than in isolation.
This is the directional gate. It does not predict the next candle; it establishes whether the sequence of swings and long-horizon references is rising, falling or unresolved before the model looks for daily evidence.
T1–T5 are nanoHedgeLABS’s in-house intelligence layer. These proprietary rule families look for specific relationships between price, structure, volatility and volume that merit a structured review. They initiate research; they do not, alone, forecast an outcome or issue an instruction.
Each family answers a different structural question. The model records which conditions are present, then carries that evidence forward; a trigger can activate without producing any final classification.
Once the evidence is present, the model maps the technical invalidation and first objective. Their measured distance creates R/R—Reward / Risk—which compares the observable space to the first objective with the distance to invalidation. These are references for inspecting the configuration, not personal stop or target instructions.
The ratio makes unlike price configurations comparable. A larger upside path is not automatically stronger: the distance required to invalidate the underlying structure is measured at the same time and remains visible beside the result.
The technical map is then read through the market environment: rates, liquidity, breadth, stress, inter-sector relationships and major indicators. Context can strengthen, constrain or leave unchanged the technical evidence; it is not a probability and does not replace the underlying structure.
These inputs are considered together rather than as isolated headlines. Their job is to test whether the surrounding market is aligned with the technical configuration and to inform the final Edge band without erasing the evidence produced earlier.
Only after the preceding stages are combined does the terminal display LONG, SHORT or WATCH, together with the evidence behind that label. It is a research classification for inspection, never an order, recommendation or position-sizing instruction. No active row simply means no current classification.
The output is therefore a compact audit trail, not a single score. Direction, active triggers, technical geometry and macro context remain beside the label so a reader can see what the model observed and where the configuration would cease to fit.
Everything above explains how a ticker reaches the research list. The guide below explains how that result is displayed on screen. It is a reading guide for the terminal, not another stage of the model.
Each row keeps a model classification beside the evidence that produced it. Begin with the instrument and classification, then verify Edge, measured geometry, macro context and active triggers. The technical boundaries complete the explanation: the label alone is never the full output and never an instruction.
Before reaching the final model, candidate parameters—including the 80/100 thresholds used in the model, trigger-activation rules and their calibrations—were repeatedly tested on the historical sample. The documented configuration was then applied systematically across six years and 12,374 simulated observations, including transaction costs and slippage. Profit Factor, Sharpe Ratio and hit rate describe how that configuration behaved in the test; they are not a live promise.
Figures aligned with the average of quantitative long/short equity funds. No management fees, no lock-up, no black box.
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