DNYL . SPACE

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

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

“Harmonic Grid Momentum Breakout”

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

Key Components:

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

Trading Conditions:

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

How It Works:

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

Exotic Edge:

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

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.

To filter and select the best strategies from your 300+ strategies in Portfolio Master, follow a systematic approach that aligns with your ranking criteria and ensures that only the most robust and stable strategies are included in the final portfolio. Here’s a step-by-step guide to filtering:

1. Apply Your Ranking Criteria

Use the criteria you’ve already set for ranking individual strategies: – CAGR/Max DD% > 1 – Sharpe Ratio > 2 – Profit Factor > 1.5 – Stability > 0.8 – Open DD% < 15%

This initial filter should remove many strategies that don’t meet the baseline performance requirements.

2. Check In-Sample (IS) and Out-of-Sample (OOS) Performance

• IS Testing: Review the performance of each strategy during the in-sample period to ensure that it was optimized correctly.
• 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.

3. Evaluate Strategy Correlation

• Use correlation analysis to ensure that strategies in your portfolio are not highly correlated. Highly correlated strategies expose you to similar risks across multiple strategies, increasing the chances of simultaneous drawdowns.
• Keep strategies with low or negative correlation in your portfolio to achieve diversification and reduce risk.

4. Monte Carlo Simulations

• Perform Monte Carlo stress tests on your selected strategies to assess how they handle market noise and randomness. Strategies that consistently perform well under Monte Carlo simulations are more robust.
• Focus on strategies with a low chance of ruin and those that maintain good metrics like Sharpe ratio and Profit Factor in the simulated results.

5. Examine Trade Frequency and Conditions

• Look for strategies that maintain consistent trade frequency and avoid excessive overtrading, which can increase transaction costs and slippage.
• Ensure that strategies follow a balance of trend-following, breakout, and swing trading methodologies (as you’ve chosen) to reduce dependency on a single market condition.

6. Filter for Drawdown Recovery

• Evaluate how fast each strategy recovers from drawdowns. Strategies that recover quickly are generally more reliable for long-term use. Use the drawdown recovery time as an additional filter.

7. Forward Testing

• Take your top strategies and conduct a small-scale forward testing or paper trading to see how they behave in current market conditions. Live market dynamics often differ from historical data, so this step is crucial for final validation.

8. Select a Balanced Portfolio

• After narrowing down the best-performing strategies, use Portfolio Master to combine them into an optimized portfolio with diverse market exposures and timeframes (H1/H4, M15/H4, etc.).
• Make sure the combined strategies adhere to your risk management rules, such as RRR > 1:3, duplicate orders, closing positions on Fridays, and break-even trailing stops.

9. Monitor Ongoing Performance

• Continue to monitor the performance of your portfolio once it’s live, making adjustments only if necessary. Pay attention to how strategies perform during different market phases (trending, ranging, volatile) to ensure long-term stability.

By following this structured filtering process, you’ll be able to select a portfolio of strategies that not only meet your criteria but are also robust, diversified, and ready for 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.

When focusing on long-term stability for your strategy, aside from CAGR/Max DD% and Sharpe Ratio, the most critical metric to consider is Profit Factor. However, other important factors like Stability and Open Drawdown (DD) also play key roles in assessing the reliability of your strategy. Let’s explore these metrics:

1. Profit Factor:

• What it Measures: The ratio of gross profit to gross loss. For example, a profit factor of 1.5 means that for every dollar lost, you are making 1.5 dollars in profit.
• Importance: A higher profit factor (typically > 1.5) is crucial for long-term viability. It shows that the strategy generates more profit than loss over time.
• Why It’s Key for Long-Term Stability: It directly reflects the strategy’s overall profitability, indicating whether it can sustain profitability through different market conditions.
• Ideal Target: Profit Factor above 1.5 is solid, while a value of 2.0 or more is very strong.

2. Stability of Returns (Stability):

• What it Measures: The consistency of your strategy’s performance over time. It looks at the smoothness of the equity curve.
• Importance: Stability indicates how reliable the strategy is at generating returns without significant fluctuations.
• Why It’s Key for Long-Term Stability: A stable equity curve suggests that your strategy can withstand various market environments without experiencing wild swings in performance.
• Ideal Target: Aim for a stability score close to 1, where 1 represents a perfectly straight equity curve.

3. Open Drawdown (Open DD%):

• What it Measures: The maximum unrealized loss during a trade (before it’s closed).
• Importance: While Max Drawdown focuses on the worst drawdown for closed trades, Open DD% shows how much risk your strategy takes while trades are still open.
• Why It’s Key for Long-Term Stability: High open drawdowns can cause emotional stress and can put your account at risk, especially during volatile periods. Keeping open drawdowns low ensures your strategy can withstand market swings without large floating losses.
• Ideal Target: Aiming for Open DD% < 10-15% is generally considered good for long-term sustainability.

Summary of Metrics (Besides CAGR/Max DD% and Sharpe Ratio):

1. Profit Factor (Most Important): Reflects the overall profitability of your strategy. Target >1.5, ideally >2.
2. Stability: Measures how smooth and reliable your equity curve is. Aim for a stability score near 1.
3. Open Drawdown %: Keep this value as low as possible to avoid large floating losses. Aim for <10-15%.

Focusing on these three metrics will help confirm your strategy’s long-term stability and profitability, making it robust enough to withstand different market conditions.

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.

Great! Now that we’re moving forward with the Trend + Breakout + Swing Trading combination, here’s the plan to proceed:

Steps for Optimization and Backtesting:

1. Initial Setup:
   • Main Timeframe: H1 (for active signals).
   • Sub Timeframe: H4 (for confirmation).
   • Open Duplicate Orders allowed, with signals from Trend Trading (ADX, Ichimoku, MACD), Breakout (Bollinger Bands, ATR, Momentum), and Swing Trading (RSI, Fractals, MACD).
   • Implement trailing stop and break-even with ATR or Parabolic SAR.
   • Ensure RRR > 1:3 and close all trades by Friday.
2. Optimization:
   • Risk and Lot Size Management: 0.01 lots per $100 balance, with risk percentage on the account.
   • Sequential optimization for additional timeframes like M30/H4, M15/H4, M5/H4 to explore robustness across different market conditions.
   • Focus on finding the optimal parameters for indicators like ATR and Ichimoku to maximize profitability across both trending and volatile markets.
3. Backtesting:
   • Using your M1 data over 20 years, run the backtest for IS (In-Sample) and OOS (Out-of-Sample) periods.
   • Analyze performance metrics: Profit Factor, RRR, Stability, Drawdown, and Win/Loss ratio.
4. Validation:
   • Post-backtest, validate on OOS data to ensure consistency and reliability in live market conditions.

To start with a combination that covers different market conditions while aligning with your desired strategy, I recommend focusing on a hybrid of Trend Trading, Breakout Strategy, and Swing Trading. These three strategies complement each other well and offer a balanced approach to both trending and volatile markets.

Combination Strategy: Trend + Breakout + Swing

Key Features:

1. Trend Trading: Capitalizes on long-lasting trends using indicators like ADX, MACD, and Ichimoku to identify strong trends. This ensures you catch larger moves.
2. Breakout Strategy: Helps in volatile conditions where price breaks out of consolidation. Use Bollinger Bands, ATR, and Momentum to identify breakouts and apply trailing stops to secure gains.
3. Swing Trading: Captures intermediate price swings between market highs and lows. MACD, RSI, and Fractals work well to time entry and exit points, ensuring you profit from reversals or corrections.

Signals for the Combination:

• Trend Trading: Use ADX (>25) to confirm a strong trend. Combine with Ichimoku for entry points in the direction of the trend. Apply MACD for further momentum confirmation.
• Breakout Strategy: Add Bollinger Bands for spotting volatility and breakout zones, while ATR helps manage trailing stops after the breakout. Momentum confirms the strength of the breakout.
• Swing Trading: Use Fractals and RSI to identify swing highs and lows. Swing trades can be opened in the direction of the trend or at key reversal points. Apply a trailing stop or break-even to secure profits on swings that move in your favor.

Application of Your Rules:

1. Open Orders with Duplicates Allowed: During strong trend or breakout conditions, duplicate trades can be placed. For instance, multiple orders can be triggered as long as ADX shows a strong trend or Bollinger Bands expand during a breakout.
2. Profit Targets: Exit trades when profit reaches 1-5% of total capital or the position exceeds 5 days. Use RSI or Fractals to close swing trades, while MACD and ATR help manage trend and breakout exits.
3. Break-Even and Trailing Stop: Apply a Parabolic SAR or ATR-based trailing stop after price moves in your favor. For trend trades, break-even should trigger when the price has moved favorably by a set percentage (e.g., 1-2%).
4. RRR > 1:3: Ensure all trades have a minimum risk-reward ratio of 1:3 by setting stop-losses based on ATR and take-profits aligned with trend direction.
5. Close by Friday: Use an exit rule to ensure all positions are liquidated before Friday’s close, even if the profit target has not been met.
6. Consecutive Wins/Losses > 2: Monitor consecutive wins or losses to adjust position sizing or avoid overtrading during losing streaks.

Expected Benefits:

• Trend Trading captures long-term movements in strong markets.
• Breakout Strategy profits from high volatility and sharp market moves.
• Swing Trading adds flexibility by allowing trades in both trending and counter-trending situations.

This combination can help you build a well-rounded system, adaptable to various market conditions while maximizing your profit potential.

My desired strategy, can you adaption with those 12 strategies above and use my signals, make it greater possible: 1. Open orders duplicate allowed 2. Close orders with a profit for 1-5% from the total capital or close by using signals that hold positions for no more than 5 days 3. Use Break-Even profits and use trailing according to appropriate signals to catch up with profits as quickly as possible 4. I need RRR more than 1:3 5. Close all orders before the market closes every Friday 6. Consec win/loss trades > 2

To adapt your desired strategy with the 12 trading methods and signals you provided, we can combine specific features from each strategy to create an enhanced, hybrid approach. Let’s structure it step by step:

Key Elements of Your Desired Strategy:

1. Open Duplicate Orders: This allows more flexibility in taking multiple trades if the conditions allow.
2. Profit Target: Closing orders with 1-5% profit from the total capital.
3. Time-Limited Holds: No position should last more than 5 days.
4. Break-Even and Trailing Stop: Secure profits with a break-even and then trail the stop to maximize gains.
5. High RRR (1:3): Ensuring risk is minimized and profits are large enough to justify trades.
6. Close by Friday: All positions are liquidated before the market closes on Friday.
7. Consecutive Wins/Losses: Focus on having more than 2 consecutive wins or limiting losses.

Strategy Adaptation Across the 12 Trading Methods:

1. Price Action Trading:

• Signals: Use Candle Patterns and Fractals to capture high-quality reversal or breakout trades.
• Adaption: These signals can be used to open multiple positions during a strong price action movement, with a stop-loss placed at key price levels. The trailing stop and break-even would be applied as soon as the price moves favorably.

2. Range Trading Strategy:

• Signals: Apply Bollinger Bands, RSI, and Stochastic for entering positions at the range extremes.
• Adaption: Open trades when the price reaches either overbought or oversold levels. Use trailing stops aggressively to capture small but consistent gains within the range. If the trade doesn’t meet profit criteria within 5 days, exit by signal.

3. Trend Trading Strategy:

• Signals: Combine ADX, Ichimoku, and Super Trend to ride strong market trends.
• Adaption: Open duplicate positions in a strong trend confirmed by ADX (>25) and Ichimoku cloud direction. Apply the trailing stop to catch the bulk of the move while closing trades that meet the 1-5% profit or exceed 5 days of holding.

4. Position Trading:

• Signals: Use Ichimoku and Hull Moving Average for longer-term trend confirmation, but limit holding time to 5 days.
• Adaption: Position trading signals are generally longer-term, but in your strategy, we can shorten this by closing trades within 5 days if the target profit is not reached.

5. Day Trading Strategy:

• Signals: Utilize VWAP, Momentum, and RSI to capture intraday moves.
• Adaption: Open multiple positions based on intraday momentum and close them by the end of the trading day or use your break-even and trailing stop to capture small profits. Ensure no trades remain open on Fridays.

6. Scalping Strategy:

• Signals: Leverage Parabolic SAR, Stochastic, and VWAP for fast entries/exits.
• Adaption: Scalping will require tighter trailing stops and quicker moves to break-even. You can open duplicate orders on small moves and close them as soon as you hit the profit target or within 5 days.

7. Swing Trading:

• Signals: MACD, RSI, and Fractals to identify swing highs/lows.
• Adaption: Swing trading offers opportunities to capture larger moves. Duplicate orders can be opened as long as the swing signal is valid, with the trailing stop applied to catch larger profits. Exit after 5 days or on the Friday close.

8. Carry Trade Strategy:

• Signals: KAMA and Directional Index for long-term bias, but constrained to short-term holding.
• Adaption: In traditional carry trade, positions are held long-term, but with your strategy, use the trailing stop to secure short-term profits within 5 days.

9. Breakout Strategy:

• Signals: Bollinger Bands, ATR, and Momentum for volatility-based breakouts.
• Adaption: Use breakout signals to open duplicate trades. Once the breakout occurs, the trailing stop will help capture further gains while securing profits. Close the trade if the price meets the 1-5% target or exits by Friday.

10. News Trading:

• Signals: ATR, Momentum, and VWAP to capture volatility spikes.
• Adaption: News trading tends to be unpredictable, so quick entries with trailing stops and a break-even move are essential. Use this approach to exit trades quickly if the market moves in your favor.

11. Retracement Trading:

• Signals: Fibonacci Retracements, RSI, and Fractals for entry signals.
• Adaption: Enter on retracement to a key Fibonacci level with multiple orders. Apply trailing stop once the price resumes the primary trend. Close the trade either at the target profit or after 5 days.

12. Grid Trading:

• Signals: Generally grid strategies are less signal-based, but ATR or Bollinger Bands can help define volatile zones.
• Adaption: In a grid strategy, you can apply the trailing stop on profitable orders while still allowing duplicate trades. Close all positions before the end of the week or after 5 days to prevent overexposure.

Enhancing the Strategy:

1. Duplicate Orders: This flexibility allows for scaling into trades during favorable conditions, especially in trending markets or during strong breakouts. Combining this with ADX for trend strength or ATR for volatility can provide an edge.
2. Close Orders Based on Capital Gain or Signal: The 1-5% capital gain target works well with signals like Ichimoku, Candle Patterns, or Bollinger Bands to time exits when trades become profitable.
3. Break-Even and Trailing Stop: Use Parabolic SAR or ATR for managing the trailing stop, ensuring that once the price has moved in your favor, the trailing stop locks in profits while allowing room for larger gains.
4. RRR of 1:3: Targeting a risk-reward ratio of 1:3 is ideal for strategies like Breakout or Swing Trading. Use Fractals, RSI, and MACD to spot high-probability trades and manage risk with well-placed stop losses.
5. Weekly Close: Ensure that all trades are automatically closed by Friday using a time-based exit rule, combined with signals to prevent holding positions over the weekend.
6. Consecutive Wins/Losses: This could be monitored in SQX by applying risk filters, ensuring the strategy does not become too risky if losses accumulate, or adding more trades if you’re on a winning streak.

By merging these trading strategies with your specifications, you’ll create a highly adaptable system that should perform well across different market conditions.

simulate for this 12 straegies method by using my signals provided: 1. Price action trading 2. Range trading strategy 3.Trend trading strategy 4. Position trading 5. Day trading strategy 6. Scalping strategy 7. Swing trading 8. Carry trade strategy 9. Breakout strategy 10. News trading 11. Retracement trading 12. Grid trading

To simulate these 12 trading strategies using the available signals in SQX, we’ll first match each strategy type with appropriate indicators from your list. This will create a structured approach for SQX to build and optimize each strategy based on specific methodologies.

1. Price Action Trading:

• Signals: Candle Patterns, Fractals, ATR
• Approach: Price action traders rely on interpreting market structure, chart patterns, and key price levels. Candle patterns combined with ATR for volatility can help pinpoint entries and exits.
• Use in H1/H4: H1 for active candle formations; H4 for larger price patterns.

2. Range Trading Strategy:

• Signals: Bollinger Bands, Keltner Channel, RSI, Stochastic
• Approach: Range trading involves identifying overbought and oversold conditions within a sideways market. Bollinger Bands and Keltner Channel are excellent for defining the range, while RSI or Stochastic can time the entry.
• Use in H1/H4: H1 for entering at range extremes; H4 to confirm the range is stable.

3. Trend Trading Strategy:

• Signals: ADX, MACD, Super Trend, Ichimoku
• Approach: Focus on capturing sustained market trends. ADX confirms trend strength, MACD signals momentum, while Ichimoku and Super Trend confirm trend direction.
• Use in H1/H4: H1 for trend signals; H4 for broader trend confirmation.

4. Position Trading:

• Signals: Ichimoku, Hull Moving Average, RSI, ADX
• Approach: Position traders take long-term trades based on macro trends. Ichimoku and Hull MA work well for spotting long-term trends, while ADX confirms trend strength.
• Use in H1/H4: H4 for trend identification; H1 for fine-tuning entries.

5. Day Trading Strategy:

• Signals: Moving Average, VWAP, RSI, Momentum
• Approach: Day traders use quick signals to profit from intraday price movements. Moving averages, VWAP (volume-weighted average price), and momentum indicators work well for this.
• Use in H1/H4: H1 for day trading entries; H4 for ensuring alignment with larger trends.

6. Scalping Strategy:

• Signals: Parabolic SAR, ATR, Stochastic, VWAP
• Approach: Scalping requires fast signals. Parabolic SAR and ATR work for precise entry/exit timing, while Stochastic identifies quick overbought/oversold conditions.
• Use in H1/H4: H1 for quick price action trades; H4 could be used to ensure macro trend direction is not against the trade.

7. Swing Trading:

• Signals: MACD, RSI, Hull Moving Average, Fractals
• Approach: Swing traders seek to profit from intermediate price swings. MACD and Hull MA identify swings, while RSI and Fractals time entry/exit points.
• Use in H1/H4: H1 for swing setups; H4 for trend alignment.

8. Carry Trade Strategy:

• Signals: KAMA (Kaufman Adaptive Moving Average), Directional Index, VWAP, ATR
• Approach: Although carry trading is more interest-rate based, technical signals like KAMA can help identify good entry points into longer-term trades, while ATR helps manage risk.
• Use in H1/H4: H4 for trend setup; H1 for trade entry and management.

9. Breakout Strategy:

• Signals: Bollinger Bands, ATR, Momentum, ADX
• Approach: Breakout strategies focus on trading volatility expansions. Bollinger Bands and ATR identify breakouts, while Momentum confirms the breakout direction.
• Use in H1/H4: H1 for breakouts; H4 for larger breakout confirmation.

10. News Trading:

• Signals: ATR, Momentum, Volume-based signals (e.g., VWAP, AVG Volume)
• Approach: News trading involves reacting quickly to market events. ATR captures volatility surges, while VWAP and momentum indicators show how the market reacts.
• Use in H1/H4: H1 for fast reaction to news events; H4 for identifying broader market sentiment.

11. Retracement Trading:

• Signals: Fibonacci retracement levels (can be coded), Stochastic, RSI, Fractals
• Approach: Retracement trading involves entering trades when price retraces within a larger trend. Stochastic and RSI work to identify the retracement, while Fractals show reversal points.
• Use in H1/H4: H1 for identifying retracement; H4 for trend direction and retracement targets.

12. Grid Trading:

• Signals: None (grid strategy), ATR, Bollinger Bands (optional for range)
• Approach: Grid trading involves placing multiple orders at set intervals without needing precise signals. However, ATR or Bollinger Bands can add volatility context to the grid.
• Use in H1/H4: Primarily H1 for grid execution; H4 for macro trend bias (optional).

Simulation Expectations:

• Price Action, Range, and Trend Strategies: These should perform well when tested with H1 signals and H4 confirmation due to their reliance on technical signals and clear exit rules.
• Day Trading and Scalping: These may require faster timeframes or tighter risk management, but with ATR and quick momentum indicators, they can be effective.
• Swing Trading and Position Trading: These will work well on higher timeframes (H1/H4), as they capitalize on medium- to long-term price movements.
• Breakout, News, and Retracement Trading: The success of these strategies will depend on volatility and momentum confirmation.
• Grid Trading: This is more mechanical and will require less signal-based input but can benefit from ATR or volatility-based indicators to avoid extreme market conditions.

By implementing these strategies and adjusting them with sequential optimization, you can test and refine how each approach behaves on various timeframes.

okin now on the way of building from above we’re talking. i’m listig all signals that i have on SQX for you: ADX, Arron, ATR, ATR Percent Rank, AVG Volume, Awesome Oscillator, Bear power, Bull power, BH Ergodic, Bollinger Bands, Candle Pattern, CCI, Choppiness Idex, Connor RSI, CSSA Market Regime, Demarker, DEMA, Didi index, Directional Index, Disparity Index, DPO, DSS Bressert, DVO, Entrophy Math, Fractal, Gann HI LO, Highest Lowest, Hull moving Average, Ichimoku, KAMA, Kaufma Efficiency Ratio, Keltner Channal, Laguerre RSI, Lin Reg, MACD, Momentum, Movinng Average, OSMA, Parabolic SAR, QQE, Reflex, ROC, RSI, RVI Schaff Trend Cycle, SR Percent rank, Std Dev, Stochastic, Super Tred, Trend, TTM Squeeze, Ulcer Index, Vortex ,VWAP, William PR.

Thanks for sharing the list of signals. With such a wide range of technical indicators available, you can create diverse strategies tailored to different market conditions and timeframes.

Recommendations for Signal Selection in StrategyQuant X (SQX):

Since you’re focusing on H1 as the main signal timeframe and H4 as confirmation, here are some suggestions for selecting and combining signals based on their roles in trend detection, volatility, momentum, and entry/exit timing:

1. Trend Detection Signals:
   • ADX (Average Directional Index): Excellent for determining the strength of a trend. Can be used to filter trades, only allowing entries when the trend strength is above a certain threshold.
   • Ichimoku: A robust all-in-one indicator for trend, momentum, and support/resistance levels. Ichimoku works well for confirmation on H4.
   • Super Trend: Simple and effective for trend-following strategies, it works well on both higher and lower timeframes for trend confirmation.
2. Momentum Indicators:
   • MACD: Classic momentum indicator, useful for detecting trend changes or continuations.
   • RSI (Relative Strength Index) and Connors RSI: Great for identifying overbought/oversold conditions. You could combine this with trend filters to avoid countertrend trades.
   • Stochastic: Similar to RSI, it can add a timing element for entries or exits when paired with trend filters.
3. Volatility & Range:
   • ATR (Average True Range) and ATR Percent Rank: Excellent for determining volatility. You could adjust stop-loss and take-profit levels based on volatility readings.
   • Bollinger Bands: Can be used for mean reversion strategies or volatility breakouts. Price reaching upper or lower bands could serve as exit points or trade triggers.
   • Keltner Channel: Useful in combination with volatility readings for breakout strategies.
4. Price-Based Signals:
   • Candle Patterns: Effective for timing entries/exits based on specific price action patterns.
   • Parabolic SAR: Can provide dynamic stop-loss levels, especially useful for trailing stops.
   • Williams %R: Good for detecting overbought/oversold levels, especially on higher timeframes like H4.
5. Additional Unique Indicators:
   • Ulcer Index: Measures downside risk. You can use it to avoid trading in risky periods.
   • Schaff Trend Cycle: Great for cyclical patterns in momentum, allowing timing entries with trend changes.
   • Vortex: Captures trend changes with crossovers; useful in combination with trend-following indicators like ADX or MACD.

Potential Strategy Combinations:

• Trend-Following Strategy:
  • Main Signal on H1: Combine ADX (trend strength) with MACD or Ichimoku to confirm trend direction and momentum.
  • Confirmation on H4: Use Super Trend or Ichimoku for trend confirmation on H4. Consider adding ATR for volatility-based stop adjustments.
• Mean Reversion Strategy:
  • Main Signal on H1: Use Bollinger Bands or Keltner Channel with RSI or Williams %R to capture reversions.
  • Confirmation on H4: Add MACD or RVI (Relative Vigor Index) for broader trend direction to confirm whether the reversion fits the larger trend.
• Volatility Breakout:
  • Main Signal on H1: Use ATR Percent Rank or Bollinger Bands for breakouts. Pair with Momentum or ROC (Rate of Change) to confirm price momentum.
  • Confirmation on H4: Add a higher timeframe confirmation such as Fractal or Directional Index for trend continuation.

Next Steps:

• You could let SQX’s Genetic Evolution process handle these combinations to find the most robust pairs.
• When doing sequential optimization across different timeframes (M30/H4, M15/H4, M5/H4), include some of these indicators to test how they perform on faster timeframes and whether they enhance your strategy’s adaptability.

This should provide a good foundation for further strategy testing and refinement.

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.

i just building and got some prototype but i asking u: Profit factor, Ret/DD ratio, Stability, Fitness, Open DD%, Win/Loss ratio and CAGR/Max DD%. Rank these values ​​from most important to least important that I should consider, including the analysis of both IS and OOS at each stage.

When evaluating a trading strategy, especially in StrategyQuant X (SQX), the metrics you mentioned are all important but serve different purposes. Ranking them depends on your specific goals (e.g., minimizing risk, maximizing returns, or focusing on long-term sustainability). Here’s a ranking from most important to least important, considering both In-Sample (IS) and Out-of-Sample (OOS) stages:

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

• Why It’s Important: This is a key metric for long-term growth and sustainability of the strategy. It balances how much the strategy grows versus how much it risks. A high CAGR with a low Max Drawdown ensures that the strategy is generating returns without taking excessive risk.
• IS/OOS Consideration: A good ratio in both IS and OOS suggests that the strategy is scalable and robust across different market environments.
  • IS: Focus on optimizing this metric to ensure strong growth.
  • OOS: Ensure consistency; a large drop in OOS could indicate overfitting.

2. Profit Factor

• Why It’s Important: Profit Factor (gross profit divided by gross loss) is essential for understanding the overall profitability of the strategy. A Profit Factor greater than 1 indicates a profitable system, with values above 1.5 or 2 being ideal.
• IS/OOS Consideration: Profit Factor must remain solid in OOS testing. If it drops significantly OOS, the strategy may be curve-fitted.
  • IS: Aim for a strong value, but don’t prioritize it at the expense of risk management.
  • OOS: Focus on stability and a consistent profit factor.

3. Ret/DD Ratio (Return-to-Drawdown Ratio)

• Why It’s Important: This metric balances returns against drawdowns. A high Ret/DD ratio indicates that the strategy generates good returns relative to the risk (drawdown) it takes.
• IS/OOS Consideration: It’s important in both IS and OOS stages because it ensures that your strategy’s performance isn’t reliant on taking high risks. Look for consistency across both.
  • IS: Focus on maximizing returns while keeping drawdowns minimal.
  • OOS: The ratio should not degrade significantly, ensuring stability.

4. Open Drawdown % (Open DD%)

• Why It’s Important: Open DD% tracks the maximum floating loss during a trade that hasn’t closed yet. This is crucial for understanding the worst-case scenarios and how your strategy behaves in adverse conditions. High Open DD% could signal that the strategy lets trades run into significant losses before closing them.
• IS/OOS Consideration: In OOS, you want to see a manageable Open DD%. A high Open DD% in OOS could indicate riskier trades.
  • IS: Optimize to keep Open DD% within acceptable risk limits.
  • OOS: Check if the Open DD% stays controlled in unseen data.

5. Stability

• Why It’s Important: Stability measures how consistent the equity curve is over time. A stable equity curve is essential for long-term profitability and avoiding emotional stress from sharp equity fluctuations.
• IS/OOS Consideration: Stability should be maintained across both IS and OOS phases to ensure that the strategy isn’t over-optimized to one specific market condition.
  • IS: High stability is desirable, but watch out for overfitting.
  • OOS: Check if stability remains intact without large drops in equity.

6. Win/Loss Ratio

• Why It’s Important: While the Win/Loss ratio is helpful, it doesn’t paint a full picture. A strategy with a lower win rate can still be profitable if the winning trades are larger than the losing ones. It’s more useful when combined with other metrics like Profit Factor or Return-to-Drawdown.
• IS/OOS Consideration: While optimizing, focus less on having a high Win/Loss ratio and more on profitability. The Win/Loss ratio may vary but shouldn’t drastically change between IS and OOS phases.
  • IS: It’s fine if the Win/Loss ratio isn’t extremely high as long as the strategy is profitable.
  • OOS: Ensure the ratio doesn’t drop significantly.

7. Fitness

• Why It’s Important: Fitness is an overall measure used by SQX to rank strategies based on multiple performance metrics. It’s a good indicator in the early stages of strategy generation but can be less reliable than specific metrics like CAGR/Max DD% or Profit Factor for deep analysis.
• IS/OOS Consideration: While a high fitness score is useful for identifying strong candidates in genetic evolution, don’t rely on it too heavily in OOS testing. Focus more on the other key metrics.
  • IS: A high fitness score helps in the strategy-building process.
  • OOS: Use other metrics to verify performance rather than fitness alone.

Stage-Specific Considerations:

In-Sample (IS) Analysis:

• In the IS phase, you are primarily focusing on optimizing your strategy.
  • Prioritize CAGR/Max DD%, Profit Factor, and Ret/DD Ratio. These three metrics ensure that you have a strategy that is growing profitably with manageable risk.
  • Pay attention to Stability and Open DD% next, as they will indicate how smooth your strategy’s equity curve is and whether you can tolerate the drawdowns during open trades.
  • Win/Loss Ratio is less critical here, as it can be misleading if isolated from other factors. A lower Win/Loss ratio can be fine as long as the profits outweigh losses.
  • Fitness is helpful early on but don’t let it overshadow more significant performance metrics.

Out-of-Sample (OOS) Analysis:

• In the OOS phase, your focus shifts to robustness and ensuring the strategy is not overfitted to past data.
  • Look for consistency in CAGR/Max DD%, Profit Factor, and Ret/DD Ratio between IS and OOS. If these metrics significantly degrade, it’s a sign of overfitting.
  • Stability becomes more crucial here. A strategy that maintains stability in OOS is likely to perform well in live markets.
  • Open DD% should be well-managed, and ideally, it should not increase dramatically in OOS testing.
  • The Win/Loss Ratio can fluctuate, but it should not collapse in OOS. Ensure that profitable trades remain larger than losing ones.

Final Ranking Summary:

1. CAGR/Max DD% (Balances growth vs. risk, essential for long-term viability)
2. Profit Factor (Key profitability measure)
3. Ret/DD Ratio (Measures return relative to risk)
4. Open DD% (Manages risk on open trades)
5. Stability (Ensures a smooth equity curve)
6. Win/Loss Ratio (Helpful but secondary to profitability metrics)
7. Fitness (Useful early but less important for final validation)

This ranking should help guide your focus when optimizing and validating strategies in SQX. Let me know if you want to dive deeper into any specific aspect of the metrics!

i use M1 data and 20yrs old of data range. too high or its ok? cuz i need to build once and leave it ran live a long period

Using M1 data and a 20-year range for backtesting in StrategyQuant X is a solid choice, especially if your goal is to build a strategy that will run live for a long period with minimal adjustments. However, there are a few considerations to keep in mind:

Pros of Using M1 Data and a 20-Year Range:

1. Accuracy in Backtesting:
   • M1 data provides the most granular view of price action, capturing every minute’s high, low, open, and close. This is beneficial for building a precise strategy, especially for exit strategies where timing is critical, such as in trailing stops or scalping.
   • A 20-year range provides a robust historical dataset that includes multiple market conditions (bullish, bearish, and volatile periods), allowing your strategy to adapt to different scenarios.
2. Long-Term Viability:
   • Since you want a strategy that can be used live for a long period, backtesting across 20 years ensures that your strategy is tested across various market cycles (e.g., financial crises, economic booms, geopolitical events).
   • This can give you confidence that your strategy is not curve-fitted to short-term conditions and will be resilient over time.
3. Diverse Market Phases:
   • Gold’s price action has changed significantly over the last 20 years. Testing your strategy on such a broad range of data ensures it performs well across different market environments (low volatility periods, high volatility during economic turmoil, etc.).

Cons and Considerations:

1. Computational Load:
   • M1 data over 20 years is a lot of data. This will significantly increase the computational load, both in terms of time and processing power needed for backtesting, especially when using genetic algorithms.
   • Make sure you have the necessary hardware resources or adjust the size of the test (e.g., use fewer strategies in the genetic evolution process, optimize parameters over smaller chunks of data).
2. Overfitting Risk:
   • With such a large dataset, there is always a risk of overfitting—where your strategy performs well in backtests but doesn’t generalize well in live trading.
   • Solution: Ensure you are using proper out-of-sample testing and walk-forward optimization to mitigate overfitting. This way, you can verify the strategy’s robustness over unseen data.
3. Execution in Live Markets:
   • M1 data focuses on short-term fluctuations. While this provides precision in backtesting, your strategy may encounter slippage and latency issues in live trading due to the speed of executions required for minute-based strategies, especially in a fast-moving asset like gold.
   • Ensure that you take these factors into account when building your risk management rules.
4. Historical Relevance:
   • Although 20 years of data ensures you cover a broad range of market environments, some past market conditions may no longer be relevant to current trading. For instance, gold’s behavior during the early 2000s might not reflect its price action post-2015.
   • Solution: While you can still backtest over 20 years, pay close attention to how the strategy performs in the last 5–10 years of data, as these periods reflect more recent market conditions.

Recommended Approach:

1. Use Data Segmentation:
   • You can break the 20-year period into different segments during backtesting:
     • In-sample (Training Period): The first 15 years of data.
     • Out-of-sample (Validation Period): The last 5 years.
   • This will allow you to refine your strategy on the first segment of data and validate its robustness on more recent market conditions.
2. Walk-Forward Testing:
   • Instead of testing the entire 20-year period as a single block, consider using walk-forward optimization to evaluate how well your strategy adapts to changing market conditions over time. This will ensure that it’s not just performing well in hindsight, but would have adapted dynamically in the real market.
3. Genetic Evolution with M1 Data:
   • When using genetic evolution on M1 data, it’s important to limit the complexity of the strategy (number of indicators, entry/exit rules) and avoid evolving too many parameters at once. This will help reduce the computational burden and minimize overfitting.
4. Live Testing:
   • After backtesting, use a small account or demo to test the strategy in live conditions for a few weeks to verify its real-world performance. Factors like execution speed, slippage, and broker conditions can impact performance differently than in backtesting.

Final Thoughts:

• M1 data and a 20-year range is definitely not “too much” if your goal is to create a long-term, robust trading strategy. Just ensure that you optimize it efficiently using techniques like walk-forward optimization and out-of-sample testing.
• Focus on building a strategy that is robust and adaptive rather than highly optimized for one specific condition, and it should serve you well over the long term in live trading.