oldtemplate.py

import os

# Set this before importing libraries that use joblib/loky
os.environ['LOKY_MAX_CPU_COUNT'] = '6'  # Replace '4' with the desired number of cores


import pandas as pd
import numpy as np
from hmmlearn import hmm
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
import time

print("Starting Bitcoin HMM analysis...")

#Load and process data
def load_and_preprocess_data(file_path):
    print(f"Loading data from {file_path}...")
    #read CSV with custom column names, no header
    df = pd.read_csv(file_path) #, names=['Open','High','Low','Close','Volume']
    
    #Uncomment this if you have a "," on the last line
    #df.drop(df.columns[-1], axis=1, inplace=True)
    
    print("Creating datetime index...")
    df.index = pd.date_range(start='2022-01-01', periods=len(df),freq='h')
    
    print(df.columns)
    
    print("calculating returns and volatility...")
    df['Returns'] = df['close'].pct_change() #'close' (lowercase), it's based on the csv column name 
    df['Volatility'] = df['Returns'].rolling(window=24).std()
    
    print("Calculating volume change...")
    df['Volume_Change'] = df['volume'].pct_change() #'volume' (lowercase), it's based on the csv column name 
    
    # Aggiungi questo blocco per gestire valori infiniti
    print("Replacing infinite values with NaN...")
    df.replace([np.inf, -np.inf], np.nan, inplace=True)
    
    print("Dropping NaN values...")
    df.dropna(inplace=True)
    
    print(f"Data preprocessed. Shape {df.shape}")
    return df

# Train HMM
def train_hmm(data, n_components=3):
    print(f"Training HMM with {n_components} components...")
    features = ['Returns', 'Volatility', 'Volume_Change']
    X = data[features].values

    print("Normalizing features...")
    scaler = StandardScaler()
    X_scaled = scaler.fit_transform(X)

    print("Fitting HMM model...")
    model = hmm.GaussianHMM(n_components=n_components, covariance_type="full", n_iter=100, random_state=42)
    model.fit(X_scaled)

    print("HMM training completed.")
    return model, scaler

# Predict states
def predict_states(model, data, scaler):
    print("Predicting states...")
    features = ['Returns', 'Volatility', 'Volume_Change']
    X = data[features].values
    X_scaled = scaler.transform(X)
    states = model.predict(X_scaled)
    print(f"States predicted. Unique states: {np.unique(states)}")
    return states

# Analyze states
def analyze_states(data, states):
    print("Analyzing states...")
    df_analysis = data.copy()
    df_analysis['State'] = states

    for state in range(model.n_components):
        print(f"\nAnalyzing State {state}:")
        state_data = df_analysis[df_analysis['State'] == state]
        print(state_data[['Returns', 'Volatility', 'Volume_Change']].describe())
        print(f"Number of periods in State {state}: {len(state_data)}")

# Plot results
def plot_results(data, states):
    print("Plotting results...")
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10), sharex=True)

    ax1.plot(data.index, data['close'])# 'close' (lowercase), it's based on the csv column name 
    ax1.set_title('Price and HMM States')
    ax1.set_ylabel('Price')

    for state in range(model.n_components):
        mask = (states == state)
        ax1.fill_between(data.index, data['close'].min(), data['close'].max(), #'close' (lowercase), it's based on the csv column name 
                         where=mask, alpha=0.3, label=f'State {state}')

    ax1.legend()

    ax2.plot(data.index, data['Returns'])
    ax2.set_title('Returns')
    ax2.set_ylabel('Returns')
    ax2.set_xlabel('Date')
    plt.tight_layout()
    print("Showing plot...")
    plt.savefig(f"data/{int(time.time())}_{os.path.splitext(os.path.basename(file_path))[0]}.png")
    plt.show()




# Main execution
print("Starting main execution...")
file_path = './BTC_1H.csv'
data = load_and_preprocess_data(file_path)

print("Training HMM model...")
model, scaler = train_hmm(data)

print("Predicting states...")
states = predict_states(model, data, scaler)

print("Analyzing states...")
analyze_states(data, states)

print("Plotting results...")
plot_results(data, states)

print("Printing transition matrix...")
print("Transition Matrix:")
print(model.transmat_)

print("\nPrinting means and covariances of each state...")
for i in range(model.n_components):
    print(f"State {i}:")
    print("Mean:", model.means_[i])
    print("Covariance:", model.covars_[i])
    print()

print("HMM analysis completed.")

#https://www.youtube.com/watch?v=JwGfdnnyUAs
#31:30

#https://www.youtube.com/watch?v=jz3tEsCcie0

#https://chatgpt.com/c/67236545-c748-8009-9cb4-774fc25ab394