Skip to main content

Silicon Agents

Silicon Silicon | Technical Analysis Swarm​

Silicon is the base material of modern computing: it turns raw electrical noise into precise, structured signals.
In QQ Omega, Silicon plays a similar role for price action: it takes noisy OHLCV data and organizes it into clear views on trend, momentum, volatility and key levels.

What Silicon Is​

The Silicon element focuses on technical and quantitative market structure:
how price moves, where liquidity is concentrated, and when volatility and momentum regimes are changing.

Some agents specialize in anchored levels like VWAP, where large players often benchmark executions.
Others map structural levels like Fibonacci retracements and extensions, highlighting where trends tend to pause, reverse or accelerate.
Another group builds supply & demand maps from raw price and volume, identifying areas where aggressive buying or selling concentrated in the past.

Silicon blends multiple schools of technical analysis — trend-following, mean reversion, price action, volume/volatility analytics — into a unified, machine-readable structure that the rest of QQ Omega can consume.

Core Dimensions Silicon Covers​

Across the swarm, Silicon continuously evaluates and updates a set of technical & quant dimensions that combine the three major schools of analysis: price action, volume and volatility.

  • Price Action Structure – trend, levels, geometry of the move
    This layer organizes raw OHLC data into a clear structural map:

    • Market structure: higher highs / higher lows vs. lower highs / lower lows, break of structure, range vs. trend identification across multiple timeframes.
    • Support & resistance: construction of key levels from repeated rejections, closes, and failed breaks, including classic horizontal SR and supply/demand zones.
    • Supply & Demand zones: zones where strong directional moves originated (imbalance candles, long wicks, gaps), classified by timeframe and strength to detect areas where large players previously stepped in.
    • Fibonacci mapping: retracements and extensions applied to significant swings and macro legs to highlight statistically common pullback areas and target zones, especially where they align with supply/demand and SR.

    The output is a multi-timeframe price action map: trend state, critical levels, and high-interest areas where the market is most likely to react.

  • Volume & Liquidity Footprint – who is actually trading where
    This layer focuses on how much participation exists and where liquidity concentrates:

    • VWAP & anchored VWAP: session and event-anchored VWAPs (e.g. listing, capitulation, breakout) to locate “fair value” zones and premiums/discounts where mean reversion or continuation becomes more probable.
    • Volume profile & node analysis: identification of high-volume nodes (HVNs) and low-volume nodes (LVNs) to map acceptance areas vs. rejection/imbalance areas, often overlapping with price action levels.
    • Volume-flow dynamics: comparison of rising/falling price vs. rising/falling volume, activity spikes on breaks and retests, and detection of fakeouts where volume doesn’t confirm the move.
    • Liquidity concentration: where trading activity clusters over time (range centers, breakout areas), building a liquidity map that complements supply/demand and SR.

    The goal is to understand where real commitment sits and whether current moves are backed by meaningful participation or just thin liquidity.

  • Volatility & Regime – how violently and cleanly price moves
    This layer classifies the “air quality” of the chart in terms of noise vs. opportunity:

    • Realized volatility & range analysis: ATR-style measures, average candle size, intraday range and gap behavior to determine if the market is compressed, balanced, or expanding aggressively.
    • Volatility clusters & squeezes: detection of prolonged low-volatility phases (compression) and subsequent expansions (breaks), used to flag potential breakout conditions vs. mean-reversion zones.
    • Trend vs. mean-reversion bias: volatility pattern + structure analysis to identify whether the environment favors breakout/trend strategies or fade/mean-reversion approaches.
    • Cleanliness of moves: presence of whipsaws, overlapping candles and erratic spikes, which degrade signal quality and increase execution risk.

    This produces a regime tag for each asset and timeframe: calm / compressed, orderly trending, choppy rotational, or chaotic, directly impacting how much weight Silicon’s signals should carry.

Combined, these dimensions give a unified, multi-timeframe view of price action (structure & levels), volume (participation & liquidity) and volatility (regime & risk).
Silicon doesn’t just say where price has been — it defines where the important battles have been fought, how many players showed up, and how dangerous it is to step in right now.

How Silicon Connects to the Rest of QQ Omega​

Silicon swarm forms the signal & execution layer of QQ Omega’s scoring and decision system.
It does not decide whether a project is fundamentally good or bad — it decides how clean or hostile the chart is for acting on a thesis.

It ensures the system always maintains a structured view of:

  • whether price action confirms or contradicts fundamentals (Carbon) and tokenomics (Gold),
  • where key levels and liquidity zones are, across multiple timeframes,
  • whether current conditions favor trend-following or mean reversion,
  • how volatile and orderly the market is for a given asset right now.

Other swarms use Silicon’s signals to:

  • time entries and exits within broader macro and fundamental views,
  • calibrate risk (position sizing, stop placement, invalidation levels),
  • de-prioritize setups where charts are structurally noisy, illiquid or technically hostile.

Silicon is where abstract conviction is forced to confront the chart: if a trade idea can’t survive the technical reality, it doesn’t move forward.