diff --git a/one_exercise_per_file/day05/ex02/readme.md b/one_exercise_per_file/day05/ex02/readme.md
index e69de29..4282625 100644
--- a/one_exercise_per_file/day05/ex02/readme.md
+++ b/one_exercise_per_file/day05/ex02/readme.md
@@ -0,0 +1,16 @@
+# Exercise 2
+
+The goal of this exercise is to learn to use Pandas on Time Series an on Financial data.
+
+The data we will use is Apple stock.
+
+1. Using `Plotly` plot a Candlestick
+
+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 ?
+
+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
+
+- (Price(t) - Price(t-1))/ Price(t-1)
+
+Using the open price compute the **daily return**. Propose two different ways **without for loop**.
+
diff --git a/one_exercise_per_file/day05/ex03/readme.md b/one_exercise_per_file/day05/ex03/readme.md
index e69de29..b811efd 100644
--- a/one_exercise_per_file/day05/ex03/readme.md
+++ b/one_exercise_per_file/day05/ex03/readme.md
@@ -0,0 +1,31 @@
+# Exercise 3 Multi asset returns
+
+The goal of this exercise is to learn to compute daily returns on a DataFrame that contains many assets (multi-assets).
+
+```python
+business_dates = pd.bdate_range('2021-01-01', '2021-12-31')
+
+#generate tickers
+tickers = ['AAPL', 'FB', 'GE', 'AMZN', 'DAI']
+
+#create indexs
+index = pd.MultiIndex.from_product([business_dates, tickers], names=['Date', 'Ticker'])
+
+# create DFs
+market_data = pd.DataFrame(index=index,
+                        data=np.random.randn(len(index), 1),
+                        columns=['Price'])
+```
+
+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:
+
+| Date                |   ('Price', 'AAPL') |   ('Price', 'AMZN') |   ('Price', 'DAI') |   ('Price', 'FB') |   ('Price', 'GE') |
+|:--------------------|--------------------:|--------------------:|-------------------:|------------------:|------------------:|
+| 2021-01-01 00:00:00 |          nan        |         nan         |          nan       |        nan        |         nan       |
+| 2021-01-04 00:00:00 |            1.01793  |           0.0512955 |            3.84709 |         -0.503488 |           0.33529 |
+| 2021-01-05 00:00:00 |           -0.222884 |          -1.64623   |           -0.71817 |         -5.5036   |          -4.15882 |
+
+Note: The data is generated randomly, the values you may have a different results. But, this shows the expected DataFrame structure.
+
+`Hint use groupby`
+
diff --git a/one_exercise_per_file/raid01/readme.md b/one_exercise_per_file/raid01/readme.md
index 94a9c0e..4155945 100644
--- a/one_exercise_per_file/raid01/readme.md
+++ b/one_exercise_per_file/raid01/readme.md
@@ -137,7 +137,7 @@ A data point (x-axis: date, y-axis: cumulated_return) is: the **cumulated return
 
 ![alt text][performance]
 
-[performance]: images/weekend/w1_weekend_plot_pnl.png "Cumulative Performance"
+[performance]: images/w1_weekend_plot_pnl.png "Cumulative Performance"
 
 ## 5. Main