Bitcoin prices will exceed $190,000 in 2025
In the long-term strategy, we dig deep into the key factors that affect the price of Bitcoin. By precisely calculating the correlation of these factors with the price of Bitcoin, we find that they are closely linked to the value of Bitcoin. In order to more effectively predict the reasonable price of Bitcoin, we built a prediction model, based on historical experience, determined the limit value of the price deviation, and calculated the upper and lower limits of the price, observing the Bitcoin price and the upper and lower limits of the price can guide the transaction, and based on the current data, calculate the upper limit of the Bitcoin price in 2025. Historical simulations prove that the model's prediction results are in good agreement with the actual price, which fully proves its reliability in predicting price Fluctuations.
As Alan Greenspan said, "When the future is unpredictable and uncertain, people often choose to stagnate, avoid risks, or even abandon their original plans." "Predictions for Bitcoin are challenging, but we've taken the first steps toward exploration.
Directory:
Step 1: Identify the factors that have the greatest impact on the price of Bitcoin
Step 2: Build a Bitcoin price prediction model
Step 3: Look for early warning indicators for Bear Market bottoms and Bull Market tops
Step 4: Predict the Bitcoin price cap in 2025
Step 5: Verify the performance of the Bitcoin Alert Indicator
Step 1: Identify the factors that have the greatest impact on the price of Bitcoin
Correlation coefficient: A mathematical concept for measuring impact
In order to predict the price movement of Bitcoin, we need to dig deeper into the factors that have the greatest impact on the price of Bitcoin. These factors or variables can be expressed mathematically or statistically as correlation coefficients. The correlation coefficient is a measure of the degree of correlation between two variables, with a value between -1 and 1. A value of 1 indicates that the two variables are completely positively correlated, and a value of -1 indicates that the two variables are completely negatively correlated.
In the case of corn and hog prices, an increase in the price of corn usually leads to a corresponding increase in the price of hogs, as corn is the main source of feed for hog farming. In this case, the correlation coefficient between corn and hog prices is about 0.3. This means that corn is a factor that affects hog prices. For example, if one shooter's performance improves, and another shooter's performance decreases due to increased psychological stress, then we can say that the former is a factor affecting the latter's performance.
Therefore, in order to find out the factors that have the greatest impact on the price of Bitcoin, we need to find the factor with the greatest correlation coefficient with the price of Bitcoin. If through the correlation analysis of Bitcoin price and on-chain data, it is found that a certain on-chain data factor has the largest correlation coefficient with Bitcoin price, then this on-chain data factor can be determined as the factor that has the greatest impact on Bitcoin price. After calculation, we found that the Bitcoin Block number is one of the factors that 🔵 have the greatest impact on the price of Bitcoin. It is evident from the historical data that
🔵 The Bitcoin Block is basically the same as the direction of the Bitcoin price. Through the analysis of the data of the last ten years, we derive:
🔵 The daily correlation coefficient between the Bitcoin Block and the Bitcoin price is 0.93.
Step 2: Build a Bitcoin price prediction model
Predictive Model: What Formula Is Used to Predict Bitcoin Price?
Among the various predictive models, the linear function is the preferred model because of its high accuracy. In the case of standard weight, the image of the linear function is a straight line, which is why we chose the linear function model. However, the price of Bitcoin and its Block Size grow extremely fast, which does not fit the characteristics of a linear function. Therefore, in order to make the two more in line with the characteristics of linear functions, we first perform a logarithmic transformation of the two. Looking at the logarithmic graph of Bitcoin price and Block Size, we can see that after logarithmic conversion, the two are more consistent with the characteristics of linear functions. Based on this feature, we chose a linear regression model to build a predictive model.
As you can see from the chart below, the actual red-green candlestick Fluctuation around the 🟢 predicted blue-green line. These predictions are based on Bitcoin's fundamental factors, which underpin Bitcoin's value and reflect its fair value. This picture coincides with Marx's theory of "price fluctuation around value" put forward in Capital.
Step 3: Look for early warning indicators at the bottom of the Bitcoin Bear Market and the top of the Bull Market
Early warning indicator: How to tell if the Bitcoin price has reached the bottom of a Bear Market or the top of a Bull Market?
Looking at the Bitcoin price logarithmic forecast chart above, we find that at the bottom of the bear market, the actual price tends to be lower than the forecast, and at the peak of the Bull Market, the actual price exceeds the forecast. This pattern shows that the deviation between the actual value and the predicted value can be used as a signal for early warning. 🟠 When the Bitcoin price deviation value is low, the chart with a green background shows that this usually means that we are at the bottom of the Bear Market; conversely, when 🟠 the Bitcoin price deviation value is high, the 🟩 🟥 chart with a red background indicates that we are at the top of the Bull Market.
This law has been verified by six Bull Market and Bear Market, and the deviation value does have an early warning effect, which can be used as an important reference indicator for us to judge the market trend.
We can find patterns by looking at the Bitcoin price logarithm and the Bitcoin price deviation chart. For example, on August 25, 2015, the Bitcoin price deviation was at the lowest value of -1.11; on December 17, 2017, 🟠
🟠 Bitcoin price deviation was at the highest value of 1.69 at that time; on March 16, 2020,
🟠 Bitcoin price deviation was at the lowest value of -0.91 at that time; on March 13, 2021,
🟠 Bitcoin price deviation was at the highest value of 1.1 at that time; on December 31, 2022,
🟠 Bitcoin price deviation was at the lowest value of -1 at that time.
For conservative reasons, we set the lower limit of the Bitcoin price deviation of the warning indicator to the greater of the three lowest values of -0.9, and the upper limit to the smaller of the two highest values of 1.
When we add the upper and lower values of the Bitcoin price deviation to the predicted price, we get the upper and 🟤 lower limits of the 🟠 price. You can intuitively guide the transaction. When the price of Bitcoin is below the price floor, buy. Sell when the Bitcoin price is above the price cap.
Step 4: Predict the Bitcoin price cap in 2025
The price cap for Bitcoin calculated based on data on February 25, 2024 is $194287, which is the upper limit for this round of Bull Market. The peak of the last Bull Market was $68,664 on November 9, 2021, and the Bear Market cycle is 4 years, so the price peak of this Bull Market is expected to be in 2025, and the Bitcoin price cap will exceed $190,000. The Bitcoin Closing Price on February 25, 2024 is $51,729, which is expected to increase by 2.7 times.
Step 5: Verify the performance of the Bitcoin Alert Indicator
Verifying the accuracy of the model: How to judge the accuracy of the Bitcoin price model?
The accuracy of the model is expressed by the coefficient of determination R, which reflects the degree of matching between the predicted value and the actual value. I divided all the historical data from August 18, 2015 into two groups, with data from August 18, 2011 to August 18, 2015 as training data to build the model. The results show that the determination coefficient R of the training period from 2011 to 2015 is as high as 0.81, which indicates that the accuracy of the model is quite high. As you can see from the Bitcoin price log forecast chart in the chart below, the forecast value does not deviate far from the actual value, which means that most of the forecast values explain the actual value well.
Verify the reliability of the model: How to confirm the reliability of the Bitcoin price model when new data is available?
The reliability of the model is achieved through model validation. I set the last day of the training period to February 2, 2024 as the "validation group" and used it as validation data to verify the reliability of the model. This means that after the model is generated, if there is new data, I use that new data with the model to make predictions, and then evaluate the accuracy of the model. If the coefficient of determination when using the validation data is similar to that of the previous training, and both remain at a high level, then we can consider the model to be reliable. The coefficient of certainty calculated from the data during the validation period and the prediction results of the model is as high as 0.83, which is similar to the previous 0.81, which further proves the reliability of the model.
Strategy: When to buy or sell, Long quantity to choose from?
We introduced the Bitcoin 5A strategy. This strategy requires us to generate trading signals based on the threshold of the warning indicator, conduct simulated trading, and count performance data for evaluation. In the Bitcoin 5A strategy, there are three key parameters: buy warning indicator, batch trading days, and sell warning indicator. Lot trading days are to ensure that after the trade signal is issued, we can trade in batches to buy at a lower price, sell at a higher price, and drop the cost of trading shocks.
In order to find the optimal threshold of the alert indicator and the number of days of batch trading, we need to repeatedly adjust these parameters and backtest them. Backtesting is a method built by looking at historical data that can help us better understand market movements and trading opportunities.
When the early warning indicator Bitcoin price deviation is lower than -0.9, that is, when the Bitcoin price is below the lower price limit, buy. When it is above 1, that is, when the Bitcoin price is above the price limit, sell. In addition, we set the number of trading days in batches to 25 days to achieve an average buy and average sell strategy. Within 25 days, we put the total money into the market evenly, buying once a day, and at the same time, we also sell position at the same pace, once a day.
Adjusting Thresholds: A Key Step to Optimize Your Trading Strategy
In the pursuit of higher performance, adjusting thresholds is an indispensable step. The following are the recommended adjustments to the number of days of batch trading and the threshold value of the warning indicator:
- Batch trading days: Experiment with different days, such as 25 days, to see how it affects overall performance.
- Buy and sell thresholds for early warning indicators: Exhaustively and iteratively adjust the buy thresholds of -0.9 and sell thresholds of 1 to find the optimal threshold combination.
With this fine-tuning, we may be able to find an optimization scheme with a lower maximum drawdown (e.g., 11%) and a higher cumulative return on Close Position trades (e.g., 474x). The following figure is the Bitcoin 5A strategy backtest transaction optimization chart, which provides us with a visual display of strategy adjustment and optimization.
In this way, we can better grasp market trends and trading opportunities, so as to achieve a more robust and efficient trading strategy.
Performance Evaluation: How to Accurately Evaluate Historical Backtesting Results?
In order to ensure the accuracy and reliability of the results after exhaustive strategy testing, we need to conduct a detailed performance evaluation of the backtest results. Key assessment metrics include:
- Equity Curve: As shown by the rose red line, it visually reflects the growth of the account's equity. By looking at the equity curve, we can understand the overall performance and profitability of the strategy.
The basic properties of this strategy are as follows:
Trading range: 2015-8-19—2024-2-18, backtesting range: 2011-8-18—2024-2-18
Initial funds: 1000USD, order size: 1 contract, pyramid: 50 orders, commission rate: 0.2%, Slippage: 20 markers.
In the strategy tester overview diagram, we also get the following key data:
- Net profit margin of Close Position trades: up to 474x, far exceeding the Benchmark, Bitcoin bought and held 210x in the Strategy Tester performance profile.
- Number of Close Position trades and percentage of wins: 100 trades are all profitable, which demonstrates the stability and reliability of the strategy.
- Drawdown to win-loss ratio: The maximum drawdown is only 11%, much lower than Bitcoin's 78%. The profit factor, i.e. the win-loss ratio of 500, further proves the strength of the strategy.
With these detailed assessments, we can clearly see the excellent balance between risk and return of the Bitcoin 5A strategy.
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