Trading is the hardest way to make easy money...
Backtesting Optimization parameters 0 replies
Optimized Parameters in Metatrader Testing? 3 replies
OrderSend parameters change? 3 replies
pivot points and MACD parameters 0 replies
Can parameters run on different periods in the same EA? 4 replies
Disliked... How can we possibly select the best parameters based on the backtest metrics?Ignored
DislikedUse Monte Carlo simulation to select the most reliable backtest parameters.Ignored
Disliked{quote} Probably off-topic, but I couldn't resist. I really like @PercyJames description of the market as a giant balancing machine... {image} Check-out the image above -- the indicator is based on DSS-Bressert. It's a simple algorithm, just a few lines -- based on double-smoothed Stochastics. Do you see the rhythmic pattern of peaks and valleys at repeating intervals? Really quite amazing. The price curve above doesn't display anything that would reflect that type of pattern. This repeating pattern of peaks and valleys occurs with...Ignored
run a series of backtests select the best parameter based on some metric trade it forward: it ends in tears.
Disliked{quote} Can you please elaborate on that? I am familiar with the Monte Carlo Permutation Method (MCP) as described in Aronson's book "Evidence-Based Technical Analysis" (short online extract here). He uses that method to compare against the Null Hypothesis. What is the likelihood the test results are pure luck? However, in the example in post #1, I backtest 1250 different parameter combinations. Do you propose to run MC's over each of these 1250 backtests? That will give 1250 profit...Ignored
Disliked{quote} Hahaha, now you sound like a fairy tale wizard speaking in riddles ;-) Not sure what to do with this advice...Ignored
DislikedThere is an essential but long forgotten thread called Systematic Trading and I strongly suggest you read at least the first 20 pages or so.Ignored
DislikedYou don't really care about the performance of any one system, because you aren't trading any one of them but a combination of multiple systems. So your average or combined performance is what matters.Ignored
DislikedThere are effectively two ways to combine multiple systems into a single equity curve: trade a portfolio of all the chosen systems with certain weights - such as modern portfolio theory, or trade a combination of the chosen systems in an ensemble (machine learning).Ignored
Disliked{quote} Hahaha, now you sound like a fairy tale wizard speaking in riddles ;-) Not sure what to do with this advice...Ignored
Dislikedthe following link is a good start to be familiar with MC application in backtesting.Ignored
DislikedI took your data and applied a 5x5 uniform kernel on it (a 2D SMA). You can see that the lucky peak gets averaged out. Then I spotted (in red) the three best local maximas.Ignored
DislikedThere is also this surprisingly good results in the forward test. Two hypothesis pop to my mind
1/ this set of parameters has no predictive power and result is randomIgnored
Disliked2/ It highly depends on the market condition which has changed between the two datasets. You said you used E/U from 2020-01-01 to 2020-07-01. Right after this period is a smoother uptrend. Can this be an explanation?Ignored
Disliked{quote} Thanks again for the link. I have read the article. What it describes is a pragmatic introduction to MC, what Aronson describes in more detail in his book. However, both the article and Aronson only consider one single system at a time. How can we apply MC for system selection? We can run MC simulations on each of the hundreds or thousands of backtests we run when varying the parameters. Using MC simulation will surely improve the accuracy of estimates like Return/DD and similar backtest metrics. As usual, we will likely see that some parameters...Ignored
DislikedThe trick is to consider each BASKET BUY and BASKET SELL as the outcomes of the synthetic symbol (like someone is trading a single asset).Ignored
DislikedThe following comments won't be well-recieved, but a little bit of controversy never hurts...Ignored
DislikedIf your over-fitted systems really worked you wouldn't need a thread to discuss how to make them workIgnored
Dislikedencourage you to favor strategies that don't rely on settings that require calibration.Ignored
DislikedOnce you become a good discretionary trader then you are in the position to design and develop a systemIgnored
DislikedNot true at all. That statement simply reflects your limited experience in trading and bias in favor of complexity.Ignored
Dislikedunfortunately skills don't necessarily transfer due to being an acquaintance.Ignored
Dislikedanother reason I know you are not a trader: with all your statistical analysis you don't mention anything about money management.Ignored
DislikedIf you couldn't code a successful strategy with the aid of your discretionary-trading friend, then how does statistical analysis overcome that defiiciency?Ignored
DislikedFor OOS you'll typically use the next chunk of data adjacent to the chunk you optimized for. The probability is very high the adjacent chunk represents similar market conditions as the chunk used for optimizing your settings. [...] your optimized settings that seemed to work on the adjacent OOS data fail miserably on the non-adjacent OOS data. It's a common misconception -- the problem is that your testing software probably forces you to use adjacent data for OOS.Ignored