Statisticalprobability — Trading Ideas on TradingView

Why Should You Care About ER?🚀 Hey Traders! Have You Ever Felt Lost in the Chaos of Market Fluctuations? What if I told you there’s a powerful tool that can help you cut through the noise and give you a statistical edge to predict SUPPORT and RESISTANCE movements with confidence? Let me take 5 minutes of your time to introduce you to something that could transform your trading game: Expected Range Volatility (ER) . What is Expected Range Volatility (ER)? The Expected Range (ER) is a framework that helps traders understand how much an asset is likely to move within a specific timeframe. Based on CME market data and Nobel Prize-winning calculations, price movements within the expected volatility corridor have a 68% probability of staying within those boundaries. 💡 Key Insight: When the price approaching certain levels, there’s a 68% chance the price won’t break through those boundaries. This means you can use ER as a powerful filter to identify more precise entry and exit points for your trades. Why Should You Care About ER? When I first discovered the ER tool, it felt like stumbling upon a gold mine in the trading world. Here’s why: It’s free and available on the CME exchange’s website. It’s underutilized —95% of traders don’t even know it exists. It provides statistical clarity in a world full of uncertainty. I remember the first time I used ER in my analysis—it completely changed the way I approached intraday trading. Now, I never make a trade without checking the ER data. It’s become an essential part of my strategy. How to Use ER in Your Trading 1️⃣ Input the Data: Head over to the CME website, plug in the necessary parameters, and get your ER values. 2️⃣ Set Boundaries: Use the ER range as a guide to set potential support and resistance levels. 3️⃣ Filter Trades: Only take trades that align with the ER framework to improve your precision. A recent example is the Japanese yen futures market. Don't be confused by the fact that we take futures levels, it can easily be plotted on a spot chart for forex market (the dollar/yen). Limitations to Keep in Mind While ER is a powerful tool, it’s not a crystal ball. Here are some limitations: Market Dynamics: Short-term price movements can be unpredictable due to sentiment, news, or economic events. ER provides a statistical estimate, but it doesn’t guarantee outcomes. Assumptions: The formula assumes price movements follow a log-normal distribution , which may not hold true in all market conditions. Your Turn: Are You Using ER in Your Strategy? 💭 Here’s the million-dollar question: Are you leveraging the power of Expected Range Volatility in your trading? If not, why not start today? 💬 Share your thoughts in the comments below: Do you currently use ER or similar statistical tools? Want to Dive Deeper? If you’re ready to take your trading to the next level, don’t miss out on our all-in-one resource designed to help you master tools like ER and other valuable sources to gain market edge! 🔥 Remember: No Valuable Data = No Edge!

A cyclical history We have all heard that the economy works in cycles, and so does the market. But what does this truly mean? Has anyone actually been able to show you where you can see these cycles occur? Well, here is a great graph that will show you how. By looking at the 6-month time frame, the percentages of stocks above the 20 daily MA, you are achieving 2 things. Seeing price action at the timeframe used to declare technical recessions Seeing the percentage of stocks in a short term uptrend or downtrend as the complement is also true Here it's quite easy to see how an important world event unfolded with a clear, repeatable pattern. When the percentage oscillates heavily, it allows for many technical resets, causing a healthy uptrend when the percentage returns to above 50% by the end of the semester. Another patter is that after a period of over-performance, a period of under-performance is followed and vice versa. When looking at world events, just remember at the end of the day we are all a number in a larger scheme. And the laws of statistics will end up controlling our outcomes, as there must be balance in all binomial systems. Even when biases can be present in distributions, the more we generalize and zoom out, the more we can see the statistical convergences in human behavior. At the end of the day, our lives are influenced by fractals, some of which we are not even aware exist.

How to read mean returns (Expand the indicator)Mean returns is a trend detection and overextension indicator. It oscillates around the value of 0. The mean return line in reality is the orange one as well as the blue one. The difference is in the number of data points into the past that they consider. Since the value of those lines is the expected value of the returns in period t, then if it's over 0 the expectation is that returns will be positive, as previously the price has been trending higher. The opposite being true as well. Meanwhile, the red and green line represent the expected upwards and expected downwards returns. That means you only take the expected value for the days in which the return was positive or negative accordingly. Therefore, if the mean returns are over the expected upwards returns the price is likely to be overextended, and vice versa. Other adjustments were made to consider the current candle. This code will remain private, as it took a lot of effort to invent. I hope you are able to understand the math. If you can't, I hope this at least allowed you to read the meaning of the indicator through this.

The most common malpractice in all of Trading: Back-testing Given ANY in- or out-of-sample time series, including purely random, synthetic data, anyone can generate (inflate) ANY Sharpe Ratio by repeatedly applying different trading or investment strategies to the same time series sample! By definition, purely random data has no discernible structure. Consequently, no method can exist to predict such a sequence - I.e., Sharpe Ratio = 0 must hold in all instances. Yet, ... See main graph! In the past It has been shown just how easy it is to generate Sharpe Ratios of 4, 5 or even >6, on any data, including on purely random, synthetic time series data when in fact, the only possible value in those instances should be S.R. = 0. As a matter of fact, this misleading (self-defeatists?) practice is so common and wide spread in finance and trading that the American Statistical Association considers it "unethical" (American Statistical Association ). (More importantly, it is a remarkably expensive way to fool oneself.) The above stems from applying the same rejection threshold for the null hypothesis under multiple testing will grossly underestimate the probability of obtaining a false positive. Unlike in the "other sciences", there is no "replication crisis" in finance or trading, simply because such checks don't even exist there - since those would be impossible to carry out. (Is that why the only two kinds of academic papers which never get revised or retracted are written in the fields of Finance and Theology?) The bottom line; In the common case of testing a trading or investment system, given a set of out-of-sample time series, one MUST increase the rejection threshold for the null hypothesis in proportion to the number of times ("peeks") such tests are carried out! (Good luck fooling yourself that way!) Anything less is just simple curve-fitting! For more in-depth explorations: Marcos López de Prado, Michael J. Lewis codemacher.com

Statistical Probability of A Price Guess A price guess has 33% to be correct because the price has only three directions. The price of a security may trade higher, may trade in a narrow range, or trade lower. Thank you for reading! Greenfield Disclosure: I am not a financial advisor. This is not a recommendation, not a representation, and not a solicitation. You should do your research and come to your own decision. Investment involves significant risks. You need to understand that you may lose your money. Past performance is not an indication of future performance. Chart reading is subjective information.