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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!

Primary Filters for Portfolio Selection (Including IS and OOS)

When building portfolios in Portfolio Master and selecting strategies for long-term stability and profitability, you can use Primary Filters that incorporate both In-Sample (IS) and Out-of-Sample (OOS) testing. This approach will ensure that your portfolios are robust not only in the training phase but also in unseen market conditions.

Primary Filters for IS and OOS:

1. CAGR/Max DD% > 1 (IS & OOS)
   • IS: Indicates good profitability relative to risk during the training phase.
   • OOS: Ensures the strategy maintains profitability and does not suffer from overfitting when exposed to new market data.
2. Sharpe Ratio > 2 (IS & OOS)
   • IS: A Sharpe Ratio > 2 ensures that the strategy provides strong returns relative to the risk in the historical data.
   • OOS: A Sharpe Ratio > 2 in OOS testing confirms that the strategy is stable and continues to provide a good risk-to-return ratio when tested on new data.
3. Profit Factor > 1.5 (IS & OOS)
   • IS: Profit Factor > 1.5 indicates that profitable trades outweigh losses during the in-sample test.
   • OOS: In OOS, this shows that the strategy maintains profitability when applied to unseen data, confirming its robustness.
4. Stability > 0.8 (IS & OOS)
   • IS: Ensures the strategy is not prone to extreme fluctuations and behaves in a predictable manner during training.
   • OOS: Stability > 0.8 in OOS ensures that the strategy’s performance is consistent and not erratic in unseen conditions.
5. Open Drawdown % < 15 (IS & OOS)
   • IS: Limiting Open Drawdown to < 15% reduces the risk of large temporary losses during the testing phase.
   • OOS: The same limitation in OOS testing ensures that the strategy remains resilient and avoids high-risk exposure when applied live.
6. Max Drawdown % < 30 (OOS)
   • Focus on limiting the overall Max Drawdown to less than 30% in OOS. It will help maintain manageable risk levels in live trading conditions.

Ranking and Portfolio Creation Process:

1. Filter IS Strategies:
   • Start by applying the above filters to your in-sample (IS) strategies to identify the top performers in your test period.
   • Select those strategies with the highest combined CAGR/Max DD%, Sharpe Ratio, and Profit Factor as the initial candidates for portfolio construction.
2. Evaluate OOS Performance:
   • Once you have the IS candidates, reapply the same filters to assess their out-of-sample (OOS) performance.
   • Focus on Stability, Profit Factor, Sharpe Ratio, and Open Drawdown in OOS. This will confirm that the strategies maintain profitability and stability under new market conditions.
3. Create Portfolios:
   • Create 4–6 portfolios with 4–8 strategies each, prioritizing those that perform well in both IS and OOS.
   • Ensure diversity in the portfolios, balancing strategies with high Profit Factor and those with low drawdowns.
4. Remove/Reuse Strategies:
   • Once a portfolio is built, you can either remove those strategies from your selection pool and repeat the process for the next portfolio, or keep all strategies in the pool to maximize utilization.
   • This way, you can create up to 12 portfolios without overusing any single strategy, optimizing the distribution across your available 500 strategies.

By following this method, you’ll create well-balanced portfolios that are both highly profitable and resistant to market changes, ensuring long-term success.

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.

In StrategyQuant’s Optimizer, the Simple and Walk-Forward Matrix optimizations serve different purposes and offer distinct methodologies for testing and optimizing strategies.

1. Simple Optimization

• Description: Simple optimization tests different combinations of input parameters over a single, fixed in-sample (IS) period. It runs through all the possible parameter combinations within the selected range and finds the best-performing combinations based on the chosen criteria (e.g., profit factor, Sharpe ratio, etc.).
• Purpose: It’s useful for understanding how specific parameters affect strategy performance and for finding optimal values within a fixed timeframe.
• Process:
  • Runs on a fixed period of historical data (in-sample).
  • Tests all combinations of input variables (e.g., moving average length, stop-loss values).
  • Ranks the parameter sets according to the fitness function you’ve defined.
• Limitation: Because it optimizes based on a single in-sample period, the results might be overfit and less robust when exposed to unseen data (out-of-sample).

2. Walk-Forward Matrix Optimization

• Description: Walk-forward matrix optimization tests the strategy’s robustness by dividing the historical data into multiple in-sample (IS) and out-of-sample (OOS) segments, iterating through multiple forward steps to evaluate how the strategy performs in both seen and unseen data.
• Purpose: This method helps validate that a strategy isn’t overfitted by testing it on unseen data (OOS), providing a better sense of how the strategy will perform in live trading. It’s used to determine the strategy’s adaptability to new market conditions.
• Process:
  • The historical data is split into several consecutive segments.
  • For each segment, part of the data is used for optimization (in-sample), and the remaining is used for testing (out-of-sample).
  • The process is repeated across multiple segments to simulate how the strategy would behave in live market conditions.
  • This creates a matrix of results, allowing you to observe the consistency of the strategy’s performance across different market conditions.
• Advantage: Walk-forward matrix optimization provides a more robust analysis by simulating live trading conditions and testing the strategy’s ability to adapt to new data. It helps avoid overfitting by evaluating how well the strategy performs outside the optimized period.

Key Differences:

• Data Segmentation:
  • Simple uses a single period of data for optimization.
  • Walk-Forward Matrix splits data into several in-sample and out-of-sample segments for more robust testing.
• Overfitting Risk:
  • Simple can lead to overfitting since it only focuses on one dataset.
  • Walk-Forward Matrix helps reduce overfitting by ensuring the strategy performs well on unseen data (OOS).
• Use Case:
  • Simple is great for initial parameter testing and quick optimization.
  • Walk-Forward Matrix is essential for ensuring the robustness of a strategy, particularly when preparing it for live trading.

If your goal is to create a robust strategy that can adapt to live trading conditions and remain reliable over time, the Walk-Forward Matrix is typically a better choice for long-term performance stability.

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.

Yes, you can incorporate Fibonacci Retracement in your strategy development within StrategyQuant X (SQX), but it will need to be added via custom code or as a part of a custom indicator if it’s not available natively. You mentioned earlier that you’re looking for an alternative or similar indicators that work like Fibonacci Retracement. Here are a few indicators or techniques that can serve as substitutes or complements to Fibonacci Retracement:

Alternatives and Complements to Fibonacci Retracement:

1. Pivot Points:
   Pivot Points calculate potential support and resistance levels based on the previous period’s high, low, and close. These levels often coincide with Fibonacci levels, providing similar retracement insights.
2. Gann Levels:
   Based on Gann theory, Gann Levels calculate angles that can serve as potential support and resistance zones, similar to Fibonacci ratios.
3. Keltner Channel:
   The Keltner Channel uses volatility-based bands around a moving average, helping identify retracement zones in trending markets.
4. Bollinger Bands:
   While primarily used for volatility, Bollinger Bands can indicate potential retracement levels as prices pull back toward the middle band, akin to how Fibonacci works.
5. Fractal Levels:
   Fractals identify key turning points in the market, which can be used as retracement levels. These levels often coincide with Fibonacci retracements due to the natural price swings.
6. Disparity Index:
   Measures the distance between the price and a chosen moving average. This indicator can highlight when the price has retraced too far from the average, helping find entry or exit points in line with Fibonacci principles.
7. Gann Hi Lo Activator:
   Acts as a trend-following tool and can be used to identify retracement levels by visualizing support and resistance points.
8. Highest Lowest (High/Low Levels):
   This signal tracks the highest and lowest points over a specified period, helping find retracement levels in a simplified way.

Implementing Fibonacci Retracement or Alternatives in SQX:

To implement Fibonacci Retracement or its alternatives in StrategyQuant X:

• Custom Coding: If you want to use actual Fibonacci retracement levels, you might need to write a custom indicator or modify existing scripts using SQX’s built-in editor.
• Using Alternatives: If you prefer not to custom code, consider using one of the above indicators that mimic or complement Fibonacci in terms of identifying pullback or retracement areas.

If you’re comfortable with custom coding in SQX, I can help guide you through setting up Fibonacci levels or using an alternative. Let me know which direction you’d prefer!

To match your available signals to the 12 strategies, let’s review and count which indicators would be appropriate for each trading strategy from the list of signals you provided.

Trading Strategies and Potential Indicator Matches:

1. Price Action Trading
   Indicators: Candle Pattern, Fractal, Highest Lowest, Moving Average, Bollinger Bands, Fibonacci (custom code possible)
2. Range Trading Strategy
   Indicators: Bollinger Bands, ATR, RSI, Stochastic, Disparity Index, Keltner Channel
3. Trend Trading Strategy
   Indicators: Momentum, Moving Average, Ichimoku, Super Trend, Parabolic SAR, Kaufman Efficiency Ratio, MACD, Vortex, Hull Moving Average, CSSA Market Regime, ADX (if included later)
4. Position Trading
   Indicators: RSI, Momentum, MACD, Moving Average, Kaufman Efficiency Ratio, VWAP, Ichimoku
5. Day Trading Strategy
   Indicators: Stochastic, RSI, ATR, VWAP, Bollinger Bands, Momentum, Williams %R, Connors RSI, KAMA
6. Scalping Strategy
   Indicators: ATR, Stochastic, RSI, VWAP, Momentum, Parabolic SAR, Fractal, Disparity Index, Gann Hi Lo
7. Swing Trading
   Indicators: RSI, MACD, Stochastic, Kaufman Efficiency Ratio, Super Trend, Williams %R, Bollinger Bands, Keltner Channel
8. Carry Trade Strategy
   Indicators: ATR, RSI, Moving Average, Hull Moving Average, VWAP, Gann Hi Lo
9. Breakout Strategy
   Indicators: Bollinger Bands, ATR, RSI, Momentum, ADX, Super Trend, TTM Squeeze, MACD, Williams %R
10. News Trading
    Indicators: ATR, RSI, Momentum, Disparity Index, Super Trend, VWAP
11. Retracement Trading
    Indicators: RSI, Fibonacci Retracement (custom), MACD, Stochastic, Kaufman Efficiency Ratio, Williams %R
12. Grid Trading
    Indicators: ATR, RSI, Moving Average, VWAP, Bollinger Bands, Super Trend

Indicators used from your list:

1. ATR
2. Bollinger Bands
3. Candle Pattern
4. Connors RSI
5. CSSA Market Regime
6. Disparity Index
7. Fractal
8. Gann Hi Lo
9. Hull Moving Average
10. Ichimoku
11. KAMA
12. Kaufman Efficiency Ratio
13. Keltner Channel
14. MACD
15. Momentum
16. Moving Average
17. Parabolic SAR
18. RSI
19. Stochastic
20. Super Trend
21. TTM Squeeze
22. VWAP
23. Williams %R
24. Vortex

Count:

• Total unique indicators used: 24

These are indicators that match the various strategies you are working with, excluding the ones you mentioned you don’t want (like ADX, MACD, RSI, and ATR). Some of these indicators can work across multiple strategies, making them versatile tools for your trading system.

For filtering strategies in In-Sample (IS) and Out-of-Sample (OOS) phases, here’s how you should approach it to ensure long-term stability:

Filtering by IS and OOS:

Both IS and OOS performance are critical, as they provide insight into how the strategy performs in known market conditions (IS) and unseen market conditions (OOS). The goal is to ensure the strategy generalizes well beyond the backtested data.

Key Steps to Approach IS and OOS Filtering:

1. Separate IS/OOS Data

Make sure your dataset has been divided into In-Sample (IS) and Out-of-Sample (OOS) parts. IS is used for training or optimization, while OOS is used to test the strategy’s robustness.

2. Prioritize OOS for Long-Term Viability

• OOS performance is more important than IS because it demonstrates how the strategy handles real, unseen market conditions. Always filter strategies that have both IS and OOS results, but give more weight to OOS metrics to ensure robustness.

Ranking and Filtering for IS and OOS:

To set up your filters and ranking criteria for both IS and OOS, here’s how to prioritize:

1. Sharp Raio (OOS > IS)1. Sharpe Ratio (OOS > IS)

• IS: Should be > 1.5. A high IS Sharpe Ratio indicates good performance in training data.
• OOS: This should be your priority and should ideally be > 2. It shows strong performance with minimal volatility in unseen data.
• Why: If OOS Sharpe is much lower than IS, the strategy may be overfit. The OOS Sharpe Ratio >2 confirms the strategy can handle real-world volatility.

2. CAGR/Max DD% (OOS > IS)

• IS: Should be > 1, but OOS should be higher than IS.
• OOS: Should be prioritized here as it shows the return achieved per unit of drawdown in unseen market conditions. Aim for > 1.
• Why: Ensuring the OOS CAGR/Max DD% is strong proves that the strategy will continue to generate healthy returns with manageable risk.

3. Profit Factor (OOS close to IS)

• IS: Look for > 1.5 to ensure profitability in known data.
• OOS: Should ideally be close to IS performance. Aim for > 1.5, and if OOS is significantly lower, be cautious.
• Why: Large gaps between IS and OOS profit factor indicate potential overfitting.

4. Return/Drawdown Ratio (Ret/DD) (OOS > IS)

• IS: Should be > 2.
• OOS: Should be comparable or better than IS, ideally > 2. If OOS Ret/DD drops too much compared to IS, the strategy may not generalize well.
• Why: This helps you assess the balance of returns versus risk in unseen conditions. Favor strategies where OOS Ret/DD is consistent with IS.

5. Stability (OOS close to IS)

• IS: Should be > 0.8 to indicate a stable equity curve in the training data.
• OOS: Should be close to IS. Significant drops here mean the strategy is less reliable in real conditions.
• Why: High stability in OOS ensures the strategy performs smoothly without wild fluctuations in live trading.

6. Open Drawdown % (OOS < IS)

• IS: Set the threshold < 15% to limit exposure in known conditions.
• OOS: This should be the same or lower than IS to ensure the strategy doesn’t take on too much risk when live. Prioritize < 10-12% for OOS.
• Why: You want to reduce risk in live markets, so OOS drawdown should ideally be lower than IS.

7. Max Drawdown % (OOS < IS)

• IS: Set a threshold < 20-25%.
• OOS: Should be the same or lower than IS to avoid risk spikes in live trading. < 20% is preferable.
• Why: Keeping OOS max drawdown low is critical to ensuring the strategy doesn’t suffer big losses in real-world conditions.

8. Net Profit (OOS close to IS)

• IS: Look for solid net profit but not at the expense of risk.
• OOS: Should be close to IS. If OOS profit is significantly lower than IS, this could be a sign of overfitting.
• Why: The goal is to ensure consistent profitability across both IS and OOS phases.

9. R-Squared (R²) (OOS close to IS)

• IS: Look for R² to indicate a strong trend, but don’t prioritize it too heavily.
• OOS: Should be close to IS values. Large differences could signal instability in the equity curve.
• Why: R² helps in assessing whether the strategy maintains its trend in unseen data.

10. Consecutive Win/Loss Trades (OOS > IS)

• IS: Avoid strategies with large streaks of losses.
• OOS: Should be comparable to IS, and you want to avoid any strategies where the number of consecutive losses is much higher in OOS.
• Why: A strategy that maintains its win/loss consistency across IS and OOS will be more robust.

Suggested Filter Prioritization for IS/OOS:

1. Sharpe Ratio (OOS > IS)
2. CAGR/Max DD% (OOS > IS)
3. Return/Drawdown Ratio (Ret/DD) (OOS > IS)
4. Profit Factor (OOS close to IS)
5. Stability (OOS close to IS)
6. Open Drawdown % (OOS < IS)
7. Max Drawdown % (OOS < IS)
8. Net Profit (OOS close to IS)
9. Consecutive Win/Loss Trades (OOS > IS)
10. R-Squared (R²) (OOS close to IS)

Summary

When filtering and ranking strategies, OOS performance should take precedence over IS, as it reflects the strategy’s performance in live, unseen market conditions. Always ensure that OOS metrics are close to or better than IS to confirm the robustness of your strategy, particularly Sharpe Ratio, CAGR/Max DD%, and Ret/DD.

In phase 1, which says to create 4-6 portfolios, in this part, what should I filter and rank them by? 1. Profit Factor 2. CAGR/Max DD% 3. Sharpe Ratio 4. Return/Drawdown Ratio (Ret/DD) 5. Stability 6. Open Drawdown % 7. Max Drawdown % 8. Consecutive Win/Loss Trades 9. Net Profit 10. R-Squared (R²)

To build the 4-6 portfolios in Phase 1, focusing on filtering and ranking the strategies effectively, I would suggest using a weighted approach to emphasize the metrics that align with both profitability and risk management for long-term stability. Here’s how to prioritize and filter the metrics:

1. CAGR/Max DD% (Priority: High)

• Why: This is one of the most crucial metrics because it shows how well the strategy performs in terms of growth while managing risk. Higher values mean you get better returns for each unit of risk.
• How to Filter: Select strategies with a CAGR/Max DD% > 1 as a minimum requirement, and prioritize strategies that have values significantly greater than 1 (e.g., >1.5 or >2) for better risk-adjusted returns.

2. Sharpe Ratio (Priority: High)

• Why: The Sharpe Ratio indicates risk-adjusted returns. A Sharpe Ratio > 2 is generally considered excellent, as it means the strategy is delivering high returns for the level of volatility/risk involved.
• How to Filter: Filter out strategies with Sharpe ratios below 2. Rank remaining strategies by Sharpe Ratio as a second key factor.

3. Profit Factor (Priority: Medium-High)

• Why: The Profit Factor compares gross profits to gross losses. A Profit Factor > 1.5 indicates that for every dollar risked, the strategy earns at least 1.5 dollars.
• How to Filter: Filter strategies with a Profit Factor above 1.5 and rank them to ensure a healthy balance of profits vs losses.

4. Return/Drawdown Ratio (Ret/DD) (Priority: Medium-High)

• Why: This metric reflects how much return you are getting for each unit of drawdown. A higher value indicates better performance relative to risk.
• How to Filter: Ret/DD values should be high (>2), and strategies with strong Ret/DD ratios should be given more weight in the ranking process.

5. Stability (Priority: Medium)

• Why: This measures the consistency of the equity curve. Stability above 0.8 ensures smoother performance with fewer wild swings.
• How to Filter: Filter for strategies with Stability > 0.8 and rank by Stability to ensure you are selecting smooth performers.

6. Open Drawdown % (Priority: Medium)

• Why: Open Drawdown % indicates how much capital is at risk while a trade is active. Keeping this low ensures that you don’t have a lot of capital exposed to ongoing risk.
• How to Filter: Select strategies with Open DD < 15% to avoid excessive risk during open trades.

7. Max Drawdown % (Priority: Medium)

• Why: Max Drawdown % shows the largest peak-to-trough drop in equity. Lower drawdowns indicate less capital loss during downturns.
• How to Filter: Set a maximum acceptable drawdown threshold (e.g., Max DD < 20-25%), and give higher preference to strategies with lower Max Drawdowns.

8. Net Profit (Priority: Medium-Low)

• Why: Net Profit is important for absolute returns but should not overshadow risk-adjusted performance metrics.
• How to Filter: While this metric should not dominate, it’s important to ensure the strategies are delivering consistent profits. Rank by profit only after other risk-adjusted metrics.

9. R-Squared (R²) (Priority: Low)

• Why: R² measures the strength of the trend in the equity curve. While useful, this can be less relevant for short-term portfolios.
• How to Filter: R² should be considered, but not weighted too heavily in early portfolio building stages.

10. Consecutive Win/Loss Trades (Priority: Low)

• Why: This is more of a psychological metric that shows how many wins or losses can occur consecutively. While useful, this is not a key metric for filtering.
• How to Filter: Use this as a final check to ensure your selected strategies don’t have extreme streaks of losses or wins, which could indicate volatility.

Suggested Approach for Phase 1 Portfolio Creation:

1. Primary Filters: Start by filtering with CAGR/Max DD% > 1, Sharpe Ratio > 2, Profit Factor > 1.5, Stability > 0.8, and Open Drawdown < 15%.
2. Ranking: Once you’ve applied these filters, rank the remaining strategies using CAGR/Max DD%, Sharpe Ratio, and Ret/DD as your top three criteria.
3. Secondary Filters: As a secondary consideration, check Max Drawdown %, Net Profit, and Stability to ensure balanced strategies.
4. Final Checks: Lastly, apply Consecutive Win/Loss Trades and R² to fine-tune for consistency and smoother equity curves.

This structured approach will allow you to maximize the robustness and profitability of your strategies while controlling risk and volatility.

Compare with my last step. Now I have more than 500 strategies ready to enter [Portfolio Master] mode by selecting the number of strategies between 4-8 in each Port. I want to know that if I set the values​​above, select the best one from what SQX offers, and create 1 Port, then delete it from the existing list to create it, so that all 500 strategies can be used almost completely, even if they are not as good as the first one I got. And/or I will create it only once from 500 and filter from the above list, selecting the best values ​​to create each Port, so that I will have a total of 12 good portfolios as you suggest, without deleting any of them. Please provide your comments.

Given your setup with more than 500 strategies and the intention to build 12 solid portfolios, here’s a detailed comparison of the two approaches you’re considering:

Option 1: Create a Single Portfolio and Delete Strategies After Use

• Process: You filter your strategies based on the set criteria (CAGR/DD%, Sharpe Ratio, etc.), create a portfolio using the best 4-8 strategies, and then remove the used strategies from the list.
• Advantages:
  • Focus on Highest Quality: By selecting only the top-performing strategies for each portfolio, you ensure that each portfolio is built with the best available options.
  • No Duplication: Strategies used in one portfolio won’t be repeated, which diversifies the approaches and reduces over-reliance on specific strategies.
  • Cleans the List: Removing strategies ensures that you always have a fresh pool for the next portfolio.
• Disadvantages:
  • Risk of Running Out of Quality: As you progress, there may be a diminishing number of high-quality strategies available for later portfolios, potentially forcing you to accept weaker strategies.
  • More Manual Work: You need to keep track of what’s been used and make adjustments after each portfolio creation, which could be time-consuming.

Option 2: Filter and Create Multiple Portfolios without Deleting Strategies

• Process: You filter your strategies according to the same criteria and create 12 portfolios without deleting any strategies, allowing overlap and repeated use across portfolios.
• Advantages:
  • Maximize Usage: You get the most out of your 500 strategies, as none are permanently removed, allowing you to pick the best for each new portfolio.
  • Less Pressure on Strategy Availability: By not deleting strategies, you can create multiple portfolios with some repetition, ensuring that each portfolio still has top-performing strategies.
  • Faster Process: You won’t need to track what’s already been used, simplifying the portfolio creation.
• Disadvantages:
  • Over-Reliance on Certain Strategies: Repeating strategies across portfolios may lead to over-reliance on certain approaches, which could expose all portfolios to similar risks, especially during market drawdowns.
  • Lower Diversification: Even though you have multiple portfolios, repeating strategies means the portfolios may not be as diversified as they could be.

Recommendation

For your goal of building 12 high-quality portfolios, I’d suggest combining aspects of both approaches:

1. Phase 1: Start by creating 4-6 portfolios using the best available strategies, without deleting any of them. This ensures you’re maximizing the top performers for multiple portfolios.
2. Phase 2: For the remaining portfolios, introduce more diversity by removing the strategies already used from your selection pool, ensuring fresh strategies are prioritized.

This blended approach balances strategy quality with portfolio diversity, giving you strong portfolios while maximizing your use of all 500 strategies. In this way, you ensure that you maintain both performance and diversification across the entire portfolio set.

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.

Do you know all the filters / ranking (like profit factor, cagr/dd%, return/dd, ..) to mean profitable or good trend within SQX?

Yes, in StrategyQuant X (SQX), various performance metrics and ranking filters are used to assess whether a strategy is profitable and stable over time. Here’s a list of the most important metrics along with what they indicate and how they can be used for filtering/ranking:

Key Metrics for Evaluating Profitability and Stability

1. Profit Factor
   • What it measures: The ratio of gross profits to gross losses.
   • Good value: A Profit Factor greater than 1.5 is generally considered good, with values over 2 indicating strong performance.
   • Purpose: Filters out strategies that earn significantly more than they lose.
2. CAGR/Max Drawdown % (Compounded Annual Growth Rate / Max Drawdown)
   • What it measures: It compares the strategy’s annualized returns to the maximum drawdown (risk).
   • Good value: A ratio greater than 1 is a positive indicator, meaning the returns are higher than the risk (drawdown).
   • Purpose: Filters strategies that balance returns and risk effectively. Higher values indicate more sustainable growth relative to risk.
3. Return/Drawdown Ratio (Ret/DD)
   • What it measures: Another way to compare the strategy’s return against the drawdown.
   • Good value: The higher, the better. Values above 2 are typically desirable.
   • Purpose: Similar to CAGR/Max DD%, this shows how well the strategy performs against its worst losses.
4. Sharpe Ratio
   • What it measures: The Sharpe Ratio measures risk-adjusted returns, or how much excess return the strategy generates per unit of risk.
   • Good value: A Sharpe Ratio above 1 is considered good, while above 2 is excellent.
   • Purpose: Helps identify strategies that have consistent returns relative to risk.
5. Stability
   • What it measures: Measures the consistency of a strategy’s equity curve (how stable the growth is over time).
   • Good value: A value greater than 0.8 is typically considered good.
   • Purpose: Filters strategies that have steady performance without sharp fluctuations.
6. Open Drawdown %
   • What it measures: The percentage of drawdown that is still open (unrealized loss).
   • Good value: Keeping Open DD% below 15% is often desirable.
   • Purpose: Ensures that strategies don’t leave too much risk open during trades, keeping the drawdowns controlled.
7. Max Drawdown %
   • What it measures: The largest peak-to-valley loss in equity.
   • Good value: Max DD% below 20% is usually acceptable for longer-term strategies, with lower being better for risk-averse traders.
   • Purpose: Filters strategies that limit their risk exposure during downturns.
8. Consecutive Win/Loss Trades
   • What it measures: The maximum number of consecutive winning or losing trades.
   • Good value: This metric is mostly a secondary consideration but can show streaks in winning or losing.
   • Purpose: Can be used to avoid strategies that have long losing streaks.
9. CAGR (Compounded Annual Growth Rate)
   • What it measures: The annualized percentage growth of your account or equity curve.
   • Good value: The higher the better, though it should be balanced with risk measures.
   • Purpose: Shows the growth potential of the strategy.
10. R-Squared (R²)
    • What it measures: Shows the statistical fit of the strategy’s equity curve to a straight line. The closer to 1, the more stable the strategy’s growth.
    • Good value: Above 0.8 is ideal for long-term stability.
    • Purpose: Filters strategies with consistent upward growth.
11. Kelly Criterion
    • What it measures: Calculates the optimal position sizing based on past performance.
    • Good value: A higher Kelly value indicates better risk-adjusted returns, but too high may suggest over-leveraging.
    • Purpose: Guides sizing to maximize growth while managing risk.
12. Net Profit
    • What it measures: The total profit after deducting losses and costs.
    • Good value: Positive and stable net profit over time is crucial, but it should be compared against risk metrics.
    • Purpose: Ensures the strategy is actually making money.
13. Sortino Ratio
    • What it measures: Similar to the Sharpe Ratio but only considers downside risk (negative returns).
    • Good value: A value above 1 is good, and higher values indicate better risk-adjusted performance focusing on negative volatility.
    • Purpose: Helps focus on strategies that minimize downside volatility while delivering good returns.

Filtering Strategies in SQX

• Primary Filters:
  • Sharpe Ratio > 2: Helps find strategies with good risk-adjusted returns.
  • CAGR/Max DD% > 1: Ensures returns are higher than drawdowns.
  • Profit Factor > 1.5: Ensures profits are sufficiently higher than losses.
• Secondary Filters:
  • Open DD% < 15: Ensures drawdowns are within acceptable limits.
  • Stability > 0.8: Ensures a smooth and steady equity curve.
• Other Considerations:
  • Max Drawdown %: This is crucial for risk management, as it can help avoid strategies with sharp drawdowns.
  • Return/Drawdown: Ensures that the strategy’s returns justify the risk it takes.

By using a combination of these filters, especially focusing on Sharpe Ratio, Stability, Profit Factor, and drawdowns, you can filter out strategies that are robust, profitable, and capable of performing well in live trading.

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.