GOOGL KVO + DeMarker Walk-Forward Strategy (with Monte Carlo Validation) (2008-2026)
📈 GOOGL KVO + DeMarker Walk-Forward Trading Strategy (2008-2026)A fully automated, research-driven quantitative trading strategy for GOOGL that combines volume momentum and price exhaustion signals with Walk-Forward Optimization and Monte Carlo robustness testing. This product is built for traders and quants who want more than a simple backtest — it delivers out-of-sample validation and statistical stress testing.This strategy uses the Klinger Volume Oscillator (KVO) to detect accumulation phases and the DeMarker indicator to identify local exhaustion points for exits. Parameters are not curve-fit once — they are continuously re-optimized using rolling walk-forward windows and then tested strictly out-of-sample.🧠 Core Features ✅ KVO + DeMarker indicator strategy logic ✅ Walk-Forward Optimization (rolling train/test windows) ✅ Automatic parameter grid search per period ✅ Out-of-sample portfolio stitching ✅ Vectorbt backtesting engine ✅ Buy & Hold benchmark comparison ✅ Trade-level analytics and performance stats ✅ Monte Carlo block-bootstrap simulation ✅ Distribution analysis for return, drawdown, and Sharpe ratio ✅ Ready-to-run Python research script 🔬 Validation Built InUnlike basic strategies, this system includes: Walk-forward parameter re-training Out-of-sample only performance aggregation 1,000-path Monte Carlo simulations Drawdown and Sharpe stability analysis Percentile risk ranges for returns and max drawdown This helps measure robustness, not just peak backtest performance.🧠Walk-Forward Performance Period: 2008 → 2026 Total Return: ~ 2800% Benchmark Return: ~ 1875% Max Drawdown: ~ 44% Win Rate: ~ 71% Profit Factor: ~ 3.55 Sharpe Ratio: ~ 1.09 Total Trades: 53 📊 What You Get Complete Python strategy code Walk-forward optimization engine Parameter selection logs per window Portfolio performance report Equity curve and trade visualization Monte Carlo simulation charts Statistical summary outputs Benchmark comparison 🎯 Ideal For Systematic traders Quantitative researchers Strategy developers Backtest validation workflows Algorithmic trading learners Indicator combination research ⚙️ Tech StackBuilt with: Python vectorbt pandas / numpy yfinance matplotlib ⚠️ NoteThis product is for research and educational use. It demonstrates a professional validation workflow (WFO + Monte Carlo), not a guaranteed profit system. Always forward-test and apply risk management before live trading.
Get it → kryptera.gumroad.com