update architecture

README.md

@@ -22,7 +22,7 @@ At minimum, at the end of this workshop, you will have an understanding how to l
 
 ## Architecture
 
-![chart](assets/chart.png)
+![chart](assets/arch.png)
 
 ## License
 
@@ -44,7 +44,7 @@ YOUR USE OF THE EXTERNAL DEPENDENCIES IS AT YOUR SOLE RISK. IN NO EVENT WILL AMA
 ## Step 0: Set up environment
 
 For the base infrastructure components (SageMaker Notebook, Athena, Glue Tables, S3 Bucket), deploy the following [CloudFormation template](https://github.com/aws-samples/algorithmic-trading/raw/master/0_Setup/ReferenceArchitecture-CF.json).
-First go to [CloudFormation](https://console.aws.amazon.com/cloudformation/home?#/stacks/new?stackName=algo) and upload the downloaded CF template. Verify that stackName is **algotrading** before creating the stack and acknowledge that IAM changes will be made.
+First go to [CloudFormation](https://console.aws.amazon.com/cloudformation/home?#/stacks/new?stackName=algotrading) and upload the downloaded CF template. Verify that stackName is **algotrading** before creating the stack and acknowledge that IAM changes will be made.
 
 This step will take ca. 5 minutes.
 
@@ -59,7 +59,7 @@ Here are a few data source options for this workshop. The daily datasets can be
 If you want to use AWS Data Exchange, you can download the following [dataset](https://aws.amazon.com/marketplace/pp/prodview-e2aizdzkos266#overview) for example. There are multiple options available, and we picked this for demonstration purposes. 
 
 To download this dataset, complete a subscription request first where you provide the required information for Company Name, Name, Email Address, and Intended Use Case. Once the provider confirms the subscription, you can navigate to [AWS Data Exchange/My subscriptions/Entitled data](https://console.aws.amazon.com/dataexchange/home?#/entitled-data).
-Then choose the latest revision for this subscription, select all assets, and click on **Export to Amazon S3**. In the new window select the root folder of the S3 bucket that starts with "*algo-data-*". Then click on **Export** and wait until your export job is completed.
+Then choose the latest revision for this subscription, select all assets, and click on **Export to Amazon S3**. In the new window select the root folder of the S3 bucket that starts with "*algotrading-data-*". Then click on **Export** and wait until your export job is completed.
 
 In order to use this dataset for algorithmic trading, we want to standardize it to a CSV file with the following columns: **dt, sym, open, high, low, close, vol**.
 Once you have successfully exported the dataset, please run the the following Jupyter notebook to format the dataset and store it in the ***hist_data_daily*** folder of your S3 bucket. Go to [Amazon SageMaker/Notebook/Notebook instances](https://console.aws.amazon.com/sagemaker/home?#/notebook-instances), then click on **Open Jupyter** for the provisioned notebook instance. Run all the cells in **1_Data/Load_Hist_Data_Daily.ipynb**.