Analyzing the Crypto Crash of May 2021 with Python: An AI/ML Perspective

The cryptocurrency market has been on a wild ride in 2021. After an exuberant bull run in the first few months of the year that saw Bitcoin and other major cryptocurrencies reach new all-time highs, the market came crashing down in May, with over $1 trillion in value wiped out in a matter of days.

In this article, we‘ll use Python to analyze price data of top cryptocurrencies from January to May 2021 to visualize the dramatic rise and fall of the market. More importantly, we‘ll take an Artificial Intelligence (AI) and Machine Learning (ML) perspective to examine the crash, exploring how these technologies are being used to analyze and trade crypto, and how they could potentially prevent such crashes in the future.

The Roller Coaster of Crypto in 2021

The year 2021 started off with the cryptocurrency market in full bull mode. Institutional investors like Tesla, Square, and MicroStrategy began adding Bitcoin to their balance sheets, driving its price above $60,000 by April.

Other major cryptocurrencies followed suit. Ethereum broke above $4,000, Binance Coin surged past $600, Cardano above $2, and even Dogecoin, the meme crypto, peaked near $0.74. The total crypto market cap crossed $2.5 trillion by mid-May.

But the party came to a swift end. By May 19, prices had plunged – Bitcoin below $40,000, Ethereum under $2500, BNB below $300. Over $1.2 trillion in market value evaporated. Liquidations and panic selling created a negative feedback loop.

Visualizing the Rise and Fall with Python

Let‘s use Python to chart this dramatic price action. We‘ll fetch data from Yahoo Finance for 7 major cryptos – Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Dogecoin (DOGE), XRP, and Polkadot (DOT) – from Jan 1 to May 29, 2021.

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

tickers = ["BTC-USD", "ETH-USD", "BNB-USD", "ADA-USD", 
           "DOGE-USD", "XRP-USD", "DOT-USD"]

start_date = ‘2021-01-01‘
end_date = ‘2021-05-29‘

crypto_data = yf.download(tickers, start=start_date, end=end_date)

prices = crypto_data[‘Adj Close‘]

We can plot the prices to visualize the boom and bust:

prices.plot(figsize=(10,7))
plt.ylabel(‘Price ($)‘)
plt.title(‘Major Crypto Prices: Jan-May 2021‘)
plt.show()

The steep rise and sudden crash in May is evident. If we look at daily percentage changes, we can see the crash was swift and severe, with multiple coins dropping over 30% on May 19 alone:

percent_changes = prices.pct_change().dropna().round(4)
percent_changes.loc[‘2021-05-19‘]

BTC-USD   -0.2979
ETH-USD   -0.4105
BNB-USD   -0.3872
ADA-USD   -0.3256
DOGE-USD  -0.3422
XRP-USD   -0.3782
DOT-USD   -0.3771

Such highly correlated crashes are not uncommon in crypto markets, which are still relatively immature and driven heavily by sentiment and speculation vs. fundamentals.

Analyzing Crypto Correlations and Crash Impact

We can further analyze correlations between the cryptos by calculating correlation coefficients:

correlations = prices.pct_change().corr(method=‘pearson‘)
print(correlations)

The high positive correlations (0.70-0.90) between most pairs suggest the cryptos tend to move in the same direction, especially Bitcoin and Ethereum as market leaders. This makes the market vulnerable to systemic shocks.

We can also compare the crash‘s impact on crypto vs. other assets like stocks and gold:

spx = yf.download(‘^GSPC‘, start=start_date, end=end_date)
spx_change = spx[‘Adj Close‘].loc[‘2021-05-19‘]/spx[‘Adj Close‘].loc[‘2021-05-01‘] - 1
gld = yf.download(‘GLD‘, start=start_date, end=end_date)  
gld_change = gld[‘Adj Close‘].loc[‘2021-05-19‘]/gld[‘Adj Close‘].loc[‘2021-05-01‘] - 1

print(f"S&P 500 (SPX) May Drawdown: {spx_change:.2%}")
print(f"Gold (GLD) May Drawdown: {gld_change:.2%}")

S&P 500 (SPX) May Drawdown: -1.01%
Gold (GLD) May Drawdown: 5.70%

While the S&P 500 saw a minor 1% dip, gold actually rallied 5.7% during the crypto crash, highlighting how digital assets can diverge from traditional markets.

AI and ML in Crypto: Insights and Future Potential

As crypto matures, AI and ML are increasingly being applied to bring more sophistication to trading and risk management. Some key applications include:

• Sentiment Analysis: Natural Language Processing (NLP) models can analyze social media and news to quantify crypto market sentiment. Sudden shifts in sentiment, as detected by AI, could provide early warning signs of an impending crash.

• Price Prediction: ML models like Long Short-Term Memory (LSTM) neural networks and Gradient Boosting can forecast short-term crypto prices. While not always accurate, they can identify patterns and aid in trading decisions.

• Algorithmic Trading: AI-powered bots can execute high-frequency trades, profiting from volatility. They can also implement stop losses to limit downside.

• On-Chain Data Analysis: ML can detect patterns in Blockchain data, like a spike in Bitcoin moving to exchanges (potentially signaling selling pressure).

• Risk Metrics: AI can calculate metrics like Value-at-Risk (VaR) and expected shortfall to quantify crash risk.

For example, let‘s build a basic LSTM model in Python to predict Bitcoin prices:

from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM

# Get BTC data and scale 
btc = prices[‘BTC-USD‘].values.reshape(-1,1)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_btc = scaler.fit_transform(btc)

# Split data into training and testing
training_size = int(len(scaled_btc) * 0.8)
train_data = scaled_btc[0:training_size]
test_data = scaled_btc[training_size:]

# Prepare data for LSTM 
def create_dataset(dataset, time_step):
    X, Y = [], []
    for i in range(len(dataset)-time_step):
        a = dataset[i:(i+time_step), 0]
        X.append(a)
        Y.append(dataset[i + time_step, 0])
    return np.array(X), np.array(Y)

# Reshape train and test data to feed into LSTM  
time_step = 15
X_train, y_train = create_dataset(train_data, time_step)
X_test, y_test = create_dataset(test_data, time_step)
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)

# Build and train LSTM model
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(loss=‘mean_squared_error‘, optimizer=‘adam‘)
model.summary()

model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=64)

# Predict on test data
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)

# Inverse transform data for plotting
train_predict = scaler.inverse_transform(train_predict)
test_predict = scaler.inverse_transform(test_predict)
original_ytrain = scaler.inverse_transform(y_train.reshape(-1,1))
original_ytest = scaler.inverse_transform(y_test.reshape(-1,1))

# Plot predictions vs. actual
look_back = time_step
trainPredictPlot = np.empty_like(btc)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict

testPredictPlot = np.empty_like(btc)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(train_predict)+(look_back*2):len(btc), :] = test_predict

plt.figure(figsize=(16,6))
plt.plot(scaler.inverse_transform(scaled_btc), label=‘Original BTC Price‘)
plt.plot(trainPredictPlot, label=‘Train Prediction‘)
plt.plot(testPredictPlot, label=‘Test Prediction‘)
plt.legend(loc=‘upper left‘)
plt.title(‘BTC Price Prediction‘)
plt.show()

The model captures some of the major price swings, showing the potential for AI to model crypto markets. However, it fails to predict the big May crash.

Therein lies the challenge – crypto is still a highly speculative, news-driven market, with extreme volatility that even advanced AI models struggle to consistently predict. As Samson Mow, Chief Strategy Officer at Blockstream, tweeted during the crash:

Most people are not ready for the volatility that will come with global adoption of Bitcoin. We‘re witnessing the transfer of trust in money, and it won‘t be a smooth transition.

The Future of AI in Crypto: Stability and Accessibility

The May 2021 crash provided a harsh lesson, but also a wealth of data for training AI models to better navigate crypto‘s boom-bust cycles. Future applications could include:

• AI-powered ‘circuit breakers‘ that temporarily halt trading during sudden crashes to prevent liquidation spirals
• Personalized AI risk management agents that adjust users‘ portfolios based on their risk tolerance
• Decentralized AI oracles that analyze on-chain and off-chain data to provide more reliable, tamper-proof crypto market insights

As Elon Musk, whose tweets played a role in the crash, stated in a recent interview:

I think there‘s a role for AI to help make the crypto ecosystem more transparent, more stable, more secure, and more accessible to a wider audience. We‘re just scratching the surface of what‘s possible.

Ultimately, the goal is for AI to help create a crypto market that is efficient, resistant to manipulation, and supportive of the technology‘s core ethos of decentralization and financial inclusion.

Python, with its rich ecosystem of AI and data science libraries, will likely play a key role in this future – from research and backtesting to implementing live trading systems.

The crypto crash of May 2021 was a baptism by fire, but also an opportunity for the industry to build back stronger with the help of AI. By learning from the past and harnessing the power of data and machine learning, a more mature and accessible crypto economy may emerge – one that can fulfill the technology‘s transformative potential.