# Cumulative Returns

![](/files/-M3wY5eKRGxx4ENcWtnC)

```
import os
import pandas as pd
import matplotlib.pyplot as plt

def symbol_to_path(symbol, base_dir="data"):
    return os.path.join(base_dir, "{}.csv".format(str(symbol)))

def get_data(symbols, dates):
    df = pd.DataFrame(index=dates)
    if 'SPY' not in symbols:  # add SPY for reference, if absent
        symbols.insert(0, 'SPY')

    for symbol in symbols:
        df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',
                              parse_dates=True, usecols=['Date', 'Adj Close'], na_values=['nan'])
        df_temp = df_temp.rename(columns={'Adj Close': symbol})
        df = df.join(df_temp)
        if symbol == 'SPY':  # drop dates SPY did not trade
            df = df.dropna(subset=["SPY"])
    return df

def plot_data(df, title="Stock prices", xlabel="Date", ylabel="Price"):
    ax = df.plot(title=title, fontsize=12)
    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    plt.show()

def compute_cumulative_returns(df):
    return ((df / df.ix[0].values) - 1)

def test_run():
    dates = pd.date_range('2012-01-01', '2012-12-31')  # one month only
    symbols = ['SPY', 'XOM']
    df = get_data(symbols, dates)
    plot_data(df)

    daily_returns = compute_cumulative_returns(df)
    plot_data(daily_returns, title="Cumulative returns", ylabel="Cumulative returns")

if __name__ == "__main__":
    test_run()
```

Here is the chart with original data.

![](/files/-M3wY5eO0rSQk2XQ0sv3)

And here is the one showing cumulative percentage.

![](/files/-M3wY5eQGQ_WkYX6LOmT)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://ondrej-kvasnovsky-2.gitbook.io/handbook-of-hidden-data-scientist-python/statistical-analysis/cumulative-returns.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.