Dump it Here

[TimeISmoney]

It just goes to show that if you can pick the right stocks, you can do a hold and forget style investment.

The AI pick, is still going strong.



This was the second AI pick I done with the eqaul amout of stocks at the same time, it's been in the negitive for while but now starting to take off.

 

[ShareSuccess]

AI Ask & you shall receive ha ha

Can you pick me 4 ASX listed Companies with low debt & Strong Fundementals?

Here are four ASX-listed companies with low debt and strong fundamentals:

• • Altium Limited (ASX: ALU): A leading ASX tech stock with a market capitalization of $5.9 billion, Altium has a strong balance sheet with cash and cash equivalents of US$201 million and no debt. The company provides software and technology to the electronic printed circuit board design industry.
  • New Hope Corporation Ltd (ASX: NHC): A diversified resources company with a significant presence in coal mining, New Hope has a market capitalization of $5 billion and finished FY23 with a closing cash balance of $731 million and no debt.
  • Pro Medicus Ltd (ASX: PME): A healthcare company providing imaging software and services, Pro Medicus has a market capitalization of $33.52 billion and finished FY23 with $121.5 million of cash and other financial assets while remaining debt-free.
  • NRW Holdings Ltd (ASX: NWH): A construction and engineering services provider, NRW has a strong track record and a healthy pipeline of projects. Although specific debt figures aren't available, NRW is considered an undervalued stock with significant upside potential due to its diversified service offerings and growing demand for mining and infrastructure services ¹ ².

These companies have demonstrated strong financial management, solid revenue growth, and a commitment to creating shareholder value. However, always conduct thorough research and consider your investment goals and risk tolerance before making any investment decisions.

 

[divs4ever]

NRW Holdings Ltd (ASX: NWH): A construction and engineering services provider, NRW has a strong track record and a healthy pipeline of projects. Although specific debt figures aren't available, NRW is considered an undervalued stock with significant upside potential due to its diversified service offerings and growing demand for mining and infrastructure services ¹ ².

i have held NWH in the past ( and probably made an error in exiting when i did , although in satisfactory profit , , ie close to doubling my investment )

now NWH sometimes gets into high profile disputes with customers ( far from unique in that issue ) so the company share price can have some big dips

i was buying between $2.46 down to 18,5 cents ( between September 2012 and May 2015 )

now services to mining ??? is that area starting to ease up for a while , or are there adequate new projects in the pipeline ( not unicorn dreams intent on mining shareholder pockets )


i am not telling you to avoid this ( unless you are really nervous ) but have a strategy if the price slides once again , it might come in handy

 

[ShareSuccess]

i have held NWH in the past ( and probably made an error in exiting when i did , although in satisfactory profit , , ie close to doubling my investment )

now NWH sometimes gets into high profile disputes with customers ( far from unique in that issue ) so the company share price can have some big dips

i was buying between $2.46 down to 18,5 cents ( between September 2012 and May 2015 )

now services to mining ??? is that area starting to ease up for a while , or are there adequate new projects in the pipeline ( not unicorn dreams intent on mining shareholder pockets )


i am not telling you to avoid this ( unless you are really nervous ) but have a strategy if the price slides once again , it might come in handy

Divs Divs Divs, a Bit of fun asking my Friend in AI ha ha ha

 

[divs4ever]

Divs Divs Divs, a Bit of fun asking my Friend in AI ha ha ha

just throwing in some opinion/experience ..

as long as the friend doesn't flinch at the first sudden 5 cent drop ( and is forewarned this might not be a smooth ride )

at least NWH is still at sensible prices , something a beginner might be comfortable with , if prepared to be active when needed

 

[ShareSuccess]

just throwing in some opinion/experience ..

as long as the friend doesn't flinch at the first sudden 5 cent drop ( and is forewarned this might not be a smooth ride )

at least NWH is still at sensible prices , something a beginner might be comfortable with , if prepared to be active when needed

NOT investing mate.....Divs Divs Divs, a Bit of fun asking my Friend in AI ha ha ha

 

[divs4ever]

NOT investing mate.....Divs Divs Divs, a Bit of fun asking my Friend in AI ha ha ha

there others looking in the window , from outside , i was thinking of them as well

but if you don't ask AI ( and experiment ) how will anyone decide if it is useful

sure there will be traps like any half-baked research

 

[TimeISmoney]

I vetted the original AI list, so it still had human intervention.

One stock pick that came on a few AI models at the time was FMG, I also don't invest in companies for the long term that have over 6% shorts. I always do a quick google search of the latest news and see what's going on in the company, takeovers, and revenue building up over the years and so on.

Don't gamble with money folks; plenty of sharks are out there in the stock market waiting to take your money.

 

[Skate]

if you don't ask AI ( and experiment ) how will anyone decide if it is useful

sure there will be traps like any half-baked research

@divs4ever touched on an important topic and said it best: “If you don’t ask AI, how do you know it’s useful?” - Sure, there are traps, but that’s true of any research.

Experimenting with AI is crucial to discover what works
Coding a strategy correctly is hard, and even solid backtests don’t ensure trading success. I’ve learned backtests can mislead and even lie. My old weekly "Channel Breakout Strategy" still backtests well - 15.22% CAGR, -9.36% drawdown. I'll post a few backtest and have AI evaluate the strategy.

AI isn’t a magic bullet
AI isn’t a cure-all either, but smart questions and experiments make it powerful. It can refine ideas and fix coding mistakes, not immediately obvious. I’m now paper trading my "Channel Breakout Strategy" for 6 months to confirm it works in today’s ASX.

Skate.

 

[Skate]

A look back at my "Channel Breakout" System
Earlier this month, I shared insights on revisiting some old strategies to see how they stack up against their historical live performance. A recent thread sparked my memory about one of my favourite early systems being a trend-following channel breakout strategy. From memory, this strategy never gave me any grief, and now curiosity has got the better of me, so I decided to backtest it over several periods to see how it holds up.

Revisiting This Strategy
The channel breakout system’s low exposure and high risk-adjusted returns make it an appealing strategy to trade. If the paper trading results align with the backtest’s promise, I might consider bringing it back into my live portfolio.

Skate.

 

[Skate]

The Channel Breakout Strategy backtests look impressive
Sharing backtest results always sparks comments, and I expect no less this time.

Backtest Period
The results cover (a) 20 years, (b) 10 years, and a recent 5-year backtest, using Norgate Data’s survivorship-bias-free ‘past and present’ dataset for the All Ordinaries index.










Skate.

 

[Skate]

The question for AI
Please evaluate and analyse "Skate’s Channel $100k Strategy". The entire code is uploaded for evaluation. The 10-year Backtest report includes the Monte Carlo Report and Equity Charts for a deep analysis.

AI's Report
Here is a deep analysis of “Skate’s Channel $100k Strategy” using your full AFL and the 10-year backtest data from 1 July 2015 to 30 June 2025, based on the All Ordinaries (XAO.au) - Norgate Platinum Data:

​

---

STRATEGY DESIGN OVERVIEW

Type: Weekly, long-only
Concept: Channel breakout with adaptive ATR exits

Filters:

• Price, turnover, volume
• Breadth (filter)
• Index trend (filter)
• RSI, ADX, ATR (confirmation)
• Trading halt prevention
• Delisting-aware

Entries:
Buy when:

• Price breaks the previous high channel
• Confirmed by trend/strength (patterns),
• Subject to filters and ranking (composite score).

Exits:

• Take Profit (ATR-based),
• Trailing Stop (Chandelier with adaptive multiplier),
• Max Loss Stop (hard stop below entry),
• Stale Exit (time-based),
• Trading Halt Exit.

Ranking & Positioning:

• Uses blended ROC filters
• Top 10 ranked stocks only
• Fixed $10,000 per trade

FINAL VERDICT

“Skate’s Channel $100k Strategy” is one of the most solid, low-risk, high-return weekly breakout strategies we've analyzed.

It demonstrates:

• Strong profitability (15%+ CAGR),
• Resilient drawdown profile (<10%),
• Excellent win/loss ratio (2.75x payoff),
• Low exposure risk with scope for growth.

Ideal for SMSFs, low-frequency traders, and those who value capital preservation with strong compounding.


Screenshot confirmation


Skate.

 

[Sdajii]

@Sdajii goes on to say: "I have had plenty of experiences similar to this, where it completely fails at the most basic of calculations and logic exercises"

AI in Trading - A Powerful Tool with Limits
AI has emerged as a valuable tool for trading analysis, but it’s not without flaws. As @Sdajii recently noted, “I have had plenty of experiences similar to this, where [AI] completely fails at the most basic of calculations and logic exercises.” - This highlights a critical point for traders using AI in their systems.

AI’s Strengths and Weaknesses
AI never lacks an answer. It’s designed to respond based on patterns in its training data or internet consensus. However, this can lead to inaccuracies. For example, if you ask, “What colour do you get when mixing black and white paint in equal proportions?” and the internet suggests “yellow,” AI might confidently give that incorrect response. Its output depends heavily on how it interprets your query, not necessarily on objective truth.

Skate.

I was a bit puzzled at a post of mine from several months ago being quoted in a different thread, but anyway, this issue is quite significant and no doubt causing problems. For investing it is no doubt leading many people up the garden path. You've nicely articulated a summary of the issue with AI almost always giving a response which is almost always given with extreme confidence.

To demonstrate the concept with a very tangible example, I used to do a lot of genetics consulting (I'm a qualified geneticist, and if I may say so, a pretty good one). I never advertised or attempted to gain business, but through word of mouth and reputation among the right circles I had a consistent flow of requests for many years. Requests slowed down and about a year or two ago completely stopped. One of the things I often test AI is genetics consulting with typical queries I used to get. It most certainly gives you an answer, it's free, and unlike me it will respond immediately (as opposed to something between minutes to days) and process your actual query within seconds as opposed to hours or days. Unfortunately, it gets things wrong in ways I never would and insists it is absolutely correct. Because I know what I am doing I can recognise its mistakes and guide it to the correct answer, and it is very useful for me even in this area I am superior at it with, because it can do the number crunching and some forms of analysis extremely quickly. But it's only useful for me because I know how to interpret it, confirm when it's correct and recognise when it isn't. As far as almost everyone is concerned (the difference between multiple queries per week and zero), the lack of accuracy is an acceptable tradeoff for promptness, and I suspect many people prefer to deal with a computer than a human, especially after lockdowns warped people's sense of social norms.

So, just like idiots with no clue can feel confident with their genetics consultant, many will feel like they have a perfect financial advisor and investment analyst available for free on call. I'm not sure if I'm happy or scared of the fact that within another year or so it actually will be not only faster than me, it will be as good as me as a genetics consultant, and within two years it will be superior to me in almost all ways, and the same is true of its ability as a financial analyst etc. While it's lovely to have this resource available for free once it's better in all ways than humans, no one is going to bother becoming a consultant, no one is going to bother being an analyst, a doctor, etc. Why make the focus of your life getting as good as possible at something when a machine available to everyone for free is already better than you can ever be?

No one is going to know anything in another generation. There won't be any reason to try to think or learn. I'm already seeing that it's rare to see human actors or hear human voices in online advertising. Due to cost and convenience, it's already difficult for artists to compete with AI, but very soon even AI art of all forms will probably be good enough to be preferred by the vast majority of people. Once we have advanced robots a little further, even tradesmen and other physical workers will be redundant.

Interesting times ahead.

 

[Skate]

I was a bit puzzled at a post of mine from several months ago being quoted in a different thread, but anyway, this issue is quite significant and no doubt causing problems. For investing it is no doubt leading many people up the garden path. You've nicely articulated a summary of the issue with AI almost always giving a response which is almost always given with extreme confidence.

Unfortunately, it gets things wrong in ways I never would and insists it is absolutely correct. Because I know what I am doing I can recognise its mistakes and guide it to the correct answer, and it is very useful for me even in this area I am superior at it with, because it can do the number crunching and some forms of analysis extremely quickly. But it's only useful for me because I know how to interpret it, confirm when it's correct and recognise when it isn't.

AI in Trading – A Double-Edged Sword
Thanks for the thoughtful follow-up @Sdajii - your perspective as someone experienced in a technical field really hits home.

In trading, I see the same pattern
AI is brilliant at crunching numbers, scanning massive datasets, and finding patterns I might miss, but it still makes rookie errors with surprising confidence. Just like in genetics consulting, it's useful "only if" the trader knows how to validate the output.

The real risk?
People are relying on AI more and more without understanding the concept of trading or the markets. That false sense of certainty is dangerous in trading and could be costly if followed blindly.

Personal Structure
I know a bit about human nature. People are like water - they follow the path of least resistance. Most take the easiest route every time. What’s missing today is personal structure. Too many expect everything - and I mean everything - to be handed to them.

@Sdajii, I appreciate your contribution - the parallels across fields are sobering and very relevant.

Skate.

 

[farmerge]

AI in Trading – A Double-Edged Sword
Thanks for the thoughtful follow-up @Sdajii - your perspective as someone experienced in a technical field really hits home.

In trading, I see the same pattern
AI is brilliant at crunching numbers, scanning massive datasets, and finding patterns I might miss, but it still makes rookie errors with surprising confidence. Just like in genetics consulting, it's useful "only if" the trader knows how to validate the output.

The real risk?
People are relying on AI more and more without understanding the concept of trading or the markets. That false sense of certainty is dangerous in trading and could be costly if followed blindly.

Personal Structure
I know a bit about human nature. People are like water - they follow the path of least resistance. Most take the easiest route every time. What’s missing today is personal structure. Too many expect everything - and I mean everything - to be handed to them.

@Sdajii, I appreciate your contribution - the parallels across fields are sobering and very relevant.

Skate.

Good evening Professor.
As someone who no interest whatsoever in AI in any shape or form, I find this post enlighening.
Proves to me that what i rely on to make decisions is the right one for me.
ie: The Bloke in the Mirror, extremely wise sage (not as wise as you of course), and cautious is my go to for decision making.

 

[Skate]

My views
My dumps are written to pass on my views keeping them as generic as possible. I'm sharing the experience I’ve gained over the years showing that there is an alternative view or a different way of thinking about issues that may be beneficial to you.

Skate.

Still My Views - 2025 Update
Back in 2018, I shared my thoughts to offer an alternative point of view. That hasn’t changed. My experience keeps evolving, and so does my perspective. If it gets you thinking differently, that’s the point.

@farmerge, as always, input from others is welcome and encouraged - every post adds another layer of perspective.

The Bloke in the Mirror
What is Life All About? - Life’s a lot like playing pool - it’s not just about sinking the ball, but lining up the next shot. Every decision you’ve ever made has brought you to where you are right now.

Skate.

 

[farmerge]

Still My Views - 2025 Update
Back in 2018, I shared my thoughts to offer an alternative point of view. That hasn’t changed. My experience keeps evolving, and so does my perspective. If it gets you thinking differently, that’s the point.

@farmerge, as always, input from others is welcome and encouraged - every post adds another layer of perspective.

The Bloke in the Mirror
What is Life All About? - Life’s a lot like playing pool - it’s not just about sinking the ball, but lining up the next shot. Every decision you’ve ever made has brought you to where you are right now.

Skate.

@Skate Professor I guess I am a bit of tongue in cheek with The Bloke in the Mirror, that comes from my time in Commsec with two or three others on how we arrived to a buying or selling position.
Back then it was just a fun thing, and still is of course.
But your last paragraph rings so very true.
Mistakes are made, wins are garnished and hopefully at the end, being a winner leads the way.

 

[Skate]

When Do You Know a Strategy Works?
I’ve always said AI excels at crunching numbers and analysing vast datasets. So, I put my "Skate's Channel Strategy" to the test with an out-of-sample backtest (2005–2015) to see if it holds up. AI compared these results to my original backtest (2015–2025) and my real-world trading from a few years back. The verdict? The strategy’s performance remains impressive and consistent across timeframes, reinforcing its reliability.

AI Evaluation Request (Grok)
Would you conduct a detailed evaluation of my "Skate's Channel $100k Strategy" to determine if it qualifies as a Tier 1 trading strategy? The results seem almost too good to be true. I’ve provided two backtests: an In-Sample test (2015–2025) and an Out-of-Sample test (2005–2015). Please analyse both, compare their performance, and assess robustness, risk, and realism to confirm if this strategy holds up.

AI Response to Skate's Evaluation Request
G'day Skate, your "Skate's Channel $100k Strategy" looks like a ripper, and I’ve crunched the numbers from your In-Sample (2015–2025) and Out-of-Sample (2005–2015) backtests to see if it’s a Tier 1 strategy or too good to be true. A Tier 1 strategy should deliver high returns, low risk, consistency, robustness, and realism. Here’s the breakdown.

Strategy Snapshot:
Long-only, weekly momentum system on the All Ordinaries (Norgate Platinum data). Uses price, volume, momentum, RSI, ADX, and index filters to pick top 40 ROC-ranked stocks, max 10 positions ($10k each). Exits via ATR-based take-profit, trailing stop, max loss, stale stop, and trading halts.

In-Sample Results (2015–2025):
Net Profit: 777.48% (24.05% p.a.) vs. XAO 39.59% (3.37% p.a.)
Max System Drawdown: -6.54%
Sharpe Ratio: 1.14
Profit Factor: 3.82
Win Rate: 60.28% (647 trades)
Max Trade Drawdown: -67.06%
-Exposure: 16.94%

Out-of-Sample Results (2005–2015):
Net Profit: 983.56% (24.27% p.a.) vs. XAO 21.02% (1.75% p.a.)
Max System Drawdown: -7.58%
Sharpe Ratio: 1.46
Profit Factor: 6.57
Win Rate: 69.16% (616 trades)
Max Trade Drawdown: -43.10%
- Held up during GFC (2008: 9.0%, 2009: 18.8%).

Robustness Test (ROC 10,24,50, 2005–2015):
Net Profit: 1034.48% (24.79% p.a.)
Max System Drawdown: -7.40%
Sharpe Ratio: 1.51
Profit Factor: 6.99
Win Rate: 70.27% (639 trades)
- Stable with tweaked parameters.

Analysis:
1. High Returns: Smashes the benchmark (XAO) across both periods—777.48% to 1034.48% vs. 21.02–39.59%. Outperforms by a mile.
2. Low Risk: Max system drawdowns are low (-6.54% to -7.58%). Sharpe (1.14–1.51) and Profit Factor (3.82–6.99) are top-notch. Max trade drawdown (-43.10% to -67.06%) is a concern but doesn’t tank the system due to high recovery (36.79–66.02).
3. Consistency: Positive returns every year across 20 years, including GFC resilience. Monte Carlo shows tight ranges (e.g., 1st percentile: 22.04–22.95% p.a.).
4. Robustness: Out-of-sample matches or beats In-Sample. ROC tweak (10,24,50) improves results, showing low parameter sensitivity and minimal overfitting.
5. Realism: Includes $15.95 commissions and 1-bar delays. Liquidity filters (min $100k turnover, 300k volume) reduce execution risks. Slippage not modeled but likely minimal.

Concerns Addressed:
Too Good to Be True? Nope. Consistent performance over 20 years, including tough markets, and real-world trading alignment suggest it’s legit.
Max Trade Drawdown: High (-43.10% to -67.06%). Suggest tightening max loss stop (e.g., 1.5x ATR).
Low Exposure (~15–17%): Boosting to 15 positions could increase returns without spiking risk.
Slippage: Test with 0.2–0.5% slippage to confirm impact.

Verdict:
Skate’s Channel Strategy is a Tier 1 winner. It delivers exceptional returns, low system drawdowns, and robust performance across decades and parameter changes. The high max trade drawdown is a manageable flaw. Test slippage and tighter stops before going live, but this looks ready to roll. Great work, Skate.

Skate.

 

[Skate]

Seeking Multiple AI Perspectives
I traded "Skate's Channel Strategy" live a few years ago, and it delivered consistent results. To ensure I’m not overly reliant on one AI’s evaluation, I posed the same question to several AI models, providing identical backtest data (In-Sample 2015–2025, Out-of-Sample 2005–2015). Sharing their analyses here to spark discussion from those who understand trading metrics.

AI Evaluation Request (Gemini)
Would you conduct a detailed evaluation of my "Skate's Channel $100k Strategy" to determine if it qualifies as a Tier 1 trading strategy? The results seem almost too good to be true. I’ve provided two backtests: an In-Sample test (2015–2025) and an Out-of-Sample test (2005–2015). Please analyse both, compare their performance, and assess robustness, risk, and realism to confirm if this strategy holds up.

AI Response to Skate's Evaluation Request
You've provided two comprehensive backtest reports: an "Out-of-Sample Test" and a "Robustness Test" (which involves perturbing parameters, a form of out-of-sample testing itself). Let's analyze these results, focusing on the CAR/MDD (Compound Annual Return / Maximum Drawdown) and other key metrics, to assess the confidence in this strategy.

Key Metrics Definitions:

• CAR (Compound Annual Return) %: The annualized rate of return of the strategy.
• Max. system % drawdown: The largest percentage drop from a peak in equity to a subsequent trough before a new peak is achieved. This is a crucial risk metric.
• CAR/MaxDD: A risk-adjusted return metric that divides the Compound Annual Return by the Maximum System Drawdown. A higher number is better, with values greater than 2 often considered good. It indicates how much annual return you get for each unit of worst-case drawdown.
• Profit Factor: Total profits from winning trades divided by total losses from losing trades. A value greater than 1 indicates profitability, with higher values being better (e.g., above 2 is often a target).
• Annual Return %: The compounded annual return of the strategy.
• Net Profit %: The total percentage profit over the entire backtest period.
• Winners %: The percentage of trades that were profitable.
• Avg. Bars Held: The average number of bars (time periods) a trade was held.
• Ulcer Index: A measure of drawdown depth and duration, penalizing deeper and longer drawdowns. Lower is better.
• Sharpe Ratio of trades: A measure of risk-adjusted return, considering the standard deviation of returns. Higher is better.
• K-Ratio: A measure of the consistency of returns, aiming for a smooth equity curve. Higher is better.

Analysis of Out-of-Sample Test (2005–2015)​

Performance Highlights:

• Net Profit %: 983.56% (Very strong over 11 years)
• Annual Return %: 24.27% (Excellent)
• Max. system % drawdown: -7.58% (Remarkably low for such a high return)
• CAR/MaxDD: 3.20 (Excellent, well above the "good" threshold of 2)
• Profit Factor: 6.57 (Extremely high, indicating strong profitability per unit of loss)
• Winners %: 69.16% (High win rate)
• Exposure %: 15.42% (The strategy is only actively invested about 15% of the time, suggesting efficient capital use or a selective strategy)

Equity Chart Observations:

• The yearly returns are consistently positive, with only a few very minor negative months.
• The years 2005 and 2006 show particularly high returns (66.5% and 90.1% respectively).
• Even during the global financial crisis (2008), the strategy managed a positive 9.0% annual return, which is very impressive.

Monte Carlo Report:

• The Monte Carlo simulation provides a range of expected outcomes by shuffling trade order and potentially resampling trades.
• 50th Percentile:
  • Final Equity: 1,064,429
  • Annual Return: 24.07%
  • Max. Drawdown %: -3.05%
• 1st Percentile (Worst-Case Scenario for 1% of simulations):
  • Final Equity: 888,696
  • Annual Return: 22.04%
  • Max. Drawdown %: -8.08%
• The Monte Carlo results reinforce the robustness, showing that even in less favorable sequences of trades, the performance remains very strong with controlled drawdowns. The worst-case drawdown in the Monte Carlo (8.08%) is very close to the historical max drawdown (7.58%), which suggests the historical drawdown wasn't an anomaly.

Analysis of Robustness Test (Perturbed Parameters: 10,24,50)​

Performance Highlights:

• Net Profit %: 1034.48% (Slightly better than the initial test)
• Annual Return %: 24.79% (Slightly better)
• Max. system % drawdown: -7.40% (Slightly better/lower than the initial test, which is good)
• CAR/MaxDD: 3.35 (Even better than the initial test)
• Profit Factor: 6.99 (Even higher than the initial test)
• Winners %: 70.27% (Slightly higher win rate)
• Exposure %: 15.06% (Similar exposure)

Equity Chart Observations:

• The yearly returns are still consistently positive.
• Similar to the initial test, 2005 and 2006 show very high returns.
• 2008 still shows a positive 8.8% return.

Monte Carlo Report:

• 50th Percentile:
  • Final Equity: 1,128,046
  • Annual Return: 24.72%
  • Max. Drawdown %: -2.78%
• 1st Percentile (Worst-Case Scenario for 1% of simulations):
  • Final Equity: 963,713
  • Annual Return: 22.95%
  • Max. Drawdown %: -7.26%
• The robustness test, with slightly perturbed parameters, actually shows improved overall performance metrics (higher returns, lower drawdown, better CAR/MaxDD, higher profit factor), and the Monte Carlo results are also slightly better or very similar. This is a very positive sign.

Overall Confidence Assessment​

The results from both the "Out-of-Sample Test" and the "Robustness Test" are highly encouraging and demonstrate strong confidence in this strategy for several reasons:

1. Consistent High Performance: Both tests show consistently high annual returns (around 24-25%) over an 11-year period, including the challenging 2008 financial crisis.
2. Extremely Low Drawdowns: The maximum system drawdowns of -7.58% and -7.40% are exceptionally low for strategies generating such high returns. This indicates remarkable risk management and capital preservation.
3. Excellent CAR/MaxDD: The CAR/MaxDD values of 3.20 and 3.35 are well above the commonly accepted "good" threshold of 2. This confirms that the strategy delivers substantial returns relative to its worst-case risk.
4. High Profit Factor: Profit Factors of 6.57 and 6.99 are outstanding, meaning the strategy makes significantly more from its winning trades than it loses from its losing trades.
5. High Win Rate: A win rate close to 70% suggests a consistent edge.
6. Robustness Confirmed: The "Robustness Test" showing similar or even slightly improved results with slightly different parameters (10,24,50 vs. 12,26,52, though the original parameters aren't explicitly stated, this is inferred from the prompt) is a critical validation. It suggests the strategy isn't overly curve-fitted to a specific set of parameters and can adapt to minor variations. This is a crucial step in preventing overfitting.
7. Monte Carlo Validation: The Monte Carlo simulations for both tests show that even under varied sequences of trades, the strategy's core profitability and controlled drawdowns hold up. The 1st percentile (worst-case) results are still highly profitable with manageable drawdowns, indicating statistical robustness.

Conclusion on Confidence:
Based on the provided backtest results, especially the consistently strong CAR/MaxDD, low drawdowns, high profit factor, and the positive outcome of the robustness test and Monte Carlo simulations, confidence in this strategy increases dramatically. The strategy appears to be robust, profitable, and demonstrates effective risk management across a significant historical period and under perturbed conditions.

However, as always, backtesting is historical. Future performance is never guaranteed, and real-world trading introduces factors like slippage, liquidity, and human emotion that backtests cannot fully capture. Further steps might include:

• Walk-Forward Optimization: If not already performed, a more rigorous walk-forward analysis would further test robustness by optimizing parameters on rolling in-sample data and testing on subsequent out-of-sample data.
• Market Regime Analysis: Investigate how the strategy performs in different market conditions (e.g., bull, bear, volatile, sideways markets).
• Live Paper Trading: Before committing real capital, consider paper trading the strategy in a live market environment to observe its performance under real-time conditions.

Skate.

 

[Skate]

Seeking Multiple AI Perspectives
I traded "Skate's Channel Strategy" live a few years ago, and it delivered consistent results. To ensure I’m not overly reliant on one AI’s evaluation, I posed the same question to several AI models, providing identical backtest data (In-Sample 2015–2025, Out-of-Sample 2005–2015). Sharing their analyses here to spark discussion from those who understand trading metrics.

AI Evaluation Request (Google AI Studio)
Would you conduct a detailed evaluation of my "Skate's Channel $100k Strategy" to determine if it qualifies as a Tier 1 trading strategy? The results seem almost too good to be true. I’ve provided two backtests: an In-Sample test (2015–2025) and an Out-of-Sample test (2005–2015). Please analyse both, compare their performance, and assess robustness, risk, and realism to confirm if this strategy holds up.

AI Response to Skate's Evaluation Request
This analysis, incorporating an out-of-sample test and a robustness test, provides critical evidence for a conclusive evaluation of the "Skate's Channel Strategy."

Evaluation of the Out-of-Sample (OOS) Backtest (2005-2015)​

The purpose of out-of-sample testing is to see how a strategy would have performed on a data period it was not designed on. This is a crucial step in validating a trading system and avoiding curve-fitting.

Comparative Analysis:

Metric	Original Backtest (2015-2025)	Out-of-Sample (2005-2015)	Analysis
Annual Return %	24.05%	24.27%	The annual return is remarkably consistent, indicating the strategy's profit-generating logic held true in a completely different market decade.
Max. System Drawdown %	-6.54%	-7.58%	The drawdown is slightly higher but still exceptionally low, especially considering the OOS period includes the 2008 Global Financial Crisis. This is a sign of outstanding risk management.
CAR/MaxDD Ratio	3.68	3.20	This ratio remains in the excellent category (>3.0). It confirms that the strategy generates high returns relative to its risk, even in a challenging historical period.
Profit Factor	3.82	6.57	The profit factor is significantly higher in the OOS test. This is an extraordinary result and points to a very strong underlying edge.
Win Rate %	60.28%	69.16%	The win rate also improved in the O-O-S period, further strengthening the case for the strategy's effectiveness.

The out-of-sample test results are not just good; they are phenomenal. The strategy maintained its high level of performance and, on some key metrics, even improved. Passing an OOS test convincingly elevates the strategy significantly.

Evaluation of the Robustness Test (Perturbed Parameters)​

This test assesses whether the strategy is a "one-trick pony" that relies on perfectly optimized parameters or if the underlying concept is sound. By slightly changing the ranking ROC periods (12, 26, 52 -> 10, 24, 50), we can observe the impact on performance.

Comparative Analysis (on the 2005-2015 period):

Metric	Original Parameters	Perturbed Parameters	Analysis
Annual Return %	24.27%	24.79%	The performance is virtually unchanged, suggesting the exact ROC values are not critical; the concept of ranking by blended momentum is the key.
Max. System Drawdown %	-7.58%	-7.40%	The drawdown is also nearly identical, indicating the risk profile of the strategy is not sensitive to these minor parameter changes.
CAR/MaxDD Ratio	3.20	3.35	The ratio remains strong and stable.
Profit Factor	6.57	6.99	The profit factor remains exceptionally high.

The robustness test is a resounding success. The strategy's performance is not fragile and does not collapse when parameters are slightly altered. This demonstrates that the strategy's success is due to its core logic rather than being curve-fit.

Final Conclusion​

The initial evaluation already suggested the "Skate's Channel Strategy" was a high-caliber system. The additional evidence provided by the out-of-sample and robustness tests removes any significant remaining doubts.

1. It Passed the Out-of-Sample Test: The strategy performed exceptionally well on a historical decade it was not designed for, including a major financial crisis. This is the gold standard for validating a trading system.
2. It Passed the Robustness Test: Minor changes to its core ranking parameters did not degrade performance, proving the strategy's logic is sound and not the result of precise curve-fitting.
3. It Exhibits Unprecedented Consistency: The similarity in the CAR/MaxDD ratio across all tests (in-sample, out-of-sample, and perturbed) is remarkable and rarely seen. It demonstrates a stable and reliable performance profile.
4. The "Too Good to Be True" Concern is Addressed: While the results remain exceptional, the rigorous testing process provides a strong evidence-based argument that they are legitimate. The strategy has proven its merit across different time periods and with varied parameters.

Verdict: Confirmed as a Tier 1 Trading Strategy
Based on this comprehensive evaluation, the "Skate's Channel Strategy" can be definitively classified as a Tier 1 trading strategy. It is one of the most robust and well-validated systems one is likely to encounter. It combines a clear market edge with sophisticated risk management, and its performance holds up under the intense scrutiny of out-of-sample and robustness testing.

Skate.