Case Study: AUDJPY Breakout

Strategy Overview

The EA is built around session breakout logic. It waits for price to break beyond a defined lookback range during major liquidity transitions, specifically:

• Asia session open

• London session open

• Fixed lookback period defining breakout levels

• Risk-defined position sizing per trade

The objective is to capture directional momentum immediately following volatility expansion rather than mean reversion.

Backtest Configuration

• Instrument: AUDJPY

• Timeframe: M5

• Data range: 1 January 2021 – 16 May 2025

• Account size: $10,000

• Risk per trade: ~$1,000 (10%)

• Execution model: breakout continuation

• Session triggers: Asia open and London open (UTC+2 broker time)

Hypothesis

AUDJPY exhibits a systematic tendency to make a directional breakout within the first 90 minutes of the Sydney session open, driven by the confluence of returning Asian liquidity, Australian institutional order flow, and the AUD/JPY cross's sensitivity to risk sentiment shifts that accumulate during the overnight New York close. The strategy exploits this recurring volatility expansion window by entering on breakout confirmation and exiting at a defined target or stop.

Performance Summary

The backtest generated strong risk-adjusted returns over the testing period.

• Total trades: 711

• Win rate: 57.95%

• Gross profit: $418,659

• Gross loss: $268,049

• Net profit: $150,609

• Sharpe ratio: 2.65

• Profit factor: 1.56

• Max drawdown: $7,582

• Return / drawdown ratio: 19.86

• Average trade: $211.83

• CAGR: ~100%

  
  

The equity curve shows consistent upward progression with manageable stagnation periods (~140 days). 

Risk Structure

The system was intentionally configured to risk approximately $1,000 per trade on a $10,000 account, equivalent to 10% exposure. This creates an intentionally aggressive testing scenario designed to stress-test drawdown behaviour.

Under this configuration:

• Maximum drawdown remained below $8,000

• Drawdown stayed within ~27% peak-to-valley range

• Winning percentage remained close to 58%

This suggests the underlying logic is resilient even under elevated risk conditions. 

Prop Firm Scenario Example

If applied to a $100,000 prop firm account, the same logic can be scaled conservatively:

• Maintain absolute risk at $1,000 per trade

• This becomes 1% risk instead of 10%

• Effective drawdown ceiling remains ~$10,000

• Keeps trading within a typical 10% max drawdown rule

This demonstrates that the strategy architecture can be adapted to stricter risk environments without altering core logic.

Trade Behaviour Characteristics

The strategy exhibits:

• Strong but realistic win rate (~58%)

• Slightly larger average wins than losses

• Limited consecutive losses (max observed: 6)

• Short trade durations (average ~14 bars)

These properties are consistent with breakout continuation systems that rely on momentum bursts rather than long trend holding. 

Session-Based Edge

Preliminary breakdowns indicate stronger performance during high-volatility session transitions. In particular:

• London open breakouts produced consistent directional follow-through

• Asia session volatility expansion also contributed materially

• Monday behaviour shows potential for optimisation

Further refinement could include:

• Restricting trading to specific weekdays

• Adjusting lookback windows per session

• Volatility filters prior to entry

These are optimisation paths rather than structural requirements.

Strategy Logic Summary

1. Identify consolidation range using defined lookback

2. Wait for breakout during Asia or London session open

3. Enter in breakout direction

4. Apply fixed risk-based position sizing

5. Exit via predefined TP/SL logic

The design emphasises simplicity, repeatability, and controlled exposure.

Key Takeaways

• The EA demonstrates stable performance across multiple years

• Drawdown remains contained relative to return

• Risk can be scaled linearly across account sizes

• Session-based volatility is the primary driver

• Additional optimisation opportunities exist

Notes on Interpretation

This is a historical backtest using broker-time UTC+2 data. Results may vary depending on:

• Broker execution

• Spread differences

• Slippage

• Liquidity conditions

The purpose of this case study is to illustrate behaviour and risk characteristics rather than guarantee performance.