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    Home»Bitcoin»Predicting Bitcoin Value by means of ML and Technical Indicators
    Predicting Bitcoin Value by means of ML and Technical Indicators
    Bitcoin

    Predicting Bitcoin Value by means of ML and Technical Indicators

    By Crypto EditorMarch 18, 2025No Comments5 Mins Read
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    import pandas as pd
    import numpy as np
    np.NaN = np.nan # For pandas_ta compatibility
    import matplotlib.pyplot as plt
    import requests
    from datetime import datetime, timedelta
    import pandas_ta as ta
    import mplfinance as mpf
    import xgboost as xgb
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.metrics import mean_squared_error
    import warnings
    warnings.filterwarnings(‘ignore’)

    class BitcoinTechnicalAnalysisML:
    def __init__(self):
    “””Arrange our instruments for predicting Bitcoin costs”””
    self.information = None # The place we’ll retailer Bitcoin data
    self.api_url = “https://api.coingecko.com/api/v3/cash/bitcoin/market_chart”
    # Our crystal ball: a wise mannequin that learns patterns, utilizing the GPU for pace
    self.mannequin = xgb.XGBRegressor(n_estimators=100, tree_method=’hist’, machine=’cuda’, random_state=42)
    self.scaler = StandardScaler() # A device to make numbers simpler for the mannequin

    def fetch_bitcoin_data(self, days=365, vs_currency=’usd’):
    “””Step 1: Seize Bitcoin’s value historical past, like checking a yr’s value of receipts”””
    strive:
    params = {‘vs_currency’: vs_currency, ‘days’: days, ‘interval’: ‘day by day’}
    response = requests.get(self.api_url, params=params)
    response.raise_for_status()

    information = response.json()

    df = pd.DataFrame({
    ‘Timestamp’: [x[0] for x in information[‘prices’]],
    ‘Shut’: [x[1] for x in information[‘prices’]],
    ‘Quantity’: [x[1] for x in information[‘total_volumes’]]
    })

    df[‘Timestamp’] = pd.to_datetime(df[‘Timestamp’], unit=’ms’)
    df.set_index(‘Timestamp’, inplace=True)

    df[‘High’] = df[‘Close’] * 1.02 # Guess the day’s excessive (just a little above shut)
    df[‘Low’] = df[‘Close’] * 0.98 # Guess the day’s low (just a little under shut)
    df[‘Open’] = df[‘Close’].shift(1) # Yesterday’s shut is right now’s open

    self.information = df.dropna() # Take away any incomplete days
    print(f”We’ve grabbed {len(self.information)} days of Bitcoin costs, from {self.information.index[0].strftime(‘%Y-%m-%d’)} to {self.information.index[-1].strftime(‘%Y-%m-%d’)}—like a year-long diary of Bitcoin’s ups and downs!”)
    return self.information

    besides requests.exceptions.RequestException as e:
    print(f”Oops! Couldn’t get the Bitcoin information as a result of: {e}. Perhaps the web’s down?”)
    return None

    def calculate_indicators(self):
    “””Step 2: Add clues to guess the place Bitcoin’s value is heading”””
    if self.information is None:
    print(“Maintain on! We’d like Bitcoin information first. Run fetch_bitcoin_data() to get it.”)
    return None

    df = self.information.copy()
    print(“Now, we’re including some good clues—like checking Bitcoin’s temper, pace, and patterns—to assist us predict its subsequent transfer.”)

    # Shifting averages: Like smoothing out a bumpy street to see the development
    df[‘SMA7’] = ta.sma(df[‘Close’], size=7) # 7-day common
    df[‘SMA25’] = ta.sma(df[‘Close’], size=25) # 25-day common
    df[‘SMA50’] = ta.sma(df[‘Close’], size=50)
    df[‘SMA99’] = ta.sma(df[‘Close’], size=99)
    df[‘SMA200’] = ta.sma(df[‘Close’], size=200)

    df[‘EMA12’] = ta.ema(df[‘Close’], size=12) # Fast 12-day development
    df[‘EMA26’] = ta.ema(df[‘Close’], size=26) # Slower 26-day development

    df[‘MA111’] = ta.sma(df[‘Close’], size=111)
    df[‘MA350x2’] = ta.sma(df[‘Close’], size=350) * 2 # Lengthy-term doubled

    macd = ta.macd(df[‘Close’], quick=12, gradual=26, sign=9) # Momentum checker
    df[‘MACD’] = macd[‘MACD_12_26_9’]
    df[‘MACD_Signal’] = macd[‘MACDs_12_26_9’]
    df[‘MACD_Hist’] = macd[‘MACDh_12_26_9’]

    df[‘SAR’] = ta.psar(df[‘High’], df[‘Low’], df[‘Close’])[‘PSARl_0.02_0.2’] # Development course

    df[‘RSI’] = ta.rsi(df[‘Close’], size=14) # Is Bitcoin overexcited or sleepy?

    stoch = ta.stoch(df[‘High’], df[‘Low’], df[‘Close’], ok=14, d=3, smooth_k=3) # Velocity gauge
    df[‘StochK’] = stoch[‘STOCHk_14_3_3’]
    df[‘StochD’] = stoch[‘STOCHd_14_3_3’]

    bbands = ta.bbands(df[‘Close’], size=20, std=2) # Value vary bands
    df[‘BB_Upper’] = bbands[‘BBU_20_2.0’]
    df[‘BB_Middle’] = bbands[‘BBM_20_2.0’]
    df[‘BB_Lower’] = bbands[‘BBL_20_2.0’]

    df[‘CCI’] = ta.cci(df[‘High’], df[‘Low’], df[‘Close’], size=14) # Overbought/oversold

    df[‘OBV’] = ta.obv(df[‘Close’], df[‘Volume’]) # Quantity development
    df[‘CMF’] = ta.adosc(df[‘High’], df[‘Low’], df[‘Close’], df[‘Volume’], quick=3, gradual=10) # Cash circulate

    df[‘ForceIndex’] = df[‘Close’].diff(1) * df[‘Volume’] # Value push
    df[‘ForceIndex13’] = ta.ema(df[‘ForceIndex’], size=13)

    df[‘ATR’] = ta.atr(df[‘High’], df[‘Low’], df[‘Close’], size=14) # Volatility

    recent_high = df[‘High’].iloc[-100:].max() # Final 100 days’ peak
    recent_low = df[‘Low’].iloc[-100:].min() # Final 100 days’ dip
    df[‘Fib_0’] = recent_low
    df[‘Fib_23.6’] = recent_low + 0.236 * (recent_high – recent_low) # Fibonacci ranges
    df[‘Fib_38.2’] = recent_low + 0.382 * (recent_high – recent_low)
    df[‘Fib_50’] = recent_low + 0.5 * (recent_high – recent_low)
    df[‘Fib_61.8’] = recent_low + 0.618 * (recent_high – recent_low)
    df[‘Fib_100’] = recent_high

    self.information = df
    print(f”Completed! We’ve added {len(df.columns)} clues—like Bitcoin’s temper swings and spending habits—to make our prediction smarter.”)
    return df

    def prepare_ml_data(self):
    “””Step 3: Get our clues prepared for the prediction machine”””
    if self.information is None:
    print(“Oops! We’d like the clues first. Run calculate_indicators() after fetching information.”)
    return None

    df = self.information.copy()
    print(“We’re organising the puzzle: tomorrow’s value is what we need to guess, utilizing right now’s clues.”)

    df[‘Target’] = df[‘Close’].shift(-1) # Tomorrow’s value is our aim
    df = df.dropna() # Skip days with lacking items

    options = [‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’, ‘SMA7’, ‘SMA25’, ‘SMA50’, ‘SMA99’, ‘SMA200’,
    ‘EMA12’, ‘EMA26’, ‘MA111’, ‘MA350x2’, ‘MACD’, ‘MACD_Signal’, ‘MACD_Hist’, ‘SAR’,
    ‘RSI’, ‘StochK’, ‘StochD’, ‘BB_Upper’, ‘BB_Middle’, ‘BB_Lower’, ‘CCI’, ‘OBV’,
    ‘CMF’, ‘ForceIndex’, ‘ForceIndex13’, ‘ATR’]

    X = df[features] # Our clue pile
    y = df[‘Target’] # The reply we’re after

    X_scaled = self.scaler.fit_transform(X) # Make clues simpler to check, like placing all of them in the identical language
    print(f”Puzzle prepared! We have now {len(X)} days to study from, with {len(options)} clues every—like components for a Bitcoin value recipe.”)
    return X_scaled, y, X.index

    def train_model(self, test_size=0.2):
    “””Step 4: Educate our crystal ball to foretell Bitcoin costs”””
    X_scaled, y, dates = self.prepare_ml_data()
    if X_scaled is None:
    return None

    X_train, X_test, y_train, y_test, dates_train, dates_test = train_test_split(
    X_scaled, y, dates, test_size=test_size, shuffle=False
    )
    print(f”We’re educating our prediction machine with {len(X_train)} days of historical past and testing it on the final {len(X_test)} days—like training with outdated climate forecasts earlier than predicting tomorrow’s rain.”)

    self.mannequin.match(X_train, y_train) # Let the machine study the patterns

    y_pred = self.mannequin.predict(X_test) # Check its guesses
    mse = mean_squared_error(y_test, y_pred)
    rmse = np.sqrt(mse)
    print(f”Coaching executed! Our machine’s guesses had been off by about ${rmse:.2f} on common (that’s the RMSE). The MSE ({mse:.2f}) is an even bigger quantity displaying the full error squared—smaller is healthier!”)

    return X_test, y_test, y_pred, dates_test

    def predict_next_day(self):
    “””Step 5: Look into the long run with our skilled crystal ball”””
    if self.information is None:
    print(“Wait! We’d like information and clues first. Run the sooner steps.”)
    return None

    last_data = self.information.tail(1)
    options = [‘Open’, ‘High’, ‘Low’, ‘Close’, ‘Volume’, ‘SMA7’, ‘SMA25’, ‘SMA50’, ‘SMA99’, ‘SMA200’,
    ‘EMA12’, ‘EMA26’, ‘MA111’, ‘MA350x2’, ‘MACD’, ‘MACD_Signal’, ‘MACD_Hist’, ‘SAR’,
    ‘RSI’, ‘StochK’, ‘StochD’, ‘BB_Upper’, ‘BB_Middle’, ‘BB_Lower’, ‘CCI’, ‘OBV’,
    ‘CMF’, ‘ForceIndex’, ‘ForceIndex13’, ‘ATR’]

    X_last = last_data[features]
    X_last_scaled = self.scaler.remodel(X_last)
    prediction = self.mannequin.predict(X_last_scaled)[0]

    last_date = self.information.index[-1]
    next_date = last_date + timedelta(days=1)
    print(f” yesterday ({last_date.strftime(‘%Y-%m-%d’)}, value was ${last_data[‘Close’].values[0]:.2f}), our crystal ball says tomorrow ({next_date.strftime(‘%Y-%m-%d’)}) will likely be ${prediction:.2f}. It’s utilizing all these clues we gathered!”)
    return prediction

    def plot_predictions(self, X_test, y_test, y_pred, dates_test):
    “””Step 6: Draw an image of our guesses vs. actuality”””
    plt.determine(figsize=(14, 7))
    plt.plot(dates_test, y_test, label=’Precise Value’, colour=’blue’)
    plt.plot(dates_test, y_pred, label=’Predicted Value’, colour=’purple’, linestyle=’–‘)
    plt.title(‘Bitcoin Value Prediction (Our Sensible Guess vs. What Actually Occurred)’)
    plt.xlabel(‘Date’)
    plt.ylabel(‘Value (USD)’)
    plt.legend()
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.present()
    print(“Right here’s an image! The blue line is what Bitcoin really did within the check days. The purple dashed line is what our machine guessed. Nearer strains imply higher guesses!”)

    if __name__ == “__main__”:
    print(“Let’s predict Bitcoin’s subsequent value, step-by-step, like baking a cake with a magic recipe!”)
    btc = BitcoinTechnicalAnalysisML()
    btc.fetch_bitcoin_data(days=365)
    btc.calculate_indicators()

    outcome = btc.train_model(test_size=0.2)
    if outcome is just not None:
    X_test, y_test, y_pred, dates_test = outcome
    btc.plot_predictions(X_test, y_test, y_pred, dates_test)
    btc.predict_next_day()



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