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# Export DataFrames to Excel with Charts in Multiple Sheets

## What will we cover in this tutorial?

In this tutorial we will learn how to export Financial data from DataFrames (Pandas/Python) into an Excel sheet. It will be in multiple sheets with colored rows and charts. And of course, all automated from Python.

## Step 1: Read financial data into a DataFrame

First we need some data. We will use the data from our CSV file. Feel free to use any other data. The CSV used here is available in my GitHub.

```import pandas as pd

```

## Step 2: Calculate the Moving Average, MACD, and Stochastic Oscillator with DataFrames

Moving Average as done previously.

```data['MA10'] = data['Close'].rolling(10).mean()
```

The MACD as calculated follows.

```exp1 = data['Close'].ewm(span=12, adjust=False).mean()
data['MACD'] = macd = exp1 - exp2
data['Signal line'] = exp3 = macd.ewm(span=9, adjust=False).mean()
```

The Stochastic Oscillator.

```high14 = data['High'].rolling(14).max()
low14 = data['Low'].rolling(14).min()
data['%K'] = pct_k = (data['Close'] - low14)*100/(high14 - low14)
data['%D'] = pct_d = data['%K'].rolling(3).mean()
```

## Step 3: Adjust the time period and reverse the DataFrame

Adjust the time period we need. This is needed as the first calculations will not be available (NaN) or incorrect.

```data = data.loc['2020-01-01':]
data = data.iloc[::-1]
```

Also notice, we reverse the data by .iloc[::-1]. This is just to have the most recent data on the top of our Excel sheet.

## Step 4: Export DataFrame to Excel with multiple sheets and chars

The for generating our Excel sheet we need the XlsxWriter.

If you don’t have it installed already you can install it by running this in a cell: !pip install XlsxWriter

The code that generated the Excel sheet.

```writer = pd.ExcelWriter("technical.xlsx",
engine='xlsxwriter',
date_format = 'yyyy-mm-dd',
datetime_format='yyyy-mm-dd')

workbook = writer.book

# Create a format for a green cell
'bg_color': '#C6EFCE',
'font_color': '#006100'
})

# Create a format for a red cell
'bg_color': '#FFC7CE',
'font_color': '#9C0006'
})

# **
# ** MA
# **
sheet_name = 'MA10'
data[['Close', 'MA10']].to_excel(writer, sheet_name=sheet_name)
worksheet = writer.sheets[sheet_name]

# Set column width of Date
worksheet.set_column(0, 0, 15)

for col in range(1, 3):
# Create a conditional formatted of type formula
worksheet.conditional_format(1, col, len(data), col, {
'type': 'formula',
'criteria': '=B2>=C2',
'format': green_cell
})

# Create a conditional formatted of type formula
worksheet.conditional_format(1, col, len(data), col, {
'type': 'formula',
'criteria': '=B2<C2',
'format': red_cell
})

# Create a new chart object.

# Add a series to the chart.
'name': 'AAPL',
'categories': [sheet_name, 1, 0, len(data), 0],
'values': [sheet_name, 1, 1, len(data), 1],
})

# Create a new chart object.

# Add a series to the chart.
'name': sheet_name,
'categories': [sheet_name, 1, 0, len(data), 0],
'values': [sheet_name, 1, 2, len(data), 2],
})

# Combine and insert title, axis names
chart1.combine(chart2)
chart1.set_title({'name': sheet_name + " AAPL"})
chart1.set_x_axis({'name': 'Date'})
chart1.set_y_axis({'name': 'Price'})

# Insert the chart into the worksheet.
worksheet.insert_chart('E2', chart1)

# **
# ** MACD
# **

sheet_name = 'MACD'
data[['Close', 'MACD', 'Signal line']].to_excel(writer, sheet_name=sheet_name)
worksheet = writer.sheets[sheet_name]

# Set column width of Date
worksheet.set_column(0, 0, 15)

for col in range(1, 4):
# Create a conditional formatted of type formula
worksheet.conditional_format(1, col, len(data), col, {
'type': 'formula',
'criteria': '=C2>=D2',
'format': green_cell
})

# Create a conditional formatted of type formula
worksheet.conditional_format(1, col, len(data), col, {
'type': 'formula',
'criteria': '=C2<D2',
'format': red_cell
})

# Create a new chart object.

# Add a series to the chart.
'name': 'MACD',
'categories': [sheet_name, 1, 0, len(data), 0],
'values': [sheet_name, 1, 2, len(data), 2],
})

# Create a new chart object.

# Add a series to the chart.
'name': 'Signal line',
'categories': [sheet_name, 1, 0, len(data), 0],
'values': [sheet_name, 1, 3, len(data), 3],
})

# Combine and insert title, axis names
chart1.combine(chart2)
chart1.set_title({'name': sheet_name + " AAPL"})
chart1.set_x_axis({'name': 'Date'})
chart1.set_y_axis({'name': 'Value'})

# To set the labels on x axis not on 0
chart1.set_x_axis({
'label_position': 'low',
'num_font':  {'rotation': 45}
})

# Insert the chart into the worksheet.
worksheet.insert_chart('F2', chart1)

# **
# ** Stochastic
# **

sheet_name = 'Stochastic'
data[['Close', '%K', '%D']].to_excel(writer, sheet_name=sheet_name)
worksheet = writer.sheets[sheet_name]

# Set column width of Date
worksheet.set_column(0, 0, 15)

for col in range(1, 4):
# Create a conditional formatted of type formula
worksheet.conditional_format(1, col, len(data), col, {
'type': 'formula',
'criteria': '=C2>=D2',
'format': green_cell
})

# Create a conditional formatted of type formula
worksheet.conditional_format(1, col, len(data), col, {
'type': 'formula',
'criteria': '=C2<D2',
'format': red_cell
})

# Create a new chart object.

# Add a series to the chart.
'name': '%K',
'categories': [sheet_name, 1, 0, len(data), 0],
'values': [sheet_name, 1, 2, len(data), 2],
})

# Create a new chart object.

# Add a series to the chart.
'name': '%D',
'categories': [sheet_name, 1, 0, len(data), 0],
'values': [sheet_name, 1, 3, len(data), 3],
})

# Combine and insert title, axis names
chart1.combine(chart2)
chart1.set_title({'name': sheet_name + " AAPL"})
chart1.set_x_axis({'name': 'Date'})
chart1.set_y_axis({'name': 'Value'})

# To set the labels on x axis not on 0
chart1.set_x_axis({
'label_position': 'low',
'num_font':  {'rotation': 45}
})

# Insert the chart into the worksheet.
worksheet.insert_chart('F2', chart1)

# End of sheets

# Close
writer.close()
```

For a walkthrough of the code, please see the video to this lesson.

This will generate an Excel sheet in called technical.xlsx. It will contain 3 sheets (MA10, MACD, Stochastic Oscillator).

A sheet will look similar to this.

This was the last part of the 8 part course of Master Technical Analysis with pandas.

If you want to check the first part start here where you learn about pandas DataFrames for financial analysis.

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