Autonomous RL LLM Agent for High‑Frequency Trading
Standora fuses reinforcement learning with large‑language‑model reasoning to plan, execute, and adapt across volatile markets in milliseconds.
Live metrics: PnL · Sharpe · DD · Latency
Features you actually need in production
Focused on speed, stability, and capital preservation—without sacrificing adaptability.
Latency-First Core
Sub-10ms decision loops with pinned cores, prewarmed models, and micro-batching.
RL LLM Planner
LLM-based planner proposes intents; RL policies score actions under risk/latency budgets.
Adaptive Reward Shaping
Reward functions swap per regime (trend, range, news) with auto detection.
Capital Protection
Exposure caps, session warm-up, drawdown locks, volatility circuit-breakers.
Signal Fabric
On-demand feature engineering; classic+learned indicators; order-book microstructure.
Safe Online Learning
Guardrails with replay buffers, off-policy updates, and anomaly filters while live.
Versioned Data Lake
Billions of ticks/klines since 2017; Parquet/Feather, catalog, lineage tracking.
Canary & Shadow
Dual-run policies; promote only on objective thresholds and stability windows.
Smart Routing
Venue-aware router: slippage simulation, partial fills, and liquidity filters.
Deterministic Backtests
Replay exact timestamps with PTP sync; JSONL decisions for reproducibility.
Ops Console
Full control panel: session modes, risk knobs, kill-switch, and live telemetry.
Auto Reports
Periodic PDF packs: PnL, Sharpe, DD, hit‑rate, latency distributions, failure modes.
How Standora works
A hierarchical loop: LLM planning, RL policy execution, and safe online learning under strict latency & risk budgets.
Market Ingestion & Retrieval
Streams: L2 order-books, ticks, klines; macro/event feeds. LLM retrieves context windows (venue state, volatility regime).
Feature Store & Indicators
Classical (RSI/MACD/BB), learned latent factors, microstructure (imbalance, queue dynamics), synthetic labels.
LLM Planning
LLM proposes intents (accumulate/flip /hedge/flat) with constraints; emits hypotheses for the RL policy layer.
RL Policy & Risk Budget
Policy head scores actions under limits: max exposure, VaR budget, latency target, inventory constraints.
Execution Engine
Router batches orders, simulates slippage, routes per venue; co-location optional; micro-burst cancellation.
Safe Online Updates
Off-policy updates with replay buffers; anomaly filters; canary then shadow before production promotion.
Monitoring & Reports
Latency tracing, JSONL decision logs, PDF performance packs; alerts on drift, liquidity, or anomaly spikes.
Case studies
Selected scenarios showing how the agent behaves under stress and regime shifts.
Crypto Volatility 2024
During a 20% BTC shock in hours, agent switched to ‘latency-adaptive’ mode, tightened risk budget, and preserved capital with micro-hedges.
Forex Flash Window
EUR/CHF spike: router throttled exposure, favored deeper venues; partial fills reduced slippage by ~18% vs naive baseline.
Earnings Season Alpha
LLM event planner filtered false positives and limited position time-in-market during high spreads.
DeFi Liquidity Crunch
Position sizing adapted to pool depth; circuit-breakers paused risk-on until spreads normalized.
Who we serve
Purpose-built for institutional execution and research workflows.
Institutional-grade execution across volatile crypto markets; cross-venue arbitrage; dynamic liquidity routing; programmatic risk.
Custom RL policies per symbol/venue; safe online adaptation; HFT in FX/equities with capital preservation.
Signal infra, compliance-friendly risk layer, plug-and-play router integration; analytics & reporting for clients.
Training & Infrastructure
Hybrid cluster engineered for low latency and high throughput—supporting offline training and safe online learning.
GPU Fleet
45× NVIDIA A100 40GB (NVLink) for distributed RL + LLM fine-tuning; burst pools A40/V100/H100.
Orchestration
Kubernetes + Ray for elastic rollouts; priority lanes for live; backpressure for research queues.
Serving
Triton/ggml backends; quantized heads for low-latency; blue/green + canary/shadow pipelines.
Data Fabric
Parquet/Feather lake; catalog + lineage; Kafka/Redpanda streaming; deterministic replay for backtests.
Networking
100GbE, kernel bypass (DPDK) where available; PTP for microsecond time sync; venue co-location options.
Storage & Checkpoints
Object store with versioned checkpoints; rollback at any point; tiered replay buffers (hot/warm/cold).
Observability
Metrics, traces, logs unified; Grafana-like dashboards; structured JSONL for audits; SLOs/SLAs.
Security
RBAC, key vault, TLS everywhere, signed artifacts; SBOM and dependency scanning CI.
Compliance & Security
We align with global best practices; Standora is a software platform, not a broker or advisor.
Regulatory Posture
Software platform for research/execution; requires user’s own brokerage/venue accounts and approvals.
Frameworks
SOC 2 Type II posture, ISO 27001 alignment, GDPR principles; data minimization and encryption in transit.
Risk Controls
Exposure caps, drawdown locks, warm-ups, anomaly halts; pre-/post-trade checks and liquidity filters.
Auditability
Immutable logs, versioned datasets, reproducible backtests; exportable PDF/JSONL packs for review.
Market Rules
Guidance for MiFID II/ESMA, SEC/FINRA, FCA, MAS contexts; stress scenarios and kill-switch procedures.
Technical FAQ
Deeper answers for quant engineers, infra operators, and compliance teams.
How do you maintain latency budgets?
Pinned CPU cores for hot paths, GPU-accelerated inference heads, prewarmed models, speculative execution, and PTP-synced clocks over 100GbE.
What does reward shaping look like in production?
Multi-objective rewards (PnL, volatility penalty, slippage, drawdown/var breaches). Regime-aware weights adapt per asset and microstructure.
How is safety enforced in live trading?
Circuit-breakers, exposure caps, session warm-ups, anomaly halts, and canary/shadow deployments. Deterministic backtests and immutable decision logs.
Can I customize policies?
Yes—per venue/symbol/timeframe, with risk budgets and activation conditions. Policies can be swapped or blended based on observed regimes.