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As motivation plays a crucial role in reaching these goals.

Day: November 21, 2024

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Suggested for 1 Portfolio Creation with IS and OOS Filtering

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.

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whats a different about ‘Simple’ and ‘Walk-forward matrix’ in Optimizer?

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.

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Lets get an ideas for more another trading style instead of major!

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.

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Can i use another like/same Fibonacci Retracement

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!

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Count and listing used indicators passed strategies.

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.