未来予測インジケーターに頼るな！フレッシュな情報こそ勝利のカギ

Hello, I’m Neko-kai from the Trade Idea Lab. An indicator that can predict the future? Haha, honestly, I used to be hooked on it too. I’ve thought, “This might help me win.” Especially those future-predicting indicators that can be installed on MT4; at first, I was excited. Thinking “I can see the future!”—but it was only for a moment.

But reality wasn’t so sweet after all. Even though they’re called future predictions, they’re basically just calculations based on past data. When you hear about financial engineering, it might sound very theoretical and smart, but the crucial part is the “past.” In other words, they’re trying to find patterns in past price movements, not directly predicting future ones. So, in the end, whether you can win in trading is a “different story.” Monte Carlo simulations feel nostalgic. About 20 years ago I even bought a Excel program that cost around 200,000 yen. There are other methods too, so I’ll introduce them.

Future-predicting indicators and theories aim to forecast future price movements from past data, and they use the following main analytical methods and theories. Each is based on financial engineering or mathematical methods and is commonly used in trading.

1. ARIMA Model (AutoRegressive Integrated Moving Average)

- An autoregressive moving average model, a forecasting method using time series data. It predicts past price movements based on data self-correlation. ARIMA specializes in data with random movements like financial data and is used to forecast future price movements, but in trading its accuracy is often low.

2. ARSM (AutoRegressive Stochastic Model)

- An autoregressive probabilistic model that incorporates random elements within time series data to predict. It analyzes past data fluctuation patterns autoregressively and probabilistically forecasts future prices. Generally used for economic data and market trend analysis.

3. Fourier Transform

- Fourier transform decomposes complex data into frequency components. In financial markets, it is used to detect cyclical movements and to extract periodic patterns in an attempt to forecast future trends. However, financial markets have strong random elements, so Fourier transform alone has limitations.

4. Kalman Filter

- A filtering method for state estimation that removes noise in real time while dynamically predicting a changing market. Used in many forecasting systems to provide the most “realistic” future predictions from time series data.

5. Neural Networks

- A type of machine learning model that predicts future prices based on past data. It inputs multiple elements such as past market data, news, and economic indicators to learn future price movements. With advances in deep learning, it is increasingly applied to price forecasting.

6. Elliott Wave Theory

- Based on the idea that market price movements follow patterns similar to waves in nature. By examining past price wave patterns, it analyzes which phase the current market is in and predicts future price fluctuations. However, this is more about theoretically situating the current market than about predicting the future.

7. SVM (Support Vector Machine)

- A machine learning algorithm used for classification and regression. Learns specific market conditions from past data and applies them to future markets to predict prices. It is often applied to stock or forex market forecasts, but requires complex computations.

8. GARCH Model (Generalized Autoregressive Conditional Heteroskedasticity)

- A generalized autoregressive variance model used to forecast volatility in financial data. Widely used to predict future risk based on past variability (volatility), but it’s more about capturing risk fluctuations than predicting prices themselves.

9. Monte Carlo Simulation

- A method that repeatedly simulates random variations to calculate probabilistic future scenarios. Based on random price movements, it explores the range and likelihood of future price movements. Not a definite prediction; it provides probabilistic futures to understand investment risk.

10. Fractal Theory

- Fractal theory is used to find complex patterns with self-similarity. Market movements are believed to have fractal structures, so it seeks recurring patterns in price fluctuations to forecast future movements.

These methods aim to predict the future from past data, but no method is perfect and a flawless future forecast is impossible. Markets are influenced by many random elements and emotional movements, so in trading it’s important to always be aware of risk and respond flexibly.

Let me share my biggest mistake. It was when I had just acquired an indicator and thought, “This will do!” and held a large position. As you can imagine, it moved against me and I blew up. That’s when I finally realized: the future isn’t something you can predict with indicators.

So how do we keep making profits? We collect fresh information every day and every week, and base our ideas on it. The market is a living thing, so it doesn’t move the same as yesterday. Even if a similar situation reoccurs, different news, fundamentals, and trader psychology can lead to completely different results.

To put it simply: just because yesterday’s coffee tasted good doesn’t guarantee today’s will taste the same at the same cafe. The barista may be in a different mood, or the beans may have changed. No matter what, the final product will be different, which is the reality of the market.

Therefore, analyzing past price movements is important, but we shouldn’t rely on the fantasy that we can predict the future. Rather, we as traders must always stay tuned to new information and craft ideas that suit the current market conditions.

Well, if there were truly an indicator that could predict the future, I’d probably be using it myself and not selling it (laughs). So, I believe the most important thing is to think for yourself rather than relying on such tools.

For details, search for “Samura Lite Radar 3-Step Method.”