Forecast Bitcoin using Machine Learning

Machine learning is a powerful technique for building a quantitative trading strategy. The cryptocurrency market has seen some bursts of activity from large traders. At certain hours and days of the week, large traders are active. This is due to the habits and work schedules of large traders.

We can analyze this activity using machine learning, namely time series analysis, which takes into account cyclicality and price variance in the past.

The main idea in building this trading strategy is to find abnormal market behavior, that is, the actions of large traders.

With the help of machine learning, we can determine the confidence limits of the normal and abnormal price movement, taking into account the cyclicality, and so on.

Many traders intuitively trade using the price channel. From a statistical point of view, this makes sense. The main problem is to create a price channel that will adapt to the current market conditions based on the cyclical nature of the market.

Through time series forecasting, we can build an optimized price channel that will help traders make decisions. Usually, after abnormal market behavior, the market returns to its normal state. That is, from the point of view of trading, price rollbacks, gap closings, and so on occur. We can use this information to build a trading strategy.

Intraday trading

Swing Trading

Seasonality within the week

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