⏺ How to Predict Bitcoin Price Movements Through Quantification?

The core logic of quantitative prediction for Bitcoin is to convert **market patterns, on-chain data, sentiment fluctuations** and other abstract information into calculable and backtestable strategies through mathematical modeling.

In the field of quantitative analysis, prediction is not about obtaining an "absolutely accurate" number, but about finding high-probability profit opportunities by calculating **win rate (Win Rate)** and **odds (Profit/Loss Ratio)**.

1. Core Data Sources (Factor Mining)

The first step in quantitative analysis is to obtain data from different dimensions. Compared to traditional stock markets, Bitcoin has unique "on-chain data."

Technical data (OHLCV): Opening price, highest price, lowest price, closing price, trading volume. This is the most basic data used to calculate technical indicators like RSI, MACD, Bollinger Bands, etc.

On-chain data: This is a metric specific to cryptocurrencies, reflecting real fund flows.

Exchange inflow/outflow volume: Large inflows to exchanges often indicate selling pressure, while outflows suggest accumulation.

Holder distribution: Monitoring the movements of "whale" addresses.

MVRV ratio: The ratio of market value to realized value, commonly used to determine if the price is in a bubble or at a bottom.

Derivative Data

Funding Rate: Reflects the battle between longs and shorts; extreme rates often accompany reverse manipulation.

Open Interest: Measures market activity and can be a precursor to volatility spikes.

Sentiment Data: Using natural language processing (NLP) to analyze keywords from Twitter, Reddit, and news to calculate fear and greed indices.