## What will we cover?

In this lesson we will learn about **correlation** of assets, calculations of **correlation**, and **risk** and **coherence**.

The learning objectives of this tutorial.

- What is
**correlation**and how to use it - Calculate
**correlation** - Find
**negatively correlated**assets

## Step 1: What is Correlation

**Correlation** is a statistic that measures the degree to which two variables move in relation to each other. **Correlation** measures association, but doesn’t show if x causes y or vice versa.

The correlation between two stocks is a number form -1 to 1 (both inclusive).

- A positive correlation means, when stock x goes up, we expect stock y to go up, and opposite.
- A negative correlation means, when stock x goes up, we expect stock y to go down, and opposite.
- A zero correlation, we cannot say anything in relation to each other.

The formula for calculating the correlation is quite a mouthful.

## Step 2: Calculate the Correlation with DataFrames (pandas)

Luckily, the **DataFrames** can calculate it for us. Hence, we do not need to master how to do it.

Let’s get started. First, we need to load some time series of historic stock prices.

See this tutorial on how to work with portfolios.

```
import pandas as pd
import pandas_datareader as pdr
import datetime as dt
import numpy as np
tickers = ['AAPL', 'TWTR', 'IBM', 'MSFT']
start = dt.datetime(2020, 1, 1)
data = pdr.get_data_yahoo(tickers, start)
data = data['Adj Close']
log_returns = np.log(data/data.shift())
```

Where we also calculate the log returns.

The correlation can be calculated as follows.

```
log_returns.corr()
```

That was easy, right? Remember we do it on the log returns to keep it on the same range.

```
Symbols AAPL TWTR IBM MSFT
Symbols
AAPL 1.000000 0.531973 0.518204 0.829547
TWTR 0.531973 1.000000 0.386493 0.563909
IBM 0.518204 0.386493 1.000000 0.583205
MSFT 0.829547 0.563909 0.583205 1.000000
```

We identify, that the correlation on the diagonal is 1.0. This is obvious, since the diagonal shows the correlation between itself (AAPL and AAPL, and so forth).

Other than that, we can conclude that AAPL and MSFT are correlated the most.

## Step 3: Calculate the correlation to the general market

Let’s add the S&P 500 to our **DataFrame**.

```
sp500 = pdr.get_data_yahoo("^GSPC", start)
log_returns['SP500'] = np.log(sp500['Adj Close']/sp500['Adj Close'].shift())
log_returns.corr()
```

Resulting in this.

Where we see that AAPL and MSFT are mostly correlated to S&P 500 index. This is not surprising, as they are a big part of the weight of the market cap in the index.

## Step 4: Find Negative Correlated assets when Investing using Python

We will add this helper function to help find correlations.

We are in particular interested in negative correlation here.

```
def test_correlation(ticker):
df = pdr.get_data_yahoo(ticker, start)
lr = log_returns.copy()
lr[ticker] = np.log(df['Adj Close']/df['Adj Close'].shift())
return lr.corr()
```

This can help us find assets with a negative correlation.

Why do we wan that? Well, to minimize the risk. Read my eBook on the subject if you want to learn more about that.

Now, let’s test.

```
test_correlation("TLT")
```

Resulting in this following.

The negative correlation we are looking for.

## Step 5: Visualize the negative correlation

This can be visualized to get a better understanding as follows.

```
import matplotlib.pyplot as plt
%matplotlib notebook
def visualize_correlation(ticker1, ticker2):
df = pdr.get_data_yahoo([ticker1, ticker2], start)
df = df['Adj Close']
df = df/df.iloc[0]
fig, ax = plt.subplots()
df.plot(ax=ax)
```

With **visualize_correlation(“AAPL”, “TLT”)** we get.

Where we see, when AAPL goes down, the TLT goes up.

And if we look at **visualize_correlation(“^GSPC”, “TLT”)** (the S&P 500 index and TLT).

## What next?

Want more?

This is part of a full FREE course with all the code available on my GitHub.