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)

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.