# Lesson 13

### Problem: Basic For Loops¶

Construct a for loop to sum the numbers between 1 and N for any N. A for loop that does nothing can be written:

```
n = 10
for i in range(n):
pass
```

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### Problem: Compute a compound return¶

The compound return on a bond that pays interest annually at rate r is given by $cr_{t}=\prod_{i=1}^{T}(1+r)=(1+r)^{T}$. Use a for loop compute the total return for £100 invested today for $1,2,\ldots,10$ years. Store this variable in a 10 by 1 vector cr.

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### Problem: Simulate a random walk¶

(Pseudo) Normal random variables can be simulated using the command `np.random.standard_normal(shape)`

where `shape`

is a tuple (or a scalar) containing the dimensions of the desired random numbers. Simulate 100 normals in a 100 by 1 vector and name the result `e`

. Initialize a vector `p`

containing zeros using the function zeros. Add the 1st element of `e`

to the first element of `p`

. Use a for loop to simulate a process $y_{i}=y_{i-1}+e_{i}$. When finished plot the results using

```
%matplotlib inline
import matplotlib.pyplot as plt
plt.rc('figure', figsize=(16,6))
plt.plot(y)
```

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### Problem: Nested Loops¶

Begin by loading momentum data used in an earlier lesson. Compute a 22-day moving-window standard deviation for each of the columns. Store the value at the end of the window.

When finished, make sure that `std_dev`

is a `DataFrame`

and plot the annualized percentage standard deviations using:

```
ann_std_dev = 100 * np.sqrt(252) * std_dev
ann_std_dev.plot()
```

```
# Setup: Load the momentum data
import pandas as pd
momentum = pd.read_csv("data/momentum.csv", index_col="date", parse_dates=True)
momentum = momentum / 100 # Convert to numeric values from percentages
```

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### Exercises¶

#### Exercise¶

- Simulate a 1000 by 10 matrix consisting of 10 standard random walks using both nested loops and
`np.cumsum`

. - Plot the results.

**Question to think about**

If you rerun the code in this Exercise, do the results change? Why?

#### Exercise: Compute Drawdowns¶

Using the momentum data, compute the maximum drawdown over all 22-day consecutive periods defined as the smallest cumulative produce of the gross return (1+r) for 1, 2, .., 22 days.

Finally, compute the mean drawdown for each of the portfolios.

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