Description
Strategy Name: Bollinger Bands Mean Reversion and Momentum Strategy
Author: ApexVector
Symbol: GBPJPY
Timeframe: m9
Leverage Used: 1:200
Testing Period: 17/07/2024 – 10/11/2025
Spread: Fixed 0.9 pip(s)
📈 Overview (GJ version optimized) First 10 copies were sold for $19. After that, the price will rise to $34.
Developed by ApexVector, a fintech student passionate about quantitative finance and algorithmic trading, this strategy blends Bollinger Bands mean reversion with momentum confirmation to capture both pullbacks and short-term breakouts. It dynamically adjusts to volatility, filtering false signals and optimizing trade timing.
⚙️ Core Logic
- Mean Reversion Module: Identifies price extremes using Bollinger Bands to trade reversals back to the midline.
- Momentum Filter: Confirms entries using directional momentum strength to avoid premature countertrend trades.
- Dynamic Position Management: Applies stop-loss, take-profit, and trailing parameters to balance risk and reward.
- No Martingale / No Grid: Focuses on clean, single-entry trades for transparency and reliability.
💡 Key Backtest Results (GBPJPY, 1Y3M)
MetricResult
ROI: +58.46%
Net Profit: $5,845.82 (from $10,000 starting capital)
Profit Factor: 1.76
Total Trades: 89
Winning Rate: 42.7%
Largest Win / Loss: $935.44 / -$236.41
Average Trade: $65.68
Max Consecutive Wins / Losses: 5 / 8
Drawdown (approx.): Low to Moderate
🔍 Trade Behavior
- Balanced performance across long and short trades (Long: $3,039 | Short: $2,806)
- Works efficiently under typical GBPJPY volatility environments
🧩 Recommended Usage
- Suitable for both semi-automated and fully automated traders
- Best run on ECN accounts with low spreads
- Ideal for medium-risk traders looking for sustainable, consistent growth over time
🧭 Developer’s Note
I’m a fintech student exploring algorithmic trading and crowdfunding innovation. This strategy represents months of testing and optimization around Bollinger dynamics and volatility filters. My goal is to make data-driven trading accessible and scalable for everyone.
