How to Technically Predict Bitcoin Prices Successfully?

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Is this a goldmine for quick wealth? Well, I could even hand you the code for the model above so you can use it yourself…

But let’s get serious—don’t do it! Avoid using such models for trading. While these seemingly accurate results may be tempting, they’re often deceptive.

Here’s why.

When Something Seems Too Good to Be True

In recent weeks and months, I’ve encountered countless articles showcasing cryptocurrency price predictions using methods similar to the one above.

However, these unbelievably precise forecasts should immediately raise red flags. “If it sounds too good to be true, it probably is.”

Let me demonstrate why that’s the case.

(Don’t misunderstand—I’m not dismissing the efforts behind those articles. Many approaches are theoretically sound. This article aims to explain why they fail practically and why their predictions aren’t suitable for real trading.)

Using LSTM to Predict Bitcoin Prices

To illustrate, I’ll introduce an example using a multidimensional LSTM (Long Short-Term Memory) neural network to predict Bitcoin prices and generate the forecasted results shown earlier.

LSTM, a specialized type of Recurrent Neural Network (RNN), excels in time-series problems, making it popular for cryptocurrency and stock market predictions.

👉 Learn more about LSTM networks here

Data Collection

First, I downloaded Bitcoin’s historical price data (you can use any cryptocurrency data). Using CryptoCompare’s API, I fetched:

import json  
import requests  
import pandas as pd  

endpoint = 'https://min-api.cryptocompare.com/data/histoday'  
res = requests.get(endpoint + '?fsym=BTC&tsym=USD&limit=2000')  
hist = pd.DataFrame(json.loads(res.content)['Data'])  
hist = hist.set_index('time')  
hist.index = pd.to_datetime(hist.index, unit='s')  
hist.head()

(Sample of Bitcoin’s historical price data)

This provided 2000 days of BTC data, ranging from October 10, 2012, to April 4, 2018.

Train-Test Split

Next, I split the data into training (90%) and testing (10%) sets, with the cutoff at September 14, 2017.

def train_test_split(df, test_size=0.1):  
    split_row = len(df) - int(test_size * len(df))  
    train_data = df.iloc[:split_row]  
    test_data = df.iloc[split_row:]  
    return train_data, test_data  

train, test = train_test_split(hist, test_size=0.1)

(Visualizing the split: Training vs. Test Data)

Model Architecture

I built a simple neural network with:

Trained for 50 epochs with a batch size of 4, using MAE (Mean Absolute Error) and the Adam optimizer.

(Note: Hyperparameters weren’t fine-tuned—this is just an example.)

Results: The Flaw

The model’s predictions mirrored the previous day’s prices, as seen below:

n = 30  
line_plot(targets[-n:], preds[-n:], 'actual', 'prediction')

Problem? The predictions were essentially a 1-day lagged version of actual prices.

When shifted, the curves matched perfectly, proving the model merely memorized past prices instead of forecasting future trends.

Key Takeaways

  1. LSTMs learn well—but their "strategy" might just minimize error by copying past data.
  2. Single-point price predictions based solely on historical data are unreliable for trading.

  3. Better approaches:

    • Use additional data (beyond price history).
    • Optimize network architecture & hyperparameters.

👉 Explore advanced trading strategies

FAQs

Can LSTM models predict Bitcoin prices accurately?

While they can fit historical data well, their real-world predictive power is limited without additional market indicators.

Why do most price prediction models fail in trading?

They often overfit past trends and ignore external factors like news, regulations, or market sentiment.

What’s a better alternative for price forecasting?

Combining technical analysis, fundamental data, and machine learning yields more robust results.

Disclaimer: This is not investment advice. Models are for educational purposes only.

(Original Source: HackerNoon)

Keywords: Bitcoin price prediction, LSTM neural networks, cryptocurrency trading, time-series forecasting, deep learning in finance

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