Anantaka Quant Capital

The Brief

Anantaka Quant Capital needed a trading system built to an institutional standard. The requirement was not just a working strategy, but one that had been rigorously tested, optimised, and validated before going anywhere near live capital.

What We Built

Strategy Development The strategy was developed in Pine Script on TradingView, incorporating a custom volume analysis component. We reverse-engineered and replicated the logic of a third-party proprietary volume indicator to understand how it worked and rebuild it natively within the strategy.

Optimisation The Pine Script strategy was converted to MQL5 to run large-scale parameter sweeps in MetaTrader 5. We ran multiple rounds of genetic optimisation across 12 different timeframes, testing hundreds of thousands of parameter combinations in total. Walk-forward optimisation was used throughout to validate that results held up on out-of-sample data.

Sensitivity Analysis We ran a Python-based sensitivity analysis across the optimisation results to identify which parameters had the most influence on performance. Using permutation importance, we ranked each input and narrowed the degrees of freedom before finalising the presets.

Portfolio Optimisation A Markowitz efficient frontier analysis was run across multiple strategy presets to determine the optimal capital allocation between them. The best portfolio achieved a Sharpe ratio of 1.60 with an annualised return of 225%.

Live Deployment The final system was deployed on a VPS as a Python automation backend. It receives TradingView alerts via webhooks, executes orders on Binance Futures, and sends real-time trade updates and status notifications through a Telegram bot.

Results

A production trading system running on Binance Futures, built on a strategy that went through genetic optimisation, walk-forward validation, sensitivity analysis, and portfolio-level fund allocation before deployment.