Hyper-Gemma AI Trader: Autonomous Bitget Futures Execution with Trinity v2 Quant Engine

1. The Perils of LLM Hesitation in Futures Trading

In hyper-liquid cryptocurrency derivative markets like Bitget Futures, execution speed and statistical certainty are paramount. Traditional attempts to integrate Large Language Models (LLMs) directly into high-frequency order execution loops quickly encounter fatal obstacles: token-generation latency ranging from 400ms to 3,000ms, non-deterministic decision drift, and hallucinations during sudden volatility spikes.

When a leveraged perpetual futures contract faces sudden liquidation cascades or funding rate anomalies, an automated execution engine cannot afford cognitive hesitation or probabilistic prose generation. This fundamental constraint catalyzed the evolution of Hyper-Gemma AI Trader into the Trinity v2 Quant Engine: an autonomous architecture where mathematical discipline supersedes generative AI.

2. The Pure Quant Tactical Engine (Quant Trinity)

Trinity v2 operates on a Pure Quant Tactical paradigm where four deterministic mathematical models act as the supreme decision authority without human or generative interference:

• Rolling Z-Score: Computes standardized price displacement (Z = (Pt - μ) / σ) across configurable sliding windows to pinpoint statistical overextension and mean-reversion extremes.

• Hurst Exponent (H): Dynamically determines current market fractal memory via Rescaled Range (R/S) analysis. H < 0.5 designates mean-reverting regimes (enabling statistical scalp entries), H > 0.5 signals persistent trending momentum (enabling trailing breakouts), and H ≈ 0.5 flags untradable random walks.

• Volume-Weighted Average Price (VWAP): Establishes dynamic institutional liquidity equilibrium, ensuring orders are never dispatched into adverse liquidity vacuums.

• 1D Kalman Filter: Continuously filters out microstructure high-frequency noise from raw tick feeds to estimate the true unobserved price state and velocity vector.

interface TacticalState {
  regime: "MEAN_REVERTING" | "TRENDING" | "RANDOM_WALK";
  zScore: number;
  filteredPrice: number;
  action: "LONG" | "SHORT" | "HOLD";
  confidence: number;
}

export class KalmanPriceFilter {
  private estimate: number;
  private errorEstimate: number = 1.0;
  private readonly processNoise: number;
  private readonly measurementNoise: number;

  constructor(initialPrice: number, processNoise = 1e-4, measurementNoise = 1e-2) {
    this.estimate = initialPrice;
    this.processNoise = processNoise;
    this.measurementNoise = measurementNoise;
  }

  public update(measurement: number): number {
    this.errorEstimate += this.processNoise;
    const kalmanGain = this.errorEstimate / (this.errorEstimate + this.measurementNoise);
    this.estimate = this.estimate + kalmanGain * (measurement - this.estimate);
    this.errorEstimate = (1 - kalmanGain) * this.errorEstimate;
    return this.estimate;
  }
}

export class TrinityTacticalEngine {
  public static computeZScore(prices: number[], window = 20): number {
    if (prices.length < window) return 0;
    const slice = prices.slice(-window);
    const mean = slice.reduce((a, b) => a + b, 0) / window;
    const variance = slice.reduce((acc, p) => acc + Math.pow(p - mean, 2), 0) / window;
    const stdDev = Math.sqrt(variance);
    return stdDev === 0 ? 0 : (prices[prices.length - 1] - mean) / stdDev;
  }

  public static computeHurst(prices: number[]): number {
    if (prices.length < 20) return 0.5;
    const returns = prices.slice(1).map((p, i) => Math.log(p / prices[i]));
    const n = returns.length;
    const mean = returns.reduce((a, b) => a + b, 0) / n;
    const deviations = returns.map((r) => r - mean);

    let cum = 0;
    let max = -Infinity;
    let min = Infinity;
    for (const d of deviations) {
      cum += d;
      if (cum > max) max = cum;
      if (cum < min) min = cum;
    }
    const stdDev = Math.sqrt(deviations.reduce((acc, d) => acc + d * d, 0) / n) || 1e-8;
    return Math.min(Math.max(Math.log((max - min) / stdDev) / Math.log(n), 0), 1);
  }

  public static evaluateMarket(prices: number[], filter: KalmanPriceFilter): TacticalState {
    const currentPrice = prices[prices.length - 1];
    const filteredPrice = filter.update(currentPrice);
    const zScore = this.computeZScore(prices, 20);
    const hurst = this.computeHurst(prices);

    let regime: TacticalState["regime"] = "RANDOM_WALK";
    let action: TacticalState["action"] = "HOLD";

    if (hurst < 0.45) {
      regime = "MEAN_REVERTING";
      if (zScore <= -2.0) action = "LONG";
      else if (zScore >= 2.0) action = "SHORT";
    } else if (hurst > 0.55) {
      regime = "TRENDING";
      if (zScore > 1.0 && currentPrice > filteredPrice) action = "LONG";
      else if (zScore < -1.0 && currentPrice < filteredPrice) action = "SHORT";
    }

    return {
      regime,
      zScore: Number(zScore.toFixed(2)),
      filteredPrice: Number(filteredPrice.toFixed(2)),
      action,
      confidence: Math.abs(hurst - 0.5) * 2,
    };
  }
}

3. De-biasing AI: The MOCK_AI=true Strategy

In the original Hyper-Gemma design, Google's Gemma 4 model was queried locally via Ollama to evaluate multi-candle sentiment and macro narratives. While Gemma produced articulate market rationales, latency benchmarks revealed that generating inferences added hundreds of milliseconds of overhead while introducing occasional hallucinated bias during flash crashes.

Trinity v2 fundamentally restructured this dynamic by decoupling AI into a passive background advisor. When configured with MOCK_AI=true, the execution pipeline entirely bypasses synchronous LLM roundtrips, allowing the bot to submit orders to Bitget within sub-millisecond windows. When AI is enabled, it acts solely as an asynchronous macro regime classifier, unable to override mathematically validated stop-losses or risk limits.

4. Production Telemetry & Bitget Futures Execution

The system is fully implemented in TypeScript running on Node.js, backed by MongoDB for persistent trade telemetry, position audit logs, and equity curve reconstruction. Order dispatching communicates directly with Bitget's Futures V2 API via secure HMAC-SHA256 authenticated REST and WebSocket channels.

Complete open-source code, setup instructions, and quant algorithms are available in the public repository: hyper-gemma-ai-trader on GitHub.