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62 lines
2.7 KiB
62 lines
2.7 KiB
2 years ago
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##### The exercice is validated is all questions of the exercice are validated
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###### Have you checked missing values and data types ?
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###### Have you converted string dates to datetime ?
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###### Have you set dates as index ?
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###### Have you used `info` or `describe` to have a first look at the data ?
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**My results can be reproduced using: `np.random.seed = 2712`. Given the versions of NumPy used I do not guaranty the reproducibility of the results - that is why I also explain the steps to get to the solution.**
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##### The question 1 is validated if the return is computed as: Return(t) = (Price(t+1) - Price(t))/Price(t) and returns this output. Note that if the index is not ordered in ascending order the futur return computed is wrong. The answer is also accepted if the returns is computed as in the exercise 2 and then shifted in the futur using `shift`, but I do not recommend this implementation as it adds missing values !
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```console
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Date
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1980-12-12 -0.052170
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1980-12-15 -0.073403
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1980-12-16 0.024750
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1980-12-17 0.029000
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1980-12-18 0.061024
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...
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2021-01-25 0.001679
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2021-01-26 -0.007684
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2021-01-27 -0.034985
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2021-01-28 -0.037421
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2021-01-29 NaN
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Name: Daily_futur_returns, Length: 10118, dtype: float64
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```
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An example of solution is:
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```python
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def compute_futur_return(price):
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return (price.shift(-1) - price)/price
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compute_futur_return(df['Adj Close'])
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```
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##### The question 2 is validated if the index of the Series is the same as the index of the DataFrame. The data of the series can be generated using `np.random.randint(0,2,len(df.index)`.
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##### This question is validated if the Pnl is computed as: signal * futur_return. Both series should have the same index.
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```console
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Date
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1980-12-12 -0.052170
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1980-12-15 -0.073403
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1980-12-16 0.024750
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1980-12-17 0.029000
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1980-12-18 0.061024
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...
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2021-01-25 0.001679
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2021-01-26 -0.007684
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2021-01-27 -0.034985
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2021-01-28 -0.037421
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2021-01-29 NaN
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Name: PnL, Length: 10119, dtype: float64
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```
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##### The question 4 is validated if you computed the return of the strategy as: `(Total earned - Total invested) / Total` invested. The result should be close to 0. The formula given could be simplified as `(PnLs.sum())/signal.sum()`. My return is: 0.00043546984088551553 because I invested 5147$ and I earned 5149$.
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##### The question is validated if you replaced the previous signal Series with 1s. Similarly as the previous question, we earned 10128$ and we invested 10118$ which leads to a return of 0.00112670194140969 (0.1%).
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