mirror of https://github.com/01-edu/Branch-AI.git
Badr Ghazlane
3 years ago
3 changed files with 48 additions and 1 deletions
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# Exercise 2 |
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The goal of this exercise is to learn to use Pandas on Time Series an on Financial data. |
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The data we will use is Apple stock. |
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1. Using `Plotly` plot a Candlestick |
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2. Aggregate the data to **last business day of each month**. The aggregation should consider the meaning of the variables. How many months are in the considered period ? |
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3. When comparing many stocks between them the metric which is frequently used is the return of the price. The price is not a convenient metric as the prices evolve in different ranges. The return at time t is defined as |
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- (Price(t) - Price(t-1))/ Price(t-1) |
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Using the open price compute the **daily return**. Propose two different ways **without for loop**. |
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# Exercise 3 Multi asset returns |
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The goal of this exercise is to learn to compute daily returns on a DataFrame that contains many assets (multi-assets). |
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```python |
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business_dates = pd.bdate_range('2021-01-01', '2021-12-31') |
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#generate tickers |
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tickers = ['AAPL', 'FB', 'GE', 'AMZN', 'DAI'] |
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#create indexs |
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index = pd.MultiIndex.from_product([business_dates, tickers], names=['Date', 'Ticker']) |
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# create DFs |
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market_data = pd.DataFrame(index=index, |
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data=np.random.randn(len(index), 1), |
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columns=['Price']) |
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``` |
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1. **Without using a for loop**, compute the daily returns (return(d) = (price(d)-price(d-1))/price(d-1)) for all the companies and returns a DataFrame as: |
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| Date | ('Price', 'AAPL') | ('Price', 'AMZN') | ('Price', 'DAI') | ('Price', 'FB') | ('Price', 'GE') | |
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|:--------------------|--------------------:|--------------------:|-------------------:|------------------:|------------------:| |
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| 2021-01-01 00:00:00 | nan | nan | nan | nan | nan | |
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| 2021-01-04 00:00:00 | 1.01793 | 0.0512955 | 3.84709 | -0.503488 | 0.33529 | |
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| 2021-01-05 00:00:00 | -0.222884 | -1.64623 | -0.71817 | -5.5036 | -4.15882 | |
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Note: The data is generated randomly, the values you may have a different results. But, this shows the expected DataFrame structure. |
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`Hint use groupby` |
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