To be continued still adjusting….
Just survive with your strategy for almost 4 years (similar to AI TQQQ SQQQ) and you’ll be able to answer that question by yourself.
By the way, 81% of winning trades is quite concerning parameter. It describes certain way of trading leading to failure quite often.
It was good 23 days of trading.
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My data entry was bad. Stay tuned updating now.
Compare C2 Strategies Spreadsheet
I am comparing 3 different strategies in the above spreadsheet.
Strategy 1: AI TQQQ SQQQ
Strategy 2: Beta Momentum V3
Strategy 3: SP500 Futures Scalper
Here is the data from C2 for 1/19/2024 for each:
No disrespect to anyone at all. Look at the Expected profit per trade or PYPT for each. Of course Strategy 1 and 3 are much more established. This can be quantified by the Margin of error due to trading experience. More experienced have less margin of error. I have used the margin of error to adjust for differences in Expected profit per trade.
What does everyone think?
Again no disrespect to the great strategies around here. The comparison spreadsheet can be downloaded and then edited and used by all.
An analysis on when to invest in Beta Momentum V3 based on the length of time the strategy is active. The longer the strategy is active and trading the less statistical error of the expected value calculations. Of course history is past not future and future predictions are all hypothetical. So, how long do you wait?
Nope, it can not. That two strategies have seen much more different markets during their time, your strategy have seen only one month of the recent market. That Margin of error would be valid if the strategies worked during more or less the same markets.
100 trades made in a month are not the same in terms of strategy robustness compared to 100 trades made during 5 years. But margin of error will be the same.
Valid point. But I only make 1 trade per day. So a sample of 100 is 30/20 times 100 or 150 calendar days. And a sample of 200 is 300 calendar days. This is almost a year. Granted a multi year data collection encompasses all sorts of historical business cycles. But is that really good? Are the most recent events more germane to the current business climate? By having old data in your sample you may actually be adulterating it.
200 to 400 samples over a maximum of 2 years would probably be best. 200 samples over 1.5 years may or may not be better. And how much accuracy are you sacrificing by having 100 samples over 6 months? There is also an opportunity cost too. If the young strategy is making $200 per day and you wait 6 months for more data, you have lost $200 times 20 times 6 or $24,000. The PYPT or (Average $ won per winning day) minus (Average $ lost per losing day) could have been accurate all along + or -
The above shows a difference in margin of error improvement of only about 3% (200 at 6.72% and 100 at 9.71%). For only a 3% improvement are you willing to forego $24,000?
Plus an old strategy could be struggling for the past 4 months, but the old good data from years ago is obscuring this fact.
Look here. AI TQQQ SQQQ Swing has great data, but look at the recent months chop:
Last 4 to 6 months? You could have taken a beating depending on when you got in and the C2 stats would not have saved you.
You somehow thinking that if you traded 23 days earning 200$ per day, it will continue for long time. But let me show just an example of the strategy posted on the forum recently. It earned (see screenshot below) 2,300$ first month and then lost 5,000$ next month.
I am on this forum since April 2017, approximately 90% (optimistic lower boundary) of the strategies announced on the forum during their first couple months didn’t live a year. 
What prevents young strategy from struggling for the following 4 months (see example above)?
This is norm - 70% annual return with 50% drawdown.
It is not norm to make 50,000$ a year (200$ x 250 days) on 10,000$ deposit for long time. If you believe in it, you are a genius trader having super strategy or just a rookie trader.
Ultimately this means that one day wonder which accidentally made 1,000,000% on the accident market move is much better compared to strategy making steadily 20% annually during 10 years.
That is such a new word in trading, I am not ready to comment on this.
We are agreed that a small sample is not good or optimal. Waiting 100 days at one trade per day is probably a minimum prudent course. However, I disagree that statistics from old strategies are necessarily better than data from younger strategies. This is because statistics measure performance over time and if thing change and your sample is large then your data or statistics are not flexible enough to measure or account for recent changes.
There are 3 possible outcomes for each 100 day period up, down or unchanged. This is represented as $100, -$100 or 0. When you consider two 100 day periods there 9 possible outcomes. In row 9 this is the opportunity cost of waiting if the strategy was positive for two consecutive 100 day periods. Waiting and waiting allows you to not take a loss and it is only a bad decision in 2/9 of the outcomes (see column F). However, the longer you wait on a consistent up performer the greater your opportunity cost (in this example column C).
So, if I understand your example correctly, then probability to make good decision after 100 days is 3/9 = 33.3%, and probability to make good decision after 300 days is 7/9 = 77.8%. So it is definitely better to wait.
Sure it is better. Longer strategy history shows strategy robustness.
As soon as your strategy get into drawdown, recent history will be showing losses, but you continue trading (not like last time), you will be considering longer history of your strategy in your posts, which will include period of positive returns.
Since your system is currently down since inception about a month ago can we now get the sheet to extrapolate the 1 year return.
Or maybe previous returns aren’t indicative of future returns regardless of whether the history is long or short…
It should work both ways if extrapolating such a short history works.
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Of course you are right it works both ways. But for some reason my trade today was profitable and C2 counted it as a loss. You have to let the dust settle when using C2 statistics. Wait till after 4:00pm.
Agreed yesterday was a humbling day. I have to claw my way back up more.
My main point is that you are making huge extrapolations with tiny data sets. Someone looking at your strategy on January 10th by the same logic could have extrapolated a huge long term loss. That is just two weeks ago. It’s best to just not make projections with tiny data sets whether it is 1 month 2 month etc.
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This spreadsheet is a history of the daily C2 stats for Beta Momentum V3. It is estimated prior to 1/12/2024 and recorded from then on. As you can see the PYPT has not gone negative yet. I have to figure out something still with C2. The chart you posted shows a drop in value around 1/20/2024 but that day was not a trading day?
Glenn,
FYI, the drop in value in your account on 1/20/24 appears to be a charge for both your monthly subscription price and the monthly autotrading fee charge. These charges will come out every 30 days from your strategy, just like it comes out of my strategy, The ETP Whisperer System, and every other strategy on C2.
Thanks for your reply. I do still need to investigate as I am getting charged on my credit card too on a regular periodic interval. Plus I think they deduct brokerage trading fees (estimated) from the per trade results. I just need to look into it to make sure I completely understand. Thanks again.
