DNYL . SPACE

As motivation plays a crucial role in reaching these goals.

Day: February 3, 2025

Dall·e 2025 02 03 10.30.20   A Futuristic High Tech Trading Lab With Multiple Holographic Screens Displaying Financial Charts And Currency Pairs. The Screens Highlight Currency Pa

Fact Faq #5: Finalize

Which 10 currency pairs would you recommend for 10 rounds?

When selecting currency pairs for 10 rounds of testing, consider pairs with diverse volatility and market behavior. This allows for a comprehensive and effective strategy across different markets. Examples of 10 recommended currency pairs include:

EUR/USD – A popular, highly liquid pair
GBP/USD – High volatility
USD/JPY – A pair correlated with the stock market
AUD/USD – Sensitive to commodities
USD/CAD – Linked to oil
EUR/JPY – Cross-regional correlation
GBP/JPY – High volatility
EUR/GBP – A European pair
NZD/USD – Correlated with commodities, similar to AUD
USD/CHF – Often used as a safe haven asset during high market risk periods.

You can adapt this list of currency pairs to suit your needs and the market behaviors you’re interested in.


Monte Carlo process results

For the Monte Carlo process results that did not pass the walk-forward matrix in the Ending section, should we re-apply them to Genetic evo or not?

If a strategy passes the Monte Carlo process but fails the Walk-Forward Matrix (WFM) in the Ending section, it may indicate that the strategy is vulnerable to real market changes. Although it may be resilient to random sampling (Monte Carlo), it may not adapt well to changing times (Walk-Forward Matrix). Considering reapplying this strategy in a Genetic Evolution:

Benefits of reuse:

You may find good elements within the strategy, but they require adjustment to fit changing market conditions. Using Genetic Evolution may help find parameter sets that are more flexible and adaptable.

Drawbacks of reuse:

If the strategy completely fails the WFM, it may indicate a fundamental problem within the strategy, which adjustments may not resolve. It may be a waste of time to attempt to refine a strategy likely to fail in the long run.

Recommended Approach:

Analyze the cause: Before reapplying the strategy to Genetic Evolution, analyze why it failed the WFM, such as overfitting or inability to adapt to changing markets.
Apply only parts: If parts of the strategy show promise, these parts can be incorporated or improved upon in a new Genetic Evolution process.
Create new conditions: In the new Genetic Evolution round, new conditions or constraints could be added to ensure the strategy passes the WFM or becomes more resilient.

Re-applying a strategy that failed the WFM to Genetic Evolution can be a good approach if proper analysis and improvement are undertaken. This can give the strategy a better chance to adapt and succeed in a wider range of market conditions.

Dall·e 2025 02 03 10.17.24   A High Tech Trading Lab With Multiple Holographic Screens Displaying Financial Charts, Algorithmic Trading Strategies, And Market Trends. The Environm

Fact Faq #4: Summarized previously as new engine

A. Building…

Create 10 strategies using Genetic Evolution.
1.1 Breakout Strategy
1.2 Day Trading Strategy
1.3 Grid Trading
1.4 Multi-Timeframe Divergence Strategy
1.5 News Trading
1.6 Oscillator Reversion and Momentum Burst Strategy
1.7 Range Trading Strategy
1.8 Scalping Strategy
1.9 Swing Trading
1.10 Trend Trading Strategy

Use TF H1/H4 – Opened Timeframe – Ranking Profit > 1.4 Ret/DD > 4 Min trade per month > 2 – No Cross check / Robustness test
** Market Entry Method: Enter at market / Reverse / Stop / Limit pending
*** Filter each step by 10 entries per market entry method, totaling 40 in one main group, and a total of 400.
**** Enable Exit types: MoveSL2BE / SL2BE add pips / Profit target / Stop loss / Trailing stop / Trailing activation / Exit rules
***** Trading option: Friday Exit 23:00
****** Money management: Risk fixed % of account

B. Melting…

Backtest all 400 results against 10 currency pairs, separating the timeframe into 5 TFs.
2.1 H1/H4 – M30/H4 – M15/H1 – M5/H1 – M15/M30
2.2 Aggregate the results and perform Automatic Retest by changing the Precision to 1-minute data tick simulation, but maintain the original trading settings.
2.3 Add Monte Carlo trades manipulation and Sequential optimization testing.
2.4 Save the results to the @Blender folder.
2.5 Perform another Automatic Retest using only the Walk-Forward Matrix.
2.6 Save the results to the @Melted folder.

C. Ending…

Load all files from @Blender into the Initial Population to create a new set of EA, repeating the process 10 times (10 currency pairs).
3.1 Create 3 new steps: Build Martingale / ATM / Everyday Exit
3.2 Automatic Retest with the original trading settings at 3 timeframes (H1/H4 – M30/H4 – M15/H1), using 2 Monte Carlo factors (trades manipulation and retest methods) and Sequential optimization.
3.3 Another Automatic Retest with 1-minute data tick simulation, using 2 Monte Carlo factors and Sequential optimization.
3.4 Another Automatic Retest using the Walk-Forward Matrix.