how to show more area at the far right side of the Ichimoku Cloud Chart #691

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1. soshk

   added

   questionFurther information is requestedFurther information is requested

   on Mar 5, 2025
2. soshk commented on Mar 5, 2025

   

   soshk

   on Mar 5, 2025

   Last edited by soshk

   Author

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   in case it is necessart, this is my broken code

   # This allows multiple outputs from a single jupyter notebook cell:
   from IPython.core.interactiveshell import InteractiveShell
   InteractiveShell.ast_node_interactivity = "all"
   
   import yfinance as yf
   import pandas as pd
   import mplfinance as mpf
   import numpy as np
   import datetime as dt
   from datetime import timedelta, date
   
   # for fixing 
   # Error: Cannot compare tz-naive and tz-aware datetime-like objects
   import pytz
   from dateutil.relativedelta import relativedelta
   
   # Print mplfinance version for debugging
   #print(f"mplfinance version: {mpf.__version__}")
   
   def get_stock_data(stock_symbol, start, end):
       # Fetch stock data
       security = yf.Ticker(stock_symbol)
       stock = security.history(interval='1d', start=start_date, end=end_date)
       #stock = yf.download(stock_symbol, start=start_date, end=end_date)
           
       if stock.empty:
           raise ValueError(f"No data retrieved for symbol '{stock_symbol}' between {start_date} and {end_date}")
       
       # Calculate Ichimoku components, default parameters = 9, 26, 52
       # Tenkan-sen (Conversion Line): (9-period high + 9-period low)/2
       high_9 = stock['High'].rolling(window=9).max()
       low_9 = stock['Low'].rolling(window=9).min()
       stock['Tenkan_sen'] = (high_9 + low_9) / 2
       
       # Kijun-sen (Base Line): (26-period high + 26-period low)/2
       high_26 = stock['High'].rolling(window=26).max()
       low_26 = stock['Low'].rolling(window=26).min()
       stock['Kijun_sen'] = (high_26 + low_26) / 2
       
       # Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2 shifted 26 periods forward
       stock['Senkou_Span_A'] = ((stock['Tenkan_sen'] + stock['Kijun_sen']) / 2).shift(26)
       
       # Senkou Span B (Leading Span B): (52-period high + 52-period low)/2 shifted 26 periods forward
       high_52 = stock['High'].rolling(window=52).max()
       low_52 = stock['Low'].rolling(window=52).min()
       stock['Senkou_Span_B'] = ((high_52 + low_52) / 2).shift(26)
       
       # Chikou Span (Lagging Span): Close price shifted 26 periods back
       stock['Chikou_Span'] = stock['Close'].shift(-26)
       
       # Calculate MACD, default parameter = 12, 26, 9
       stock['EMA_12'] = stock['Close'].ewm(span=12, adjust=False).mean()
       stock['EMA_26'] = stock['Close'].ewm(span=26, adjust=False).mean()
       stock['MACD'] = stock['EMA_12'] - stock['EMA_26']
       stock['Signal_Line'] = stock['MACD'].ewm(span=9, adjust=False).mean()
       stock['MACD_Hist'] = stock['MACD'] - stock['Signal_Line']
       
       # Calculate RSI, default 14days RSI vs 5days SMA
       delta = stock['Close'].diff()
       gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
       loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
       rs = gain / loss
       stock['RSI'] = 100 - (100 / (1 + rs))
       
       
       return stock
   
   def plot_ichimoku_with_indicators(stock_symbol, data):
       # Prepare the data for mplfinance (OHLC format)
       ohlc_data = data[['Open', 'High', 'Low', 'Close', 'Volume']].copy()
       
       # Debugging
       print("ohlc_data head:\n", ohlc_data.head())
       print("ohlc_data columns:", ohlc_data.columns.tolist())
       print("Volume NaN count in ohlc_data:", ohlc_data['Volume'].isna().sum())
       
       # Define additional plots for Ichimoku lines
       ichimoku_lines = [
           mpf.make_addplot(data['Tenkan_sen'], color='red', label='Tenkan-sen'),
           mpf.make_addplot(data['Kijun_sen'], color='blue', label='Kijun-sen'),
           mpf.make_addplot(data['Senkou_Span_A'], color='yellow', label='Senkou_Span_A'),
           mpf.make_addplot(data['Senkou_Span_B'], color='purple', label='Senkou_Span_B'),        
           mpf.make_addplot(data['Chikou_Span'], color='black', label='Chikou Span'),        
       ]
       
       # Define the fill_between Senkou_Span_A and Senkou_Span_B for the cloud directly in the plot call
       cloud_fill = [dict(y1=data['Senkou_Span_A'].values, y2=data['Senkou_Span_B'].values, where=data['Senkou_Span_A'] >= data['Senkou_Span_B'], alpha=0.3, color='green'),
                       dict(y1=data['Senkou_Span_A'].values, y2=data['Senkou_Span_B'].values, where=data['Senkou_Span_A'] < data['Senkou_Span_B'], alpha=0.3, color='red')]
       
       # Volume = True in mpf.plot(), being panel = 1
       
       # Define MACD panel
       macd_plots = [
           mpf.make_addplot(data['MACD'], color='blue', label='MACD', ylabel='MACD', y_on_right=False, panel=2, title="MACD"),
           mpf.make_addplot(data['Signal_Line'], color='orange', label='Signal Line', panel=2),
           mpf.make_addplot(data['MACD_Hist'], type='bar', color='gray', alpha=0.5, label='Histogram', panel=2),
           mpf.make_addplot(pd.Series(0, index=data.index), color='black', linestyle='--', panel=2, width=0.5)
       ]
       
       # Define RSI panel
       rsi_plots = [
           mpf.make_addplot(data['RSI'], color='blue', label='RSI', ylabel='RSI', y_on_right=False, panel=3, title="RSI"),
           mpf.make_addplot(pd.Series(70, index=data.index), color='red', linestyle='--', panel=3, width=0.5),
           mpf.make_addplot(pd.Series(30, index=data.index), color='green', linestyle='--', panel=3, width=0.5)
       ]
   
       all_plots = ichimoku_lines + macd_plots + rsi_plots
           
       # Plot using mplfinance
       mpf.plot(ohlc_data,
                type='hollow_candle',  # Use candlestick chart, hollow
                style='yahoo',  # Yahoo-style coloring
                title=f'Ichimoku Cloud Chart - {stock_symbol}',
                addplot=all_plots,
                volume=True,   # panel = 1
                panel_ratios=(3, 1, 1, 1),  # Adjust panel sizes
                fill_between=cloud_fill, # Add cloud fill for Ichimoku
                ylabel='Price',
                ylabel_lower='Volume',
                #y_on_right=False,
                xlim=(start_date, xlim_end),
                figscale=1.5,
                tight_layout=True,
       )
   
   # Example usage
   if __name__ == "__main__":
       #Set parameters
       #ask suer for stock symbol
       stock_symbol = input("Please enter the stock symbol: ")
       start_date = dt.datetime(date.today().year - 1, date.today().month, date.today().day, tzinfo=pytz.UTC)
       end_date = dt.datetime(date.today().year, date.today().month, date.today().day, tzinfo=pytz.UTC)
       xlim_end = dt.datetime.today().replace(tzinfo=pytz.UTC) + relativedelta(months=1)
   
       # Get data and plot
       try:
           ichimoku_data = get_stock_data(stock_symbol, start_date, end_date)
           if ichimoku_data.empty:
               raise ValueError("No data returned for the specified stock symbol.")
           plot_ichimoku_with_indicators(stock_symbol, ichimoku_data)
       except Exception as e:
           print(f"Error: {str(e)}")
   
   # Requirements:
   # pip install yfinance pandas mplfinance numpy #