DNYL . SPACE

Yes, when merging strategies into a portfolio in SQX, the global trading options such as “Exit on Friday” or “Limit Trading Range” apply to the merged portfolio as a whole. This means:

1. Instrument Consistency:
   • You can mix strategies from different instruments (e.g., XAUUSD and AUDUSD), but only if the global trading options make sense across all instruments. If trading behavior (e.g., session times) differs greatly between instruments, it might create conflicts.
2. Timeframe Variance:
   • You can merge strategies using different timeframes (e.g., M15 and H1). However, the portfolio may become harder to interpret, especially if the global settings (e.g., exit times or trading hours) affect strategies differently across timeframes.
3. Strategy Options Alignment:
   • If individual strategies have specific settings that conflict with global options (e.g., one strategy relies on no-limit trading hours while another limits trading to the London session), the global options will override these.

Recommendations:

• If you want to mix different instruments or timeframes, ensure their logic and performance are robust enough to handle global settings.

• If strategies have critical unique options, avoid merging or build separate portfolios for each configuration.

Mixing different instruments and timeframes can create diversified and robust portfolios, but it must be done carefully to avoid conflicts or inefficiencies. Here’s a structured approach:

1. Key Considerations for Mixing Instruments

• Instrument Correlation:
  • Aim to mix instruments with low or negative correlations. For example:
    • Good combinations: XAUUSD and AUDUSD (moderate correlation); USDJPY and GBPUSD (low correlation).
    • Risky combinations: EURUSD and GBPUSD (high correlation).
  • Tools: Use correlation analysis to confirm.
• Trading Hours and Behavior:
  • Different instruments react to different trading sessions (e.g., XAUUSD active during London/NY, USDJPY active in Tokyo).
  • Avoid instruments with conflicting optimal trading ranges unless your global options align with all.
• Cost Structure:
  • Ensure that trading costs (spreads, commissions, swaps) don’t disproportionately impact certain instruments.

2. Key Considerations for Mixing Timeframes

• Complementary Timeframes:
  • Combine timeframes with complementary behavior:
    • Lower timeframes (M15, M30) for capturing short-term trends or scalps.
    • Higher timeframes (H1, H4) for broader trends and stability.
  • Avoid redundancy (e.g., M15 and M30 strategies doing similar trades).
• Position Size & Risk Management:
  • Mixed timeframes can increase overlapping trades. Apply portfolio-level position sizing to manage overall risk.
• Execution and Synchronization:
  • Ensure your backtesting and execution environment supports smooth transitions between timeframes without timing issues.

3. Portfolio Design Suggestions

Here’s how to structure and balance portfolios with mixed instruments and timeframes:

A. Single-Timeframe, Multi-Instrument Portfolio

• Example: XAUUSD M15, AUDUSD M15, EURUSD M15.
• Advantages: Easier to manage global options since all strategies share the same timeframe logic.
• Challenges: Instruments with highly different characteristics may need different filters/settings.

B. Multi-Timeframe, Single-Instrument Portfolio

• Example: XAUUSD M15, XAUUSD H1, XAUUSD H4.
• Advantages: Focused on a single instrument’s behavior across timeframes. Robust to changes in volatility.
• Challenges: Risk of over-concentration if XAUUSD trends or ranges unexpectedly.

C. Multi-Timeframe, Multi-Instrument Portfolio

• Example: XAUUSD H1, EURUSD M15, USDJPY H4.
• Advantages: Maximum diversification of signals, less exposure to a single market.
• Challenges: Complexity in managing global options and synchronizing trades.

4. Workflow for Building Mixed Portfolios

1. Validate Individual Strategies:
   • Test strategies on their respective instruments and timeframes. Use robust walk-forward validation.
2. Correlation Analysis:
   • Measure historical correlations between strategies’ equity curves, not just instruments.
3. Simulate Portfolio:
   • Combine strategies in SQX Portfolio Master and analyze:
     • Total return and drawdown.
     • Sharpe ratio, MAR, profit factor.
4. Apply Global Options:
   • Ensure settings like “Exit on Friday” or “Limit Trading Range” make sense across all strategies.
5. Monitor Overlap:
   • Watch for overlapping trades that could inflate risk or strain account margin.

Example Scenarios

Scenario 1: Balanced Portfolio

• XAUUSD H1 (Trend-following).
• EURUSD M15 (Mean reversion).
• USDJPY H4 (Breakout).
• Outcome: Well-diversified, different strategies complement each other.

Scenario 2: Aggressive Scalping

• XAUUSD M15 (Scalping).
• GBPUSD M5 (Scalping).
• AUDUSD M15 (Scalping).
• Outcome: High trade frequency, suitable for high-volatility periods, but riskier due to correlation.

5. Tools to Support Mixed Portfolios

• Correlation Analysis Tools (available in SQX Portfolio Master).
• Monte Carlo Simulations: Stress-test portfolio robustness with random sequence variations.
• Walk-Forward Matrix: Ensure each strategy is robust independently before merging.

Key Advice

If what you’re doing aligns with these points—correlation analysis, managing global options, ensuring diversity—it’s on the right track. If you’re skipping global coherence checks or overloading similar strategies (e.g., scalping on correlated pairs), adjust your approach.

To incorporate these elements into your SQX setup for handling maximum drawdown effectively, here are the specific parameters and considerations:

1. Portfolio Diversification:

• Multi-Strategy Approach: In SQX, build and test different strategies that target various market conditions (e.g., trend-following, breakout, mean-reversion). This diversification helps balance drawdowns since each strategy may perform differently during various market phases.
• Asset Selection: Include other assets besides XAUUSD if feasible, such as major currency pairs, to spread risk and reduce correlation.
• Correlation Analysis: Use SQX’s built-in tools to analyze the correlation between strategies. Select a mix with low correlations to ensure the portfolio’s drawdown is less affected by any single market movement.

2. Regular Monitoring:

• Backtesting and Walk-Forward Analysis: Regularly backtest and run walk-forward optimization to see how drawdown patterns evolve over different timeframes. This ensures strategies are resilient and maintain drawdowns below the 10% threshold.
• Out-of-Sample Testing: Test strategies with out-of-sample data to confirm they perform well beyond just in-sample data, helping to validate their robustness in managing drawdown.
• Monte Carlo Simulations: Utilize Monte Carlo analysis in SQX to stress test strategies under various market conditions, identifying those with a consistent maximum drawdown below the desired level.

3. Adaptive Stop Loss:

• ATR-Based Stop Loss: In SQX, configure adaptive stop losses based on a multiple of the ATR (e.g., 1.5x-2x). This makes the stop loss dynamic and better suited to market conditions, allowing trades to have room during high volatility while tightening in calm periods.
• Trailing Stop Loss: Implement a trailing stop loss feature that follows the price as it moves in your favor. Use a percentage of the ATR or a fixed percentage (e.g., 1%) that updates as the price increases.
• Break-Even Adjustments: Set rules for moving the stop loss to break-even after the trade has gained a certain number of pips or a percentage of the ATR. This reduces the risk of a profitable trade turning into a loss.

SQX Parameter Settings for Maximum Drawdown Control:

• Maximum Drawdown Filter: Configure the “Max Drawdown” filter during strategy generation to discard any strategy that shows a drawdown over 10% in backtests.
• Risk Control Rule: Add a rule that limits the number of open trades or caps exposure based on total equity or margin.
• Trailing Stop Mechanism: Set up rules in the strategy logic for trailing stops that trigger once a predefined profit threshold is reached (e.g., trailing starts at 1 ATR profit).
• Portfolio Drawdown Limitation: Use SQX Portfolio Master to set an equity stop for the portfolio, ensuring the combined strategies don’t push drawdown above 10%.

By using these techniques and parameters, you create a structured approach to control drawdown effectively, ensuring strategies are optimized for real-world trading and compliant with risk management standards like those required for FTMO.

To help you optimize for the FTMO challenge, here are detailed points addressing your questions:

1. Daily Loss Limit (5%):
   • Trade Sizing: Structure your trade sizes so that no single trade or group of trades taken on the same day exceeds 2-3% of the account. This buffer can help you stay within the 5% limit even in the worst-case scenario.
   • Stop Loss Placement: Always use stop losses to cap the potential loss on each trade. Ensure that cumulative risk from open positions stays well within the daily loss limit.
   • Limit Daily Exposure: Use a daily trading plan that sets a cap on the number of trades or risk exposure for the day, helping prevent overtrading.
2. Maximum Drawdown (10%):
   • Portfolio Diversification: Build a diversified strategy set to spread risk across uncorrelated assets or strategies.
   • Regular Monitoring: Use SQX to simulate and monitor drawdown patterns during backtesting and select strategies with a maximum drawdown comfortably below the 10% threshold.
   • Adaptive Stop Loss: Implement dynamic stop losses or trailing stops that adjust based on market volatility to protect gains and reduce drawdown.
3. Risk Management:
   • Fixed Fractional Position Sizing: Risk a fixed percentage (e.g., 0.5%-1%) of your account on each trade to limit potential losses without stifling profit potential.
   • Break-Even Stops and Trailing Stops: Use break-even stops to remove risk after a trade moves in your favor and trailing stops to lock in profit as it continues.
   • Multiple Timeframe Analysis: Use higher timeframes for trend direction and lower ones for entry, optimizing entry points and reducing the chance of high drawdowns.
4. Consistency:
   • Robust Backtesting and Optimization: Use SQX to test strategies across various time periods and ensure they perform consistently, not just in one market condition. Utilize out-of-sample testing to confirm stability.
   • Trade Filters: Apply filters based on volatility, trend strength (e.g., ADX or Moving Average slopes), or other market conditions to prevent trading in suboptimal periods.
   • Maintain Simplicity: Avoid overfitting by keeping strategy rules straightforward and logical.
5. Adapting to Different Market Conditions:
   • Volatility-Based Adjustments: Incorporate volatility measures such as ATR to adapt stop loss and take profit levels according to market conditions.
   • Multi-Strategy Approach: Combine trend-following, mean-reversion, and breakout strategies to cover various market phases.
   • Monitoring Correlations: Regularly check correlations between strategies or instruments to avoid compounding risk when market conditions change.
6. Position Sizing:
   • Dynamic Sizing: Adjust position size based on current account equity and market volatility. This can be done using ATR-based sizing to ensure that each position aligns with your risk tolerance.
   • Scaling In and Out: Gradually scale into trades to mitigate entry risk and scale out to lock in partial profits, balancing risk and reward.
7. Stop Loss Placement:
   • ATR-Based Stops: Use a multiple of ATR (e.g., 1.5-2x ATR) to set a stop loss that adapts to current market volatility, allowing enough room for price fluctuations.
   • Technical Levels: Place stops beyond key support/resistance or Fibonacci levels to avoid being stopped out prematurely.
   • Time-Based Stops: Consider using time-based exits in combination with price-based stop losses to limit exposure during uncertain market periods.

These strategies and tips can help you meet FTMO’s trading rules while optimizing for profitability and risk management.

To ensure your trading strategy aligns with FTMO guidelines and efficiently trades XAUUSD while managing risk effectively, here’s how to set up, rank, and parameterize your strategies in StrategyQuant X (SQX):

Setup/Parameters in SQX:

1. Trade Sizing Setup:
   • Money Management Rules: Configure the strategy to use fixed fractional position sizing, targeting 1%-2% of account risk per trade.
   • Max Daily Risk Cap: Implement a daily risk cap in the money management module to prevent exceeding 3%-5% total risk exposure.
   • Volatility-Based Adjustment: Use ATR-based position sizing to dynamically adjust the lot size according to market conditions.
2. Stop Loss (SL) and Take Profit (TP):
   • SL Parameters: Set stop loss at 1.5-2x the ATR value to adapt to XAUUSD’s volatility.
   • TP Parameters: Configure TP at 2-3x ATR or use a predefined RRR (e.g., 1:2 or 1:3) in the setup.
   • Trailing Stop: Enable trailing stops based on ATR or a percentage of the move to capture gains while protecting profits.
3. Break-Even (BE) Settings:
   • BE Activation: Implement a rule that moves the SL to breakeven when the price moves in your favor by 0.5%-1%.
   • Dynamic BE: Use ATR-based calculations for adjusting the break-even point based on market volatility.
4. Entry Criteria:
   • Indicators: Configure indicators like RSI, CCI, or Moving Averages for signal generation.
   • Confirmation Filters: Include filters like ATR to ensure the market is in a trending state before taking trades.
   • Multi-Timeframe Analysis: Utilize an H4 timeframe for trend confirmation and an H1 for trade entries.
5. Exit Strategies:
   • Time-Based Exit: Set a maximum trade holding period (e.g., up to 5 days).
   • Signal-Based Exit: Add indicators like Super Trend or Parabolic SAR to trigger exit conditions.
   • End-of-Day Exit: Configure exits that close positions before significant economic releases or weekends.

Ranking Criteria in SQX:

To choose the most robust strategies for XAUUSD trading that align with FTMO rules, apply the following ranking criteria:

1. Performance Metrics:
   • CAGR/Max Drawdown: Set a threshold where CAGR/Max Drawdown > 1 for risk-adjusted performance.
   • Sharpe Ratio: Ensure a Sharpe Ratio > 2 for good risk-adjusted returns.
   • Profit Factor: Select strategies with a Profit Factor > 1.5 to ensure more winning trades compared to losing ones.
   • Stability: Use a stability metric of > 0.8 to identify strategies with consistent performance.
   • Open Drawdown: Filter out strategies with Open DD% exceeding 15%.
2. Risk Management Parameters:
   • Max Daily Loss Check: Filter out strategies that, during backtesting, exceed a 5% daily loss.
   • Max Drawdown Filter: Discard strategies with a maximum drawdown over 10% in backtests.
3. Consistency Metrics:
   • Consecutive Winning/Losing Trades: Rank strategies with greater than 2 consecutive winning trades and manage strategies that handle consecutive losses effectively.
   • Win Rate and Expectancy: Ensure that the strategy has a win rate that supports profitability, even if lower, as long as the RRR is high.
4. Adaptive Parameters:
   • Volatility Adaptation: Include strategies that adjust their entries, exits, or stop levels based on ATR changes to handle varying market conditions.
   • Market Regime Testing: Use SQX to test strategies in different market regimes (e.g., trending vs. ranging) to ensure adaptability.

By structuring and ranking strategies this way in SQX, you create a robust, compliant approach for trading XAUUSD under FTMO rules.

If your results exceed the values I recommended, that’s an excellent indication that you have exceptionally robust and high-performing strategies. Here’s what you should do next to make the most of these outstanding strategies:

1. Prioritize Top-Performing Strategies:

• Select the Best Strategies: Prioritize strategies that exceed all or most of the values stated. For example, if a strategy has a Ret/DD ratio significantly above 4, a profit factor above 2, and a Sharpe ratio above 2, it’s a top candidate for inclusion in your portfolio.
• Diversify Within Top Performers: Ensure that your high-performing strategies aren’t too correlated with each other. A diversified set of high-quality strategies will help mitigate risk.

2. Create High-Performance Portfolios:

• Balance Strategy Types: Build a portfolio that combines different types of strategies (e.g., trend-following, mean reversion, breakout) to create a more resilient and balanced approach.
• Allocate Capital Strategically: Allocate more capital to strategies that have proven consistent and robust over all testing phases, but keep an eye on overall drawdown to avoid overexposure.
• Test Portfolio Stability: Run portfolio-level backtests and simulations to check for overall performance metrics like cumulative profit factor, Sharpe ratio, and maximum drawdown.

3. Stress Test and Validation:

• Monte Carlo Stress Testing: Perform Monte Carlo simulations to see how the portfolio performs under various simulated market conditions. This will help confirm that the strategies maintain high performance even with slight data variations or unexpected market behavior.
• Out-of-Sample (OOS) and Forward Testing: Ensure the strategies that exceed your stated metrics continue to perform well in OOS data and in live or demo forward testing.

4. Evaluate Long-Term Performance:

• Assess Stability and Adaptability: Check if the top-performing strategies maintain their edge over longer timeframes and during different market conditions (e.g., trending vs. ranging markets).
• Monitor Key Metrics: Continuously track the profit factor, Ret/DD ratio, Sharpe ratio, drawdown percentages, and CAGR/Max DD% to ensure these metrics remain favorable over time.

5. Prepare for Live Trading:

• Simulate Live Trading: Before deploying your portfolio for real capital, simulate live trading with historical data as a final check.
• Set Risk Management Rules: Establish stop-loss, take-profit, and position sizing rules that align with your high-performing strategy metrics.

6. Performance Monitoring:

• Regular Performance Review: Once live trading starts, monitor your strategies closely to see if they continue to exceed expectations. Adjust your portfolio as needed based on performance changes.
• Rebalance Portfolio: Periodically review and rebalance your portfolio to ensure it continues to contain only top-performing strategies that exceed your target metrics.

Benefits of Strategies That Exceed Your Metrics:

• High Reliability: Strategies with metrics that exceed your thresholds indicate a strong and reliable edge in the market.
• Risk Mitigation: Exceeding drawdown and stability requirements means these strategies are safer and less likely to encounter major losses.
• Scalability: Such strategies may allow for greater position sizing or capital allocation due to their high return-to-risk ratios.

Final Thoughts:

If your results are consistently exceeding these values, you are in a very strong position for creating an elite portfolio of trading strategies. Ensure that you maintain rigorous validation and risk management practices as you move forward to capitalize on these high-performing strategies.

Yes, you can definitely use the filters you listed for building your trading portfolio. Applying these filters ensures that only the most robust and reliable strategies make it into your portfolio, creating a diversified and resilient set of strategies for trading. Here’s how to use these filters effectively when constructing a portfolio:

Steps for Using Filters to Create a Portfolio:

1. Apply the Filters to Select Strategies:
   • Use your filters (e.g., Profit Factor > 1.4, Ret/DD ratio > 4, Sharpe Ratio > 1.5, etc.) to narrow down your list of strategies.
   • Apply these filters consistently across all testing stages (IS, OOS, and different data precisions) to ensure that the selected strategies are robust and well-suited for real trading.
2. Select a Diverse Set of Strategies:
   • Mix Strategy Types: Include different types of strategies such as trend-following, mean reversion, breakout, and range trading to diversify your portfolio.
   • Multiple Timeframes: Ensure that your portfolio contains strategies that trade on different timeframes (e.g., H1, M30, M15, M5) to spread risk across various market conditions.
   • Trading Approaches: Include strategies with different entry and exit techniques (e.g., indicators, price action, volatility-based) for a balanced portfolio.
3. Ensure Correlation Is Low:
   • Check for correlation between the selected strategies. Including highly correlated strategies may increase the overall risk as they might behave similarly during specific market conditions.
   • Aim for strategies that complement each other, so that if one performs poorly, others may compensate for it.
4. Backtest the Portfolio:
   • Backtest the combined portfolio using your existing filters to evaluate the overall performance metrics such as profit factor, drawdown, Sharpe ratio, and stability.
   • Use a weighted approach for strategy allocation to balance exposure within the portfolio.
5. Simulate Portfolio with Out-of-Sample (OOS) Data:
   • Run the portfolio through OOS testing and check the combined performance metrics.
   • Ensure that the portfolio maintains acceptable levels of maximum drawdown, stability, and profitability.
6. Optimize Strategy Allocation:
   • Adjust the capital allocation for each strategy to balance the portfolio risk. For example, allocate more capital to strategies with a higher Sharpe ratio or a better Ret/DD ratio.
   • Ensure the overall portfolio drawdown stays within a risk-tolerant level that aligns with your trading goals.

Benefits of Using These Filters for Portfolio Creation:

• Consistency: Applying these filters helps ensure that your portfolio strategies are consistently high-quality, reducing the risk of underperformance.
• Robustness: By selecting strategies that meet your strict criteria across all stages of testing, your portfolio will be more adaptable to various market conditions.
• Risk Management: Including maximum drawdown and stability filters keeps your portfolio within manageable risk levels.

Final Tips:

• Monitor Portfolio Performance: After constructing your portfolio, continue to monitor its performance periodically and replace or adjust strategies as needed based on live or forward-test results.
• Forward Test: Always forward test the portfolio in a demo or live environment with minimal risk to confirm that it behaves as expected.

Using your existing filters as a benchmark for selecting strategies ensures that you build a strong, diversified portfolio capable of sustaining various market conditions.

Suggested Approach for Phase 1 Portfolio Creation with IS and OOS Filtering

In phase 1, the goal is to create several robust portfolios by filtering and ranking strategies to achieve high performance and stability, with a specific focus on their behavior in both in-sample (IS) and out-of-sample (OOS) testing.

1. IS and OOS Filtering:
   • IS: Ensure strategies perform consistently during the in-sample period by prioritizing parameters like CAGR/Max Drawdown (DD%), Sharpe Ratio, and Stability. These metrics demonstrate the strategy’s profitability and risk management in the training phase.
   • OOS: Use filters that emphasize robustness and adaptability to unseen data. High values for metrics like Sharpe Ratio, Stability, and low OOS drawdowns indicate resilience. Also, analyze parameters such as Open DD% and consecutive losses to evaluate risk in live scenarios.
2. Prioritize Key Performance Metrics for Filtering and Ranking:
   • CAGR/Max DD%: A high ratio here is essential for long-term profitability.
   • Sharpe Ratio: Select for a Sharpe Ratio > 2 in both IS and OOS to ensure that returns consistently outweigh risk.
   • Profit Factor: A Profit Factor > 1.5 across IS and OOS indicates that profitable trades outweigh losses.
   • Stability: Aim for Stability > 0.8, ensuring strategies are less likely to be overfitted.
   • Open DD% and Max DD%: Filter for Open DD% < 15% to limit real-time exposure to high drawdowns, with Max DD% providing an additional safeguard.
   • Net Profit and R-Squared (R²): These are supportive metrics, with higher values indicating stronger trends in profit generation.
3. Portfolio Structuring:
   • From the filtered strategies, construct 4–6 portfolios, each containing 4–8 strategies that exhibit the highest OOS Sharpe Ratios, Stability, and low drawdowns. Ensure each portfolio balances a mix of high-return and stable strategies.
   • Once these portfolios are built, remove selected strategies from the list for the next round of portfolio creation, or, alternatively, create all portfolios from the initial list to maximize the use of all high-performing strategies without deletion.

This approach ensures each portfolio is diversified in both strategy type and performance metrics, optimizing for long-term stability across various market conditions.

If you prefer not to use ADX, MACD, RSI, or ATR, we can build the strategy using other signals from your available list in SQX. Below is an alternative setup using simpler, yet effective indicators:

“Momentum-Based Price Action Grid Strategy”

Indicators Used:

• Candle Patterns: For price action and reversal detection.
• Bollinger Bands: To capture volatility and breakout opportunities.
• Moving Averages: For trend direction and entry triggers.
• Fractals: To identify local highs/lows for breakouts or retracements.

Strategy Setup in SQX:

1. Entry Signals

• Candle Pattern: Use bullish or bearish reversal candle patterns (like engulfing, hammer, or doji) as entry triggers.
• Moving Average Crossover:
  • Buy when a faster-moving average (e.g., 10-period) crosses above a slower-moving average (e.g., 50-period).
  • Sell when the faster MA crosses below the slower MA.
• Bollinger Bands: If the price closes above the upper band, consider a buy (bullish breakout). If it closes below the lower band, consider a sell (bearish breakout).

How to Set It Up:

• In Entry Conditions, use the Candle Pattern indicator for reversal patterns.
• Add Moving Average Crossover as a trend confirmation filter.
• Include a condition using Bollinger Bands to detect breakouts and confirm volatility.

2. Exit Strategy

• Fractal Exit: Exit positions when the price hits a recent fractal high (for buy trades) or a fractal low (for sell trades).
• Bollinger Bands Reversal: Close the position if the price reverses and closes inside the Bollinger Bands after breaking out.
• Time-based Exit: Close all positions after 5 days to limit the holding period.
• Friday Close: Automatically close all trades at 23:30 on Fridays to avoid weekend risk.

How to Set It Up:

• In Exit Conditions, use the Fractal Indicator to detect recent highs and lows for exit points.
• Set up a rule to exit when the price re-enters the Bollinger Bands after a breakout.
• Define a Time-based Exit rule to close trades after 5 days or on Friday.

3. Risk Management

• Fixed Lot Size: Trade with 0.01 lots per $100 of the account balance.
• Risk Management: Set a fixed percentage of risk per trade (e.g., 1% of the total account).
• Grid Layer (Optional): Place pending buy/sell orders at intervals (e.g., 10 pips) from the current price to capture retracements or price movements.

How to Set It Up:

• In Money Management, set your fixed lot size and risk percentage.
• If you want a grid, use the Pending Orders functionality to create a grid around the current price.

Additional Layers:

1. Duplicate Trades: Enable multiple trades to open in the same direction.
2. Break-Even: Move the stop loss to break-even once the trade has moved in your favor by a certain distance (e.g., a fixed pip value or after hitting a fractal level).

Final Steps:

1. Backtest: Run your backtests using M1 data over 20 years.
2. Optimize: Adjust moving average periods, Bollinger Band deviations, and fractal periods to find the best combination.
3. OOS Testing: Validate the strategy’s performance on out-of-sample data.

By removing ADX, MACD, RSI, and ATR, this setup focuses more on price action with simple indicators that track volatility and trend strength.

Let’s simplify things by using the signals you already have in your system. Here’s a strategy you can set up with your available indicators:

“Simple Trend Momentum Grid Strategy”

Objective:

This strategy will capture both trend-following and breakout moves with simple signals, using indicators already present in SQX.

Key Components:

1. Primary Indicators:
   • ADX: For confirming strong trends.
   • MACD: For detecting momentum and direction.
   • RSI: To help with overbought/oversold levels.
   • ATR: For volatility-based risk management.

Strategy Setup in SQX:

1. Entry Signals

• MACD Crossover: Enter a buy when the MACD line crosses above the signal line, and enter a sell when the MACD line crosses below the signal line.
• ADX > 25: Confirm that the market is trending before allowing an entry.
• RSI Confirmation:
  • If RSI > 70, avoid buying (overbought signal).
  • If RSI < 30, avoid selling (oversold signal).

How to Set It Up:

• In SQX, use Entry Conditions with MACD crossover as the trigger.
• Add an additional filter where ADX > 25 to confirm trend strength.
• Add RSI as a filter to avoid extreme market conditions.

2. Exit Strategy

• Take Profit and Stop Loss: Use ATR-based levels. For example:
  • Take Profit: 2x ATR(14).
  • Stop Loss: 1x ATR(14).
• Trailing Stop: Trail the stop loss based on ATR, moving the stop higher/lower as the trend progresses.
• Exit by Time: Close all positions every Friday at 23:30 to avoid weekend gaps.

How to Set It Up:

• In Exit Conditions, define take profit and stop loss levels based on ATR values.
• Use the Trailing Stop function with ATR.
• Set a rule in Exit Conditions to close all trades at a specific time on Friday.

3. Risk Management

• Use Lot Size: 0.01 lots per $100 of the account balance.
• Risk %: Define risk management to ensure that no more than a specific percentage (e.g., 1%) of the total capital is risked on each trade.

How to Set It Up:

• In SQX, use the Money Management tab to set the lot size and risk percentage.

4. Grid Layer (Optional)

• If you want a grid layer to capture additional profits, set up pending orders at regular intervals around the current price (e.g., every 10 pips) with small lot sizes.
• You can also adjust the grid based on market volatility using ATR.

Final Setup Steps:

1. Backtest: Use your M1 data over the 20-year range to evaluate how the strategy performs.
2. Optimize: Fine-tune the parameters like ATR multiples, grid intervals, and trailing stops for the best results.
3. Out-of-Sample Testing: Ensure that your strategy performs well in unseen data to confirm robustness.

This simpler approach will help you use the signals you’re familiar with while maintaining solid trend-following and risk management.

By merging these trading strategies with your specifications, you’ll create a highly adaptable system that should perform well across different market conditions. Would you like to test this setup with a focus on any particular method or combination first? ———— make 1 of random uniqe and exotic trading style

Here’s an exotic and unique trading strategy that merges multiple aspects of traditional techniques but with an unconventional twist:

“Harmonic Grid Momentum Breakout”

Concept: This strategy combines the principles of grid trading, harmonic patterns, and momentum breakouts. The goal is to capture both trending and ranging market conditions while managing risk dynamically. It thrives on detecting strong directional moves and harmonic reversals, using grid placement to capture both breakouts and retracements.

Key Components:

1. Grid Trading Layer:
   • A grid of orders is placed at regular intervals above and below the current price, with a predetermined spacing (e.g., 10 pips).
   • The grid can be expanded dynamically based on market volatility using ATR.
   • If price moves into a trending market, breakouts are captured, while in ranging conditions, price oscillations fill and close the grid orders profitably.
2. Harmonic Pattern Recognition:
   • The strategy identifies harmonic patterns (e.g., Gartley, Bat, Butterfly) using Fibonacci retracements and extensions.
   • When a pattern completes, it signals a reversal or continuation of the trend.
   • Entry orders are placed based on the harmonic reversal points, with grids enhancing profit capture from both retracement and continuation.
3. Momentum Confirmation (RSI + MACD):
   • RSI is used to confirm momentum. If RSI > 70, look for shorting opportunities; if RSI < 30, look for buying opportunities.
   • MACD crossover signals trend strength and confirms breakout conditions. If the MACD lines cross and the grid captures the breakout, positions are allowed to ride the momentum.
4. Dynamic Risk Management:
   • Trailing Stop: Adjusts dynamically based on ATR, ensuring profits are locked in while allowing the trade to run.
   • Break-even Stop: Once price moves halfway to the profit target, the stop loss moves to break even, ensuring no loss on that trade.
5. Weekly Market Reset:
   • All open trades are closed by the end of the trading week, ensuring no positions are held over the weekend to avoid gaps.
   • The grid resets at the beginning of the new week with fresh levels based on the week’s opening volatility.

Trading Conditions:

• Timeframes: H1 for main signal entries, H4 for confirming harmonic patterns and momentum direction.
• Market: Gold (XAU/USD).
• Lot Sizing: 0.01 lot per $100 of the account balance, with risk managed by risk percentage of total capital (as per your initial design).
• Risk/Reward Ratio: Aimed at 1:3 or higher.

How It Works:

1. Setup Phase: The grid is initialized with buy and sell orders at regular intervals.
2. Harmonic Detection: As the market moves, harmonic patterns are identified, and additional grid orders are aligned to these patterns, looking for precise reversals.
3. Momentum Confirmation: If the market shows momentum through RSI and MACD crossovers, additional positions are opened based on the breakout strength.
4. Profit Capture: As price fluctuates within the grid, orders are closed for profits. If momentum is strong, the trailing stop secures profits as the price trends.

Exotic Edge:

• Multi-Layer Strategy: This strategy captures both ranging and trending conditions. The harmonic patterns add precision, while the grid ensures profits from oscillations, and momentum ensures strong breakouts are capitalized on.
• Versatile Profit Capture: Whether the market moves wildly or remains calm, this strategy can adapt, always seeking opportunities across multiple layers.

A Sharpe ratio greater than 1 is a positive indicator for a trading strategy, but when it’s over 2, it signifies even better performance. Here’s a breakdown:

• Sharpe Ratio > 1: Indicates that the strategy is generating more returns than the risk it’s taking. A ratio above 1 means the excess returns (returns above the risk-free rate) are higher than the volatility of the strategy’s returns.
• Sharpe Ratio > 2: This is considered excellent performance. A Sharpe ratio over 2 means the strategy is delivering significantly higher returns relative to the amount of risk (volatility) it’s exposed to. It suggests the strategy is not only profitable but also doing so with low volatility and risk. This kind of performance is desirable for long-term strategies, as it indicates a strong balance between risk and reward.

What a Sharpe Ratio Over 2 Means:

1. Efficient Risk Management: The strategy takes relatively low risk compared to the high returns it generates.
2. Low Volatility: The equity curve should be relatively smooth, with fewer large drawdowns.
3. Consistency: It’s a sign of strong risk-adjusted returns—the strategy is not reliant on a few lucky trades but generates returns consistently over time.

In summary, a Sharpe ratio over 2 indicates an outstanding risk-reward balance, and such a strategy is highly desirable, especially if the ratio remains consistent across both In-Sample (IS) and Out-of-Sample (OOS) testing. However, always double-check for overfitting or data snooping—make sure that this performance translates well in OOS and live trading conditions.

do it your best with my need to build on main TF H1, sub chart H4. H1 for active signal H4 for confirmation. basse on 0.01 : 100$ and Risk % on account.

To build your strategy in StrategyQuant X with the main TF (H1) for active signals and H4 for confirmation, along with risk management based on 0.01 lots per $100 balance and a percentage-based risk, here’s an optimal setup.

1. Timeframe Setup:

• Main Timeframe (H1): This will be the active trading timeframe where signals are generated and trades are executed.
• Sub-chart Timeframe (H4): This higher timeframe will be used to confirm the direction of the trend before executing trades on the H1 chart.

2. Signal and Confirmation Logic:

• H1 (Active Timeframe):
  • Use indicators like Moving Average (MA), RSI, or MACD for generating buy/sell signals.
  • For example:
    • Buy Signal: Price crosses above the 50-period Moving Average (MA).
    • Sell Signal: Price crosses below the 50-period Moving Average (MA).
• H4 (Confirmation Timeframe):
  • This timeframe will confirm the trend direction before executing trades.
  • Confirmation example:
    • Only buy if the 200-period MA on H4 is pointing upwards.
    • Only sell if the 200-period MA on H4 is pointing downwards.

3. Position Sizing:

• For position sizing, your specification is to trade 0.01 lots per $100 of balance. To implement this:
  • Set the lot size based on account balance: Use a custom rule in StrategyQuant that dynamically calculates lot size.
  • For every $100 in balance, set lot size = 0.01.
  • Risk Percentage: Use the account balance and risk percentage to calculate the stop loss based on the total risk per trade.
    • Example: If risk per trade is 1%, on a $100 account, this equals $1 risk. The stop loss distance should be calculated based on the value of 0.01 lots.

4. Exit Strategies:

Combine different exit strategies to close trades frequently while maintaining profitability:

• Trailing Stop:
  • Set a trailing stop that follows the price as it moves in favor of your trade. This will ensure that you secure profits when the price reverses slightly.
  • Trailing Activation: Activate the trailing stop after a certain number of pips in profit.
• Profit Target:
  • Set a modest profit target that can be hit frequently. Given gold’s volatility, a reasonable target might be 20-50 pips, but you can optimize this during backtesting.
• Stop Loss:
  • Calculate stop loss based on the percentage risk setting. For example, if you are risking 1% of the account per trade, the stop loss should reflect this value based on the lot size and account balance.
• Exit After Bars:
  • You could add a rule to exit the trade if it hasn’t hit the profit target or stop loss within a specific number of bars (e.g., after 50 bars on H1).
• Exit Rule:
  • Use a custom indicator or logic for additional precision. For example, exit trades if the RSI is overbought/oversold on H1, or when the MACD histogram flips direction.

5. Risk Management:

• Risk % on Account:
  • Set a risk percentage (e.g., 1% or 2%) for each trade. This will limit the amount of capital you are risking on each position.
  • This can be implemented in StrategyQuant X by setting the risk to a fixed percentage of the account balance for each trade. The software will adjust the stop loss distance or position size accordingly.
  • Leverage: Ensure that leverage is factored in, especially with volatile assets like gold.

Example Strategy Steps in StrategyQuant X:

1. Entry Conditions:
   • H1: Enter a long trade when the price crosses above the 50-period MA, and the RSI is above 50 (indicating upward momentum).
   • H4: Confirm the trade only if the 200-period MA is sloping upwards (trend confirmation).
2. Exit Conditions:
   • Exit when the trailing stop is hit.
   • Exit when the profit target (e.g., 30 pips) is hit.
   • Exit after 50 bars if none of the other exit conditions are met.
3. Position Size:
   • Set 0.01 lots per $100 balance.
   • Risk 1-2% of the account balance on each trade.
4. Backtesting & Optimization:
   • Run the strategy through backtests on historical data for gold to optimize parameters like:
     • Profit target and trailing stop distance.
     • Moving average periods (e.g., experiment with 20, 50, 200 MA).
     • Risk percentage.
   • Use Monte Carlo simulations to assess how the strategy performs under different market conditions.