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KI

Python:

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tabulate import tabulate

# Fetch Apple's daily stock data
daily_data = yf.download('AAPL', start='2020-01-01')

# Fetch Apple's weekly stock data
weekly_data = yf.download('AAPL', start='2020-01-01', interval='1wk')

# Function to calculate Ichimoku components
def calculate_ichimoku(data):
    high_window = 9
    low_window = 9
    base_window = 26
    span_b_window = 52
    displacement = 26
    
    data['Tenkan-sen'] = (data['High'].rolling(window=high_window).max() + data['Low'].rolling(window=high_window).min()) / 2
    data['Kijun-sen'] = (data['High'].rolling(window=base_window).max() + data['Low'].rolling(window=base_window).min()) / 2
    data['Senkou Span A'] = ((data['Tenkan-sen'] + data['Kijun-sen']) / 2).shift(displacement)
    data['Senkou Span B'] = (data['High'].rolling(window=span_b_window).max() + data['Low'].rolling(window=span_b_window).min()) / 2
    data['Chikou Span'] = data['Close'].shift(-displacement)
    
    return data

# Calculate Ichimoku for daily data
daily_data = calculate_ichimoku(daily_data)

# Calculate Ichimoku for weekly data
weekly_data = calculate_ichimoku(weekly_data)

# Set up Seaborn style
sns.set(style="whitegrid")

# Create a Seaborn plot for daily Ichimoku Cloud
plt.figure(figsize=(14, 7))
plt.plot(daily_data['Close'], label='Close Price', color='black', linewidth=1.5)
plt.plot(daily_data['Tenkan-sen'], label='Tenkan-sen', color='red', linewidth=1.5)
plt.plot(daily_data['Kijun-sen'], label='Kijun-sen', color='blue', linewidth=1.5)
plt.plot(daily_data['Senkou Span A'], label='Senkou Span A', color='green', linewidth=1.5)
plt.plot(daily_data['Senkou Span B'], label='Senkou Span B', color='orange', linewidth=1.5)
plt.plot(daily_data['Chikou Span'], label='Chikou Span', color='purple', linewidth=1.5)

# Fill the cloud
plt.fill_between(daily_data.index, daily_data['Senkou Span A'], daily_data['Senkou Span B'],
                 where=daily_data['Senkou Span A'] >= daily_data['Senkou Span B'], color='lightgreen', alpha=0.5)
plt.fill_between(daily_data.index, daily_data['Senkou Span A'], daily_data['Senkou Span B'],
                 where=daily_data['Senkou Span A'] < daily_data['Senkou Span B'], color='lightcoral', alpha=0.5)

plt.title('Ichimoku Cloud - Daily Apple Stock')
plt.xlabel('Date')
plt.ylabel('Price')
plt.legend()
plt.grid()
plt.show()

# Prepare multitimeframe trends table
def trend_analysis(data):
    most_recent = data.iloc[-1]
    
    trend = ""
    if most_recent['Close'] > most_recent['Senkou Span A'] and most_recent['Close'] > most_recent['Senkou Span B']:
        trend = "Bullish"
    elif most_recent['Close'] < most_recent['Senkou Span A'] and most_recent['Close'] < most_recent['Senkou Span B']:
        trend = "Bearish"
    else:
        trend = "Sideways"
    
    return trend

daily_trend = trend_analysis(daily_data)
weekly_trend = trend_analysis(weekly_data)

# Create a summary table
summary = pd.DataFrame({
    'Timeframe': ['Daily', 'Weekly'],
    'Current Trend': [daily_trend, weekly_trend]
})

# Print the summary table
print(tabulate(summary, headers='keys', tablefmt='psql'))

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