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Capital at risk -- demo environment only.

This public experience streams signed, sandboxed market data for illustrative purposes. No live trading or performance guarantees.

How It Works

From raw data to executed trades: 7 layers of institutional-grade automation

Our pipeline processes market data through 486 ML models and 380 strategy configurations, protected by Kelly Criterion sizing, VaR monitoring, and automatic circuit breakers. Every decision is cryptographically signed and auditable.

01. DataMarket feeds
02. AnalysisTechnical signals
03a. StrategyRule-based
03b. ML ModelAI predictions
04. RiskPosition sizing
05. ExecuteOrder routing
06. MonitorLive tracking
System Architecture

7-Layer Pipeline: Data to Execution

Every trade flows through a rigorous pipeline. Each layer is observable, each decision is signed, and every control must pass before capital is deployed.

1
Multi-Source Aggregation

Data Layer

CCXT integration with 100+ exchanges via WebSocket streaming. Tick-by-tick order book data, trade flow, and funding rates. Alternative data from NewsAPI, FRED economic indicators, and LunarCrush social sentiment.

  • Real-time WebSocket streams from 100+ exchanges
  • Order book depth (L2/L3 data)
  • Funding rates, open interest, liquidation data
  • NewsAPI, FRED, LunarCrush integrations
2
40+ Technical Indicators

Analysis Layer

Feature engineering pipeline with 40+ technical indicators: MACD, RSI, Bollinger Bands, ATR, OBV, ADX, and more. VPIN calculations for toxic flow detection and microprice modeling for true value estimation.

  • 40+ technical indicators (MACD, RSI, BB, ATR, OBV, ADX)
  • VPIN toxic flow detection
  • Microprice calculations
  • Order flow imbalance analysis
3
380 Configurations

Strategy Layer

Strategy Expander generates 380 configurations from 63 core templates. Categories: Technical (15), ML-driven (2), Arbitrage (3), Event-driven (1), Grid bots (5), Hedge fund replication (22), Microstructure (6), Advanced (7).

  • 63 core templates → 380 configurations
  • Mean reversion, momentum, arbitrage, microstructure
  • Hedge fund strategy replication (22 templates)
  • Performance-based auto-selection
4
486 Model Configurations

ML Layer

Model Factory generates 486 unique configurations from 25+ base architectures: LSTM, GRU, Transformers, TCN, BiLSTM, Random Forest, XGBoost, LightGBM. Each expanded across 5 lookback windows and 8 feature sets.

  • 25+ architectures: LSTM, Transformers, TCN, XGBoost
  • 5 lookback windows × 8 feature sets
  • Automatic hyperparameter optimization
  • Ensemble methods and model stacking
5
Institutional-Grade Controls

Risk Layer

Kelly Criterion position sizing (0.25-0.5 fractional), dynamic leverage caps (3x crypto, 2x stocks), VaR monitoring, drawdown throttling, correlation limits, and automatic circuit breakers.

  • Kelly Criterion (0.25-0.5 fractional sizing)
  • VaR monitoring with dynamic leverage
  • Drawdown throttling and circuit breakers
  • Correlation limits across positions
6
Smart Order Routing

Execution Layer

Adaptive smart order routing splits flow across venues. Real-time slippage estimation, market impact modeling, and order splitting algorithms. Latency-optimized execution with microsecond precision.

  • Multi-venue smart order routing
  • Slippage estimation and market impact modeling
  • TWAP, VWAP, POV execution algorithms
  • Latency optimization < 5ms
7
Real-Time Observability

Monitoring Layer

Signed telemetry for every decision. Cryptographic audit trails, real-time P&L tracking, latency monitoring, and automated alerting. Full post-trade analysis with replayable execution logs.

  • Cryptographically signed audit trails
  • Real-time P&L and latency monitoring
  • Automated alerting and incident response
  • Replayable execution logs
Model Factory

486 ML Configurations, Automatically Generated

Our Model Factory generates 486 unique configurations from 25+ base architectures. Each variant is backtested, scored, and ranked. Top performers are auto-selected for live trading.

25+
Base Architectures
LSTM, Transformers, TCN, XGBoost, and more
5
Lookback Windows
1h, 4h, 1d, 1w, 1m time horizons
8
Feature Sets
Technical, sentiment, on-chain, fundamental
486
Final Configurations
Unique model variants for selection

Deep Learning

LSTM
Long Short-Term Memory networks for sequential pattern recognition
GRU
Gated Recurrent Units with faster training convergence
BiLSTM
Bidirectional LSTM for both past and future context
Transformer
Attention mechanisms for long-range dependencies
TCN
Temporal Convolutional Networks for causal convolutions
TFT
Temporal Fusion Transformers for multi-horizon forecasting

Ensemble Methods

Random Forest
Bagged decision trees for robust predictions
XGBoost
Gradient boosted trees with regularization
LightGBM
Leaf-wise gradient boosting for large datasets
CatBoost
Categorical feature handling with ordered boosting
AdaBoost
Adaptive boosting with weighted weak learners
Stacking
Meta-learning with heterogeneous base models
Strategy Expander

380 Strategies from 63 Core Templates

Each template expands across multiple timeframes, parameter sets, and market conditions. Strategies compete for activation based on live performance metrics.

Technical Strategies

15
Trend FollowingMean ReversionBreakoutRSI DivergenceMACD Cross

ML-Driven Strategies

2
Prediction EnsembleRegime Classification

Arbitrage Strategies

3
Cross-ExchangeTriangularStatistical Pairs

Microstructure Strategies

6
Order FlowVPIN-BasedMicropriceToxicity AvoidanceQueue PriorityLatency Arb

Hedge Fund Replication

22
Momentum FactorValue FactorCarry TradeVol TargetingRisk Parity

Grid & Advanced

12
Grid TradingDCAMartingaleAnti-MartingalePyramiding
63 templates × 6 expansions = 380 configurations

Top 10-15 auto-activated based on Sharpe ratio, win rate, and drawdown

Risk Engine

Institutional-Grade Risk Controls

Capital preservation comes before profit pursuit. Every trade passes through Kelly Criterion sizing, VaR monitoring, drawdown throttling, and automatic circuit breakers.

Kelly Criterion Position Sizing

Fractional Kelly (0.25-0.5) with volatility scaling and confidence adjustments. Mathematically optimal bet sizing prevents overexposure while maximizing long-term geometric growth.

f* = (bp - q) / b

Where f* is optimal fraction, b is odds, p is win probability, q is loss probability

Value at Risk (VaR) Monitoring

Real-time VaR calculations at 95% and 99% confidence intervals. Dynamic position adjustment when portfolio risk exceeds thresholds. Monte Carlo simulation for tail risk estimation.

VaR = μ - σ × z(α)

95% VaR: maximum expected loss with 95% confidence

Drawdown Throttling

Automatic position reduction at 10%, 15%, and 20% drawdown levels. Complete trading halt at 24% max drawdown. Recovery protocols ensure measured re-entry after drawdown events.

10% → 50% position reduction
15% → 75% reduction
20% → 90% reduction
24% → Full halt

Circuit Breakers

Multi-level circuit breakers for flash crash protection. Automatic pause on unusual volatility, liquidity gaps, or execution anomalies. Manual override requires dual-key authorization.

Volatility spike > 3σ
Liquidity gap > 2%
Execution failure > 3 consecutive
Exchange API timeout
Evaluation Methodology

No Cherry-Picked Backtests

All performance metrics include realistic transaction costs, dynamic slippage, and market impact. No look-ahead bias. No fantasy assumptions.

0.1-0.5%
Realistic Transaction Costs

All backtests include maker/taker fees, funding rates, and borrowing costs. No fantasy assumptions about zero-fee execution.

Adaptive
Dynamic Slippage Modeling

Order size-dependent slippage estimation based on historical order book depth and volume profiles. Larger orders get more slippage.

Strict
No Look-Ahead Bias

Walk-forward optimization with out-of-sample testing. Parameters are never fitted on data that would be unavailable at trade time.

Square Root
Market Impact Modeling

Impact = σ × √(Volume/ADV). Larger trades move the market against you. We account for this in all simulations.

Validated Performance Ranges

52-68%
Win Rate Range
Across top strategies
1.2-2.8
Sharpe Ratio
Risk-adjusted returns
12-24%
Max Drawdown
With recovery protocols
25-85%
Annual Return
Gross, before costs

*Performance metrics based on comprehensive backtesting with realistic transaction costs (0.1-0.5%). Past performance does not guarantee future results.

Ready to See the System in Action?

Schedule a private walkthrough with our engineering team. No sales pitch — just a transparent demonstration of the pipeline, risk controls, and execution quality.