Miraclez

Crypto insights through Data Science 🤓

[Miraclez @melifarox]

Indicators? #Elliott waves? fractals? everyone knows these words, as well as beautiful graphs.
Let's look at #bitcoin from a new angle.
Rocket science applied to cryptocurrency! 🤓
Meet - recurrent graph analysis. let's look globally at the price chart with another area of ​​the brain!

How to read?
features of price dynamics are reflected in graph patterns. similar patterns = similar behavior.

For what?
- revealing hidden patterns
- identifying and predicting market modes
- is bitcoin broken or is past behavior reflected today?
Notice how the structure changes after each halving!

This method complements traditional analysis and helps to see the market through the eyes of a data scientist.
What do you see on this chart?

#cryptocurrency #Data_Science #BitcoinAnalysis
#CryptoTopology
#BTCPricePatterns #BitcoinTechnicals
#CryptoResearch
#BlockchainAnalytics
#BTCMarketCycles
#CryptoPriceAnalysis
#AdvancedTrading
#FinancialComplexity
#CryptoTrends
#BitcoinMetrics

[Miraclez @melifarox]

@anisssia_ Phuket 😉

[Miraclez @melifarox]

@alt_layer @oh_ottie_ Awesome! 🥳

[Miraclez @melifarox]

@Dorothy_defi @berachain @monad_xyz I think the cycle has not yet begun, everything is ahead

[Miraclez @melifarox]

Who else has any doubts that Bitcoin's price behavior is determined by Gaussian distributions? 😉 #bitcoin #prediction

[Miraclez @melifarox]

🚀 Rocket Science meets Crypto: Lesson #2

🔮 What will be the price of bitcoin in the next 365 days? (Spoiler: It's just for fun!)

📊 Technical analysis, wave theory, modern deep learning (boostings, LSTMs, GANs... ) - all great, but what does the old school say? How about building a Hidden Markov Model for Bitcoin price?

🧠 Let's assume Bitcoin's behavior is determined by a mixture of Gaussian distributions (think about it, almost everything in our lives is just a mix of Gaussians).

🔬 The experiment:
1. Use historical data (strictly before Sept 2022)
2. Calculate the parameters of this mixture
3. Validate by forecasting 2 years up to today
4. Compare with real data (we have it, right?)

📈 If the forecast correlates well with the actual price, let's peek into the future and predict the price for the next year. And here's what we got!

🤯 The similarity between the predicted price and the actual price over the last 2 years is striking, isn't it?

Remember: This is just a thought experiment, not financial advice! But isn't it fascinating how simple models can sometimes capture complex behaviors?

#Bitcoin #DataScience #MachineLearning #CryptoForecast #Mathematics #Statistics #BTC
#Crypto #CryptoTrading #Blockchain #FinTech #QuantitativeFinance #CryptoInvesting #Halving

[Miraclez @melifarox]

@0xGans @GMNetwork_AI We can’t wait ☺️

[Miraclez @melifarox]

@RobTG4 I support the idea that not every token should strive to be 'money'. This will allow ETH to focus on its unique role.
ETH does not necessarily have to be 'money', its true value is in enabling decentralized applications and services

[Miraclez @melifarox]

@UmojaProtocol Umoja and Forbes - both brands are like Swiss knives in the world of investments. They should be together :)

[Miraclez @melifarox]

Bitcoin Correlation with the Stock Market? Stop! Enough! 🤓
🔍 Conducted deep analysis of various assets' influence on #Bitcoin.
Here's what found:
1️⃣ S&P 500 does affect BTC, but not immediately. First 4 days post-impulse, BTC might move in the opposite direction!
2️⃣ The real surprise: a cybersecurity ETF turned out to be the best predictor of Bitcoin's movements 😮
3️⃣ Cointegration analysis > simple correlation for understanding short-term and long-term relationships with trend resistance

Unlocking Bitcoin's true influencers provides a strategic edge for predicting market movements and optimizing investments.
🧵 Details below ⬇️

(1/5) I think everyone has heard about the correlation of Bitcoin with the SP500, and even tried to use SP500 to predict Bitcoin. But why do we often hear that "Bitcoin has become untied from the stock market"? Let's investigate!

(2/5) Correlation is simple and visible, but it only accounts for momentary dependencies. We need to predict Bitcoin's movements in advance. Enter Granger causality analysis - but it only considers linear relationships and depends heavily on lag choice.

(3/5) The solution? Cointegration analysis. It accounts for trends, reveals hidden non-linear relationships (both short and long-term), and works with non-stationary data. Perfect for finding assets that influence Bitcoin in advance.

(4/5) We ran Engle-Granger cointegration tests on 10+ assets (SP500, Nasdaq, gold, oil, Dow Jones, etc.). Surprise leader: the cyber security ETF. Bitcoin follows this asset best over recent years.

(5/5) Impulse response function shows: Bitcoin barely reacts to SP500 impulse movements in the first 4 days, sometimes moving oppositely. It starts following the impulse only on the 5-10th trading day!

PS. This is just a short tutorial example. For detailed research code, DM me.
#BitcoinAnalysis #CryptoResearch #FinancialModeling #Cryptocurencies #priceprediction #crypto #FinancialEducation #Finances #StockMarket

[Miraclez @melifarox]

@GMNetwork_AI GM!

[Miraclez @melifarox]

@chessxyz GM as a style and way of life 😉

[Miraclez @melifarox]

@100trillionUSD Miners May Need a Motivational Speaker After This Halving 😉

[Miraclez @melifarox]

@GMNetwork_AI Great idea. Stay with GM, stay Healthy 💪

[Miraclez @melifarox]

@binance Lesson 1: "How to Explain Blockchain to Grandma"
Lesson 2: Law: "How to Create a Whitepaper Without Attracting the SEC's Attention
Lesson 3: Psychology: "Managing FOMO and FUD in Stressful Situations

[Miraclez @melifarox]

FEAR & GREED INDEX vs FUNDING RATE: BATTLE OF THE TITANS! 🥊

🧠 "Be fearful when others are greedy and greedy when others are fearful" - Warren Buffett
🐰 "Mass shorts = liquidation of hamsters" - Crypto traders

Let's test these claims with t-tests from math stats!
(Spoiler: negative funding is our bro!)

📉 Extreme Fear (index < 20):
T-stats: -0.32, P-value: 0.75
Verdict: Price behaves as usual. Sorry, Buffett! 🤷‍♂️

📈 Extreme Greed (index > 80):
T-stats: 1.2, P-value: 0.22
Verdict: No magic here either. Just normal price behavior. 😴

🚀 Negative Funding (funding rate < 0):
T-stats: 2.1, P-value: 0.035
Verdict: BINGO! Statistically significant daily price change increase! 🎉

💡 Key Insight: With negative funding, price daily rises on average by 0.75%, which is 8 times more than with positive funding (0.09%)!

Conclusion: Hamsters, prepare to be sheared! Negative funding is our true friend in the crypto trading world. 💇‍♂️🐰

#Cryptocurrencies #Bitcoin #TradingStrategies #DataScience #Trading #fintech #cryptoinvesting #crypto #investingtips #cryptotips

P.S. Data since 2020. Funding from Binance, fear&greed index from open sources. Tests were conducted comparing daily price change percentages under conditions with normal Bitcoin price changes. Not investment advice, DYOR! 📚🔍

So, was Buffett wrong? Maybe, but perhaps he meant that fear is always followed by an upward reversal, and vice versa. We'll find out in the next episodes 🤓

P.P.S. If you need experiment's code, DM me.

[Miraclez @melifarox]

@BitcoinMagazine Satoshi deserves a Nobel Prize for making economists do programming and programmers do economics! 🤓💱

[Miraclez @melifarox]

@Ismael_H_R The synergy between ZK proofs and restaking is fascinating. It addresses two major challenges in crypto - scalability and capital efficiency - in one elegant solution. Bravo, LFG! :)

[Miraclez @melifarox]

@BitcoinMagazine Bitcoin: The Only 'Dead Man' That Keeps Breaking Records

[Miraclez @melifarox]

@cb_doge Free speech and Dogecoin have a lot in common: everyone is talking about it, but no one knows what its future will be 🤓

[Miraclez @melifarox]

@elonmusk doge no longer responds to tweets 🥱
specific actions needed :)

[Miraclez @melifarox]

🚀 Rocket Science meets Crypto: Lesson #2

🔮 What will be the price of bitcoin in the next 365 days? (Spoiler: It's just for fun!)

📊 Technical analysis, wave theory, modern deep learning (boostings, LSTMs, GANs... ) - all great, but what does the old school say? How about building a Hidden Markov Model for Bitcoin price?

🧠 Let's assume Bitcoin's behavior is determined by a mixture of Gaussian distributions (think about it, almost everything in our lives is just a mix of Gaussians).

🔬 The experiment:
1. Use historical data (strictly before Sept 2022)
2. Calculate the parameters of this mixture
3. Validate by forecasting 2 years up to today
4. Compare with real data (we have it, right?)

📈 If the forecast correlates well with the actual price, let's peek into the future and predict the price for the next year. And here's what we got!

🤯 The similarity between the predicted price and the actual price over the last 2 years is striking, isn't it?

Remember: This is just a thought experiment, not financial advice! But isn't it fascinating how simple models can sometimes capture complex behaviors?

#Bitcoin #DataScience #MachineLearning #CryptoForecast #Mathematics #Statistics #BTC
#Crypto #CryptoTrading #Blockchain #FinTech #QuantitativeFinance #CryptoInvesting #Halving