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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.

In backtesting against 20 years of gold data, if my cost is around $1000, should I be concerned with the 2003-2010 period, since gold prices were significantly different after 2010, or should I add more cost for this test?

When backtesting against 20 years of gold data with a cost of $1000, the period from 2003 to 2010 warrants special attention for a few reasons:

Why Focus on 2003–2010?

1. Market Regime Change:
   • Gold underwent a major bull market during 2003–2010, rising from approximately $300 to over $1200 by the end of the period.
   • After 2010, gold experienced new price regimes, including the 2011 peak and subsequent corrections. These changes may mean that strategies working well before 2010 might fail in later, more volatile markets.
2. Cost Significance:
   • A $1000 cost might have represented a larger relative percentage of the price during 2003–2010 (e.g., around 5%-10% of gold’s price) compared to post-2010 periods when gold prices have often been above $1500.
3. Volatility Differences:
   • The volatility structure of gold has evolved over time. Pre-2010, trends were stronger and more directional, while post-2010 has seen more erratic and range-bound movements.

Should You Be Concerned?

Yes, you should carefully evaluate this period because:

1. Disproportionate Impact:
   • If a strategy heavily depends on the trends of 2003–2010, its robustness in post-2010 periods (with more range-bound action) could be in question.
   • Costs may have a larger impact on profitability for strategies built in this regime compared to more recent data.
2. Regime Bias:
   • A strategy optimized for pre-2010 conditions may overfit to that regime and not generalize well for the flatter periods seen later.
3. Risk of Misleading Results:
   • Even if the backtest shows strong performance overall, the pre-2010 period may dominate the results due to high trends and low relative costs.

Adjusting Costs for Realism

To ensure realistic testing:

• Increase the Cost:
  • You can apply a scaled transaction cost to simulate the higher relative impact of costs during periods when gold prices were lower. For example:
    • Use a percentage-based cost relative to price (e.g., 0.2% of the price per trade) instead of a fixed $1000.
  • Alternatively, segment the backtest into multiple periods and apply different costs for each based on gold’s price levels.
• Separate Analysis:
  • Evaluate performance metrics for 2003–2010 and post-2010 separately. This will help you determine if the strategy is overly reliant on the earlier period’s trends.

Practical Steps:

1. Segment Backtest Results:
   • Divide results into multiple time frames (e.g., 2003–2010, 2011–2020, and 2021–present) and analyze performance metrics separately for each.
2. Simulate Robust Costs:
   • Apply higher costs for lower price periods and re-run the backtest to evaluate its robustness across varying market conditions.
3. Evaluate Strategy Adaptability:
   • Check how the strategy performs during flat or range-bound periods (e.g., 2012–2015) compared to strong trends (e.g., 2005–2010).
4. Compare Across Assets:
   • Test the strategy on other metals like silver or correlated assets. This helps assess whether the strategy is robust to different price and volatility structures.

Conclusion:

You should absolutely scrutinize the 2003–2010 period. Adjusting costs based on relative price levels and segmenting your backtest will provide better insight into your strategy’s robustness. Additionally, focusing on OOS performance in post-2010 regimes is critical to avoid overfitting to historical bull markets.

Setting up parameters for trading XAUUSD (Gold) can be pivotal due to its volatility and sensitivity to global economic news. Here’s a guide on configuring entry, take profit (TP), stop loss (SL), break-even (BE), and exit strategies, along with general advice:

1. Entry Parameters:

• Signal Confirmation: Use multiple indicators for confirmation, such as a combination of a trend indicator (e.g., Moving Average or Kaufman Efficiency Ratio) and a momentum indicator (e.g., RSI or CCI).
• Timeframes: Focus on the main timeframe (e.g., H1 for execution) and a higher timeframe (e.g., H4) for trend confirmation.
• Volatility Filter: Apply ATR or Bollinger Bands to avoid entries during low-volatility periods when Gold often consolidates.
• Breakout Strategy: Consider entries above/below recent highs/lows or breakout levels using Super Trend or Donchian Channels.

2. Take Profit (TP) Setup:

• Fixed TP Levels: Set TP levels at 2-3 times the ATR value or use Fibonacci extensions (e.g., 161.8% or 261.8%) to target strong price levels.
• Dynamic TP: Implement a trailing TP based on a percentage of price movement (e.g., 1-2% daily move in XAUUSD).
• Partial Profit Taking: Lock in partial profits at the first TP level (e.g., 1:1 RRR), while letting the remaining position run to maximize gains.

3. Stop Loss (SL) Placement:

• ATR-Based SL: Set stop losses at 1.5x-2x the ATR to allow for natural price movement and prevent early exits during typical gold fluctuations.
• Support/Resistance SL: Place SL just beyond major support or resistance levels identified by pivot points or Fibonacci retracements.
• Technical Patterns: Ensure SL is positioned outside significant chart patterns (e.g., triangles or channels) to confirm validity.

4. Break-Even (BE) Strategy:

• Threshold BE: Move SL to breakeven after price moves in your favor by a certain percentage (e.g., 0.5%-1% of the price).
• Volatility-Adjusted BE: Adjust the break-even point based on current ATR to avoid being stopped out prematurely during high volatility.
• Two-Step BE: Initially move the SL to breakeven after a moderate profit (e.g., 1 ATR). As the trade progresses, trail the SL based on new ATR readings or key levels.

5. Exit Strategy:

• End-of-Day Exits: Close positions before key market events or the end of the trading week to avoid overnight gaps and weekend volatility.
• Time-Based Exits: Set a maximum holding period (e.g., no more than 5 days) to reduce exposure to unexpected market changes.
• Signal-Based Exits: Use a trailing indicator like the Parabolic SAR or Super Trend for signal-based exits when the trend reverses.
• Multi-Condition Exits: Combine exit signals from indicators such as RSI (overbought/oversold) and CCI divergences for a robust exit plan.

6. General Advice:

• Risk-Reward Ratio (RRR): Maintain an RRR of at least 1:2 or greater for each trade to ensure long-term profitability.
• Volatility Considerations: Gold is sensitive to economic releases like U.S. Non-Farm Payrolls, CPI data, and FOMC meetings. Plan around these dates by reducing position size or closing trades to manage risk.
• Adaptive Position Sizing: Use a volatility-based position sizing model (e.g., ATR-adjusted) to align trade size with current market conditions.
• Backtesting and Optimization: Use StrategyQuant X to test the strategy over long periods and different market cycles, ensuring robustness.
• Portfolio Approach: Consider pairing your Gold strategy with other correlated and non-correlated assets to spread risk and smooth out performance.

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.

After completing Step 3 (the Walk-Forward Matrix with real tick data), the next step for a final review and comparison involves thorough analysis and filtering to confirm the robustness and reliability of the strategies. Here’s what you should do:

1. Aggregate Results from All Steps:

• Compile the performance metrics from Step 1 (Selected TF + Monte Carlo), Step 2 (1-Minute Data Tick), and Step 3 (Real Tick Data with Walk-Forward Matrix).
• Metrics to compare include:
  • Profit Factor
  • Sharpe Ratio
  • CAGR/Max DD%
  • Drawdown (Max and Open)
  • Stability
  • Win/Loss Ratio
  • Net Profit

2. Consistency Check Across Steps:

• Compare Performance Metrics: Ensure that strategies performing well in Steps 1 and 2 still show good results in Step 3.
• Consistency Across Steps: Prioritize strategies that maintain strong performance and meet your criteria in all testing phases. For example, a strategy with a profit factor consistently above your threshold across all steps is likely more robust.
• Stability Analysis: Verify that the stability metric (e.g., > 0.8) is maintained across the steps, which indicates a consistent equity curve and reduced risk of erratic behavior.

3. Look for Outliers:

• Identify strategies that show significant performance drops or volatility in Step 3 compared to Steps 1 and 2. A drop in metrics such as the Sharpe ratio or a spike in drawdown during the Walk-Forward analysis can signal potential weaknesses under live trading conditions.

4. Select the Most Robust Strategies:

• Filter strategies that pass your final criteria (e.g., consistent Sharpe ratio > 2, profit factor > 1.5, drawdown < 20%).
• Prioritize those that show minimal variation in performance metrics across different testing data sets, as this indicates adaptability and robustness.

5. Portfolio Creation:

• Create Portfolios: Use the most robust strategies to create diversified portfolios. Ensure that these portfolios balance different strategy types and timeframes to reduce risk.
• Simulate Portfolio Performance: Run a final simulation or backtest of your chosen portfolios on real tick data to evaluate how they perform together and assess their combined drawdown and profit factor.

6. Forward Testing and Live Demo:

• Forward Test: Run your selected strategies or portfolios in a live demo account for a few weeks to a few months. This will help validate the strategies’ performance in real-time conditions without risking capital.
• Monitor Performance: Continuously track key metrics and look for consistency with backtested and Walk-Forward results.

7. Compare and Review the Results:

• Final Review Report: Create a comparison report summarizing the performance of each strategy across all testing steps and during the forward test. Highlight key metrics, any significant deviations, and overall performance trends.
• Decision Making: Choose to proceed with strategies or portfolios that maintain robust performance throughout all phases, as they are likely to be the most reliable for live trading.

This comprehensive review ensures that the strategies you move forward with are robust, adaptable, and optimized for real trading environments.

In StrategyQuant X (SQX), setting up trailing stops based on indicators allows you to adaptively protect profits as market conditions change. Here’s a step-by-step guide on how to configure trailing stops that follow indicator levels rather than a fixed price distance.

1. Choose an Indicator for Trailing Activation

You’ll want to pick an indicator that provides reliable support or resistance levels for trailing stops. Common indicators for trailing stops include:

• Moving Average (e.g., EMA or SMA) – Trails based on the trend’s moving average.
• Parabolic SAR – Trails based on the SAR dots, adjusting dynamically as trends change.
• ATR (Average True Range) – Trails based on market volatility, keeping stops further away during high volatility and closer during low volatility.
• Bollinger Bands – Uses upper/lower bands as dynamic stop points.

2. Set Up Indicator-Based Trailing Stops in SQX

Here’s how to configure indicator-based trailing stops in SQX, using a Moving Average as an example:

1. Define the Trailing Stop Condition:
   • Go to Trading Strategy > Money Management in SQX.
   • Enable Trailing Stop and select Custom for more control over the conditions.
2. Create a Rule for Trailing Stop Activation:
   • In the Trailing Stop settings, set the rule condition by adding the indicator as a condition for trailing activation.
   • For example, choose Moving Average from the indicator list and set the trailing stop to activate when the price moves a specific distance from the Moving Average line.
3. Adjust Distance and Frequency:
   • Define the distance between the indicator and the trailing stop:
     • For a Moving Average, you might set the stop to trail at a certain number of pips or percentage below the MA for long positions (or above for short positions).
     • For ATR, you could set the trailing stop to a multiple of the ATR value (e.g., 1.5x ATR).
   • Set the frequency for updating the trailing stop, usually with every new price tick, to ensure it follows the indicator closely.

3. Example Configurations for Different Indicators

Here are configurations for some commonly used indicators as trailing stops:

• Moving Average (MA) Trailing Stop:
  • Set trailing stop to activate when the price is a certain distance (in pips) away from the selected MA.
  • Example: If the price is 20 pips above a 50-period EMA, set the trailing stop 10 pips below the EMA to follow the trend.
• ATR Trailing Stop:
  • Activate the trailing stop when ATR exceeds a certain threshold, then trail the stop by a multiple of the ATR.
  • Example: If ATR(14) = 15 pips, set the stop at 1.5x ATR (22.5 pips) from the entry price, adjusting as ATR changes.
• Parabolic SAR Trailing Stop:
  • Set the trailing stop to follow the SAR dots, which adjust to market reversals.
  • Example: Set the trailing stop to match the SAR level and update with each new SAR value, keeping the stop at the SAR level for trend-following entries.
• Bollinger Bands:
  • Use the lower band as a trailing stop for long trades and the upper band for short trades.
  • Example: Place the trailing stop at the lower Bollinger Band for a long trade and adjust as the bands move based on volatility.

4. Define the Exit Condition in SQX:

• Go to the Exit Rules section and add the indicator-based trailing stop as an exit rule.
• Set the trailing stop to close the position if the price touches or moves past the defined trailing level.

Example: Setting Up a Moving Average-Based Trailing Stop in SQX

1. Activate Trailing Stop: In the Money Management section, enable Trailing Stop and set the trailing condition based on a 50-period EMA.
2. Set Distance: Define the trailing stop to trail at a distance of 10 pips below the EMA for long trades.
3. Update Frequency: Choose on every tick to ensure the stop adjusts as the EMA value changes.
4. Exit Rule: Set the rule to exit when the price crosses the trailing stop level.

Advantages of Indicator-Based Trailing Stops

• Adaptability: The trailing stop adjusts to market conditions, following trends and respecting support/resistance.
• Noise Reduction: Indicator-based stops reduce premature exits, as they often respond to genuine trend shifts rather than small price fluctuations.
• Dynamic Profit Protection: Stops adapt to volatility and trend strength, helping you lock in profits effectively.

This approach lets you create a responsive trailing stop in SQX that aligns with current market dynamics rather than rigid price points, which is especially useful for trend-following and volatility-based strategies. Let me know if you’d like more specific setup help with any particular indicator!

In StrategyQuant X (SQX), if you want to activate a trailing stop without relying on a specific timeframe, you can do this by setting up the trailing stop condition based on price movement or indicator levels rather than on a timeframe. Here are a few ways to implement this:

1. Activate Trailing Based on Distance from Entry Price (Price-Based)

• Setup: Set the trailing stop to activate once the price has moved a specific distance (e.g., a certain number of pips or a percentage move) from the entry price.
• Configuration:
  • In SQX, configure the trailing stop to trigger when the price has moved a specified amount in your favor.
  • Set up the trailing distance as a fixed pip amount, or based on an indicator like ATR or a custom value.
• Advantage: This activates the trailing stop purely based on price movement, so it doesn’t rely on the passage of time or specific candles.

2. Indicator-Based Trailing Activation (No Timeframe Required)

• Setup: Use an indicator to dynamically activate the trailing stop when a trend or threshold condition is met, independent of timeframe.
• Example:
  • Moving Average: Trailing stop activates when the price crosses above a moving average by a certain distance.
  • Parabolic SAR: Use SAR points as a trailing stop activation level, adjusting the stop if the SAR shifts.
  • ADX or Momentum: Activate the trailing stop when a trend strength indicator (like ADX) crosses a threshold, indicating a strong trend continuation.
• Configuration: In SQX, set the activation condition by configuring the trailing stop rule with an indicator level.

3. Profit-Based Trailing Activation

• Setup: Activate trailing when the trade reaches a predefined profit level, regardless of timeframe.
• Configuration:
  • Define a minimum profit level (e.g., 1% or 50 pips) in SQX, and set the trailing stop to activate once this profit threshold is met.
• Advantage: This keeps the trailing stop inactive until the trade has generated enough profit, helping to secure gains while allowing room for the trade to move.

4. ATR or Volatility-Based Activation

• Setup: Use ATR (Average True Range) or other volatility-based indicators to activate the trailing stop once the market reaches a certain level of volatility, independent of a fixed timeframe.
• Configuration: Set the trailing stop activation to trigger when ATR reaches a threshold value. For instance, if ATR goes above a certain level, it may indicate increased momentum, prompting trailing stop activation.
• Advantage: Volatility-based activation allows the trailing stop to adjust dynamically to market conditions without a reliance on a specific timeframe.

Each of these methods allows you to activate the trailing stop based on market conditions, price movement, or profit levels rather than timeframe-specific events. This flexibility can help secure profits in trending markets while allowing trades enough room to develop in your favor. Let me know if you’d like guidance on configuring any of these methods in SQX!

Here’s an updated version of unique trading styles using three or more signals from your list. This will increase complexity while combining multiple indicators for cross-confirmation and more refined trading decisions.

1. Volatility Compression Breakout Strategy

• Concept: Use Choppiness Index, ATR, and Disparity Index to detect volatility compression and breakout potential. This strategy identifies low-volatility conditions followed by sharp breakouts.
• Indicators: Choppiness Index, ATR, Disparity Index
• Entry: When Choppiness Index and ATR both show low volatility, and Disparity Index confirms price compression below a critical threshold.
• Exit: Exit on the first sign of ATR expansion or Disparity Index divergence from the breakout direction.

2. Oscillator Reversion and Momentum Burst Strategy

• Concept: Use Bear Power, Bull Power, and CCI to identify momentum reversals, coupled with Momentum to confirm strong directional movement. This strategy works best in swing trading environments.
• Indicators: Bear Power, Bull Power, CCI, Momentum
• Entry: Buy when Bull Power overtakes Bear Power, and CCI indicates oversold with rising Momentum.
• Exit: Exit when Bear Power gains dominance, or CCI signals overbought.

3. Trend Channel Momentum Strategy

• Concept: This strategy combines Keltner Channel, Didi Index, and Directional Index to capture trades within price channels while confirming momentum with Didi and Directional Index.
• Indicators: Keltner Channel, Didi Index, Directional Index
• Entry: Buy when price touches the lower Keltner Channel, confirmed by a Didi Index crossover and rising Directional Index.
• Exit: Exit when price reaches the upper channel or Directional Index weakens.

4. Mean Reversion on Trend Weakness

• Concept: Use Hull Moving Average, RVI, and Directional Index to identify trend weakening and trade counter-trend mean reversions. Ideal for volatile markets prone to snapbacks.
• Indicators: Hull Moving Average, RVI, Directional Index
• Entry: Buy when Hull MA indicates weakening trend, confirmed by falling RVI and Directional Index showing weak trend strength.
• Exit: Exit once Hull MA indicates a trend reversal, or RVI turns neutral.

5. Cycle-Based Trend Reentry Strategy

• Concept: Combine DSS Bressert, Laguerre RSI, and Momentum to time re-entries into an existing trend during market cycles. This is useful for trading in wave-like price movements.
• Indicators: DSS Bressert, Laguerre RSI, Momentum
• Entry: Enter when DSS Bressert signals an upcycle, Laguerre RSI crosses into bullish territory, and Momentum confirms.
• Exit: Exit when DSS Bressert cycle nears completion or Laguerre RSI diverges.

6. Sentiment and Momentum Reversal Strategy

• Concept: This strategy uses Bull Power, Bear Power, and RVI to gauge market sentiment, alongside DVO for volume-based confirmation of momentum reversals.
• Indicators: Bull Power, Bear Power, RVI, DVO
• Entry: Buy when Bull Power rises above Bear Power, confirmed by RVI signaling bullish momentum and DVO indicating high volume.
• Exit: Exit when Bear Power overtakes Bull Power or RVI momentum fades.

7. Market Chaos and Divergence Strategy

• Concept: Combine Entropy Math, CCI, and QQE to identify chaotic markets and exploit price divergence. Ideal for detecting sudden directional shifts or corrections.
• Indicators: Entropy Math, CCI, QQE
• Entry: Enter when Entropy Math decreases (chaos subsides) and both CCI and QQE show bullish divergence.
• Exit: Exit when CCI indicates overbought conditions or Entropy increases again.

8. Dynamic Momentum and Price Rejection Strategy

• Concept: Use Directional Index, Momentum, and Williams %R to identify strong price rejection in trending markets, trading short-term corrections or pullbacks.
• Indicators: Directional Index, Momentum, Williams %R
• Entry: Buy when Williams %R signals an oversold condition during a trending market confirmed by strong Directional Index and Momentum.
• Exit: Exit when Momentum begins to fade or Directional Index weakens.

9. Volume-Driven Reversal Strategy

• Concept: Use VWAP, Disparity Index, and AVG Volume to trade reversals based on volume imbalances and price deviation from its average price.
• Indicators: VWAP, Disparity Index, AVG Volume
• Entry: Buy when price deviates significantly below VWAP, confirmed by low Disparity Index and rising AVG Volume.
• Exit: Exit when price returns to VWAP or Disparity Index peaks.

10. Multi-Timeframe Divergence Strategy

• Concept: Combine Fractal, Directional Index, and Momentum across multiple timeframes (e.g., M5 for entry, H1 for confirmation) to spot fractal-based divergences.
• Indicators: Fractal, Directional Index, Momentum
• Entry: Enter when a bullish Fractal forms on M5, confirmed by a rising Directional Index and Momentum on H1.
• Exit: Exit when a bearish Fractal forms or Directional Index weakens on the higher timeframe.

These strategies leverage three or more signals, which should give you more robustness and cross-confirmation in your trading. You can now explore these combinations based on your market preferences and risk tolerance.

Added ability to filter by IS and OOS in each topic to see which IS/OOS should be used.

When adding the ability to filter by In-Sample (IS) and Out-of-Sample (OOS) performance in each metric or topic, it becomes critical to differentiate between the two phases of strategy testing. The IS data is used to develop and optimize the strategy, while the OOS data tests the strategy on unseen, untouched market conditions to assess its robustness.

Here’s how to approach filtering each metric for IS and OOS:

IS (In-Sample) vs OOS (Out-of-Sample) Explanation

• In-Sample (IS): The period used for building and optimizing the strategy. High IS performance may indicate overfitting if OOS performance is poor.
• Out-of-Sample (OOS): The unseen data used to test the robustness of a strategy. Good OOS performance indicates that the strategy generalizes well to unseen market conditions.

Metric Prioritization and IS/OOS Expectations

1. Profit Factor
   • IS Target: Should be > 1.5.
   • OOS Target: A good profit factor in OOS should remain above 1.3 or higher to show that the strategy is profitable without overfitting.
   • Purpose: This shows how well the strategy is balancing profits against losses both in-sample and out-of-sample.
2. CAGR/Max DD%
   • IS Target: > 1 (This indicates returns are higher than the risk during optimization).
   • OOS Target: Should ideally remain above 0.8 in OOS. If the value dips significantly, it might indicate over-optimization in the IS phase.
   • Purpose: It helps compare growth to risk over the strategy’s life cycle. Stability in this metric across IS and OOS is important for long-term performance.
3. Sharpe Ratio
   • IS Target: > 2 (This shows good risk-adjusted performance in-sample).
   • OOS Target: Should remain above 1.5 in OOS. Sharpe ratios dropping significantly in OOS may indicate risk or volatility problems in unseen data.
   • Purpose: This measures the strategy’s ability to handle risk consistently, and OOS results are crucial to validate its stability in live trading.
4. Return/Drawdown Ratio (Ret/DD)
   • IS Target: > 2 (Shows solid returns in comparison to risk).
   • OOS Target: Ideally, you want this to stay above 1.5 to confirm stability.
   • Purpose: A high Ret/DD ratio in IS shows good returns relative to risk, but if the OOS version drops sharply, it indicates the strategy may not handle risk well in real market conditions.
5. Stability
   • IS Target: > 0.8 (A smooth, consistent equity curve).
   • OOS Target: Stability should remain > 0.7 in OOS. A significant drop indicates the strategy’s performance may become inconsistent in live conditions.
   • Purpose: High stability across IS and OOS ensures a smooth equity curve, avoiding sharp performance drops in real trading.
6. Open Drawdown %
   • IS Target: < 15% (Keeps risk exposure in check during optimization).
   • OOS Target: Should remain below 20% in OOS testing. If OOS drawdowns significantly exceed the IS values, the strategy may struggle in volatile or unseen markets.
   • Purpose: Managing open drawdowns is critical for risk control, and seeing how this behaves OOS ensures you’re not taking on excessive risk in real trading.
7. Max Drawdown %
   • IS Target: < 20% (Manages maximum peak-to-valley losses).
   • OOS Target: Should not exceed 25% in OOS. Drawdown stability between IS and OOS indicates the strategy is capable of handling stress.
   • Purpose: This metric helps manage risk, ensuring that no single market condition causes extreme losses.
8. Consecutive Win/Loss Trades
   • IS Target: > 2 (Aims for win streaks without significant drawdowns).
   • OOS Target: Should be similar to IS results, or show a smooth transition. Long losing streaks in OOS can be a warning sign.
   • Purpose: This shows how often you can expect consecutive wins or losses. Stability between IS and OOS is a good indicator of robustness.
9. Net Profit
   • IS Target: Positive net profit in IS with steady growth.
   • OOS Target: Should maintain a positive net profit. If OOS performance is significantly lower, this indicates overfitting.
   • Purpose: Maintaining positive net profit in both IS and OOS is crucial for real trading profitability.
10. R-Squared (R²)
    • IS Target: > 0.8 (A high R² shows smooth, predictable growth).
    • OOS Target: Should remain above 0.7 to avoid overfitting. A significant drop in OOS means the strategy is inconsistent.
    • Purpose: This metric helps you find strategies that perform smoothly without high volatility in their returns.
11. Sortino Ratio
    • IS Target: > 1.5 (Indicates good risk-adjusted returns considering only downside volatility).
    • OOS Target: Above 1.2 in OOS. A sudden drop in Sortino indicates the strategy is exposing itself to more downside risk than it did during optimization.
    • Purpose: This is a good alternative to Sharpe Ratio, focusing on negative risk rather than total risk. Stability in OOS is critical for downside protection.

How to Use These Filters in SQX:

1. First pass with IS filters:
   • During the initial build/optimization phase, filter strategies based on high values for key metrics in the IS data. Use Profit Factor > 1.5, CAGR/Max DD% > 1, Sharpe Ratio > 2, Ret/DD > 2, and Stability > 0.8.
2. Second pass with OOS filters:
   • After IS optimization, evaluate the strategies in the OOS phase. Set slightly lower but still acceptable values: Profit Factor > 1.3, Sharpe Ratio > 1.5, CAGR/Max DD% > 0.8, Ret/DD > 1.5, and Stability > 0.7.
3. Compare IS and OOS performance:
   • Discrepancies between IS and OOS are normal, but they should not be drastic. If a strategy performs well in IS but poorly in OOS, it’s likely overfitted. Favor strategies with consistent metrics across both phases.

By maintaining these IS/OOS targets in SQX, you can ensure your strategies are robust and able to handle live trading without deteriorating due to overfitting.

OOS Testing: More importantly, assess the performance during out-of-sample testing to ensure the strategy is robust in unseen market conditions. Strategies with a high OOS Sharpe ratio and low OOS drawdowns are more likely to perform well in live trading.

this OOS should be > ?

 

When assessing out-of-sample (OOS) performance, the following benchmarks can help determine if a strategy is robust enough for live trading:

1. OOS Sharpe Ratio:

• A Sharpe ratio greater than 1 indicates that the strategy has delivered excess returns relative to its volatility.
• A Sharpe ratio of greater than 2 is considered excellent, suggesting strong performance and favorable risk-adjusted returns.

2. OOS Drawdown:

• Aim for a maximum drawdown of less than 15%. This benchmark helps ensure that the strategy does not experience severe losses that could jeopardize the trading account.
• Additionally, the recovery time from drawdown is important; faster recovery is generally preferable.

3. Profit Factor:

• A profit factor greater than 1.5 is generally favorable. This means that the strategy makes 1.5 times more profit than it loses.

4. Consistency:

• Look for consistency in OOS performance metrics, such as stable returns and low volatility, over a sufficient sample size (e.g., at least 6-12 months of trading).

Summary:

• OOS Sharpe Ratio > 1 (ideally > 2)
• Maximum OOS Drawdown < 15%
• Profit Factor > 1.5

Meeting these benchmarks can indicate that the strategy is likely to perform well in live trading.

Yes, it is possible to find or build a strategy that meets these criteria, but it will depend on several factors, including the quality of your data, the optimization process, and the strategy type.

Here’s a breakdown of what each criterion implies and how achievable it is:

1. CAGR/Max DD% > 1:

• Achievability: This is a reasonable and important target, as it ensures your strategy produces more growth than risk. You can achieve this through proper risk management, consistent profits, and by minimizing drawdowns.
• Tuning Tips: Focus on risk control, such as using tight stop losses and proper position sizing (e.g., your 0.01 lot per $100 account balance rule).

2. Sharpe Ratio > 2:

• Achievability: A Sharpe Ratio above 2 is challenging but realistic, especially with a well-optimized strategy. It implies high risk-adjusted returns with low volatility.
• Tuning Tips: To achieve this, focus on strategies that reduce large fluctuations in returns. Use techniques like trailing stops, break-even exits, and conservative risk management. Avoid strategies with excessive drawdowns or those that rely on highly volatile instruments without proper hedging.

3. Profit Factor > 1.5:

• Achievability: This is a common target for profitable trading strategies. A profit factor above 1.5 indicates that your gross profits are at least 1.5 times larger than your gross losses.
• Tuning Tips: Optimize entry/exit rules, avoid overtrading, and focus on strategies that aim for higher reward-to-risk ratios, such as your target of RRR > 1:3.

4. Stability > 0.8:

• Achievability: Stability near 0.8 is a realistic goal for most well-performing strategies. It shows a relatively smooth and reliable equity curve, which is necessary for long-term performance.
• Tuning Tips: Stability can be enhanced by reducing the frequency of large drawdowns, fine-tuning exit rules, and optimizing over a range of market conditions (using IS and OOS testing).

5. Open DD% < 15%:

• Achievability: This is achievable but can be difficult if you’re targeting high returns. Controlling open drawdown is crucial to keep the account safe from volatile market swings.
• Tuning Tips: You can keep open drawdown in check by adjusting trade size, using dynamic stop-loss management (e.g., ATR-based stops), or employing partial exits as trades move into profit.

Finding Such a Strategy:

1. Backtesting and Optimization: You’ll likely need extensive backtesting with 20 years of M1 data (as you’re already using) to find strategies that meet all these criteria. Sequential optimization across timeframes (H1, H4, etc.) can help refine strategies for long-term stability.
2. Strategy Combination: You may need to combine trend following, breakout, and swing trading methods, as these tend to perform well in various market conditions.
3. Fine-Tuning Parameters: Parameters like risk management rules, trailing stops, and exit timing (closing trades before Friday or after 5 days) will play a key role in managing drawdowns and ensuring the other metrics hold up.

It will take work, but it is possible to find a strategy that fits within these boundaries. Be prepared for iterative testing and refinement in StrategyQuant X to optimize across these performance metrics.

what’s the most priority betwee CAGR/Max DD% and Sharp ratio ?

When prioritizing between CAGR/Max DD% and Sharpe Ratio, it depends on your strategy’s goals and risk tolerance. Here’s how to assess their importance:

1. CAGR/Max DD% (Compound Annual Growth Rate / Maximum Drawdown Percentage):

• What it Measures: The growth of your account over time (CAGR) relative to the worst peak-to-trough decline (Max Drawdown).
• Importance: It tells you how much risk (drawdown) you’re taking to achieve returns. A higher CAGR/Max DD means your returns are efficient in relation to the risk you’ve taken.
• When it’s Critical:
  • When you aim for long-term, steady growth with controlled risk.
  • If you prioritize minimizing drawdowns, which is crucial for strategies that need to stay resilient in volatile markets (like your long-term trading approach).

2. Sharpe Ratio:

• What it Measures: The return per unit of risk (volatility). It measures how well your strategy compensates you for the risk taken (volatility of returns).
• Importance: A higher Sharpe ratio (above 1, or ideally above 2) means the strategy offers a better risk-adjusted return. It’s a good metric for comparing different strategies to see which one generates smoother returns relative to volatility.
• When it’s Critical:
  • When you care about consistent performance and want to compare risk-adjusted returns between strategies.
  • If your strategy experiences large fluctuations in returns, the Sharpe ratio helps determine if the higher returns are worth the risk (volatility).

Comparison in Your Strategy:

• CAGR/Max DD%: Since you are building a strategy for long-term use, minimizing drawdowns (while still growing your account) is a key consideration. This metric is more focused on the capital preservation aspect and will be vital in avoiding large losses that could wipe out significant portions of your account.
• Sharpe Ratio: It’s essential for ensuring that your strategy has consistent returns relative to risk. However, if your drawdowns are well-managed (as per the focus on risk control), the Sharpe Ratio becomes secondary in importance.

Which is More Important?

For your strategy goals (focused on long-term performance and risk management): 1. CAGR/Max DD% should be the top priority because it balances growth with minimizing drawdowns. 2. Sharpe Ratio is important but secondary to CAGR/Max DD%. You should still aim for a Sharpe ratio above 1 (preferably 2+), but don’t compromise your drawdown management to chase a higher Sharpe ratio.

Conclusion:

Focus on maximizing CAGR/Max DD% to ensure your strategy can grow steadily with minimal drawdowns. A high Sharpe Ratio (above 2) is an excellent goal but secondary to ensuring that your strategy survives long-term market swings with controlled drawdowns.

then i got 400 from builds type : Exit everday 23:00, Exit on Friday 23:40, Exit Rules no fix exit day or time but hold not over 10 day and Repeat/Duplicate orders trade. from base H1/H4 TF, so can i use Sequantial optimization by +30% -30% with 10 step for another TF like M30/H4 M15/H4 and M5/H4. what do you think i can get some great results ?

Using sequential optimization by adjusting parameters +30% and -30% with 10 steps for testing other timeframes like M30/H4, M15/H4, and M5/H4 is a smart approach to explore the robustness and adaptability of your strategy across different timeframes. Here’s what to expect and consider:

Benefits of Sequential Optimization:

1. Broader Testing Range: By adjusting the parameters within the 30% range, you’ll ensure that you’re not just fine-tuning for one specific set of market conditions but testing for variability across different timeframes.
2. Identify Timeframe Sensitivity: You’ll be able to see how your strategy behaves with different levels of granularity (e.g., faster-moving timeframes like M5 vs slower ones like M30). This can help you identify if your strategy is overly reliant on a specific timeframe or whether it’s flexible across multiple.
3. Refining Exit Strategies: Since your strategies include time-based exits (e.g., exiting at 23:00 daily or on Fridays at 23:40), sequential optimization will allow you to see if different exit timings yield better results on shorter timeframes. The hold-not-over-10-days rule may work differently on lower timeframes where market volatility plays a bigger role.
4. Exploration of New Opportunities: By shifting to M30/H4, M15/H4, and M5/H4, you’ll open your strategy to new trading opportunities as faster timeframes capture more price fluctuations. This could either increase profitability or increase risk, depending on how well your strategy manages shorter-term price swings.

Things to Watch Out for:

1. Overfitting: The more timeframes and parameters you optimize, the higher the risk of overfitting. To avoid this, focus on keeping core elements of the strategy consistent and avoid over-optimizing for one specific timeframe.
2. Higher Volatility on Lower Timeframes: When moving to lower timeframes (like M15 or M5), strategies can be more prone to whipsaws and false signals due to increased market noise. Sequential optimization can help adjust, but ensure your risk management rules (like SL, TP, and trailing stops) are adaptable to these faster movements.
3. Execution Speed and Slippage: Shorter timeframes (M15 and M5) might experience more slippage and execution issues in live trading. Make sure that the strategy can handle the increased trade frequency without degrading profitability.
4. Strategy Robustness: Pay attention to how the strategy behaves during out-of-sample testing after the optimization across different timeframes. You’re looking for a strategy that performs consistently across multiple timeframes, not just optimized for one.

Expected Results:

• You might find that your strategy performs better on intermediate timeframes like M30/H4 or M15/H4 because these timeframes strike a balance between fast execution and less noise.
• M5/H4 can be more volatile, and while it might open more trading opportunities, it could also increase drawdowns or risk of false signals. Sequential optimization can help fine-tune it to still be effective.

In summary, using sequential optimization with a +30%/-30% range across timeframes like M30/H4, M15/H4, and M5/H4 is a strong approach to test the adaptability of your strategy. If applied well, you could discover new opportunities on lower timeframes while maintaining a solid performance on H1/H4.

Additional Tip:

• Walk-forward optimization after sequential optimization can further ensure robustness by simulating how the strategy would adapt over time in live markets. This can be especially useful for testing how the strategy would react in different timeframes.

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.