Most of Larry's discussion centers on being able to detect the difference between random market data and non random market data. Once that is distinguished then, and only then, you can design system solutions to attack the non-random elements....but only once it has been distinguished.
When you mentioned random market data vs non random market data, are you really referring to data or are you referring to EA that are generated through randomness vs EA generated via trading theory which is not created via randomness by through proper understanding of underlying market behaviours? At least this is my interpretation of random system vs non random system/EA, ie. EA developed with underlying understanding of market behaviours.
Our systems need to respond to signals in the 'otherwise' noisy environment. We must be able to detect the signals in the market data.
We can only know when it is noise only when the trend or direction of the price movement didn't materialized. Meanwhile it is termed noise because of market forces, ie. Demand (buy) vs Supply (short) are at play (traders aren't clear of the direction, equal amount of buying and selling to force prices to range at a particular price point. Till there is a clear break, then price break out of the price point. Hence trend starts. So these "uncertainty" are termed as noises but there are part and parcel of market forces, they are signals too, if you understand what market condition it is in, i.e Ranging Market. To the Trend traders, these are noises, to the Ranging Traders, these are signals.
We don't have that ability in EA Studio as we only can undertake Monte Carlo sims with trade results, not underlying market data.
Monte Carlo Simulation, also known as the Monte Carlo Method or a multiple probability simulation, is a mathematical technique, which is used to estimate the possible outcomes of an uncertain event
I don't use MC, I don't understand why we need to introduce more randomness to an already random generator. That randomly generate systems/EA without any theoretical basis. My challenge is to find a sound theory System/EA and why would I want to messy it up by messing up with the parameters further...I don't understand the logic behind it. MC is for uncertain event like weather, but forex is not uncertain....it's driven by market forces, Demand vs Supply, it's not uncertain. It's the traders/speculators that are uncertain of which direction they are going as a "whole" because there is no "coordinated" effort to decide (that's why banks and liquidity providers can determine the direction) which direction the market forces are going because everyone has different interpretation, different goals, long term gain vs short term gain etc and hence traders are the ones who are uncertain. But once there is a "coordinated" recognition of where the market is going, prices begin to trend, that's where trader go with the trend and no longer wants to fight with the price point.
The problem we face when only working with our trade results is that if those trade results are a response to random market data and not market signals....then we have a problem Houston. This means that we simply cannot tell whether or not our systems developed through these processes possess these statistical biases.
Yes, we do have a problem here that Popov don't seem to recognized. The need to generate EA/System that is rule based, ie. Market signals based on trading theory and not rules that are pluck randomly that are illogical to manual traders but somehow survived for a period of randomness or survived in the backtest/software environment and failed when put in live/demo account.
This is irrespective of the amount of post-treatment we apply through Monte Carlo Sims or Walk Forward Methods we use to attempt to remove this bias. These methods can simply further complicate the issue and exponentially magnify the existing bias unless we really know what we are doing. To assume we can simply use these methods without a comprehensive understanding of what we are doing is going to end in tears.
Exactly, to apply so much work to Randomised System is a total waste of time that's why users after all the MC, WF, Acceptance Criteria etc ended up with no or little success.
So the video serves as a heads up to make us at least be very careful with what tools we use and how we do it in our data mining experiences.
Some key ideas to consider in addition to Larry's ideas.
1. Adopt Design First Logic without Optimisation to configure your system through Design Principles to deliberately respond to a market condition you are targeting. Don't let the system generator decide for you. It will over-fit to the data which 'mostly is noise'.
Yes, it's time to allow users in EAS to have a choice to ensure our Generator goes by the trading rules and not some weird randomness. Rules that I've mentioned in my previous post. https://forexsb.com/forum/topic/7293/in … cess-rate/
2. Avoid reducing collection sizes by performance metrics. If there is randomness in the equity curve you are only exacerbating 'fictional' performance as you are removing the adverse random elements that make up the equity curve in your selection process.
To me, if it is random, it doesn't matter whether I have 1000 vs 10,000. They are all useless.
3. Most Monte Carlo methods applied to trade results themselves (as opposed to market data) tell you nothing as they disrupt the serial correlation in the equity curve (related to the market signal) and not the noise element of the equity curve (related to the randomness in the market data). They turn the entire equity curve into noise.
Multi Market validation would be a more useful way of applying "MC" to market data, random market data without messing up your System's theory. Don't confuse Random Market data with Random System/EA. EA generated by going through all the random curve fitting is a random System.
4. Walk Forward should only be used for Predictive techniques that are seeking to capture a repeatable market condition. They are not applicable for trend following or momentum methods. A nice straight equity curve for a single system can only be maintained if market conditions remain favorable over the entire extent of that equity curve. That is only relevant to predictable methods for the period of time that the predictable market condition persists. Every equity curve must display periods when they are performing and periods when they are under-performing as we know that no system can address all market conditions.
5. Straight equity curves for the long term are the result of a portfolio of many different successful systems attacking many different conditions.
Agree, those going for "perfect" equity are asking the software to look for some random system that can fit into an unrealistic expectation. To ask for near perfect Equity curve is as good as asking for random system, ie asking the software to look for failure.
"Perfect" equity curve can only be achieved in a portfolio that managed to balance the account's equity curve through the use of different system/EA, diversification of systems and markets (different currency pairs) so that when Trend EA are not performing, the Ranging or mean reversal EA can help to soften the fall and hence Equity curve can look more linear, perfect.
6. Always data mine using the most data you can get your hands on. This is the only way you can reduce the impact of randomness in your overall performance results. Data mining over a few years of data is asking for trouble.
Another way to have more data and to reduce randomness is to use Multi Market validation. It means, these system are facing many types of market data randomness/conditions/fluctuations, volatilities, that it takes a good system to pass it and hence reduce some form of randomness but not totally. If any random system pass such vigorous system, then it's purely magical and luck. 1 in a billion and not because of some effective software but just pure randomness.
7. The market condition determines your ultimate fate. A good system simply allows you to extract the signal from the market data. Bad systems......well they are just bad in every shape and form.
Introducing Trading theory in a software determine your "fate" in finding success in your process. Not having theory in your generating search is like hoping to win in a lottery system. That's why I tend to use Preset indicators (introduce theory based rules) to increase my chances. Best of luck.