Learned from Behavioral Analysis
After repeatedly analyzing my trading behavior, I concluded that success comes from properly handling chart patterns, accurately interpreting higher timeframes, and making informed decisions based on key elements. However, despite being aware of these principles and repeatedly executing trades, my performance did not improve dramatically.
I found myself in a dilemma: “The theories I’ve studied and observed should work in practice, but the results just don’t follow.”
The most challenging aspects were the proper handling of chart patterns, interpreting higher timeframes, and entering near previous highs and lows—the very fundamentals of trading. Even though I felt I understood these elements conceptually, I lacked clear, actionable criteria during actual trades, and time just kept slipping away.
Often, I would question whether the answers I discovered were truly correct, only to feel betrayed by the outcome moments later. I simply couldn’t master these aspects effectively.
At one point, it struck me: Forex trading is essentially an attempt to predict whether prices will rise or fall based on historical chart data and patterns. In many ways, it felt similar to answering language comprehension questions on a test. In such tests, you must read a passage, understand the intent of the author or question writer, and derive the optimal answer based on that understanding.
This realization led me to redefine my approach by focusing on two key principles:
- Letting go of unnecessary elements.
- Extracting probabilities from past data.
I had to accept that I am not a trading prodigy loved by the “Forex gods.”
Letting Go
I decided to completely disregard the following elements:
- Chart patterns
- Higher timeframes
- Currency strength/multiple currency pairs
- Previous highs and lows
- Breakouts
Extracting Probabilities from Historical Data
The probability of hitting red or black on a roulette wheel is approximately 47.3%. In baccarat, the player has around a 44.62% chance of winning, and in horse racing, the odds for the favorite horse to win are roughly 30–40%.
An ideal strategy would have a 1:1 risk-reward ratio and a win rate of around 60%, but such a perfect strategy may not exist.
By letting go of the unnecessary elements mentioned above, I shifted my focus to carefully analyzing recent historical data to identify entry points where:
- The risk-reward ratio is 1:1.
- The win rate exceeds 52%.
If these points also show consistent patterns across long-term historical data, then I will have found a strategy worth pursuing.