Providing access to a production-ready laboratory core designed for predictive analysis of market microstructure via quantum math. This is not a standard signal generator; it is an institutional-grade filter for Order Flow Toxicity.
The architecture is built for extreme workloads: asynchronous I/O, C-compiled pipelines via Numba (@njit), and vectorized backtesting using VectorBT, allowing for simulation of millions of ticks in milliseconds.
Key Technical Features:
Adaptive Fractional Differencing (AFD): Advanced implementation of the 2025/2026 methodology for maintaining long-memory properties of time series. Transforms non-stationary price data into a clean mathematical signal without information loss.
Hawkes Processes: Integration of self-exciting point processes to detect and quantify order book imbalance and liquidity cascades before they manifest in price action. (now integrated to PocketOption)
State-Dependent Risk Management: Dynamic sizing logic based on Hurst exponent and Markov Chain regime switching, shifting exposure between entropy-neutral and trend-following regimes.
Telegram Infrastructure: Fully integrated with high-speed automated signal routing.
Application:
The engine is API-agnostic and designed for integration with bin.opt infra. While initially validated on rapid price dynamics, the core logic is universal for high-frequency environments where micro-efficiency determines the alpha.
Note:
Real-time proof of performance and backtesting analytics are available upon request via DM. Only serious inquiries regarding algorithmic integration and institutional-grade microstructure analysis
