PPO-SMI Fusion Strategy
Developing a High-Performing HES Stock Trading Strategy with the Stochastic Momentum Index and Percentage Price Oscillator
Key Performance Metrics Past 5 Years (2020-01-01 until 2025-01-01) HES Stock
Trading Strategy Return: 129.05%
Trading Strategy Max Drawdown: 36.43%
Buy and Hold Return: 111.59%
Buy and Hold Max Drawdown: 58.93%
Total Trade: 37 trades
Winrate: 45.95%
Profit Factor: 1.82
Sharp Ratio: 0.81
Warning: The returns and results presented are solely based on backtesting using historical data. Although the system has undergone tests to enhance its robustness and resilience to market conditions, there is no guarantee that it will achieve similar profitability in the future. Markets carry inherent risks and volatility. Therefore, you should only invest money that you can afford to lose without causing financial distress.
Hess Corporation (HES) is not a stock I chose to invest in this year, but because it has performed well, I want to create a trading system for this stock using 2 indicators: the Stochastic Momentum Index (SMI) and the Percentage Price Oscillator (PPO). Now, let's first get to know these 2 indicators.
Strategy Overview
The strategy focuses on trading a single stock — in this case, Hess Corporation (HES) — over 10 years (2015–2025). The goal is to maximize returns using signals generated from the SMI and PPO indicators, each of which offers unique insights into market momentum.
SMI measures the closing price relative to the median of the high-low range over a specified period, providing a smoothed version of the traditional stochastic oscillator.
PPO compares two exponential moving averages of price to detect momentum shifts and is similar in concept to the MACD indicator.
The core of the approach lies in walk-forward optimization, a method that simulates how a real trader would adapt their strategy over time based on historical performance.
Walk-Forward Optimization Process
The process begins by dividing the dataset into yearly segments. For each test year, the previous four years of data are used for training. The training phase involves iterating through various combinations of SMI and PPO parameters to identify the best-performing setup based on total return.
Once the optimal parameters are selected for the training period, they are applied to the following year (the test year) to simulate real-world trading conditions. Performance metrics from each test year are recorded, providing a timeline of how the strategy adapts annually.