###### Does the structure of the project is as below ?
```
project
│ README.md
@ -25,15 +29,19 @@ project
│ outliers.txt
```
- The readme file contains a description of the project and explains how to run the code from an empty environment. It also gives a summary of the implementation of each python file. The preprocessing which is a key part should be decribed precisely. Finally, it should contain a conclusion that gives the performance of the strategy.
- The environment has to contain all libraries used and their versions that are necessary to run the code.
###### Does the readme file contain a description of the project, explain how to run the code from an empty environment, give a summary of the implementation of each python file and contain a conclusion that gives the performance of the strategy?
###### Does the environment contain all libraries used and their versions that are necessary to run the code ?
- The notebook has to contain:
- Missing values analysis. **Example**: number of missing values per variables or per year
- Outliers analysis
- Histogram of average price for companies for all variables (save the plot with the images). This is required only for `prices.csv` data.
- Describe at least 5 outliers ('ticker', 'date', price). To check the outliers it is simple. Search the historical stock price on Google at the given date and compare. The price may fluctuate a bit. The goal here is not to match the historical price found on Google but to detect a huge difference between the price in our data and the real historical one.
###### Does the notebook contain a missing values analysis? **Example**: number of missing values per variables or per year
###### Does the notebook contain an outliers analysis?
###### Does the notebook contain a Histogram of average price for companies for all variables (saved the plot with the images) ? This is required only for `prices.csv` data.
###### Does the notebook describe at least 5 outliers ('ticker', 'date', price) ? To check the outliers it is simple: Search the historical stock price on Google at the given date and compare. The price may fluctuate a bit. The goal here is not to match the historical price found on Google but to detect a huge difference between the price in our data and the real historical one.
Notes:
@ -43,50 +51,49 @@ Notes:
## Python files
### 1. memory_reducer.py
The memory_reducer is validated if:
- The `prices` data set weights less than **8MB** (Mega Bytes)
- The `sp500` data set weights less than **0.15MB** (Mega Bytes)
- For float data the smaller data type used is np.float32. Smaller data type may alter the precision of the data.
###### Do the `prices` data set weights less than **8MB** (Mega Bytes) ?
###### Do the `sp500` data set weights less than **0.15MB** (Mega Bytes) ?
###### Do the data type is greater than `np.float32` ? Smaller data type may alter the precision of the data.
### 2. preprocessing.py
The preprocessing is validated if:
#### Prices
- The data is agregated on a monthly period and only the last element is kept
- The outliers are filtered out by removing all prices bigger than 10k $ and smaller than 0.1 $
- The historical return is computed using only current and past values.
- The futur return is computed using only current and futur value. (Reminder: as the data is resampled monthly, computing the return is straightforward)
- The outliers in the returns data is set to NaN for all returns not in the years 2008 and 2009. The filters are: return > 1 and return <-0.5.
- The missing values are filled using the last value available **for the company**. `df.fillna(method='ffill')` is wrong because the previous value can be the return or price of another company.
- The missing values that can't be filled using a the previous existing value are dropped.
- The number of missing values is 0
##### The data is agregated on a monthly period and only the last element is kept
##### The outliers are filtered out by removing all prices bigger than 10k $ and smaller than 0.1 $
##### The historical return is computed using only current and past values.
##### The futur return is computed using only current and futur value. (Reminder: as the data is resampled monthly, computing the return is straightforward)
##### The outliers in the returns data is set to NaN for all returns not in the years 2008 and 2009. The filters are: return > 1 and return <-0.5.
##### The missing values are filled using the last value available **for the company**. `df.fillna(method='ffill')` is wrong because the previous value can be the return or price of another company.
##### The missing values that can't be filled using a the previous existing value are dropped.
##### The number of missing values is 0
Best practice:
Do not fill the last values for the futur return because the values are missing because the data set ends at a given date. Filling the previous doesn't make sense. It makes more sense to drop the row because the backtest focuses on observed data.
Do not fill the last values for the futur return because the values are missing because the data set ends at a given date. Filling the previous doesn't make sense. It makes more sense to drop the row because the backtest focuses on observed data.
### 3. create_signal.py
The signal creation is validated if:
##### The metric `average_return_1y` is added as a new column if the merged DataFrame. The metric is relative to a company. It is important to group the data by company first before to compute the average return over 1y. It is accepted to consider that one year is 12 consecutive rows.
- The metric `average_return_1y` is added as a new column if the merged DataFrame. The metric is relative to a company. It is important to group the data by company first before to compute the average return over 1y. It is accepted to consider that one year is 12 consecutive rows.
- The signal is added as a new column to the merged DataFrame. The signal which is boolean indicates wether, within the same month, the company is in the top 20. The top 20 corresponds to the 20 companies with the 20 highest metric within the same month. The highest metric gets the rank 1 (if rank is used the parameter `ascending` should be set to `False`).
##### The signal is added as a new column to the merged DataFrame. The signal which is boolean indicates whether, within the same month, the company is in the top 20. The top 20 corresponds to the 20 companies with the 20 highest metric within the same month. The highest metric gets the rank 1 (if rank is used the parameter `ascending` should be set to `False`).
### 4. backtester.py
The backtester is validated if:
##### The PnL is computed by multiplying the signal `Series` by the **futur returns**.
##### The return of the strategy is computed by dividing the PnL by the sum of the signal `Series`.
##### The signal used on the SP500 is the `pd.Series([20,20,...,20])`
##### The series used in the plot are the cumulative PnL. `cumsum` can be used
##### The PnL on the full historical data is **smaller than 75$**. If not, it means that the outliers where not corrected correctly.
###### Does the plot contain a title ?
###### Does the plot contain a legend ?
###### Does the plot contain a x-axis and y-axis name ?
- The PnL is computed by multiplying the signal `Series` by the **futur returns**.
- The return of the strategy is computed by dividing the PnL by the sum of the signal `Series`.
- The signal used on the SP500 is the pd.Series([20,20,...,20])
- The series used in the plot are the cumulative PnL. `cumsum` can be used.
- The PnL on the full historical data is **smaller than 75$**. If not, it means that the outliers where not corrected correctly.
![alt text][performance]
@ -95,4 +102,4 @@ The backtester is validated if:
### 5. main.py
**The command `python main.py` executes the code from data imports to the backtest and save the results.** It shouldn't return any error to validate the project.
###### The command `python main.py` executes the code from data imports to the backtest and save the results? It shouldn't return any error to validate the project.
Badr Ghazlane
2 years ago