EUR/GBP strategy
EUR/GBP strategy
The Basis of The Strategy
This particular trading strategy effectively utilizes the Dark Venus Expert Advisor, which is a free trading strategy in Metatrader 4 and 5. This specific strategy operates specifically on the 1-hour timeframe, executing only short trades due to their statistically higher win rate compared to long positions.
Short positions are only opened when the 1h bar closes above the bollinger band, see figure 1. The settings for the Bollinger bands are period 16, standard deviation 0.7. A time filter is used to filter out any false positive entries. If the conditions are met again while the trade is still open, another larger short trade is opened, see figure 2.
Take profit and stop loss are determined at 100 and 500 ticks respectively.
Figure 1. Single trade
Figure 2. Double trade
Backtesting Results and Statistics
Figure 3 is generated based on a 0.01 lot size, taking into account a starting capital of 10,000 USD. The equity curve is relatively smooth, with periods of drawdowns. Preventing large losses is crucial for maintaining trader motivation and consistently achieving sustained long-term profits.
Table 1 presents the backtesting results observed over the past 10 years, providing a comprehensive overview of the strategy's performance. The strategy is characterized by a high win rate, largely due to its approach of employing a small take profit while simultaneously utilizing a large stop loss. While this methodology may appear unreasonable or counterintuitive to numerous traders, the extensive backtesting clearly demonstrates that it is effective in practice. Notably, the Sharpe ratio stands at a positive 1.87, indicating a favorable risk-adjusted return, while the maximum drawdown remains low at just 1.45%. However, it is important to note that this drawdown should be evaluated over specific timeframes, such as a year, during which it could become significant. We'll explore these dynamics when we increase the lot size in the analysis below.
| Net P/L (EUR) | Trades Total | Win % | Sharpe Ratio | Max Drawdown (%) | Avg. Trade Duration (hh:mm) |
|---|---|---|---|---|---|
| 852 | 1271 | 84.74% | 1.87 | 1.45% | 16:57 |
Figure 3. Equity curve and account margin plotted from 1/1/2015 to 1/1/2025
Boosting The Return
Since I'll be running multiple algorithms simultaneously and I want to protect my account from large losses, I aim for a maximum drawdown of 20% over 10 year backtesting period. This should provide enough risk management that it will not blow up the account when running other strategies at the same time. For this strategy, I've set the risk percentage to 3% in the expert advisor (which does not match with the actual risk), enabling dynamic lot sizing over the backtest period. The results are shown in Figure 4.
The backtesting results in Table 2 show a compound annual growth rate (CAGR) of 27.5% and a maximum drawdown of 19.2%. Personally, this trade-off is better than investing in the stock market. Let’s find out of these backtesting results match with the actual performance!
| Net P/L (EUR) | Trades Total | Win % | Sharpe Ratio | Max Drawdown (%) | Avg. Trade Duration (hh:mm) | CAGR |
|---|---|---|---|---|---|---|
| 117756 | 1271 | 84.74% | 1.75 | 19.14% | 16:57 | 27.5% |
Figure 4. Equity curve and account margin plotted from 1/1/2015 to 1/1/2025 for the boosted results with a max drawdown of 19%.
Running the Strategy Live
Unlike common practice typically observed in algorithmic trading, I did not conduct a traditional out-of-sample test prior to implementing my trading strategy. Instead, I have backtested the strategy across a comprehensive time period of 10 years, during which it has consistently yielded a steady return each year, with an average of over 120 trades executed annually.
Given these favorable results, I made the decision to subsequently test the strategy using a small lot size of 0.01 over the course of the first 6 months, effectively acting as my out-of-sample test. After the conclusion of these 6 months, I will thoroughly review the performance metrics to ensure that the backtesting results align closely with the actual performance observed in the market, thereby allowing me to confirm that this strategy indeed possesses a tangible edge.
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