Python Graphing Attribute Error

Question

I edited the code with the suggestions and currently receive this error Traceback (most recent call last): File "C:\Users\Jonathan.HollowayMainPc\Documents\Inchimoku Kinko Hyo.py", line 111, in ichimoku_chart() File "C:\Users\Jonathan.HollowayMainPc\Documents\Inchimoku Kinko Hyo.py", line 97, in ichimoku_chart facecolor='green', alpha=0.2, interpolate=True) File "C:\Python27\lib\site-packages\matplotlib\pyplot.py", line 2826, in fill_between interpolate=interpolate, **kwargs) File "C:\Python27\lib\site-packages\matplotlib\axes_axes.py", line 4345, in fill_between raise ValueError("Argument dimensions are incompatible") ValueError: Argument dimensions are incompatible

My code is below not sure what is causing it. Any help would be appreciated.

import urllib
import string
import sys
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt
import pandas.io.data as web
import datetime
#from stooq_helper_functions import data_to_dataframe
stocks = []
#^ list of for stocks 
#for stock in stocks:
    #Everything gets tabbed here. 
stock = "ebay"

data = {'Close': [], 'High': [], 'Low': [], 'Open': [], 'Date':[], 'Volume':[]}
#^Above is done on each stock but only one for now to test. 
url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1y/csv'
page = urllib.urlopen(url)
for line in page:
    new_string = string.split(line, ',')
    if len(new_string) == 6:
        if new_string[0].isdigit() == True:
            #print new_string
            data[stock]= new_string
            todays_high = float(data[stock][2])
            todays_low = float(data[stock][3])
            todays_open = float(data[stock][4])
            todays_close = float(data[stock][1])
            todays_volume = data[stock][5]
            todays_date = data[stock][0]
            data['High'].append(todays_high)
            data['Low'].append(todays_low)
            data['Open'].append(todays_open)
            data['Date'].append(todays_date)
            data['Close'].append(todays_close)
            data['Volume'].append(todays_volume)

matplotlib.style.use('ggplot')


def ichimoku_chart():
    global data, stock
    # Prepare the data
    #pos = len(data) - days
    close_prices = pd.DataFrame(data['Close'])
    high_prices = pd.DataFrame(data['High'])
    low_prices = pd.DataFrame(data['Low'])
    data['Date'] = pd.to_datetime(data['Date'], format='%Y%m%d')
    # workaround, so matplotlib accepts date axis
    #data['Date'].set_index('Date')

    # Ichimoku chart components

    # 1. Tenkan-sen (Conversion Line): (9-period high + 9-period low)/2))
    period9_high = pd.rolling_max(high_prices, window=9)
    period9_low = pd.rolling_min(low_prices, window=9)
    tenkan_sen = (period9_high + period9_low) / 2
    data['tenkan_sen'] = tenkan_sen

    # 2. Kijun-sen (Base Line): (26-period high + 26-period low)/2))
    period26_high = pd.rolling_max(high_prices, window=26)
    period26_low = pd.rolling_min(low_prices, window=26)
    kijun_sen = (period26_high + period26_low) / 2
    data['kijun_sen'] = kijun_sen

    # 3. Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2))
    # plotted 26 periods ahead
    senkou_span_a = ((tenkan_sen + kijun_sen) / 2).shift(26)
    data['senkou_span_a'] = senkou_span_a

    # 4. Senkou Span B (Leading Span B): (52-period high + 52-period low)/2))
    # plotted 22 periods ahead
    period52_high = pd.rolling_max(high_prices, window=52)
    period52_low = pd.rolling_min(low_prices, window=52)
    senkou_span_b = ((period52_high + period52_low) / 2).shift(22)
    data['senkou_span_b'] = senkou_span_b

    # 5. The most current closing price plotted 22 time periods behind
    chikou_span = close_prices.shift(-22)
    data['chikou_span'] = chikou_span

    #data = data[pos:]
    date_values = data['Date'].values

    fig = plt.figure()

    plt.plot_date(date_values, data['Close'], '-', linewidth=1.4, label='Close')
    plt.plot_date(date_values, data['tenkan_sen'], '-', label='Tenkan Sen')
    plt.plot_date(date_values, data['kijun_sen'], '-', label='Kijun Sen')
    plt.plot_date(date_values, data['senkou_span_a'], '-', linewidth=0)
    plt.plot_date(date_values, data['senkou_span_b'], '-', linewidth=0)
    plt.plot_date(date_values, data['chikou_span'], '-', label='Chikou Span')

    plt.fill_between(date_values, data['senkou_span_a'], data['senkou_span_b'],
                     where=data['senkou_span_a'] >= data['senkou_span_b'],
                     facecolor='green', alpha=0.2, interpolate=True)
    plt.fill_between(date_values, data['senkou_span_a'], data['senkou_span_b'],
                     where=data['senkou_span_a'] < data['senkou_span_b'],
                     facecolor='red', alpha=0.2, interpolate=True)

    fig.set_tight_layout(True)
    plt.legend(loc='upper left')
    plt.show()


#if __name__ == '__main__':
    #days = sys.argv[1]
    #stock = sys.argv[2]
    #ichimoku_chart(data_to_dataframe(stock + '.txt'), int(days))
ichimoku_chart()

Answer 1

There are multiple issues

• url = url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1yr/csv' should be url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1y/csv', i.e. range=1y instead of range=1yr. Otherwise no data will be returned
• high_prices is a list but rolling_max expects a DataFrame (http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.rolling_max.html). Try high_prices = pd.DataFrame(data['High'])
• Even with those two issues addressed, your plotting function plt.plot_date(date_values, data['Close'], '-', linewidth=1.4, label='Close') will fail because close_prices = data['Close'] will always be empty since no data is written to data['Close']

Some smaller issues:

• todays_volume = data[stock][5] has a newline character \n attached
• the line data[stock]= new_string is not needed, it is always overwritten by last read line

---

Update for the edited code and new error message

ValueError: Argument dimensions are incompatible

If you look at the dimensions of your DataFrames you will see that they have different shapes.

>>> date_values.shape
(252,)
>>> data['senkou_span_a'].shape
(252, 1)

Changing your parameter to data['senkou_span_a'][0] will give a plot. I cannot tell whether the plot makes sense and shows the correct data but at least the Python statement is formally correct.