Quote from: cryptoaddictchie on Today at 09:29:55 AM

Quote from: LossLeader on August 30, 2024, 09:45:04 AM

I realized that many of the traditional methods are either outdated or irrelevant to today’s markets. So, I embarked on my own research journey.

Can you cite an example of those traditional that is barely useful now for trading? Cause I only knew of basic trading concept and method but still employing up to now and it seems working fine for me.

Quote from: LossLeader on August 30, 2024, 09:45:04 AM

As an economist with experience in big data analytics, I started working with historical data, focusing on price action. After a year of research, I’ve gained a deeper understanding of market processes but also recognized the limits of my own skills.

So in your research what can you say about historical data does it help determine the potential future price for the market? As it seems many are reliable on this info as basis too.

I believe many trading concepts are based on abstract assumptions and share a common origin, with some being outright misconceptions. Take Fibonacci levels, for example—waiting for a 50% retracement is essentially the same as waiting for a moving average crossover. When it comes to supply and demand, it’s complete nonsense in the context of BTC. While these concepts might apply to markets like grains or coffee, where seasonal supply and strategic demand are factors, BTC operates differently. Here, market makers provide liquidity 24/7, and market efficiency is their goal.

Risk management is another area that’s often misunderstood. The idea of risking 1% of your bankroll isn't efficient while 99% of it is not working. And strategies like risk-reward ratios of 1:3 can fail over time. Even in a vacuum, there are numerous combinations of losses that could eventually break you; it's only a matter of time.

The ICT approach is a compilation of these misconceptions, combining many flawed ideas into one.

As for historical data, numbers reflect specific states of affairs—quantitative aspects of reality as opposed to abstract ones. When combined with a deep understanding of market processes, this data can indeed help make more accurate predictions. You’ll see that the market is far from random. Statistics show that 95% of retail traders lose money. If you think that’s what randomness looks like, you’re mistaken—this is the result of misconceptions spreading and someone's well-executed market strategies.