GeoHorizon — The Hedge Fund Everyone Can Use:
AI-Powered Risk Protection Across Crypto and Traditional Markets

Every forecast cycle runs a three-agent adversarial debate. The Bull Advocate (temp 0.4) builds the strongest credible upside case with quantified catalysts, probability adjustments, and fear-opportunity scores. The Bear Risk Devil's Advocate (temp 0.3) maps tail risks, 3-step cascade pathways, structural vulnerabilities, and black swan flags. Both run in parallel. The Debate Arbitrator (temp 0.2) applies Bayesian synthesis: nets adjustments, caps at ±15pp per forecast, injects black swan entries when flagged, regresses extreme probabilities toward the mean, and produces a calibrated arbitration summary.

The EpisodeStore is a SQLite-backed episodic memory system that persists every forecast cycle as a structured episode: regime, keywords, top forecasts, actual outcomes (scored later), and strategy performance. Before each Forecaster run, the system computes TF-IDF cosine similarity between the current context and up to 500 historical episodes, applying a +0.15 bonus for regime matches. The top-3 most similar episodes are formatted and injected into the Forecaster's prompt — giving the LLM a working memory of what happened last time conditions looked like this.

Spawned automatically when the next FOMC meeting is within 7 days. A rates strategist persona trained on 30+ years of FOMC statements decodes policy language, models the Fed reaction function, and quantifies the probability of hawkish vs dovish surprises. Outputs precise BTC and ETH price impact estimates for each scenario, key watch phrases, and a specific positioning recommendation for the 48 hours surrounding the decision.

Activates when VIX exceeds 35 or when the Regime Agent classifies conditions as bear_trending or crisis. A former CIA analyst persona maps escalation and de-escalation trigger inventories, cites 2-3 historical precedents with quantified outcomes, assesses whether crypto will behave as a safe haven or risk-off asset in the specific crisis type, and outputs a concrete position sizing recommendation.

Activates when COIN, MSTR, MARA, NVDA, or MSFT have earnings within the current week. Models beat/miss/inline scenarios with historical probability estimates and quantified BTC impact ranges (e.g., COIN miss historically causes -6 to -12% BTC within 24 hours). Computes a symmetric volatility band addition to existing forecasts and provides a specific strategy recommendation for the 24 hours before and after the key earnings release.

At the start of every cycle, three intelligence modules run concurrently in a single async gather call before any agent crew starts: (1) Social Intelligence — Reddit sentiment across r/bitcoin, r/ethereum, r/CryptoCurrency, r/CryptoMarkets, r/solana + CryptoPanic news ranked by importance votes, combined social bias, trending narratives, and risk keyword detection. (2) On-Chain Intelligence — DeFiLlama global TVL and top protocols, Coinglass exchange flow analysis, liquidation heatmap cluster analysis, and whale transaction monitoring via Etherscan/blockchain.info. (3) Options Flow Intelligence — Deribit IV term structure (inverted/normal/humped), put/call ratio, 25-delta risk reversal skew, and gamma exposure with flip level computation. Results are injected into Sentiment (social) and Forecaster (onchain + options + specialist) context.

Runs once per 4h cycle. Reads per-(strategy, regime) win-rate breakdowns, flags gaps below 50%, and proposes a concrete improvement variant via LLM. Walk-forward analysis tests each variant across sliding unseen windows — training on one period, testing on genuinely out-of-sample data repeatedly. Promotion requires a 2-of-3 cross-timeframe gate and adversarial stress testing (40% floor across 5 scenarios: crisis, flash crash, liquidity crunch, high-vol, bear grind). Promoted variants blend into ensemble hybrids weighted by WF stability and TIMG confidence. Every decision generates a plain-English explanation surfaced on the dashboard. Live variants (capped at 3) inject into the Hedging agent's context as active sizing guidance. Auto-retire after 50 cycles.

Before the Hedging agent runs each cycle, the Unified Portfolio Engine computes a real-time blended snapshot of all open positions across crypto leverage, equity options, stock leverage, and live-test strategies. Net dollar delta, long/short split, per-strategy allocation, and unrealised PnL are calculated and injected as context. The engine actively flags conflicts: opposing directional positions on the same asset waste capital and are surfaced as amber alerts; single-cluster concentration exceeding $20k net exposure triggers a concentration warning. The Hedging agent uses this snapshot to size new positions relative to the full book — not in isolation.

Every 8 hours, the system runs a full reflection cycle against real outcomes: (1) backtest prior forecasts against actual price moves, (2) compute per-agent Brier scores, (3) extract calibration lessons via LLM reflection, (4) inject updated patterns before the next run. An agent that was overconfident about a direction that reversed gets recalibrated before it can compound that error into the next cycle's hedge sizing. Protection gets measurably sharper every 8 hours.

Accurately calibrated forecasts are the prerequisite for effective hedges — a hedge that activates based on a systematically overconfident forecast wastes premium on conditions that don't materialise. Each forecast is scored using the Brier proper scoring rule. Agents that consistently assign 70% probability to events that occur only 30% of the time are identified and recalibrated automatically. The result: hedge activation is informed by forecasts that are statistically honest about uncertainty.

A strategy that was effective in the prior regime may silently become a source of losses in the current one. A 7-day vs 30-day win rate comparison runs per strategy every cycle — when recent performance drops >20pp below its historical baseline (≥5 trades), the strategy is automatically de-risked: position sizes reduced 20% without any manual intervention. Strategies are retired before they become a liability, not after.

A strategy's effectiveness is meaningless without its regime context — what works in RISK_ON destroys capital in CRASH_FEAR. Every position is tagged with its regime at entry. The system maintains separate win rate statistics per (strategy, regime) pair: once 10+ trades accumulate, selection blends live regime win rate (70%) with historical baseline (30%). Capital is never deployed using performance statistics from a different market regime.

A strategy with a high win rate that occasionally produces catastrophic losses is worse than a strategy with a lower win rate and tightly bounded downside. Each strategy's 30-day rolling Sharpe contributes 10% to its composite score (Sharpe=1→0.5, Sharpe=3→1.0), systematically favouring consistent risk-adjusted returns over raw win rate. Strategies are selected for their ability to protect capital across a full regime cycle, not just to win the most individual trades.

Patterns that have appeared in ≥4 confirmed historical episodes are elevated to permanent crisis pattern status — the system's highest-conviction risk templates. These are the recurring structures that consistently preceded drawdowns or recovery: specific regime signatures, correlation breakdowns, volatility surface inversions. Permanently encoded into the knowledge base, they are surfaced ahead of every similar setup to inform hedge positioning before the pattern completes.

Every cycle is saved as a structured episode: regime, VIX level, extracted risk keywords, top-3 forecasts, composite risk score, and alert level. When outcomes are confirmed, a quality score weights the episode by its predictive accuracy. At each new cycle, TF-IDF retrieval surfaces the 3 most similar historical episodes — giving every agent a working memory of how analogous conditions resolved, including whether the prior hedge was sufficient and what signals preceded the move. The system remembers every crisis it has seen.

Three independent Forecaster instances run in parallel across separate model families — Claude, Gemini, and Grok — each dispatched through the task-aware ModelRouter so the model matches the regime and the confidence the call requires. Their outputs are merged via confidence-weighted voting: each forecast's escalation probability is the weighted average across all three, with weights derived from per-forecast confidence levels (high=1.0, medium=0.7, low=0.4). The Reflection Ensemble gate applies on top — ≥90% agreement boosts conviction, <70% forces posture to neutral — so disagreement across the ensemble reduces risk-on exposure rather than being averaged away.

The full Bull/Bear/Arbitrator debate runs every cycle, functioning as an active calibration mechanism. The Arbitrator's output — net probability shift, bull weight, bear weight, and calibration action (widened/tightened/maintained) — is stored in the HedgeReport and contributes to the backtesting feedback loop. Over time, this allows the system to learn whether its debate-adjusted forecasts are better calibrated than the raw ensemble output.

A nightly compaction job groups TIMG trajectories older than 60 days by regime, computes centroid embeddings across each group, and distils them into a single high-weight regime lesson entry (success_score=0.95 — equivalent to 3× retrieval weighting). The originals are deleted. This process prevents memory dilution from thousands of individually stored older trajectories, keeps retrieval fast and relevant, and permanently consolidates the swarm's long-term institutional knowledge into high-signal lesson nodes that outcompete stale individual entries in cosine similarity rankings.

When the Elite Regime Detector signals a regime transition, the system automatically runs a distillation pass over the prior regime's trade history. Key statistics — win rate, average P&L, best-performing assets — are condensed into a structured lesson memory stored at weight=3.0, three times the standard TIMG entry weight. The next time a similar regime appears, these distilled lessons are preferentially retrieved, accelerating adaptation. This creates a continuous audit trail of what worked in each past regime, compounding knowledge across the full market cycle history.

An always-on anomaly detector compares the swarm's rolling forecast accuracy against the TIMG baseline (derived from stored trajectory success scores). When live directional accuracy drops more than 15 percentage points below the TIMG baseline across a 30-trade rolling window, the system enters review mode: high-conviction trade sizes are reduced to 10% of normal, and shadow-testing cycles for new Strategy Lab variants are doubled. Review mode clears automatically after six cycles of accuracy recovery — or when the deviation falls below half the threshold. State is persisted in Redis for cross-process consistency.

Human feedback — thumbs-up/down votes on proposed trades — is injected into the TIMG system as high-weight memories. Previously all votes carried a fixed 2.0× weight. The updated system scales weight dynamically by the user's historical accuracy: users with no feedback history receive a conservative 1.0× baseline; users whose past votes correctly predicted outcomes receive progressively higher weights up to 4.0× for users with ≥85% historical accuracy. This means consistently insightful users have up to four times the influence on swarm learning compared to new or inconsistent voters — preserving signal quality as the feedback dataset grows.

The Anti-TIMG subsystem is the failure-learning counterpart to the episodic memory store. While TIMG stores calibrated successes and regime lessons, Anti-TIMG specifically captures high-confidence wrong predictions — forecasts with probability ≥60% where the directional outcome was incorrect. These failure trajectories are stored in a ChromaDB failure store, tagged by asset class, regime, and forecast magnitude. Before each new forecast on the same asset class and regime, the top-3 most similar failures are retrieved and injected as explicit anti-patterns into the Forecaster prompt. The Markets Forecast Generator applies automatic conservative damping when active: predicted impact magnitudes are reduced by 15% and probability confidence is capped at 65%. The failure store is initialized as a registry singleton at startup — the system learns from what it got wrong with conviction, not just from what it got right.

A micro-structure paper-trading engine running 15–20 minute cycles on a dedicated $50k NAV pool. Evaluates seven independent entry signals — VWAP deviation, order-book imbalance, volume delta, fair-value gap, liquidity sweep, momentum divergence, and volume-profile level — using Kelly-fraction sizing (÷4 safety factor) and ATR-based stops (1.5× ATR). Strictly gated by the World Model: execution is blocked in crisis, extreme_bear, and bear regimes. The ScalpTrader feeds every trade as a labelled trajectory into ChromaDB, accelerating intra-day regime learning. The Scalp Trader now operates with regime-aware dynamic leverage up to 20×, volatility-adjusted profit targets, trailing stops, and advanced micro-structure signals including cumulative volume delta (CVD), delta divergence, and final order-flow confirmation. These features allow the trader to capture high-conviction setups while maintaining strict risk controls.

An HMM-inspired Bayesian state machine that maintains a full probability simplex across eight mutually exclusive macro regimes: RISK_ON, RISK_OFF, HIGH_VOLATILITY, LOW_VOLATILITY, LIQUIDITY_CRUNCH, MEAN_REVERSION, TRENDING, and GEO_SHOCK. At each 30-minute cycle the detector extracts 13 features across six synthetic timeframes (1m → 1D), computes per-state likelihoods, and applies a Bayesian update against an expert-calibrated 8×8 transition prior. Tracks velocity, acceleration, and persistence. Six early-warning channels (CORRELATION_BREAKDOWN, LIQUIDITY_STRESS, VOL_ACCELERATION, SENTIMENT_DIVERGENCE, MOMENTUM_EXHAUSTION, GEO_SPIKE) inject context into every downstream agent via the World Model. Brier-score calibration adjusts per-state confidence weights automatically.

Tracks Pearson correlations across seven asset pairs (BTC/SPY, BTC/DXY, ETH/BTC, SPY/VIX, BTC/GOLD, ETH/SPY, SOL/BTC) at 15m, 1h, 4h, and 24h resolutions simultaneously. Anchored to structural baseline values — BTC/SPY +0.45, BTC/DXY −0.30, ETH/BTC +0.88, SPY/VIX −0.78 — with ±0.15 shift thresholds triggering regime alerts. Outputs a cross_asset_regime label (RISK_ON, RISK_OFF, STRESS, DECOUPLED, NEUTRAL) that is injected into the World Model after every cycle, enriching downstream correlation and risk agent context.

Disaggregates total protocol P&L into five strategy sources — Crypto Leverage, Options, Stock Leverage, Scalp Trader, and Macro Hedge — across any window from 1 to 30 days. For each category: win rate, average R:R, approximate Sharpe ratio, contribution percentage, and per-trade extremes. A natural-language narrative summary is generated describing which strategies drove performance and why. Outputs trajectory metadata into ChromaDB so that future regime detection cycles can correlate strategy performance with macro state history.

A user-facing control layer that lets GEO stakers adjust the relative weighting between Macro Hedge, Scalp Trader, Options, and Crypto Leverage strategies via persistent sliders (0–100, stored in Redis). The Meta-Orchestrator reads current mix weights before each cycle and blends strategy selection accordingly. Critical constraint: mixer weights shift emphasis within the allowed solution space but never override the World Model's macro posture. In a crisis regime, even a 100% Crypto Leverage slider will not open new crypto positions.

Continuously compares live strategy performance against backtested benchmarks for five strategy types. Drift score is a weighted composite of win-rate deviation, R:R deviation, and Sharpe deviation. Thresholds: Warn (drift > 20%), Size ÷2 (drift > 40%), Pause (drift > 60%). When a strategy enters pause status, it is automatically halted until the next learning cycle completes and calibration improves. Sizing multipliers are surfaced via API and consumed by both the Orchestrator and the Scalp Trader.

Provides a real-time per-asset risk concentration map across all four position categories (Crypto, Equity, Scalp, Options). Each cell shows exposure in USD, risk percentage of total NAV, and a heat classification — HIGH (> 20% concentration), MED (8–20%), or LOW (< 8%). The heatmap summary includes BTC/SPY 24h correlation and the largest single concentration, giving traders an immediate view of where the protocol's risk is concentrated at any point in time.

A deterministic keyword-matching engine that scores every ingested news headline for expected volatility impact and directional bias at zero LLM cost. Now enhanced with real-time X/Twitter integration that monitors verified accounts and high-engagement posts for breaking geopolitical events (bombings, sanctions, military escalations), automatically flagging them as high-impact for immediate World Model injection. Micro-cap and niche catalyst detection routes asset-specific signals (contract wins, FDA approvals, sudden volume spikes) to the relevant swarm with priority labelling. Enriched event objects — including breaking priority tags — are stored in Redis and injected into the TIMG trajectory system.

The Regime-Strategy Performance Matrix (RSM) maintains per-(regime, strategy) win/loss tallies in Redis with a 90-day TTL, tracking the empirical track record of every strategy in every macro regime separately from the global win-rate statistics. A Thompson Sampling bandit algorithm draws from Beta(wins+1, losses+1) posteriors for each (regime, strategy) pair, blending 30% exploration against 70% RSM exploitation. This ensures strategies with limited history in a given regime are still tested occasionally, while consistently underperforming pairs see their posterior mean drift lower automatically — reducing selection probability without any manual parameter tuning. After each scored outcome, the corresponding (regime, strategy) posterior is updated, creating a continuous closed loop between trade outcomes and future strategy selection within the current macro regime.