The logic of quantitative analysis in the cryptocurrency world is actually very simple. It mainly predicts the price of cryptocurrencies through various indicators and verifies its effectiveness through historical data. This is also a quantitative analysis method used by many people. Based on our years of experience, we can simply summarize the following points: 1. Prices can be predicted, but price fluctuations cannot be predicted 2. Indicators such as volatility, amplitude, and turnover rate can predict price fluctuations 3. Historical trends can verify whether the indicators are effective 4. When there is a problem with the indicator, the indicator should be corrected in time 5. If an error occurs when using indicators for prediction, stop loss in time

1. Prices can be predicted, but ups and downs cannot be predicted

In our quantitative analysis system, we hope to separate the price from the rise and fall, and not use the rise and fall of the price to judge the trend. Because many people have seen too many price rises and falls, but have overlooked one problem: the reasons for the price drop and rise are the same, which is determined by the supply and demand relationship. For example, the reason for the sharp drop in Bitcoin in 2017 was mainly because when Bitcoin production was reduced in March 2017, a large number of low-priced chips appeared in the market, which caused the price of the currency to fall. When Bitcoin rose sharply in March 2018, a large number of high-priced chips appeared in the market, which caused the price of the currency to rise. Therefore, in the quantitative analysis system, we do not want to use the rise and fall of the currency price as the basis for judging the market trend, but rather want to use data to analyze the reasons behind the trend of the currency price.

2. Indicators such as volatility, amplitude, and turnover rate can predict price fluctuations

Indicators such as volatility, amplitude, and turnover rate are important tools for predicting price fluctuations. Volatility can determine the market's recognition of the current price, amplitude can determine the market's recognition of the current price, and turnover rate is an important indicator used to reflect the market's capital activity and trading willingness. Predicting price fluctuations through indicators such as volatility, amplitude, and turnover rate is not inconsistent with predicting price trends based on fundamental analysis, because they can complement each other. Therefore, when we use indicators such as volatility, amplitude, and turnover rate to predict the rise and fall of currency prices, we generally do not use a single indicator alone.

3. Historical trends can verify whether the indicator is effective

As we all know, predicting prices is a difficult task, and it is even more difficult to accurately predict the rise and fall of currency prices. The reason why many people cannot predict the trend of currency prices is because they lack analysis and summary of historical data. Therefore, when we use various indicators for prediction, we need to find patterns in historical trends and verify the effectiveness of indicators through historical trends. For example: we can verify the effectiveness of BTC prices through their historical performance. If the BTC price has risen or fallen more than 5 times in the past 30 days, it means that the indicator is effective in predicting price increases and decreases.

4. If you use indicators for prediction, stop loss in time when errors occur

The above are some basic logic and methods for quantitative analysis of the currency circle. We do quantitative analysis mainly to predict the currency price, so we also need to use some indicators, such as volatility, amplitude, turnover rate and other indicators we use. When using these indicators for prediction, we need to consider the relationship between these indicators and the currency price, as well as the problems of these indicators themselves. For example, when we use volatility to make predictions, if the volatility is too low, we need to consider adjusting the calculation method of volatility, or use other indicators to make up for the loss caused by too low volatility; when we use amplitude and turnover rate to make predictions, if the amplitude and turnover rate are too large, we need to consider correcting the calculation method of amplitude and turnover rate; if we use moving averages to make predictions, when there is a problem with the moving average, we need to correct the moving average in time. Of course, in addition to these indicators, many other indicators can be used for prediction.