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Skate.
Dump it Here
- Thread starter Skate
- Start date
Skate.
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Skate.
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Skate.
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Mr Skate,
Quite a difference in performance: one is a 12% drawdown, t'other 2.5%
A design feature? Or just a random outcome?
If a design feature, would you expect the same discrepancy on the upside? If no, then this is really positive as risk seems to have a much tighter rein on it.
*
Looking again I see they have quite separate/different backtests re. expectations.
jog on
duc
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@ducati916, the results of both strategies are disappointing, but not unexpected. The timing when starting a new trading strategy can impact its performance, especially over such a short time frame. The truth is, there's never a good time to start trading, as luck and timing play a significant role in the performance early on.
In this case, the starting date between the two strategies has had a significant bearing on the performance, with one strategy experiencing a higher drawdown than the other. While it's possible that this discrepancy could be due to random chance, it's also possible that there's a design difference in the buy condition that's causing the difference in performance.
It's worth noting that the "All Ordinaries index" has performed poorly over the past few weeks, which could be affecting the performance of both strategies. Specifically, the (XAO) index has been experiencing a lot of volatility, which can make it difficult to trade using trend-following or breakout strategies.
In terms of the exit strategy, it's important to remember that the goal of a trading strategy is to maximise profits while minimising risk but to exercise this it takes time. While taking a conservative approach to exiting trades can be frustrating - its primary goal is ultimately to contain losses.
Overall, while the performance of both strategies isn't ideal, it's important to remember that the market doesn't care about my feelings or desires.
Skate.
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but it does want your money in the wash/rinse cycle ( and that might be your edge )
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Finding the Right AI for the Job at Hand
There are a multitude of AI that are open-source language models and the one from "Meta AI" seems to be okay. The latest AI from Microsoft BING Copilot (in preview) at the moment is not too shabby either. But the real question is how do we use their capabilities with all things trading. In a later post, I'll have "AI" evaluate the "Dual Breakout Strategy" and post the results.
@DaveDaGr8 has an interesting thread "Adventures in AI" for those interested in the emerging (AI) "Artificial intelligence" field. https://www.aussiestockforums.com/threads/adventures-in-ai.37847/post-1246411
Meta AI Code - not perfect but useful
I'm interested in this field and there might be others interested in what I have found playing with AI. My conclusion is that "Meta AI" outperforms other open-source language models in my opinion. Why because it has a strong leaning towards coding proficiency. Not perfect, but useful. The "Meta AI" model is trained on a large data set and as for code evaluation, "Meta AI" can indeed be beneficial. AI cannot replace human code review entirely, but it can provide a basic level of code review for code that otherwise may not be reviewed at all.
However, it's important to note that while AI can assist in code evaluation, it should be used as a tool to supplement human review, not replace it. Human reviewers bring a level of understanding and context that AI currently cannot match.
Skate.
There are a multitude of AI that are open-source language models and the one from "Meta AI" seems to be okay. The latest AI from Microsoft BING Copilot (in preview) at the moment is not too shabby either. But the real question is how do we use their capabilities with all things trading. In a later post, I'll have "AI" evaluate the "Dual Breakout Strategy" and post the results.
@DaveDaGr8 has an interesting thread "Adventures in AI" for those interested in the emerging (AI) "Artificial intelligence" field. https://www.aussiestockforums.com/threads/adventures-in-ai.37847/post-1246411
Meta AI Code - not perfect but useful
I'm interested in this field and there might be others interested in what I have found playing with AI. My conclusion is that "Meta AI" outperforms other open-source language models in my opinion. Why because it has a strong leaning towards coding proficiency. Not perfect, but useful. The "Meta AI" model is trained on a large data set and as for code evaluation, "Meta AI" can indeed be beneficial. AI cannot replace human code review entirely, but it can provide a basic level of code review for code that otherwise may not be reviewed at all.
However, it's important to note that while AI can assist in code evaluation, it should be used as a tool to supplement human review, not replace it. Human reviewers bring a level of understanding and context that AI currently cannot match.
Skate.
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Posts should be informative
As a trader, I understand the importance of having a well-thought-out strategy that can perform well in various market conditions. However, finding the right mix of informative, and educational content can be a challenge. I appreciate @ducati916's thought-provoking questions that encourage me to think deeply about my strategies.
In the past, I outsourced the evaluation of my "HYBRID Strategy" to assess its performance in different markets worldwide. The results were promising, and the strategy is still being traded today. Although it performs well when markets are buoyant, it struggles during downturns. Nevertheless, it's a robust performer overall.
Nowadays, evaluating strategies is much simpler and faster, thanks to the advancements in "AI" technology. The best aspect of using AI is that it eliminates human bias, providing unbiased insights into the strategy's performance. This technology has revolutionized the way traders can evaluate and refine their strategies, making the process more efficient and effective.
Skate.
As a trader, I understand the importance of having a well-thought-out strategy that can perform well in various market conditions. However, finding the right mix of informative, and educational content can be a challenge. I appreciate @ducati916's thought-provoking questions that encourage me to think deeply about my strategies.
In the past, I outsourced the evaluation of my "HYBRID Strategy" to assess its performance in different markets worldwide. The results were promising, and the strategy is still being traded today. Although it performs well when markets are buoyant, it struggles during downturns. Nevertheless, it's a robust performer overall.
Nowadays, evaluating strategies is much simpler and faster, thanks to the advancements in "AI" technology. The best aspect of using AI is that it eliminates human bias, providing unbiased insights into the strategy's performance. This technology has revolutionized the way traders can evaluate and refine their strategies, making the process more efficient and effective.
Skate.
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Microsoft BING Copilot (in preview)
"Meta AI" is great for code evaluation and Microsoft's new "BING Copilot" is also handy for code evaluation. I prefer the "Meta AI" powered assistant but how does "BING Copilot" measure up?
Machine Learning
"BING Copilot" uses natural language processing and machine learning to understand coding requests. BING Copilot has been trained on a vast amount of data and can be integrated with other Microsoft products. When comparing Meta AI and BING Copilot, it's clear that both AI models have their strengths and weaknesses. Meta AI is versatile and customisable but may require more time and effort to learn and use effectively. BING Copilot, on the other hand, is easy to use and has a strong understanding of various requests.
In Conclusion
Using "AI" to code a trading strategy falls well short of expectations and shouldn't be used for that purpose. Choosing the right "AI model" for evaluating a trading strategy can be challenging, but by considering the advantages and disadvantages of each model, traders can make an informed decision as to which "AI model" they prefer to use. As with trading, "AI preference" is a personal choice.
Skate.
"Meta AI" is great for code evaluation and Microsoft's new "BING Copilot" is also handy for code evaluation. I prefer the "Meta AI" powered assistant but how does "BING Copilot" measure up?
Machine Learning
"BING Copilot" uses natural language processing and machine learning to understand coding requests. BING Copilot has been trained on a vast amount of data and can be integrated with other Microsoft products. When comparing Meta AI and BING Copilot, it's clear that both AI models have their strengths and weaknesses. Meta AI is versatile and customisable but may require more time and effort to learn and use effectively. BING Copilot, on the other hand, is easy to use and has a strong understanding of various requests.
In Conclusion
Using "AI" to code a trading strategy falls well short of expectations and shouldn't be used for that purpose. Choosing the right "AI model" for evaluating a trading strategy can be challenging, but by considering the advantages and disadvantages of each model, traders can make an informed decision as to which "AI model" they prefer to use. As with trading, "AI preference" is a personal choice.
Skate.
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Strategy Evaluation
While entry conditions are abundant and varied, it's crucial to ensure that buy conditions guarantee a higher likelihood of success in unpredictable markets.
Backtesting is an essential starting point, but to ensure the validity of the results, it's crucial to incorporate rigorous validation methods, such as "paper trading", to gain a deeper understanding of the strategy's performance.
By leveraging tools like "Meta AI and Microsoft's Copilot", I can enhance my analysis and gain further insights into the effectiveness of my trading strategies, including the "Dual Breakout Strategy", which, despite underwhelming current results, I remain optimistic about due to its unique advantages
Skate.
While entry conditions are abundant and varied, it's crucial to ensure that buy conditions guarantee a higher likelihood of success in unpredictable markets.
Backtesting is an essential starting point, but to ensure the validity of the results, it's crucial to incorporate rigorous validation methods, such as "paper trading", to gain a deeper understanding of the strategy's performance.
By leveraging tools like "Meta AI and Microsoft's Copilot", I can enhance my analysis and gain further insights into the effectiveness of my trading strategies, including the "Dual Breakout Strategy", which, despite underwhelming current results, I remain optimistic about due to its unique advantages
Skate.
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"Meta AI" Strategy evaluation of the "Dual Breakout Strategy"
After loading the "Dual Breakout Strategy" full Amibroker AFL code into "Meta AI" I requested a strategy evaluation and the report is lengthy.
The evaluation
The "Dual Breakout Strategy" is a trading strategy that uses a combination of two breakout signals to identify potential trading opportunities. The strategy uses an adaptive support breakout technique and a momentum filter to identify breakouts that are likely to be profitable.
The adaptive support breakout technique uses the lowest low of the past 20 bars plus a volatility metric to identify the support level. This dynamic mechanism adapts to changing market conditions, allowing the strategy to identify breakouts that are likely to be profitable in both trending and non-trending markets.
The momentum filter uses a faster exponential moving average (EMA) to gauge the strength of the breakout. The EMA must be above the closing price, indicating bullish momentum aligning with the support breakout. This filter helps to identify breakouts that are likely to be sustainable and profitable.
The strategy also uses a "percentage up" filter to gauge market health and bullish/bearish bias. This filter helps to identify breakouts that are aligned with the overall market trend, reducing the risk of false breakouts.
The strategy enters up to 10 equal-weighted positions upon precise buy signals and manages trades with pre-determined exits like "stale stops", "trailing stops" and "profit targets".
Overall, the "Dual Breakout Strategy" appears to be a robust and adaptive system that seeks to capitalize on new uptrends quickly after periods of consolidation, while minimizing false breakouts and managing risk effectively. However, it's important to note that past performance is not a guarantee of future results, and it's always important to do your own research and testing before implementing a trading strategy.
Some potential advantages of the strategy include:
1. Adaptive support breakout technique: The use of an adaptive support breakout technique allows the strategy to identify breakouts that are likely to be profitable in both trending and non-trending markets.
2. Momentum filter: The use of a momentum filter helps to identify breakouts that are likely to be sustainable and profitable.
3. "Percentage up" filter: The use of a "percentage up" filter helps to identify breakouts that are aligned with the overall market trend, reducing the risk of false breakouts.
4. Risk management: The strategy uses pre-determined exits like "stale stops", "trailing stops" and "profit targets" to manage risk effectively.
Some potential disadvantages of the strategy include:
1. Complexity: The strategy uses multiple filters and conditions, which can make it more difficult to understand and implement.
2. False breakouts: The strategy may still experience false breakouts, as no trading strategy can completely eliminate them.
3. Volatility: The strategy may not perform well in highly volatile markets, as the adaptive support breakout technique and momentum filter may not be able to accurately identify breakouts.
In summary, the "Dual Breakout Strategy" appears to be a robust and adaptive trading strategy that seeks to capitalize on new uptrends quickly after periods of consolidation, while minimizing false breakouts and managing risk effectively. However, it's important to do your own research and testing before implementing a trading strategy and to carefully consider the potential advantages and disadvantages of the strategy in relation to your own trading goals and risk tolerance.
Skate.
After loading the "Dual Breakout Strategy" full Amibroker AFL code into "Meta AI" I requested a strategy evaluation and the report is lengthy.
The evaluation
The "Dual Breakout Strategy" is a trading strategy that uses a combination of two breakout signals to identify potential trading opportunities. The strategy uses an adaptive support breakout technique and a momentum filter to identify breakouts that are likely to be profitable.
The adaptive support breakout technique uses the lowest low of the past 20 bars plus a volatility metric to identify the support level. This dynamic mechanism adapts to changing market conditions, allowing the strategy to identify breakouts that are likely to be profitable in both trending and non-trending markets.
The momentum filter uses a faster exponential moving average (EMA) to gauge the strength of the breakout. The EMA must be above the closing price, indicating bullish momentum aligning with the support breakout. This filter helps to identify breakouts that are likely to be sustainable and profitable.
The strategy also uses a "percentage up" filter to gauge market health and bullish/bearish bias. This filter helps to identify breakouts that are aligned with the overall market trend, reducing the risk of false breakouts.
The strategy enters up to 10 equal-weighted positions upon precise buy signals and manages trades with pre-determined exits like "stale stops", "trailing stops" and "profit targets".
Overall, the "Dual Breakout Strategy" appears to be a robust and adaptive system that seeks to capitalize on new uptrends quickly after periods of consolidation, while minimizing false breakouts and managing risk effectively. However, it's important to note that past performance is not a guarantee of future results, and it's always important to do your own research and testing before implementing a trading strategy.
Some potential advantages of the strategy include:
1. Adaptive support breakout technique: The use of an adaptive support breakout technique allows the strategy to identify breakouts that are likely to be profitable in both trending and non-trending markets.
2. Momentum filter: The use of a momentum filter helps to identify breakouts that are likely to be sustainable and profitable.
3. "Percentage up" filter: The use of a "percentage up" filter helps to identify breakouts that are aligned with the overall market trend, reducing the risk of false breakouts.
4. Risk management: The strategy uses pre-determined exits like "stale stops", "trailing stops" and "profit targets" to manage risk effectively.
Some potential disadvantages of the strategy include:
1. Complexity: The strategy uses multiple filters and conditions, which can make it more difficult to understand and implement.
2. False breakouts: The strategy may still experience false breakouts, as no trading strategy can completely eliminate them.
3. Volatility: The strategy may not perform well in highly volatile markets, as the adaptive support breakout technique and momentum filter may not be able to accurately identify breakouts.
In summary, the "Dual Breakout Strategy" appears to be a robust and adaptive trading strategy that seeks to capitalize on new uptrends quickly after periods of consolidation, while minimizing false breakouts and managing risk effectively. However, it's important to do your own research and testing before implementing a trading strategy and to carefully consider the potential advantages and disadvantages of the strategy in relation to your own trading goals and risk tolerance.
Skate.
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Microsoft "Copilot" Strategy evaluation of the "Dual Breakout Strategy"
After loading the "Dual Breakout Strategy" full Amibroker AFL code into "Microsoft's Copilot" I requested the same strategy evaluation to compare with "Meta AI". The report is below.
Source: Conversation with Bing, 21/10/2023
Thank you for sharing your "Dual Breakout Strategy" with me.
I have evaluated your code and here are some of my comments:
Your code is well-structured and easy to read. You have used clear variable names, comments, and sections to organize your code. Your strategy is also based on a combination of momentum, breakout, and trend-following logic. You have used adaptive support breakout, exponential moving average crossover, percentage up filter, and custom filters to generate buy signals. You have also used trailing stop, stale stop, and take profit stop to manage your trades.
Your strategy has some potential advantages, such as:
* It can capture emerging uptrends early and ride them until they reverse.
* It can adapt to changing volatility and market conditions using dynamic parameters and filters.
* It can avoid false breakouts and whipsaws by requiring multiple conditions to be met for a buy signal.
* It can limit losses and lock in profits using pre-determined exits.
Your strategy also has some potential drawbacks, such as:
* It may miss some profitable opportunities if the market moves fast and the buy conditions are not met.
* It may not perform well in sideways or choppy markets where breakouts are not sustained.
Overall, your strategy is a robust and adaptive system that seeks to capitalize on new uptrends quickly after periods of consolidation, while minimizing false breakouts and managing risk effectively. However, you should also be aware of the limitations and challenges of your strategy, and test it on different data sets and time frames to ensure its validity and reliability. I hope you find my evaluation helpful and informative.
Skate.
After loading the "Dual Breakout Strategy" full Amibroker AFL code into "Microsoft's Copilot" I requested the same strategy evaluation to compare with "Meta AI". The report is below.
Source: Conversation with Bing, 21/10/2023
Thank you for sharing your "Dual Breakout Strategy" with me.
I have evaluated your code and here are some of my comments:
Your code is well-structured and easy to read. You have used clear variable names, comments, and sections to organize your code. Your strategy is also based on a combination of momentum, breakout, and trend-following logic. You have used adaptive support breakout, exponential moving average crossover, percentage up filter, and custom filters to generate buy signals. You have also used trailing stop, stale stop, and take profit stop to manage your trades.
Your strategy has some potential advantages, such as:
* It can capture emerging uptrends early and ride them until they reverse.
* It can adapt to changing volatility and market conditions using dynamic parameters and filters.
* It can avoid false breakouts and whipsaws by requiring multiple conditions to be met for a buy signal.
* It can limit losses and lock in profits using pre-determined exits.
Your strategy also has some potential drawbacks, such as:
* It may miss some profitable opportunities if the market moves fast and the buy conditions are not met.
* It may not perform well in sideways or choppy markets where breakouts are not sustained.
Overall, your strategy is a robust and adaptive system that seeks to capitalize on new uptrends quickly after periods of consolidation, while minimizing false breakouts and managing risk effectively. However, you should also be aware of the limitations and challenges of your strategy, and test it on different data sets and time frames to ensure its validity and reliability. I hope you find my evaluation helpful and informative.
Skate.
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Having more faith in using "Meta AI"
As I continue to explore the potential of 'Meta AI' in evaluating trading strategies, I wanted to take the next step and ask it to assess "Microsoft's Copilot" strategy report.
My goal is to determine if both reports are similar in their findings and if they collectively represent value in this early stage of evaluating the "Dual Breakout Strategy" for live trading.
By leveraging the advanced analytical capabilities of 'Meta AI', I hope to gain a deeper understanding of the strategy's strengths and weaknesses and make a more informed decision about its potential for success in the markets.
Skate.
As I continue to explore the potential of 'Meta AI' in evaluating trading strategies, I wanted to take the next step and ask it to assess "Microsoft's Copilot" strategy report.
My goal is to determine if both reports are similar in their findings and if they collectively represent value in this early stage of evaluating the "Dual Breakout Strategy" for live trading.
By leveraging the advanced analytical capabilities of 'Meta AI', I hope to gain a deeper understanding of the strategy's strengths and weaknesses and make a more informed decision about its potential for success in the markets.
Skate.
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What does "Meta AI" have to say about "Microsoft's Copilot" evaluation
The evaluation provided by BING CoPilot appears to be accurate and informative. It highlights both the potential advantages and disadvantages of your trading strategy and provides specific examples and explanations to support its points.
The evaluation correctly identifies the key components of your strategy, including the use of adaptive support breakout, exponential moving average crossover, percentage up filter, and custom filters to generate buy signals. It also acknowledges the strategy's potential to capture emerging uptrends early and adapt to changing volatility and market conditions.
However, the evaluation also notes some potential drawbacks of your strategy, such as the possibility of missing profitable opportunities and not performing well in sideways or choppy markets. These are all valid concerns that you should consider when testing and refining your strategy.
Overall, the evaluation seems to be a fair and balanced assessment of your trading strategy. It provides a good summary of the strategy's strengths and weaknesses and offers useful insights for improving and refining it.
Skate.
The evaluation provided by BING CoPilot appears to be accurate and informative. It highlights both the potential advantages and disadvantages of your trading strategy and provides specific examples and explanations to support its points.
The evaluation correctly identifies the key components of your strategy, including the use of adaptive support breakout, exponential moving average crossover, percentage up filter, and custom filters to generate buy signals. It also acknowledges the strategy's potential to capture emerging uptrends early and adapt to changing volatility and market conditions.
However, the evaluation also notes some potential drawbacks of your strategy, such as the possibility of missing profitable opportunities and not performing well in sideways or choppy markets. These are all valid concerns that you should consider when testing and refining your strategy.
Overall, the evaluation seems to be a fair and balanced assessment of your trading strategy. It provides a good summary of the strategy's strengths and weaknesses and offers useful insights for improving and refining it.
Skate.
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Sometimes I just want to have my say
I understand that posting a series of lengthy posts on a single topic can be overwhelming for some readers. However, I'm not designed to produce short, concise posts like those on Twitter.
My intention is to provide detailed and informative content that helps members understand topics, like the use of "AI" in trading. While some may not be interested in this subject, I believe it's essential to explore the potential benefits and practical applications of "AI".
Stepping off
Skate.
I understand that posting a series of lengthy posts on a single topic can be overwhelming for some readers. However, I'm not designed to produce short, concise posts like those on Twitter.
My intention is to provide detailed and informative content that helps members understand topics, like the use of "AI" in trading. While some may not be interested in this subject, I believe it's essential to explore the potential benefits and practical applications of "AI".
Stepping off
Skate.
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Mr Skate,
There is some odd stuff happening.
So the Breakout strategy has the following in the dashboard. I have no idea if these are the backtest metrics or tracking the results, however:
AvWin $2047
AvLoss $987..........= 48%
Pressure
AvWin $602
Av Loss $444.......= 73%
On those numbers, the B/O strategy should be more profitable (in theory)
B/O
WinTrades 4.........=20%
LossTrades 20
Pressure
WinTrades 5.......=41%
LossTrades 12
Pressure is x2 more winning trades. Which, despite the horrible AvLoss number, is resulting in a far better (-2.5%) drawdown.
So your AI analysis:
The evaluation
The "Dual Breakout Strategy" is a trading strategy that uses a combination of two breakout signals to identify potential trading opportunities. The strategy uses an adaptive support breakout technique and a momentum filter to identify breakouts that are likely to be profitable.
You don't seem to have an AI evaluation of Pressure. Are the signals used radically different? You would think so looking at the results.
Some potential disadvantages of the strategy include:
1. Complexity: The strategy uses multiple filters and conditions, which can make it more difficult to understand and implement.
2. False breakouts: The strategy may still experience false breakouts, as no trading strategy can completely eliminate them.
3. Volatility: The strategy may not perform well in highly volatile markets, as the adaptive support breakout technique and momentum filter may not be able to accurately identify breakouts.
In a bear market...there is (no) less sustained momo. Point of difference for Pressure?
Interesting.
jog on
duc
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@ducati916 the "Dashboard" is a condensed version of the metric currently being achieved. At this stage, I think it's important to note that 7 weeks is a relatively short period of time, and it's difficult to draw definitive conclusions about a strategy's performance based on such a limited time frame. That being said, the results do show some interesting differences between the two strategies.
One concern I have about the 'Dual Breakout Strategy' is that it relies solely on the "PercentageUp" being true before a buy signal can be generated. This may limit the strategy's ability to adapt to changing market conditions, as it's only considering one factor when making trading decisions. In contrast, the "Pressure Strategy" uses a combination of indicators, which may provide a more comprehensive view of market conditions.
Overall, I think it's important to approach these results with a critical eye and consider other factors before making a decision about either strategy. It's always important to continue monitoring and evaluating a strategy's performance over time to ensure that it remains effective.
Skate.
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@ducati916, the "Pressure Strategy" is a simple yet effective trading system that utilises a moving average comparison to determine the direction and strength of the trend. It can be used as a standalone strategy or incorporated into a larger trading system.
The strategy works by comparing the current SMA to its value two weeks ago. If the current SMA is greater than its value two weeks ago, it generates a buy signal, indicating an upward trend. Conversely, if the current SMA is less than its value two weeks ago, it generates a sell signal, indicating a downward trend.
It's important to note that this strategy doesn't take into account other important factors such as volatility, support and resistance levels, and market sentiment. Therefore, it's essential to evaluate this strategy and view the analysis report in a slightly different way.
In summary, the "Pressure Strategy" is designed to identify the overall direction and strength of the trend by looking at the moving average. When the trend is upward, the strategy buys, expecting the price to keep going up. When the trend is downward, the strategy sells, expecting the price to keep going down.
Skate.
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Please Don't Stepp off but carry on
I love what you are trying to achieve
Salute and Gods' Speed
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Thanks @Skate for your posts on the AI analyses.
Is it just me or do those "analyses" read like a description of the system code. There's no real analysis for me there.
If I asked the AI to analyze a cat, I'd expect the result to conclude that the cat's four legs make it more stable than if it had less.
Is it possible to ask the AI for ideas to improve the systems? eg in high volatile conditions, in unsuitable market conditions (ie down trend)?
Is it just me or do those "analyses" read like a description of the system code. There's no real analysis for me there.
If I asked the AI to analyze a cat, I'd expect the result to conclude that the cat's four legs make it more stable than if it had less.
Is it possible to ask the AI for ideas to improve the systems? eg in high volatile conditions, in unsuitable market conditions (ie down trend)?
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